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The application of spin–orbit torque (SOT) devices to neuromorphic computing platforms is focused on the development of hardware circuit architectures. However, the inter-device variability, the integration modes of devices and peripheral circuits, and appropriate application scenarios are still unclear, limiting the development of SOT devices in neuromorphic computing. To solve this problem, this paper first proposes a circuit compensation scheme for the difference in resistance values of SOT devices, which solves this variability problem at the circuit level. Moreover, a synergistic scheme with the circuit is developed based on the correspondence between the multistate resistance characteristics of the SOT devices and a convolutional algorithm. To achieve this, a multichannel SOT convolutional kernel circuit architecture is built, which implements an image edge recognition application. Finally, based on a simulation model, an image edge recognition hardware circuit based on our CoPt-SOT devices is implemented, which is capable of performing image edge recognition with an accuracy of 96.33%. This scheme provides technical support and development prospects for SOT devices in neural network hardware applications. [ABSTRACT FROM AUTHOR]

While spin–orbit torque (SOT) devices are extensively investigated due to their potential for use in neural network computation, it remains challenging to explore the hardware for neural networks. In this paper, the field‐free memristive SOT switching of the CoPt single layer is used to propose a neuromorphic hardware circuit for detecting edges in images. Owing to its threefold symmetry of inversion, the polarity of SOT switching can be reversed by rotating the current by 60°. Moreover, the process of current‐induced SOT switching exhibits stable multi‐state magnetic switching behavior, and can be controllably tuned by using the pulse of the current. As the slope of the applied ramp pulse current increased, the wave of the anomalous Hall resistance changed from a curve with normally memristive property to trigonometric, and finally to cosine. The design of the hardware circuit for a single SOT device is subsequently formulated to detect the edges in images. The results of experiments verified the capability of this device to detect the edge lines in images with high accuracy, which confirms its potential for use in the hardware of neuromorphic computing platforms. The work here provides guidance for the application of SOT‐based devices to neuromorphic hardware. [ABSTRACT FROM AUTHOR]

For real-world applications, it is desirable to realize field-free spin–orbit torque (SOT) switching in thin films with high perpendicular magnetic anisotropy (PMA). In this paper, we report that field-free SOT switching in a L10-FePt single layer with a large switching ratio of 26% is obtained by using a MgO ⟨100⟩⋀8°/⟨100⟩ miscut substrate. It is found that field-free switching depends on the direction of the imposed pulse current. Only when the electric current is along the y (010)-direction but not along the x (100)-direction does field-free switching happen, which can be attributed to the tilted PMA induced symmetry breaking in the x–z plane. Furthermore, under the field-free condition, our FePt single layer system exhibits stable multi-state magnetic switching behavior and nonlinear synaptic characteristics. This work paves the way to realize field-free SOT switching in the L10-FePt single layer, which will have significant impact on spin memory devices and synaptic electronics. [ABSTRACT FROM AUTHOR]

• Binary synapse based on the deterministic switching of the magnetization states by encoding high and low resistance states. • Sigmoid neuron based on the stochastic switching probability of the magnetization in the free layer. • All-spin BWN based on the multi-functional nanoscale SOT devices achieves ∼81.73 % recognition accuracy on the CIFAR-10 dataset. Spintronic devices driven by spin–orbit torque (SOT) have a lot of potential for future neuromorphic computing hardware platforms. Nevertheless, problems such as multistate loss, weight time-dependent variability, and output discontinuity ensue when the device size is reduced to the nanoscale, which are contradictory to the requirements of traditional high-precision neural networks. In this study, we propose a high-precision all-spin neural network based on magnetization switching of a simple nanoscale multi-functional SOT device. The all-spin neural network is implemented by the Binary Weight Network, in which the binary synapse is encoded by the two states formed by the device's deterministic switching and the practical neuron is realized by its stochastic switching probability. Furthermore, we use a difference derivation training algorithm for the general-purpose network in a novel way to be more compatible with the discontinuous neuron output. Using this strategy, our network can reach ∼81.73 % recognition accuracy on the universal CIFAR-10 dataset, paving the way for future practical hardware implementation of nanoscale SOT devices in high-precision compact neuromorphic computing. [ABSTRACT FROM AUTHOR]