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Fluorite-structured antiferroelectric hafnium-zirconium oxide for emerging nonvolatile memory and neuromorphic-computing applications

神经形态工程学 萤石 材料科学 反铁电性 氧化锆 氧化物 计算机科学 光电子学 并行计算 冶金 铁电性 人工神经网络 机器学习 电介质
作者
Kangli Xu,Tianyu Wang,Jiajie Yu,Yongkai Liu,Zhenhai Li,Chen Lu,Jieru Song,Jialin Meng,Hao Zhu,Qingqing Sun,David Wei Zhang,Lin Chen
出处
期刊:Applied physics reviews [American Institute of Physics]
卷期号:11 (2) 被引量:3
标识
DOI:10.1063/5.0175439
摘要

The rapid progress of the internet of things, cloud computing, and artificial intelligence has increased demand for high-performance computing. This demand has led to a focused exploration of novel nonvolatile memory (NVM) and brain-inspired neuromorphic-computing electronics, with research efforts directed at identifying materials compatible with complementary metal-oxide-semiconductor technology. Exploring fluorite-structured hafnium-zirconium oxide (HZO) mixed oxides has revealed promising ferroelectric (FE) and memristor characteristics, suggesting potential applications in emerging technologies. However, certain intrinsic properties of HZO-based FEs, such as high coercive fields (Ec) and polarization metastability, may pose challenges for commercial viability. Recent investigations of fluorite-structured HZO-based antiferroelectrics (AFEs) have highlighted their advantages, including lower energetic barriers, higher switching speeds, and a uniform phase distribution. These inherent benefits position fluorite-structured HZO-based AFEs as potential candidates within the NVM landscape. Furthermore, the accumulated polarization and spontaneous depolarization characteristics of fluorite-structured HZO-based AFEs make them worthy of potential integration into neuromorphic-computing because they resemble certain aspects of neuron behavior. Despite these positive aspects, a more thorough exploration and consideration are needed to address existing challenges. This review aims to present fluorite-structured HZO-based AFE materials and 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 optimization and design of HZO-based AFE materials and devices for emerging NVM and neuromorphic-computing applications.
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