Shape-based Magnetic Domain Wall Drift for an Artificial Spintronic Leaky Integrate-and-Fire Neuron

Abstract

Spintronic devices based on domain wall (DW) motion through ferromagnetic nanowire tracks have received great interest as components of neuromorphic information processing systems. Previous proposals for spintronic artificial neurons required external stimuli to perform the leaking functionality, one of the three fundamental functions of a leaky integrate-and-fire (LIF) neuron. The use of this external magnetic field or electrical current stimulus results in either a decrease in energy efficiency or an increase in fabrication complexity. In this work, we modify the shape of previously demonstrated three-terminal magnetic tunnel junction neurons to perform the leaking operation without any external stimuli. The trapezoidal structure causes shape-based DW drift, thus intrinsically providing the leaking functionality with no hardware cost. This LIF neuron therefore promises to advance the development of spintronic neural network crossbar arrays.

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• Controllable reset behavior in domain wall-magnetic tunnel junction artificial neurons for task-adaptable computation
• Theoretical Conditions for Field-free Magnetization Switching Induced by Spin-orbit Torque and Dzyaloshinskii-Moriya Interaction
• CMOS-Free Multilayer Perceptron Enabled by Four-Terminal MTJ Device
• Maximized Lateral Inhibition in Paired Magnetic Domain Wall Racetracks for Neuromorphic Computing
• Unsupervised Competitive Hardware Learning Rule for Spintronic Clustering Architecture
• Domain Wall Leaky Integrate-and-Fire Neurons with Shape-Based Configurable Activation Functions