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It Began With… “Tell me a dark story about a guy who makes pizza with a twist ending” Those were the first words I ever wrote to ChatGPT. Seriously. I was testing out this “new chat thing” that was supposedly very good. And we were all impressed. I read that story aloud to the friends I was hanging out with that night. We were all so blown away by the details and intrigue it seemed to pull out of thin air in a matter of seconds. My prompts and relationship with ChatGPT have come a long way since then.
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Continuing on the theme of using machine learning to speed up electromagnetic simulations, we've got some new work to share! This paper goes beyond the 2D example previously explored and now looks at full-wave 3D solutions. Most importantly, we wanted to show that this is possible with geometry generalizations. For example, many papers pass things like via drill diameter and other parameters directly as input to their neural networks. We instead mesh the geometry into a 3D rectangular grid and pass this as input to the neural network instead. The result was quite impressive!
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Update I'm happy to report that this paper was a Best SIPI Paper finalist at the conference! While I was unable to attend in-person due to a missed flight, the paper has been published to IEEE Xplore here.
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Some initial research points to yes, for certain applications. Here's a preview of a paper I'll be sharing at this year's IEEE EMC+SIPI conference in Spokane. There's still a lot of work to be done, and I'm admittedly quite a machine learning amateur. Even so, I think there are areas in signal integrity where we can find smart ways to apply machine learning to speed up design cycle time.
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