Shreya S, Jenkins A S, Rezaeiyan Y, Li R, Böhnert T, Benetti L, Ferreira R, Moradi F, Farkhani H
Electrical and Computer Engineering Department, Aarhus University, 8200, Aarhus, Denmark.
International Iberian Nanotechnology Laboratory (INL), Braga, Portugal.
Sci Rep. 2023 Oct 4;13(1):16722. doi: 10.1038/s41598-023-43923-z.
In this paper, we investigate the granularity in the free layer of the magnetic tunnel junctions (MTJ) and its potential to function as a reservoir for reservoir computing where grains act as oscillatory neurons while the device is in the vortex state. The input of the reservoir is applied in the form of a magnetic field which can pin the vortex core into different grains of the device in the magnetic vortex state. The oscillation frequency and MTJ resistance vary across different grains in a non-linear fashion making them great candidates to be served as the reservoir's outputs for classification objectives. Hence, we propose an experimentally validated area-efficient single granular vortex spin-torque nano oscillator (GV-STNO) device in which pinning sites work as random reservoirs that can emulate neuronal functions. We harness the nonlinear oscillation frequency and resistance exhibited by the vortex core granular pinning of the GV-STNO reservoir computing system to demonstrate waveform classification.
在本文中，我们研究了磁性隧道结（MTJ）自由层中的粒度及其作为储层计算储层的潜力，其中在器件处于涡旋状态时，颗粒充当振荡神经元。储层的输入以磁场的形式施加，该磁场可将涡旋核心固定在处于磁涡旋状态的器件的不同颗粒中。振荡频率和MTJ电阻以非线性方式在不同颗粒间变化，这使其成为用于分类目标的储层输出的理想候选者。因此，我们提出了一种经过实验验证的面积高效的单颗粒涡旋自旋扭矩纳米振荡器（GV-STNO）器件，其中钉扎位点充当可模拟神经元功能的随机储层。我们利用GV-STNO储层计算系统的涡旋核心颗粒钉扎所表现出的非线性振荡频率和电阻来演示波形分类。
