Editor’s note: This interview has been edited for length and clarity.

Engineering.com: Can you tell us about your background and what led you to your role as Vice President at Siemens Digital Industries Software?

Michael Munsey: After college, I began my career as a design engineer at IBM, where I worked on semiconductor development for IBM mainframes for many years. While working there, I became interested in EDA tools and methodologies. I had the opportunity to work in the EDA industry, where I joined Viewlogic Systems and worked there for several years.

I later joined a startup called Sente, which worked on RTL power analysis, long before power became a crucial aspect in the development of semiconductor devices. Then I went to Cadence, where I focused on design verification using Verilog and the entire functional verification suite. Then I moved to another startup that worked on semiconductor layout. I went back to Cadence to work on their enterprise solutions.

After this, I decided to venture out of the EDA industry, so I joined Dassault Systèmes, where I led their semiconductor industry and strategy. In this role, I learned about enterprise software and how to integrate semiconductor design and manufacturing within the enterprise software. Then I joined Methodics as Vice President for marketing, corporate strategy, and business development, and stayed with them through the Perforce acquisition of the company.

I joined Siemens in 2021 as Vice President of the Semiconductor Industry. I lead the semiconductor industry, focusing on EDA software, product lifecycle management tools, multi-physics simulation tools and AI platforms. All of these converge at the Digital Industries Software level, so I get to manage solutions that come across our entire portfolio of products.

How can semiconductor companies lean into digitalization and leverage the comprehensive digital twin for both semiconductor design and fab?

If you look at the semiconductor industry as a whole, the design and verification processes already leverage the digital twin. Engineers use advanced EDA tools to get digital design files that are handed off to the fab. We already have an industry that has done an excellent job on the design side of digitalization.

The gap exists on the manufacturing side. Unlike other industries that have embraced digital twins to simulate and optimize their production environments, semiconductors still rely heavily on real-time, physical experimentation, like running test wafers through fabs and bringing up new designs directly in real time manufacturing. This approach is both costly and time-consuming, especially at advanced nodes.

Now is the time to extend digitalization beyond design into semiconductor manufacturing. This means collaborating with fabs and equipment manufacturers to create digital twins of not only the chip designs but also the equipment and the fab processes themselves and use the digital design data as the foundation for simulating how a design interacts with a virtualized fab environment. Also, ramping up process nodes virtually so new designs can be brought up in a simulated fab before moving to physical production.

If the industry can achieve this, the payoff is substantial and will include significant time savings, reduced costs, faster time-to-yield and quicker time-to-volume of semiconductor devices. In short, bringing the digital twin to manufacturing is the logical next step for semiconductor companies to remain competitive at the leading edge.

What are the key benefits the comprehensive digital twin can provide for a business’s semiconductor ecosystem?

There’s a feed-forward and a feedback aspect to this. The first benefit, which I already mentioned, is doing things virtually. There are considerable cost savings because you are not wasting materials, electricity, and resources by doing this. Ideally, doing things virtually also allows you to do many more experiments for ramping up a process node.

A digital twin setup also gives semiconductor businesses the ability to optimize various critical processes. That’s definitely the feed-forward aspect of it. But there’s also feedback that enables shift left at the same time. For example, fabs can leverage real-time information from manufacturing to improve the PDKs continuously. This allows customers to make more manufacturable and yieldable designs up front that can get delivered into the fab.

What role do software-defined products play in the semiconductor industry, now and into the future?

When we talk about software-defined products, it is difficult to think about something that isn’t impacted by software today. If you look at the cars you drive, there is more and more software both in the way you interact with the vehicles and also behind the scenes in terms of how the car actually operates.

Unless you’re still building steam locomotives, chances are the product you are creating is somehow, some way, software-defined. What this really means is that there are numerous different products available, and with so much customization, the notion of having standardized compute platforms to build software-defined products on top of them is changing.

You rarely say, “I’m going to buy this microprocessor and build my platform around it.” What you see is customized processing platforms tailored toward the product and the software that needs to run on those platforms. That is changing the way software and semiconductors are developed.

The software and semiconductors are being developed in parallel. But software is being shifted to the left, where it’s starting to lead semiconductor development. Therefore, you need to ensure that the compute platforms can handle the software workload required to run on them when making those decisions. This is about making system-level decisions that are much more important.

How should companies in the semiconductor industry approach fab commissioning and operation?

In the past, the entire concept of building a new fab was to replicate one fab infrastructure to the next, with a few tweaks along the way. But when we consider the broader issues, such as sustainability and efficiency, there are more effective ways to build fabs. When you look at the macro impact of fab commissioning and operation, you want to make sure that it is being run as efficiently as possible. As a result, things like electricity and water usage, HVAC systems and filtration systems have a considerable impact.

The new approach would require digital analysis for building fabrication facilities, supported by real-time, detailed information about the projects within these fabs. This strategy will make sure that we’re building and commissioning green fabs that are energy-efficient and resource-optimized.

How are artificial intelligence and machine learning impacting the semiconductor industry?

We’re looking at AI from multiple angles. First, the features built into products need to be supported by the underlying semiconductor designs. Second, the impact of actually running AI workloads on those products. And third, how AI itself is transforming the way we design and manufacture those products.

So, it’s pervasive throughout the entire industry. The design and verification tools are much more advanced than they were even a year ago because of the AI algorithms that are being integrated within them. On the manufacturing side of things, it gives the ability to understand the way fabs operate and be able to have intelligence at the industrial level.

You could query the AI platform, asking questions on the operability of the manufacturing equipment, potential errors and fixing solutions. AI solutions will enable factory floor operators to automate the actual fixing of mistakes in the manufacturing facilities. We’re seeing things changing at an exponential rate, all the way from design and verification to manufacturing. Ultimately, we see AI having drastic effects that are going to allow us to significantly improve the way we design, verify and manufacture semiconductors moving forward.

To learn more about how Siemens is impacting the semiconductor industry, visit siemens.com/us/en/industries/semiconductors.

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Engineering.com: Please tell us about your background and your role at Siemens.

Nand Kochhar: I spent nearly 30 years working at Ford Motor Company in roles of increasing responsibility. I was a chief engineer in multiple functions, including global safety systems, where I was responsible for vehicle crash safety of all Ford and Lincoln products globally. I also held many engineering roles in product development and manufacturing at Ford.

I was on the Ford Technical Advisory Board and held an additional responsibility as the Executive Technical Leader of Simulation, shaping future automotive and transportation technologies. During my tenure at Ford, I served for several years on the Society of Automotive Engineers (SAE) in several different roles. I served as Chairman of the Motor Vehicle Council for seven years, and later as Vice Chairman and then Chairman of the Technical Standards Board. I worked closely with SAE International to develop standards for autonomy, where we brought in the definition of SAE levels one through five for autonomous vehicles, which most of the industry has adopted globally.

I also had the opportunity to work in Germany for three years, launching a product for Ford Motor Company. I focused on building automotive capabilities in Asia Pacific as well.

I joined Siemens in 2020 as Vice President of Automotive and Transportation. I’m responsible for the automotive and transportation vertical, driving product development input for future capabilities, and supporting the marketing side of the organization by working with sales within our global sales and customer satisfaction organization.

What has caused the shift toward software-defined vehicles, and what is the impact of SDVs on the automotive and transportation industry?

Over the last few decades, more electronics and semiconductors have been introduced — not just in vehicles, but in the majority of consumer products, whether it’s your cell phone or appliances at home. It’s even more fascinating that today’s automobiles now include many of those same chips, semiconductors, printed circuit boards and electronic control units. Many people refer to modern cars and vehicles as computers on wheels.

Software has to work on something, and that something is electronics. That’s the logic behind bringing more electronics into the vehicle — and it’s what has caused everything, whether it’s a user function or connectivity, to be driven by software. That, in my mind, gave rise to the term software-defined vehicle. Some people originally called it a software-driven vehicle, but that’s the foundation behind the shift.

The impact of SDVs on the automotive and transportation industry is huge. The auto industry has matured around traditional powertrain technologies and how vehicles function. But in recent years, trends like connected vehicles, autonomy, and electrification have all emerged — and they’re enabled by software-defined vehicles.

In other words, all those trends — connected, autonomous, shared mobility and electrification — are embraced and delivered through SDVs. Electronics and semiconductors now play a major role alongside the electromechanical functions that vehicles have relied on for years. It’s bringing the two worlds together.

The software-defined vehicle is a channel that brings together electronics, semiconductors and the vehicle itself to deliver both the user interface and new business opportunities for OEMs and the broader automotive supply ecosystem.

There’s a level of expertise in the automotive industry. The software industry brings its own processes, like Agile product development. Now those two worlds have come together so companies can develop a holistic experience for the end user. That’s the importance of SDVs in this industry.

What are some challenges facing automakers as they transition from hardware to software-based innovation?

There are legacy companies — what we usually call brownfields — the ones that have been developing products, and then there are the newcomers. Each faces different challenges.

Established companies all over the globe, in every region, have siloed functions they have developed over the years. Chassis, body, electronics, electrical — and they have developed those functions separate from each other. The skillsets they have hired, matured and trained over the years are all focused on, even in a model-based systems engineering context, developing holistic systems.

But now, with software-defined vehicles and software-based innovations, it’s a different skillset. Either companies must retrain their workforce, or they need to bring in new people.

While legacy companies must figure out how to bring these two different skillsets together to deliver the end product, the greenfield companies — the startups — are often starting with a clean slate so they can build a team that has the new skillset. The newcomers, instead of being faced with a different set of challenges that includes infrastructure requirements, which carries a significant investment in development tools, cost management, time-to-market pressure and quality assurance.

How important is digital transformation to auto manufacturing in navigating the complexities of software-defined vehicles?

I would say digital transformation is the key — and it’s a journey. The business is complex today for many reasons: how you engage the entire ecosystem, how you work with suppliers. From time to time, there are different pressures: where the raw materials are coming from, COVID-related challenges, then the chip shortage, and now the reshoring of things in terms of tariffs.

Now, you lay another layer on top of that with the software-defined vehicle, and the complexity goes through the roof. One of the best ways to address that complexity is digital transformation. Instead of being challenged by it, digital transformation allows companies to turn complexity into a competitive advantage. Companies that adopt digital transformation can run “what if” scenarios, perform simulation and leverage tools like the digital twin and digital thread — all of which help them manage complexity and deliver business results.

This shift also presents new opportunities, including entirely new business models. Connected and autonomous vehicles enable new ways of generating revenue that the industry wasn’t focused on before.

In the case of the software-defined vehicle, one important feature is the over-the-air update. It helps improve quality, reduce warranty costs and — when there’s a recall — software-related issues can be fixed remotely, potentially saving millions of dollars.

It impacts not only automotive companies but also the entire ecosystem. The role of suppliers is changing, whether they’re technology providers or traditional component manufacturers. It’s quite a bit of disruption. On the positive side, you could say everybody is creating new business models — not only at the OEM level, but across the entire ecosystem. It is bringing the worlds of electronics and semiconductors together with mechanical systems.

How does the comprehensive digital twin help optimize product design, enhance interdisciplinary collaboration and ensure seamless over-the-air updates for SDVs?

Everybody is talking about digital twins. This is one of the key differentiators for Siemens, we talk about the “comprehensive digital twin.” The digital twin brings the real and virtual worlds together. What makes it comprehensive is that it’s not just in the design phase. It builds into the manufacturing of the same product for the same companies — and more importantly, after the product has left the factories, into the operations or the lifecycle of that product. Bringing this digital twin to have trusted traceability, and a continuous thread from product development to manufacturing to service, is what makes it unique. That’s the comprehensive nature of it. And that part of over-the-air updates for SDVs I already touched on — that’s one element of the comprehensive digital twin.

In each phase, there are different values that come out of a comprehensive digital twin. These are enabled by interdisciplinary collaboration. For example, when you use these technologies in product development, you can optimize for weight and cost, continuously learning from the field and having a closed-loop process. You’re bringing the lessons learned from the launch and from the field back into product development and incorporating them.

There are very important systems, like electrical and electronic systems. These technologies allow you to reduce the amount of wiring used in an electric vehicle — not only saving weight but also cost. So, there is a lot of innovation happening because of the comprehensive digital twin.

What should companies do to manage the challenges around SDV development, and how can they benefit from it?

Adopt these technologies — and it’s a journey. It’s not something that gets done in one quarter or two. You might have a small win with your biggest challenge, which helps companies learn and see the benefits of deploying these SDV development methods — and then grow that outward from that particular area.

In terms of benefits — in design, manufacturing and beyond — the biggest one we already touched on is quality improvement, warranty recalls and software-enabled updates. Those are happening, along with things like optimization and innovating your next generation of products. And you can beat the time to market — that’s the biggest one.

The mobility industry is at the cutting edge of innovation and changing every day. The solutions we experience today will evolve into new features and functions each year, along with the introduction of new business models driven by these technologies.

To learn more about how Siemens Digital Industries Software is helping companies on their software-defined vehicles journey, visit sw.siemens.com/en-US/digital-thread/mbse/software-defined-vehicle/

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