Light-Stimulated Synaptic Transistor with High PPF Feature for Artificial Visual Perception System Application

Optoelectronic synaptic devices, which combine the functions of photosensitivity and information processing, are essential for the development of artificial visual perception systems. Nevertheless, improving the paired pulse facilitation (PPF) index of optoelectronic synaptic devices, which is an urgent problem in the construction of high-precision artificial visual perception systems, has received less attention so far. Herein, a light-stimulated synaptic transistor (LSST) device with an ultra-high PPF index (approximate to 196%) is presented by introducing an ultra-thin carrier regulator layer hexagonal boron nitride (h-BN) into a classic graphene-based hybrid transistor frame (graphene/CsPbBr3 quantum dots). Crucially, analysis of the rate-limiting effect of h-BN on photogenerated carriers reveals the mechanism behind the LSST ultra-high PPF index. Furthermore, a two-layer artificial neural network connected by LSST devices demonstrate approximate to 91.5% recognition accuracy of handwritten digits. This work provides an effective method for constructing artificial visual perception systems using a hybrid transistor frame in the future.

Automated summary

The study introduces a light-stimulated synaptic transistor (LSST) device with an ultra-high paired pulse facilitation (PPF) index, achieved by introducing an ultra-thin carrier regulator layer hexagonal boron nitride (h-BN) into a classic graphene-based hybrid transistor frame. Analysis of the rate-limiting effect of h-BN on photogenerated carriers reveals the mechanism behind the ultra-high PPF index of LSST. A two-layer artificial neural network connected by LSST devices demonstrates a high recognition accuracy of handwritten digits.