ArticleNature communications2023
Graphene/silicon heterojunction for reconfigurable phase-relevant activation function in coherent optical neural networks.
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 17 papers.
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Who cites it
17 citing papers in PubMed.
- Emerging integrated photonic neural network technologies for artificial intelligence: from devices to systems.National science review · 2026Review
- 192-dimensional wavelength-/mode-/polarization-multiplexed photonic architecture for highly parallel optical computing.National science review · 2026Article
- Spatiotemporal all-optical diffractive neural networks empowered by spatiotemporal light field manipulation.Light, science & applications · 2026Article
- On-chip non-volatile all-optical residual neural network accelerator.Light, science & applications · 2026Article
- Compact photonic spiking neuron with inherent stochasticity based on phase-change material for probabilistic computing.Nature communications · 2026Article
- Microcomb-enabled parallel self- calibration optical convolution streaming processor.Light, science & applications · 2026Article
- Femto-joule threshold reconfigurable all-optical nonlinear activators for picosecond pulsed optical neural networks.Light, science & applications · 2026Article
- Multi-wavelength diffractive optical neural network integrated with 2D photonic crystals for joint optical classification.Nanophotonics (Berlin, Germany) · 2025Article
- Image processing with Optical matrix vector multipliers implemented for encoding and decoding tasks.Light, science & applications · 2025Article
- A low-complexity M-shaped reconfigurable intelligent meta-surface for mitigating pathloss in wireless systems.Scientific reports · 2025Article
- Article
- Sub-milliwatt threshold power and tunable-bias all-optical nonlinear activation function using vanadium dioxide for wavelength-division multiplexing photonic neural networks.Scientific reports · 2025Article
- TOPS-speed complex-valued convolutional accelerator for feature extraction and inference.Nature communications · 2025Article
- Multimodal In-Sensor Computing System Using Integrated Silicon Photonic Convolutional Processor.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Ultra-broadband all-optical nonlinear activation function enabled by MoTeNature communications · 2024Article
- Optical neural networks: progress and challenges.Light, science & applications · 2024Review
- Polarization and wavelength routers based on diffractive neural network.Frontiers of optoelectronics · 2024Article
Corrections and comments
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Authors and funding
21 authors.
Funding
Abstract
Optical neural networks (ONNs) herald a new era in information and communication technologies and have implemented various intelligent applications. In an ONN, the activation function (AF) is a crucial component determining the network performances and on-chip AF devices are still in development. Here, we first demonstrate on-chip reconfigurable AF devices with phase activation fulfilled by dual-functional graphene/silicon (Gra/Si) heterojunctions. With optical modulation and detection in one device, time delays are shorter, energy consumption is lower, reconfigurability is higher and the device footprint is smaller than other on-chip AF strategies. The experimental modulation voltage (power) of our Gra/Si heterojunction achieves as low as 1 V (0.5 mW), superior to many pure silicon counterparts. In the photodetection aspect, a high responsivity of over 200 mA/W is realized. Special nonlinear functions generated are fed into a complex-valued ONN to challenge handwritten letters and image recognition tasks, showing improved accuracy and potential of high-efficient, all-component-integration on-chip ONN. Our results offer new insights for on-chip ONN devices and pave the way to high-performance integrated optoelectronic computing circuits.
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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.