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.
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