Humidity-induced synaptic plasticity of ZnO artificial synapses using peptide insulator for neuromorphic computing

Neuromorphic devices inspired by the human brain have attracted significant attention because of their excellent ability for cognitive and parallel computing. This study presents ZnO-based artificial synapses with peptide insulators for the electrical emulation of biological synapses. We demonstrated the dynamic responses of the device under various environmental conditions. The proton-conducting property of the tyrosine-rich peptide enables time-dependent responses under ambient conditions such that various aspects of synaptic behaviors are emulated by the devices. The transition from short-term memory to long-term memory is achieved via electrochemical doping of ZnO by protons. Furthermore, we demonstrate an image classification simulation using a multi-layer perceptron model to evaluate the potential of the device for use in neuromorphic computing. The neural network based on our device achieved a recognition accuracy of 87.47% for the MNIST handwritten digit images. This work proposes a novel device platform inspired by biosystems for brain-mimetic hardware systems.