Machine Learning Assisted Inverse Design of Microresonators

TL;DR

This paper presents a machine learning approach to inverse design microresonator geometries based on dispersion profiles, demonstrating high accuracy and experimental validation with silicon nitride devices.

Contribution

It introduces a novel ML-based method for determining microresonator geometries from dispersion data, improving design efficiency and accuracy.

Findings

Random Forest achieved below 15% average error.

The method was validated experimentally with silicon nitride microresonators.

ML algorithms outperform traditional design approaches.

Abstract

The high demand for fabricating microresonators with desired optical properties has led to various techniques to optimize geometries, mode structures, nonlinearities and dispersion. Depending on applications, the dispersion in such resonators counters their optical nonlinearities and influences the intracavity optical dynamics. In this paper, we demonstrate the use of a machine learning (ML) algorithm as a tool to determine the geometry of microresonators from their dispersion profiles. The training dataset with ~460 samples is generated by finite element simulations and the model is experimentally verified using integrated silicon nitride microresonators. Two ML algorithms are compared along with suitable hyperparameter tuning, out of which Random Forest (RF) yields the best results. The average error on the simulated data is well below 15%.