Zhang Zezhou, Yang Chuanchuan, Qin Yifeng, Feng Hao, Feng Jiqiang, Li Hongbin
Peking University Shenzhen Graduate School, Peking University, Shenzhen 518055, China.
Peng Cheng Laboratory, Shenzhen 518055, China.
Nanophotonics. 2023 Oct 4;12(20):3871-3881. doi: 10.1515/nanoph-2023-0292. eCollection 2023 Oct.
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-parameters 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-parameters 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.
传统的超原子设计严重依赖研究人员的先验知识以及使用全波模拟进行的反复试验搜索，这导致过程既耗时又低效。基于优化算法（如进化算法）和拓扑优化的逆向设计方法已被引入用于超材料设计。然而，这些算法都不够通用，无法完成多目标任务。最近，以生成对抗网络（GAN）为代表的深度学习方法已被应用于超材料的逆向设计，该方法可以根据S参数要求直接生成高自由度的超原子。然而，GAN的对抗训练过程使网络不稳定，并导致高建模成本。本文提出了一种基于扩散概率理论的新型超材料逆向设计方法。通过学习将原始结构转化为高斯分布的马尔可夫过程，该方法可以从高斯分布开始逐步去除噪声，并生成满足S参数条件的新的高自由度超原子，这避免了GAN对抗训练过程引入的模型不稳定性，并确保了更准确和高质量的生成结果。实验证明，我们的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。
