Inverse Design of Photonic Systems

• Published in: Laser & photonics reviews Vol. 18; no. 5
• Main Authors: MacLellan, Benjamin, Roztocki, Piotr, Belleville, Julie, Romero Cortés, Luis, Ruscitti, Kaleb, Fischer, Bennet, Azaña, José, Morandotti, Roberto
• Format: Journal Article
• Language: English
• Published: Weinheim Wiley Subscription Services, Inc 01.05.2024
• Subjects: Algorithms; Design optimization; Inverse design; Machine learning; Noise generation; Noise reduction; Parameter identification; Photonics; Quantum optics; Software; Topology; Waveforms
• ISSN: 1863-8880; 1863-8899
• DOI: 10.1002/lpor.202300500

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. The article presents advancements in inverse design for photonic systems. While computational tools mature for the identification of optimal parameters, challenges remain in determining suitable system topologies, especially for complex photonic schemes. The presented framework introduces an efficient approach, leveraging automatic differentiation and discrete topological changes, facilitating the discovery of physically‐feasible photonic systems for applications like waveform generation and sensing.