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13 March 2024 Diffractive unidirectional imager designed by deep learning
Jingxi Li, Tianyi Gan, Yifan Zhao, Bijie Bai, Che-Yung Shen, Songyu Sun, Mona Jarrahi, Aydogan Ozcan
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Proceedings Volume PC12903, AI and Optical Data Sciences V; PC129030Y (2024) https://doi.org/10.1117/12.3001969
Event: SPIE OPTO, 2024, San Francisco, California, United States
Abstract
We introduce a unidirectional imager that facilitates polarization-insensitive and broadband operation using isotropic, linear materials. This design comprises diffractive layers with hundreds of thousands of learnable phase features, trained using deep learning to enable power-efficient, high-fidelity imaging in the forward direction (A-to-B), while simultaneously inhibiting optical transmission and image formation in the reverse direction (B-to-A). We experimentally tested our designs using terahertz radiation, providing a good match with our simulations. Furthermore, we demonstrated a wavelength-selective unidirectional imager that performs unidirectional imaging along A-to-B at a predetermined wavelength, while at a second wavelength, the unidirectional operation switches from B-to-A.
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Jingxi Li, Tianyi Gan, Yifan Zhao, Bijie Bai, Che-Yung Shen, Songyu Sun, Mona Jarrahi, and Aydogan Ozcan "Diffractive unidirectional imager designed by deep learning", Proc. SPIE PC12903, AI and Optical Data Sciences V, PC129030Y (13 March 2024); https://doi.org/10.1117/12.3001969
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KEYWORDS
Imaging systems
Deep learning
Image acquisition
Design and modelling
Light sources and illumination
Education and training
Image processing
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