Hybrid Opto-Electrical Excitation of Spin-Transfer Torque Nano-Oscillators for Advanced Computing

Felix Oberbauer; Tristan Joachim Winkel; Tim Böhnert; Marcel S. Claro; Luana Benetti; Ihsan Çaha; Leonard Francis; Farshad Moradi; Ricardo Ferreira; Markus Münzenberg; Tahereh Sadat Parvini

arXiv · 2501.00813

Hybrid Opto-Electrical Excitation of Spin-Transfer Torque Nano-Oscillators for Advanced Computing

Felix Oberbauer·Tristan Joachim Winkel·Tim Böhnert·Marcel S. Claro·Luana Benetti·Ihsan Çaha·Leonard Francis·Farshad Moradi·Ricardo Ferreira·Markus Münzenberg·Tahereh Sadat Parvini

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Abstract

Neuromorphic computing, inspired by the brain's parallel and energy-efficient processing, offers a transformative approach to artificial intelligence. In this study, we fabricated optimized spin-transfer torque nano-oscillators (STNOs) and investigated their dynamic behaviors using a hybrid excitation scheme combining AC laser illumination and DC bias currents. Laser-induced thermal gradients generate pulsed thermoelectric voltages ($V_{\text{AC}}$) via the Tunnel Magneto-Seebeck (TMS) effect, while the addition of bias currents enhances this response, producing both $V_{\text{AC}}$ and a DC component ($V_{\text{DC}}$). Magnetic field sweeps reveal distinct switching between parallel (P) and antiparallel (AP) magnetization states in both voltage components, supporting multistate memory applications. Millivolt-range thermovoltage signals in open-circuit conditions demonstrate CMOS compatibility, enabling simplified, scalable neuromorphic systems. Under biased conditions, enhanced thermovoltage outputs exhibit intriguing phenomena, including spikes correlated with Barkhausen jumps and double-switching behavior, offering insights into magnetization dynamics and vortex transitions. These features resemble neural spiking behavior, suggesting applications in spiking neural networks, reservoir computing, multistate logic, analog computing, and high-resolution sensing. By bridging spintronic phenomena with practical applications, this work provides a versatile platform for next-generation AI technologies and adaptive computing architectures.

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Felix Oberbauer, Tristan Joachim Winkel, Tim Böhnert, Marcel S. Claro, Luana Benetti, Ihsan Çaha, Leonard Francis, Farshad Moradi, Ricardo Ferreira, Markus Münzenberg, Tahereh Sadat Parvini. 2025-01-01. Hybrid Opto-Electrical Excitation of Spin-Transfer Torque Nano-Oscillators for Advanced Computing. https://arxiv.org/abs/2501.00813

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