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Abstract
This paper introduces a self-powered in-sensor computing paradigm using a ferroelectric photosensor network (FE-PS-NET) for real-time machine vision applications. The FE-PS-NET, composed of ferroelectric photosensors (FE-PSs) with tunable photoresponsivities, allows simultaneous image capture and processing. Each FE-PS utilizes photovoltaic responses modulated by the remanent polarization of a Pb(Zr0.2Ti0.8)O3 layer, enabling the representation of signed weights in a single device. The interconnected FE-PSs function as an artificial neural network, performing in situ multiply-accumulate (MAC) operations. Experiments demonstrate successful real-time image processing, including binary classification and edge detection with 100% accuracy and F-Measure of 1, respectively.
Publisher
Nature Communications
Published On
Mar 31, 2022
Authors
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, Jun-Ming Liu
Tags
self-powered computing
ferroelectric photosensor
machine vision
image processing
artificial neural network
real-time applications
binary classification

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