Inverse design of metasurfaces with customized transmission characteristics of frequency band based on generative adversarial networks

Abstract

In recent years, deep learning (DL) has demonstrated significant potential in the inverse design of metasurfaces, and the generation of metasurfaces with customized transmission characteristics of frequency band remains a challenging and underexplored area. In this study, we propose a DL-assisted method for the inverse design of transmissive metasurfaces. The method consists of a generative adversarial network (GAN)-based graph generator, an electromagnetic response predictor, and a genetic algorithm optimizer. By integrating these components, we can obtain customized metasurfaces with desired transmission characteristics of frequency band. We demonstrate the effectiveness of the proposed method through examples of inverse-designed three-layer cascaded transmissive metasurfaces with wideband, dual-band, and stopband responses in the 8∼12 GHz frequency range. Specifically, we realize three different types of dual-band metasurfaces, namely double-wide, front-wide and rear-narrow, and front-narrow and rear-wide configurations. Additionally, we analyze the accuracy and reliability of the inverse design method by employing data from the training dataset, self-defined objectives, and bandwidth-reduced target responses scaled from the wideband type as design inputs. Quantitative evaluation is performed using metrics such as mean absolute error and average precision. The proposed method successfully achieves the desired effect as intended.