de Orio Roberto L, Ender Johannes, Fiorentini Simone, Goes Wolfgang, Selberherr Siegfried, Sverdlov Viktor
Institute for Microelectronics, TU Wien, Gußhausstraße 27-29/E360, 1040 Vienna, Austria.
Christian Doppler Laboratory for Nonvolatile Magnetoresistive Memory and Logic at the Institute for Microelectronics, TU Wien, 1040 Vienna, Austria.
Micromachines (Basel). 2021 Apr 15;12(4):443. doi: 10.3390/mi12040443.
Spin-orbit torque memory is a suitable candidate for next generation nonvolatile magnetoresistive random access memory. It combines high-speed operation with excellent endurance, being particularly promising for application in caches. In this work, a two-current pulse magnetic field-free spin-orbit torque switching scheme is combined with reinforcement learning in order to determine current pulse parameters leading to the fastest magnetization switching for the scheme. Based on micromagnetic simulations, it is shown that the switching probability strongly depends on the configuration of the current pulses for cell operation with sub-nanosecond timing. We demonstrate that the implemented reinforcement learning setup is able to determine an optimal pulse configuration to achieve a switching time in the order of 150 ps, which is 50% shorter than the time obtained with non-optimized pulse parameters. Reinforcement learning is a promising tool to automate and further optimize the switching characteristics of the two-pulse scheme. An analysis of the impact of material parameter variations has shown that deterministic switching can be ensured for all cells within the variation space, provided that the current densities of the applied pulses are properly adjusted.
自旋轨道矩存储器是下一代非易失性磁阻随机存取存储器的合适候选者。它将高速运行与出色的耐久性相结合，在高速缓存应用中特别有前景。在这项工作中，一种双电流脉冲无磁场自旋轨道矩切换方案与强化学习相结合，以确定该方案中导致最快磁化切换的电流脉冲参数。基于微磁模拟，结果表明，对于亚纳秒级定时的单元操作，切换概率强烈依赖于电流脉冲的配置。我们证明，所实现的强化学习设置能够确定一种最优脉冲配置，以实现约150皮秒的切换时间，这比使用非优化脉冲参数获得的时间短50%。强化学习是一种很有前景的工具，可用于自动化并进一步优化双脉冲方案的切换特性。对材料参数变化影响的分析表明，只要适当调整所施加脉冲的电流密度，就可以确保在变化空间内的所有单元实现确定性切换。
