Deep Learning for the Design of Toroidal Metasurfaces

Abstract

In recent years, the toroidal dipoles have had a profound impact on several fields including electromagnetism. However, the on-demand design of toroidal metasurfaces is still a very time-consuming process. In this paper, a method of neural network simulating the nonlinear relationship between the structural parameters of metasurfaces and its multipole scattered powers is proposed based on a deep learning algorithm. The forward network can quickly predict the scattered powers from input structural parameters, which can achieve an accuracy comparable to the electromagnetic simulations. In addition, with the required scattering spectrum as input, the appropriate parameters of the structure could be automatically calculated and then output by the inverse network which can achieve a low mean square error of 0.074 in training set and 0.18 in the test set. Compared with the conventional design process, the proposed deep learning model can guide the design of the toroidal dipole metasurface faster and pave the way for the rapid development of toroidal metasurfaces.