# Designing Silicon Photonic Devices using Artificial Neural Networks

## TL;DR

This paper introduces a neural network-based framework for designing silicon photonic devices, significantly reducing computational costs and simplifying device modeling, with experimental validation on waveguides and Bragg Gratings.

## Contribution

It presents a practical neural network approach applicable to various photonic devices, enabling faster and more intuitive design processes compared to traditional methods.

## Key findings

- Over 4 orders of magnitude reduction in computational cost
- Successful experimental validation of neural network predictions
- Effective abstraction of device models to key parameters

## 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.