The magnetic reversal in dot arrays recognized by the self-organized adaptive neural network

TL;DR

This paper simulates the remagnetization dynamics in a 2D spin model of dot arrays, using an adaptive neural network to classify complex intra- and inter-dot magnetic configurations and analyze reversal mechanisms.

Contribution

It introduces a neural network-based classification method for intra-dot and inter-dot magnetic states during reversal in a simulated dot array system.

Findings

Formation of nonlinear wave/anti-wave pairs during reversal

Dependence of reversal dynamics on initial conditions and parameters

Neural network effectively classifies intra- and inter-dot magnetic configurations

Abstract

The remagnetization dynamics of monolayer dot array superlattice XY 2-D spin model with dipole-dipole interactions is simulated. Within the proposed model of array, the square dots are described by the spatially modulated exchange-couplings. The dipole-dipole interactions are approximated by the hierarchical sums and spin dynamics is considered in regime of the Landau-Lifshitz equation. The simulation of reversal for $40 000$ spins exhibits formation of nonuniform intra-dot configurations with nonlinear wave/anti-wave pairs developed at intra-dot and inter-dot scales. Several geometric and parametric dependences are calculated and compared with oversimplified four-spin model of reversal. The role of initial conditions and the occurrence of coherent rotation mode is also investigated. The emphasis is on the classification of intra-dot or inter-dot (interfacial) magnetic configurations done by adaptive neural network with varying number of neurons.