平面磁畴壁人工突触中的脉冲时间依赖可塑性学习
Spike-timing-dependent-plasticity learning in a planar magnetic domain wall artificial synapsis
AI总结：
本研究提出基于Co/Pt多层膜的自旋忆阻器作为人工突触，验证了其可塑性与STDP特性，并揭示相关磁学机制，为神经形态架构的人工突触构建提供新方案。
摘要：
未来的神经形态架构将需要数百万个人工突触，因此必须理解其可塑性功能背后的物理机制。本研究中，我们提出了一种简化的自旋忆阻器，其电阻可通过磁场脉冲调控，该器件基于具有垂直磁各向异性的Co/Pt多层膜构建，用作突触模拟器。我们在该器件中验证了可塑性与脉冲时间依赖可塑性（STDP），并利用克尔显微镜成像与霍尔磁输运测量探究了其潜在的磁学机制。明确的磁化反转阈值，以及与微磁构型相关的连续电阻态，是该器件实现可塑性与STDP学习机制的基础特性。
英文摘要：
Future neuromorphic architectures will require millions of artificial synapses, making understanding the physical mechanisms behind their plasticity functionalities mandatory. In this work, we propose a simplified spin memristor, where the resistance can be controlled by magnetic field pulses, based on a Co/Pt multilayer with perpendicular magnetic anisotropy as a synapsis emulator. We demonstrate plasticity and spike time dependence plasticity (STDP) in this device and explored the underlying magnetic mechanisms using Kerr microscopy imaging and Hall magneto-transport measurements. A well-defined threshold for magnetization reversal and the continuous resistance states associated with the micromagnetic configuration are the basic properties allowing plasticity and STDP learning mechanisms in this device.
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