KLA Corporation ($KLAC)

Please welcome KLA EVP, CFO and Global Operations, Bren Higgins.

Good morning. Thank you for being here for our 2026 KLA Investor Day. It's great to be back in New York. I'm going to make a few comments. First, I'm going to walk through the agenda overall. So I'll make a few comments, and then I'll transition to our President and CEO, Rick Wallace, who will talk about compounding sustainable outperformance of the company, where we've been and where we're going, some of the dynamics that are driving the ecosystem and how that plays through to opportunities for KLA relevance. Ahmad Khan, who's the President of our Semiconductor Products and Customers business, will then stand up and talk about process control in the AI era, some of the dynamics that are driving our business, how we're collaborating and engaging with customers that drives our innovation model and ultimately, how we execute. And our strategy is to take advantage of what looks like a very exciting business environment moving forward. We'll take a break. And then Brian Lorig, who runs our service business, will talk about service. And we're excited with some of the dynamics despite some of the export control effects on service, about what's happening in service in terms of how our service business and how our customers are relying on it to drive higher performance out of the installed base and higher levels of availability. After Brian, I will talk, come back up on stage and talk about our path to 2030, what's happening, what we've done over the last several years and what's happening in the market that then translates into how we're operating the company, what that means in terms of financial performance as we move forward. And then I'll walk through a 2030 model based on the various assumptions that get us there. At the end of the day, there's always those unknowns and questions. We're drawing some lines over time. But I think what matters is the story that we have around the ability for KLA to drive our relevancy and execute the business model over time. We have a credible history of execution. And so as we talk about that, I think our history is indicative of how we're going to run the company moving forward. We'll then transition to a Q&A session, roughly 45 minutes. And then after that, we'll go to lunch, and we'll do some round-robin with the executive team rotating around the various tables to answer some additional questions. So pretty big program here, 9:00 to roughly 2:00 today, and we hope you can all stay with us. You see the mention of the AR/VR demos. If you haven't had a chance to do those already, we also have some things we brought in terms of show and tell. So as you go through the day, if you haven't had a chance, I'd encourage you to take a look at some of what we have here. The AR/VR demos, you get to get up close and personal with a couple of our very high-end systems, a Gen 5 wafer inspector and our new TeraBeam 8xx multi-column electron beam inspector for reticle inspection. Get a sense for the complexity, scale of what we do at KLA. I'm going to let everyone read this, so I'll take 10 minutes maybe. Yes. Look, we're going to make forward-looking statements today. Those statements are subject to risk. We have an exhaustive list of risk factors that are in our SEC filings. I would encourage you to take a look at them. You can find them at our website at kla.com. So as it relates to the March quarter, we did a press release this morning. We affirmed our guidance for the March quarter: revenue at $3.35 billion, non-GAAP diluted EPS of $9.08. We got 3 weeks to go, more or less. Quarter is going as expected. So no real update to the March quarter, and we're comfortable with the guidance that we provided at earnings last January. So a couple of comments about the industry. So if you look at where the industry is today, we continue to see very strong momentum across all segments, foundry/logic, memory and advanced packaging, that the wafer equipment market, including advanced packaging, is now expected to be in the range of $135 billion to $140 billion. We talked about a mid-30s profile. We think that the momentum that we're seeing, particularly as it relates to what's happening in the second half, is driving incrementally up that view. Now the other thing that's pretty clear today as we engage with customers is that the visibility for 2027 is very high. Our customers are building new facilities. And so there's new equipment demand that is tied to those facilities and the schedules for that construction. And so our visibility into '27, to have this level of visibility 12 to 24 months out is really uncommon but gives us a lot of comfort to be able to stand up here in March of '26 and talk about '27 and having a growth rate in 2027 that we think is at the similar level or perhaps even higher than what we're seeing in 2026. So that's the growth rate -- year-over-year growth rate of the industry investment in wafer equipment in '27 versus '26. So as it relates to KLA, we expect to continue to see quarter-to-quarter sequential growth as we have for the last several quarters. Our views on 2026 are strengthening based on our visibility into the second half for semi PC systems. Our view for 2026 now is that we expect the total company to be up somewhere in the high teens in terms of year-over-year growth versus 2025. And given that's the total company, our semi process control systems growth should be faster than that. So we'll look at a couple of points more or less faster than that. We'll see how the year plays out. Half-to-half dynamics, I'm sure we'll talk about more of this in the Q&A, half-to-half dynamics. I don't really want to get into it at this point. Let's finish the March quarter. We'll talk about June, and then we'll give you some sense on what that looks like. But at least in terms of the overall year, we think the year translates into high teens growth today. We made 2 announcements around capital allocation today, very consistent with our history at KLA, our 17th consecutive annual dividend increase, which was a 21% increase to $2.30 per quarter from $1.90 and additional share repurchase authorization of $7 billion on top of the $3.9 billion that was on our remaining authorization as of the end of December, so roughly $11 billion in authorization to support what we expect to be a pretty robust environment over the next couple of years in terms of cash flow generation. And so we'll generate the cash. As everyone generally knows about KLA, we are big believers in assertive capital allocation and allocating every dollar of cash. So our share repurchase programs are consistent over time, and this is in support of that and our outlook moving forward. Let me stop. Thank you for being here today, and welcome to the KLA 2026 Investor Day. [Presentation]

Please welcome President and CEO, Rick Wallace.

November 30, 2022. Do you guys remember what happened on November 30, 2022? I was in a review at KLA with our Chief AI Officer. His name's Kris Bhaskar. And he said, that week, something had happened that only happened twice in his 40-year career that was as profound. That was the week ChatGPT dropped. And his view was that was going to change the industry in ways that we couldn't imagine, and he was right. We couldn't imagine at that time. I had not heard of ChatGPT, but I called my kids at the end of the week, college kids and said, "Have you guys used it?" All 3 had. It got 1 million downloads in the first week. So let me ask, how many of you use chat or some other chatbot on a routine basis? Okay. How many of you have created an agent? How many? Only a couple, 3. So my prediction is that, by the end of the year, you all will have created an agent. It's not very hard. You can do it in half an hour. You can create an agent. What will the agent do? It will do 2 major things. It will do a lot more work for you, and it will also drive a ton of demand for compute. ChatGPT drives 10x what an Internet search does, and reasoning and agents drive another 10x. So part of the build-out that we're seeing around AI is the fact that so many people are starting to leverage these tools, and that's what's driving consumption. What we didn't understand at the time, it was this interface to chat that was going to drive the demand. So what you're going to hear today is how well positioned KLA is for this inflection. We said in the video, Bren said, if we had to design a market, this would be it. We're going to talk about our customer relationships and how that positions us, and we'll talk about our technology and the investments that we've made, some of which you can see in the room. And we thought we'd bring these to you because the times where we've had investors come on site and see the hardware, it's made quite an impression. And one example over here is that's a lens in a BBP system right there. That's a lens. When I started in the industry, it was an off-the-shelf lens. That's not off the shelf. Who are we? We were founded 50 years ago. This is our 50th anniversary. And the original idea for the company was to automate what was done manually. Can we be automated mask -- photomask inspection because it was being done by microscopes? And so we started in the industry doing that, but we've expanded, of course, to a large portfolio. And we're now the leaders in process control, and we'll talk about what that means and why that's important in the age of AI. The leadership team, what you really need to know, you're going to hear from Ahmad and Brian, as you heard, and you -- Bren will come back. What you need to know as an investor about this leadership team is this is the leadership team that committed to our 2019 Investor Day and hit it. This is the leadership that committed to the 2022 Investor Day and hit it. And this is the leadership team that's committing to the 2030 plan we're going to share with you today. So we've been through it. We're very excited about the prospects that we have, and we're going to show our work today on why that's the case. I know you know this, but I want to add a little context to this hierarchy. Semiconductors is in service of overall electronics. Historically, electronics have grown GDP-ish, not now, not with these data centers. It's significantly above that. A lot of customers -- or investors will ask what's the bottleneck now? And I'll show and we'll share -- ask me in 3 months. Ask me 3 months ago. It's moving. The bottleneck is moving. But one thing that is clear is there aren't enough fabs. When we talk to our customers, there are definitely not enough fabs. And there's not even enough fabs contemplated in what we'll share for 2030 to meet all the stated demand out there. And if more of you start doing agents, that's going to drive up the demand for compute radically. And we know that's going to happen. It's already happening. And the agent's probably 100x the compute demand of the original search. So we're pretty excited about this. And we're going to make the case that none of this happens without process control. Without process control, ChatGPT would not have dropped, and certainly, we wouldn't be where we are today. So I'll talk a little bit -- of course, you know the landscape, but I want to give you a specific perspective on this landscape. And it is more about who designed the chips than the overall revenue and why that's so profound for KLA and for the industry. Up until we started seeing mobile and smartphone, there are a couple of companies that designed it. They needed their own silicon, right, one in Cupertino, one in Korea, that they wanted their own silicon for their phones because they could better optimize. That happened mid-, early 2004, '05, '06, '07 in there, and they started doing design for silicon. And we thought that was interesting, but it wasn't really a profound difference. It just shifted who was doing it and who was making it. There weren't that many more designs. The real inflection for our business and for this business, if I go back, it's really what happened when things went to the cloud. More so, that was the beginning of what we're seeing now when it went to the cloud because it changed the economics of who wanted to drive the design of semiconductors. We saw the first company in AWS, was one of the first ones to acquire design capability to build their own chips. And we're like why are they doing that. And if you fast forward it today, there's so many more players designing their own silicon. So it went from everybody buys the same microprocessor to people start designing their own. So that flipped the dynamic, and we'll show you the design starts go up. There's more players in it because the economic advantage for these players, it's now viewed as their differentiation. Yesterday, there was another hyperscaler that announced 4 new designs. Yesterday. That was on 3-nanometer. There's over 100 designs on 3-nanometer now. So this dynamic has changed significantly because it's in the economic interest of the cloud provider and the hyperscalers to have an advantage in their own compute stack. And so originally, they're buying from the same players, and we'll show you some data about how it impacted KLA and the transition we made. At the same time, comes along new ways to do compute. We've been locked in the CPU construct for a long time of how to do compute, right? And there were a lot of people that said that was not the most efficient way to do compute, but there are so many barriers to make that transition. And I'll share our example. This KLA system, and what we really ship, is a very high-end microscope with lenses like that and sensors like that attached to a very high-performance compute network or a compute data center, if you will, that sits. Now this is an example of one of our products, an edge server or -- it's really high performance. And we say cost was 1 -- and in this case, there are many examples like this in the company. It was literally $1 million of cost to us. So as we're going to -- that's the COGS of our compute inside of the system. So we're going to the next generation trying to figure out do we stay on CPU architecture or do we transition to GPU. We had already been experienced with GPUs and programming them. In fact, we had AI algorithms as early as 2018 in our products, but they were CPU-based. We hadn't made the transition. We wanted to, but what was the big barrier was the massive amount of rewrite of code. But when we looked at staying on CPUs, we knew, yes, we'd have minimal code change, but the cost curve was going to go up pretty dramatically and the power consumption. Once we bit the bullet and decided to go to the CPU-based architecture -- and we did it for the algorithms. We did it for the algorithms, but look what's happened. The cost only goes up from 1 to 1.2, and the power consumption actually goes down. Power goes down. So if you're a hyperscaler and you're paying your own power bill, that going down, that's enough just to do that. But if you're now also hosting models to run -- so our view of this was like there's no going back. It's way more efficient. And by the way, GPUs, because they're parallel process and Ahmad will talk to this in detail, they're a better way to do compute, but they're not the most efficient way to do compute. There's custom silicon being designed for specific tasks that's even more efficient than GPUs, which is why there are so many more designs happening. GPU is better, custom better yet. So what's interesting about this transition is once we make this transition as a company, we're never going back. And we leverage this across every one of our product platforms, the savings, and now we're on a different cost curve and a different performance curve, so huge implications for us. Now the challenge is, you all know -- first of all, one of the challenges is these GPUs are very large die. Why does that matter? It really matters to our customers because the yield challenges are much higher on large die. And the process control requirements are much higher, and we'll share the intensity that goes with that. But the other thing is if you've got one of these very high-powered processors, you've got to feed it data. So suddenly, the memory out of nowhere becomes a critical component. I mentioned the shortages. Right now, it's a memory shortage. High-bandwidth memory is a shortage, right? That is the thing that's a shortage because you got to feed all of these processors to keep them active because you paid so much for the processor, you got to feed it data. That's changed the device of a memory device significantly. The first change in years, high-bandwidth memory. And it all favors the need for more process control because of the size of the memory that's going up, the complexity, the lack of redundancy. And so for those of you that think KLA is not a memory player, it's not true. That was true. Actually, if you go back far enough, it wasn't true. We grew up on memory. And then it got less true as the memory guys figured out how to do redundancy, and now it's back in a big way. And we'll share why the intensity in memory manufacturers has gone up so much. The other thing that's amazing about packaging is we got into the packaging business -- Ahmad will show our road map -- years ago thinking more than more. Remember that? We were like -- we did our first acquisition in this in 2008, so yes, we were early. But boy, we're glad we're there now because this advanced packaging looks like a semiconductor process, which is why there's some confusion about capital investment, capital spend as it pertains to front end and packaging, and we'll try to clear up some of that. I mentioned this. This is crazy. This number of people doing advanced designs at the [ lead ], this has flipped on its head. So many of you, if we've done this Investor Day 10 years ago, you would have said that every node, there's going to be fewer players at the leading edge and only a few people can afford it, and therefore, the number of designs was going to go down. So a couple of things happened. One, scaling resumed and then the hyperscalers enter. So those 2 factors, you get scaling and you get more players who determine that silicon is strategic for them. So now we see -- and this is just within the first year. I just mentioned there are a bunch of new designs announced yesterday for 3-nanometer. Yesterday. And so you see the demand for 2-nanometers going up because 2-nanometer is a more efficient node. So what does this mean for process control? Well, if you have a lot of designs going through a factory and you have a lot of process flows -- because if I'm a hyperscaler and I want my custom silicon, I want my custom process flow, too. I don't want to buy what everybody else buys. I want the thing that I think is best. Well, if I'm providing that capability, I got to make sure the variations are low. So that's what's driven up process control. Look, this industry is all about economics. Nobody does anything if there's not an economic return. And the economic return on managing these complex fabs are now enormous, especially when there are shortages, especially when there are shortages. So what's the role of process control? Well, process control is a funny term actually because -- confession, I started as a controls engineer in the paper industry. Okay? There's nothing like this industry. I mean it's super exciting and all, but nothing like this industry. But it was just a simple closed-loop control. Why is the semiconductor industry so different? Because of constant change. You can't get something under control because it's constantly changing. So we talk about what do you do in process control. We originally, as a company, thought it was about finding defects and assuring quality. It is, but it's far more than that. And we learned this -- it was probably in the '90s. We learned this when there was a big study on competitiveness in semiconductor manufacturing, and the conclusion was because every semiconductor manufacturer essentially buys the same equipment, the competitive advantage is a learning rate, how fast they learn and their ability to learn and improve the process. Think about this. You're building a $15 billion, $20 billion fab and you start at 0 yield. So KLA is about learning faster. And learning faster is the only sustainable advantage in semiconductor manufacturing. It's the only one. I could argue it's the only one in tech, is the ability to learn faster. And so what our systems are used for -- and years ago, we had people think it was just for quality control or final check or whether we're a measurement company. No, it's about learning. And we'll show you the challenges of when people bring out this new process. So they adopt more capability, and they ask for our expertise because they've got to learn how to ramp that new process up because the stakes are so high. What can go wrong in the semiconductor manufacturing process? There are defects everywhere, and they're not stable, and they change all the time. And so the beauty for us is nothing is stable in a fab. You could run along with a process that seems to be working and there's a hiccup because some of the material -- starting material comes in and it's not what it's supposed to be. We've had customers that have suddenly had an experience because something changed in all these processes. And then you've got to make all these measurements. So the history of process control is you put a process in statistical control and you let it run into control. I can tell you this industry is never in control. It just isn't. Well, that's not true. If you make analog wafers, you can be in control. Truly, they don't change that much. So the other thing that's interesting, there's a couple of areas we inspect everything in. And one of them is a reticle, and I'll show you what a reticle is. This is a mask. This is a template to print and Ahmad is going to talk about this more, to print one of the many layers in a semiconductor process. They call it lithography because you're doing a lithograph, right? When I started in the industry, I know, a long time ago, $2,000 for a mask. And this little thing, this was a Saran wrap on top of it. That was a pellicle. Why did that exist, is because if a particle fell, it would be out of focus. That's what a pellicle was about. And I used to accidentally break them all the time. So my first job in a fab was doing inspection on these reticles. They were $2,000. This reticle today for EUV, $200,000 and I need sets of them. So that gets inspected a lot because if there's a defect on this -- and by the way, this is what we'll call a single die reticle because it's -- that means there's only one of these, and it's printing. It reduces when it prints, but it's printing. And if there's a defect on there, it prints on everything. You can't have that. So that gets inspected 100%. Almost nothing else does. Everything else, when we talk about process control intensity, everything else is sampling. So we have 1,600 apps engineers that work with our customers determining their strategy for sampling -- for sampling and inspection. I was apps #7 at KLA. So we've ramped a bit. And the reason is because we've got to help customers figure out where to inspect and where to measure, and it's dynamic. We get a lot of questions about process control intensity and how it's changed. And it has changed. And I gave you my example of analog. So the way to interpret this graph is from the left to the right is the relative percent of process control intensity in those different markets. And you can see analog is very low. Now the line is the amount that was spent last year on equipment. So it's low process control intensity, but they didn't spend much because not much changes. Far right, almost half of the spend for mask shop is process control, almost half. And the reason -- and it's gone up since 2018 because the cost of the mask, the number of single buy, everything has driven that number up. The 3 things that are in the middle of this, DRAM, WLP, advanced foundry, that's all about AI. The reason those have all gone up of process control intensity is because of the challenges, and we'll share more about those with AI. And Ahmad is going to go into more detail. But I think it's important to understand what drives process control intensity. It's a combination of the economics associated with failure as in a mask and the economics associated with the managing complexity. So we have multiple offerings. One of our strengths as a company is we have a portfolio. And the reason this matters to our customers is because we don't go in with a single solution and say this is what you need. So this graphic of e-beam to BBP laser scanning, look, the throughput different between BBP and e-beam is like 1,000x. So it's not a linear graph. But there are specific things that you can only find with e-beam, and there are specific things that you can use laser scanning for. And our customers are always going to look for the most cost-effective portfolio answer. And on top of all these inspections, there's the measurement. The benefit that we get as a company from having all these things is we can invest in common core technologies that get reused. Ahmad is going to show an example of why we've been able to make such an impact in e-beam, leveraging a lot of technology we already have in the company. The other competitive advantage we have in addition to our portfolio is our huge installed base. I'm saying that because we learn a lot about what to do for our customers. And it turns out now customers buy our equipment, and Brian is going to share our installed base support for customers. It's a great revenue stream, but it's a competitive advantage, and it creates a lot of value for our customers. The other thing that's happened, and this is kind of fascinating, is the life of an average tool has gone up dramatically. And again, Brian is going to share that with you. So we're a measurement inspection company. How do we measure success? We have an operating model we talked about in the video. You'll hear more about it of how we run the place. But for your purposes as investors, what do we focus our team on? What do the 4 of us, when we're in reviews, look at? We spend a lot of time on intensity. What is our relevance? What is our share of wallet? And we do that because we know if we create more value for our customers, they're going to buy more of our equipment. It's straight economics for our customers. If they get leverage out of buying KLA, they're going to buy KLA. The other thing we look at and focus on heavily is market share, but we constrain market share by gross margin. So you can have a high market share, but if you're buying the market, that's not great for -- as an investment. So we spend a lot of time looking at what's our gross margin and how efficient are we and what's our operating margin. And then do we have the talent? Can we find the people to continue to turn this crank? We have many product divisions, and so we need to hire a lot of people and get them into the KLA system. So how have we done on intensity? Well, since -- and Bren will show some other cuts because I know of you like to pick your end date. Pick your start date and you can get a different story. But 2019 until now, process control has grown about 16%. Lithography has grown similar, and we've outgrown it because we've gained share. So we'll talk more about our share and our approach to share gain. And we look at the overall totality of process control, but every one of our divisions, we look at what are they doing in terms of driving their position competitively to drive process control. This isn't -- it required an investment, big R&D investment. And if you think about what we invest as a company relative to our closest peers, typically, we're investing more than their revenue just in R&D. And we're investing in new platforms, new capabilities and then common things that we share across. And we view this as going -- this pretty much goes up every year. This is not -- and this isn't counting the 1,600 apps engineers that we have. This is just the R&D. So what are the areas of focus that we have? We look at all these areas -- just think about a microscope. Remember high school physics. You have a microscope. You got an illumination source. You have a stage that moves things around. You have an objective. You have your eyes. Every one of those things is a subsystem. When I joined the company, one of those things was custom for KLA. The only thing that was custom for KLA back then was the image processing. Everything else was off the shelf. Everything now is custom. We have custom development in every single area. Our competitors do not. They can't afford it. So we get the leverage out of those investments across here, which allows us to provide -- we don't do it to win competitively. We do it to serve the market, but it sure does help competitively. So this is one of our show and tell. That thing over there, as I mentioned, is a lens that's used in the 39xx. It weighs 300 kilos, which I'm told is a lot for a lens. The lens on the 2020, as I said, would have fit in my pocket, right? One of the biggest changes we made was -- when we started doing custom lenses was about 1999, was the 23xx. And this was -- we realized and the model show why we need the spectrum of capabilities to provide it. But every one of these generations, we provide more capability. There are those -- I know none of you who, years ago, said optics was going to run out of gas and e-beam was going to take over. This is Gen 5. Last year, we sold more Gen 4 than Gen 5. So optical is not out of gas. And the reason is because we keep innovating. We keep driving new capabilities and our customers want the most cost-effective way to solve their problem. So that's -- you'll get to look at that closely. It's an engineering marvel. We own the design. There's one place in the world that can make those things, and we're pretty proud of what it allows us to do. So how have we done in share? We're up 2 points from 2021, and we're now 6.5x our nearest competitor in process control. People look at the ability to defend markets. Back to my R&D. It's a pretty big spread. It was 4x 7 years ago. So we invest heavily. We focus heavily on market share, and we also focus on growth of service. So Brian is going to share his story, but that's a pretty good chart for service revenue. And we'll talk about why that's the case and why it's even going to grow at a faster rate going forward. But our installed base is also a massive competitive advantage, not just the revenue from service but the fact that we have that to leverage. We spend a lot of time on gross margin. Bren and I and Ahmad and Brian will ask if general managers come in and say they're differentiated, we'll say show us your gross margin. If you are differentiated, you'll have a better gross margin. And we'll do acquisitions, and somebody will say, "We have a really great product." And we go, "Well, because your margin is not that good." So is it your cost too high? Are you not pricing? What is it? And we'll drive those up over time. And Bren will show we absorbed a company with lower margins, both gross margin and operational, and we've been able to improve it using the KLA operating model. We're proud of our operational excellence. Bren talked about our cash generation. Those are things we're measured on. You look at management incentive. It's on relative free cash flow. We focus pretty heavily on operating performance. And of course, we got to get the talent to make this happen. Our job got easier in the last few years, I'll be honest, because for a while, nobody cared much about the semiconductor industry. We're back. So we're able to hire great people. And when we ask them how they feel about being part of KLA, they're proud. They can't explain to their family what they do, but they're proud. So where are we now? AI is clearly driving the industry. The outsized growth that we're seeing is because of AI. There's no way to do it without process control. You're going to hear more about this. And here's a point to take away. KLA wins no matter who wins. It doesn't matter which hyperscaler wins. It doesn't matter which semiconductor company wins. Doesn't matter which processor wins. Doesn't happen without process control. And process control is going to outgrow the market, and we're going to outgrow process control. So here it is, the assumption for 2030. We see semi growth, and Bren is going to double click or triple click on this, 11% because, look, there -- if the semi industry grows at that rate, it will still massively underserve all the data centers that have been announced as well. Capital intensity is going to grow faster. We're showing $215 billion. And this includes what you think of as WFE and WLP packaging and semi, plus or minus $20 billion, big range. Process control is going to outgrow it, and we're going to gain share. In service, we're upping our growth expectations of service based on what we've seen and what's in the pipeline. So what does that get us to? $26 billion in 2030 revenue, plus or minus $2.5 billion; $84 a share, plus or minus $8. We've done this before. We've signed up to what looked like really big goals before. We're generally pretty conservative, but if the spend -- like people will say this -- don't ever say this time is different. I'm not saying this time is different. This time is just much bigger. This build-out is much bigger than anything we've seen before, this data center build. There's just so many reasons to drive this build-out, and that's what drives our model. So the rest of the day, we're going to spend more time going through and explaining, showing our work on how we get to this. And the first person that gets to do that is Ahmad Khan, who's done an outstanding job running our product divisions and who's going to share deep dive on his businesses. Ahmad?

Good morning, everyone. My name is Ahmad Khan. I run the systems business for KLA and also represent the customer, meaning that I engineer the products and then I have to make sure that our customers take them. So it is a humbling process if the customer doesn't take them. I have nobody else to blame. So it's a good org design. Today, I'm going to talk about how the compute is changing process control intensity at our customers. That's the bulk of my presentation. I'll give you several examples of what is changing in the industry on chip design and how our customers are yielding them and why they are using more process control. To set the goals earlier -- as early as possible, inflections, share gains and portfolio expansions, all 3 of them are going to drive share of WFE for KLA higher than it was today. Today, it is around 7.4%; and by 2030, we're assuming that it's going to get to around 9%. So share of WFE is going up. We work very closely with our customers, very strong customer collaborations. Why is that important? It is best for your customer to vote what we should develop, and therefore, they are bought into buying that system to solve their problems. So that collaboration is very critical to us, and we work very hard to maintain that customer trust. We're focusing on innovations in hardware and also in AI. I bring that topic up because in AI, you can create models easily and replicate somebody else's performance. That is not the case for KLA systems. We build very complex systems to reach to very pure signals. That pure signal coupled with complex models leads to defect detection and measurements that solves our customers' problems. All of this will drive growth. Calendar '25 to '30, wafer equipment is going to grow between 11% to 13%, and the systems business would be around 15% to 17%. So what do we do? Rick said it's really about learning rate. It is correct. We do, do detection of defects and measurements of parameters, critical parameters. We collect lots of data at the customer fab, but our customers have insatiable demand for data because the output of process systems is ti nitride or tungsten or something else. The output of KLA systems is 1s and 0s. It's just information. And that information, coupled with the right people, helps solve customer problems. That enables customers to yield and make more profit. Now we participate in all segments of semiconductor manufacturing from infrastructure, which is reticle manufacturing, wafer manufacturing to logic, DRAM, specialty and now advanced wafer-level packaging and also component inspection. And finally, when the chip goes on to the PCB board, we also inspect and write the PCB board. So we are in the entire supply chain of semiconductor, gives us a lot of insight on what is happening in the market because we look at each of the segments and how they're doing. We are a portfolio company. When we go to a customer, we don't have a single product that we are positioning with the customer to solve their mission-critical problems. We're a portfolio company. We go in, and Rick mentioned the 1,500 applications engineers. We send those applications engineers to the customers to determine what is the problem in the line. Then, we use the right system with the best cost of ownership and performance to solve the customer problems. It's a full portfolio company, inspection, metrology, data analytics. These boxes are only a subset of what we make. It's much larger, but the font would be very small. We use the collaboration, innovation and execution method as our flywheel to drive performance with our customers. Collaboration is very critical because, as I said earlier, it is best for the customer to tell us what they want us to work on. Then, we innovate, and execution delivers the revenue. You will see this being used across my presentation. So now let's look at the systems business in numbers. Calendar 2025, $9.8 billion in revenue. $9 billion of the $9.8 billion came from the process control segment, $0.8 billion from the rest. 19% outperformance from calendar '19 to '25. The market grew 14% in the same period. So outperformance is a general thing that we work on. Every product group, every product division is focused on how to drive intensity and outperformance in the market. 62.8% overall KLA gross margin. Gross margin is an indicator that we have a differentiated product. R&D is very important. We really focus on reinvestment back in the business to ensure the problems of 5 to 7 years from now will be solved. The one thing to remember is when a customer wants to develop a new method, let's say, a vertical channel transistor, they have a pretty good understanding of what doping materials you're going to use. They can go to other customers and say -- other suppliers and say I need this doping material and I need this etch capability, well before it comes out to the -- goes into the production. But our customers do not design defects. They have no plan to design defects. Their plan is to have a defect-free process. But it isn't. So we have to think and develop systems that have wide-enough capability that when the process comes to high-volume manufacturing that we are able to do that detection and solve the customer problem. We don't have 4 years after the process shows up in HVM. So that goes into our design philosophy. How do we predict what will the customer do and how it will lead to a failure that we need to be able to detect and design today, which will take about 4 years and come out in the marketplace? This is why we are a portfolio company. We have many, many instruments, and each instrument has thousands of modes to go fine-tune the system to find that defect. This is why reinvestment back into the business is a very critical aspect of our methods. There are 2 numbers that I look at constantly and all of my product general managers look at very closely. One of them is market share, and the second one is process control intensity, which is the amount of process control that our customer is going to use to yield the device. So let's double click on those 2 numbers. So this slide has 2 lines. One of them is the annualized process control intensity. On the top, you have WFE or wafer equipment market. And at the bottom, you have 2 lines. One is the annualized, and the second is the 3-year rolling average. So you can see that process control intensity was around about 5.3% back when WFE was around $60 billion and fairly flat but something happened in calendar '21 onwards. And generally, if you back up, the main inflection that changed is customers started to print smaller and smaller devices because of EUV, high-end EUV, many of other things. And this drives large variability in the fab, and I'm going to describe that in detail later. And therefore, the amount of process control customers needed to yield the devices started to go up. And you can see a clear trend, 5.8%, 6.4%, 6.7%, 7%, 7.1%, absolute number, 7.4% intensity. And we think the trend continues because I think we can all agree that complexity of semiconductor devices will go up, not down in the next 5 to 10 years. Large dies, high variability, all of those factors are driving process control intensity to go up. This is why we feel that now in the 2030 period, this number goes from 7.4% to 9%. The second thing is in a growing market, we want to continue to grow share. We made a commitment in the 2019 Investor Day that we would grow share by 0.5% year-over-year, and I'm happy to say that we've been doing that. We are now at 56.5% share, up 2% since 2021. When I was here last time, I said we were 4.5x versus the nearest competitor. We are now 6.5x versus the nearest competitor, meaning the next competitor is below 10% in share. In the recent past, we have gained share in optical, in electron beam. Electron beam is a new segment for KLA. The last time I was presenting to you, the e-beam market for KLA was between $75 million to $100 million revenue. Today, we are at $400 million. And I'm going to describe why we got into e-beam and why it's very important for future and why -- how we are leveraging it for our entire portfolio. We gained in advanced packaging. About 4 years ago, we were less than 10% share in advanced packaging. We are now in #1 position in advanced packaging. And mask inspection, Rick spoke about reticle inspection a few minutes ago, and we are gaining share in that segment as well. So those 2 numbers are very critical. But it's not just me and the senior staff that look at this number. Every product group in my organization is driving those metrics. So I'll give you a few examples. Reticle inspection. So one unique thing about reticle inspection different from wafer inspection, in wafer inspection, you're generally looking for signature of the defects. There are some defects here and some defects here. If you catch 25% to 30% of the defects, it is good enough for a process engineer to now make changes to improve the defectivity level. That is not the case for mask inspection. We have to detect 100% of the defects. 99% is not good enough because that 1 killer defect will print on 100% of the dies and your yield would be low, 0, if it's a killer defect. So it's very critical that you detect 100% of the defects. Our method to detect 100% of the defects is to use a suite of systems for best cost of ownership and performance. If you bring a single system to do this, the chances you detect 100% is low. So that drives intensity up. Unpatterned inspection, very important. So as you know, about 4, 5 years ago, a semiconductor wafer fab with equal real estate was producing about 50,000 wafer starts per month in logic. That same real estate now only produces about 25,000 wafer starts per month. What happened? The number of process steps to make the advanced nodes has gone up significantly. In order to have more process steps, you need more process tools. Those process tools now generate just as much defects; and therefore, unpatterned inspection qualifies all those process tools to ensure they are clean before you use them for high-volume manufacturing. So unpatterned has grown significantly for us. Optical inspection, we used to use a single system for a single layer. That is no longer the case. We have many layers where the types of defects on a particular layer is 3 to 4 types. And therefore, our customers will use Gen 4, then they will use Gen 5, then they'll use Gen 5 again, and then they'll use electron beam system, all on that same wafer to detect all the different types of defects. This is driving intensity. E-beam. E-beam, we got into for 2 reasons. One, for buried defects we were not in e-beam prior because most of the architectures were planar. Now you have a 3D architecture. So you have subtle defects buried in. We're able to detect most of them with Gen 4 because it is a longer wavelength, and we can penetrate, but electron beam helps as well. At the same time, because we have so many Gen 4 and Gen 5 systems, an electron beam system provides intelligence for the setup of our Gen 4 and Gen 5 system, and I'm going to go through that in detail. This enabled us to grow from $75 million to $100 million in revenue to now $400 million in revenue, all driving intensity and share. I'll pick on one other example, which is data analytics. All of these systems, data systems, are generating petabytes of data every single day. And our customers can use that petabytes of data for an instant or do analysis when their customers come back with reliability failures. We now have a suite of software and data analytics systems that we provide to almost all customers for advanced packaging and for front-end manufacturing where customers can do overlay analysis and CD analysis across the board. So this suite of -- this business now is $250 million, software business that is doing quite well. So all of this is driving intensity and share. Okay. So going back to collaboration, innovation, execution, I'm going to talk about first, the collaboration and what is happening in logic. AI-based inflections are really driving a lot of change in logic. The logic from 5 years ago is very different than the logic of today. So what is changing in the logic segment? So prior to going into the technical details, I will quickly explain what is happening in the market. The first initial build-out for AI was in training. That is in its late stage now fully implemented. The second phase is inference. And I think we -- most of us are experiencing the inference world now as token usage continues to go up on a weekly basis and our customers are able to generate revenue from that. After that, there is deep reasoning. And as Rick talked in, elaborate detail about agentic AI and autonomous work, each of these phases has multiplicative effect in growth of semiconductors. We see it in our own systems, and we see it in the marketplace. This is going to continue to drive growth. Now this growth is affecting all chip designs across the semiconductors. It is affecting logic, in CPU, CPUs are primarily now used for complex compute and data orchestration; GPU, which is very, very good at single-mode processing, and we use it across the board in our systems; high-bandwidth memory, HBM, is very critical because you have to feed these GPUs with millions and billions of transistors data. And if you don't feed the GPUs, billions of transistors, the data, you will slow down the processing. So memory went from traditional DRAM to high-bandwidth memory so that you are able to load that data as fast as possible. DRAM growth for reliability reasons and also for conversations, and we now see growth in eSSDs for conversational history reasons. All segments of semiconductor devices are growing at massive rates. So if we look at -- and we have examples later on, you can see here is a full board of Blackwell mixed with other devices. In order to make full system, you need a couple of large GPUs, surrounded by several HBM guys, and then you take all of that and integrate it on an interposer and then on a PCB board. So this is advanced packaging. And then PCB. This is logic. This is memory. All of that comes together to make the device. So let's talk about leading-edge logic, then leading-edge memory, and then I'll talk about advanced packaging and how KLA plays in each of these areas. So what is happening in logic? The first thing is, there is a term that I'm trying to introduce, which is called variability. Most fabs deeply understand what variability is, and I'll give you a very detailed description of it later. But variability is inversely proportional to the printed feature size. And this is why I brought up that 5.6% intensity going to 9% intensity. As people started to print smaller and smaller lines and spaces with EUV, the variability in the fab goes up. If variability in the fab goes up, you need more process control to bring that variability down or detect those defects. The details of this is going to come later on in my presentation. The second biggest inflection is chip size. If you have 600 chips on a wafer and a 1% yield loss, and you do nothing else but increase the chip size and you have 60 chips per wafer, that equivalent process will have a 10% yield loss. So chip size -- and process companies are governed by the size of the wafer, and the size of the wafer is fixed. You deposit the same amount of material on a large chip and a small chip, but process control is governed by the size of the chip. If you print very, very, very small chips, 1 killer defect will kill 1 chip. But if you print only 2 chips on a wafer and you have 1 killer defect, your yield is 50%. So as chip size goes up, process control intensity goes up. And this is true for logic and also for DRAM. So what is happening in the 3D architecture? This is a 3D channel transistor. And the key thing to remember is the channel -- the worst channel is going to determine the performance of the transistor. And foundry customers are not allowed to bend their devices, meaning you have a high-performing transistor, and you can sell it to customer A and low performing, it doesn't work. You have to have the same performance. So KLA comes in to look for subtle defects with our Gen 4 systems, and these are many different types of defects like SiGe residue and our electron beam systems to ensure that this is defect-free, and it will perform well. And at the same time, we have metrology solutions ensuring that the dipole engineering that is done on these channel transistors is done really well. Because if your dipole engineering is not doing well, you're not able to provide multiple VTs to your customer, and therefore, the value of that product is far less. So we have a suite of tools that are doing this. So if you summarize all the things that are happening in logic, which is variability, large die, buried defects, we see significant growth in KLA process control, 30% increase in total inspection steps in logic. If you look at this color, the first one is the surface defects, the bread and butter of KLA, we are very, very good at doing that, and we've been doing it for years. Buried defects were introduced, followed by electrical defects and followed by new applications like print check. If you add EUV, you need to detect repeater defects, and we have new systems that do that. So N minus 2 to N minus 1, we see 30% increase in intensity and then N minus 1 to N, another 30% increase. Incremental value versus N minus 2 to 4, KLA is $1 billion in logic. I will now switch to DRAM and HBM. So what is happening in memory? The first thing that is happening is you are -- the customers are trying to increase overall density inside DRAM. Why? Because they need more DRAM transistors. There's a couple of things you can do. You can add EUV, and the other thing you can do is reduce the capacitor size, which takes a lot of space. So as you reduce the capacitor size, the amount of electrons that are available to you to detect if a transistor is on or off is very low. If you have less electrons, you have to make very, very good logic circuitry inside DRAM to detect if the transistor is on or off. So the main thing that is happening in DRAM as it pertains to KLA that it is more or less now becoming like a logic device. It is not like a traditional DRAM device. That is what's driving process control intensity. Rick said, stop thinking that KLA's DRAM intensity is low. That's exactly the point. Why? Because it's becoming more like logic. So as the capacitor sizes go up, customers have to design better logic to ensure that they're able to detect it when a transistor is on and off. To drive packing density, you're introducing EUV. Some customers are now to 5 to 7 layers, others are 1 to 2 layers, but the trend is in that direction. With EUV, the great advantage of EUV is that it prints smaller lines and spaces. But it also prints smaller defects because the wavelength is short. So if the wavelength is short, it will print smaller wavelengths, but it will also print smaller defects. And therefore, high sensitivity systems from KLA are needed to detect those defects. Also new applications like EUV print check and mask recall, all of those things happen because more EUV is happening. Large chips, DRAM and HBM chip sizes are increasing on a regular basis. And the reason they're increasing is because you have to add more transistors and because in order to do high bandwidth, you add TSVs from top, all the way to the bottom, that takes a lot of space. They're not very small. They're large. So that is driving chip size. And then eventually, less circuit redundancy. Customers used to have immense amount of redundancy in DRAM. One of the reasons is that the iPhone -- generally, I mean, we're all sitting here, most of the iPhones are idle. And when you sleep, they are idle. So the DRAM performance requirements are very different than a server-class DRAM, which is performing 24/7. So all of those redundancy things also have reduced pretty significantly. So now the details, I explained everything already. EUV prints repeater defects and also prints smaller defects and therefore, KLA needs to come in and detect those defects. The chip sizes in DRAM are going up pretty significantly. This is a DDR chip and HBM3, not 4, 4E, even bigger, is 3x the size. And then this part, this is the non-memory content and the memory content. The memory content from DDR to HBM reduces pretty significantly and the logic portion increases pretty significantly. And therefore, you have to ensure that this part of the chip, which is the same place where this is and the same place where this is, yields very well. This drives higher process control intensity because now you're essentially making a logic chip, and you're calling it a memory chip. Therefore, overall intensity in memory has gone up significantly. KLA incremental intensity gain is $450 million in calendar '25. That does not include the fact that market went up. So if you look at the same point differently, DRAM wafer equipment market went up by 2x. KLA DRAM revenue went up by 3x. And the reason for that is all the things that I just said. Okay. So now we have a logic wafer. We've spent a bunch of money making sure that, that wafer yields. We have a DRAM wafer, and we have spent a bunch of money making sure that, that yields. And now you need to package it. And in packaging, you will have lots of losses because of cutting the wafers and then doing 2.5D or 3D packaging. In HBM case, you are packaging 12 dies one after the other. And then in CoWoS, you're doing the rest. The total number of chips for an advanced AI chip is greater than 100 chips before a system is available, right? So you can just quickly do the math, 12 DRAM chips times 6, and then you have a couple of logic chips, all of that go well above 100 chips before a first system is available. And these are very, very valuable dies. This is why advanced packaging became very important, very important for KLA and very important for industry. This is not wire-bonding packaging, right? That is fan-out packaging. That is low end. This is very high end, and the intensity is high because you are going to lose good die. And in some cases, your logic customer who is doing the HBM integration will have to buy new HBM if they lose the HBM, right? Because the HBM manufacturer is not going to say that you lost some die, so here's some free ones. So it is a complex problem, and KLA is all over this area, and let me explain how. So as Rick said, we did an acquisition in 2008 for ICOS Systems, which is a component inspector. And then we also built -- did a second acquisition, and we bought another system called CIRCL, details, not important. But what these 2 systems enabled us to do is to get into the specialty and packaging market, and we started to learn about everything to do with how do you handle packaging substrates. Our problem is never that can -- do we have the next 10 systems with resolution. We have all of that. Our KLA's packaging problem is not that. Some other competitors may have that problem. We don't have that problem. We're doing 10-nanometer detection in the front end. Packaging defects are not that big. So that is not an issue for us. The only issue is can we handle all these different types of substrates. So our teams started working from 2008 to 2015 on all substrate handling, glass, silicon, other materials, ABF, all of those types of things. So we can handle them and are able to detect the defects and measure the curvature and all of those things. When advanced packaging became important, our core customers, the top 4, 5, you can guess who they are, said, "Please take all your front-end systems and make them packaging compatible." We were able to use that $1.44 billion that I talked about earlier and divert a large portion of it to doing full integration of packaging as fast as possible for all of our front-end systems. And today, even though Kronos is the high sensitivity, high throughput system and the workhorse of packaging, we have the next 3 systems already ready, PUMA and there's a bunch of versions of PUMA, then Voyager, a bunch of versions of Voyager and our Gen 4 system, all ready for packaging because we have deployed that handling capability. At the same time, we are not just dealing with round things, wafers. We are now deploying panel capability on all of our systems. So we'll be able to handle panels. Why panels? I think you all know because the chip sizes are going up. Even though everything is full field reticle, right? It's one field reticle, but it's a 4x reduction after that. But in Blackwell's case or Rubin's case, then you take 4 of those and tile them together. That's how you get scale because EUV systems cannot -- today cannot handle bigger reticles. So all of that is driving panels. So we have a full suite of systems to do that. Now results, I'll talk about results in a minute. There's also inflections in packaging. Today, in DRAM, almost everything is micro bumps, and we are able to do micro bump inspections. And in the future, near future, for logic, SoIC is going to go to hybrid bonding. And for that reason, we have a whole suite of systems for hybrid bonding. And in the future, if DRAM goes to hybrid bonding -- and we don't have -- we don't care if they do or not because it doesn't affect us significantly, we are able to sell micro bump systems or hybrid bonding systems. We will have those systems. Now the one difference is that the specifications are significantly tighter, in many cases, 10x tighter. And this is why the next 4 systems I've already developed to ensure that if customers go to hybrid bonding, we will be able to detect very, very small defects for them with no issues. So we're watching this inflection very closely. And our customers, as I said, collaboration, innovation, execution, collaboration, customers constantly telling us when they're going to do this switch and -- but we're ready for that. Okay, results. We had 10% share in packaging in 2020. So we were #4 in a 3-player market. And then 4x increase in share since 2021. $950 million in advanced packaging revenue in 2025 and #1 share position in packaging for process control. Okay. So I talked about technology inflections. I would now want to speak about high-volume manufacturing. I get this question from time to time from investors with a slight doubt that KLA does really well during the initial stages of R&D. But as customers ramp up wafer starts, they slow down purchasing process control. This used to be the case. It is not the case anymore, but I will give you some data as to why that is the case. So now let's first talk about variability. A day in the life of high-volume manufacturing fab manager, what is this person dealing with? First, feature sizes are becoming smaller. Second, 3D architectures are going up. Third, number of process steps are significantly increasing. I talked about the size of the fab and what the same real estate produces today. Fourth, Rick spoke about it, number of designs going significantly up. We see -- this is process steps, 1.2x, 1.4x, and then number of designs going up pretty significantly. Chip sizes going up. In-line process control specs coming down, right, because you need to detect smaller defects, EUV, all of this. All of this leads to high variability in the fab, all of this. The next thing is what do you do with this variability? Should you do inspection everywhere? That is a difficult thing to do because the cost will be very high. So how do you go about determining what is sampling, right? You have 1,500 steps to make a semiconductor device. In the 1,500 steps, each step has a lot, right? It has 25 wafers going through each of those steps, each one of those steps. Inside the lot, there's 25 wafers. Inside the wafer, you can decide to just sample this bit, or you can sample the entire wafer. Inside that wafer, you can decide to do low resolution or very high resolution. How do you do this? How do you determine what you're going to do? So that's where the 1,500 engineers of KLA come in. It's a competitive advantage for us. We understand how yield works. We deploy those 1,500 engineers across all the fabs. And then we try to determine what particular problem this one customer is having. Every customer has a different problem. So we deploy those engineers and we process map out what is happening and then we help solve the problem. But in general, this slide is going up. Sampling in general is going up. The number of overlay points on a wafer used to be about 250. Today, there's many layers, well above 3,000 points, to make sure that the alignment between Metal 0 and Metal 1 is perfect. And if it isn't, you will have to do rework. If you have to do rework -- what does rework mean? You just used an EUV scanner to print. Now you strip it and print again. That second EUV print costs you a lot of money. So people don't want to do that. Metrology is far cheaper to do, and that's what they do. So how do we process map? Rick showed a slide about what's happening in the fab, right? There's deposition, etch, everything else. So we go through the full loop until a wafer is etched into silicon. The pattern is etched into silicon. We do defect inspection. If it's clean, it's great. If it's not clean, now we need to do source analysis. We then segment the line. We have a number of different systems that segment the line. We look at what happened in CMP. We looked at what happened in etch. We looked at what happened in deposition. We segment the line. And then come up with the right suite of solutions. You need that many bare wafer systems. You need that many high throughput, lower resolution, darkfield systems, and then you need so many brightfield systems to make sure this variability is controlled. That's how we do it. 1,500, 1,600 employees that are across the world doing this on a regular basis, helping our customers. Now collaboration is very important. If a customer thinks they can do it, then the chances of success is A, but most customers depend on KLA to do this work so that we can help dial the yield down -- yield up, sorry. There's many types of defects also, right? There is what we call systematic defects, right, which is you kind of understand why the defect would happen. And those are easier to solve, and we help solve those. We don't want customers to have systematic defects. We just don't. They won't yield. If they don't yield and, Rick says, every time, all the time with this thing that it's about economics. So we want them to resolve the systematic defects as fast as possible. The second is stochastic defects. These are subtle defects. They'll happen and then they don't happen, and then they happen and they don't happen. So you have to determine where they happen. Why they happen. They're related to design. They're related to how much dose you're using. What your focus is. And then there is random defectivity, which just requires consistent monitoring. Otherwise, you will have a line problem. And it is about economics. I mean the high-end logic wafers are $25,000 a wafer with 50,000 wafer starts. So you can just do the math if you have a 3,000 wafer excursion. That's a meeting that you don't want to be in. It's a tough situation. So I skipped those. So if you looked at process control, buying behavior, N minus 2, the slope wasn't 1, N minus 1 and now N. Customers are buying process control at a fairly linear rate as they add capacity. And we see this now for advanced logic, and we see this for memory as well. So I hope that helps answer the question. I've been asked this question several times, so we thought that we would add this section in the presentation to answer how customers' buying behavior changes as they do high-volume manufacturing. All of this, again, results. Semi-PC relative outperformance from 2018 to 2025 delivered $8.5 billion in outperformance during that period. So this is above-market performance. I'm not talking about what the market was because market is -- we are supposed to perform at least at market. This is the outperformance number, $8.5 billion during that period for process control. So first is that they do this and second is you have the numbers. Okay. So now let's go into point product differentiation and something that I introduced last time, which was co-intelligence and what does that mean? So as Rick said, what do we do, we make sophisticated microscopes, optical and e-beam. We connect them with supercomputers, and then we report results. There was a customer who asked me, "How come it took you about 6 years, 7 years to be able to do e-beam?" I said, well, we didn't do any of this for e-beam. We didn't do any of this for e-beam. We only just needed to change the microscope. And this applies everywhere in the KLA portfolio. When we decided to do advanced packaging, we're using AI for advanced packaging today. Why would we have to use AI for advanced packaging? You can say that the size of the defect is so large, you should be able to easily detect it. The one big difference is, advanced packaging uses organic materials. Organic materials play up very differently in optical scans than what we have in silicon wafers. So now all the learning that we developed on models and detection capability in AI and electron beam systems, we are able to use that and deploy it on advanced packaging. So this story goes across the board constantly all the time. And our collaboration inside the company is very strong. We have engineering conferences that our CTO runs where we share algorithms that were developed in Group 1, algorithms that were developed in Group 2. And these are very technical conversations that enable us to disseminate our learning across the board. And it's better. We can afford doing it more than once, but it's faster and cheaper to be able to do it this way. So we would make a large investment in algorithm development, let's say, in e-beam, but a very small development cost of deploying that algo on a packaging tool. But if a packaging company has to do that, they have to start from scratch and do it that way. So the portfolio acceleration is quite large, and we really like it. So we do innovations in light source optics sensors. During the breaks, we have several people here, including our CTO, that can go through these innovations in detail. Optics is very important. I don't think AI can help solve that problem. So if you don't have a pure signal, then the chances of you being able to do defect detection is very low. And that's why I think KLA, with its systems and its algorithms, helps customers solve problems. So light. So deep UV broadband source, 2x brighter than the surface of the sun. It is -- we are right now one atmosphere here, which is 760 torr. There's several atmospheres inside this bulb. It's a very tiny thing, but it generates a lot of light. And we are a big light company because we have to do light for optical systems, electron beam systems, Gen 4, Gen 5. Now we're working on a new source for Gen 6. So a big light company. The second is a 300-kilogram catadioptric objective, very difficult to make and manufacture, but it has the ability to detect very small defects, and that enables us to solve customer problems. And then the third is this custom sensor, right? If you look at, this is the chip itself, which is able to take simultaneous 2,000 iPhones every second. That's the image generation capability of this thing. It looks very small, but to run it, you need this big thing. There's a lot of electronics, a lot of cooling. It generates a lot of power, all that stuff. And this is all done custom inside KLA. We make our own sensors on light sources. So why do we develop systems that are this capable. The earlier story, I'll just repeat it quickly. Our customers don't design defects. Our customers design logic circuitry and DRAM circuitry, and then comes the defects. So we have to build systems with a lot of capability before we know what defect they will generate. This is why we build these broadband systems with lots of resolution and capability so that a customer when generates a defect type will be able to detect it. And every customer is different. Every customer integration is different. I know people say gate-all-around. No, gate-all-around, customer A is different than gate-all-around to customer B. So we have to detect all of those defects. I'll just give you one quick example of why broadband, right? This is the spectrum of the wavelength of light. The type of image that we are generating is the same, but you would have no contrast of that image in this wavelength of light, but you will have very good contrast of that image in this wavelength of light. Now if I designed a line tool, otherwise known as a single wavelength system, then when the defect shows up, I may not have the right wavelength. And now a customer is like, "Okay, KLA, you are supposed to be #1 in the segment and you cannot find my defect." That's another meeting I would don't want to go to. So we need to make sure that we can predict all of those permutations for our customer. This is why we have several systems with different wavelengths, 21 x-axis, 28, this thing goes from 240 nanometers to 450 nanometers. This goes from 190 to 240. And then now we started to design our Gen 6 system with even a shorter wavelength. And we continue to increment each of these systems every couple of years to bring additional capability. Now just to think about how much data is generated. I'm going to make a case of why KLA wants to make their own HPC computers. Why do we make our supercomputers? Not because we want to do engineering because it's interesting, but there's a good reason for it. So an average brightfield system takes 78 trillion pixels of images in an hour. This, we then arrange this into 65 million frames, then that leads to 40 giga pixels a second of image data rate. Some more details. 12 terabytes an hour. That's what we generate. 1 petabyte in 8 hours. Total GPT-4, I believe, was 4 petabytes of data, total GPT-4. If you ask GPT sometimes it says it was 1 petabyte. I like to use 1, but I don't want to be inaccurate. We generate that much data in a shift, on a single tool, on a single wafer. That's the amount of data we're generating. And then, that data, we cannot store. If we try to store this data, we will have fabs of hard drives. We cannot store. So at that data rate, you have to process everything. This is why we have a supercomputer. Because if we try to store it, it's too much. It's literally drinking from a fire hose. I mean, if you can imagine that, right? That's the amount of water that is coming in. There's no way you can store this. You have to process at the data rate of the system. This is why we build supercomputers, and we designed them to ensure that the system has this. Now we can build low sensitivity systems, like I can reduce the pixel size by half, and now the data rate goes by half, but now you don't have sensitivity. That's the reason we do it. And once we do it for one product, we can replicate it. We just need to reduce the amount of computers you need, the architecture is done. KLA also has a 5-layer cake for AI, right? Okay. So you know the reference. I was thinking maybe you'll get it, maybe not, but no. Okay, you got the reference. So physical subsystem is very critically important. That thing there. That thing over there. High-performance compute, I just talked about the HPC is very critical. Software infrastructure, to deal with all this data that is coming through. We're not a process company. So this process company software is very low, very low, right, because you're opening actuators and things like that. Software infrastructure is very critical. AI models, we use foundational models. We use all the different types of large language models and detection models. But the most important part is that we have the context of understanding what is inside the optics and what is inside the wafer, and I'll describe that in a minute. And then lastly, the application layer. So our 5 layer ensures that we're able to provide the result to the customer. So let's double-click on a couple of them, right? So this is why do you design your own HPC? Why do you design your own HPC with CPUs and GPUs. As I said earlier, that if we go to a very large pixel inspection, so now the resolution is low, right, because the pixel size is very large, the data rate goes down, the data rate goes down, but the sensitivity goes down. You will throw away the defect. If the defect is here, you won't be able to detect it. So what we try to do every single -- every 2 years with Gen 4 and Gen 5, we bring in more light. We make the sensor faster, and we improve the optics. Why? So we can increase the resolution. We increased the resolution. But the moment we increase the resolution, what happens, we take more pictures. We take more pictures, we need to scale the data rate. So this is why we custom design everything inside KLA. We don't go out and buy a rack from somebody, right? The second thing is that we want to make sure the images that we're taking are extremely accurate, right? This is the optical image. We align that optical image to the design. Our customers provide us the design. Most inspection companies don't get design. We get design. We align the optical image with the design, then we build an e-beam system, and we coordinate match the e-beam system with the optical system. So now the chances that there's a defect here and you miss it is very low because the design is telling you something, the e-beam inspection image is telling you something and the optical image is telling you something. All 3 of those come together. And then we can add other information from design from the customer. This area is more important because there is this thing next to that thing that will cause a defect. All of those enable us to reduce what the customer calls this nuisance defect. You can always find differences. You can always find differences. It's not the difference that we care about. It is the defect we care about. So customers tell us, "Yes, I can see that there is a difference here, but I don't care about this. I only care about that." That's the other part we have to do. We're not in the job of reporting differences. We are in the job of reporting the critical defect that is going to cause the yield loss. And all of this, with our systems, we're able to do. So I hope that answers the questions. Now I'm going to talk about a concept of how we bring the full portfolio to do defect detection. Okay. So mask inspection, I'm going to start with that. So what happens in a mask shop in a wafer fab. So first, you take a plate, this is a glass plate, and you want to print the design on to this. You use an electron beam system to write the design. The customer has the design, and they write it. Now the design is written, you need to do a pattern inspection to ensure that there is no defects on this plate. Then, after you've done that, you take this plate, you put it in an EUV scanner and print. During that print, you can add defects onto the plate because the EUV uses a source that can inherently generate 10 particles, and you can get on that. So what we have to do is after this plate is printed, we have to find all the defects. And after this plate is sent to the wafer fab, if there's new defects added, we have to detect those. So there's 2 places a mask goes through. One is a mask shop and the second is the wafer fab. In the mask shop, you're using the electron beam writer to make the mask, and it needs to be defect-free. And when you ship it to the wafer fab, you start printing wafers with it, and it needs to be defect-free. Now how many reticles do you have in a wafer fab? You think, maybe 10, 20, 30, 100, that's a lot of reticles. Now why do you have so many reticles? First, number of designs. You have many designs. Number two, number of lithography layers, right? You have a lot of lithography steps, and lithography steps are going up. Number three, re-spins. A customer brings a design, the design doesn't work, you need to print another reticle. And number four, extra reticles because you will have a defect on a reticle and you need to send it for repair. All of this causes a number of reticles in the mask shop. We believe it's about 700,000 reticles per year, new reticles per year. And we have to do defect inspection on all of them. Now in advanced logic, there are 50 193, 193i immersion reticles and about 20 EUV reticles, okay? For inspecting those, you would think you would need systems that are 193 and EUV. That's not the case. Our 193 system is able to do all 193 reticles and are able to do most EUV reticles, not all, but most EUV reticles. What's the difference? It is called printed pitch. If the printed pitch, which is the pitch of the design, is larger, even though it's EUV, we will be able to detect the defects with 193. And if the printed pitch is smaller, very small, then we won't be able to do it with a 193 system, and you need a high sensitivity system, okay? So that's how it works. So it's about 3 to 5 reticles in advanced logic that are very tight dimensions. Everything else we can do with a 193 system. Now in advanced memory, the same thing happens. It's 25 plus 6. 30 of those can be done with a 193 system, and maybe 1 or 2 reticles require very high sensitivity to ensure that we can do defect detection. So this is the suite of systems that we use to do defect detection. As I said earlier, it depends on printed pitch, right? So relaxed printed pitch, 70-nanometer. 30 to 40-nanometer, we can easily do with our optical system, and then we can easily do with an optical system with EUV capability, most of those. And then in the wafer fab -- and I'll go through this in a minute. And in the wafer fab, you are just looking for particles, right? You're not looking for design issues. We have a 193 system that does most of the work. And then we have something called Gen 5 EUV print check, which can determine reticle repeater defects. We do that with that -- with -- sorry, EUV and then with Gen 5. And all of those capture most of the defects. Now we're talking about a few subset of defects with very small pitch, how are you able to detect those? There's 2 ways to do it. One is you can develop an actinic inspection system. It has the same wavelength as the EUV scanner, 13.5 nanometers, or you could do something else, which is an e-beam system, which has very high resolution. So we're doing both, and I will show you results for both. So the first one, TeraBeam 8xx mask inspection system, we just brought it out to the marketplace. We've been developing it for the last 5 years at a customer, very close collaboration. And that customer is TSMC because we have published a paper recently. It's a massive technology. If you get a chance later on, you can see it. This is the 12x12 column array. And why 12x12 column array because we have to cover the mask completely. e-beam is slow, but if you had a lot of e-beam outlets, you'll be able to do it fairly fast. So this column array, we have 24 columns, 12 of them are always running. It's 100,000 pounds system if you're buying by weight. And we have an AR/VR model in the other room. So why this system? Because as I said earlier, the printed pitch matters quite a bit, right? So 36-nanometer, this is TSMC paper, so I'm just going to repeat what they said. So a deep UV system can cover easily 36-nanometer and greater, even though it's an EUV print. And an e-beam system has ultimate resolution, so it can go all the way to 20-nanometer printed pitch. This is reticle pitch, right? So 4x after that on wafer. So this is very, very aggressive. EUV, high-NA EUV covered both. No issue. Nobody is printing 20-nanometer printed pitch on reticle for wafer. So they've built reticles that are very difficult to test this machine. And you can easily see that with actinic, you will have a slight resolution problem with e-beam, you can easily do it. Now why do people do actinic? There's 2 reasons. There's types of -- again, there's always a type of defect problem. Currently, we don't have a type of defect problem that deep UV and e-beam cannot capture, but there could be a type of defect problem. And the second is that it is easier to do because you are able to do 24-nanometer pitch, which is in the middle. So for the next couple of years, you could continue using this system before you need this. But our customer decided to develop this system with us because then it has ultimate resolution. An actinic system will always require change in NA each time you change the NA of the scanner, right? So that's why 0.33 NA is one system, 0.55 NA is another system, 0.77 will be another system. And then after that, if you change the wavelength from 13.5 to 6.5, you will change the system again. This system is more -- longer capability. But regardless, we're developing actinic inspection also. We have taken our first images on our prototype system in our lab, and we have done our first inspection a couple of weeks ago, and the results look quite impressive. So that was the mask shop. And in the wafer fab, you need multiple defenses. Now in every case, you need multiple defenses because as I said earlier, in reticle inspection, you have no choice but to detect 100% of the defects. Defense 1, our EUV 670e XP2. It's a deep UV system. Defense 2 Gen 5. Now you would print -- you would do this inspection every 250 wafers. Every 250 wafers you shoot under an EUV scanner, you're going to do a deep UV inspection. So the throughput of an EUV scanner is 250 wafers an hour. After 1 hour, you will do this. Defense 2, Gen 5, every 4 lots. So maybe like 800 wafers, you will do a second inspection. And this one, maybe around 2,000 to 5,000 wafers. It's really an insurance policy inside the wafer fab because it's a cost of ownership on an actinic system for recall is quite bad. So most customers use these 2. You can always add a third defense to catch all the defects. Okay. So that is a co-intelligent solution for reticle inspection. Now how are we doing on a co-intelligent solution for e-beam. So why did we get into e-beam? As I said, there are class of defects, very subtle defects that you may not be able to detect with optical. We still think the ratio of optical and e-beam is greater than 80-20. 80-20? The actual number is 88 and whatever the other number. So it's on the fly mask. So that's what the ratio today is. So it's 80-20 is what we loosely call it. But there's class of defects that e-beam is very good at. And as 3D became important, those subtle defects can be detected with e-beam. So the question is, if you can detect with optical, will you use e-beam? No. There's no physics and no economics that will enable you to do that. So 50 nanometers all the way to you go, you decide where it stops or where it starts to struggle. The second thing is, if I can do what I showed you earlier, I have the brightfield image, I have the design image, and now I'm going to provide it e-beam intelligence, the brightfield will be able to extend more. So now we are using e-beam to extend our brightfield systems more. And thirdly, I said, in HBM, they're buying a lot of brightfield systems. Well, there are 1,000 modes per brightfield system and multiplied by the number of 1,500 layers. When an electron-beam system can help our AI model to determine what is the best brightfield mode for the type of defect. So these 3 reasons is why we got into it. Now we thought we would just get into one segment of e-beam, but that was not a good plan. I mean, generally, to get an all segment of e-beam from an engineering perspective is very hard, but we decided to do that. We have a single-beam e-beam inspection, eSi50; a multi-beam e-beam inspection 300-beam e-beam inspection, eSV100. Both of them in HBM today. EDRX1 for review and eM200 for metrology. All of these systems are now in HBM. They're not in development. So already moved to HBM. So I showed this slide, which is this is a brightfield test image, and this is the difference image, meaning that you took an image, you subtracted the pattern, and this is a difference image. Where is the defect? It's very hard to tell. It could be this, it could be this, it could be this, or it could be this. The defect that the customer cares about is only this one. Nothing else they care about. And if I report this, this, this, this and that defect, guess what? You have to do a lot of e-beam review to verify. Now my relevance goes down. So in order to fix that, this is what we're doing. We're connecting our e-beam inspection systems with our brightfield systems and e-beam review systems with our brightfield systems. So this gives the example of what the defect looks like. This does the workhorse of full wafer inspection and then you can send the results to e-beam review. And then all of this is connected through a computer. Computer is a weak word for this. It's a huge machine that is updating the models continuously. So this is an example of a co-intelligent portfolio delivering questions -- I mean delivering answers to our customers. Brightfield optical inspection success, $8.5 billion since 2022 to 2025. That's just the Gen 4 and Gen 5 systems that we have sold. It grew greater than 30%. The market in the same period, I believe, was around 14%, 15%. Greater than 600 Gen 4 systems, and Rick pointed that out that we're shipping more Gen 4 systems. And greater than 180 Gen 5 systems shipped during this period. Okay. That's the technical part. I will go into the model. So I spoke about logic inflections; memory inflections; advanced packaging inflections; high-volume manufacturing, we participate in high-volume manufacturing at the same curve; Brightfield optical inspection; reticle inspection; and then e-beam. But as I said in the very beginning, right, $9.5 billion, the systems business came -- the process control was $9 billion. There's a bunch of other things that we do. But if I have to present on that, we'll be here for a longer time. But I'll give you a little bit of flavor, right? Extra metrology. We developed an extra metrology systems for vertical NAND and DRAM capacitor, and it's used in high-volume manufacturing today. We purchased a chemical analysis company, and it's tagged with plating systems. This metrology system is scaling as interconnect density is going up, more customers are buying this. Data analytics, this is a $250 million software business now, connecting all of our systems together to ensure that we're able to help customers do detection. Plasma dicing. As customers go to more valuable die instead of doing saw dicing, they're going to start doing plasma dicing. We have a suite of systems that are going to help with plasma dicing. Component inspection, I spoke about in PCB inspection. All of this is roughly about $1 billion on top of the Semi PC business. Lastly, to the model, $9.8 billion in 2025. Market baseline growth rate would be $7.6 billion. That is an 11% to 13% CAGR growth. Outperformance would be $3.1 billion, reaching $20.5 billion. Overall Semi PC CAGR -- overall systems business CAGR would be 4% and Semi PC would outgrow the overall business by 4.5%. All because of our collaboration, innovation execution. That's all I had. Thank you. [Break]

Now it's time for a 10-minute break. Please help yourself to refreshments outside the room and join us back here in 10 minutes.

Right. Good morning. Welcome back. So as you heard from Rick and Ahmad, the market dynamics set up extremely well for KLA. And we've got a strong portfolio of products to support our customers' most difficult challenges. And that's good for our service business as well because one of the bookends of our growth strategy is growing the installed base and the additional value-added services that we provide on those new systems. The other bookend is the enduring value of our systems. I started at KLA 28 years ago working on products that we now call the class of '95. Many of those products are still running in customer fabs around the world today, generating more than $100 million in annual recurring service revenue. So it's this powerful combination of new installed base growth, coupled with the enduring value of our systems that makes our service business so durable. So we have 3 key messages for today's section. First, we are a customer-focused organization. Our service excellence is aligned with customer outcomes to ensure that they succeed. And we manage that through the entire life cycle of the customers' products. Next, as you heard from Rick and Ahmad, the KLA operating model is a framework we use to work with our customers. It's rooted deeply in data, and we use that data to improve both customer experience and KLA profitability and productivity. And finally, the headline message for our service section today. We are increasing our service growth rate from 12% to 14% to 13% to 15%, which means at the upper end of that range, we'll double the service revenue by 2030. So before we get into some of the details about how we're helping our customers solve some of their challenges, I want to give a little bit of perspective on KLA infrastructure, a little bit about why process control service is different and close with some financial highlights. So first, from an infrastructure perspective, we have more than 57,000 tools installed worldwide at more than 4,000 customer facilities. Every one of those customer facilities is driving unique requirements. And we have to invest in order to support each one of those. And we invest in the form of people, more than 3,500 service engineers and in parts, more than 260,000 unique assemblies and spare parts. And that all comes together, that infrastructure comes together in comprehensive service contracts, which reduce overall cost of ownership for our customers. So we've talked about this in the past. We have a unique signature in our installed base. We're high mix, high complexity, lower redundancy and long life. And I'm going to unpack each one of these in the coming slides. Before I do that, I want to touch briefly on this graphic on the right-hand side, the product plus service. So as Ahmad went through in his presentation, we are the market leader in process control, and that's because we deliver the highest throughput at the highest sensitivity. The only way that our customers can maintain that highest throughput and highest sensitivity is by leveraging KLA service. We deliver our product, our service product is availability at performance. So we have to meet those throughput and sensitivity on a day-to-day basis at very, very high availability rates. This concept of product plus service 1 plus 1 is greater than 2, it's a competitive advantage for KLA. But let's talk a little bit about some of the characteristics of our business. So first, from a mix perspective. So what do we mean when we say mix? Well, each one of those pluses on the left-hand side is a KLA product division. That in and of itself would suggest a high mix. We are not a single product company. But when you double-click on one of these, and if you haven't visited the AR/VR, as we talked about, we've got our BBP system, our latest gen BBP system there. That's one of -- one tool model configuration that you see. If you double-click on the division, you see there are more than 30 different product models and every one of those product models has 5 different configurations. It means 150 different configurations that we're managing just for a single product division. So -- and you can imagine what that looks like when you go back and look at that through each one of these divisions, the mix that we are managing across our installed base. We also have -- it's also a complex business, more than 1,200 unique parts and assemblies. You can get some perspective on that. You see our objective over here, our sensor over here. These are 2 examples of assemblies that we -- that are also field replaceable units. So more than 1,200, we'll talk a little bit more about that, and more than 25,000 different specifications. So again, this is for one product division. It's representative of what we deal with across the rest of the installed base. This is one of our biggest challenges as a service organization is supporting this high mix, high complexity. It's also one of our greatest advantages because we are uniquely positioned to be able to support this. We're also differentiated in a couple of different ways. So Ahmad outlined, ultimately, we're a portfolio company, and our customers are going to select different KLA products to put in line and use those to drive improved overall yield. So if you go into a customer fab, you're going to see a lot of white panels and purple stripes, lots of KLA tools. But when you double-click on that back to this high mix, there are lots of different tool types in there, which means there's not a lot of redundancy for any one of those given tools. Because there is low redundancy, our utilization of our systems doesn't correlate very well to overall fab output. It's inelastic. Means whether you're running 10,000 wafers, 25,000 wafers, you're still going to use your process control tools at very high availability rates. That means we have -- that contributes to the durability of our business. You're not going to idle some of that equipment. You're going to use our equipment. You might change some of your sampling rates, but you're going to use our equipment at very, very high availability rates, which is why, again, we see very little fluctuation in our overall service and why it's so predictable. This is differentiated from others in the semi equipment ecosystem. Two other important differences is we are -- our part content, as you see both in the AR/VR demos and as you work through looking at some of these examples that we have, these are very proprietary in nature, these crews. And they take -- we spend a long time developing these with suppliers. Bren will talk about they're -- many of them are sole sourced. And so KLA is uniquely positioned to be able to stock those parts and be able to service the equipment. In addition, we have very diverse labor content because of the unique nature of all those parts, the unique nature of the failure mechanisms that we have. So all this, again, contributes to the durability of our service business. So a little bit about how we've performed over time. If you think about our service business, the way to think about the growth equation is you've got this existing installed base, are you able to drive greater revenue and how stable is the existing installed base? And then what's happening on the new tools that you're shipping? Are you attaching at a higher rate, higher contract penetration on those? Are you generating more revenue? And if you do all those things, then it should lead to improved overall growth. So good news for our business is we're moving on all of those. So first, from a tool lifetime, we'll touch on this in a little bit more detail later in the presentation. But median time to tool retirement for KLA systems has increased from 10 years to more than 24 years from 2010 to 2025. Very important for our customers. Next, from attach rate. When I was here in 2019, we talked about service contract -- or excuse me, service revenue from contracts at about 70%. We've increased that 10 points closing in 2025. And that's, of course, driven by even higher contract penetration on the new systems that we're shipping. That's why this is increasing because the complexity is increasing, therefore, the reliance on KLA service is increasing. And next, we're also generating more service revenue per tool. From the class of 2000 to the class of 2020, we're up about 3x. So important drivers. This is what's leading us to improve overall projected CAGR. So in 2019, we said we were going to grow the business at 9% to 11%. In 2022, we increased that to 12% to 14%. And today, we increased that to 13% to 15%. A very important revenue stream for KLA. Bren talked about 17th consecutive annual increase in our dividend. That dividend is serviced by cash generated from our service business. And so it's that predictability and confidence that allows us to continue to drive increase in the dividend. But ultimately, it's about serving our customers. Again, we use the KLA operating model framework, collaborate, innovate and execute. And we support our customers, whether they're ramping new technologies, new fabs or if they're trying to extend the asset lifetime of their existing tools. So first, from a collaboration perspective, our service engineers are very much part of the fabric of our customers' operations. And we are recognized for it. We see recognition whether we're helping a customer ramp a new technology, ramp a new fab, ramp a new geography. We're recognized for the Best Supplier of the Year Award or in the event when our customers experience unexpected events like an earthquake, our engineers are in the fab in hours to help our customers get those tools back recovered so that they don't miss any of their production commitments. And while recognition is important, ultimately, our customers vote with their checkbooks. And as we mentioned, our service contract penetration has increased over time. So back in 2005, we were about 60% coming from revenue on service contract. Today, we're more than 80%. And these are long-term service contract agreements. These are more than 3 years. And so this is a lot of trust with our customers. But this long-term commitment allows us to not just solve for today's problem, but also to solve for their road maps, especially in this ever-evolving geographic expansion environment that we're operating in today. Additionally, we talk a lot about the service contract being 80%. But if you look at this pink, this is what we call KLA time and materials. So this is a little bit more variable spend, but it's still spend with KLA. So if you take the purple and the pink, that's basically the revenue that we generate from those 57,000 tools in the installed base. Every one of those individual tools over a 2-year period recurs in revenue 95% of the time. So again, our predictability on our service revenue, our line of sight on ability to sustain the level of growth that we've talked about and our confidence to continue to deliver that is very high because of these dynamics. But next, of course, in the semi industry, we've got to continue to innovate. And our innovation in our service business, we think about it in 2 distinct buckets. First, how are we trying -- how are we improving customer or tool performance in customer fab. And next, how are we improving every part of the value delivery chain that delivers that performance in the fab. And that usually comes in the form of people, parts and knowledge. These 2 things come together around KLA data systems. And we're a process control company. We've been collecting data for many decades. It's all about sorting signal from noise. We've done a very good job of this, and we mine this data in order to improve both customer experience as well as KLA productivity. So I'm going to spend a little bit of time giving you some examples of some of the things that we're working on in terms of improving in-fab performance of our customers' tools. The way that we do that is we leverage our service applications portfolio. Both Rick and Ahmad talked about our applications engineers. For example, our systems are connected to recipe development servers for accelerated recipe deployment that allows them to store, modify and deploy uniformly across the installed base. Some additional examples that I'm going to go through are predictive tool, health insights, autonomous synchronization and then on-demand agentic expertise. So first, in this period where customers demand is significantly outstripping supply, any unscheduled downtime for one of our products has a material impact on our customers' manufacturing. So in the historical model, we would wait for something to break and then this period down here meant that the tool was down, you would fix it and then you'd come back up. With our predictive tool health insights, we're now able to track assemblies like these 2 off to the side, what's performance of those assemblies against predefined performance specifications to understand when the assembly begins to drift. Again, KLA is a system of systems. It's a bunch of these complex assemblies that come together to create the performance that we want. If one of those assemblies begins to drift, total tool performance begins to drift. And so because we have predictive tool health insights in place, we're able to track that, and we can now preposition material and labor in order to support that, reducing total unscheduled downtime for our customers. That's also a value unlock for KLA because now it can be much more productive with our deployment of labor as well as inventory. And we do that again across those 1,200 unique assemblies on that one BBP platform. So again, you imagine how this scales when you look across the entire installed base. Similarly, we have an autonomous synchronization application. And the challenge here is you heard again from Ahmad, the high mix environment that our customers are operating in. And in that high mix environment, you want to make sure that the tool that you have in place for whatever application, it's serving inside the customer fab is aligned to the control reference, whatever they want that, so that they have 100% of the performance, 100% of the time. And our autonomous synchronization system allows us to do that and ensure that we do -- we catch those issues or deviations before we return the tool to production, which reduces overall variability inside the customer fab and improves our scalable operational infrastructure. Semi manufacturing, highly automated, but still requires skilled engineers. And our customers are hiring a lot of skilled engineers, and we have to hire a lot of skilled engineers. This is important to be able to meet ramp time lines. So one of the biggest challenges is accelerating time to proficiency. And the way that we accelerate time to proficiency is in addition to some of the AR/VR tools that you saw today, we also use our on-demand agentic expertise. So again, I talked about we've been collecting data off our systems and our business processes for decades. But correlation does not mean causation. So it's great that we have all this data. We also have the contextual information and the subject matter expertise to understand what's really happening. That allows us to synthesize the data and put it into dynamic knowledge model, and that then converts to on-demand delivery guidance for our customer support engineers to be able to reduce mean time to repair. So ultimately, what we're trying to do is take the tacit knowledge of a master engineer and transfer that as quickly as possible to a new engineer, whether that's a new hire or an engineer that's now learning one of the new products that Ahmad talked about we're introducing. But ultimately, we're an execution -- our service business is built for execution. And we execute in many different ways. I'm going to talk about how we're helping customers solve against new technology inflections, how we're helping them ramp in new geos and then also how we help them extend the lifetime of some of their assets. So first, Ahmad went through this in great detail, HBM inflections that we're seeing. This is driving increased device complexity, increased advanced product models from KLA and tighter requirements. That has a pull-through -- a natural pull-through for our service business. And so we're able to align on new and tighter availability at performance requirements with our customers and deliver to that commitment. And because we do that, we see revenue growth increase by about 5x from 2022. This is one of the catalysts for our 2030 plan. Similarly, advanced packaging, also a great business, as Ahmad outlined, and good for our service business. We talked about pulling front-end systems, configuring front-end systems for back-end application. Again, that puts tighter requirements on running those tools in the back end, that puts pressure on our service organization. And so we've been able to modernize our back-end support with the same rigor and intensity that we see in the front end. We've also been able to leverage our global infrastructure to support this significant ramp that we've seen in advanced packaging, again, a competitive advantage for KLA. And we've seen strong revenue growth of about 8x from '22 to '25, again, another catalyst for our 2030 plan. Geo expansion, we know our customers are -- all of our customers -- many of our customers are expanding geographically. The last thing they want to worry about is whether or not their equipment suppliers can support them. They've got plenty to deal with on their own. But what our customers want is they want to match the culture, the intensity, the discipline that they see in the home country. So we can partner very closely with our customers to do that. And our scope and scale allows us to move resources from the home country to the new geography, ramp up, hire people, get them trained in customer processes to allow faster yield ramp. We've been very successful in doing this over the last couple of years. And as we look at the number of ramps that we've got to support through 2030, we feel very good about our position. So we've spent the majority of the presentation here talking about the left side of this life cycle, R&D, ramp through HBM. So -- and that's about 70% of our services business. But the other 30% is supporting this legacy HBM. And this is a business -- many of our customers, these are profitable businesses for our customers because they have depreciated their equipment. And so they're able to run a very profitable business, and KLA plays an important role in that. And the way that we play an important role in that is by extending tool lifetime. So we talk about this increase. If you go back to the year 2000, the median time to tool retirement for KLA system was about 4 years. And that goes back to the 18-month or 2-year tick-tock strategy. We'd introduce a new technology, a new node. And then 2 years later, we'd retire that equipment. That began to change as you move into the 2010s and the 2020s as you see some of these super nodes that came into play. And now we see median time to tool retirement up at 24 years, significantly longer than our customers' depreciation cycle. Of course, this does not come for free. KLA invests heavily in making this 24-year to medium time retirement come true. First off, we structurally changed the way we design and service tools. So all those products that Ahmad talked about, we're developing when we sit in reviews, we're not asking about how to deliver those tools for 2 years or 4 years or support them for 2 or 4 or 6 years. We're talking about supporting them for 25 years. And this is an important value proposition for our customers. It's one of the reasons that they pick us is because they know that we will be there to support them for the long term. We also have active obsolescence management, hundreds of obsolescence problems that we solve and modernizing those systems on a regular basis to ensure that they can continue to get the performance that they need to support the tool. So that all comes together in what we call our class of chart. So if there's one chart in my deck that I think both shows KLA's 50 years of market leadership, but also the -- how our service business has performed over time, it's this chart. So on the Y-axis, this is service revenue. On the X-axis is time. And as you see on the legend over here, these are cohorts. So the way that we define a cohort is if you were a product that was introduced in the year 2020, '21, '22, '23, '24, you're part of the 2020 cohort. So I mentioned at the beginning, I started in the class of '95. And if you squint really hard, you can see a little bit of blue starting to come up for service revenue in the class of '95. And you follow it over and it peaks in about 2008. But then as you follow it all the way to 2025, you see it's still there. That's the $100 million of service revenue that we're still generating on the class of 1995 products, not dissimilar to the amount of revenue that we're currently generating from the class of 2020. So this is why we're very confident as you look out into 2030, of course, that 2020 is going to continue to grow. We also expect that the class of '95, 2000, 2005, [ 2010 ] will continue to deliver value for our customers and continue to deliver revenue for KLA. And again, this is why we're confident in our growth rate projections through 2030. So in closing, so we use the collaborate, innovate and execute to deliver more value for our customers. We deliver more value for our customers. We deliver more value for KLA. I was here in 2019. We were $1 billion. I talked about -- it took us 40 years to get to $1 billion of service revenue. When we came back in 2022, we had added another $1 billion. So we went to $2 billion. 2025, we're now another $1 billion, $3 billion. And again, as I said, at the upper end of our range, we would expect to double that revenue to nearly $6 billion and at the upper end of the 13% to 15% range in 2030. So never a better time to be in the semi industry, never a better time to be a part of KLA. Thank you.

As a CFO, I've been the CFO of KLA since 2013. You love that business, right? It keeps you warm, right? It's a business that has a lot of real positive attributes. It's unique in the industry, and those attributes are getting better. So pretty excited to have it. And it certainly is something that as you think about just how -- what happens in the world, having this anchor that drives growth consistently is a pretty powerful base to have as we think about operations, as we think about capital allocation and so on. So in 2019, I stood up here and said, hey, the world is changing, and it's changing in a real positive way for KLA, for the industry and for KLA. For the industry, we were going to move from a period of time where you had significant capital intensity decline to all those drivers of it sort of played out and that going forward, we would have capital intensity flat to rising. And so that would create structural growth in the industry. So semi equipment will grow at the rate of semi. The other good thing that was happening is we finally had a scaling road map after years of delay with EUV. And as a result of that, you would have the 7-nanometer node. And later on with DRAM, but the 7-nanometer node where you had a lot of design activity. And you saw Rick's chart earlier, first time bigger than 28-nanometer. And so because of that, you had designs, you had a big difference between intensity in logic and foundry versus memory. And so as a result of that, it was going to drive the relevancy of KLA higher. 2022, we stood up here and said, all those things are true. There's more momentum as we saw the successive nodes driving more designs. And that the industry was broadening in demand. You had a lot of legacy demand. Semiconductor content was rising and all the digitization related to COVID was part of that. Legacy demand is really good for our customers because of their profitability. And so you had what was a GDP plus growth rate for semi revenue start to accelerate. And then, of course, as we moved into '24 and '25, we saw the high-performance compute drive. And all of that, as you saw on Ahmad's chart, took KLA's process control intensity or share of the overall market up about 200 basis points. And translated from '19 to '25 into best in industry growth levels and a very strong financial model. So of course, over the last 12 months or so, I get lots of questions from investors asked in very different ways. But really, the crux of the question is, can the next 5 years be better than the last 5 because the last 5 were pretty good for KLA. Can the next 5 years be better than the last 5? And if you look at the conditions that you heard a lot about today that are driving the next 5 years, the next 5 years are going to be better than the last 5. So what I'm going to cover today is, as you've heard, we're well positioned to drive sustainable outperformance across all different segments, and that's contributed to increasing process control intensity. We're excited about that. The second message, I'm going to talk a lot about the operating model and how it translates into the financial model and how we run the company, how we make decisions and what's underneath the hood, if you will, in terms of our -- how we engage with customers, how we have to invest, what's driving the income statement and balance sheet over time. We have to execute, and I'll talk about some of the execution drivers in the business and how that translates to a go-forward view. And then finally, I'll wrap it up with a discussion of the 2030 target model, work through some of those assumptions. And that model is really based on a history of credible execution. We've done, I think, a pretty good job against the public targets that we've established. As Rick said, we define winning inside the company. There's no daylight between the external plans and the internal plans. This is how we measure ourselves. This is how we evaluate the company. This is how we're incentivized. And this is how we measure our success over time. The 2030 revenue plan, $26 billion, plus or minus $2.5 billion, and I'll walk through that. Non-GAAP EPS of $84 and should continue to drive strong free cash flow generation, consistent with our history, and I'll share some of that. And a capital returns target that was greater than 85% on a go-forward basis, expected to be greater than 90% of the free cash flow we'll generate. So go back to last Investor Day, where were we and where are we today? So we talked about $14 billion and $38. And if you look at where we are today, you can see that particularly given the commentary that I gave you earlier around our expectations for the year, we're in excess of that. We said we wanted to improve our share of market by 100 basis points. We improved it by over 150. We said service would grow 12% to 14%. Service grew right in line with that target range. And as I said earlier, had some disruption related to access to fabs. So pretty happy about that. Certainly, the strength of the systems and some of the drivers of incremental opportunity were a factor in that. We said we were going to broaden our portfolio. We're going to invest in reticle inspection. We're going to invest in e-beam, and we were going to expand our market leadership. And as Ahmad showed you, we have done that. And so we feel very good about how we're positioned from that point of view. And advanced packaging. We didn't talk about it before, but it is a significant market and a significant market opportunity, and I'll talk a little bit more about why we see it the way we do. We were going to integrate acquisitions, which we did. We put them on a trajectory. They were dilutive, put them on a trajectory to corporate level incremental margins moving forward. We're going to invest to support our growth. We're going to deliver our incremental operating margin model, which we did. 2025 operating margins were 43.6%. We said our target range was 41% to 43%, and that's despite tariffs and some other things that we hadn't contemplated back then. And then our capital returns was just slightly in excess of 85% since 2022, so in line with our target there. So where we're going? This all translates, $26 billion translates into 15% at the midpoint in terms of revenue growth. We're going to gain another 150 basis points of share in terms of share of the overall market. Service revenue, as Brian just talked about, 13% to 15%. We're going to continue to optimize our business through technology leadership, how do we leverage the portfolio. It's unique and competitive advantage for KLA. But we're also driving productivity improvement across the enterprise, certainly have a strong growth environment, but opportunities for us to continue to drive our productivity. And if we do all that, we'll sustain our model at a higher growth rate, we should be in the higher end of the range of our 40% to 50% target model, and we'll drive 45% to 47% operating margins. And I talked about the capital return change. So it's interesting when you look at the revenue over time, and this was that time where there wasn't much happening. You had capital intensity that was down. A lot of the growth for the company came from service. And so you had this period, then you had 7-nanometer node, you had EUV into DRAM, EUV into logic. You had a down year in '29. This was a $1 billion increase year-to-year in a down year. And then we saw this path to mid-teens growth. And so 2019, 2025, mid-teens and basically, our plan is a continuation of that. You can see a lot of the drivers on what are the characteristics of the target revenue mix and why. But in an environment where we feel like there's -- again, the conditions are strong and that it will sustain our ability to grow in a mid-teens range. Now if you look back to the 3 Investor Days, what's happened? As Rick mentioned, we were about -- just to acquire a company, integrated the company, drove our margins up. Best among the leaders in the semiconductor industry, a lot of focus, the indicator of differentiation. And then driving leverage on our infrastructure as we scaled over time and incremental margins at the top end of our 60% to 65%, so incremental gross margins, 60% to 65%. Operating margins, differentiation, unless your profitability drives your operating margins. And we have, again, a superior margin profile versus peers. I'm going to talk more about this, but this extendability of product platforms and what does that mean to the R&D intensity, how much we spend on R&D. The industry has a pace, a cadence of innovation that isn't as fast as it used to be, but it's fast enough that it drives the end markets. And it's really good for structural profitability for our customers because they can amortize cost of development and cost of capacity over a broader period of time and more volume, but also good for us. So I'll talk more about that in upcoming slides. Obviously, focus on productivity and how we drive the business and then our performance on incremental operating margins at 51%, so slightly ahead of the top end of the model. Earnings leverage, operating margins, less some of the capital structure decisions, taxes, all those sorts of things. We improved share outstanding, so drove our shares outstanding down about 18%. And we had a target model that we would deliver 1.5x the revenue growth rate in terms of EPS growth. And so revenue grew 16% over this time frame, diluted EPS at 26%. So achieved that as well. And then free cash flow margin. We have a history of strong free cash flow margin. It's trending above 30%. I'll share some data on that. Our business model is a unique one. Obviously we have the profitability levels we do. We invest in working capital. spent a lot on inventory, have a lot committed there. If there's a thing that we accept to drive our differentiation that goes to a lot of what we talked about earlier in terms of design and capabilities, we accept that the asset velocity of inventory turns will be lower, but it positions us to enable the differentiation and to meet the dynamics in our business. And I'll talk a little bit more about that. And then CapEx, absolute value of CapEx, we've been spending more, but generally between 2% and 4% of revenue. So as a result, a lot of that profitability drops to free cash flow margin. It's not just about semi equipment. You can see on the chart when you compare -- take a long term, different drivers, different regions, other investments, different cycles, different drivers in the end markets, consistently against the SOX, average performance over the last 10 years, ranked in the top 5 of all 3. Only 3 companies are in the top 5 of all 3, NVIDIA, KLA and Analog Devices. So it matters a lot in terms of long-term performance, lots of opportunities where things can go a different way and a continuation across the board of we'll call best-in-class performance in the overall semiconductor industry. It translates into very strong cash flow. So free cash flow as a percent of revenue and the 92nd percentile of the entire S&P. So it's that model that I talked about, profitability but also an asset-light business that enables very strong free cash flow performance, which has allowed us over the years to be very thoughtful and very explicit about capital allocation. The capital doesn't get valued unless it's deployed productively. That businesses need to be financed with a tiered debt. All the sort of philosophy of KLA and how we thought about it has been really driven by the free cash flow sustainability of the company. And you can see in this trend, first, the dividend, right, mid-teens growth rate target, 2 targets, the growth rate, how do we drive the growth rate relative to the growth rate in our free cash flow. But also, is it 25% to 30%, so we can maintain a consistent cadence of 25% to 30% of the free cash flow we generate. And so that drives our dividend. And then you can see on the share repurchase side, we have the additional authorization today. And our strategy is really about consistently again putting the capital to work consistently over time and using capital structure where prudent to augment the returns or accrete the value of KLA to our shareholders, and we have one of those actions back here. So a consistent approach over time, explicit, so it can be modeled and valued. Brian talked a lot about the dividend and service and the dividend. I take it one step further. It's not just the dividend, it's the after-tax cost of debt for the company. It really services service, services both those. So if you talk about the market, this was that world that I talked about. We basically went from a 4% GDP-type business, GDP plus to this transition over the last few years that we think has a continuation of approximately low double-digit growth. And you can see a lot of the drivers from it. Obviously, discipline and pricing has been good. The value of what's happening in high-performance compute is also a factor. But lots of good factors that are driving growth, the diversification of demand. But what's really important for us, as you can see, as you take 2030 and you think about an environment of $1.3 trillion to $1.5 trillion, $1.4 trillion is 11%. And that's where we put the stake in the ground as we think about growth. Some people think it will be higher, some people think it will be lower, but that's how we thought about it, 11%. But you can see the composition goes from about 25% to about 50%. That's high-performance compute, high-end enterprise server market. And so you get growth in these other markets and you need them because they consume a lot of semiconductors. But what's driving the growth is the area of semiconductor revenue spending that KLA is the most relevant in. And so we think it's a big opportunity as the market moves, which is why we have a comfort that really across the board that we can continue to grow our share of the overall market. Our customers are doing pretty well. So this is the top 5 customers, their revenue trend. We looked at their semiconductor-only businesses and also their EBITDA trend. And so you can see that the customer profitability is at pretty high levels these days. So as I talked in the opening remarks about the outlook, we feel pretty good about our customers' ability to spend more and also that's driving their profitability. The structural profitability of the industry is pretty healthy. And so the trajectory over the next couple of years and what's happening with the underlying dynamics, we feel pretty good about where it extends over the next several years. I'm not going to go into the details of these. What I tried to do here was just summarize all the reasons why the KLA relevancy increases, why the relevancy of process control increases in advanced logic, what's happening in memory that's now bridging sort of the delta in terms of how our customers spend on high-bandwidth memory relative to logic as a percent of their equipment spending, and of course, what's happening in advanced packaging, high sample rates, a lot of processing, front-end like processing that happens in packaging. So in the situation where you have rising complexity on process, lots of opportunities for our customers and very high-value devices to have challenges. And so as that road map scales, we think it creates opportunities for us moving forward. It's really hard to gain share in a market. What usually drives it is that there's a technology inflection or there's something that's changing in the underlying economics of the industry. So as we've seen over time, of course, lithography, the introduction of EUV was good for share of market, but also the growth in process control. And because we've gained share overall, the KLA process control grew faster than above. So over this time frame, and I picked a couple of other points, starting '18, starting '19, starting '20, you generally get the same results. The 2 markets that are gaining share of WFE or share of the equipment market are lithography and process control. And as you think about the packaging market, you can see back here, we don't really talk a lot about packaging. And you'll see in the next slide, you can see why. You can see not a lot of growth in advanced packaging. And this is wafer-level packaging includes dicing, includes film frames. Eventually, we will include panels, nothing today. But you can see fairly flat, not a very high level. And this is our internal forecast, but also aligns with where TechInsights and Gartner forecast the market. And of course, you see this acceleration with high-performance compute over the last few years. What you have here on the right, and this is not a statement on performance or market share. But you see that as this market has moved to more front-end like, all the traditional front-end players are targeting these opportunities. So as we talk to investors a lot about share of market and how important that is, I think it's important to kind of get the denominator right because everybody is reporting in their earnings or the revenue numbers, they're reporting in their numerator the opportunities that exist in this part of the market. So we think there was some confusion about how to think about it. But when you look across all the major markets, front-end players are targeting lots of opportunity and expected growth that's slightly faster than traditional WFE over time. You can see why we don't really think about it and really maybe even fully understand it, right? We were 50 basis points share of that overall market back in this time frame. But of course, that's changed in a very meaningful way, right? So from 2022 forward, almost over 400% increase in our share of the overall packaging market. So very relevant market. We think we're well positioned. I'll talk a little bit more about why that's the case and how it affects how we've invested in it over the years, but something that we're focused on, and we think it, again, creates opportunities over the next several years. A version of the operating model, the same sort of framework around collaborate, innovate and execute, but then how does it affect how we drive the business and how that flows through the financial statements of the company. From a collaboration point of view, we have to work closely with our customers in early process development. We have to understand the -- it takes a long time to develop the capabilities to meet their needs. So we have to stay close so we understand where the problems are going to be so we can pick and to solve the right problems. We can't solve every problem. We need to fix the problems that we can differentiate, solve uniquely and those problems scale, scale to production. We have to work closely with our key suppliers. The supplier who does this work, this doesn't happen in a short period of time. So we have to make sure that we can engage early enough to make sure our suppliers are ready. It's an important part of our differentiation. And we have to make sure that given our structure as a company that we can work across the company, reuse applicable technology, Ahmad talked about some of the examples there. Then we have to innovate. We invest heavily. Rick showed the trend. I'll show a little bit more of that and a little bit more context behind it. Well ahead of the market requirements. We are investing today in capabilities that drive revenue outside the target revenue window, so beyond 2030. So there's consistent investment that's happening there, N+2, N+3 investment. And then, of course, we have to invest and go get talent. We have to invest around the world. We have a worldwide talent strategy. We have engineers in a number of locations. We have to go to where the talent is. It's an important part about how we execute the company. Then we have to execute overall. We consistently meet against the public financial targets. I showed that performance earlier. a cadence of new product introduction. New product introduction matters a lot to KLA, and I'll talk about why that is. It's never over. You can always be more efficient and disciplined in your investments in your operations. And so there's always focus on that. Critical supply chain. I'm going to talk about supply chain, but we need to make investments to ensure we have the capacity, not only can develop the capability, but also the capacity to meet our needs and then continue to deliver sustainable performance over time. This is our spend. And so you see R&D, this is what Rick showed you. We've talked a lot about applications, and it's about 1,600 people. I count these people. So Ahmad said 1,500, it's actually like close to 1,600 or it will be this year. But applications, advanced degree people that are out working with our customers to drive value out of KLA tools, a really big differentiator for us. Obviously, a lot of that insight comes back and influences product development. So 70% of our OpEx is technical investments. It's R&D or apps. So when we talk about improving our SG&A as a percent of revenue over time, you have this in there and it scales with volume, right? More tools in the field, we need more applications people. So when I talk about our plan and what it means, it's offsetting an area of natural growth. Now AI will help us here as these folks start to use that capability more assertively, particularly as they help do recipe creation and so on. But it's in general, something that scales with volume. So we have to offset that as we think about the long-term growth of the company. We're investing a lot in big programs. So what this slide shows you is the number of programs in the company that are greater than $100 million. So engineering programs that are great in development that are greater than $100 million. And used to be a fairly small number, and we've seen that increase significantly. It's expensive. It takes a long time to develop the products we have. Yes, R&D intensity is favorable, but there's a lot of investment that's happening. So this is an area we spend a lot of time as an executive team and program reviews to make sure that we're achieving what we need to achieve in product development because these are big bets, and they're important to maintaining and driving the model of KLA. And then, of course, the average spend per program has also increased about 70% bigger over this 10- to 15-year time frame. Rick showed an example when we talk about what's happened in our investments in AI in GPUs and how long that's taken. And so what I have here are a couple of examples that we certainly are enabling high-performance compute, but we're a user, and we've been doing it for a long time. So we started over 10 years ago, first product 2018, across the whole portfolio today and a road map for more capability to optimize over time. If you look at the image computer GPU benefit versus a CPU baseline, so we -- the requirement capability is represented data rate in this BBP product, about 60% improvement in the Voyager product here, laser scanning closer to 100%. So if we had stayed on CPUs, it would be more expensive to use more power because we converted and we did the programming work and moved the portfolio to GPUs, it's changed our cost curve, and you can see in our power usage, right? So you can see savings on power down 30% to 40% savings in cost versus the CPU baseline of about 40% to 50%. So if you think about what's driving customers' customers, hyperscalers and how they're thinking about the investments they're making and the robustness and cost effectiveness of this compute and the fact that they have to pay power, this is a pretty good microcosm given our use cases and what their use cases are. And Ahmad talked a lot about what our use cases are. So we think it's pretty compelling. We're a user of it, we're an enabler of it, but we have to invest and think in the long term to make these things happen, particularly across the entire portfolio. So what you have here, these are our products, product families, and they're not to scale, right? You've got Gen 4, Gen 5, which is obviously pretty big. You have component, which is pretty small, right? But we go down all the way through here. And in a lot of cases, there's multiple products. This is the product introduction cadence across these different product lines. We introduce products quickly. We need to do it. We think it's important. Our customers want it, but it also allows us to do some other things. It makes us a moving target for competition, and that matters. Our salespeople always have new capability to sell. So that's good in any environment. The third thing is, and particularly over the last few years where we've dealt with a lot of cost dynamics and so on is it allows us to reset as we're introducing new capability and improving cost of ownership, take a look at price versus cost and make adjustments. And of course, the last few years, that's been a big focus area, but we are a value sell. It's about returns. that our customers get because my costs went up, doesn't necessarily change the return profile of what our customers see. So we have to be thoughtful about that. But the way we do it is we introduce new products. We introduced them a lot faster, and it allows us to do 2 things: share -- introduce improvement in the tool, capability, cost of ownership and share in some of that. And so it's an important part of the operating model as we execute. So when you look at service, a couple of things -- or this is -- or I'm sorry, you look at manufacturing as a go forward, and we're going to talk about our gross margin model. You can see incremental margin improvement expectations. So we think versus this '22 to '25 baseline, we'll see about a 1.3% increase in the points or 1.3 points of improvement in incremental gross margins. Part of that are the value differentiation things you see on the right, but also part of that is our ability to scale our manufacturing organization. We have growth scale, obviously, given the growth rate expectations of the business. We're driving leverage through factory consolidation to our bigger factories, drive more efficiency and leverage on the investments we're making. And then a lot of focus on driving productivity and how we introduce products and how we deliver those products and drive quality and so on. And you have a version in service, again, increasing value at the top on the right for service. But the incremental margin expectations in service are about 1.3x what they were at the beginning of the decade. And one of the big drivers for that is that you can see service and parts expense. We have invested a lot to support our customers' regional efforts. And as those regional efforts come to scale, you start to get a return on those investments. And so we would expect on a go-forward basis, the incremental margins of service. Service carries a lower gross margin. But given the growth rate of systems and the improvement in the incremental it becomes less of a gross margin headwind over time. There's also a lot of productivity and Brian talked about is so I won't belabor it around how we're designing for product development, how we're leveraging AI and automation, parts, planning, predictive maintenance too. As contract goes up to optimize our cost structure under those contracts. And that's the best thing about our contracts, we know what we have to deliver to. We can make sure that the costs underneath it are optimized. And so there are a lot of focus in this area. And I think the combination of these things will drive some incremental gross margin improvement up at a pretty high level as we go forward. Supply chain. With the growth over the next couple of years, I'm getting lots of questions from investors on supply chain. And can we ramp to support the business. So I thought it would be prudent to spend a little bit of time talking about, well, how do we manage the supply chain? How do we think about it? And you can see our supply chain, I really break down our components into three types. So our complex subsystems like this, sole-sourced relationships, long-term relationships with customers, things we have a few more options but still long-term relationships and our higher volume critical components and then more of a traditional supply chain in this Tier 3. Now one of the things you have here in the lower left is that what is our time fence. When we start to order material to the time we ship it, the natural time fence is almost 60 weeks. It takes a lot of time to procure glass and deliver volume level optics. And so we have to do a lot to manage against that back to my comments around inventory where we buy long lead time materials, we build hedge kits to try to shorten that so we can action for customers early. It still takes time to do that. One of the issues in the first half of this year that we talked about at earnings is that the momentum in the industry picked up really, really fast. And when we were making a lot of decisions around the first half capacity we were inside a lead time, and that momentum picked up. Now as we get in the second half, we have a lot more flexibility and capacity, and it's influenced the outlook I provided earlier. So we had these decade-long relationships. We work very closely with engineering, supply chain, long-term commitments. We provide our suppliers 8 quarters of visibility. We don't cancel orders. You go back to '23 and '24, the industry corrected a bit. Our inventory levels went up. I need them to be positioned to support us going forward. And we've invested about $250 million over the last couple of years in supplier capacity. So we invest a lot here. This drives the total volume here as well. And then we have the opportunity to manage all the usual issues that pop up in a constrained environment. This doesn't matter so much today. Rearview mirror. You all remember this. But maybe the best indicator of future performance is what happened in the past and the validation of the model, and I think we're better positioned today in terms of how we manage supply chain. But when the last time the industry was supply constrained. Our ability to ship tools and get them out and recognize revenue was at a level that exceeded all our peers. And so I think we're in a better position today. We learned from that experience. We've leaned in. So I feel pretty good about our ability to compete over the long run and to meet the market demand that's out there. I don't -- generally, for high-value components, we don't compete against our competitors because of relationships are mostly so sourced we compete within KLA sometimes with these suppliers, but we don't compete. So when industry strained, I'm not going to have to worry about an allocation necessarily. But you do have the challenges of ensuring that you're well positioned, and I think we've made the investments to do that. This is the trajectory of the OpEx curve over time. And you can see -- you can see the R&D curve and you can see SG&A. And as you look at our 2030 plan, we think the R&D stays about 11%, which is an improvement versus the last plan with 13%. But SG&A basically comes down from where we were around 8.5% or so, 9% down to about 7% moving forward. So more leverage in our model and OpEx as we go forward. When you look at -- this is this R&D concept I talked about earlier, and I'll show some examples of this. But when you look at the -- about the pace of innovation. So what you have here is you have different revenue levels in these 5-year periods, and then what you have is the translation in the R&D as a percent of revenue in the middle. And then what you have over here is the ROI, so the return on investment. So what this means is that we can invest over a longer period of time to sustain our products to deliver new capability to customers. And at the same time, we can improve R&D intensity, but also drive up our returns. Back to my point, is it moves fast enough, we can we can make investments. We can do them over a longer period of time, and it drives a structural profitability benefit. And when you look at a couple of our products, and this is the Gen 5 product, you can see we have the best way ahead, so accomplishing a few things in my messages here, but like 6 years. And then if you look at here, you can see the revenue, then there's more work to do, right? And then it finally scales. So you have to invest way ahead of the of the requirements. But what happens today because the pace of innovation is not every 18 to 24 years, this spending, if it were that tight, this spending would contract and you'd have to do a lot more of it in a much shorter period of time. And we're also investing in the next-generation platform, which is the platform, meaning that it's a new architecture, wavelength of a system. So you can change a lot here, but you can invest and you can spread that investment out, you can optimize it over time. You can still deliver unique value to customers. And you can see what the revenue ramp looks like. And this particular product you see an 8:1 return to KLA as we measure it. This is laser scanning. And you can see, in this case, a much longer period of investment before requirements, and then we see a similar trajectory of revenue growth over time. So the ability to spend over a broader time frame, all translates into lower R&D as a percent of revenue. In this case, it was a 4:1. So good examples. We have some that aren't as good. But in general, every product has a similar sort of investment cycle and then translates into new capability with moving customers. So our customers benefit and we benefit. Here's the example from packaging. So Ahmad talked a lot about investing in the handling requirements of packaging very different than 300-millimeter hard wafer is very consistent. And so this is macro inspection. Which is the market that -- or the product that serves most of the packaging marketing. You see back here, this was the front-end revenue, this lighter color. What you have here in this is this is the packaging investment that we were making over time to be able to support this market. Then, of course, we see this accelerate, and this represents the #1 position in this part of the market. But all of this R&D is transferable to the rest of the portfolio. So the rest of the portfolio, which has higher capability. So when the design rules become more aggressive over time, we're very well positioned to deliver without incremental R&D to support the requirements of that market. Very hard to compete against that. I think we're well positioned. And ultimately, the operating margin translation because the R&D investment has been made and now ported puts us in a very good position in terms of how it affects our R&D spending over time. Now let's talk about AI. So the way we're looking at AI, not about our products and product development, but how we're looking at it in terms of business operations across the company. We are a very data-driven company. We generate data everywhere. As you might imagine, we generate the most data in the fab, so that translates back into how we run KLA. So we have access, we have data. So how do we accelerate the access to that? And all these things are little small things, but across whether manufacturing service or corporate operations, you start doing them, picking them off, start saving time and then it starts to fundamentally change how people work. So we're big believers that we can do this. There's also process automation. Where do you have humans in the process, repetitive tasks and so on. So there's actions that are happening here. And then we're also doing things that drive multiple vectors of return as you look at machine learning, mass correlation pattern recognition type activities. We are integrating a lot of heterogeneous data to drive either cost improvement, asset optimization and so on, predictive maintenance. And so we're doing those efforts, bigger lifts. So we're spending money today, but we expect, over time, we're going to start to see meaningful benefit to the company in all these areas. And you can see some of the things here. It's not a full list of some of the things we're doing that I thought people would recognize that are things that are in-flight inside the company today. Ultimately, as I tell our employees got to save money, you got to bend the cost curve, got to drive up revenue per head count. And so what you have here is revenue per SG&A head count. We have a target that we think that we can drive it from 1.3x at the beginning of the decade baseline to 1.9x. And it's a big factor in how we're thinking about how we're scaling SG&A over time and how we offset some of the dynamics related to applications and other drivers in our business that are more volume intensive. So let's talk about the target model. So when you look at the target model, what you have here, traditional WFE and WLP and then you have the wafer equipment intensity over time. So the way we thought about it was a range of 14% to 16% or so, so about 15% in terms of the overall capital intensity or equipment intensity in the market. And we thought that this was a reasonable place to think about it with a range. Some people have a different view of growth. Some people have a different view of capital intensity, but plus or minus roughly 10%. That's how we thought about it. And within that, the $215 billion about 20, low 20s advanced packaging versus core WFE. And you can see a lot of the drivers of wafer equipment intensity over time. So our model is in the semiconductor revenue is growing 11%, that WFE will grow or wafer equipment will grow about 12%. If you look at what will happen to the next thing, so what size of the market, but then also how does it affect -- how does KLA relevance change? We talked about -- we gained 50 bps here, 150 bps here. We think it goes up another 150. Part of our range includes whether it's 9.25, so plus or minus 25 basis points on 9%. You can see some of the assumptions in the middle. Obviously, what's changing the most in our long-term revenue growth model is what's happening structurally in the industry. And then what are the contributions to the overall growth rate from systems, which is going to be between 1% and 3% of the overall service, which drives an incremental 1% CAGR on our long-term growth. That translates, and you saw versions of this earlier, right? $12.7 billion in 2025, $10.6 billion for systems, $2.7 billion for service gets us to $26 billion. If you look at semi PC service -- or semi PC systems, 16.5% CAGR against the industry at 12%. So we think we're positioned to continue to outform, and that's how that 1.5% on the $215 million translates back in terms of growth rates relative to the market baseline and our expectation from our business. In terms of capital allocation, given our expectations about, look, we like the businesses we're in, our focus is on driving those businesses and capitalizing on this opportunity. Most of the capital, if you're looking about the capital base and what's changed from '22 to '25 versus '26 to '30 will be share repurchases as we deploy that cash. And you can also see a change in expectations around SG&A was 14% coming to 11% as an indicator or a driver of scale in the model. We need $4 billion to $5 billion to run the company. We think that's a reasonable and conservative place to be and how we'll think about cash on the balance sheet as we move forward. Leverage ratio target at 1.5 to 2x gross leverage, we're about 1x today, so have some capacity implicit in our ratings that we would flex that over time but live more or less in our target range. You see the announcements we made earlier. And so here's the target model. You can see 2030 model versus 2026, $26 billion, plus or minus $2.5 billion, 63.5% gross margin, plus or minus 50 basis points. Obviously, offsetting what looks like a 50 basis point headwind related to tariffs, if things kind of continue the way we think. R&D at 11%. SG&A at 7% translates into 45% to 47%, so 46% at the midpoint, up from 42% at the midpoint, $84 in earnings, $8 plus or minus $8 in target free cash flow of 90%. You can see the macro assumptions on the right. One of the assumptions around mix of business. We expect foundry logic to be 60% plus but kind of in that kind of lower end 60% plus a little bit as we go forward. And I think if you -- and we've got -- our EPC business, which is not in the Semi PC calc is grows more mid- to high single in terms of how we're modeling it. Tax rates are about a point higher than the last plan for completeness. So before we move to the Q&A, I think the key takeaways from the Investor Day today. First, you heard Rick talk about best-in-class outperformer. We think we're uniquely positioned. We understand the market drivers. We think they favor and drive KLA relevance. We're excited about them. You heard Ahmad talk about. We think we understand the market pretty well. We have good strategies. Our portfolio is well positioned. We can meet customers' technical requirements and their economic ones. And those change in the different stages of maturity in a process node ramp. As Brian talks about the service business, great business, that gets better in every metric as we go forward. And in terms of our 2030 target model builds on a credible history of execution and operating leverage growth and consistent capital allocation. So structural semi growth is changing and raising, we like it, industry 12% WFE, 11% semi, intensity and share gains, greater than 150 basis points. Operating leverage through the model at the high end of our operating margin -- incremental operating margin target, continuation of a disciplined capital allocation and return strategy leads to durable EPS compounding. And so I think the answer to the question, as I asked earlier before, is it's going to be better in the next 5 years. Thank you.

Now it's time for our Q&A session. Please welcome back to the stage, Bren Higgins, Rick Wallace, Ahmad Khan and Brian Lorig.

Okay. So we have two mics. One over here. With me, one over on the other side with Ed. Raise your hands, obviously, for Q&A, and I'll come to you. So start over here with Atif.

Thank you for a great presentations. Ahmad, I have a question. You made a good point that as the HBM based die becomes a bigger portion of the area you -- it plays in to KLA's hands in terms of higher process intensity on the logic side. My question is, if the industry were to pivot more towards SRAM-based accelerators for ultrafast inferencing. How will that impact -- obviously, some opportunity will come out of HBM and play more into the logic side? How will that impact your process intensity?

Yes. Great question. First, to clarify, I didn't mean to say that just the base die is logic. What I was actually emphasizing is the DRAM die itself, which is above the base die, the logic content of that DRAM die is increasing significantly because of the sense amps, which is closer to now logic. Plus you have a base die which is all logic. So those are the two factors today. We love SRAM, in short. SRAM is very logic-ish, and it's hard to yield. And our process control intensity in SRAM is high. And that's the reason why process control intensity and logic is high. So SRAM would be beneficial to process control. The trend is real.

Well allow us speaking about it. I don't have anything to add to that one.

Sorry. Stacy Rasgon with Bernstein. Bren, I wanted to ask you a little bit about the medium-term numbers that you gave. So as strong as calendar year '26 WFE is, it's a constrained year. We're missing clean rooms and everything else. They come online starting into next year. And so I guess what I'm asking is like why would WFE not actually grow faster in '27 versus '26 given the constraints on clean room ease? And I guess, number two, is it reasonable to assume that you should outgrow WFE in '27 as that happens, your lead times are longer, you're maybe more dependent on those clean rooms. And your overall market seems to suggest you can outgrow WFE's process control.

Yes, the first question, so I did say at least as fast. I think if you look at expectations moving forward, we feel pretty good about the growth rate. So I think, look, we'll see we're in March of '26. We'll see when we get there. But yes, there's a ton of momentum. There's a lot of sites that are being built. There will be -- customers are trying to align schedules with that construction schedule. But we feel very good about how well we're positioned, what we're seeing in terms of customer engagement, what we're seeing in terms of backlog. And I think as you move to greenfield, I think it also creates opportunities as new fabs are fully equipped. So I feel pretty good about our ability to continue to -- our track record of outperformance over the next couple of years.

And just the current calendar year, you took it up to high teens, right, versus whatever it was before. My math suggests that something like a 20% half over half, depending on what you want to assume for next quarter. Is that kind of what you have in mind? That seems where it stands.

We'll see how this quarter finishes and how we guide June. But yes, I would expect the half-to-half to be pretty strong in the second half. And that the overall company high teens, the equipment business, Semi PC will be closer to 20%. I don't what we see today. And we'll see as we go, what happens in terms of customer momentum and whether that changes. We have more flexibility in the second half. We'll see how that goes. But for now, that's where we are.

Joe Quatrochi, Wells Fargo. Maybe one for Brian on the services side. How do you think about just kind of -- you talked about some of the ramp in services you've seen in advanced packaging and in DRAM, but how do you think about like the service intensity across the different end markets? And how is that changing as we move to 2030?

Yes. Thanks, Joe. So certainly, there are some differences across the different segments, but we also think about servicing products. And the products, as Ahmad outlined, are now being used traditional front-end projects used in advanced packaging used with tighter requirements in HBM. And so service intensity increases in those areas. And that's part of the reason why we showed that really good growth from 2022 to 2025. And those are catalysts for growth as we look out through 2030.

So Joe, I think some of the billable business happened in memory segments, packaging segments in the past. So as the requirements for those customers and the capacity constraints that have given sold out in a lot of cases, has started to change the service mindset of a lot of those customers. And so as we think about growth, that's one of the elements of growth is that, that service mindset is going to change because the value of the process control and the value of what they're inspecting is increasing.

Chris Caso from Wolfe Research. The question is the mix of advanced logic versus memory within this forecast. And you had talked about, obviously, process control intensity increasing in memory side because of HBM and such. Does that mean that memory will become a larger part percentage of your business as you go through this period. Obviously, that has -- also has to do with the relative growth rates between those markets.

Yes, I think so. If you go out a few years ago, the industry was high 60s, mid to high 60s in terms of percent of equipment. And so our model here has a view that it moves from that into the low 60 range. We'll see how it plays out over time. The changes in intensity in memory mean that the mix effect has less of an effect on KLA. So -- but we thought in terms of how we would model it, we thought low 60s made sense given the growth expectations that are happening in and around high-performance compute.

Put differently, because of that then actually, perhaps you're more agnostic as far as mix. And if memory grows a little faster or logic grows a little faster, maybe that doesn't actually make a difference.

Yes. No, that's true. Look, in high bandwidth memory, we are seeing in some product lines today. We're seeing intensity levels that are as high as high-end logic. So as that's a bigger percentage of the total, it creates opportunities. We'll see how that flows through the rest of the portfolio. But yes, we're less -- the business is less sensitive to the mix of business moving forward.

Harlan Sur at JPMorgan. I caught up with one of the largest fabless AI, XPU ASIC chip companies recently. They designed a high-volume custom XPU chip for one of the top hyperscaler titans in the world. They're currently getting about 60% yields at 3-nanometer, an increase of those yields by just 3 percentage points, right, drives incremental revenue per wafer that pays for the entire cost of that 3-nanometer wafer. And their foundry partner is already at historical targets for defect density and yields. But in this tight supply environment, larger die sizes, customers are pushing their foundry customers to drive even higher than historical yield targets, and that's on their foundry partners existing capacity footprint. That's not even on next year's greenfield fabs, right? These companies need these chips now. What is the KLA team doing to sort of help your customers on this front, improving yields beyond target historical defect densities on existing technology, existing capacity footprint. And how is the team monetizing this opportunity? I assume it's both the tools and services opportunity, but wanted to get your views.

Yes. So. I didn't fully cover this point earlier, but we do, do some estimates on what the incremental revenue generation is with improvement of yield. Rough numbers are like 1% improvement in yield could lead to at least $150 million in profit because you have already spent the money buying the equipment and everything else. So it's a pretty substantial number. And this is one of the reasons why intensity is going up. We keep seeing this. That's why I felt comfortable with this 7.4% number going to 9%. On what -- how we do it is what I described, we are working very closely with all of the high-end logic customers to drive yields up. But it is a complex -- it is a very complex thing. To do it requires time and effort and eventually the yields do go up.

Is there a services aspect to it? In other orders, helping your customers maybe implementing different yield implementation methodologies and so on?

So we do have a team of people, we call it, PCS. These are not KLA experts, but they are device physics experts. So we send those people out to specific locations where they will have a yield problem on a particular module. Then we deep dive it and then we go work on those issues. And this team is in very high demand. I don't have enough of these people and they are able to really help customers drive yields up.

The sampling is pretty valuable, right? So as it translates to not only the performance of the systems across the fleet and that they have to match, but also the availability at that performance level becomes even more critical. So it is a factor in how we think about how we monetize the opportunity as we work closely to be able to enable that. So there's a service element that's, hey, look, this information is as valuable today given the cost of what we're inspecting. So the fleet needs to be up and active and they rely on us to enable that.

And Harlan, it's not just in the case of the 1 that you're talking about, even with leading-edge customers that if they get increased demand at legacy nodes, and they're yielding in the '80s, they recognize more yield is worth a lot, especially when their customers feel supply constrained. And when we talk to our customers' customers, they're all frustrated that they're supply constrained, like for sure.

It's Krish Sankar from TD Cowen. Another question for Ahmad. If I heard it right, you mentioned e-Beam revenue was about $450 million last year. It seems like it's growing pretty nicely. Is it mainly coming from the multi-column product for reticle? Or are you actually gaining share on the wafer die size also?

First, I said $400 million and $450 million. And second is the mask part is not included in the e-Beam revenue number. I -- that is a separate segment. So therefore, I didn't mix it, even though the mask system is e-Beam related. Those are very expensive systems and the numbers would go up pretty significantly if I add those. So this is a wafer part.

So it's in its e-Beam, it's e-Beam inspection.

It's e-beam inspection, which is a single e-Beam inspection, very high resolution between 1 to 4 nanometer. It's voltage contrast, also e-Beam inspection, where we look at electrical defects. It is multi-beam. We are very proud of introducing a 300 beam -- multi-beam system for primary voltage contrast applications and then eventually it will go to physical as well. And it includes e-Beam review, which is after you do a brightfield inspection, you review your results, and that's the e-Beam review part, small portion of e-Beam metrology. Those are the 4 segments that equate to that rough number of $400 million.

Vivek Arya from Bank of America Securities. Thanks for hosting a very informative Analyst Day. Actually, first, I just wanted to clarify, I think we kind of know the answer, but still any impact, disruption from whatever is going on in the Middle East, whether it's to you, whether it's your customers, right, in terms of any supply disruption. I hope you know the answer, but still would love anything that you might have.

Not currently any disruption.

Okay. Very good. So my main question is, if I go back to your Analyst Day slides, right, from the '22 Analyst Day, your assumption at that time was foundry logic was going to be 55% of the mix, right, and it ended up being much higher. So how much of the share gain that you had was just because the foundry mix turned out to be a much bigger part of the industry. And if, let's say, over the next few years, if, let's say, memory becomes a bigger part of the industry, I don't know, 45%, 50%, whatever, just because there is so much faster growth expected in memory. What does that do to your share gain potential? Because I think the assumption is that if there is greater share of wallet that goes into more greenfield, more memory, more capacity rather than technology, that it naturally favors, right, some of your competitors. So I would love to hear your perspective on that.

Well, as we said in the earlier question, right, we're a lot less sensitive going forward than we were historically. So less concerned about that. The other thing is our long-term model assumes a pretty high level of efficiency in the industry, right? So if you think about the last 5 or 6 years, the leading edge was incredibly efficient. And if you think going forward, we are multiple players that are investing in leading and near leading edge. So that creates kind of a market inefficiency that I think that would create an opportunity for us. The other thing is that I put up those R&D charts where I talked about R&D efficiency and I showed Gen 5 and I showed Voyager, and you see the revenue ramp and then it dips down and then goes back up again. There was a period of time of a lot of legacy investment. And we think the legacy investment, which has a lower process control than advanced logic will be a smaller percentage of the business going forward. I think the legacy business will be 25% to 30% of overall WFE. And all that is generally logic investment in terms of just what's accessible to us. So I think for a combination of those factors, we feel pretty good about the plan we laid out.

The only thing I would add is when we were making the slides for 2022, the chips were very different than we were making in the slides for 2026. These chips are very different, large die, much more logic, it's a very different packaging, the way it comes together. It's a very different world.

And no different greenfield versus upgrades in memory? NAND versus DRAM.

Greenfield requires a lot more process control in the beginning because it is difficult to ramp a greenfield. So I think to your earlier question on 2027, there's going to be more greenfield, and that is also very beneficial.

And not to confuse things, but there's this blending happening too. As you know, the foundries are making memory and the memory guys are making a lot of logic, right? So it's kind of getting a little more muddled as we look at how that matches. So I think the bigger than that mix, the bigger change that we're experiencing is the move to the hyperscalers as customers and away from consumer. The consumer tolerated a lot more than -- and just a GPU versus CPU example, the guys who made CPUs used to bin them based on defectivity, right? You can't do that. You can't do that in high-performance compute, right? You can't bin them. So all these -- all the stresses in the system have gone up because these things are going into data centers. And all the stresses in the fab have gone up because more people are doing designs. I think that's the tension that we're seeing with our customers in the fab is that they have got tremendous pressure from so many more people driving design. So it's -- it's in that -- we really didn't anticipate that in 2022, how dramatically that was going to change. And I think my comment earlier about how much chat is being used or how much of these reasoning models, that's just driving tremendous demand for compute. I mean it's just -- so that's part of why I think this industry is going to underserve quite a bit the demand that's been out there for the next few years. And the bottlenecks will move. But memory is a different game for us now for sure, with high-bandwidth memory, it's a different game.

And packaging, right? And how that influences the share of the total equipment as we add that in and look at that over time, those numbers are based on the total market opportunity. So that's a factor that wasn't something we considered in the '22 plan for '26 and is a more meaningful factor going forward.

CJ Muse with Cantor Fitzgerald. Thank you very much for today. Great presentations. I wanted to focus on advanced packaging. So when you think about process control share gains of 150 bps over the 4 years. How much of that do you think will come from advanced packaging? And moreover, when you think about how many more kind of dies are going into these packaging, are you seeing the vision for more process control insertions? And are customers, as the back end looks more like the front end, are you seeing customers willing to pay higher and higher premium versus 5 years ago where the ASPs were significantly lower than what you guys do typically in the front end?

Maybe Ahmad will talk the story and then you can fill it in. The the conversation we had with our customer a few years ago on packaging.

Which?

The one in Taiwan. Well, I'll start and then you. I got pulled into the meeting with Ahmad. And usually, these guys talk about, well, depends where we are in the cycle. Your stuff is too expensive versus we have these missing defects. But what we were getting was we have to -- you have to help us with EUV adoption and their second topic was packaging. And this was the guys out of the front end. We're like literally -- and literally, Ahmad and I were like, are you sure because we're not going to discount our equipment for packaging. And they said, no, we definitely need you to take your portfolio and modify it so it can work in packaging. And we're in Taiwan. And they said, and we'll have a team in California next week go over your plans. And with this customer, you respond.

And it was December 10. And next week, they were going to show up. So we had to figure out how to get them to come on January 5. But that's the intensity, not the process control intensity, but that's the intensity we deal with. So I will leave the share comment to Bren, but about insertion of process control, I'll take that one. So as you know, in HBM, there is two different processes that are being used to stack dies. There's one company that has a process and everybody else uses the second process. And there's more latitude in the first process but almost no latitude in the second one. So when you go with the second process, as the dies stack goes up, your failure rates are on a log scale. So it's not very easy unless you do heavy process control. This is part of the reason why people are thinking of doing hybrid bonding. And they may do wafer-to-wafer bonding and hybrid bond, and they might do that. And in that case, process control intensity goes up then why does process control intensity go up. We participate very heavily in CoWos, right? And we do a great job for our customers in CoWos. But those same customers are trying to do 2.5D SoIC. All of that is hybrid bonding. The specs I showed you for hybrid bonding were 10x tighter, 10x tighter. So our Kronos systems are not going to be sufficient. We believe Puma will be successful, Voyager and most likely, you will need to use brightfield systems for doing that type of inspection. And in brightfield we need to ship the system with that thing that is on that corner and this thing. So the cost is just -- it is, but the value of the package is quite high. So we believe that the future of packaging, the specifications are going to get tighter. Now I don't personally like to get into the when which road map will happen. We are going to grow intensity no matter what because hybrid bonding will drive intensity but even the TCF bonding drives intensity up. So we're good either way. So we're not choosing, but the trends are all positive. So we think it continues to grow because of that. That's why we feel comfortable with signing up to a higher intensity number.

And look, we're going to be greater than 50% in that ballpark in 2025, a share of process control. Most of our share today is more concentrated in leading-edge logic. There are opportunities in memory as we move forward. Some of the things that are happening in logic will drive higher value systems. There's OSAT engagement as you start to see some offloading to OSAT. So we feel -- I'm not going to put the stake in the ground of a specific share number, but we feel very good about the trajectory of our share, and we think we're pretty well positioned with existing platforms that customers know how to use that have demonstrated value in the front end that are very applicable to the back end or the packaging requirements.

Shane Brett from Morgan Stanley. One of your leading-edge logic customers has more openly talked about opening up their foundry data externally and how external vendors, including yourself, has helped drive up their yield. To the extent that you can, can you talk about how you contribute to that yield improvement and how this broadening of leading-edge logic spend contributes to your growth?

Yes. So I spoke about earlier. I will give you another preview of what we did in front end and then now we are doing in packaging. But all of the systems that KLA makes and all the systems that our competitors may generate petabytes of data. So we had this vision about 15, 18 years ago, that the complexity is going to go up, and we need to provide a solution by which customers can organize the data and then after that, run models. So we have a system that enables us to collect all the data that comes out of KLA systems. We're agnostic in this area as to what customers buy. So we will take competitors' data also, organize it. We have KLA models that enable you to find correlations, meaning that if you have overlay performance problem in a particular layer, does that affect -- defect and we can correlate those to and we can now do like 6 to 8 parameters of different types of things that are causing a problem. So we do that -- but the system is also open, customer can write their own algorithms and import this on. We have now changed that from wafer to tiles, and we're now providing that same system capability for advanced packaging. So every die can be traced back to every wafer, every stacked die can be traced to wafers, chambers, everything. So we are developing the advanced packaging module. I hope that answers the question.

[indiscernible]

I see. You were talking about that part. We fully engaged with them as well. It's hard for me to give specific customer details. But as I said earlier, one of the first things we are doing is using our process control team, the PCS team to help segment their line and figure out where the defect forces are. And then our engagement is pretty open. I mean, their CEO has made comments about KLA engagement with them. So we're very engaged in driving and helping improve yields.

Collaboration levels are high, and we talked about the broader base of investment. We're seeing it leading and near leading-edge investment. And so we're optimistic. I think again, the collaboration is high.

Yes, I think once you have to go to foundry model, you cannot bin devices. That's one issue. And the second is variability goes up because as designs come in. So at 10-nanometer, which is the last successful node for some customers, the margins were pretty large. But if you go to 2-nanometer, that variability is very tight. I mean, you just cannot use that book. You've got to let go of that book, all the things you have learned, 2-nanometer is a very different world, 3, 5-nanometer very different world.

All right. This is Charles Shi from Needham. I have a question about the reticle inspection side of the business. For what it's worth, there's always some debate about KLA, the inspection metrology technology, a few years -- I mean, probably more than 10 years is optical versus e-Beam and I mean 80-20, I think the debate is probably more or less settled. But a more recent one, I think it feels like it's about the reticle inspection. It's 193 Gen 4, Gen 5 print check versus Actinic. And now I think you talked a little bit more about e-Beam. So going back a couple of years, I think you kind of tied to a tight Actinic inspection adoption to at least on the wafer fab side to the pelliclization, I didn't hear much. You talk about pelliclization, but we kind of feel like pelliclization rate seems to be going up. And does that change how you feel about that mix of the market between 193, Print Check, e-Beam and Actinic? And is pelliclization is still a factor there?

Yes. Let me start, and then you take it. Okay. So this idea that it's settled is just never settled with you guys. I mean, I remember hearing 20 years ago, when I became CEO that optical was going to run out of gas and e-Beam was going to take over. Look, the thing you ought to remember is we have a portfolio. We're typically attacked with products and with messaging with people that have a single product. So what they're going to say is that product is going to take over, right? Because that's kind of got to be their pitch. And so in general, it's not really how our customers respond or behave. Our customers keep trying to optimize the cost of inspection, the efficiency of it and to get it done. So they use this portfolio, which is what we try to explain today. Specifically, as it gets to this question around Actinic and Ahmad talked to it, and I'll let him take it. But I think in general, it's easy to take a shot at us and say, we're going to win in this certain segment, but it's not actually how the market ends up behaving. And if you see the products that are being used for reticle, it would be nice if it was such a simple thing that one system did everything, but it's never actually been that way. There's multiple systems that provide the capability, which is why in aggregate, if you look at KLA share over time, and if you plotted that against all the announcements of people taking share, right, it would be hard to reconcile the two.

Right. So I'll try to be concise, but it's -- you asked a very detailed question. So just for everybody else, this is the mask and there is a plastic film on it, and that's called the pellicle if you -- but this is a 193 reticle not an EUV reticle. So if there's a plastic thing on it, then how can a particle drop and affect the design, that's where the debate is. EUV reticles are not sealed pellicles, number one. So even if you see -- if you put a pellicle, the particle can get in from the side, number one. Number two, if you add pellicles, it reduces the throughput of an EUV scanner. And therefore, now you are spending more money doing that. The pellicles do burst. And because of that, you have to clean EUV scanners, there's costs associated to it. There's two religions here. One people say, do not do pelliclization, do very, very few for very few layers and do heavy metrology. And that is 1 religion. There was another company that has a different religion. Now there is changing, which is you pellicalize everything, and then you don't have to worry about it, but the problem is pellicles not sealed. So that's where the debate is. Now if in the future, there's going to be CNT pellicles that are very clear. And then it wouldn't matter if you pellicalize or don't pellicalize because a DPV system will work and EUV system would work. e-Beam system would work because e-Beam is done before palletization. So I don't think there is a big technical debate. We truly believe that this is a portfolio solution. Why? Because of the requirement of 100%. If the requirement was to just catch 70% of the defects, I'll build 1 tool, and it will work. The problem is that they want 100% of the defects caught. That's why it's a combination of DUV, e-Beam. We will develop -- we are developing an Actinic inspection system and Print Check, and Print Check doesn't care about pellicles. I hope that is clear, but this is a very detailed subject, one, I'd be glad to spend more time on. It's not a lack of confusion in our mind.

And we will have more time for that at lunch. I'm looking at the clock. We're going to have time for one last question here in a moment. It will come from the front. But just some final announcements just before we end, because I know that otherwise, everyone will be moving and up. I want to thank everyone again for obviously, your attendance and your attention, and we hope it was both informative and educational. We will be moving outside after this last question, where we'll get the tables will be set up, and it will be round robin set up. So get your food go sit down. The executives will fill in the tables and we'll be rotating every 15 minutes for the next hour. Of course, the demos will remain open until 2:00. Some of you folks got your early and saw them, that's great. If not, I highly encourage you to. And last but not least, if you didn't get a chance, we're going to have our CTO, Ben Tsai, here during the lunch hour and afterwards to talk through a lot of the technology showcase items that are here in this room. So I encourage you also if you have time to come back in and explore that a little bit further. But with that, we'll have the last question coming here.

Thank you and sorry to keep everybody from their lunch. I'm Melissa Weathers from Deutsche Bank. You talked about how your users of AI and a lot of your products and how that's helping to improve your own performance. I assume your customers are also trying to use AI as much as they can to improve their yields and even some of your semi cap peers are probably using it as well. So can you talk about what are you seeing from them in their efforts to improve yields on their own through AI processes?

Yes. So in AI, something very important is context, right? So our machines provide us immense context for problems that we can zero in. So that context comes from design. It comes from optical architecture. It comes from the patch we're looking at. That problem is what I would call it is a high variability problem. That's what we do. Slow variability problem, slow variability problem, temperature of the fab changing. You have some other issues, humidity changing. You have all sorts of others overlay over there changing and then CD over here changing. Those types of things you can do on general data. So this is a collaboration. They do that type of work. We do this type of work. I don't think this is in conflict for process control. Hybrid metrology ten years ago, was going to just take over everything, but nothing -- it doesn't work. You need sensors on the problem. So I think we are working on two different things. We are actually also working on that based on that thing that I talked about, the entire fab and data, all of those things and the customers can plug in their algorithms. So I don't think we're in conflict yet.

I think in terms of in the process, world, people using it. We have a slight glimpse into that because we have a division that makes process tools. There's just so much less data. I mean, most of what we are is data processing and image processing as a company. So we're naturally more inclined and we've been working on algorithms literally for the whole 50 years of the company. I think that for ASML has talked about having AI inside of their systems. They have a lot of parameters inside to try to optimize. But I think this notion that we're all going to use it to be more productive is true, but there are certain things that it's -- you have to have access as some odds to the data, and then you have to have -- the challenge in our image computing is it's such a high data rate that we have to process it locally in order to be able to provide information to our customers. So it's been quite a driver for us, and we think that it's even inside the algorithms we've seen developed in the last 6 months are giving us more capability of the existing platforms than what we thought 6 months ago. And that's going to be really important for our customers because it will enable us to provide even more of an upgrade path as we go forward for these investments that they've made, that we've made.

So that will conclude the Q&A session. Again, thank you, everyone. It will also conclude the webcast. I want to thank everyone who is remote who joined us as well. Thank you very much.

Thank you all.

That concludes the formal part of our Investor Day and webcast. Now please join us for lunch with management and make sure you check out the AR/VR models of KLA products before you leave.