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Polarization-modulated programmable photovoltaic performance of a designed ferroelectric heterojunction

  • Meijiao Men
  • , Zunyi Deng
  • , Zijing Zhao
  • , Lingchen Liu
  • , Yuanyuan Cui
  • , Shuaizhao Jin
  • , Yiyang Xie
  • , Xingan Jiang
  • , Wuyang Ren
  • , Jiawang Hong
  • , Jiang Wu
  • , Sang Wook Cheong
  • , Pengying Chang*
  • , Lili Wang*
  • , Xueyun Wang*
  • , Xiaolei Wang*
  • *此作品的通讯作者
  • School of Physics and Optoelectronic Engineering
  • Beijing University of Technology
  • Beijing Institute of Technology
  • Engineering Research Center of Integrated Circuit Packaging and Testing
  • Tianshui Normal University
  • CAS - Institute of Semiconductors
  • Key Laboratory of Optoelectronics Technology
  • Institute of Micro/Nano Materials and Devices
  • Ningbo University of Technology
  • Institute of Fundamental and Frontier Sciences
  • University of Electronic Science and Technology of China
  • Department of Physics and Astronomy
  • Rutgers - The State University of New Jersey, New Brunswick

科研成果: 期刊稿件 › 文章 › 同行评审

摘要

Conventional photovoltaic devices based on PN or Schottky junctions are inherently constrained by the material band gap, resulting in limited photoelectric conversion efficiency. The van der Waals ferroelectric material CuInP2S6 offers strong potential for efficient photocurrent generation via bulk photovoltaic effect, while heterostructure engineering provides additional degrees of freedom for performance modulation. Yet, the specificity in asymmetric CuInP2S6 heterojunctions remains poorly understood. Here, we design and fabricate an asymmetric photovoltaic device, consisting of Pt/CuInP2S6/Graphene heterostructure. The ferroelectric photovoltaic effect couples with the Cu+ migration dynamics, which could be manipulated by asymmetric interface barriers. Remarkably, the photovoltaic current is increased by 10 times and exhibits a positive-negative switching through polarization modulation. The heterojunction device achieves on-demand photovoltaic programming for in-sensor computing tasks, including edge detection with recognition rate F-score ~ 1 and binary pattern classification with 100% accuracy, which provides new insights for next-generation visual technology.

源语言英语
期刊论文编号2096
期刊Nature Communications
卷17
期1
DOI
出版状态已出版 - 12月 2026
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