Benchmarking Optimization Methods Enabling Efficient Designs for Diverse Nanophotonic Applications

Abstract Advances in fabrication processes and materials have driven the demand for intricate nanophotonic designs that enable active, multifunctional, and high‐performance devices. Selecting an appropriate optimization method is crucial, considering the characteristics of both the problem and algorithms, such as the number of design parameters and simulation methods. In this study, nine optimization algorithms are modularized and applied them to six representative nanophotonic problems using rigorous coupled‐wave analysis (RCWA) and scalar diffraction theory. This framework facilitates benchmarking and performance evaluation of optimization methods across diverse problems. Particle swarm optimization (PSO) excels in low‐dimensional cases, while genetic algorithms (GAs) performs better in higher dimensions. For optimizing phase profiles with tens of thousands of variables, automatic differentiation‐based methods are most effective. This research provides a practical guide for selecting optimization methods based on problem characteristics and lays the foundation for modular, scalable inverse design tools in nanophotonics.