Work

Recurrent Network Classifier for Ultrafast Skyrmion Dynamics

Year: 2019

Type: article

Abstract: Drowning in data: Autonomous classification of skyrmion dynamics is important for creating next-generation data storage, spintronics, and quantum technologies. The extremely large amount of experimental data in this field cannot be processed manually; there is a need for automation. This study uses an artificial recurrent neural network to classify various dynamical processes exhibited by skyrmions (topologically protected spin structures) under the influence of a picosecond magnetic pulse. It is hoped that this machine-learning approach will speed the development of devices that use skyrmions.

Source: Physical Review Applied

DOI: 10.1103/physrevapplied.12.054026

Authors: A. Y. Deviatov, I. A. Iakovlev, V. V. Mazurenko

Institution: Ural Federal University

Language: English

Topic: Magnetic properties of thin films

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funder: Russian Science Foundation

Award: G7999443837