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

• DOI: 10.1038/s41467-022-33118-x
• Publication Date: 2022-09-14T13:07:59Z
• Authors: 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; Yao Guo
• Keywords: Artificial intelligence; Advanced Materials for Smart Windows; Dielectric; Polymers and Plastics; Ferroelectricity; Science; Memristive Devices for Neuromorphic Computing; Materials Science; Wearable Nanogenerator Technology; Biomedical Engineering; Piezoresponse force microscopy; FOS: Medical engineering; Quantum mechanics; 7. Clean energy; Article; Carbon nanotube; 03 medical and health sciences; Engineering; Triboelectric Technology; FOS: Electrical engineering, electronic engineering, information engineering; Nanotechnology; Electrical and Electronic Engineering; Optoelectronics; FOS: Nanotechnology; 0303 health sciences; Physics; Q; Molecule; van der Waals force; Computer science; Materials science; Tungsten disulfide; Nanotube; Physical Sciences; Metallurgy; Synaptic Plasticity

AbstractIntelligent 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.