ArticleNature communications2024
Multimodal deep learning using on-chip diffractive optics with in situ training capability.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Analyzing the enhancement of CNN-YOLO and transformer based architectures for real-time animal detection in complex ecological environments.Scientific reports · 2025Pooled it
- How nanophotonics can drive optical computing toward practical applications.Nature nanotechnology · 2026Review
- Integrated Neuromorphic Photonic Computing for AI Acceleration: Emerging Devices, Network Architectures, and Future Paradigms.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Emerging Multimodal Artificial Sensory Fusion Paradigms Toward Advanced Embodied Intelligence.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- On-chip non-volatile all-optical residual neural network accelerator.Light, science & applications · 2026Article
- High-precision integrated diffractive optical networks enabling regression analysis.Nature communications · 2026Article
- Broadband, Compact, and Training-Free Optical Processors for Parallel Image Classification.ACS nano · 2026Article
- Photonic Mixture-of-Experts for scalable multi-task on-chip optical neural networks.Nature communications · 2026Article
- Compact photonic spiking neuron with inherent stochasticity based on phase-change material for probabilistic computing.Nature communications · 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
- Inverse-designed nanophotonic neural network accelerators for ultra-compact optical computing.Nature communications · 2026Article
- Multifunctional movable-type coding metasurface enabling reconfigurable diffractive neural networks.Light, science & applications · 2026Article
- Anti-interference diffractive deep neural networks for multi-object recognition.Light, science & applications · 2026Article
- Physical neural networks using sharpness-aware training.Nature communications · 2026Article
- Multisensory Neuromorphic Devices: From Physics to Integration.Nano-micro letters · 2026Review
- Self-configuring high-speed multi-plane light conversion.Nature communications · 2025Article
- High computational density nanophotonic media for machine learning inference.Nature communications · 2025Article
- Programmable diffractive deep neural networks enabled by integrated rewritable metasurfaces.Scientific reports · 2025Article
- Scaling up for end-to-end on-chip photonic neural network inference.Light, science & applications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Multimodal deep learning plays a pivotal role in supporting the processing and learning of diverse data types within the realm of artificial intelligence generated content (AIGC). However, most photonic neuromorphic processors for deep learning can only handle a single data modality (either vision or audio) due to the lack of abundant parameter training in optical domain. Here, we propose and demonstrate a trainable diffractive optical neural network (TDONN) chip based on on-chip diffractive optics with massive tunable elements to address these constraints. The TDONN chip includes one input layer, five hidden layers, and one output layer, and only one forward propagation is required to obtain the inference results without frequent optical-electrical conversion. The customized stochastic gradient descent algorithm and the drop-out mechanism are developed for photonic neurons to realize in situ training and fast convergence in the optical domain. The TDONN chip achieves a potential throughput of 217.6 tera-operations per second (TOPS) with high computing density (447.7 TOPS/mm
Identifiers
What OpenQuestion holds
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.