Non-Volatile Vortex MTJs with Opto-Electrical and Spin-Diode Nonlinearities as Multifunctional Neuromorphic Platforms

TL;DR

This paper introduces a multifunctional spintronic platform using vortex magnetic tunnel junctions that unifies non-volatile memory, optoelectrical nonlinear computation, and multilevel readout, enabling efficient neuromorphic processing.

Contribution

It demonstrates a novel vortex MTJ device integrating non-volatile weights, optoelectrical nonlinearities, and multilevel readout within a single nanopillar, advancing neuromorphic hardware capabilities.

Findings

Achieved image classification accuracy of 95.4% with bTMS mode.

Demonstrated non-volatile tuning of vortex resonance over 15 MHz.

Smaller devices outperform larger ones, highlighting nonlinear-response engineering.

Abstract

The human brain achieves exceptional energy efficiency by co-locating memory and processing, yet reproducing this principle in hardware remains challenging because many neuromorphic devices require standby power, offer limited programmability, or separate state storage from nonlinear computation. Here we demonstrate a multifunctional spintronic platform based on storage-layer-enabled vortex magnetic tunnel junctions (MTJs) that unifies non-volatile weight storage, optoelectrically driven nonlinear computation, and multilevel readout within a single nanopillar. A thermally programmable FM/AFM storage layer retains analog synaptic weights with zero standby power and enables non-volatile tuning of the vortex gyrotropic resonance over ${\sim}15$~MHz. Under optoelectrical operation, combined laser heating and dc bias drive the junction into the bias-enhanced tunnel magneto-Seebeck (bTMS) regime, where the thermoelectric response exhibits a pronounced cubic nonlinearity providing a compact, hardware-native transfer function for weighted analog computation. The electrical and thermoelectric channels switch at matched coercive fields but with distinct amplitudes, yielding an effective four-level readout space. Crossbar-array simulations parameterized by measured device response maps evaluate two neuromorphic modes -- a bTMS mode (optical input, dc-bias weights) and a spin-diode mode (RF-frequency input, RF-power weights) -- achieving image-classification accuracies of $95.4\%$ and $94.9\%$, comparable to a digital single-layer network with sigmoid activations. Smaller 600~nm devices consistently outperform larger ones, identifying nonlinear-response engineering as a key device-level lever. Because bTMS and spin-diode rectification coexist in the same junction, a combined regime could enable nonlinear multi-input interactions, including quadratic cross-terms, within a single nanoscale element.