Deep learning to explain and design complex nanophotonic structures

Abstract

A central challenge in the development of nanophotonic structures and metamaterials is identifying the optimal design for a target functionality and understanding the physical mechanisms that enable the optimized device’s capabilities. In this talk, we will describe deep learning-driven strategies to both design complex nanophotonic structures, including across multiple device categories, as well as understand their behavior. We will highlight potential pathways to making deep learning a tool for global inverse design across multiple device categories, while also opening up the 'black box' of the machine learning algorithm to understand why a particular optimized design works well.