ArticleNature communications2023
Learning diffractive optical communication around arbitrary opaque occlusions.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Diffractive Optical Neural Networks: Pathways Toward Intelligent and Scalable Photonic Computing.Nanophotonics (Berlin, Germany) · 2026Review
- Image processing with Optical matrix vector multipliers implemented for encoding and decoding tasks.Light, science & applications · 2025Article
- Universal point spread function engineering for 3D optical information processing.Light, science & applications · 2025Article
- Optimizing structured surfaces for diffractive waveguides.Nature communications · 2025Article
- Memory-less scattering imaging with ultrafast convolutional optical neural networks.Science advances · 2024Article
- All-optical complex field imaging using diffractive processors.Light, science & applications · 2024Article
- Diffractive optical computing in free space.Nature communications · 2024Review
- All-optical image denoising using a diffractive visual processor.Light, science & applications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
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.
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Registered trials
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