A Generative Machine Learning-Based Approach for Inverse Design of Multilayer Metasurfaces

TL;DR

This paper introduces a generative machine learning method to automate the inverse design of multilayer metasurfaces, significantly reducing the time and expertise needed compared to traditional simulation-based approaches.

Contribution

It presents a novel ML-based inverse design framework for multilayer metasurfaces that handles complex multiobjective optimization and inter-layer coupling effects.

Findings

Successfully designed metasurfaces with specific scattering properties.

Demonstrated the method's ability to handle multi-layer and multi-objective problems.

Validated designs across various frequency ranges and applications.

Abstract

The synthesis of a metasurface exhibiting a specific set of desired scattering properties is a time-consuming and resource-demanding process, which conventionally relies on many cycles of full-wave simulations. It requires an experienced designer to choose the number of the metallic layers, the scatterer shapes and dimensions, and the type and the thickness of the separating substrates. Here, we propose a generative machine learning (ML)-based approach to solve this one-to-many mapping and automate the inverse design of dual- and triple-layer metasurfaces. Using this approach, it is possible to solve multiobjective optimization problems by synthesizing thin structures composed of potentially brand-new scatterer designs, in cases where the inter-layer coupling between the layers is non-negligible and synthesis by traditional methods becomes cumbersome. Various examples to provide specific magnitude and phase responses of $x$- and $y$-polarized scattering coefficients across a frequency range as well as mask-based responses for different metasurface applications are presented to verify the practicality of the proposed method.