Constrained tandem neural network assisted inverse design of metasurfaces for microwave absorption

Abstract

Designing microwave absorbers with customized spectrums is an attractive topic in both scientific and engineering communities. However, due to the massive number of design parameters involved, the design process is typically time-consuming and computationally expensive. To address this challenge, machine learning has emerged as a powerful tool for optimizing design parameters. In this work, we present an analytical model for an absorber composed of a multi-layered metasurface and propose a novel inverse design method based on a constrained tandem neural network. The network can provide structural and material parameters optimized for a given absorption spectrum, without requiring professional knowledge. Furthermore, additional physical attributes, such as absorber thickness, can be optimized when soft constraints are applied. As an illustrative example, we use the neural network to design broadband microwave absorbers with a thickness close to the causality limit imposed by the Kramers-Kronig relation. Our approach provides new insights into the reverse engineering of physical devices.