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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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 2041-1723 |