Topological Insulators Boost Ultralow-Power Neuromorphic Spintronics: Advancing Handwritten Digit Recognition with High SOT Efficiency.

Journal: ACS applied materials & interfaces
Published Date: Jun 4, 2025

Abstract

Neuromorphic spintronics devices driven by spin-orbit torque (SOT) offer advantages in integration density, durability, and scalability for high-performance artificial intelligence systems. However, the development of ultralow-power neuromorphic computing is hindered by the low SOT efficiency ( < 1) in conventional heavy metals. In this work, we demonstrate low-power artificial synapses and neuron devices that simultaneously achieve long-term potentiation/depression and excitatory/inhibitory postsynaptic potential processes with an ultralow activation current density of 1.8 × 10 A/cm, which is 1-2 orders of magnitude lower than that in traditional heavy metal systems, owing to the exceptional SOT efficiency of (BiSb)Te ( = 1.11). Furthermore, an artificial neural network utilizing our low-power synapses and neurons achieved 92.8% accuracy in handwritten digit recognition, highlighting topological insulators as promising candidates for low-power neuromorphic spintronics.

Authors

  • Xi Guo
    Key Laboratory of Poyang Lake Watershed Agricultural Resources and Ecology of Jiangxi Province, Jiangxi Agricultural University, Nanchang 330045, China.
  • Junwei Zeng
    Institute for Quantum Information & State Key Laboratory of High-Performance Computing, College of Computer, National University of Defense Technology, Changsha 410073, China.
  • Jijun Yun
    Shaanxi Key Laboratory of Condensed Matter Structures and Properties, and MOE Key Laboratory of Materials Physics and Chemistry under Extraordinary Conditions, School of Physical Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China.
  • Pengxiang Zhao
    School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China.
  • Yuhan Chang
    School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China.
  • Wenjie Song
  • Yalu Zuo
    School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China.
  • Guoqiang Yu
  • Hao Wu
    Zhejiang Institute of Tianjin University (Shaoxing), Shaoxing, China.
  • Li Xi
    School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China.
  • Baoshan Cui
    School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China.

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