Accelerating silicon photonic parameter extraction using artificial neural networks

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.