Enhancing adjoint optimization-based photonics inverse design with explainable machine learning

Abstract

A fundamental challenge in the design of nanophotonic devices is the optimization of subwavelength structures to achieve tailored and high-performance electromagnetic responses. To this end, topology or shape optimizers based on the adjoint variables method have been widely adopted to push the performance limits of electromagnetic systems. However, the understanding of such freeform structures remain obscure, and such gradient-based optimizers can get trapped in low-performance local minima. Accordingly, to elucidate the relationships between device performance and nanoscale structuring, while mitigating the effects of local minima trapping, we present an inverse design framework that combines adjoint optimization, AutoML, and explainable AI.