Gate-Tunable Synaptic Dynamics of Ferroelectric-Coupled Carbon-Nanotube Transistors.

Abstract

Artificial neural networks (ANNs) based on synaptic devices, which can simultaneously perform processing and storage of data, have superior computing performance compared to conventional von Neumann architectures. Here, we present a ferroelectric coupled artificial synaptic device with reliable weight update and storage properties for ANNs. The artificial synaptic device, which is based on a ferroelectric polymer capacitively coupled with an oxide dielectric via an electric-field-permeable, semiconducting single-walled carbon-nanotube channel, is successfully fabricated by inkjet printing. By controlling the ferroelectric polarization, synaptic dynamics, such as excitatory and inhibitory postsynaptic currents and long-term potentiation/depression characteristics, is successfully implemented in the artificial synaptic device. Furthermore, the constructed ANN, which is designed in consideration of the device-to-device variation within the synaptic array, efficiently executes the tasks of learning and recognition of the Modified National Institute of Standards and Technology numerical patterns.