Optoelectronic Properties of Printed Photogating Carbon Nanotube Thin Film Transistors and Their Application for Light-Stimulated Neuromorphic Devices.

Abstract

Artificial synapses/neurons based on electronic/ionic hybrid devices have attracted wide attention for brain-inspired neuromorphic systems since it is possible to overcome the von Neumann bottleneck of the neuromorphic computing paradigm. Here, we report a novel photoneuromorphic device based on printed photogating single-walled carbon nanotube (SWCNT) thin film transistors (TFTs) using lightly n-doped Si as the gate electrode. The drain currents of the printed SWCNT TFTs can gradually increase to over 3000 times of their starting value after being pulsed with light stimulation, and the electrical signals can maintain for over 10 min. These characteristics are similar to the learning and memory functions of brain-inspired neuromorphic systems. The working mechanism of the light-stimulated neuromorphic devices is investigated and described here in detail. Important synaptic characteristics, such as low-pass filtering characteristics and nonvolatile memory ability, are successfully emulated in the printed light-stimulated artificial synapses. It demonstrates that the printed SWCNT TFT photoneuromorphic devices can act as the nonvolatile memory units and perform photoneuromorphic computing, which exhibits potential for future neuromorphic system applications.