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Quantitative phase contrast imaging with a nonlocal angle-selective metasurface

Author

Listed:
  • Anqi Ji

    (Stanford University)

  • Jung-Hwan Song

    (Stanford University)

  • Qitong Li

    (Stanford University)

  • Fenghao Xu

    (Stanford University)

  • Ching-Ting Tsai

    (Stanford University)

  • Richard C. Tiberio

    (Stanford University)

  • Bianxiao Cui

    (Stanford University)

  • Philippe Lalanne

    (University of Bordeaux)

  • Pieter G. Kik

    (University of Central Florida)

  • David A. B. Miller

    (Stanford University)

  • Mark L. Brongersma

    (Stanford University)

Abstract

Phase contrast microscopy has played a central role in the development of modern biology, geology, and nanotechnology. It can visualize the structure of translucent objects that remains hidden in regular optical microscopes. The optical layout of a phase contrast microscope is based on a 4 f image processing setup and has essentially remained unchanged since its invention by Zernike in the early 1930s. Here, we propose a conceptually new approach to phase contrast imaging that harnesses the non-local optical response of a guided-mode-resonator metasurface. We highlight its benefits and demonstrate the imaging of various phase objects, including biological cells, polymeric nanostructures, and transparent metasurfaces. Our results showcase that the addition of this non-local metasurface to a conventional microscope enables quantitative phase contrast imaging with a 0.02π phase accuracy. At a high level, this work adds to the growing body of research aimed at the use of metasurfaces for analog optical computing.

Suggested Citation

  • Anqi Ji & Jung-Hwan Song & Qitong Li & Fenghao Xu & Ching-Ting Tsai & Richard C. Tiberio & Bianxiao Cui & Philippe Lalanne & Pieter G. Kik & David A. B. Miller & Mark L. Brongersma, 2022. "Quantitative phase contrast imaging with a nonlocal angle-selective metasurface," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34197-6
    DOI: 10.1038/s41467-022-34197-6
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    References listed on IDEAS

    as
    1. Gordon Wetzstein & Aydogan Ozcan & Sylvain Gigan & Shanhui Fan & Dirk Englund & Marin Soljačić & Cornelia Denz & David A. B. Miller & Demetri Psaltis, 2020. "Inference in artificial intelligence with deep optics and photonics," Nature, Nature, vol. 588(7836), pages 39-47, December.
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