Reconfigurable and nonvolatile ferroelectric bulk photovoltaics based on 3R-WS2 for machine vision

Yue Gong, Ruihuan Duan, Yi Hu, Yao Wu, Song Zhu, Xingli Wang (), Qijie Wang, Shu Ping Lau, Zheng Liu () and Beng Kang Tay ()
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Yue Gong: Nanyang Technological University
Ruihuan Duan: Nanyang Technological University
Yi Hu: Nanyang Technological University
Yao Wu: Nanyang Technological University
Song Zhu: Nanyang Technological University
Xingli Wang: Nanyang Technological University
Qijie Wang: Nanyang Technological University
Shu Ping Lau: The Hong Kong Polytechnic University
Zheng Liu: Nanyang Technological University
Beng Kang Tay: Nanyang Technological University

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: Abstract Hardware implementation of reconfigurable and nonvolatile photoresponsivity is essential for advancing in-sensor computing for machine vision applications. However, existing reconfigurable photoresponsivity essentially depends on the photovoltaic effect of p-n junctions, which photoelectric efficiency is constrained by Shockley-Queisser limit and hinders the achievement of high-performance nonvolatile photoresponsivity. Here, we employ bulk photovoltaic effect of rhombohedral (3R) stacked/interlayer sliding tungsten disulfide (WS2) to surpass this limit and realize highly reconfigurable, nonvolatile photoresponsivity with a retinomorphic photovoltaic device. The device is composed of graphene/3R-WS2/graphene all van der Waals layered structure, demonstrating a wide range of nonvolatile reconfigurable photoresponsivity from positive to negative ( ± 0.92 A W−1) modulated by the polarization of 3R-WS2. Further, we integrate this system with a convolutional neural network to achieve high-accuracy (100%) color image recognition at σ = 0.3 noise level within six epochs. Our findings highlight the transformative potential of bulk photovoltaic effect-based devices for efficient machine vision systems.

Date: 2025
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DOI: 10.1038/s41467-024-55562-7

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