Reversing the training effect in exchange biased CoO/Co bilayers
Steven Brems (1), Dieter Buntinx (1), Kristiaan Temst (1), Chris Van, Haesendonck (1), Florin Radu (2), Hartmut Zabel (2) ((1) Laboratorium voor, Vaste-Stoffysica en Magnetisme, Katholieke Universiteit Leuven, (2), Experimentalphysik/Festk\"orperphysik, Ruhr-Universit\"at Bochum)

TL;DR
This study investigates the training effect in exchange biased CoO/Co bilayers, revealing how magnetic domains nucleate and can be partially erased, affecting magnetization behavior and offering insights into magnetic domain control.
Contribution
It demonstrates that the untrained state can be partially reinduced by specific hysteresis measurements, showing a new way to manipulate antiferromagnetic domains.
Findings
Magnetic domain nucleation occurs during initial magnetization reversal.
Partial erasure of antiferromagnetic domains is possible with perpendicular field measurements.
The untrained state can be reinduced, altering the hysteresis loop characteristics.
Abstract
We performed a detailed study of the training effect in exchange biased CoO/Co bilayers. High-resolution measurements of the anisotropic magnetoresistance (AMR) are consistent with nucleation of magnetic domains in the antiferromagnetic CoO layer during the first magnetization reversal. This accounts for the enhanced spin rotation observed in the ferromagnetic Co layer for all subsequent reversals. Surprisingly, the AMR measurements as well as magnetization measurements reveal that it is possible to partially reinduce the untrained state by performing a hysteresis measurement with an in plane external field perpendicular to the cooling field. Indeed, the next hysteresis loop obtained in a field parallel to the cooling field resembles the initial asymmetric hysteresis loop, but with a reduced amount of spin rotation occurring at the first coercive field. This implies that the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
