Machine-learning reprogrammable metasurface imager

Lianlin Li; Hengxin Ruan; Che Liu; Ying Li; Ya Shuang; Andrea Alù; Cheng-Wei Qiu; Tie Jun Cui

doi:10.1038/s41467-019-09103-2

Nature Communications (Mar 2019)

Machine-learning reprogrammable metasurface imager

Lianlin Li,
Hengxin Ruan,
Che Liu,
Ying Li,
Ya Shuang,
Andrea Alù,
Cheng-Wei Qiu,
Tie Jun Cui

Affiliations

Lianlin Li: State Key Laboratory of Advanced Optical Communication Systems and Networks, Department of Electronics, Peking University
Hengxin Ruan: State Key Laboratory of Advanced Optical Communication Systems and Networks, Department of Electronics, Peking University
Che Liu: State Key Laboratory of Millimeter Waves, Southeast University
Ying Li: Department of Electrical and Computer Engineering, National University of Singapore
Ya Shuang: State Key Laboratory of Advanced Optical Communication Systems and Networks, Department of Electronics, Peking University
Andrea Alù: Photonics Initiative, Advanced Science Research Center, City University of New York
Cheng-Wei Qiu: Department of Electrical and Computer Engineering, National University of Singapore
Tie Jun Cui: State Key Laboratory of Millimeter Waves, Southeast University

DOI: https://doi.org/10.1038/s41467-019-09103-2
Journal volume & issue: Vol. 10, no. 1
pp. 1 – 8

Abstract

Read online

Conventional imagers require time-consuming data acquisition, or complicated reconstruction algorithms for data post-processing. Here, the authors demonstrate a real-time digital-metasurface imager that can be trained in-situ to show high accuracy image coding and recognition for various image sets.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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