Cu/MgO-based resistive random access memory for neuromorphic applications

Abstract

Resistive Random Access Memory (ReRAM) is considered to be a suitable candidate for future memories due to its low operating voltage, fast access speed, and the potential to be scaled down to nanometer range for ultra-high-density storage. In addition, its ability to retain multi-level resistance states makes it suitable for neuromorphic computing applications. In this paper, we report the resistive switching performance of Cu/MgO/Pt ReRAM. Repetitive resistive switching transitions with low switching voltages (around 1 V), 102 storage windows, and multi-level memory capabilities have been obtained. Biological synaptic plasticity behavior, such as long-duration potentiation/depression and paired-pulse facilitation, has been realized by the Cu/MgO/Pt ReRAM. The simulation of convolutional neural network for handwritten digit recognition is carried out to evaluate its potential application in neuromorphic systems. Finally, the conduction mechanism of the device is studied, and a resistive switching model based on Cu conducting filaments is proposed according to the dependence of I–V results on temperature and electrode size as well as the element distribution in the device. These findings indicate the potential of Cu/MgO/Pt device as high-performance nonvolatile memories and its utilization in future computer systems and neuromorphic computing.