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; Beng Kang Tay

doi:10.1038/s41467-024-55562-7

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

Nat Commun. 2025 Jan 2;16(1):230. doi: 10.1038/s41467-024-55562-7.

Authors

Yue Gong^#^{1

2}, Ruihuan Duan^#³, Yi Hu^{2

4}, Yao Wu³, Song Zhu², Xingli Wang^{5

6}, Qijie Wang², Shu Ping Lau⁴, Zheng Liu⁷, Beng Kang Tay^{8

9}

Affiliations

¹ Interdisciplinary Graduate School, Nanyang Technological University, Singapore, 639798, Singapore.
² School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore.
³ School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore.
⁴ Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, China.
⁵ School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore. [email protected].
⁶ IRL3288 CINTRA (CNRS NTU THALES), Nanyang Technological University, Singapore, 637553, Singapore. [email protected].
⁷ School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore. [email protected].
⁸ School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore. [email protected].
⁹ IRL3288 CINTRA (CNRS NTU THALES), Nanyang Technological University, Singapore, 637553, Singapore. [email protected].

^# Contributed equally.

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.