Tunable, Nucleation-Driven Stochasticity in Nanoscale Silicon Oxide Resistive Switching Memory Devices

Abstract

Resistive switching memory devices hold extensive possibilities for realizing artificial neural networks along with nonconventional computing paradigms. Studying and understanding phenomena arising at single resistive switching elements is necessary for utilizing their particular traits for computation. Tuning the variability of the set time-the timespan before the onset of the transition from a high-resistance OFF state to a low-resistance ON state-is key for making use of the inherently stochastic nature of the resistance switching effect. Here, we study the set time statistics in nanometer-sized graphene-SiOx-graphene resistive switching memory devices. For dedicated OFF state configurations, we demonstrate a universal variance of the logarithmic set time values, which is characteristic to a nucleation-driven crystallization process. Furthermore, we observe clear correlation between the OFF state resistance and the set time, and hence we explore the tunability of the set time statistics via changing the reset amplitude parameter in sequential pulsed measurements. The latter phenomenon could prove useful for controlling stochasticity in memristor-based probabilistic computing applications via the control of the active volume's nanostructure.