Diffusion Probabilistic Model Based Accurate and High-Degree-of-Freedom Metasurface Inverse Design

TL;DR

This paper introduces a diffusion probabilistic model for inverse design of high-degree-of-freedom metasurfaces, offering a more stable and accurate alternative to GAN-based methods, with faster convergence and better quality.

Contribution

It presents a novel diffusion-based inverse design approach that overcomes GAN limitations, enabling efficient and precise generation of metasurface structures.

Findings

Outperforms GANs in convergence speed

Achieves higher accuracy in meta-atom generation

Produces higher-quality metasurface designs

Abstract

Conventional meta-atom designs rely heavily on researchers' prior knowledge and trial-and-error searches using full-wave simulations, resulting in time-consuming and inefficient processes. Inverse design methods based on optimization algorithms, such as evolutionary algorithms, and topological optimizations, have been introduced to design metamaterials. However, none of these algorithms are general enough to fulfill multi-objective tasks. Recently, deep learning methods represented by Generative Adversarial Networks (GANs) have been applied to inverse design of metamaterials, which can directly generate high-degree-of-freedom meta-atoms based on S-parameter requirements. However, the adversarial training process of GANs makes the network unstable and results in high modeling costs. This paper proposes a novel metamaterial inverse design method based on the diffusion probability theory. By learning the Markov process that transforms the original structure into a Gaussian distribution, the proposed method can gradually remove the noise starting from the Gaussian distribution and generate new high-degree-of-freedom meta-atoms that meet S-parameter conditions, which avoids the model instability introduced by the adversarial training process of GANs and ensures more accurate and high-quality generation results. Experiments have proven that our method is superior to representative methods of GANs in terms of model convergence speed, generation accuracy, and quality.