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

Boyuan Cui, Zhen Fan (), Wenjie Li, Yihong Chen, Shuai Dong, Zhengwei Tan, Shengliang Cheng, Bobo Tian, Ruiqiang Tao, Guo Tian, Deyang Chen, Zhipeng Hou, Minghui Qin, Min Zeng, Xubing Lu, Guofu Zhou, Xingsen Gao and Jun-Ming Liu
Additional contact information
Boyuan Cui: South China Normal University
Zhen Fan: South China Normal University
Wenjie Li: South China Normal University
Yihong Chen: South China Normal University
Shuai Dong: South China Normal University
Zhengwei Tan: South China Normal University
Shengliang Cheng: South China Normal University
Bobo Tian: East China Normal University
Ruiqiang Tao: South China Normal University
Guo Tian: South China Normal University
Deyang Chen: South China Normal University
Zhipeng Hou: South China Normal University
Minghui Qin: South China Normal University
Min Zeng: South China Normal University
Xubing Lu: South China Normal University
Guofu Zhou: South China Normal University
Xingsen Gao: South China Normal University
Jun-Ming Liu: South China Normal University

Nature Communications, 2022, vol. 13, issue 1, 1-12

Abstract: 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(Zr0.2Ti0.8)O3 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.

Date: 2022
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DOI: 10.1038/s41467-022-29364-8

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