Non-interleaved chiral metasurfaces and neural networks enhance the spatial resolution of polarimetry

Jaewon Jang; Minsu Park; Yeonsang Park

doi:10.1038/s41377-024-01397-2

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Non-interleaved chiral metasurfaces and neural networks enhance the spatial resolution of polarimetry

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- Non-interleaved chiral metasurfaces and neural networks enhance the spatial resolution of polarimetry
- Light: Science & Applications Vol. 13, Issue 3, Pages: 396-398(2024)
- Affiliations：
  
  1.Department of Physics, Chungnam National University, Daejeon 34134, Korea
  2.Institute of Quantum Systems, Chungnam National University, Daejeon 34134, Korea
- Author bio：
  
  Yeonsang Park (yeonsang.park@cnu.ac.kr)
- Funds：
- DOI：10.1038/s41377-024-01397-2
  CLC：
- Published Online：20 February 2024，
  
  Published：31 March 2024
- Accepted：
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Jang, J., Park, M. & Park, Y. Non-interleaved chiral metasurfaces and neural networks enhance the spatial resolution of polarimetry. Light: Science & Applications, 13, 396-398 (2024).
DOI：

Jang, J., Park, M. & Park, Y. Non-interleaved chiral metasurfaces and neural networks enhance the spatial resolution of polarimetry. Light: Science & Applications, 13, 396-398 (2024). DOI： 10.1038/s41377-024-01397-2.

摘要

Abstract

Non-interleaved chiral metasurfaces for high-spatial-resolution polarimetry are proposed and demonstrated. Furthermore

a convolutional neural network is incorporated to analyze interferometric images with the polarization state of light

and it results in accurate Stokes parameters.

关键词

Keywords

references

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