A Surrogate-Assisted Extended Generative Adversarial Network for Parameter Optimization in Free-Form Metasurface Design
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866910288151838720 |
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| author | Dai, Manna Jiang, Yang Yang, Feng Chattoraj, Joyjit Xia, Yingzhi Xu, Xinxing Zhao, Weijiang Dao, My Ha Liu, Yong |
| author_facet | Dai, Manna Jiang, Yang Yang, Feng Chattoraj, Joyjit Xia, Yingzhi Xu, Xinxing Zhao, Weijiang Dao, My Ha Liu, Yong |
| contents | Metasurfaces have widespread applications in fifth-generation (5G) microwave communication. Among the metasurface family, free-form metasurfaces excel in achieving intricate spectral responses compared to regular-shape counterparts. However, conventional numerical methods for free-form metasurfaces are time-consuming and demand specialized expertise. Alternatively, recent studies demonstrate that deep learning has great potential to accelerate and refine metasurface designs. Here, we present XGAN, an extended generative adversarial network (GAN) with a surrogate for high-quality free-form metasurface designs. The proposed surrogate provides a physical constraint to XGAN so that XGAN can accurately generate metasurfaces monolithically from input spectral responses. In comparative experiments involving 20000 free-form metasurface designs, XGAN achieves 0.9734 average accuracy and is 500 times faster than the conventional methodology. This method facilitates the metasurface library building for specific spectral responses and can be extended to various inverse design problems, including optical metamaterials, nanophotonic devices, and drug discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_02961 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Surrogate-Assisted Extended Generative Adversarial Network for Parameter Optimization in Free-Form Metasurface Design Dai, Manna Jiang, Yang Yang, Feng Chattoraj, Joyjit Xia, Yingzhi Xu, Xinxing Zhao, Weijiang Dao, My Ha Liu, Yong Machine Learning Computer Vision and Pattern Recognition Image and Video Processing Optics Metasurfaces have widespread applications in fifth-generation (5G) microwave communication. Among the metasurface family, free-form metasurfaces excel in achieving intricate spectral responses compared to regular-shape counterparts. However, conventional numerical methods for free-form metasurfaces are time-consuming and demand specialized expertise. Alternatively, recent studies demonstrate that deep learning has great potential to accelerate and refine metasurface designs. Here, we present XGAN, an extended generative adversarial network (GAN) with a surrogate for high-quality free-form metasurface designs. The proposed surrogate provides a physical constraint to XGAN so that XGAN can accurately generate metasurfaces monolithically from input spectral responses. In comparative experiments involving 20000 free-form metasurface designs, XGAN achieves 0.9734 average accuracy and is 500 times faster than the conventional methodology. This method facilitates the metasurface library building for specific spectral responses and can be extended to various inverse design problems, including optical metamaterials, nanophotonic devices, and drug discovery. |
| title | A Surrogate-Assisted Extended Generative Adversarial Network for Parameter Optimization in Free-Form Metasurface Design |
| topic | Machine Learning Computer Vision and Pattern Recognition Image and Video Processing Optics |
| url | https://arxiv.org/abs/2401.02961 |