Inverse Design of Metamaterials with Manufacturing-Guiding Spectrum-to-Structure Conditional Diffusion Model

TL;DR

This paper introduces a novel conditional diffusion model framework for the inverse design of metamaterials, enabling accurate spectral predictions, diverse pattern generation, and manufacturing guidance, demonstrated through a thermal camouflage application.

Contribution

The paper presents a new spectrum-to-structure conditional diffusion model that improves inverse metamaterial design accuracy and diversity, aiding fabrication.

Findings

Superior spectral prediction accuracy

Generated diverse metamaterial patterns

Successfully fabricated a thermal camouflage metamaterial

Abstract

Metamaterials are artificially engineered structures that manipulate electromagnetic waves, having optical properties absent in natural materials. Recently, machine learning for the inverse design of metamaterials has drawn attention. However, the highly nonlinear relationship between the metamaterial structures and optical behaviour, coupled with fabrication difficulties, poses challenges for using machine learning to design and manufacture complex metamaterials. Herein, we propose a general framework that implements customised spectrum-to-shape and size parameters to address one-to-many metamaterial inverse design problems using conditional diffusion models. Our method exhibits superior spectral prediction accuracy, generates a diverse range of patterns compared to other typical generative models, and offers valuable prior knowledge for manufacturing through the subsequent analysis of the diverse generated results, thereby facilitating the experimental fabrication of metamaterial designs. We demonstrate the efficacy of the proposed method by successfully designing and fabricating a free-form metamaterial with a tailored selective emission spectrum for thermal camouflage applications.