Steering magnons by noncollinear spin textures

One of the grand challenges in cutting edge quantum and condensed matter physics is to harness the spin degree of electrons for information technologies. While spintronics, based on charge transport by spin polarized electrons, made its leap in data storage by providing extremely sensitive detectors in magnetic hard-drives [1], it turned out to be challenging to transport spin information without great losses [2]. With magnonics, a visionary concept inspired researchers worldwide: Utilize spin waves-the collective excitation quanta of the spin system in magnetically ordered materials-as carriers for information [3-8]. Spin waves, which are also called magnons, are waves of the electrons’ spin precessional motion. They propagate without charge transport and its associated Ohmic losses, paving the way for a substantial reduction of energy consumption in computers. While macroscopic prototypes of magnonic logic gates have been demonstrated [9, 10], the full potential of magnonics lies in the combination of magnons with nano-size spin textures. Both magnons and spin textures share a common ground set by the interplay of dipolar, spin-orbit, and exchange energies, rendering them perfect interaction partners. Magnons are fast, sensitive to the spins’ directions, and easily driven far from equilibrium. Spin textures are robust, nonvolatile, and still reprogrammable on ultrashort timescales. The vast possibilities offered by combining these magnetic phenomena add value to both magnonics and the fundamental understanding of complex spin textures. The scope of this chapter is about experimental studies on magnon transport in metallic ferromagnetic microstructures with focus on actively controlling the magnon propagation. Two inherent characteristics of magnons enable for lateral steering: the anisotropy of the magnon dispersion and its sensitivity to changes in the internal magnetic field distribution. We intend to give an idea of how these magnon features can be utilized toward realizing functionalized magnonic networks. © 2017 Pan Stanford Publishing Pte. Ltd.