Integrated platforms and techniques for photonic neural networks

Neural networks (NNs) have been widely applied in computer vision, natural language processing, and beyond. As the miniaturization of semiconductor transistors nears its limits, thereby bringing Moore’s law to an end, photonic neural networks (PNNs), which leverage photons rather than electrons as information carriers, have emerged as promising hardware accelerators for NNs. PNNs offer ultrafast processing speed, ultra-low energy consumption, and extremely high throughput. Moreover, advances in photonic integrated circuits provide compact and reliable hardware platforms for constructing PNNs. Here, we provide an overview of recent advances in integrated PNN components, including linear and nonlinear operators based on diverse optical elements, as well as devices that enable optical fan-in and fan-out. We further summarize the development in on-chip PNN implementations and discuss the future challenges associated with their monolithic integration.

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Publication Details

Journal
npj Nanophotonics
Published
2025-10-22
DOI
https://doi.org/10.1038/s44310-025-00088-z
Citations
1
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
2.48

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Integrated platforms and techniques for photonic neural networks

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Integrated platforms and techniques for photonic neural networks

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article en
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Abstract

Neural networks (NNs) have been widely applied in computer vision, natural language processing, and beyond. As the miniaturization of semiconductor transistors nears its limits, thereby bringing Moore’s law to an end, photonic neural networks (PNNs), which leverage photons rather than electrons as information carriers, have emerged as promising hardware accelerators for NNs. PNNs offer ultrafast processing speed, ultra-low energy consumption, and extremely high throughput. Moreover, advances in photonic integrated circuits provide compact and reliable hardware platforms for constructing PNNs. Here, we provide an overview of recent advances in integrated PNN components, including linear and nonlinear operators based on diverse optical elements, as well as devices that enable optical fan-in and fan-out. We further summarize the development in on-chip PNN implementations and discuss the future challenges associated with their monolithic integration.

npj NanophotonicsVol. 2(1)
Beijing University of Posts and Telecommunications (CN), Chinese University of Hong Kong (HK), Wuhan National Laboratory for Optoelectronics (CN), Huazhong University of Science and Technology (CN), Swinburne University of Technology (AU), Tsinghua University (CN)
National Natural Science Foundation of China, Beijing University of Posts and Telecommunications
Openalex Percentile: Top 8%
Neural Networks and Reservoir Computing
2.48
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