Inverse Design of Two-Function Transmission-type Reconfigurable Polarization Control Metasurfaces Based on Deep Learning

Abstract

Abstract Compared with single-function metasurfaces, the design difficulty of multi-function metasurfaces increases significantly. This paper introduces an inverse design method based on deep learning to address this challenge. By this method, a transmission-type reconfigurable polarization control metasurface (TRPCM) with two functions is rapidly designed. The network model used in the method consists of an electromagnetic parameter reconstruction network model (EMPRNM) and an inverse prediction network model (IPNM). The combination of the two models can solve the problem of difficulty in defining high-dimensional inputs in traditional inverse design, and achieve accurate prediction of metasurface structure parameters under given design targets. To optimize the hyperparameters of the neural network model, a genetic algorithm (GA) was introduced. To solve the non-uniqueness problem of inverse design, a method for eliminating similar data by calculating Euclidean Distance (ED) was introduced. Both schemes further improve the predictive performance of the proposed network model. Finally, six design targets were set based on the TRPCM. The structural parameters of the metasurface were successfully predicted using two neural network models and achieved the required performance. On this basis, a set of parameters was selected for experimental validation. By controlling the ON or OFF of the PIN diodes, the fabricated metasurface achieves two functions: linear-to-circular polarization conversion (LTCPC) and linear polarization maintenance (LPM) in the range of 2-3.6 GHz. Study results show that the inverse design scheme proposed in the paper is feasible and practical for solving the rapid optimization design of complex multi-function metasurfaces.