Kinematic Model of Magnetic Domain Wall Motion for Fast, High-Accuracy Simulations

TL;DR

This paper introduces a phenomenological model for magnetic domain wall motion that achieves high accuracy and speed, enabling efficient large-scale simulations for memory, logic, and neuromorphic devices.

Contribution

A novel phenomenological domain wall model inspired by classical force analogies, offering improved speed and accuracy over existing models for large-scale system design.

Findings

Predicts DW motion within 1.2% of micromagnetic simulations

Seven times faster than collective coordinate models

Fourteen times more accurate than hyper-reduced models

Abstract

Domain wall (DW) devices have garnered recent interest for diverse applications including memory, logic, and neuromorphic primitives; fast, accurate device models are therefore imperative for large-scale system design and verification. Extant DW motion models are sub-optimal for large-scale system design either over-consuming compute resources with physics-heavy equations or oversimplifying the physics, drastically reducing model accuracy. We propose a DW model inspired by the phenomenological similarities between motions of a DW and a classical object being acted on by forces like air resistance or static friction. Our proposed phenomenological model predicts DW motion within 1.2% on average compared with micromagnetic simulations that are 400 times slower. Additionally our model is seven times faster than extant collective coordinate models and 14 times more accurate than extant hyper-reduced models making it an essential tool for large-scale DW circuit design and simulation. The model is publicly posted along with scripts that automatically extract model parameters from user-provided simulation or experimental data to extend the model to alternative micromagnetic parameters.