Ferroelectric photosensor network: an advanced hardware solution to real-time machine vision

计算机科学 光电探测器 神经形态工程学 铁电性 材料科学 计算机硬件 光电子学 人工神经网络 人工智能 电介质
作者
Boyuan Cui,Zhen Fan,Wenjie Li,Yihong Chen,Shuai Dong,Zhengwei Tan,Shuangshuang Cheng,Bobo Tian,Ruiqiang Tao,Guo Tian,Deyang Chen,Zhipeng Hou,Minghui Qin,Min Zeng,Xubing Lu,Guofu Zhou,Xingsen Gao,Jun‐Ming Liu
出处
期刊:Nature Communications [Nature Portfolio]
卷期号:13 (1) 被引量:93
标识
DOI:10.1038/s41467-022-29364-8
摘要

Abstract Nowadays the development of machine vision is oriented toward real-time applications such as autonomous driving. This demands a hardware solution with low latency, high energy efficiency, and good reliability. Here, we demonstrate a robust and self-powered in-sensor computing paradigm with a ferroelectric photosensor network (FE-PS-NET). The FE-PS-NET, constituted by ferroelectric photosensors (FE-PSs) with tunable photoresponsivities, is capable of simultaneously capturing and processing images. In each FE-PS, self-powered photovoltaic responses, modulated by remanent polarization of an epitaxial ferroelectric Pb(Zr 0.2 Ti 0.8 )O 3 layer, show not only multiple nonvolatile levels but also sign reversibility, enabling the representation of a signed weight in a single device and hence reducing the hardware overhead for network construction. With multiple FE-PSs wired together, the FE-PS-NET acts on its own as an artificial neural network. In situ multiply-accumulate operation between an input image and a stored photoresponsivity matrix is demonstrated in the FE-PS-NET. Moreover, the FE-PS-NET is faultlessly competent for real-time image processing functionalities, including binary classification between ‘X’ and ‘T’ patterns with 100% accuracy and edge detection for an arrow sign with an F-Measure of 1 (under 365 nm ultraviolet light). This study highlights the great potential of ferroelectric photovoltaics as the hardware basis of real-time machine vision.
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