Compounding meta-atoms into meta-molecules with hybrid artificial intelligence techniques

TL;DR

This paper introduces a hybrid AI framework combining deep learning and evolutionary algorithms to efficiently design complex meta-molecules for metasurfaces, enabling advanced control of light properties with experimental validation.

Contribution

It presents a novel AI-based approach that decomposes meta-molecule design into smaller tasks, improving efficiency and effectiveness over traditional methods.

Findings

Successfully designed metallic meta-molecules for polarization control

Validated designs through experimental measurements

Demonstrated rapid and systematic metasurface development

Abstract

Molecules composed of atoms exhibit properties not inherent to their constituent atoms. Similarly, meta-molecules consisting of multiple meta-atoms possess emerging features that the meta-atoms themselves do not possess. Metasurfaces composed of meta-molecules with spatially variant building blocks, such as gradient metasurfaces, are drawing substantial attention due to their unconventional controllability of the amplitude, phase, and frequency of light. However, the intricate mechanisms and the large degrees of freedom of the multi-element systems impede an effective strategy for the design and optimization of meta-molecules. Here, we propose a hybrid artificial intelligence-based framework consolidating compositional pattern-producing networks and cooperative coevolution to resolve the inverse design of meta-molecules in metasurfaces. The framework breaks the design of the meta-molecules into separate designs of meta-atoms, and independently solves the smaller design tasks of the meta-atoms through deep learning and evolutionary algorithms. We leverage the proposed framework to design metallic meta-molecules for arbitrary manipulation of the polarization and wavefront of light. Moreover, the efficacy and reliability of the design strategy are confirmed through experimental validations. This framework reveals a promising candidate approach to expedite the design of large-scale metasurfaces in a labor-saving, systematic manner.