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Spin-Orbit-Torque-Driven Two-Terminal Giant Magnetoresistance Memristive Devices for In-Memory Computing.

Tianli Jin1,2, Bo Zhang1,3, Dihua Wu4

  • 1School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore.

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Summary

This study presents a two-terminal spin-orbit torque-driven giant magnetoresistance memristive device for scalable processing-in-memory. The device integrates storage and logic, achieving high accuracy in AI tasks and demonstrating synaptic plasticity for neuromorphic computing.

Keywords:
GMR memristive deviceartificial synapseslogic functionmultiple magnetization statesspin−orbit torque

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Area of Science:

  • Materials Science
  • Spintronics
  • Neuromorphic Computing

Background:

  • Increasing complexity of AI and data-intensive applications necessitates advanced computing architectures.
  • Current spintronic processing-in-memory (PIM) solutions, while promising, face scalability issues due to their large device footprints.
  • Memristive devices are crucial for high-performance and scalable PIM.

Purpose of the Study:

  • To demonstrate a scalable two-terminal spin-orbit torque (SOT)-driven giant magnetoresistance (GMR) memristive device.
  • To integrate data storage and logic functions into a single unit for efficient in-memory computing.
  • To explore the potential of this device for artificial intelligence (AI) and neuromorphic applications.

Main Methods:

  • Fabrication of a two-terminal GMR memristive device using a Pt/Co/Cu/CoTb stack.
  • Modulation of SOT current amplitude to achieve multiple nonvolatile resistance states.
  • Tuning of CoTb alloy composition to create additional resistance states with opposite polarities.
  • Demonstration of synaptic plasticity (long-term potentiation and depression) via current pulse modulation.
  • Simulation of a deep neural network (DNN) utilizing the GMR device.

Main Results:

  • Achieved ten nonvolatile resistance states by modulating SOT current.
  • Created additional states with opposite polarities by tuning alloy composition.
  • Successfully demonstrated synaptic plasticity functions.
  • Simulated DNN achieved 92% accuracy in handwritten digit recognition and image visualization.
  • Exhibited basic Boolean logic functions, confirming processing capabilities.

Conclusions:

  • The developed two-terminal GMR memristive device offers a scalable solution for advanced memristive memory and in-memory computing.
  • This device integrates storage and logic, paving the way for more efficient AI hardware.
  • The demonstrated capabilities in synaptic plasticity and Boolean logic highlight its potential for neuromorphic computing applications.