Deep learning-designed unidirectional imagers

Jingxi Li; Tianyi Gan; Yifan Zhao; Bijie Bai; Che-Yung Shen; Songyu Sun; Mona Jarrahi; Aydogan Ozcan

doi:10.1117/12.2678255

28 September 2023 Deep learning-designed unidirectional imagers

Jingxi Li, Tianyi Gan, Yifan Zhao, Bijie Bai, Che-Yung Shen, Songyu Sun, Mona Jarrahi, Aydogan Ozcan

Proceedings Volume PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023; PC126550K (2023) https://doi.org/10.1117/12.2678255
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States

Abstract

We present the first demonstration of unidirectional imaging that permits image formation along only one direction, from an input field-of-view to an output field-of-view, while eliminating optical transmission in the reverse direction. This unidirectional imager is formed by diffractive layers composed of isotropic linear materials spatially-coded with thousands of phase features optimized using deep learning. We experimentally tested our diffractive design using a terahertz setup and 3D-printed diffractive layers, which revealed a good agreement with our numerical simulations. The designs of these diffractive unidirectional imagers are compact and can be scaled to operate at different parts of the electromagnetic spectrum.

Conference Presentation

Citation Download Citation

Jingxi Li, Tianyi Gan, Yifan Zhao, Bijie Bai, Che-Yung Shen, Songyu Sun, Mona Jarrahi, and Aydogan Ozcan "Deep learning-designed unidirectional imagers", Proc. SPIE PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, PC126550K (28 September 2023); https://doi.org/10.1117/12.2678255

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