Artificial neural networks (ANNs), inspired by the structure and function of the human brain, have achieved remarkable success in various fields. However, ANNs implemented using conventional complementary metal oxide semiconductor technology face significant limitations. This has prompted exploration of nonvolatile memory technologies as potential solutions to overcome these limitations by integrating storage and computation within a single device. These emerging technologies can retain resistance values without power, allowing them to serve as analog weights in ANNs, mimicking the behavior of biological synapses. While promising, these nonvolatile devices often exhibit inherent nonlinear relationships between resistance and applied voltage, complicating training processes and potentially impacting learning accuracy. This article proposes a magnetic synapse device based on the spin–orbit torque effect with geometrically controlled linear and nonlinear response characteristics. The device consists of a magnetic multilayer stack patterned into a designed shape, where the width variation along the current flow direction allows for controllable magnetic domain wall propagation. Through finite element method simulations and experimental studies, we demonstrate that by engineering the device geometry, a linear relationship between the applied current and the resulting Hall resistance can be achieved, mimicking the desired linear weight-input behavior in artificial neural networks. Additionally, this study explores the influence of current pulse width on the response curves, revealing a deviation from linearity at longer pulse durations. The geometric tunability of the magnetic synapse device offers a promising approach for realizing reliable and energy-efficient neuromorphic computing architectures.

中文翻译：

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用于可靠神经形态计算的可调谐磁性突触


人工神经网络（ANN）受人脑结构和功能的启发，在各个领域取得了令人瞩目的成功。然而，使用传统互补金属氧化物半导体技术实现的人工神经网络面临着巨大的局限性。这促使人们探索非易失性存储器技术作为潜在的解决方案，通过在单个设备中集成存储和计算来克服这些限制。这些新兴技术可以在没有电源的情况下保留电阻值，使它们能够充当人工神经网络中的模拟权重，模仿生物突触的行为。虽然很有前景，但这些非易失性设备通常在电阻和施加电压之间表现出固有的非线性关系，使训练过程复杂化并可能影响学习准确性。本文提出了一种基于自旋轨道扭矩效应的磁突触装置，具有几何控制的线性和非线性响应特性。该器件由图案化为设计形状的磁性多层堆叠组成，其中沿电流方向的宽度变化允许可控磁畴壁传播。通过有限元方法模拟和实验研究，我们证明，通过设计器件几何形状，可以实现所施加的电流和所得霍尔电阻之间的线性关系，模仿人工神经网络中所需的线性权重输入行为。此外，本研究探讨了电流脉冲宽度对响应曲线的影响，揭示了较长脉冲持续时间下的线性偏差。 磁性突触设备的几何可调性为实现可靠且节能的神经形态计算架构提供了一种有前景的方法。