Manipulation of Magnetic Skyrmion for Future Electronic Application

Abstract

Most magnetic materials show collinear magnetic ordering due to the large exchange interaction between neighbouring magnetic moments. If provided the Dzyaloshinskii-Moriya interaction (DMI) is strong enough to overcome the exchange interaction, magnetic spins tend to align in a non-collinear manner with fixed homochirality. In any structures where the broken inversion symmetry leads to sufficiently large DMI, non-trivial small cylindrical swirling spin structures, called magnetic skyrmions can be energetically stable. Magnetic skyrmions are topologically protected spin textures that have nanoscale dimensions and can be manipulated by an electric current. These properties make the structures potential information carriers in data storage, processing and transmission devices. The room temperature stabilization of magnetic skyrmions and their current pulse-induced displacement on nano-tracks has been reported in magnetic heterostructures. While such current controlled skyrmion motion is applicable to an actual device scheme, the deterministic writng and deleting of a single isolated magnetic skyrmion is required for fully functional skyrmionic devices. In order to utilize skyrmion for any electrical electronic devices, we have studied current driven dynamics of skyrmions and inhibition of the skyrmion Hall effect which is detrimental to application. We have shown that the current-induced creation, motion, detection and deletion of skyrmions at room temperature can be used to store information as well as to mimic the potentiation and depression behaviours of biological synapses. In particular, the accumulation and dissipation of magnetic skyrmions in ferrimagnetic multilayers can be controlled with electrical pulses to represent the variations in the synaptic weights. In this talk, I will briefly introduce current induced dynamics of skyrmions and discuss about future application of skyrmions in terms of non-volatile memory and synapse devices for neuromorphic computing.