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.

该研究提出了改进的 Transformer 和条件生成对抗神经网络（CGAN）来实现基于太赫兹多重共振吸收光谱的石墨烯超表面的反向设计，并证明这项工作可以促进人工智能生成超材料的设计过程，并对利用生成神经网络开发基于二维材料的复杂太赫兹超表面提供有用指导。