Data-Efficient Machine Learning Algorithms for the Design of Surface Bragg Gratings

Abstract

Deep learning models, with a prerequisite of large databases, are common approaches in applying machine learning for inverse design in photonics. For these models, less expensive, approximate methods are usually used to generate large databases, which limit their applications. In this study, we compare the performance of data-efficient machine learning (ML) models for predicting the characteristics of surface Bragg gratings in semiconductor ridge waveguides. We employ the 3D finite-difference time-domain method which is very accurate but time-consuming to generate a database. We analyze the performance of different ML models including support vector regression and extreme gradient boosting (XGBoost) on our limited data. We show that the XGBoost significantly outperforms other models on a smaller database. Our results pave the way for the data-efficient design of integrated photonic components with accurate but time-demanding simulations.