Reconfigurable and nonvolatile ferroelectric bulk photovoltaics based on 3R-WS2 for machine vision
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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55562-7 |
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| author | Yue Gong Ruihuan Duan Yi Hu Yao Wu Song Zhu Xingli Wang Qijie Wang Shu Ping Lau Zheng Liu Beng Kang Tay |
| author_facet | Yue Gong Ruihuan Duan Yi Hu Yao Wu Song Zhu Xingli Wang Qijie Wang Shu Ping Lau Zheng Liu Beng Kang Tay |
| author_sort | Yue Gong |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-91c34fdbc646417190831f84f20db5ff |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-91c34fdbc646417190831f84f20db5ff2025-01-05T12:37:51ZengNature PortfolioNature Communications2041-17232025-01-0116111210.1038/s41467-024-55562-7Reconfigurable and nonvolatile ferroelectric bulk photovoltaics based on 3R-WS2 for machine visionYue Gong0Ruihuan Duan1Yi Hu2Yao Wu3Song Zhu4Xingli Wang5Qijie Wang6Shu Ping Lau7Zheng Liu8Beng Kang Tay9Interdisciplinary Graduate School, Nanyang Technological UniversitySchool of Materials Science and Engineering, Nanyang Technological UniversitySchool of Electrical and Electronic Engineering, Nanyang Technological UniversitySchool of Materials Science and Engineering, Nanyang Technological UniversitySchool of Electrical and Electronic Engineering, Nanyang Technological UniversitySchool of Electrical and Electronic Engineering, Nanyang Technological UniversitySchool of Electrical and Electronic Engineering, Nanyang Technological UniversityDepartment of Applied Physics, The Hong Kong Polytechnic UniversitySchool of Materials Science and Engineering, Nanyang Technological UniversitySchool of Electrical and Electronic Engineering, Nanyang Technological UniversityAbstract 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.https://doi.org/10.1038/s41467-024-55562-7 |
| spellingShingle | Yue Gong Ruihuan Duan Yi Hu Yao Wu Song Zhu Xingli Wang Qijie Wang Shu Ping Lau Zheng Liu Beng Kang Tay Reconfigurable and nonvolatile ferroelectric bulk photovoltaics based on 3R-WS2 for machine vision Nature Communications |
| title | Reconfigurable and nonvolatile ferroelectric bulk photovoltaics based on 3R-WS2 for machine vision |
| title_full | Reconfigurable and nonvolatile ferroelectric bulk photovoltaics based on 3R-WS2 for machine vision |
| title_fullStr | Reconfigurable and nonvolatile ferroelectric bulk photovoltaics based on 3R-WS2 for machine vision |
| title_full_unstemmed | Reconfigurable and nonvolatile ferroelectric bulk photovoltaics based on 3R-WS2 for machine vision |
| title_short | Reconfigurable and nonvolatile ferroelectric bulk photovoltaics based on 3R-WS2 for machine vision |
| title_sort | reconfigurable and nonvolatile ferroelectric bulk photovoltaics based on 3r ws2 for machine vision |
| url | https://doi.org/10.1038/s41467-024-55562-7 |
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