Compact Model of Dzyaloshinskii Domain Wall Motion-Based MTJ for Spin Neural Networks

Recent progress has demonstrated that current-induced domain wall motion (CIDWM) is able to achieve efficient and ultrafast magnetic switching in the case of spin-orbit torque (SOT) and Dzyaloshinskii-Moriya interaction (DMI). CIDWM-based devices are taken as promising candidates for the next-generation nonvolatile artificial neurons and synapses due to its excellent programmability, fast operation speed, low write power, and so on. In this article, we present a physics-based model of CIDWM magnetic tunnel junction (MTJ), which exhibits high performance based on experimental results. The proposed model integrates the CIDWM dynamics and nanowire MTJ resistance, showing great agreement with extensive physical simulation. A learning circuit based on CIDWM-MTJ, as a hybrid MTJ/CMOS circuit example, has been designed and simulated to validate its functionality. The proposed SPICE-compatible compact model will be useful for high-performance circuit and system evaluation and is expected to promote the research and development of CIDWM-based spintronics devices.