Learning diffractive optical communication around arbitrary opaque occlusions

Journal Article · 26 October 2023 · Nature Communications

DOI: https://doi.org/10.1038/s41467-023-42556-0 · OSTI ID:2203768

Rahman, Md Sadman Sakib ^[1]; Gan, Tianyi  ^[1]; Deger, Emir Arda  ^[1]; Işıl, Çağatay  ^[1]; Jarrahi, Mona  ^[1]; Ozcan, Aydogan  ^[1]

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1. Univ. of California, Los Angeles, CA (United States)

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.

• Research Organization: Univ. of California, Los Angeles, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE); USDOE
• Grant/Contract Number: SC0023088
• OSTI ID: 2203768
• Alternate ID(s): OSTI ID: 2341442
• Journal Information: Nature Communications, Vol. 14, Issue 1; ISSN 2041-1723
• Publisher: Nature Publishing GroupCopyright Statement
• Country of Publication: United States
• Language: English