Femto-joule threshold reconfigurable all-optical nonlinear activators for picosecond pulsed optical neural networks

Ruizhe Liu; Zijia Wang; Chuyu Zhong; Yan Chen; Boshu Sun; Jialing Jian; Hui Ma; Dawei Gao; Jianyi Yang; Lan Li; Kaihui Liu; Xiaoyong Hu; Hongtao Lin

doi:10.1038/s41377-025-02175-4

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Femto-joule threshold reconfigurable all-optical nonlinear activators for picosecond pulsed optical neural networks

Original Articles

- Femto-joule threshold reconfigurable all-optical nonlinear activators for picosecond pulsed optical neural networks
- Light: Science & Applications Vol. 15, Issue 5, Pages: 1464-1475(2026)
- Affiliations：
  
  1.The State Key Lab of Brain-Machine Intelligence, Key Laboratory of Micro-Nano Electronics and Smart System of Zhejiang Province, College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
  2.Shenzhen Technology University, College of Integrated Circuits and Optoelectronic Chips, Shenzhen 518118, China
  3.Zhejiang Key Laboratory of 3D Micro/Nano Fabrication and Characterization, Westlake Institute for Optoelectronics, Fuyang, Hangzhou, Zhejiang 311421, China
  4.Zhejiang Key Laboratory of 3D Micro/Nano Fabrication and Characterization, School of Engineering, Westlake University, Hangzhou, Zhejiang 310030, China
  5.College of Integrated Circuits, Zhejiang University, Hangzhou 310027, China
  6.Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
  7.State Key Laboratory for Mesoscopic Physics, Frontiers Science Center for Nano-optoelectronics, School of Physics, Peking University, Beijing 100871, China
- Author bio：
  
  Lan Li (lilan@westlake.edu)
  Kaihui Liu (khliu@pku.edu.cn)
  Xiaoyong Hu (xiaoyonghu@pku.edu.cn)
  Hongtao Lin (hometown@zju.edu.cn)
- Funds：
- DOI：10.1038/s41377-025-02175-4
  CLC：
- Received：16 February 2025，
  
  Revised：2025-11-19，
  
  Accepted：19 December 2025，
  
  Online First：27 February 2026，
  
  Published：31 May 2026
- Accepted：
Scan QR Code
Liu, R. Z. et al. Femto-joule threshold reconfigurable all-optical nonlinear activators for picosecond pulsed optical neural networks. Light: Science & Applications, 15, 1464-1475 (2026).
DOI：

Liu, R. Z. et al. Femto-joule threshold reconfigurable all-optical nonlinear activators for picosecond pulsed optical neural networks. Light: Science & Applications, 15, 1464-1475 (2026). DOI： 10.1038/s41377-025-02175-4.

摘要

Abstract

Achieving optical computing with thousands of tera-operations per second per watt per square millimeter (TOPs/W/mm

) is the key to surpassing electrical computing. This realization requires a breakthrough in the design of a new optical computing architecture and nonlinear activation functions. By leveraging the Kerr effect of silicon and the saturable absorption of graphene

we designed an all-optical nonlinear activator based on a graphene-silicon integrated photonic crystal cavity. The ultralow-threshold

high-speed

compact

and reconfigurable all-optical nonlinear activator could achieve a saturable absorption energy threshold of 4 fJ and a response time of 1.05 ps

a reconfigurable nonlinear activation threshold of 30 fJ and a response time of 4 ps

and an ultrasmall size of 15 μm × 10 μm. This device provides foundation blocks for the picosecond pulsed optical neural network chip to achieve 10

TOPs/W/mm

level optical computing.

关键词

Keywords

references

LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521 , 436–444 (2015)..

Leiserson, C. E. et al. There's plenty of room at the Top: What will drive computer performance after Moore's law?. Science 368 , eaam9744 (2020)..

Khan, H. N., Hounshell, D. A. & Fuchs, E. R. H. Science and research policy at the end of Moore's law. Nat. Electron. 1 , 14–21 (2018)..

Shastri, B. J. et al. Photonics for artificial intelligence and neuromorphic computing. Nat. Photonics 15 , 102–114 (2021)..

Chen, Z. J. et al. Deep learning with coherent VCSEL neural networks. Nat. Photonics 17 , 723–730 (2023)..

Bai, B. W. et al. Microcomb-based integrated photonic processing unit. Nat. Commun. 14 , 66 (2023)..

He, T. et al. On-chip optoelectronic logic gates operating in the telecom band. Nat. Photonics 18 , 60–67 (2024)..

Yan, T. et al. All-optical graph representation learning using integrated diffractive photonic computing units. Sci. Adv. 8 , eabn7630 (2022)..

Zhou, H. L. et al. Photonic matrix multiplication lights up photonic accelerator and beyond. Light Sci. Appl. 11 , 30 (2022)..

Wu, N. et al. Intelligent nanophotonics: When machine learning sheds light. eLight 5 , 5 (2025)..

Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361 , 1004–1008 (2018)..

Lich, J. et al. Single-shot 3D incoherent imaging with diffuser endoscopy. Light Adv. Manuf. 5 , 218–228 (2024)..

Shen, Y. C. et al. Deep learning with coherent nanophotonic circuits. Nat. Photonics 11 , 441–446 (2017)..

Zuo, Y. et al. All-optical neural network with nonlinear activation functions. Optica 6 , 1132–1137 (2019)..

Wetzstein, G. et al. Inference in artificial intelligence with deep optics and photonics. Nature 588 , 39–47 (2020)..

Xu, Z. H. et al. Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence. Science 384 , 202–209 (2024)..

Rahman, M. S. S. et al. Massively parallel and universal approximation of nonlinear functions using diffractive processors. eLight 5 , 32 (2025)..

Feldmann, J. et al. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature 569 , 208–214 (2019)..

Feldmann, J. et al. Parallel convolutional processing using an integrated photonic tensor core. Nature 589 , 52–58 (2021)..

Wei, M. L. et al. Electrically programmable phase-change photonic memory for optical neural networks with nanoseconds in situ training capability. Adv. Photonics 5 , 046004 (2023)..

Destras, O. et al. Survey on activation functions for optical neural networks. ACM Comput. Surv. 56 , 35 (2023)..

Fard, M. M. P. et al. Experimental realization of arbitrary activation functions for optical neural networks. Opt. Expr. 28 , 12138–12148 (2020)..

Ashtiani, F., Geers, A. J. & Aflatouni, F. An on-chip photonic deep neural network for image classification. Nature 606 , 501–506 (2022)..

Amin, R. et al. An ITO–graphene heterojunction integrated absorption modulator on Si-photonics for neuromorphic nonlinear activation. APL Photonics 6 , 120801 (2021)..

Xu, Z. F. et al. Reconfigurable nonlinear photonic activation function for photonic neural network based on non-volatile opto-resistive RAM switch. Light Sci. Appl. 11 , 288 (2022)..

Zhong, C. Y. et al. Graphene/silicon heterojunction for reconfigurable phase-relevant activation function in coherent optical neural networks. Nat. Commun. 14 , 6939 (2023)..

Becker, S., Englund, D. & Stiller, B. An optoacoustic field-programmable perceptron for recurrent neural networks. Nat. Commun. 15 , 3020 (2024)..

Slinkov, G. et al. All-optical nonlinear activation function based on stimulated Brillouin scattering. Nanophotonics 14 , 2711–2722 (2025)..

Jha, A., Huang, C. R. & Prucnal, P. R. Reconfigurable all-optical nonlinear activation functions for neuromorphic photonics. Opt. Lett. 45 , 4819–4822 (2020)..

Shi, Y. et al. Nonlinear germanium-silicon photodiode for activation and monitoring in photonic neuromorphic networks. Nat. Commun. 13 , 6048 (2022)..

Xu, X. Z. et al. Ultrafast growth of single-crystal graphene assisted by a continuous oxygen supply. Nat. Nanotechnol. 11 , 930–935 (2016)..

Bonaccorso, F. et al. Graphene photonics and optoelectronics. Nat. Photonics 4 , 611–622 (2010)..

Tari, H. et al. Sigmoid type neuromorphic activation function based onsaturable absorption behavior of graphene/PMMA composite for intensity modulation of surface plasmon polariton signals. Plasmonics 17 , 1025–1032 (2022)..

Liao, K. et al. Matrix eigenvalue solver based on reconfigurable photonic neural network. Nanophotonics 11 , 4089–4099 (2022)..

Guo, X. X. et al. Backpropagation through nonlinear units for the all-optical training of neural networks. Photonics Res. 9 , B71–B80 (2021)..

Zhou, Z. W. et al. Ultrafast silicon/graphene optical nonlinear activator for neuromorphic computing. Adv. Opt. Mater. 12 , 2401686 (2024)..

Li, G. H. Y. et al. All-opt ical ultrafast ReLU function for energy-efficient nanophotonic deep learning. Nanophotonics 12 , 847–855 (2023)..

Mourgias-Alexandris, G. et al. An all-optical neuron with sigmoid activation function. Opt. Express 27 , 9620–9630 (2019)..

Vandoorne, K. et al. Parallel reservoir computing using optical amplifiers. IEEE Trans. Neural Netw. 22 , 1469–1481 (2011)..

Opala, A. et al. Perovskite microwires for room temperature exciton-polariton neural network. Adv. Mater. 37 , e07612 (2025)..

Tyszka, K. et al. Leaky integrate-and-fire mechanism in exciton–polariton condensates for photonic spiking neurons. Laser Photonics Rev. 17 , 2100660 (2023)..

Matuszewski, M. et al. Energy-efficient neural network inference with microcavity exciton polaritons. Phys. Rev. Appl. 16 , 024045 (2021)..

Teo, T. Y. et al. Programmable chalcogenide-based all-optical deep neural networks. Nanophotonics 11 , 4073–4088 (2022)..

Pflüger, M. et al. Experimental reservoir computing with diffractively coupled VCSELs. Opt. Lett. 49 , 2285–2288 (2024)..

Tanabe, T. et al. All-optical switches on a silicon chip realized using photonic crystal nanocavities. Appl. Phys. Lett. 87 , 151112 (2005)..

Baba, T. Slow light in photonic crystals. Nat. Photonics 2 , 465–473 (2008)..

Yan, S. Q. et al. Slow-light-enhanced energy efficiency for graphene microheaters on silicon photonic crystal waveguides. Nat. Commun. 8 , 14411 (2017)..

Li, S. H. & Cai, X. H. High-contrast all optical bistable switching in coupled nonlinear photonic crystal microcavities. Appl. Phys. Lett. 96 , 131114 (2010)..

Rumi, M. & Perry, J. W. Two-photon absorption: An overview of measurements and principles. Adv. Opt. Photonics 2 , 451–518 (2010)..

Lin, H. T. et al. Chalcogenide glass-on-graphene photonics. Nat. Photonics 11 , 798–805 (2017)..

Gu, T. et al. Regenerative oscillation and four-wave mixing in graphene optoelectronics. Nat. Photonics 6 , 554–559 (2012)..

Marini, A., Cox, J. D. & García De Abajo, F. J. Theory of graphene saturable absorption. Phys. Rev. B 95 , 125408 (2017)..

Zhong, C. Y., Li, J. Y. & Lin, H. T. Graphene-based all-optical modulators. Front. Optoelectron. 13 , 114–128 (2020)..

Chen, C. D. et al. Ultra-broadband all-optical nonlinear activation function enabled by MoTe2/optical waveguide integrated devices. Nat. Commun. 15 , 9047 (2024)..

Rasmussen, T. S., Yu, Y. & Mork, J. All-optical non-linear activation function for neuromorphic photonic computing using semiconductor Fano lasers. Opt. Lett. 45 , 3844–3847 (2020)..

Xu, X. Y. et al. 11 TOPS photonic convolutional accelerator for optical neural networks. Nature 589 , 44–51 (2021)..

Miscuglio, M. et al. All-optical nonlinear activation function for photonic neural networks [In vited ] . Opt. Mater. Expr. 8 , 3851–3863 (2018)..

Wang, C. et al. Integrated lithium niobate electro-optic modulators operating at CMOS-compatible voltages. Nature 562 , 101–104 (2018)..

Piggott, A. Y. et al. Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer. Nat. Photonics 9 , 374–377 (2015)..

Dong, B. W. et al. Partial coherence enhances parallelized photonic computing. Nature 632 , 55–62 (2024)..

Qin, J. W. et al. All-optical Fourier neuralnetwork using partially coherent light. Chip 4 , 100140 (2025)..

Wu, B. et al. Scaling up for end-to-end on-chip photonic neural network inference. Light Sci. Appl. 14 , 328 (2025)..

Wei, M. L. et al. Monolithic back-end-of-line integration of phase change materials into foundry-manufactured silicon photonics. Nat. Commun. 15 , 2786 (2024)..

Kim, Y. Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha: Association for Computational Linguistics, 2014, 1746-1751.

He, K. et al. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas:IEEE, 2016, 770-778.

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