Tunable intermediate states for neuromorphic computing with spintronic devices

Abstract

In the pursuit of advancing neuromorphic computing, our research presents a novel method for generating and precisely controlling intermediate states within heavy metal/ferromagnet systems. These states are engineered through the interplay of a strong in-plane magnetic field and an applied charge current. We provide a method for fine-tuning these states by introducing a small out-of-plane magnetic field, allowing for the modulation of the system’s probabilistic response to varying current levels. We also demonstrate the implementation of a spiking neural network (SNN) with a tri-state spike timing-dependent plasticity (STDP) learning rule using our devices. Our research furthers the development of spintronics and informs neural system design. These intermediate states can serve as synaptic weights or neuronal activations, paving the way for multi-level neuromorphic computing architectures.