In this study, we developed a quantitative description of the dynamics of spin-torque vortex nano-oscillators (STVOs) through an unconventional model based on the combination of the Thiele equation approach (TEA) and data from micromagnetic simulations (MMS). Solving the STVO dynamics with our analytical model allows to accelerate the simulations by 9 orders of magnitude compared to MMS while reaching the same level of accuracy. Here, we showcase our model by simulating a STVO-based neural network for solving a classification task. We assess its performance with respect to the input signal current intensity and the level of noise that might affect such a system. Our approach is promising for accelerating the design of STVO-based neuromorphic computing devices while decreasing drastically its computational cost.

翻译：在本研究中，我们通过结合Thiele方程方法(TEA)与微磁模拟(MMS)数据的非常规模型，建立了自旋矩涡旋纳米振荡器(STVO)动力学的定量描述。利用我们的解析模型求解STVO动力学，相较于MMS可将模拟速度加速9个数量级，同时达到同等精度。在此，我们通过模拟基于STVO的神经网络执行分类任务来展示该模型。我们评估了其针对输入信号电流强度及可能影响此类系统的噪声水平的性能。我们的方法有望在大幅降低计算成本的同时加速基于STVO的神经形态计算设备的设计。