Neural network assisted high-spatial-resolution polarimetry with non-interleaved chiral metasurfaces

Research context

Polarimetry plays an indispensable role in modern optics. Nevertheless, the current strategies generally suffer from bulky system volume or spatial multiplexing scheme, resulting in limited performances when dealing with inhomogeneous polarizations. Here, we propose a non-interleaved, interferometric method to analyze the polarizations based on a tri-channel chiral metasurface. A deep convolutional neural network is also incorporated to enable fast, robust and accurate polarimetry. Spatially uniform and nonuniform polarizations are both measured through the metasurface experimentally. Distinction between two semblable glasses is also demonstrated. Our strategy features the merits of compactness and high spatial resolution, and would inspire more intriguing design for detecting and sensing.

Keywords: Polarimetry, Interferometry, Polarization (electrochemistry), Computer science, Image resolution, Optics, Convolutional neural network, Physics, Artificial intelligence, Scattering, Physical chemistry, Chemistry

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70 citations · OpenAlex · observed 2026-09-08

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Cite / 引用

Chen Chen, Xingjian Xiao, Xin Ye, Jiacheng Sun, Jitao Ji, Rongtao Yu, Wange Song, Shining Zhu, Tao Li. Neural network assisted high-spatial-resolution polarimetry with non-interleaved chiral metasurfaces. Light: Science & Applications (2023). https://doi.org/10.1038/s41377-023-01337-6

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