Designing Silicon Photonic Devices using Artificial Neural Networks

Abstract

We develop and experimentally validate a novel neural network design framework for silicon photonics devices that is both practical and intuitive. The framework is applicable to nearly all known integrated photonics devices, but as case studies we consider simple waveguides and chirped Bragg Gratings. By using artificial neural networks, we decrease the computational cost relative to traditional design methodologies by more than 4 orders of magnitude. We also demonstrate the abstraction of the device models to a few simple input and output parameters relevant to designers. We then apply the results to various design problems and experimentally compare fabricated devices to the neural network's predictions.

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