Thermally Driven Leaky-Integrate-and-Fire Spintronic Neurons With Stray-Field-Enabled Self-Reset for Neuromorphic Computing

Spiking neural networks (SNNs) have garnered significant attention due to their potential for low energy consumption, fewer hyperparameters, and reduced network complexity compared to deep neural networks. However, the performance of SNNs is largely constrained by the limitations of spiking neurons, including high energy consumption, low operational speed and weak readout signals. Designing an efficient spiking neuron is thus critical for advancing SNN capabilities. In this work, we present a compact and energy-efficient neuron device with self-reset and leaky-integrate-and-fire (LIF) characteristics, enabled by a single spin-orbit torque magnetic tunnel junction (SOT-MTJ). The LIF functionality is realized within a few nanoseconds by leveraging current induced SOT effect and thermal accumulation. The firing probability can be finely controlled by modulating the SOT current, spike width, and spike interval with only 49 fJ for each spike. Moreover, the device autonomously resets after firing due to the stray field engineered from the reference layer. Using this experimentally validated neuron device, we construct a three-layer SNN to classify the Modified National Institute of Standards and Technology (MNIST) dataset, achieving an accuracy of 96.9%. This system showcases exceptional performance in recognition and classification tasks, underscoring the potential of spintronic devices for energy-efficient SNN applications.