Magnetic tunnel junctions driven by hybrid optical-electrical signals as a flexible neuromorphic computing platform
Felix Oberbauer, Tristan Winkel, Tim Böhnert, Clara C. Wanjura, Marcel Santos Claro, Luana Benetti, İhsan Çaha, Francis Leonard Deepak, Farshad Moradi, Ricardo Alberto Neto Ferreira, Markus Münzenberg, Tahereh Sadat Parvini · Communications Physics · 2025
Abstract Magnetic tunnel junctions (MTJs) offer a promising pathway toward energy-efficient neuromorphic computing due to their nanoscale footprint, nonvolatile switching, and intrinsic nonlinear dynamics that emulate synaptic behavior. However, generating large thermoelectric voltages with bias-tunable nonlinearities for neuromorphic use remains largely unexplored. Here, we introduce a hybrid opto-electrical excitation scheme—combining pulsed laser heating with DC bias—to drive MTJs into the nonlinear bias-enhanced tunnel magneto-Seebeck regime. This regime yields thermoelectric voltages in the tens of millivolts with a strong contrast between magnetic states, while also revealing spiking and double-switching behavior linked to vortex dynamics and fixed-layer depinning. The thermovoltage exhibits cubic dependence on bias current, enabling tunable synaptic weights. We simulate a single-layer neuromorphic network using optically encoded inputs and achieve 93.7% classification accuracy on handwritten digits. These results establish hybrid-driven MTJs as a compact, CMOS-compatible platform for neuromorphic computing, integrating optical input with spintronic functionality.