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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.
Mou et al. (Mon,) studied this question.