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突触


受人脑结构和功能的启发，人工神经网络 （ANN） 在各个领域都取得了显着的成功。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重大限制。这促使人们探索非易失性存储器技术，将其作为通过在单个器件中集成存储和计算来克服这些限制的潜在解决方案。这些新兴技术可以在没有动力的情况下保持电阻值，使它们能够用作人工神经网络中的模拟权重，模拟生物突触的行为。虽然前景广阔，但这些非易失性器件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。该器件由一个设计形状的磁性多层堆栈组成，其中沿电流流动方向的宽度变化允许可控的磁畴壁传播。通过有限元方法仿真和实验研究，我们证明，通过设计器件几何形状，可以实现施加的电流和产生的霍尔电阻之间的线性关系，从而模拟人工神经网络中所需的线性权重输入行为。此外，本研究还探讨了电流脉冲宽度对响应曲线的影响，揭示了在较长脉冲持续时间下与线性度的偏差。 磁性突触器件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途的方法。