Orbit-Torque Spintronic Devices for Variation-Robust Neuromorphic Computing

Brain-inspired spintronic artificial neural networks (ANNs) have emerged as promising candidates for next-generation computing systems, yet conventional spin-orbit torque (SOT) devices face challenges of high current density ( $10^{{11}}$ – $10^{{12}}$ A/m2) and Joule heating-induced variability. Here, we introduce orbit torque (OT) derived from light metal Ti (effective orbit Hall angle $\approx ~0.4$ ) to drive ferromagnetic synapses and neurons with a biologically inspired continuously differentiable exponential linear unit (CeLu) activation function. We systematically characterize device variations, including cycle-to-cycle (CTC) and device-to-device (DTD) fluctuations, and reveal that Joule heating significantly contributes to CTC variability through finite element and micromagnetic simulations. Optimizing the neural network depth reduces error propagation induced by CTC variation. Moreover, the network exhibits higher tolerance to DTD variations compared to CTC variations. Our OT-driven all-spin ANN achieves a recognition accuracy of $90.1 \pm ~0.2$ % on the MNIST dataset under combined synaptic and neural CTC and DTD variations. This work provides a viable path towards low-power neuromorphic computing systems by leveraging OT’s advantages of reduced thermal dissipation and stable switching characteristics.