Inverse design of meta-surfaces based on generative adversarial networks

Hui Chen; Zhaoxian Zhang

doi:10.1117/12.3040163

16 August 2024 Inverse design of meta-surfaces based on generative adversarial networks

Hui Chen, Zhaoxian Zhang

Proceedings Volume 13231, 4th International Conference on Laser, Optics, and Optoelectronic Technology (LOPET 2024); 132312Y (2024) https://doi.org/10.1117/12.3040163
Event: 4th International Conference on Laser, Optics, and Optoelectronic Technology (LOPET 2024), 2024, Chongqing, China

Abstract

Metasurfaces, as artificial structures capable of precise manipulation of optical wavefronts, demonstrate immense potential in the fields of optoelectronics and wireless communications. However, traditional design methods for metasurfaces often rely on intuition and trial-and-error, which are not only inefficient but also struggle to meet complex design requirements. The rise of deep learning technologies in recent years has offered a new perspective for addressing this issue. This study designs and improves a novel deep convolutional network architecture that integrates Wasserstein GANs and attention mechanisms for the efficient inverse design of metasurface structures. After testing on internal and external test sets and random spectra, the attention-based WGAN network model achieved an image accuracy of 0.98 and a spectral accuracy of 0.97, proving its efficiency and accuracy in designing elliptical metasurface structures.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hui Chen and Zhaoxian Zhang "Inverse design of meta-surfaces based on generative adversarial networks", Proc. SPIE 13231, 4th International Conference on Laser, Optics, and Optoelectronic Technology (LOPET 2024), 132312Y (16 August 2024); https://doi.org/10.1117/12.3040163

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