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Harnessing Time-Dependent Magnetic Texture Dynamics via Spin-Orbit Torque for Physics-Enhanced Neuromorphic

Yifan Zhang1,2, Yu Li1,2, Huai Lin1,3

  • 1State Key Laboratory of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|November 11, 2025
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Summary
This summary is machine-generated.

Researchers developed new magnetic texture devices for neuromorphic computing. These devices efficiently perform visual recognition and combinatorial optimization tasks simultaneously, paving the way for advanced AI hardware.

Keywords:
advanced neuromorphic hardwarecombinatorial optimization problemsdomain wall motionmagnetic texturepattern recognitionspin‐orbit torque

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Area of Science:

  • Neuromorphic computing
  • Spintronics
  • Materials science

Background:

  • Simultaneous visual recognition and combinatorial optimization on single platforms offer efficiency and real-time processing benefits.
  • Hardware constraints currently limit advances in multifunctional neuromorphic computing.

Purpose of the Study:

  • To report a new type of all-electrically controlled labyrinth magnetic texture (MT) devices.
  • To demonstrate the devices' capability for simultaneous visual recognition and combinatorial optimization.
  • To overcome hardware limitations in current neuromorphic computing platforms.

Main Methods:

  • Utilized the spin-orbit torque (SOT) effect to create, manipulate, and detect trainable MT devices at room temperature.
  • Modulated nonlinear magnetoresistance via a dynamic conductance matrix, mimicking biological synaptic plasticity.
  • Employed a cross-bar array with SOT-MT devices for in-memory computing tasks.

Main Results:

  • Achieved over 93% test accuracy on MNIST for pattern recognition.
  • Exceeded 95% success rate in solving the 8-city traveling salesman problem using a Hopfield network.
  • Demonstrated efficient and reliable operation of SOT-MT devices at room temperature.

Conclusions:

  • The developed SOT-MT devices show significant potential for diverse in-memory computing tasks.
  • Synergistic tailoring of fluctuations enhances the performance of these neuromorphic devices.
  • This work advances dynamic network MT devices, enabling efficient fusion of cognition and optimization on single neuromorphic hardware.