期刊
APL MATERIALS
卷 10, 期 12, 页码 -出版社
AIP Publishing
DOI: 10.1063/5.0122943
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资金
- College of Engineering at the University of Michigan
This study demonstrates high-performance self-powered deep UV photodetectors based on the unique optoelectronic properties of an ultrawide bandgap nitride ferroelectric material. These photodetectors exhibit high responsivity and detectivity, low illumination intensity requirements, fast and stable switching response time, and excellent rejection to other light sources. The significant findings reported in this work provide a viable route to achieve high-performance self-powered ferroelectric UV photodetectors for energy-efficient applications.
The efficient photoelectric conversion based on the ferroelectric property of a material has attracted widespread attention in advanced optoelectronic systems. Such an electrically reconfigurable photovoltaic effect offers a unique opportunity for the development of self-powered ultraviolet (UV) photodetectors for a broad range of applications from the military to human health and the environment. To date, however, the low performance metrics of such photodetectors have hindered their integration with existing platforms. By exploring the unique optoelectronic properties of an ultrawide bandgap nitride ferroelectric (ScAlN), we demonstrate, for the first time, polarization dependent high-performance self-powered deep UV photodetectors. The responsivity at 193 nm illumination reached up to a maximum of 15 mA/W with a detectivity of 1.2 x 10(11) Jones at an extremely low illumination intensity of 0.12 mW/cm(2). Furthermore, the photodetectors exhibit wake-up free and reconfigurable photo-response, and fast and stable switching response time (< 0.06 s) with excellent rejection to UV-A and visible illumination. The significant findings related to the growth, fabrication, and characterization reported in this work construct a viable route to realize unprecedentedly high performance self-powered ferroelectric UV photodetectors toward energy-efficient applications. (C) 2022 Author(s).
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