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Granular Magnetization Switching in Pt/Co/Ti Structure with HfOx Insertion for In-Memory Computing Applications.

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This study introduces a new HfOx layer for enhanced spin-orbit torque (SOT) efficiency in magnetic devices. This innovation enables efficient multistate storage and artificial neural network (ANN) applications.

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Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Electrical Engineering

Background:

  • In-memory computing relies on devices utilizing domain wall (DW) position for multiple states.
  • Spin-orbit torque (SOT) is key for driving DW motion, but efficient DW pinning is crucial.
  • Conventional devices face limitations in SOT efficiency and switching current density.

Purpose of the Study:

  • To develop a novel device structure for efficient DW manipulation in magnetic memory applications.
  • To investigate the effect of an HfOx insertion layer on SOT efficiency and switching mechanisms.
  • To demonstrate the potential of the new device for multistate storage and artificial neural networks (ANNs).

Main Methods:

  • Incorporation of an HfOx insertion layer at the Co/Ti interface within a Pt/Co/Ti structure.
  • Experimental characterization of magnetization switching, SOT efficiency, and switching current density.
  • Device testing for stable multistate storage and synaptic plasticity using pulsed currents.
  • Simulation of artificial neural networks (ANNs) utilizing the developed device.

Main Results:

  • Achieved granular magnetization switching via a transition from DW motion to DW nucleation.
  • Demonstrated enhanced SOT efficiency and reduced switching current density compared to conventional structures.
  • Realized stable multistate storage and synaptic plasticity in the Pt/Co/HfOx/Ti device.
  • ANN simulations achieved a 91% accuracy rate for digital recognition tasks.

Conclusions:

  • The Pt/Co/HfOx/Ti device enables efficient DW nucleation, overcoming limitations of DW motion-based devices.
  • The HfOx insertion layer significantly improves SOT efficiency and reduces power consumption.
  • The developed device shows strong potential for advanced multistate storage and practical ANN applications.