The emergence of light-tunable synaptic transistors provides opportunities to break through the von Neumann bottleneck and enable neuromorphic computing. Herein, a multifunctional synaptic transistor is constructed by using 2,7-dioctylbenzothieno[3,2-b]benzothiophene (C8-BTBT) and indium gallium arsenide (InGaAs) nanowires (NWs) hybrid heterojunction thin film as the active layer. Under illumination, the Type-I C8-BTBT/InGaAs NWs heterojunction would make the dissociated photogenerated excitons more difficult to recombine. The persistent photoconductivity caused by charge trapping can then be used to mimic photosynaptic behaviors, including excitatory postsynaptic current, long/short-term memory and Pavlovian learning. Furthermore, a high classification accuracy of 89.72% can be achieved through the single-layer-perceptron hardware-based neural network built from C8-BTBT/InGaAs NWs synaptic transistors. Thus, this work could provide new insights into the fabrication of high-performance optoelectronic synaptic devices.
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Electrical and Electronic Engineering
- Materials Chemistry