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Area of Science:

  • Spintronics
  • Neuromorphic Engineering
  • Materials Science

Background:

  • Current artificial neural network (ANN) research aims to mimic multistate synapses using magnetic tunnel junction (MTJ) technology.
  • Existing spin transfer torque (STT) MTJ ANNs face challenges like poor thermal reliability and high critical current densities.
  • Spin-orbit torque (SOT) MTJ ANNs using domain wall motion exhibit small readout signals and scalability issues.

Purpose of the Study:

  • To propose and demonstrate a novel multistate synaptic device concept using compound SOT-MTJs.
  • To mimic spin-based artificial neural networks with improved synaptic weight representation.
  • To provide an industry-compatible platform for neuromorphic computing applications.

Main Methods:

  • Developed a compound MTJ device with multiple SOT-MTJs (n=1-4) on a shared write channel.
  • Tuned synaptic resistance states (n+1 states) using voltage pulses, pulse durations, and in-plane magnetic fields.
  • Fabricated and characterized the compound MTJ devices, measuring tunneling magnetoresistance (TMR) differences.

Main Results:

  • Achieved tunable resistance states (synaptic weights) by controlling voltage pulses (±1.5-1.8 V) and durations (100-300 ns).
  • Observed a significant TMR difference (>13.6%) between consecutive states in the 4-cell compound MTJ, a 4-fold improvement.
  • Demonstrated high learning accuracy (up to 95.75%) for digital recognition tasks using ANNs built with the compound MTJ.

Conclusions:

  • The proposed multistate SOT-MTJ compound device effectively mimics synaptic behavior for bioinspired computing.
  • The device offers significant improvements in TMR difference and synaptic state resolution compared to prior art.
  • This technology presents an industry-compatible route for integrating advanced synapses into neuromorphic architectures.