Magnetic tunnel junction based implementation of spike time dependent plasticity learning for pattern recognition

Aijaz H. Lone*, S. Amara, H. Fariborzi*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


We present a magnetic tunnel junction (MTJ) based implementation of the spike time-dependent (STDP) learning for pattern recognition applications. The proposed hybrid scheme utilizes the spin-orbit torque (SOT) driven neuromorphic device-circuit co-design to demonstrate the Hebbian learning algorithm. The circuit implementation involves the (MTJ) device structure, with the domain wall motion in the free layer, acting as an artificial synapse. The post-spiking neuron behaviour is implemented using a low barrier MTJ. In both synapse and neuron, the switching is driven by the SOTs generated by the spin Hall effect in the heavy metal. A coupled model for the spin transport and switching characteristics in both devices is developed by adopting a modular approach to spintronics. The thermal effects in the synapse and neuron result in a stochastic but tuneable domain wall motion in the synapse and a superparamagnetic behaviour of in neuron MTJ. Using the device model, we study the dimensional parameter dependence of the switching delay and current to optimize the device dimensions. The optimized parameters corresponding to synapse and neuron are considered for the implementation of the Hebbian learning algorithm. Furthermore, cross-point architecture and STDP-based weight modulation scheme is used to demonstrate the pattern recognition capabilities by the proposed neuromorphic circuit.

Original languageEnglish (US)
Article number024003
JournalNeuromorphic Computing and Engineering
Issue number2
StatePublished - Jun 1 2022


  • domain wall synapse
  • magnetic tunnel junction
  • neuromorphic computing
  • pattern recognition
  • spike time dependent plasticity
  • spin orbit torque

ASJC Scopus subject areas

  • Artificial Intelligence
  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials


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