Learning diffractive optical communication around arbitrary opaque occlusions

Md Sadman Sakib Rahman, Tianyi Gan, Emir Arda Deger, Çağatay Işıl, Mona Jarrahi and Aydogan Ozcan ()
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Md Sadman Sakib Rahman: University of California
Tianyi Gan: University of California
Emir Arda Deger: University of California
Çağatay Işıl: University of California
Mona Jarrahi: University of California
Aydogan Ozcan: University of California

Nature Communications, 2023, vol. 14, issue 1, 1-17

Abstract: Abstract Free-space optical communication becomes challenging when an occlusion blocks the light path. Here, we demonstrate a direct communication scheme, passing optical information around a fully opaque, arbitrarily shaped occlusion that partially or entirely occludes the transmitter’s field-of-view. In this scheme, an electronic neural network encoder and a passive, all-optical diffractive network-based decoder are jointly trained using deep learning to transfer the optical information of interest around the opaque occlusion of an arbitrary shape. Following its training, the encoder-decoder pair can communicate any arbitrary optical information around opaque occlusions, where the information decoding occurs at the speed of light propagation through passive light-matter interactions, with resilience against various unknown changes in the occlusion shape and size. We also validate this framework experimentally in the terahertz spectrum using a 3D-printed diffractive decoder. Scalable for operation in any wavelength regime, this scheme could be particularly useful in emerging high data-rate free-space communication systems.

Date: 2023
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DOI: 10.1038/s41467-023-42556-0

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