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Distinguishing artificial spin ice states using magnetoresistance effect for neuromorphic computing

Author

Listed:
  • Wenjie Hu

    (Fudan University
    Shanghai Qi Zhi Institute)

  • Zefeng Zhang

    (Fudan University
    Fudan University)

  • Yanghui Liao

    (Fudan University
    Shanghai Qi Zhi Institute)

  • Qiang Li

    (Fudan University
    Shanghai Qi Zhi Institute)

  • Yang Shi

    (Fudan University
    Shanghai Qi Zhi Institute)

  • Huanyu Zhang

    (Fudan University
    Shanghai Qi Zhi Institute)

  • Xumeng Zhang

    (Fudan University)

  • Chang Niu

    (Fudan University
    Shanghai Qi Zhi Institute)

  • Yu Wu

    (Fudan University
    Shanghai Qi Zhi Institute)

  • Weichao Yu

    (Fudan University
    Fudan University)

  • Xiaodong Zhou

    (Fudan University
    Shanghai Qi Zhi Institute
    Fudan University)

  • Hangwen Guo

    (Fudan University
    Shanghai Qi Zhi Institute
    Fudan University)

  • Wenbin Wang

    (Fudan University
    Shanghai Qi Zhi Institute
    Fudan University)

  • Jiang Xiao

    (Fudan University
    Shanghai Qi Zhi Institute
    Fudan University
    Shanghai Research Center for Quantum Sciences)

  • Lifeng Yin

    (Fudan University
    Shanghai Qi Zhi Institute
    Fudan University
    Shanghai Research Center for Quantum Sciences)

  • Qi Liu

    (Fudan University
    Fudan University)

  • Jian Shen

    (Fudan University
    Shanghai Qi Zhi Institute
    Fudan University
    Shanghai Research Center for Quantum Sciences)

Abstract

Artificial spin ice (ASI) consisting patterned array of nano-magnets with frustrated dipolar interactions offers an excellent platform to study frustrated physics using direct imaging methods. Moreover, ASI often hosts a large number of nearly degenerated and non-volatile spin states that can be used for multi-bit data storage and neuromorphic computing. The realization of the device potential of ASI, however, critically relies on the capability of transport characterization of ASI, which has not been demonstrated so far. Using a tri-axial ASI system as the model system, we demonstrate that transport measurements can be used to distinguish the different spin states of the ASI system. Specifically, by fabricating a tri-layer structure consisting a permalloy base layer, a Cu spacer layer and the tri-axial ASI layer, we clearly resolve different spin states in the tri-axial ASI system using lateral transport measurements. We have further demonstrated that the tri-axial ASI system has all necessary required properties for reservoir computing, including rich spin configurations to store input signals, nonlinear response to input signals, and fading memory effect. The successful transport characterization of ASI opens up the prospect for novel device applications of ASI in multi-bit data storage and neuromorphic computing.

Suggested Citation

  • Wenjie Hu & Zefeng Zhang & Yanghui Liao & Qiang Li & Yang Shi & Huanyu Zhang & Xumeng Zhang & Chang Niu & Yu Wu & Weichao Yu & Xiaodong Zhou & Hangwen Guo & Wenbin Wang & Jiang Xiao & Lifeng Yin & Qi , 2023. "Distinguishing artificial spin ice states using magnetoresistance effect for neuromorphic computing," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38286-y
    DOI: 10.1038/s41467-023-38286-y
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    References listed on IDEAS

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    1. Yann Perrin & Benjamin Canals & Nicolas Rougemaille, 2016. "Extensive degeneracy, Coulomb phase and magnetic monopoles in artificial square ice," Nature, Nature, vol. 540(7633), pages 410-413, December.
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    Cited by:

    1. Kilian D. Stenning & Jack C. Gartside & Luca Manneschi & Christopher T. S. Cheung & Tony Chen & Alex Vanstone & Jake Love & Holly Holder & Francesco Caravelli & Hidekazu Kurebayashi & Karin Everschor-, 2024. "Neuromorphic overparameterisation and few-shot learning in multilayer physical neural networks," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    2. 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.

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