Quantification of Competing Magnetic States and Switching Pathways in Curved Nanowires by Direct Dynamic Imaging

For viable applications, spintronic devices based, for example, on domain wall motion need to be highly reliable with stable magnetization states and highly reproducible switching pathways transforming one state to another. The existence of multiple stable states and switching pathways in a system is a definitive barrier for device operation, yet rare and stochastic events are difficult to detect and understand. We demonstrate an approach to quantify competing magnetic states and stochastic switching pathways based on time-resolved scanning electron microscopy with polarization analysis, applied to the technologically relevant control of vortex domain wall chirality via field and curvature in curved wires. As a pump-probe technique, our analysis scheme nonetheless allows for the disentanglement of different occurring dynamic pathways, and we can even identify the rare events leading to changes from one magnetization switching pathway to another pathway via temperature- and geometry-dependent measurements. The experimental imaging is supported by micromagnetic simulations to reveal the mechanisms responsible for the change of the pathway. Together the results allow us to explain the origin and details of the domain wall chirality control and to quantify the frequency and the associated energy barriers of thermally activated changes of the states and switching pathways.

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:14

Enthalten in:

ACS nano - 14(2020), 10 vom: 27. Okt., Seite 13324-13332

Sprache:

Englisch

Beteiligte Personen:

Schönke, Daniel [VerfasserIn]
Reeve, Robert M [VerfasserIn]
Stoll, Hermann [VerfasserIn]
Kläui, Mathias [VerfasserIn]

Links:

Volltext

Themen:

Automotion
Chirality
Domain walls
Journal Article
Magnetic imaging
Scanning electron microscope with polarization analysis
Spintronics
Switching pathways

Anmerkungen:

Date Revised 30.10.2020

published: Print-Electronic

Citation Status PubMed-not-MEDLINE

doi:

10.1021/acsnano.0c05164

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM316182133