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S-Parameters from a Neural Network

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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Can Machine Learning Replace Field Solvers?

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. — 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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