
Nanjing University, National Laboratory of Solid State Microstructures, Key Laboratory of Intelligent Optical Sensing and Manipulations, Jiangsu Key Laboratory of Artificial Functional Materials, College of Engineering and Applied Sciences, 210093 Nanjing, China
Tao Li (taoli@nju.edu.cn)
Published:31 December 2023,
Published Online:04 December 2023,
Received:06 July 2023,
Revised:31 October 2023,
Accepted:12 November 2023
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Chen, C. et al. Neural network assisted high-spatial-resolution polarimetry with non-interleaved chiral metasurfaces. Light: Science & Applications, 12, 2732-2741 (2023).
Chen, C. et al. Neural network assisted high-spatial-resolution polarimetry with non-interleaved chiral metasurfaces. Light: Science & Applications, 12, 2732-2741 (2023). DOI: 10.1038/s41377-023-01337-6.
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.
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