Inverse Design of Photonic Systems

Abstract Inverse design methods use optimization and learning algorithms to pair desired functionalities with the corresponding high‐performing systems. Such methods have significant potential for discovering novel photonics solutions, with inverse design techniques already mediating significant milestones in nanophotonics, quantum optics, and lens systems. However, while computational tools for identifying optimal system parameters (i.e., component settings) have reached significant maturity, the identification of suitable system topologies (i.e., component choice and arrangement) has remained challenging, especially for the design of complex photonic schemes. Here, a framework for the inverse design of practical photonic systems is presented, capable of efficiently and automatically searching for high‐performance topologies and their associated operational parameters. It is demonstrated that the approach can aid in the discovery of practical photonic systems, that are both physically feasible and non‐trivial, by leveraging system‐level automatic differentiation and discrete topological changes. The versatility of the platform is supported with example designs for waveform generation, noise suppression, and sensing, among others.