Deep learning-enabled inverse design of polarization-selective structural color based on coding metasurface

Abstract Structural colors based on metasurfaces have very promising applications in areas such as optical image encryption and color printing. Herein, we propose a deep learning-enabled reverse design of polarization-selective structural color based on coding metasurface. In this study, the long short-term memory (LSTM) neural network is presented to enable the forward and inverse mapping between coding metasurface structure and corresponding color. The results show that the method can achieve 98% accuracy for the forward prediction of color and 93% accuracy for the inverse design of the structure. Moreover, a cascaded architecture is adopted to train the inverse neural network model, which can solve the non-uniqueness problem of the polarization-selective color reverse design. This study provides a new path for the application and development of structural colors.