ArticleLight, science & applications2022
Polarization multiplexed diffractive computing: all-optical implementation of a group of linear transformations through a polarization-encoded diffractive network.
Article in Light, science & applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Emerging integrated photonic neural network technologies for artificial intelligence: from devices to systems.National science review · 2026Review
- Optical phased array LiDAR chips towards polarization-resolved 4D sensing.Nature communications · 2026Article
- How nanophotonics can drive optical computing toward practical applications.Nature nanotechnology · 2026Review
- Parallel execution of nonlinear logic circuits using reconfigurable free-space diffractive optics.Nature communications · 2026Article
- Snapshot 3D image projection using a diffractive decoder.Light, science & applications · 2026Article
- Programmable Three-dimensional Photonic Neural Network Chip.Nature communications · 2026Article
- Diffractive Optical Neural Networks: Pathways Toward Intelligent and Scalable Photonic Computing.Nanophotonics (Berlin, Germany) · 2026Review
- Anti-interference diffractive deep neural networks for multi-object recognition.Light, science & applications · 2026Article
- High-speed all-optical neural networks empowered spatiotemporal mode multiplexing.Light, science & applications · 2025Article
- Optimizing structured surfaces for diffractive waveguides.Nature communications · 2025Article
- Article
- Bioinspired single-shot polarization photodetector based on four-directional grating arrays capped perovskite single-crystal thin film.Science advances · 2024Article
- Optical neural networks: progress and challenges.Light, science & applications · 2024Review
- Nonlinear encoding in diffractive information processing using linear optical materials.Light, science & applications · 2024Article
- A perspective on the artificial intelligence's transformative role in advancing diffractive optics.iScience · 2024Review
- All dielectric metasurface based diffractive neural networks for 1-bit adder.Nanophotonics (Berlin, Germany) · 2024Article
- Diffractive optical computing in free space.Nature communications · 2024Review
- Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding.Light, science & applications · 2024Article
- All-optical image denoising using a diffractive visual processor.Light, science & applications · 2024Article
- Vectorial adaptive optics: expanding the frontiers of optical correction.Light, science & applications · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Research on optical computing has recently attracted significant attention due to the transformative advances in machine learning. Among different approaches, diffractive optical networks composed of spatially-engineered transmissive surfaces have been demonstrated for all-optical statistical inference and performing arbitrary linear transformations using passive, free-space optical layers. Here, we introduce a polarization-multiplexed diffractive processor to all-optically perform multiple, arbitrarily-selected linear transformations through a single diffractive network trained using deep learning. In this framework, an array of pre-selected linear polarizers is positioned between trainable transmissive diffractive materials that are isotropic, and different target linear transformations (complex-valued) are uniquely assigned to different combinations of input/output polarization states. The transmission layers of this polarization-multiplexed diffractive network are trained and optimized via deep learning and error-backpropagation by using thousands of examples of the input/output fields corresponding to each one of the complex-valued linear transformations assigned to different input/output polarization combinations. Our results and analysis reveal that a single diffractive network can successfully approximate and all-optically implement a group of arbitrarily-selected target transformations with a negligible error when the number of trainable diffractive features/neurons (N) approaches [Formula: see text], where N
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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.