神经形态计算中基于几何钉扎的磁畴壁器件的类二极管特性
Diode Like Attributes in Magnetic Domain Wall Devices via Geometrical Pinning for Neuromorphic Computing
摘要：
神经形态计算（NC）被认为是实现节能人工智能（AI）的潜在工具。为了实现NC，正在研究几种材料系统。其中，自旋轨道矩（SOT）驱动的畴壁（DW）器件是一个潜在的候选者。为了将这些设备实现为神经元和突触，即NC的构建模块，研究人员提出了不同的设备设计。然而，基于数据仓库设备的数控系统的实验实现还处于初级阶段。在这项研究中，我们提出并研究了松树形DW设备，基于拉普拉斯力的弹性DW，实现突触功能。当DW由SOT电流驱动时，我们已经成功地观察到多个磁化状态。关键的观察结果是当DW在两个相反的方向上移动时（定义为xhard和xeasy），器械的非对称钉扎强度。这显示了这些DW器件作为DW二极管的潜力。我们已经使用微磁模拟来理解实验结果，并估计拉普拉斯压力的各种设计参数。这项研究导致了设备制造的路径，其中突触特性是通过不对称的钉扎电位实现的。
英文摘要：
Neuromorphic computing (NC) is considered as a potential vehicle for implementing energy-efficient artificial intelligence (AI). To realize NC, several materials systems are being investigated. Among them, the spin-orbit torque (SOT) -driven domain wall (DW) devices are one of the potential candidates. To implement these devices as neurons and synapses, the building blocks of NC, researchers have proposed different device designs. However, the experimental realization of DW device-based NC is only at the primeval stage. In this study, we have proposed and investigated pine-tree-shaped DW devices, based on the Laplace force on the elastic DWs, for achieving the synaptic functionalities. We have successfully observed multiple magnetization states when the DW was driven by the SOT current. The key observation is the asymmetric pinning strength of the device when DW moves in two opposite directions (defined as, xhard and xeasy). This shows the potential of these DW devices as DW diodes. We have used micromagnetic simulations to understand the experimental findings and to estimate the Laplace pressure for various design parameters. The study leads to the path of device fabrication, where synaptic properties are achieved with asymmetric pinning potential.
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