Electronegative metal dopants improve switching variability in Al2O3 resistive switching devices

Abstract

Resistive switching random access memories (RRAMs) promise to overcome the limitation of time- and energy-consumption set by increased training demand in the deep neural network. These devices enable the colocation of memory and processing by storing and utilizing information in the form of conductive networks, such as those made of oxygen vacancies. However, the inherent stochastic nature of atomic motion results in poor reliability and high switching variability in these devices, hindering their widespread use. In this paper, the authors propose a method to substantially reduce the switching variability of RRAM devices by doping the RRAM oxide electrolyte with electronegative metals. They find that electronegative metals reduce the oxygen vacancy formation energy, thereby pinning the conductive filament formation along fixed, predictable paths. This improved reliability enables multibit switching and can facilitate integration into large-scale hardware neural networks.