Magnetic-field-free current-controlled switching of perpendicular magnetization via spin–orbit torque (SOT) is necessary for developing a fast, long data retention, and high-density SOT magnetoresistive random access memory (MRAM). Here, we use both micromagnetic simulations and atomistic spin dynamics (ASD) simulations to demonstrate an approach to field-free SOT perpendicular magnetization switching without requiring any changes in the architecture of a standard SOT-MRAM cell. We show that this field-free switching is enabled by a synergistic effect of lateral geometrical confinement, interfacial Dyzaloshinskii–Moriya interaction (DMI), and current-induced SOT. Both micromagnetic and atomistic understanding of the nucleation and growth kinetics of the reversed domain are established. Notably, atomically resolved spin dynamics at the early stage of nucleation is revealed using ASD simulations. A machine learning model is trained based on ~1000 groups of benchmarked micromagnetic simulation data. This machine learning model can be used to rapidly and accurately identify the nanomagnet size, interfacial DMI strength, and the magnitude of current density required for the field-free switching.

通过自旋轨道转矩（SOT）实现垂直磁场的无磁场电流控制切换对于开发快速，长数据保留和高密度SOT磁阻随机存取存储器（MRAM）是必要的。在这里，我们同时使用微磁模拟和原子自旋动力学（ASD）模拟来演示一种无场SOT垂直磁化切换的方法，而无需更改标准SOT-MRAM单元的架构。我们表明，这种无场切换是通过横向几何约束，界面Dyzaloshinskii-Moriya相互作用（DMI）和电流感应SOT的协同效应实现的。建立了对反向域的成核和生长动力学的微磁和原子学理解。值得注意的是 使用ASD模拟可以揭示成核早期的原子分解自旋动力学。基于约1000组基准微磁仿真数据来训练机器学习模型。该机器学习模型可用于快速准确地识别纳米磁体的大小，界面DMI强度以及无场切换所需的电流密度的大小。