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; Jun-Ming Liu

doi:10.1038/s41467-022-29364-8

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

Nat Commun. 2022 Mar 31;13(1):1707. doi: 10.1038/s41467-022-29364-8.

Authors

Boyuan Cui¹, Zhen Fan², Wenjie Li¹, Yihong Chen¹, Shuai Dong¹, Zhengwei Tan¹, Shengliang Cheng¹, Bobo Tian³, Ruiqiang Tao¹, Guo Tian¹, Deyang Chen¹, Zhipeng Hou¹, Minghui Qin¹, Min Zeng¹, Xubing Lu¹, Guofu Zhou⁴, Xingsen Gao¹, Jun-Ming Liu^{1

5}

Affiliations

¹ Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, 510006, China.
² Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, 510006, China. fanzhen@m.scnu.edu.cn.
³ Key Laboratory of Polar Materials and Devices, Ministry of Education, East China Normal University, Shanghai, 200241, China.
⁴ National Center for International Research on Green Optoelectronics, South China Normal University, Guangzhou, 510006, China.
⁵ Laboratory of Solid State Microstructures and Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China.

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_0.2Ti_0.8)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.