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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2512.12625 |
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| _version_ | 1866908711262355456 |
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| author | Xu, Borui Shao, Jingzhu Zhao, Xiangyu Xu, Haishan Tian, Yudong Chen, Nanxi Sun, Jielin Lin, Han Bao, Qiaoliang Mai, Yiyong Wu, Chongzhao |
| author_facet | Xu, Borui Shao, Jingzhu Zhao, Xiangyu Xu, Haishan Tian, Yudong Chen, Nanxi Sun, Jielin Lin, Han Bao, Qiaoliang Mai, Yiyong Wu, Chongzhao |
| contents | Recent advances in meta-optics have enabled diverse functionalities in compact optical devices; however, conventional forward design approaches become inadequate as device complexity and scale grow. Inverse design offers a powerful alternative but often requires massive computational resources and neglects mutual coupling effects. Here, we propose and experimentally validate a deep-learning-enabled framework for rapid inverse design of large-scale, aperiodic metasurfaces with full-wave accuracy.The framework integrates an inverse design network responsible that maps target near-field responses to metasurface geometries in a non-iterative and scalable manner. A lightweight forward prediction network, integrated as a full-wave solver surrogate within the framework, enables efficient end-to-end training of the inverse design network while capturing mutual coupling effects by considering both local and neighboring geometries.The framework's effectiveness is experimentally verified through a multi-foci metalens and a holographic metasurface. This framework enables the inverse design from micrometer to centimeter scales (> 20kλ), with near-field responses discrepancies less than 3% compared to full-wave solvers at subwavelength (< λ/10) resolution.Moreover, it is generalizable to metasurfaces of arbitrary size and operates efficiently without high-performance resources, overcoming the computational bottlenecks of previous inverse design methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_12625 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep-learning-enabled inverse design of large-scale metasurfaces with full-wave accuracy Xu, Borui Shao, Jingzhu Zhao, Xiangyu Xu, Haishan Tian, Yudong Chen, Nanxi Sun, Jielin Lin, Han Bao, Qiaoliang Mai, Yiyong Wu, Chongzhao Optics Recent advances in meta-optics have enabled diverse functionalities in compact optical devices; however, conventional forward design approaches become inadequate as device complexity and scale grow. Inverse design offers a powerful alternative but often requires massive computational resources and neglects mutual coupling effects. Here, we propose and experimentally validate a deep-learning-enabled framework for rapid inverse design of large-scale, aperiodic metasurfaces with full-wave accuracy.The framework integrates an inverse design network responsible that maps target near-field responses to metasurface geometries in a non-iterative and scalable manner. A lightweight forward prediction network, integrated as a full-wave solver surrogate within the framework, enables efficient end-to-end training of the inverse design network while capturing mutual coupling effects by considering both local and neighboring geometries.The framework's effectiveness is experimentally verified through a multi-foci metalens and a holographic metasurface. This framework enables the inverse design from micrometer to centimeter scales (> 20kλ), with near-field responses discrepancies less than 3% compared to full-wave solvers at subwavelength (< λ/10) resolution.Moreover, it is generalizable to metasurfaces of arbitrary size and operates efficiently without high-performance resources, overcoming the computational bottlenecks of previous inverse design methods. |
| title | Deep-learning-enabled inverse design of large-scale metasurfaces with full-wave accuracy |
| topic | Optics |
| url | https://arxiv.org/abs/2512.12625 |