Mesoscopic sliding ferroelectricity enabled photovoltaic random access memory for material-level artificial vision system

Yan Sun, Shuting Xu, Zheqi Xu, Jiamin Tian, Mengmeng Bai, Zhiying Qi, Yue Niu, Hein Htet Aung, Xiaolu Xiong, Junfeng Han, Cuicui Lu, Jianbo Yin, Sheng Wang, Qing Chen, Reshef Tenne, Alla Zak () and Yao Guo ()
Additional contact information
Yan Sun: Beijing Institute of Technology
Shuting Xu: Beijing Institute of Technology
Zheqi Xu: Beijing Institute of Technology
Jiamin Tian: Peking University
Mengmeng Bai: Beijing Institute of Technology
Zhiying Qi: Beijing Institute of Technology
Yue Niu: Beijing Institute of Technology
Hein Htet Aung: Beijing Institute of Technology
Xiaolu Xiong: Beijing Institute of Technology
Junfeng Han: Beijing Institute of Technology
Cuicui Lu: Beijing Institute of Technology
Jianbo Yin: Beijing Graphene Institute
Sheng Wang: Peking University
Qing Chen: Peking University
Reshef Tenne: Weizmann Institute of Science
Alla Zak: Holon Institute of Technology
Yao Guo: Beijing Institute of Technology

Nature Communications, 2022, vol. 13, issue 1, 1-8

Abstract: Abstract Intelligent materials with adaptive response to external stimulation lay foundation to integrate functional systems at the material level. Here, with experimental observation and numerical simulation, we report a delicate nano-electro-mechanical-opto-system naturally embedded in individual multiwall tungsten disulfide nanotubes, which generates a distinct form of in-plane van der Waals sliding ferroelectricity from the unique combination of superlubricity and piezoelectricity. The sliding ferroelectricity enables programmable photovoltaic effect using the multiwall tungsten disulfide nanotube as photovoltaic random-access memory. A complete “four-in-one” artificial vision system that synchronously achieves full functions of detecting, processing, memorizing, and powering is integrated into the nanotube devices. Both labeled supervised learning and unlabeled reinforcement learning algorithms are executable in the artificial vision system to achieve self-driven image recognition. This work provides a distinct strategy to create ferroelectricity in van der Waals materials, and demonstrates how intelligent materials can push electronic system integration at the material level.

Date: 2022
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DOI: 10.1038/s41467-022-33118-x

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