铁电性
小型化
纳米技术
材料科学
神经形态工程学
非易失性存储器
计算机科学
铁电RAM
工程物理
光电子学
工程类
人工智能
电介质
人工神经网络
作者
Haiyan Chen,Xuefan Zhou,Lin Tang,Yonghong Chen,Hang Luo,Xi Yuan,Chris Bowen,Dou Zhang
出处
期刊:Applied physics reviews
[American Institute of Physics]
日期:2022-02-02
卷期号:9 (1)
被引量:62
摘要
Nonvolatile memories are in strong demand due to the desire for miniaturization, high-speed storage, and low energy consumption to fulfill the rapid developments of big data, the Internet of Things, and artificial intelligence. Hafnia (HfO2)-based materials have attracted significant interest due to the advantages of complementary-metal–oxide–semiconductor (CMOS) compatibility, large coercive voltage, and superior ferroelectricity at an ultra-thin thickness. The comparable ferroelectricity to that of traditional perovskite materials and size advantage of HfO2 result in fascinating storage performance, which can be readily applicable to the fields of integrated non-volatile memories. This Review provides a comprehensive overview of recent developments in HfO2-based ferroelectrics with attention to the origin of ferroelectricity, performance modulation, and recent achievements in the material. Moreover, potential solutions to existing challenges associated with the materials are discussed in detail, including the wake-up effect, long-term fatigue behavior, and imprint challenges, which pave the way for obtaining HfO2-based ferroelectric materials and devices with long service life and high stability. Finally, the range of potential applications for these fascinating new materials is presented and summarized, which include non-volatile memories and neuromorphic systems. This Review intends to present the state-of-the-art HfO2-based ferroelectrics and to highlight the current challenges, possible applications, and future opportunities and can act as an update for recent developments in these intriguing materials and provide guidance for future researchers in the design and optimization of HfO2-based ferroelectric materials and devices.
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