MetaDiT: Enabling Fine-grained Constraints in High-degree-of Freedom Metasurface Design

TL;DR

MetaDiT is a new framework that enables precise, high-fidelity design of metasurfaces with complex parameters, overcoming previous limitations by using contrastive learning and diffusion transformers for better spectral accuracy.

Contribution

It introduces MetaDiT, a novel generative model combining contrastive learning and diffusion transformers to explore large, unconstrained metasurface design spaces with high spectral fidelity.

Findings

MetaDiT outperforms existing methods in spectral accuracy.

The spectrum encoder improves physical relationship modeling.

Extensive ablation studies validate the approach.

Abstract

Metasurfaces are ultrathin, engineered materials composed of nanostructures that manipulate light in ways unattainable by natural materials. Recent advances have leveraged computational optimization, machine learning, and deep learning to automate their design. However, existing approaches exhibit two fundamental limitations: (1) they often restrict the model to generating only a subset of design parameters, and (2) they rely on heavily downsampled spectral targets, which compromises both the novelty and accuracy of the resulting structures. The core challenge lies in developing a generative model capable of exploring a large, unconstrained design space while precisely capturing the intricate physical relationships between material parameters and their high-resolution spectral responses. In this paper, we introduce MetaDiT, a novel framework for high-fidelity metasurface design that addresses these limitations. Our approach leverages a robust spectrum encoder pretrained with contrastive learning, providing strong conditional guidance to a Diffusion Transformer-based backbone. Experiments demonstrate that MetaDiT outperforms existing baselines in spectral accuracy, we further validate our method through extensive ablation studies. Our code and model weights will be open-sourced to facilitate future research.