Jamming-controlled stochasticity in metal-insulator switching

TL;DR

This study investigates how nano-domain jamming influences stochastic switching in vanadium dioxide devices, revealing control mechanisms for deterministic or stochastic behavior crucial for neuromorphic applications.

Contribution

It demonstrates how electrical currents induce a jamming transition in nano-domains, controlling switching stochasticity in Mott insulator devices for neuromorphic computing.

Findings

Repetitive above-threshold currents lead to a jammed, deterministic state.

Sub-threshold currents erase short-term memory, restoring stochastic switching.

Nano-domain reconfiguration occurs over thousands of seconds after switching.

Abstract

Understanding and controlling phase transitions is a fundamental part of physics and has been central to many technological revolutions, from steam engines to field-effect transistors. At present, there is strong interest in materials with strongly coupled structural and electronic phase transitions, which hold promise for energy-efficient technologies. Utilizing a structural phase transition and controlling its plasticity naturally leads to built-in memory, a key feature for emulating neurons and synapses in neuromorphic technologies. Here, $\textit{operando}$ Bragg X-ray photon correlation spectroscopy is used to study the evolution of the nano-domain distribution at the micron-scale in neuromorphic devices made from the archetypal Mott insulator vanadium dioxide. It is found that after electrical switching, slow nano-domain reconfiguration occurs on timescales of thousands of seconds and that the domains undergo a jamming transition, offering control over switching stochasticity at the micron scale. More precisely, repetitive above-threshold currents plastically drive the system into a jammed/glassy state where switching becomes deterministic, while sub-threshold currents erase the short-term memory contained in the nano-domain distribution, recovering stochastic switching, thus offering a path for in-device learning. The results illustrate the importance of studying the nanoscale physics associated with phase transitions in strongly correlated materials, even for macroscopic devices, and offer guidance for future device operation schemes.