Neuromorphic Vision Sensor driven by Ferroelectric HfAlO

Abstract

The rapid progress in bioinspired machine vision supported by intelligent computing has attracted attention of the modern electronics due to their wide applications in image recognition, artificial intelligence, and medical applications. The ferroelectric materials are considered as most reliable for the fabrication of these devices due to their nonvolatile and dense memory capacity as well as stability. Here, we report the neuromorphic vision sensor (NVS) based on a self-grown ferroelectric interfacial HfAlO layer. The unique properties of graphene (Gr) and silicon are integrated through the ferroelectric layer to fabricate the NVS, which can mimic the human eye system. The sensor efficiently demonstrates the basic neural functions including pair-pulsed facilitation, depression, inhibition, and excitation under electric as well as optical stimuli. The optical spike rate (2 Hz–10 Hz) as well as the amplitude (0.5 μW/cm2-1.5 μW/cm2) dependent plasticity, exhibited excellent facilitation and depression index of 1.62% and −1.86% with the minimum energy consumption of only 3.5 fJ per spike. The high endurance of 1 × 107 cycles confirm its reliability and data security. Our device with excellent features of optical signal detection, information processing, and data storage with its simple structure can be efficiently utilized as an image sensor for robotics and intelligent machine vision systems.