Go ahead. All right, everybody. Everybody's filtering in. Thanks for joining us. Our next presentation is with Cadence, and we've got Anirudh Devgan, their President, CEO, and member of the board of directors. First, a brief statement by Richard from the investor relations team. Hey, good morning, everyone. Thank you, thank you for having us. I'll be real quick, just get this out of the way. So today's discussion will contain forward-looking statements. Due to risks and uncertainty, actual results may differ materially. For information on factors that could cause actual results to differ, please refer to our SEC filings, including our most recent Forms 10-K and 10-Q. With that out of the way, back to you, Jim. All right. Thanks, Richard. All right, Anirudh, so you became CEO in 2021. Before that, you were president, and before that, you were Executive Vice President, General Manager of Digital and Sign-Off and System Verification Group. So quite a career at Cadence. It's been remarkable to watch Cadence's growth under your leadership. It's one of the best performing stocks on Nasdaq since then. So what have you done to drive some of this remarkable growth in your time as CEO? Well, first of all, thank you for all the interest. Thank you for the question. Yeah, we have done well in the last 5-10 years, and what I would say is that we are probably better positioned now than we were five years ago. Okay, and there are multiple reasons for that. I mean, it's a combination of, like, our own products and culture and our strategy, and then what's happening in the marketplace. You know, the importance of silicon now, the importance of system companies doing silicon. So I think it's a combination of good team, you know, good technology, and what the customer trends are there, and we are well positioned to capitalize that going forward. Fantastic. Mm-hmm. So let's do just a quick overview and a refresher of Cadence. If you could, for the audience, just kind of talk about EDA. Where does it sit in the broader kind of semis ecosystem? And then, you know, as you think about the EDA landscape, what are some of your competitive strengths? Yes. You know, it's, like, difficult to give an overview because some of you might be, like, super experts already, and then some of you may not know. So it's always tricky, you know, to give overview to it. Like, some of them may be more experts than even me. So I think the main thing about what we do is we make software, you know, and we call it computational software, because this is not regular software. This is very mathematical, numerical, technical software to design chips and electronic systems, okay? Because these chips are, as you may know, very, very complicated. Like, you know, the chips these days, you know, 1 inch by 1 inch may have like 100 billion transistors. So there's no way that they can be designed by hand, and they haven't been designed by hand for, like, 20 years. So we make the software that makes that possible. So we are used by all the companies that are designing chips and in all geographies, in all vertical segments. So it has the business has become more and more essential to, you know, digital transformation and AI. So that's the EDA part. That's our core business, and we are the most diversified, the biggest portfolio. We are, you know, the leader in core EDA, okay? And then the question has been in EDA, okay, you're good at designing chips, but can you do other things with it, right? So we do some IP, you know, which is like existing building blocks, okay? The real expansion in the last five years is into system design and analysis. What I call SDA, along with EDA, and of course, in AI. The big drivers are SDA and AI. The reason for the system move is because right now, about 45% of our customers are system companies, you know, and they are designing more silicon, so we sell them EDA, but also there are big issues of thermal, of power management, of aerodynamics. We have this whole area of SDA, which can, you know, double our TAM with SDA, and then AI can further, you know, increase it. In terms of our growth drivers for the future, you know, apart from core EDA, the drivers are SDA, you know, AI, and then this emergence of chiplets or System-i n- Package. Let's kind of take those in order. As you kind of think about the past couple years, a lot of companies have faced macro headwinds from the Mm-hmm. - economy. You guys have continued to grow. What are some of the technology drivers behind that, in terms of driving that growth? I think the main reason, in my opinion, is that we are part of the R&D process, okay? R&D is anyway, especially for these big companies, essential for growth. So even though there is some fluctuation in revenue, even in some of our semiconductor customers in the last couple of years, you know, these things take years to design, you know, so the customers still invest in R&D for the future. And so I think the essential part is that we are part of R&D of our customers. Then the second reason of our resilience is that most of our revenue is recurring in nature. So we have, you know, 85%-90% recurring revenue, so we normally have three-year contract cycles, so that gives a certain predictability, and for that reason, we invest heavily in R&D because we have a recurring revenue source. And then third reason is that we are very, very diversified. So almost all geographies where chips and electronic systems are designed, you know, all verticals. So what happens is sometimes some verticals are doing well, some are not doing well, but there's always some new thing. Of course, AI is a big thing, or data centers or automotive. So if some verticals are weaker or some geographies are weaker, some others are stronger. And in general, there is going to be more and more silicon. So in general, the verticals that are stronger are more stronger than the verticals that are weaker. So I see that continuing for some time. There will be more and more silicon design, and as long as we are best in class, we will be critical in that process, yeah. And so one of those end markets that's obviously top of mind right now for everybody is artificial intelligence. Mm. So can you talk a little bit about what you've been doing to drive product innovation around AI? Mm-hmm. What's in your product portfolio today, and where does the future take it? Yeah, AI is a big topic, you know, and of course, everything—everybody calls everything AI. That's a little bit of an issue right now, but that's good in some ways. But we have done AI for some time because, you know, even EDA, the core algorithms, a lot of kind of computational methods are used for a very long time. And the way we look at the world is that there's silicon, then system, and then data, right? Perfect example is like electric car. You have all the navigation data, then you have the actual car, which is electrical plus mechanical, hardware plus software, and then the silicon that drives the car. Okay. So then if you overlay our core strength, which is computational software, this kind of numerical software, into these three circles. So the innermost circle, that's EDA, right? Then the next circle, if you put computational software, that's SDA, that's simulation and analysis and system design. And if you put computational software on data, that's AI, because a lot of the AI algorithms are very similar to what, you know, like inference is like matrix multiply, training is like some type of conjugate gradient. So we have done these kind of algorithms for a very, very long time, okay. So then as these emerge, we can apply this into our own products and also the build-out of AI in general. So to me, you know, AI has like three phases to it, and some of you may know all this anyway, but... So the first phase is like the build-out of the infrastructure. You know, whether it's GPUs, and then, of course, NVIDIA is a big partner of Cadence, or like car companies doing AI, like Tesla. You know, Tesla is a big partner of Cadence. Or these hyperscale companies like Google and all these others have talked about their own silicon, so, and then other companies, GPU companies. So and then there's AI in these phone chips and all that. So the first part is the infrastructure build-out. So we are very, very well positioned, and a lot of the- not just from a software standpoint, but also from what we call hardware-assisted verification. You know, we have special systems that design these AI chips, okay. So that's one, and that is still- We don't break it out of the $4 billion that our revenue, but that's a big portion and driving a lot of growth, okay? So that's the first way we participate in AI, because anyway, you can't design any of these systems without, you know, using products like ours. Now, the second part of AI is applying it to our own products, okay? So, and we have now five major AI platforms or GenAI platforms, which are based on all these reinforcement learning, generative AI technologies. And, they are giving... You know, I can give some examples, giving, like, massive improvement in productivity, but also what's even more important, I think, is improvement in performance. So what would take, like, three to six months, can be done in about two weeks. But what is more interesting is that it can give better results than what a human can do in lots of cases, and I'll tell you why that is, okay. But we have five major AI platforms right now. We have done that for, you know, released it probably two years ago, before all the buzz about AI. And that can drive a lot of growth for the future because these chips are getting more and more complicated, so you need AI to make the problem more tractable. So that's the second big part of AI, and I think that was your main question. But I come back to that. But the third part. I just want to comment on the three phases in my mind, okay? And then the third phase of AI is like new verticals that will emerge because of AI. Now, this, the second part is applying AI to our existing products, and make them much better. But I think AI will also drive new solutions, of course, just like internet did or other things did. So then the question is like, what is that new thing? And there could be a lot of them, but in my mind, the biggest one has to be life sciences, okay. So we invested last year in biosimulation, and you will see a lot more activity for it. Now, this is a little bit further out because the third phase takes the longest, but could have the most transformative effect. So just like we can do chip design software, and then we can do system design software, like planes and cars, the similar methods can be applied for molecular design, and drug discovery. So we acquired a company last year, more for the long run. So those are the three phases. But the second phase, you know, it can drive a lot of productivity benefit, performance benefit. Like, I'll give you example. In lots of cases, it's not that it's the design is faster, but the design is better because automation has not been applied to certain parts of the design process that we can do with reinforcement learning now in AI. So, in some cases, we're getting 8%-10% power performance in what we call PPA, Power Performance Area benefit. Just to put that in context, you know, if you go from one node to another node, the improvement these days are like maybe 10%-20%. Because, you know, there's more maturation of Moore's Law. So you're getting half of that or two-thirds of that from better software.... You don't have to go to the And of course, people will still go to the next node because there are benefits to that, but that's a huge benefit that you can get by better software. Well, and that's a good thing 'cause we have seen Moore's Law kinda slowing down- Yeah. which leads to a lot of the interest in kinda 3D integrated circuits. You mentioned. Mm-hmm. chiplets as one aspect of that. Maybe you could talk a little bit about how you're positioned in that kinda core chip design area as it gets tougher and tougher to get to the next node. How does that affect- Yes. -Cadence? I think Moore's Law has been slowing down for a very long time. Mm-hmm. Of course, some people say Moore's Law is alive and well. Some people say it's dead already. Okay. I think it depends on what you... This is problem with everything, like, what you define as Moore's Law to be, okay? Same thing with AI. So the Moore's Law, you know, the classical Moore's Law, I think, is done for a long time already, okay? You know, I used to work with Bob Dennard in IBM, who actually wrote the Dennard scaling. So by that definition, it's done like 15-20 years ago, okay. What has happened in the last 5-10 years is area scaling. Mm-hmm. That means, you know, when you go from 10 nanometer, let's say, just pick any two nodes. When you go—I mean, it's easiest to look at 10 versus 7 because, you know, 10 by 10 is 100, 7 by 7 is 49. So when you go from 10 nanometer to 7 nanometer, what's happening is effectively, number of transistors are doubling on the same area. Okay, so you have double the transistor, so you can do more things with them. So if you look at even last 10 years, it's not that the chips are getting faster. I mean, your typical mobile CPU is still 3 GHz or so, but instead of 1, you have, like, 8, and then you have, like, GPUs in it, you have neural engines, all this kind of... So the main thing that has driven Moore's Law is area scaling, okay? That's why GPUs have also done well, because they have a lot of cores, and you can have a lot of them, right? Now, what is the extension of that area scaling? I think this can continue, like we are at 3-nanometer right now. We go to 2, 1.4, and 1, okay? It can continue for, like, 10 years or so, easily. In parallel, the other way to do area scaling is, instead of just putting more things in a chip and reaching the reticle limit, you can put multiple chips in a package. Of course, we talked about it in the 1990s, too, right? It's nothing new, but I think the economics now make it more feasible to augment Moore's Law with a, with you wanna call it Moore's Law or some other dimension of area scaling, which is in a package, okay. And it has a lot of other advantages, as you know, because, you know, like if you look at these chips now, especially in it started in HPC, high-performance computing, and I think it will percolate through the entire value chain. Is that like, even, like, these are all public, like Amazon has Graviton, it has, like, seven chiplets in a package, right? And then when you go to the next version, you don't have to design all redesign all seven. You can redesign some critical pieces of it. So it's much more cheaper and cost-effective to do next generation than IP reuse and design reuse. So, so for area scaling benefit and also for cost benefits, I think chiplet is gonna be a huge trend, okay. So then the question is: How are we positioned in that, right? So the good thing with Cadence is that we, you know, to do chiplets well. Now, I mean, from a design software standpoint, I'm not talking about manufacturing and. Because in general, we know, we focus on the software part of it. There you need three basic components. So you need the IC design, of course, tools. So we are the leadership in, you know, analog and digital because, you know, these are mixed signal chips and because some of them are IP, some of them are interface chiplets, some of them are compute chiplets. So that's first layer. Then the second layer, you need package design tools. Of course, it's system in a package. And then the third layer on top is you need analysis tools which are specific for that. And the biggest thing is, like, thermal simulation or electromagnetics, because you have a lot more power being generated. So if you talk to TSMC, they'll say thermal is a big issue in 3D IC. So the 3D—and 3D IC is a generalized term for 2.5D and 3D. Not everything is stacked. I mean, some of them are stacked, but some of them are next to each other in interposer. But to really do it well, you need the IC tools, the packaging tools, and the analysis tools. So Cadence is the only company that has all of them because we are the leader in packaging tools. You know, Allegro is a flagship. Now, some of this is good luck, okay, because Allegro has been there for 30 years, and now packaging became super critical. It was not that critical 5, 10 years ago, and some of it is, is good planning. So Allegro- And then 5 years ago, we started a lot of system analysis tools. So we have a lot of thermal tools and, you know, electromagnetics tools. So we have a one- So when TSMC announced, like, 3D blox flow last year and all the big foundries, so we are working closely with them to enable this kind of, 3D IC. So I feel we are very, very well positioned in this chiplet work. Yeah. And so then as we kinda move up from the silicon design to the SDA segment, as you mentioned- Mm-hmm. The kind of system design analysis, what all goes into that, and how do you kind of define that opportunity for Cadence? Yes, so, I mean, the chiplet is like a bond between the semi and the system side. Mm-hmm. So it's a little bit of system, but not really all of it. But the fact is that 45% of our customers are system companies, you know, these big phone companies or car companies or, you know, automotive companies. So of course, we can work with them on the silicon side, but silicon is just one part of the system. You know, like, there is the actual mechanical, electrical system, the hardware, software system, and all the data. So the key thing is that a lot of those problems, there is a lot of R&D synergy with EDA. To give you example, like, I mean, without getting too into the details, like, like, like computational fluid dynamics, right? Like, if you have electric car, you know, the range of the electric car depends on. You know, that's why they're all rounded now, you know. It depends on the shape of the car, it has probably the biggest... Or if you look at a F1 car, you know, we have all this collaboration with McLaren and other. You know, the aerodynamics is the damn fastest determinant of which car is the best, right? And or thermal is a big issue in cars or phones and data centers. So like thermal simulation and CFD simulation, in terms of R&D algorithms, are very similar to EDA. Actually, they are simpler than EDA. Because EDA, you're doing transistors at very large scale, like 100 billion transistors, and transistors are non-linear, and it's the most difficult simulation problem. So about one-third of EDA is simulation, and we are the world's best at all these simulation algorithms. So we can apply those same algorithms to simulate thermal and aerodynamics, and we have done that organically and with some acquisition. So that's the first part. There's a lot of R&D synergy. The second part is that there's a lot of customer synergy, because the big customers, you know, we have much deeper relationship with them because of silicon. Okay? And third part is that there is a lot of growth of simulation and also very profitable business, because simulation is always profitable. And then this emergence of digital twin, and, you know, whether it's for designing planes or cars or molecules, is gonna be a big, big area. So that's an entire big space, and that's about $10 billion TAM expansion opportunity for us, and it's very synergistic in R&D, synergistic with customers, and it's a good, good area to grow. So that's why we have. Now, that's about roughly 12% of our revenue. This year, it's about... So we started this about 5 years ago, so it's about $500 million revenues, growing, like, about 20%-25% a year for last several years. Very good. Mm-hmm. Now, with Arm's IPO this year- Uh. intellectual property or IP Yeah. got a lot of buzz. You mentioned before, Cadence has a, an IP portfolio. Yeah. How do you think about that part of your business? Well, IP is a good business, you know, because, you know, we have software to design these things, and then in some cases, we also sell, like, pre-designed components. Uh. Okay. Now, but IP business is very diversified, okay? So there are parts of IP which are very differentiated, you know, like Arm. Arm is very differentiated and also they, they, and they're a great partner with Cadence, by the way, you know, for the last 10 years. And, you know, typically, you, you buy that, and you harden that with, with your, you know, with software like, like Cadence. And then part of the IP business is more commoditized, you know, like vanilla, like PLLs or, you know, interface IP, things like that. So we have good IPs in about $500 million. Of our $4 billion is IP, so that's also roughly 12%-13%. But we wanna make sure that we focus it on the more profitable parts of it, right? So... But it can grow a lot, so that's a good thing. But we wanna make sure the growth with profitability. That's always our motto, even in our regular business. Because in the end, the investors want not just, you know, they want take-home pay, you know. So what's the profit? What's the EPS, right? Not just what's the growth. So we just want to be careful. So we have, like, Tensilica is a big part of our IP business. That's very profitable. So Tensilica model is very similar to the Arm model. So these are specialized DSP and audio/vision processors, AI processors for the edge, and we have royalty in that. It's very similar to the Arm model. And then in the interface IP, we do some critical IPs like UCIe, you know, DDR, PCIe, which are critical for chiplets and AI infrastructure. It's a good business, and we will continue to invest in it with a focus on growth plus profitability. Yeah. Fantastic. Well, we've got plenty more to go through- Mm. I do wanna take a pause, see if there's any questions from the audience. Mm. Can you comment on the China exposure of the business and how that differs from the non-China part of the business? Yeah, China, I think, is roughly 15%-16% of revenue for Cadence. It moves around a little bit. It has grown over the years, last five years, even though there are sometimes some regulatory changes in the past, right? So China, I think it's true in any other region. I mean, the semiconductors are so essential that there are a lot of companies in China designing chips for, you know, a variety of applications, right? Whether it's washing machines or phones or TVs or cars or, you know. And that's true in a lot of other parts of the world as well. So, you know, we are pretty diversified, okay? But we are, you know, working with all the major Chinese companies that are designing chips, just like we are in other parts of the world. Now, there are some regulatory environment, but that's roughly stable for Cadence, okay? So most of the regulatory is focused. We have some effect on it, but most of it is focused on certain kind of chips. You know, like certain, you know, in, in the news, even yesterday, some kind of AI chips, and then certain kind of manufacturing below 14 nanometer. But we are on the design side, and we are in design of all kinds of things. So even though some small, like AI chips, may have a effect, but given that we are so diversified, it's not that pronounced. Okay. All right. Another one in the back there. Mm-hmm. ... Thank you. I think we've heard, we've seen from semicaps, especially these past couple of quarters, strong manufacturing activity in China. And so you does also assume that you'd see also strong design. But I think so one of your closest competitor on their last call mentioned weakness in China. So I was wondering how, I mean, how you can reconcile that. Are you seeing any strong design activity in China? Yeah. Yeah, I mean, I think there are two parts to the China. I mean, just like any other. There's one extra part, but there's the regulatory environment, I think is roughly stable at the moment, you know, there's some small changes. And the other is the macro environment. You know, so I think the macro will dominate the regulatory, in my opinion, in China, just like it would do in U.S. So we'll see. We still see sort of design activity. I mean, it's same thing in China like here. You know, some parts are very strong. Like, for example, autos, it's phenomenal, right? And all those auto companies are, you know, it's all public, designing their own chips and moving. It's remarkable what's happening there. And some parts may be weaker, you know, if the recovery is not, not as strong. But overall, I still see a lot of activity, so we just have to, you know, in terms of what we see, we just have—you know, this year is good. We just have to see where we end up, then January, February, we'll have a better idea for next year. Yeah. But I think it'll be more macro issues than regulatory issues, is my guess. Yeah. One right down here. Mm-hmm. With your expertise, which you've described, in computational software, can you bring that to areas other than semiconductors? I think you've mentioned biosciences in the past, and can you update us on where you are in pushing into sort of other areas and how much resource you'll put into that? Absolutely. I mean, that's the whole. I mean, that's a huge growth opportunity for Cadence. So the first area. I mean, bio is there. Okay, bio, and bio, I'm very optimistic long term, but in the middle term or short to middle term is the system simulation. So if you look at system simulation, you know, there are like 80,000, you know, there could be 70,000-80,000 customers. So the number of customers is much higher than for silicon, and there are three or four big areas in there. So one big area is what they would normally call like high tech. So that's like similar to our customers, you know, like, like electronics companies and silicon companies. But a lot of electronics companies, you know, like they have to do all the simulation of thermal or aerodynamics, electromagnetics, or aerodynamics. But the other two parts of the system business is of course automotive, and then aerospace. So we are now working with a lot of the big automotive companies. Now, some of them are also designing chips, so that helps, but some of them are not. Actually, a lot of them are not. So then but simulating, like I was saying, you know, the range of the car or simulating the heat of the car, and then the same thing we are working with. And we did several acquisitions also with a lot of aerospace companies, for design of planes and... So in the system simulation business, now, but it is a different area, but it's the high tech electronics, so semiconductors plus electronics, plus cars, plus planes. Now, of course, there's a lot of commonality in that, too. So that's about a $10 billion market, and the EDA is about $13 billion-$14 billion. So that's a very good opportunity, and Cadence is leading that merger of this semi and systems. Awesome. Well, we are at time. Thank you, everybody, for your questions. Anirudh, thank you for sharing so much about Cadence. Yes. Thank you. Okay, I think we're ready to start. Good morning. My name is Giel Rutten. I'm the CEO of Amkor Technology. And what we'll do this morning, I'll give a short introduction of Amkor, and after that, we do a couple of... Now, Amkor is a pioneer or was a pioneer in the outsourced manufacturing industry. It was established 55 years ago, so early on in the semiconductor industry, and it established over the last 55 years a deep relationship with all leading customers in the industry, both fabless companies as well as the IDM companies in the semiconductor industry. We build a trusted cooperation with most of these companies, and we serve them from a very diverse manufacturing base around the world, where we have about 2-2.5 million sq ft of manufacturing space in six countries around the world. That global presence is important to offer our customers a resilient manufacturing supply chain. Now, we invest in the future. We are a leading company in advanced packaging. We're considered a technology leader. We're investing in both R&D as well as manufacturing capability and scale. In 2022, we invested close to $1 billion in that. So we have a large R&D base, about 700 engineers in Korea, where we support and work closely together with our lead customers. Overall, we employ about 30,000 people around the world, and we hold a couple of important leadership positions. I mean, we are number one OSAT in the automotive industry. But besides that, we also hold very strong positions in the premium-tier smartphone industry, as well as in the high-performance computing market, where we offer a complete package portfolio. In 2022, we did $7.1 billion. This year, we are in a declining industry. We declined about 9% year-on-year in an industry that is declined between 15%-20%. So overall, we're doing better than the industry. We are an advanced packaging leader, so we're not engaging very much in the, let's say, commodity side of the technologies, but we're focusing our our support and services to advanced packaging, and that starts with everything that relates to bumping, flip chip, up to 2.5D package technologies, where we offer a complete portfolio to our customers. So we're not on the wire bond side, too much of our business, although that's still part of our portfolio, and we see that as a complementary part to support our customers. Now, where are we in the supply chain? I think we are an integral part of a semiconductor supply chain. On the left-hand side of this slide, you'll see the, let's say, the design companies. These could be, fabless companies or IDMs, and more and more, we see the OEMs also, let's say, building up design capabilities to design their own, their own products to protect their IP. So then, of course, we have wafer foundries or wafer fabs that companies maintain internally, and then, you know, wafers move into an OSAT space, and some companies do their assembly and test internally. We ship directly, of course, to the contract manufacturers. There are a couple of trends in this, in this supply chain. First of all, I think the percentage of outsourced manufacturing is increasing. Certainly for advanced packaging, we see that companies that currently have in-house manufacturing capabilities, when their products move to more advanced silicon or to advanced packaging, that they tend to outsource more and not invest internally. Secondly, we see that a lot of the industry mega trends in the semiconductor industry are supported by advanced silicon as well as advanced packaging. So the growth in the industry is almost exclusively supported with advanced products. And, thirdly, I think the geopolitical tensions currently, and also the uncertainties in the supply chain due to COVID, actually result that location and geography becomes more important. Customers are willing to take less risk to ship products around the world, to have them assembled in different locations. I think co-location gets more important, and also customers look for a stable, sustainable supply chain that can maintain supply for multiple years to go. In the automotive industry, for example-