Barkhausen noise from formation of 360$^{\circ}$ domain walls in disordered permalloy thin films

Fuente: arXiv
Enregistré dans:
Détails bibliographiques
Auteurs principaux: Kaappa, Sami, Laurson, Lasse
Format: Preprint
Publié: 2022
Sujets:
Accès en ligne:
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
_version_ 1866916294341689344
author Kaappa, Sami
Laurson, Lasse
author_facet Kaappa, Sami
Laurson, Lasse
contents Barkhausen noise in disordered ferromagnets is typically understood to originate primarily from jerky field-driven motion of domain walls. We study the magnetization reversal process in disordered permalloy thin films using micromagnetic simulations, and find that the magnetization reversal process consists of gradual formation of immobile 360$^{\circ}$ domain walls via a sequence of localized magnetization rotation events. The density of 360$^{\circ}$ domain walls formed within the sample as well as the statistical properties of the Barkhausen jumps are controlled by the disorder strength.
format Preprint
id arxiv_https___arxiv_org_abs_2209_10847
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Barkhausen noise from formation of 360$^{\circ}$ domain walls in disordered permalloy thin films
Kaappa, Sami
Laurson, Lasse
Materials Science
Disordered Systems and Neural Networks
Barkhausen noise in disordered ferromagnets is typically understood to originate primarily from jerky field-driven motion of domain walls. We study the magnetization reversal process in disordered permalloy thin films using micromagnetic simulations, and find that the magnetization reversal process consists of gradual formation of immobile 360$^{\circ}$ domain walls via a sequence of localized magnetization rotation events. The density of 360$^{\circ}$ domain walls formed within the sample as well as the statistical properties of the Barkhausen jumps are controlled by the disorder strength.
title Barkhausen noise from formation of 360$^{\circ}$ domain walls in disordered permalloy thin films
topic Materials Science
Disordered Systems and Neural Networks
url https://arxiv.org/abs/2209.10847