Enhancing Adjoint Optimization-based Photonics Inverse Design with Explainable Machine Learning

TL;DR

This paper introduces a novel inverse design framework combining adjoint optimization, AutoML, and explainable AI to improve photonic device performance and understand structure-performance relationships, surpassing traditional methods.

Contribution

The work presents an integrated framework that enhances adjoint-based inverse design with explainable machine learning, enabling better performance and interpretability in photonic device optimization.

Findings

Achieved 39-74% performance improvements over state-of-the-art methods.

Revealed structural contributions to device performance using explainable AI.

Successfully applied the framework to waveguide design across telecom wavelengths.

Abstract

A fundamental challenge in the design of photonic devices, and electromagnetic structures more generally, is the optimization of their overall architecture to achieve a desired response. To this end, topology or shape optimizers based on the adjoint variables method have been widely adopted due to their high computational efficiency and ability to create complex freeform geometries. However, the functional understanding of such freeform structures remains a black box. Moreover, unless a design space of high-performance devices is known in advance, such gradient-based optimizers can get trapped in local minima valleys or saddle points, which limits performance achievable through this inverse design process. 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, automated machine learning (AutoML), and explainable artificial intelligence (XAI). Integrated with a numerical electromagnetics simulation method, our framework reveals structural contributions towards a figure-of-merit (FOM) of interest. Through an explanation-based reoptimization process, this information is then leveraged to minimize the FOM further than that obtained through adjoint optimization alone, thus overcoming the optimization's local minima. We demonstrate our framework in the context of waveguide design and achieve between 39% and 74% increases in device performance relative to state-of-the-art adjoint optimization-based inverse design across a range of telecom wavelengths. Results of this work therefore highlight machine learning strategies that can substantially extend and enhance the capabilities of a conventional, optimization-based inverse design algorithm while revealing deeper insights into the algorithm's designs.