Imaging non-collinear antiferromagnetic textures via single spin relaxometry
Aurore Finco, Angela Haykal, Rana Tanos, Florentin Fabre, Saddem, Chouaieb, Waseem Akhtar, Isabelle Robert-Philip, William Legrand, Fernando, Ajejas, Karim Bouzehouane, Nicolas Reyren, Thibaut Devolder, Jean-Paul Adam,, Joo-Von Kim, Vincent Cros, Vincent Jacques

TL;DR
This paper introduces a novel all-optical method using a single nitrogen-vacancy defect in diamond to image non-collinear antiferromagnetic textures at the nanoscale by detecting local magnetic noise from thermal magnons.
Contribution
The study demonstrates a new nanoscale imaging technique for antiferromagnetic textures using NV center relaxometry, enabling visualization of complex spin structures without net magnetization.
Findings
Successfully imaged domain walls, spin spirals, and skyrmions in synthetic antiferromagnets.
Proved the method's effectiveness for various spin textures.
Extended the potential for studying antiferromagnetic physics and magnonics.
Abstract
Antiferromagnetic materials are promising platforms for next-generation spintronics owing to their fast dynamics and high robustness against parasitic magnetic fields. However, nanoscale imaging of the magnetic order in such materials with zero net magnetization remains a major experimental challenge. Here we show that non-collinear antiferromagnetic spin textures can be imaged by probing the magnetic noise they locally produce via thermal populations of magnons. To this end, we perform nanoscale, all-optical relaxometry with a scanning quantum sensor based on a single nitrogen-vacancy (NV) defect in diamond. Magnetic noise is detected through an increase of the spin relaxation rate of the NV defect, which results in an overall reduction of its photoluminescence signal under continuous laser illumination. As a proof-of-concept, the efficiency of the method is demonstrated by imaging…
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.
