Conditional Generative Adversarial Networks for Inverse Design of Multi-functional Microwave Metasurfaces

Abstract

Electromagnetic (EM) metasurfaces can present a versatile platform for realization of multiple diverse EM functionalities with incident wave frequency, polarization, propagation direction, or power intensity through appropriate choice of unit cells structures. However, the inverse design of multi-functional metasurfaces relies on massive full-wave EM numerical simulations to obtain an optimized solution. This article proposes a step-by-step procedure based on conditional Generative Adversarial Networks (cGANs) integrated with Gramian Angular Fields (GAFs) to reduce the computational time required for the EM simulations in the inverse design of multifunctional microwave metasurfaces. The proposed procedure initially implements GAFs to encode the desired multi-objective scattering parameters to images and then passes them through the cGAN model to map them to three-layer metasurfaces. The present study uses a robust dataset with a wide range of values and designs, including 54,000 metasurface structures and corresponding scattering parameters to train and validate the cGAN model. This article also presents a case study example using a multi-functional metasurface with three independent functionalities and full-space coverage to justify the performance of the proposed procedure in the inverse design of multi-functional microwave metasurfaces. The results demonstrate that the performance of the proposed model is promising, and the proposed procedure can be used to efficiently and accurately design multi-functional metasurfaces.