Artificial Intelligence-Generated Terahertz Multi-Resonant Metasurfaces
  via Improved Transformer and CGAN Neural Networks
Yangpeng Huang Naixing Feng Yijun Cai
It is well known that the inverse design of terahertz (THz) multi-resonant
graphene metasurfaces by using traditional deep neural networks (DNNs) has
limited generalization ability. In this paper, we propose improved Transformer
and conditional generative adversarial neural networks (CGAN) for the inverse
design of graphene metasurfaces based upon THz multi-resonant absorption
spectra. The improved Transformer can obtain higher accuracy and generalization
performance in the StoV (Spectrum to Vector) design compared to traditional
multilayer perceptron (MLP) neural networks, while the StoI (Spectrum to Image)
design achieved through CGAN can provide more comprehensive information and
higher accuracy than the StoV design obtained by MLP. Moreover, the improved
CGAN can achieve the inverse design of graphene metasurface images directly
from the desired multi-resonant absorption spectra. It is turned out that this
work can finish facilitating the design process of artificial
intelligence-generated metasurfaces (AIGM), and even provide a useful guide for
developing complex THz metasurfaces based on 2D materials using generative
neural networks.