Neuromorphic spintronics simulated using an unconventional data-driven Thiele equation approach

TL;DR

This paper introduces a novel data-driven Thiele equation model for simulating spin-torque vortex nano-oscillators, significantly speeding up simulations and aiding the design of neuromorphic spintronic devices.

Contribution

The study presents an unconventional analytical model combining the Thiele equation with micromagnetic data, enabling rapid and accurate simulation of STVO dynamics for neuromorphic applications.

Findings

Simulation speed increased by 9 orders of magnitude

Model accurately predicts neural network performance

Effective in assessing noise impact on device operation

Abstract

In this study, we developed a quantitative description of the dynamics of spin-torque vortex nano-oscillators (STVOs) through an unconventional model based on the combination of the Thiele equation approach (TEA) and data from micromagnetic simulations (MMS). Solving the STVO dynamics with our analytical model allows to accelerate the simulations by 9 orders of magnitude compared to MMS while reaching the same level of accuracy. Here, we showcase our model by simulating a STVO-based neural network for solving a classification task. We assess its performance with respect to the input signal current intensity and the level of noise that might affect such a system. Our approach is promising for accelerating the design of STVO-based neuromorphic computing devices while decreasing drastically its computational cost.