Inverse design of transmission-type linear-to-circular polarization control metasurface based on deep learning

Abstract

To solve the time-consuming and complex design problems, the deep learning method is used to realize the inverse predictive design of a transmission-type linear-to-circular polarization control metasurface (TLCPCM). Firstly, the target-generation neural network model (TGNNM) is constructed based on a fully connected neural network. The model selects the critical features of the required electromagnetic performance as design targets, and maps low-dimensional design targets to high-dimensional electromagnetic performance. Secondly, taking the output data of the TGNNM as input data, an inverse-mapping neural network model (IMNNM) is constructed by a convolutional neural network. The prediction performance of the IMNNM is compared with two other inverse-mapping models. The research results show that the IMNNM outperforms the other two networks. Finally, combining TGNNM and IMNNM, four sets of TLCPCM structural parameters are predicted. The research results show that the electromagnetic performances of the metasurface determined by the predicted structural parameters are generally consistent with the given design targets. On this basis, one experimental sample is manufactured. The measurement results are consistent with the simulation results. The research results demonstrate the validity and feasibility of the inverse predictive design method proposed in this paper.