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.

本文提出了一种基于扩散概率理论的新型超材料反设计方法，该方法可以逐步从高斯分布中去除噪声并生成满足 S 参数条件的新型高自由度元电池，并且证明实验表明该方法在模型收敛速度、生成精度和质量方面优于 GAN 的代表性方法。