Uniform multilevel switching of graphene oxide-based RRAM achieved by embedding with gold nanoparticles for image pattern recognition

Abstract

Traditional metal-oxide semiconductor devices are inadequate for use in artificial neural networks (ANNs) owing to their high power consumption, complex structures, and difficult fabrication techniques. Resistive random access memory (RRAM) is a promising candidate for ANNs owing to its simple structure, low power consumption, and excellent compatibility with CMOS. Moreover, it can mimic synaptic functions because of its multilevel resistive switching (RS) behavior. Herein, we demonstrate highly uniform RS and a high on/off ratio of RRAM based on graphene oxide by embedding gold nanoparticles into the device. This allowed reliable multilevel storage. Further, multilevel RRAM based on spike-timing-dependent-plasticity learning rules was used for image pattern recognition. These findings may offer a route to develop reliable digital memristors for ANNs.