Intrinsic Resistive Switching in Ultrathin Snox/Siox Heterostructure for Neuromorphic Inference Accelerators

Abstract

Memristors based on two-dimensional (2D) materials are promising candidates for non-volatile memory elements with low device variability and fast switching speed. However, 2D layered materials generally require sophisticated deposition techniques at the expense of limited large-scale homogeneity and high growth temperatures. Here, we report an intrinsic memristor based on ultrathin 2D-like nonlayered SnOx/SiOx heterostructure which is derived from a native oxidation process at ambient temperature, where the interfacial SnOx layer acts as an oxygen reservoir. Such 2D-like heterostructure memristors demonstrate low switching variability (3.7%), nanosecond switching speed, good endurance (>10^6 cycles) and high retention (> 10^3 s at 80 oC). We verified the resistive switching mechanism via in-depth X-ray photoemission spectroscopy (XPS) analysis at different resistance states and confirmed the oxygen vacancies movement under the electric field between the SnOx reservoir layer and the SiOx layer. Moreover, Simulation results for an MNIST image classifier based on the ultrathin SnOx/SiOx heterostructure memristor show a high recognition accuracy of ~99%, manifesting its potential for the practical implementation of nonlayered 2D-like materials based neural network inference accelerator.

Keywords: memristor, switching variability, 2D-like nonlayered, SnOx/SiOx heterostructure, oxygen reservoir, neuromorphic inference