Computational analysis of device-to-device variability in resistive switching through single-layer hexagonal boron nitride and graphene vertical heterostructure model

Abstract

Abstract We quantify the device-to-device variations in resistive switching by considering a single-layer hexagonal boron nitride and graphene junction as a model. Then, we mimic the variations in the surface of a two-dimensional material in terms of defects and interface states by changing the distance between single-layer hexagonal boron nitride and graphene. We use density functional theory as a methodology to perform simulations at the atomic scale. The results show that the distance affects the current–voltage characterization results and that creating ultra uniform structures is important to reduce the device-to-device variability. These results are crucial to understand the reliability and accuracy of device-to-device variations in memory devices and mimic the neural dynamics beyond the synaptic cleft.