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

2025-06-08

AI Summary

In metasurface inverse design, challenges persist due to strong nonlinearity and complex one-to-many spectral-structural mappings, alongside low manufacturability. To address these, this work proposes a fabrication-aware conditional diffusion generative model. By integrating spectral embedding guidance and diversity-aware sampling, it explicitly incorporates fabrication constraints into the end-to-end spectral-to-structural mapping for generating manufacturable, freeform structures tailored to target optical responses. Freeform parameterization and post-generation structural diversity analysis significantly enhance coverage of the solution space. Validated on thermal cloaking applications, the method successfully designs and fabricates spectrally selective emitters: spectral prediction error is reduced by 32%, and structural diversity increases 3.1× compared to baselines. This demonstrates synergistic optimization across accuracy, diversity, and manufacturability.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Inverse design of metamaterials with nonlinear structure-spectrum relationships
• Addressing fabrication challenges in complex metamaterial manufacturing
• Generating diverse manufacturable designs for tailored optical properties

Innovation

• Conditional diffusion models for inverse design
• Spectrum-to-structure parameter customization
• Manufacturing-guided diverse pattern generation