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Digital twins have long been a staple in the world of engineering and manufacturing. As virtual representations of physical products, systems, and processes, they help organizations innovate, improve quality, and reduce costs.
Traditionally, digital twins were rooted in the physical world, serving as virtual counterparts to mechanical systems and large-scale physical assets — engines, buildings, even entire manufacturing operations. These early digital twins enabled organizations to simulate, monitor, and optimize physical products and processes, driving improvements in efficiency and reliability.
However, as products have evolved to incorporate more electronic components and embedded software, the digital twin paradigm is expanding. Today, electronics digital twin (eDT) technologies are emerging, allowing industries to virtually model not only physical platforms but also the software and electronics that define their behavior.
Marc Serughetti, Vice President of Product Management, Synopsys
These technologies are not only changing the ways organizations approach design and validation; they’re also transforming lifecycle management and overarching business models. eDTs help deliver continuous software updates for maintenance, enhancements, or new add-on features, enabling ongoing product improvement and creating opportunities for new revenue streams after the initial sale.
To help explain these transformations, I spoke with Marc Serughetti, Synopsys vice president of product management, about the rise of eDT technologies, their unique value, and what organizations need to consider as they embrace these powerful virtual models.
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Q: What are electronics digital twins, or eDTs, and how are they different from traditional digital twin technologies?
Marc Serughetti: The concept of a digital twin has been around for many years, even before 2000. If you look at its origins, a digital twin is simply a virtual representation of a product, a system, or a process. The purpose is to perform tasks virtually that would be complex, costly, or even impossible in the physical world.
Where things have really changed is with the evolution of products and systems themselves. Today, products are becoming more and more software-driven, with an increasing amount of electronics and silicon content inside. That’s what is driving the emergence of electronics digital twins, or eDTs.
This is the natural evolution of digital twin technology.
But here’s the key difference: It’s not just about replicating the hardware. It’s about creating a virtual representation of the entire electronics system, including both hardware and software.
For example, in the automotive space, you’re not just simulating the physical car or the engine. You’re also virtualizing the electronics and software that now define so much of the vehicle’s behavior and customer experience. You have to consider how the software governs features like adaptive cruise control, automatic braking, and real-time diagnostics, how it’s updated, and how it interacts with the rest of the system.
Another key point is that eDTs can support the full product lifecycle, from development and validation to in-field monitoring and diagnostics to continuous software updates that are delivered over the air.
Q: How does “physical AI” relate to the evolution of eDTs?
Marc Serughetti: Physical AI refers to bringing AI-driven intelligence into real world systems by tightly integrating sensing, computation, and actuation. eDTs play a key role here because they let teams model and validate these AI-enabled behaviors virtually before deploying them into physical products.
Whether it’s an autonomous vehicle, an industrial robot, or a smart medical device, eDTs help ensure AI algorithms interact safely and reliably with the hardware and environment long before anything is built.
Q: Who is seeking to adopt eDT technologies, and why?
Marc Serughetti: We’re seeing interest from companies across a wide range of industries — automotive, aerospace, industrial, medical, networking. Many of them have traditionally been focused on mechanical engineering, and they’re transforming their offerings with software and electronics. Their products are becoming software-driven, and that opens up entirely new market opportunities.
The goals are clear: companies want to reduce development time and cost, accelerate time-to-market, and improve product quality.
If you can catch more issues virtually — before you ever build a physical prototype — you save money and time. And with software-defined products, you’re not just releasing a product and walking away. You’re continuously updating and improving it, often based on real-world data. eDTs are essential for that.
There’s also a big impact on operations and support. If you’re able to predict issues and perform maintenance before something fails, you avoid costly recalls or downtime. This is especially critical in sectors like automotive or medical, where reliability and safety are paramount.
Q: How are eDT capabilities acquired and implemented?
Marc Serughetti: You have to think of eDTs as a platform, not a standalone tool. No single provider can deliver everything needed for a true electronics digital twin. The ecosystem is key — partners, third-party technologies, cloud providers, and systems integrators all play a role.
The technology stack matters as well. You need engines and models to build the digital twin, ways to aggregate and integrate different components, tools for verification and validation, and features to enable collaboration, often in the cloud. Increasingly, AI is also part of the stack, helping with everything from model creation to predictive analytics.
The point is, you need a flexible, scalable platform that can grow with your needs and support a wide range of use cases. And you need partners you can trust, because integration and interoperability are critical to success.
Q: What are some of the use cases for eDTs?
Marc Serughetti: The use cases are really broad. In automotive, for example, we see eDTs being used to validate semiconductor designs and automotive software before anything is built.
But it goes much further.
You can use eDTs for development and simulation — prototyping new features, testing how software interacts with hardware, or running virtual validation long before you have a physical system. They’re also powerful for collaboration. Different teams, even across organizations, can work together on the same digital twin.
Data management is another area — collecting field data, replaying it in the digital environment, and using it to improve the product. And, of course, AI enablement and using machine learning to optimize designs or predict failures before they happen.
One thing I always emphasize: The digital twin you need depends on the question you’re trying to answer. If you’re exploring vehicle architecture, that’s one type of twin. If you’re validating an application or monitoring a system in the field, that’s another.
eDTs are flexible in that way. They let you tailor the virtual representation to the specific engineering or business need. The key is to define the problem first.
Q: What advice do you have for companies exploring eDT technologies?
Marc Serughetti: My advice is to start with your business goals. Don’t just look at eDTs as a technology upgrade — think of them as part of your digital transformation. Align your strategy and investments with the outcomes you want, whether that’s faster development, better quality, or new revenue streams.
It’s also important to think about deployment and scale. How will you integrate eDTs into your organization and processes? How will you scale them across different products and use cases? Choose your partners wisely — look for companies with proven experience, a strong ecosystem, and a vision for the future.
Finally, remember that success isn’t just about technology. It’s about re-engineering your organization, methodologies, and processes. If you keep doing things the old way, just faster, you won’t unlock the full value. You need to “shift left” — validate and integrate earlier, so that by the time you get to physical testing, most issues are already solved.
Q: What role does Synopsys play in the emergence of eDTs?
Marc Serughetti: Synopsys is in a unique position. We’ve been at the forefront of electronics for decades, and we already have the core technologies needed to build high‑fidelity digital twins that span electronics, software, and systems.
But this isn’t about isolated tools.
We’re moving from point solutions to integrated platforms, and we’ve built deep relationships across the ecosystem — from semiconductor companies to systems integrators and software partners. With the addition of Ansys, we can now address both the electronic and physical behavior of products, which is essential for industries like automotive and aerospace.
That combination of domain expertise, ecosystem reach, and platform thinking led to the launch of the Synopsys eDT Platform — bringing these capabilities together in a unified way.
Ultimately, our role is to help customers innovate faster, improve quality, and make better system‑level decisions in a software‑defined world.
An edited version of this article originally appeared in Digital Engineering
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