Metasurface color routers inverse-designed with a deep-learning-accelerated genetic algorithm for high-efficiency imaging

Abstract

Full-wave electromagnetic simulations provide accurate field distributions for nanophotonic structures, but their high computational cost limits their direct use in large-scale inverse design. Here, we introduce a deep-learning-assisted inverse-design framework in which a neural network is trained with finite-difference time-domain (FDTD)-calculated field maps and used to estimate the transmitted field distribution of candidate structures during genetic optimization. This approach allows the genetic algorithm to evaluate a large number of structures while using FDTD-derived spatial field information for the design objective. As a model system, we apply this framework to a metasurface color router, where red, green, and blue light must be directed to prescribed sub-pixel regions at the photodiode plane. The router is implemented as a single-layer Si3N4 metasurface within a conventional 2 µm × 2 µm Bayer unit cell, discretized into a 16 × 16 grid with a 125 nm pitch for experimental validation. For this design, the corresponding simulations predict a total transmission efficiency of 95.4% and RGB routing efficiencies of 72.9%, 68.6%, and 45.1%, with crosstalk values of 37.1%, 53.4%, and 39.5%, respectively. We further show that the same trained model can be reused for different photodiode-aperture layouts without generating new FDTD training data or retraining the network. These results demonstrate a reusable inverse-design framework for optimization of complex nanophotonic devices.