ArticleLight, science & applications2025
High-speed all-optical neural networks empowered spatiotemporal mode multiplexing.
Article in Light, science & applications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Spatiotemporal all-optical diffractive neural networks empowered by spatiotemporal light field manipulation.Light, science & applications · 2026Article
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Authors and funding
5 authors.
Funding
Abstract
Orbital angular momentum (OAM) beams, characterized by a helical phase structure and phase singularity, have emerged as a powerful resource for high-capacity optical communications through mode-division multiplexing (MDM). Traditional OAM multiplexing systems operating solely in the spatial domain face significant challenges, including increased system complexity, inter-modal crosstalk, and limited scalability. Recent advances have explored hybrid multiplexing schemes combining OAM with wavelength or polarization degrees of freedom, demonstrating Pbit/s level transmission capacities. However, these systems predominantly rely on continuous-wave lasers and external modulators, which constrain their applicability in challenging environments, whereas pulsed lasers provide superior peak power, enhanced transmission robustness, and the potential for implementation of OAM lasers, which generally emit pulsed OAM beams. Here, we report an OAM-based spatiotemporal multiplexing (OAM-STM) technique that synergistically implements pulsed OAM beams with a diffractive deep neural network (D
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.