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

• URL: http://arxiv.org/abs/2304.13038v1
• Date: Tue, 25 Apr 2023 08:25:23 GMT
• Title: Diffusion Probabilistic Model Based Accurate and High-Degree-of-Freedom Metasurface Inverse Design
• Authors: Zezhou Zhang, Chuanchuan Yang, Yifeng Qin, Hao Feng, Jiqiang Feng, Hongbin Li
• Abstract summary: Inverse design methods based on optimization algorithms have been introduced to design metamaterials. Deep learning methods represented by Generative Adversarial Networks (GANs) have been applied to inverse design of metamaterials. This paper proposes a novel metamaterial inverse design method based on the diffusion probability theory.
• Score: 14.18549701990854
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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