Spike-timing-dependent-plasticity learning in a planar magnetic domain wall artificial synapsis

J. O. Castro, B. Buyatti, D. Mercado, A. Di Donato, M. Quintero, M. Tortarolo
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Abstract

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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平面磁域壁人工突触中的尖峰计时依赖性可塑性学习
未来的神经形态架构将需要数以百万计的人工突触,因此必须了解其可塑性功能背后的物理机制。在这项工作中,我们提出了一种简化的自旋晶闸管,其电阻可由磁场脉冲控制,以具有垂直磁各向异性的 Co/Pt 多层板为突触模拟器。我们在该器件中展示了可塑性和尖峰时间依赖性可塑性(STDP),并利用克尔显微镜成像和霍尔磁传输测量探索了其潜在的磁机制。定义明确的磁化反转阈值和与微磁配置相关的连续电阻状态是该器件中可塑性和 STDP 学习机制的基本特性。
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