# Accelerating Silicon Photonic Parameter Extraction using Artificial Neural Networks

## TL;DR

This paper introduces a fast and accurate neural network-based method for extracting parameters from complex silicon photonic devices, validated on fabricated chirped Bragg gratings.

## Contribution

The paper presents a novel neural network approach that can extract parameters for any silicon photonic device with multiple design parameters, surpassing traditional analytic methods.

## Key findings

- Method is fast and accurate
- Capable of modeling complex device features
- Validated on fabricated chirped Bragg gratings

## Abstract

We present a novel silicon photonic parameter extraction tool that uses artificial neural networks. While other parameter extraction methods are restricted to relatively simple devices whose responses are easily modeled by analytic transfer functions, this method is capable of extracting parameters for any device with a discrete number of design parameters. To validate the method, we design and fabricate integrated chirped Bragg gratings. We then estimate the actual device parameters by iteratively fitting the simultaneously measured group delay and reflection profiles to the artificial neural network output. The method is fast, accurate, and capable of modeling the complicated chirping and index contrast.