Reconfigurable and nonvolatile ferroelectric bulk photovoltaics based on 3R-WS<sub>2</sub> for machine vision.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 39747133.
- Also identified by DOI 10.1038/s41467-024-55562-7 and PMC identifier 11695928.
- Licence recorded as CC BY-NC-ND.
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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 (WS<sub>2</sub>) to surpass this limit and realize highly reconfigurable, nonvolatile photoresponsivity with a retinomorphic photovoltaic device. The device is composed of graphene/3R-WS<sub>2</sub>/graphene all van der Waals layered structure, demonstrating a wide range of nonvolatile reconfigurable photoresponsivity from positive to negative ( ± 0.92 A W<sup>-1</sup>) modulated by the polarization of 3R-WS<sub>2</sub>. 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.