Strain-gradient-driven synthetic antiferromagnetic skyrmion neuron with velocity-affine LIF dynamics for neuromorphic computing

Abstract This work presents a strain-gradient-driven synthetic antiferromagnetic (SAF) skyrmion neuron that implements leaky-integrate-fire (LIF) functionality through voltage-controlled magnetoelastic actuation. The proposed device comprises a SAF nanotrack elastically coupled to a piezoelectric PZT-5 H thin film, where the application of small voltages establishes programmable compressive or tensile strain gradients to drive skyrmion motion. Micromagnetic simulations demonstrate that the SAF configuration effectively suppresses the skyrmion Hall effect, ensuring rectilinear propagation and enabling a precise linear mapping between strain-gradient amplitude and skyrmion velocity. Building on these drift characteristics, a velocity-affine LIF model is formulated, in which integration ( K int ) and leakage ( V leak ) coefficients are quantitatively extracted from displacement-time trajectories and shown to scale linearly with the applied gradient. The derived neuron model is integrated into the SpikingJelly framework and evaluated within a 784–128–10 spiking neural network trained on the MNIST dataset. Network-level simulations reveal that the skyrmion-based neuron achieves training and inference accuracies exceeding 97%, matching the performance of standard LIF models while exhibiting reduced trial-to-trial variance. To assess hardware-level programmability and design robustness, a two-dimensional parameter sweep across the ( K int , V leak ) space is performed. The resulting analysis identifies a broad operational region where inference accuracy remains robust, coinciding with a highly energy-efficient regime of approximately 0.5–1.0 pJ per spike. These results establish a skyrmion neuron architecture that is physically interpretable, energy-efficient, and tunably programmable, bridging nanoscale strain engineering with reliable system-level neuromorphic computation.