Easy-plane spin Hall nano-oscillators as spiking neurons for neuromorphic computing

TL;DR

This paper demonstrates that easy-plane spin Hall nano-oscillators can mimic biological neuron spiking behavior, offering a promising hardware platform for neuromorphic computing through analytical modeling, micromagnetic simulations, and neural network block demonstrations.

Contribution

It introduces a novel application of easy-plane spin Hall nano-oscillators as spiking neurons, combining analytical, simulation, and neural network implementations.

Findings

Oscillators exhibit Josephson junction-like phase dynamics.

Spiking behavior is preserved in realistic nano-architecture.

Neural network blocks demonstrate spike summation and weighted outputs.

Abstract

We show analytically using a macrospin approximation that easy-plane spin Hall nano-oscillators excited by a spin-current polarized perpendicularly to the easy-plane have phase dynamics analogous to that of Josephson junctions. Similarly to Josephson junctions, they can reproduce the spiking behavior of biological neurons that is appropriate for neuromorphic computing. We perform micromagnetic simulations of such oscillators realized in the nano-constriction geometry and show that the easy-plane spiking dynamics is preserved in an experimentally feasible architecture. Finally we simulate two elementary neural network blocks that implement operations essential for neuromorphic computing. First, we show that output spikes energies from two neurons can be summed and injected into a following layer neuron and second, we demonstrate that outputs can be multiplied by synaptic weights implemented by locally modifying the anisotropy.