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A complete photonic integrated neuron for nonlinear all-optical computing

Nature Computational Science volume 5, pages 1202–1213 (2025)Cite this article

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Abstract

The field of photonic neural networks has experienced substantial growth, driven by its potential to enable ultrafast artificial intelligence inference and address the escalating demand for computing speed and energy efficiency. However, realizing nonlinearity-complete all-optical neurons is still challenging, constraining the performance of photonic neural networks. Here we report a complete photonic integrated neuron (PIN) with spatiotemporal feature learning capabilities and reconfigurable structures for nonlinear all-optical computing. By interleaving the spatiotemporal dimension of photons and leveraging the Kerr effect, PIN performs high-order temporal convolution and all-optical nonlinear activation monolithically on a silicon-nitride photonic chip, achieving neuron completeness of weighted interconnects and nonlinearities. We develop the PIN chip system and demonstrate its remarkable performance in high-accuracy image classification and human motion generation. PIN enables ultrafast spatialtemporal processing with a latency as low as 240 ps, paving the way for advancing machine intelligence into the subnanosecond regime.

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Fig. 1: PIN: a complete neuron for nonlinear all-optical computing.
Fig. 2: Time delay, nonlinearity and computation characteristics of PIN.
Fig. 3: Image classification with PIN.
Fig. 4: Natural and diverse human motion generation with PIN.

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Data availability

The raw datasets are all publicly available. MNIST42, Fashion-MNIST58, N-MNIST59 and MSR-Action3D4 datasets are downloaded online from previous works. Source data are provided with this paper.

Code availability

The software code is available via Zenodo at https://doi.org/10.5281/zenodo.14975352 (ref. 67).

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Acknowledgements

This work is supported in part by National Science and Technology Major Project under contract no. 2021ZD0109901 (L.F.), in part by Natural Science Foundation of China (NSFC) under contract nos. 62125106 (L.F.) and 62427804 (L.F.), in part by the Beijing Outstanding Young Scientist Program under contract no. JWZQ20240101009 (L.F.), in part by the XPLORER PRIZE (L.F.) and in part by the Shuimu Tsinghua Scholar Program (T.Y. and T.Z.) and the China Postdoctoral Science Foundation (CPSF) grant BX20240196 (T.Y.).

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Author notes

  1. These authors contributed equally: Tao Yan, Yanchen Guo, Tiankuang Zhou.

Authors and Affiliations

  1. Beijing National Research Center for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, Beijing, China

    Tao Yan, Yanchen Guo, Tiankuang Zhou, Guocheng Shao, Shanglong Li & Lu Fang

  2. Department of Automation, Tsinghua University, Beijing, China

    Tao Yan, Yanchen Guo, Guocheng Shao & Qionghai Dai

  3. Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

    Yanchen Guo, Guocheng Shao & Ruqi Huang

  4. Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China

    Qionghai Dai & Lu Fang

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Contributions

L.F., Q.D. and R.H. initiated and supervised the project. T.Y., T.Z. and L.F. conceived the idea. T.Z., T.Y. and Y.G. designed the photonic integrated circuit. T.Y., Y.G., G.S. and S.L. performed the simulations and constructed the experiments. T.Y., T.Z. and Y.G. developed the methods. All the authors analyzed the results. T.Y., Y.G., T.Z. and L.F. prepared the paper with input from all the authors.

Corresponding authors

Correspondence to Ruqi Huang, Qionghai Dai or Lu Fang.

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Nature Computational Science thanks Thomas van Vaerenbergh and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Jie Pan, in collaboration with the Nature Computational Science team.

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Yan, T., Guo, Y., Zhou, T. et al. A complete photonic integrated neuron for nonlinear all-optical computing. Nat Comput Sci 5, 1202–1213 (2025). https://doi.org/10.1038/s43588-025-00866-x

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