Synaptic behaviors in flexible Au/WOx/Pt/mica memristor for neuromorphic computing system

Abstract

Neuromorphic computing, composed of artificial synapses and neural network algorithms, is expected to replace the traditional von Neumann computer architecture to build the next-generation intelligent systems due to its more energy-efficient features. In this work, the flexible Au/WOx/Pt/Mica memristor with simple structure is fabricated by RF magnetron sputtering, and the highly adjustable resistance states, the function of biological synapses and neurons in different states, such as short/long-term plasticity, paired-pulse facilitation, and spike-time-dependent plasticity, were demonstrated in flexible WOx memristor. Furthermore, we established a convolutional neural networks (CNNs) architecture for the Mixed National Institute of Standards and Technology (MNIST) pattern categorization and demonstrated that the recognition performance is comparable to that of a software-based neural network. These results provide a feasible approach for the realization of flexible neuromorphic computing systems in the future.