Non-local generative machine learning-based inverse design for scattering properties

Abstract

Metamaterials are created by arranging small scatterers in a regular array throughout a space to manipulate electromagnetic waves. However, current design methods view metasurfaces as independent meta-atoms, which limits the range of geometrical structures and materials used, and prevents the generation of arbitrary electric field distributions. To address this issue, we propose an inverse design method based on generative adversarial networks (GANs), which includes both a forward model and an inverse algorithm. The forward model utilizes dyadic Green’s function to interpret the expression of non-local response, realizing the mapping from scattering properties to generated electric fields. The inverse algorithm innovatively transforms the scattering properties and electric fields into images and generates datasets with methods in computer vision (CV), proposing an architecture of GAN with ResBlock to achieve the design for the target electric field pattern. Our algorithm improves upon traditional methods, as it achieves greater time efficiency and generates higher quality electric fields. From a metamaterial perspective, our method can find optimal scattering properties for specific generated electric fields. Training results and extensive experiments demonstrate the algorithm’s validity.