Application of artificial synapse based on all-inorganic perovskite memristor in neuromorphic computing

Abstract

Artificial synapse inspired by the biological brain has great potential in the field of neuromorphic computing and artificial intelligence. The memristor is an ideal artificial synaptic device with fast operation and good tolerance. Here, we have prepared a memristor device with Au/CsPbBr₃/ITO structure. The memristor device exhibits resistance switching behavior, the high and low resistance states no obvious decline after 400 switching times. The memristor device is stimulated by voltage pulses to simulate biological synaptic plasticity, such as long-term potentiation, long-term depression, pair-pulse facilitation, short-term depression, and short-term potentiation. The transformation from short-term memory to long-term memory is achieved by changing the stimulation frequency. In addition, a convolutional neural network was constructed to train/recognize MNIST handwritten data sets; a distinguished recognition accuracy of ∼96.7% on the digital image was obtained in 100 epochs, which is more accurate than other memristor-based neural networks. These results show that the memristor device based on CsPbBr₃ has immense potential in the neuromorphic computing system.