Stochastic spin-orbit-torque device as the STDP synapse for spiking neural networks.

Neuromorphic hardware, as a non-Von Neumann architecture, has better energy efficiency and parallelism than the conventional computer. Here, with the numerical modeling spin-orbit torque (SOT) device using current-induced SOT and Joule heating effects, we acquire its magnetization stochastic switching probability as a function of the interval time of input current pulses and use it to mimic the spike-timing-dependent plasticity learning behavior like actual brain working. We further demonstrate that the artificial spiking neural network (SNN) built by this SOT device can perform unsupervised handwritten digit recognition with an accuracy of 80% and logic operation learning. Our work provides a new clue to achieving SNN-based neuromorphic hardware using high-energy efficiency and nonvolatile spintronics nanodevices. [ABSTRACT FROM AUTHOR]