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Domain wall magnetic tunnel junction-based artificial synapses and neurons for all-spin neuromorphic hardware

Author

Listed:
  • Long Liu

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Di Wang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Dandan Wang

    (Hubei Jiufengshan Laboratory)

  • Yan Sun

    (Chinese Academy of Sciences)

  • Huai Lin

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Xiliang Gong

    (Chinese Academy of Sciences)

  • Yifan Zhang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Ruifeng Tang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Zhihong Mai

    (Hubei Jiufengshan Laboratory)

  • Zhipeng Hou

    (South China Normal University)

  • Yumeng Yang

    (ShanghaiTech University)

  • Peng Li

    (University of Science and Technology of China)

  • Lan Wang

    (Hefei University of Technology)

  • Qing Luo

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Ling Li

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Guozhong Xing

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Ming Liu

    (Chinese Academy of Sciences
    Fudan University)

Abstract

We report a breakthrough in the hardware implementation of energy-efficient all-spin synapse and neuron devices for highly scalable integrated neuromorphic circuits. Our work demonstrates the successful execution of all-spin synapse and activation function generator using domain wall-magnetic tunnel junctions. By harnessing the synergistic effects of spin-orbit torque and interfacial Dzyaloshinskii-Moriya interaction in selectively etched spin-orbit coupling layers, we achieve a programmable multi-state synaptic device with high reliability. Our first-principles calculations confirm that the reduced atomic distance between 5d and 3d atoms enhances Dzyaloshinskii-Moriya interaction, leading to stable domain wall pinning. Our experimental results, supported by visualizing energy landscapes and theoretical simulations, validate the proposed mechanism. Furthermore, we demonstrate a spin-neuron with a sigmoidal activation function, enabling high operation frequency up to 20 MHz and low energy consumption of 508 fJ/operation. A neuron circuit design with a compact sigmoidal cell area and low power consumption is also presented, along with corroborated experimental implementation. Our findings highlight the great potential of domain wall-magnetic tunnel junctions in the development of all-spin neuromorphic computing hardware, offering exciting possibilities for energy-efficient and scalable neural network architectures.

Suggested Citation

  • Long Liu & Di Wang & Dandan Wang & Yan Sun & Huai Lin & Xiliang Gong & Yifan Zhang & Ruifeng Tang & Zhihong Mai & Zhipeng Hou & Yumeng Yang & Peng Li & Lan Wang & Qing Luo & Ling Li & Guozhong Xing & , 2024. "Domain wall magnetic tunnel junction-based artificial synapses and neurons for all-spin neuromorphic hardware," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48631-4
    DOI: 10.1038/s41467-024-48631-4
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    References listed on IDEAS

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