Magnetic Skyrmion Dynamics in Wedge-shaped Nanotrack and Its Potential Applications.

摘要

Magnetic skyrmions are swirling topological configuration [1], mostly induced by chiral exchange interactions between atomic spins in non-centrosymmetric magnetic bulks or in thin films with broken inversion symmetry. With the rapid advances made in this field [2-3], the development of skyrmion-based spintronics holds promise for future applications owing to the topological nature, nanoscale size, and ultralow current density for motion. Furthermore, the standby energy consumption and heat generation during the processing and transportation of information can be efficiently reduced thanks to the nonvolatility. In this abstract, we present firstly our investigations on skyrmion dynamics in terms of size, velocity, energy, stability in a wedge-shaped nanotrack via micromagnetic and theoretical studies [4]. We find some interesting results compared to previous research. For example, the size of a skyrmion decreases as the nanotrack width decreases because of the compression by the nanotrack edge (see Fig. 1a), thus this property can be harnessed to adjust the dimension of skyrmions to acheive ultra-dense storage in racetrack memory [5]. Inspired by the findings in wedge-shaped nanotracks, we draw a conclusion about the tradeoff between the nanotrack width (storage density) and the skyrmion motion velocity (data access speed) by further analyzing the skyrmion dynamics in parallel nanotracks (see Fig. 1b). Our results may provide guidelines in designing skyrmion racetrack memory and other related skyrmionic applications. We also model a novel compact neuron device based on this wedge-shaped nanotrack. Under the coaction of the exciting current pulse and the repulsive force exerted by the edge of the nanotrack, the dynamic behavior of the proposed skyrmionic artificial neuron device corresponds to the leaky-integrate-fire (LIF) spiking function of a biological neuron (see Fig. 2). We believe that our study makes a significant step because such a compact artificial neuron can enable energy-efficient and high-density implementation of neuromorphic computing hardware [6].