Diffusion probabilistic model based accurate and high-degree-of-freedom metasurface inverse design
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-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.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Diffusion probabilistic model based accurate and high-degree-of-freedom metasurface inverse design ; volume:12 ; number:20 ; year:2023 ; pages:3871-3881 ; extent:11
Nanophotonics ; 12, Heft 20 (2023), 3871-3881 (gesamt 11)
- Creator
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Zhang, Zezhou
Yang, Chuanchuan
Qin, Yifeng
Feng, Hao
Feng, Jiqiang
Li, Hongbin
- DOI
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10.1515/nanoph-2023-0292
- URN
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urn:nbn:de:101:1-2023101214030549029088
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 11:01 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Zhang, Zezhou
- Yang, Chuanchuan
- Qin, Yifeng
- Feng, Hao
- Feng, Jiqiang
- Li, Hongbin
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