Neural Network-Assisted End-to-End Design for Dispersive Full-Parameter Control of Meta-Optics
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916307582058496 |
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| author | Chi, Hanbin Hu, Yueqiang Ou, Xiangnian Jiang, Yuting Yu, Dian Lou, Shaozhen Wang, Quan Xie, Qiong Qiu, Cheng-Wei Duan, Huigao |
| author_facet | Chi, Hanbin Hu, Yueqiang Ou, Xiangnian Jiang, Yuting Yu, Dian Lou, Shaozhen Wang, Quan Xie, Qiong Qiu, Cheng-Wei Duan, Huigao |
| contents | Flexible control light field across multiple parameters is the cornerstone of versatile and miniaturized optical devices. Metasurfaces, comprising subwavelength scatterers, offer a potent platform for executing such precise manipulations. However, the inherent mutual constraints between parameters of metasurfaces make it challenging for traditional approaches to achieve full-parameter control across multiple wavelengths. Here, we propose a universal end-to-end inverse design framework to directly optimize the geometric parameter layout of meta-optics based on the target functionality of full-parameter control across multiple wavelengths. This framework employs a differentiable forward simulator integrating a neural network-based dispersive full-parameter Jones matrix and Fourier propagation to facilitate gradient-based optimization. Its superiority over sequential forward designs in dual-polarization channel color holography with higher quality and tri-polarization three-dimensional color holography with higher multiplexed capacity is showcased. To highlight the universality, we further present polarized spectral multi-information processing with six arbitrary polarizations and three wavelengths. This versatile, differentiable, system-level design framework is poised to expedite the advancement of meta-optics in integrated multi-information display, imaging, and communication, extending to multi-modal sensing applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_00559 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural Network-Assisted End-to-End Design for Dispersive Full-Parameter Control of Meta-Optics Chi, Hanbin Hu, Yueqiang Ou, Xiangnian Jiang, Yuting Yu, Dian Lou, Shaozhen Wang, Quan Xie, Qiong Qiu, Cheng-Wei Duan, Huigao Optics Applied Physics Flexible control light field across multiple parameters is the cornerstone of versatile and miniaturized optical devices. Metasurfaces, comprising subwavelength scatterers, offer a potent platform for executing such precise manipulations. However, the inherent mutual constraints between parameters of metasurfaces make it challenging for traditional approaches to achieve full-parameter control across multiple wavelengths. Here, we propose a universal end-to-end inverse design framework to directly optimize the geometric parameter layout of meta-optics based on the target functionality of full-parameter control across multiple wavelengths. This framework employs a differentiable forward simulator integrating a neural network-based dispersive full-parameter Jones matrix and Fourier propagation to facilitate gradient-based optimization. Its superiority over sequential forward designs in dual-polarization channel color holography with higher quality and tri-polarization three-dimensional color holography with higher multiplexed capacity is showcased. To highlight the universality, we further present polarized spectral multi-information processing with six arbitrary polarizations and three wavelengths. This versatile, differentiable, system-level design framework is poised to expedite the advancement of meta-optics in integrated multi-information display, imaging, and communication, extending to multi-modal sensing applications. |
| title | Neural Network-Assisted End-to-End Design for Dispersive Full-Parameter Control of Meta-Optics |
| topic | Optics Applied Physics |
| url | https://arxiv.org/abs/2407.00559 |