Semiconductor Insiders - Podcast EP324: How Dassault Systèmes is Creating the Next Generation of Semiconductor Design and Manufacturing with John Maculley

Episode Date: December 26, 2025

Daniel is joined by John Maculley, Global High-Tech Industry Strategy Consultant at Dassault Systèmes. John has over 20 years of experience advancing innovation across the semiconductor and electronics sectors. Based in Silicon Valley, he works with leading foundries, OSATs, design houses, and research institutes worldwide… Read More

Hello, my name is Daniel Nenny, founder of semi-Wiki, the open forum for semiconductor professionals. Welcome to the Semiconductor Insiders podcast series. My guest today is John McCulley, Global High Tech Industry Consultant at DeSoe Systems. John has over 20 years of experience advancing innovation across the semiconductor and electronic sectors. based in Silicon Valley, he works with leading foundries, OSATs, design houses, and research institutes worldwide to accelerate technology, co-optimization, and strengthen ecosystem resilience. Welcome to the podcast, John. Hi, Dan. Thanks for having me. The first question I like to ask is, what brought you to your current position? What brought you to DeSoe Systems?

Yeah, thanks for that question. You know, DeSos Systems is such a visionary company. You know, I started actually in the aerospace industry at the beginning of my career and switched to the semiconductor industry in 2006, working for Micron Technology. I spent 10 years in their research and development organization, and then the last two years I spent as a global director of operational excellence where I was deploying different information systems and PLM systems across their FAB network. I left in 2019, I became a management consultant for four years, working with semiconductor companies, basically helping them improve time to market and lower cost. And I decided to go work with the SO systems just because it was the perfect blend of technology, the application of fantastic software across the high tech industry in general is the the group I'm in, but the semiconductor space in particular.

So excited to be with DESO. I'm based in Silicon Valley. I'm kind of in the heart of the semiconductor world and excited to work with companies across the ecosystem. Yeah, I'm in Silicon Valley as well. It's been an excellent experience in semiconductors. I mean, Silicon Valley really is the heart of the industry. So can you share how your team?

theme is transforming semiconductor intellectual property into generative IP intelligence? And what does that mean for the broader design ecosystem? Yeah, great question. So this is an exciting moment for our industry. We're finally beginning to treat knowledge, not just silicon, it's a true strategic asset. And actually, your audience may not realize that among companies in the S&P 500, intangible assets, which include intellectual property and know-how, now account for 90% of of total market value.

Just an amazing statistic and it's really fundamental to what we're focused on now with some of our solutions. Let's unpack what that means a little bit. First of all, IP is no longer just icy blocks that you would traditionally think of from EDA tools. Knowledge and know-how have really become the new IP. And we now see the need to codify and curate curate that IP from end to end.

And so IP management is shifting to governance and intelligence. So with AI augmented IP engineers can now design with manufactureability in mind from day one, which is pretty exciting. And so you can imagine a virtual manufacturing expert sitting next to every new engineer. And this can be applied to any discipline. So having that, what we call virtual companion, you know, as a mentor is a real game changer.

So in the semiconductor industry, capturing tribal knowledge, you know, has never been more critical. We just can't let decades of know-how walk out the door. And I'm sure many of your listeners are seeing this or have seen it over the last decade. So this surprised me, you know, here's an alarming statistic. approximately one-third of U.S. semiconductor employees are age 55 or older and nearing retirement. That's a real wake-up call for the industry, and, you know, it's an opportunity to leverage AI, you know, not only for improving operations, efficiency, but also in workforce development, you know, using AI to train the next-generation workforce.

So how is this, though, helping, you know, companies along. this transformation we we just released a new solution that makes a lot of this possible we call it semiconductor engineer and it's part of our anovia brand so here's a little a little bit about it at its core it's all about improving collective intelligence you know we promote real design collaboration by breaking down silos and surprisingly even the most prestigious high-tech companies that you can think of in the semiconductor industry still have silos and it's kind of a legacy structure that's slowly being dismantled and so our solution kind of helps streamline that dismantling. So we manage IP with this solution on a unified model-based platform that we

call a 3D experience platform. It's a real differentiator for us in our market. And with it, we democratize the design process, you know, by federating metadata. It enables non-experts to use AI to make informed decisions. And we, you know, enable with the platform end-to-end traceability, which is a key differentiator as well. So what does all this mean? Engineers can get a holistic view of where IP comes from and how it evolves. In fact, knowing the lineage of IP is becoming just. as important as the IP itself, critical element, especially as companies start working within

ecosystems. And so speaking of that, if we talk about the broader ecosystem, collective intelligence is really laying the foundation for the AI era of engineering. For example, the US Chipsack is helping drive a lot of this. And the Smart USA Institute, which is a digital twin consortium that's funded by the Chipsack is already looking at how to scale this nationally and DeSso Systems is a member of Smart USA. We're actively engaged in this effort. And so virtual twins, which is what we call digital twins, and I won't get into the differentiation, but virtual twins are becoming the ideal way to embed IP into these large language models. You know, they become kind of the encapsulation of knowledge and know-how across enterprises.

And virtual twins, they're living models, they learn from real-world data, they evolve, and they're becoming more valuable than the physical products they represent, which is interesting to think about that concept. So we believe that innovation now depends on leveraging collective intelligence and companies need to treat know-how

as a first-class asset, not just ICBlock. and they need to build the digital infrastructure to govern it. It's a real game changer. So to quote Mark Zuckerberg, the best tech companies don't just build tools, they build communities.

And Mark's point is exactly what collective intelligence depends on. And when people and knowledge are connected, the organization learns faster, they're able to solve harder problems and they can innovate as a unified force. Interesting. So with the searching demand for

AI and high-performance computing, what new challenges are emerging around IP management protection and collaboration across partners? Yeah, we're seeing several big shifts happen all at once, and they're putting real pressure on how companies manage and protect their IP. For example, design complexity is skyrocketing. It's generating far more IP variance than dependencies than traditional systems can handle. And that, you know, the complexity is not only on the front end with the silicon, it's in the back-end packaging and couple that with the supply chain and the ecosystem aspect and it's a

compounding complexity. We're also seeing collaboration expanding globally. It's increasing the need for secure, consistent IP exchange across these diverse partners and tool chains. And the collaboration is happening within regional ecosystems, connecting to other regional ecosystems. And, you know, with onshoreing, the entire supply chain is being modified, you know, as we speak. So being able to cure that IP across these global regional ecosystems is real, really a key. And AI and compliance pressures are also rising. You know, they're introducing new concerns around IP protection, sovereignty, as we just talked about, and responsible data use. data use. I could go on with more examples. But these challenges are exactly why we're seeing

so much interest in virtual twins and model-based governance. They're giving companies a way to collaborate confidently without losing control of what makes them competitive. Got it. Yeah, I agree completely. So what do you see as a key neighbor of global semi-connected ecosystems collaboration in 2026 and beyond? Yeah, 2026 is an exciting upcoming year. There's a few big enablers that are starting to shape, you know, how global collaboration work going forward. Obviously, as I talked about earlier, shared virtual twins with model-based governance. It gives all the partners, you know, real-time, trusted view of design and process intent.

Federated, secure collaboration networks. You know, these span metadata platforms. supply chain, provenance, and protected IP exchanges across the ecosystem. And AI-enabled engineering and strong ecosystem structures is also a big enabler. These enhance cross-disciplinary decision-making and ensure a skilled aligned workforce. When you combine these enablers, you start to see a more open, resilient, and innovation-driven and global ecosystem, one that can actually keep up with the PACE AI as putting on the industry.

And this is really just the beginning. And from your perspective, what's the most exciting innovation you expect to see in the chip design or manufacturing the next year? And what breakthroughs might redefine the industry over the next decade? Great question.

There are a few innovations that I think will really stand out in 2026, and some that could reshape the entire industry over the next 10 years. So AI driven design and manufactureability intelligence. It blends AI-generated architectures, physics-based simulations, and real-time process guidance built-on captured know-how. So this is basically the democratization of knowledge,

which is kind of an important concept to understand as we're talking about these different topics. Secondly, virtual twin-centric development and closed-loop fabs. for design, process optimization, and infab telemetry continuously informed each other in a unified space. We call those 3D universes. And those are areas where these virtual twins are just continuously informed in learning and increasing in value. Third would be advanced packaging and interoperable chiplet ecosystems, including hybrid bonding at scale. You know, these are going to redefine performance as scaling flows with silicon, and it elevates a virtual twin as the primary asset, which is key for what we're doing with our customers. So going forward, I think the next decade will be defined by how well we merge human expertise, AI, and virtual twins. That's where the real transformation happens.

Yeah, yeah, AI is changing everything. I think it's one of the most disruptive technologies we'll see for a long time, especially in the semiconductor industry. So you mentioned co-optimization. So how is fabrication technology co-optimization influencing chip performance, efficiency, time-to-market. I mean, where do you see the biggest opportunities ahead? Yeah, FTCO or fabrication technology co-optimization. You know, it's one of the many aspects of technology co-optimization.

And traditionally over the last, you know, several decades design technology for optimization was a big driver. That's an area I worked quite heavily on with Micron Technology. FTCO is relatively new. It's becoming one of the most important levers for performance, though, and competitiveness. And we're seeing its impact across the entire roadmap. So I see three key opportunities emerging with FTCO, you know, design, fab convergence and early insight. where cross-disciplinary co-optimization can improve performance, predicts defects, accelerates yield, and reduces costly fab iterations. So imagine, you know, fewer physical prototypes,

because you're able to do these with a combination of virtual twins and, and AI optimization. Also closed-loop data-driven optimization, you know, with inline telemetry feeding design models and where every wafer is improving the next generation of process and product. And this begins with short-loop optimizations, which is a project we're partnered with Purdue University on. The North Star here is the full flow, optimizing the full flow with AI. And that's a ways off, but that's what we're all across the industry working towards. And then finally, advanced packaging and unified FTCO environment, enabling nanometer scale gains and power and bandwidth while integrated materials, equipment, processes, system requirements, all of this in real time. Extending FTCO to the back end is a recent development.

It's been, at least in the last couple of years, focused on the front end, but we're seeing the application. back end as well. So it's still in the early stages, but I really see FTCO evolving into a central pillar of competitiveness and as complexity skyrockets like we talked about, the companies that co-optimized across the entire stack from materials to systems will be the ones that win in the end. So the bottom line is, you know, when when people, data and know-how come together with AI, you know, innovation accelerates, and that's exactly where this industry is headed. Yeah, I agree completely. Thank you for your time, John.

That was a great conversation. You know, one final question. Where's your favorite coffee house in Silicon Valley? Well, it's actually my, my espresso machine, I think, is my favorite coffee house. I've become kind of an espresso addict and get the best. pod delivered to my home. I don't, I don't go to too many coffee houses just because I like an espresso so much, but funny questions. Thank you. Well, I'll meet you at your house then. I'm ready. That sounds great. Let's do it. Thanks, John. Thank you so much, Dan. Appreciate your time. Thanks for

having me on. That concludes our podcast. Thank you all for listening and have a great day. Thank you.