自旋力矩振荡器模型在联想记忆中的应用

G. Csaba, M. Pufall, D. Nikonov, G. Bourianoff, A. Horváth, T. Roska, W. Porod
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引用次数: 41

摘要

我们提出了单个和耦合自旋力矩纳米振荡器(STNOs)的基于物理的模型。这样的stno可能会成为类似cnn的动态计算架构的构建模块。我们讨论了模型的层次结构,从包含详细几何和物理的微磁模型扩展到基于从底层物理描述中提取的参数的紧凑模型。这些模拟还包括单个STNOs之间的耦合,包括通过自旋波和通过电互连。利用这种建模方法,我们演示了阵列中sto之间的频率夹带和相位同步,从而实现了计算功能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spin torque oscillator models for applications in associative memories
We present physics-based models for both individual and coupled spin torque nano oscillators (STNOs). Such STNOs may become as building blocks for CNN-like dynamic computing architectures. We discuss a hierarchy of models, extending from micromagnetic models, which include the detailed geometry and physics, to compact models, which are based on parameters extracted from the underlying physical description. These simulations also include coupling between individual STNOs, both via spin waves and via electrical interconnects. Using this modeling approach, we demonstrate frequency entrainment and phase synchronization between STOs in the array, which enable computing functions.
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