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

TL;DR

This paper demonstrates a hybrid opto-electrical approach to excite spin-transfer torque nano-oscillators, revealing multistate magnetization switching and neural-like spiking behaviors suitable for advanced neuromorphic computing.

Contribution

It introduces a novel hybrid excitation scheme combining laser and electrical stimuli to control spintronic nano-oscillators for neuromorphic applications.

Findings

Thermovoltage signals support multistate memory functions.

Enhanced thermovoltage exhibits neural-like spikes and double-switching.

CMOS-compatible thermovoltage signals enable scalable neuromorphic systems.

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.