Intrinsic resistive switching in ultrathin SiOx memristors for neuromorphic inference accelerators

Abstract

Two-dimensional (2D) materials are promising memristive materials owing to their outstanding physical properties, electrical tunability and fast switching capability. However, 2D layered materials generally require sophisticated deposition techniques at the expense of limited large-scale homogeneity and high growth temperatures. Furthermore, the variability and reliability of the 2D materials based memristor should be further improved. Here, we report an intrinsic memristor based on ultrathin 2D-like nonlayered amorphous SiOx which is derived from a native oxidation process at ambient temperature. Such 2D-like memristors demonstrate low switching variability (3.7 %), nanosecond switching speed (15 ns), good endurance (>106 cycles) and high retention (>103 s at 85 °C). 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 SiOx memristor show a high recognition accuracy of ∼ 98 %, manifesting its potential for the practical implementation of nonlayered 2D-like materials based neural network inference accelerator.