ReviewLight, science & applications2024
Optical neural networks: progress and challenges.
Review in Light, science & applications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
40 citing papers in PubMed.
- Deep diffractive optical neural networks for detecting skyrmionic topologies of light and their live training.Nature communications · 2026Article
- AI for bioactive materials: From material design to biological applications.Bioactive materials · 2026Review
- Spatiotemporal all-optical diffractive neural networks empowered by spatiotemporal light field manipulation.Light, science & applications · 2026Article
- How nanophotonics can drive optical computing toward practical applications.Nature nanotechnology · 2026Review
- Ferroelectrically programmable lanthanide luminescent memristor.Nature communications · 2026Article
- Dual-Mode MOSCAP Silicon Microring Resonator for Energy-Efficient Analog Optical Computing.ACS photonics · 2026Article
- 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 Electromagnetic Field Prediction at Unseen Wavelengths via Physics-Guided Neural Operators.Nanophotonics (Berlin, Germany) · 2026Article
- Wavelength-encoded neuromorphic inference enabled by microcavity MoSNature communications · 2026Article
- Parallel execution of nonlinear logic circuits using reconfigurable free-space diffractive optics.Nature communications · 2026Article
- 2D Materials Powering Neuromorphic Intelligence.Nano-micro letters · 2026Review
- Photonic Mixture-of-Experts for scalable multi-task on-chip optical neural networks.Nature communications · 2026Article
- Optical in-memory computing using laser array.Light, science & applications · 2026Article
- Integrated van der Waals waveguides for all-optical nonlinear photonic circuits.Nature communications · 2026Article
- Machine Learning for Intelligent and Adaptive Communication Systems: From Optimization to Emerging Paradigms.Sensors (Basel, Switzerland) · 2026Article
- Optical Bistability in Photonic Topological Hypercrystals and Its Applications in Photonic Neural Network.Nanomaterials (Basel, Switzerland) · 2026Article
- Compact and programmable large-scale optical processor in free space.Light, science & applications · 2026Article
- Origin of Threshold Voltage Instabilities in Indium Oxide Transistors.ACS applied materials & interfaces · 2026Article
- Inverse-designed nanophotonic neural network accelerators for ultra-compact optical computing.Nature communications · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Artificial intelligence has prevailed in all trades and professions due to the assistance of big data resources, advanced algorithms, and high-performance electronic hardware. However, conventional computing hardware is inefficient at implementing complex tasks, in large part because the memory and processor in its computing architecture are separated, performing insufficiently in computing speed and energy consumption. In recent years, optical neural networks (ONNs) have made a range of research progress in optical computing due to advantages such as sub-nanosecond latency, low heat dissipation, and high parallelism. ONNs are in prospect to provide support regarding computing speed and energy consumption for the further development of artificial intelligence with a novel computing paradigm. Herein, we first introduce the design method and principle of ONNs based on various optical elements. Then, we successively review the non-integrated ONNs consisting of volume optical components and the integrated ONNs composed of on-chip components. Finally, we summarize and discuss the computational density, nonlinearity, scalability, and practical applications of ONNs, and comment on the challenges and perspectives of the ONNs in the future development trends.
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.