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Artificial intelligence (AI) has been making inroads into a variety of industries in the past decade or so, from automobiles to medical devices. Naturally, EDA tool companies are taking a look at AI. Does AI offer a way forward for PCB design tool developers?
I recently interviewed Paul Musto, director of marketing for the Board Systems Division of Mentor, A Siemens Business. We discussed Mentor’s plans for integrating AI into EDA tools, and why we may be at the very beginning of understanding the pros and cons of this new technology.
Andy Shaughnessy: Paul, what do you think about AI and where it is right now?
Paul Musto: Artificial intelligence, where computers are trained to perform tasks that normally require human intelligence, will have a place in almost every facet of our lives. We are really at the beginning of understanding the implications and opportunities of AI. As the technology matures, it’s not hard to imagine that AI will have a critical role in product development.
Shaughnessy: How likely it is that we’ll see more AI in EDA tools? With chunks of AI in EDA tools already, like for reuse and things like that, is there a place for more AI in EDA tools?
Musto: It’s very likely, and we are at the beginning of this journey. If you look at the mechanical engineering world, you see examples, like generative design. With AI software and the cloud-based computing power, generative design enables engineers to create thousands of design options by simply defining their design problem, such as basic parameters like height, weight, strength, and material options. As AI becomes more integrated, you will see better, more productive application of generative design principals. There is reason to expect that to happen in the EDA world and in PCB design. Mentor, as part of Siemens, is well positioned to take advantage of Siemens’ delivery of a digital thread, which includes product design, manufacturing process, and product performance. We will be able to capture and rationalize data from a myriad of sources like requirements, actual manufacturing data, or field performance, and use that intelligent data to drive better design practices. The basis of our future AI won’t be built on design automation alone, but on predictive principals based on actual manufacturing and field performance.
Shaughnessy: Can you describe what Mentor is doing in AI?
Musto: Being part of Siemens gives us real advantages to deliver cutting-edge solutions to our customers. A key area of investment is in simulation technologies and in design exploration and design recommendations.
Shaughnessy: What consumer applications have you seen that use AI?
Musto: Obviously, I am a gadget freak. I have home automation assistants in my house. All of us are familiar with Amazon and digital services like Netflix or Spotify that give users recommendations based on past consumption behavior and search history. Can you imagine these applications in EDA? Like implementation wizards based on predictive analytics, simulation models, manufacturing resources and availability, or field performance.
To read this entire interview, which appeared in the September 2018 issue of Design007 Magazine, click here.