论文标题
部分可观测时空混沌系统的无模型预测
Diode Like Attributes in Magnetic Domain Wall Devices via Geometrical Pinning for Neuromorphic Computing
论文作者
论文摘要
神经形态计算(NC)被认为是实施节能人工智能(AI)的潜在工具。为了实现NC,正在研究几种材料系统。其中,自旋轨道扭矩(SOT)驱动的域壁(DW)设备是潜在的候选者之一。为了将这些设备作为神经元和突触实施,NC的基础是研究人员提出了不同的设备设计。但是,基于DW设备的NC的实验实现仅在原始阶段。在这项研究中,我们根据弹性DWS的拉普拉斯力提出并研究了松树形的DW设备,以实现突触功能。当DW由SOT电流驱动时,我们已经成功观察到了多个磁化状态。关键观察结果是当DW朝两个相反的方向移动时,设备的不对称固定强度(定义为Xhard和Xeasy)。这显示了这些DW设备作为DW二极管的潜力。我们已经使用微磁模拟来了解实验发现,并估算各种设计参数的拉普拉斯压力。该研究导致了装置制造的路径,其中具有不对称固定电势实现突触性能。
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.