Bibliografische Daten
ISBN/EAN: 9783956065323
Sprache: Englisch
Umfang: 178 S., 64 farbige Illustr., 66 Illustr., 2 Tab.
Auflage: 1. Auflage 2020
Einband: Paperback
Beschreibung
The use of non-trivial spin textures such as domain walls and skyrmions holds promise in future applications like magnetic high-density memories and magnetic logic devices. Domain walls in perpendicular magnetized nanowires are narrow and can be driven by spintorques. Skyrmions due to their particular nontrivial topology makes them to be robust against external perturbations, are small in size and can be driven in the same manner as domain walls with spin-torques. The precise detection of position and motion of these non-trivial spin textures is crucial for future applications. Usually the detection and characterization are performed electrically by their signature in the anomalous Hall effect (AHE), among other magneto-resistive effects. In the case of the AHE the detection is limited to Hall crosses and an electrical current is needed for the detection. The results in this work show that thermoelectric effects such as the anomalous Nernst effect (ANE), which is the thermoelectric analogue of the AHE, allows a non-invasive measurement, detection of the position, the counting and the characterization of nontrivial spin textures. By using domain detection and observation techniques such as magneto optical Kerr microscopy and magnetic force microscopy the presence of the nontrivial spin structures in the devices is corroborated. Not only the presence of the nontrivial spin textures is verified, but also the ANE based detection is demonstrated. This allowed to detect with nanometer precision the movement of domain walls and the signature of individual skyrmions in the devices. Furthermore, a new procedure, the "twoheater" procedure, is presented which allows to separate the contributions of the different thermal gradients to the total measured thermoelectrical signal. It can be used to remove signal contributions arising from unwanted thermal gradients.
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