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Local bifurcation with spin-transfer torque in superparamagnetic tunnel junctions

Author

Listed:
  • Takuya Funatsu

    (Tohoku University)

  • Shun Kanai

    (Tohoku University
    PRESTO, Japan Science and Technology Agency
    Tohoku University
    Tohoku University)

  • Jun’ichi Ieda

    (Advanced Science Research Center, Japan Atomic Energy Agency)

  • Shunsuke Fukami

    (Tohoku University
    Tohoku University
    Tohoku University
    Tohoku University)

  • Hideo Ohno

    (Tohoku University
    Tohoku University
    Tohoku University
    Tohoku University)

Abstract

Modulation of the energy landscape by external perturbations governs various thermally-activated phenomena, described by the Arrhenius law. Thermal fluctuation of nanoscale magnetic tunnel junctions with spin-transfer torque (STT) shows promise for unconventional computing, whereas its rigorous representation, based on the Néel-Arrhenius law, has been controversial. In particular, the exponents for thermally-activated switching rate therein, have been inaccessible with conventional thermally-stable nanomagnets with decade-long retention time. Here we approach the Néel-Arrhenius law with STT utilising superparamagnetic tunnel junctions that have high sensitivity to external perturbations and determine the exponents through several independent measurements including homodyne-detected ferromagnetic resonance, nanosecond STT switching, and random telegraph noise. Furthermore, we show that the results are comprehensively described by a concept of local bifurcation observed in various physical systems. The findings demonstrate the capability of superparamagnetic tunnel junction as a useful tester for statistical physics as well as sophisticated engineering of probabilistic computing hardware with a rigorous mathematical foundation.

Suggested Citation

  • Takuya Funatsu & Shun Kanai & Jun’ichi Ieda & Shunsuke Fukami & Hideo Ohno, 2022. "Local bifurcation with spin-transfer torque in superparamagnetic tunnel junctions," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31788-1
    DOI: 10.1038/s41467-022-31788-1
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    References listed on IDEAS

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    3. Alice Mizrahi & Tifenn Hirtzlin & Akio Fukushima & Hitoshi Kubota & Shinji Yuasa & Julie Grollier & Damien Querlioz, 2018. "Neural-like computing with populations of superparamagnetic basis functions," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
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    Cited by:

    1. Nihal Sanjay Singh & Keito Kobayashi & Qixuan Cao & Kemal Selcuk & Tianrui Hu & Shaila Niazi & Navid Anjum Aadit & Shun Kanai & Hideo Ohno & Shunsuke Fukami & Kerem Y. Camsari, 2024. "CMOS plus stochastic nanomagnets enabling heterogeneous computers for probabilistic inference and learning," Nature Communications, Nature, vol. 15(1), pages 1-9, December.

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