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Photonically linked three-dimensional neural networks based on memristive blinking neurons

Nature Electronics (2026)Cite this article

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Abstract

The continuing development of artificial intelligence requires more powerful computing architectures. However, the large footprint of complementary-metal–oxide–semiconductor-based neurons and constraints on electric routing hinder the scaling of conventional artificial neurons and their synaptic connectivity. Here we show that memristive blinking neurons can be used to build scalable photonically linked three-dimensional neural networks. Our artificial neuron is based on a silver/poly(methyl methacrylate)/silver metal–insulator–metal memristive switching in-plane junction. Its resistive switching relies on atomic-scale filamentary dynamics and the device emits photon pulses on integrating a critical number of incoming electrical spikes, which eliminates the need for bulky peripheral circuit read-out and electrical wiring for transmitting signals. We use the memristive blinking neuron, which has a footprint of 170 nm × 240 nm, to build a photonically linked three-dimensional spiking neural network. We show that the network can perform a four-class classification task within the Google Speech dataset with an accuracy of 91.51%. We also create a high-density artificial neuron array with a pitch of 1 μm and show that it can perform an MNIST classification task with an accuracy of 92.27%.

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Fig. 1: Optical and electrical characteristics of the MBN.
Fig. 2: Transient-mode electroluminescence of the MBN.
Fig. 3: MBN’s LIF process.
Fig. 4: Photonically linked SNN for a KWS task.
Fig. 5: Photonically linked SNN for handwritten digits classification task.

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Data availability

The data that support the plots and findings in this paper are available from the corresponding author upon request.

Code availability

The code for the SNN is publicly available via GitHub at https://github.com/Neuromorphic-Electronics-Photonics-Lab/Photonic-Linked-3D-Spiking-NN.

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Acknowledgements

This work has been funded by the Young Scientists Fund of the National Natural Science Foundation of China (grant numbers 62305278 (to B.C.) and 62405255 (to R.X.)), the Werner Siemens Foundation (to J.L.) and the French Agence Nationale de la Recherche (ANR-17-EURE-0002 to A.B.). All experiments in this study are performed at Novel IC Exploration Facility (NICE), Guangzhou, China, and Laboratoire Interdisciplinaire Carnot de Bourgogne (ICB), Dijon, France. We thank the operations team of NICE and ICB for their help and support in the experiment setup. All samples in this study are fabricated at the cleanroom facilities of the Binnig and Rohrer Nanotechnology Center (BRNC), Ruschlikon, Switzerland. We thank the cleanroom operations team of the BRNC for their help and support in sample fabrication.

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Author notes

  1. These authors contributed equally: Yue Zhou, Yuetong Fang.

Authors and Affiliations

  1. The Hong Kong University of Science and Technology (Guangzhou), Microelectronics Thrust, Guangzhou, China

    Yue Zhou, Yuetong Fang, Hongwei Ren, Haotian Fu, Zelin Ma, Yulong Huang, Renjing Xu & Bojun Cheng

  2. ETH Zurich, Institute of Electromagnetic Fields, Zurich, Switzerland

    Raphael Gisler & Juerg Leuthold

  3. Laboratoire Interdisciplinaire Carnot de Bourgogne, UMR 6303 CNRS, Université de Bourgogne Europe, Dijon, France

    Alexandre Bouhelier

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Contributions

B.C., A.B. and J.L. conceived the concept and supervised the project. R.G. fabricated the samples. Y.Z. carried out the measurements. Y.F. and H.R. designed and trained the network. H.F., Z.M. and Y.Z. optimized the experimental setup. Y.Z., Y.H., R.X. and B.C. analysed the data. Y.Z. performed the FEM simulation. Y.Z., Y.F., R.G., A.B., J.L. and B.C. wrote the manuscript. All authors discussed and commented on the manuscript.

Corresponding author

Correspondence to Bojun Cheng.

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Nature Electronics thanks Qionghai Dai, Ilia Valov and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Sections I–XI and Figs. 1–16.

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Zhou, Y., Fang, Y., Gisler, R. et al. Photonically linked three-dimensional neural networks based on memristive blinking neurons. Nat Electron (2026). https://doi.org/10.1038/s41928-025-01529-5

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