An On-Demand Inverse Design Method for Nanophotonic Devices Based on Generative Model and Hybrid Optimization Algorithm

The inverse design of nanophotonic devices has been widely concerned by researchers, and on-demand design is the difficulty of inverse design. In inverse design, researchers usually define a target spectrum based on the performance indicators and experiences and then inverse design the structural parameters from the target spectrum. Due to the uncertainty of inverse design and “one-to-many” problem, it is not usually possible to guarantee that the target spectrum is sure to correspond to a real nanostructure. In order to solve these problems, an inverse design method combining generative model and genetic algorithm is proposed in this paper. Before the inverse design, the real spectrum is compressed into a latent space by the generation model, and then, the target spectrum is decoded from the latent space according to the performance index. Finally, the hybrid optimization algorithm combining genetic algorithm and forward prediction network is used to optimize the generated spectrum. The design method follows the process from performance indicators to target spectrum to structural parameter, and we successfully realized the inverse design of multilayer nanofilms on demand by using this method in the experimental part. The inverse design method proposed in this paper provides a possible solution for the inverse design of nanophotonic devices on demand.