Progress in Computational Understanding of Ferroelectric Mechanisms in HfO$_2$
Tianyuan Zhu, Liyang Ma, Shiqing Deng, and Shi Liu

TL;DR
This review summarizes computational advances in understanding the complex ferroelectric mechanisms of HfO$_2$, highlighting the roles of structural phases, dopants, vacancies, and novel simulation techniques.
Contribution
It provides a comprehensive comparison of metastable phases, discusses stabilization factors, and explores machine learning methods to address unresolved issues in ferroelectric HfO$_2$.
Findings
Comparison of various polar phases and stabilization factors
Insights into the interplay of polymorphs, dopants, and vacancies
Potential of machine learning to enhance modeling and address high coercive fields
Abstract
Since the first report of ferroelectricity in nanoscale HfO-based thin films in 2011, this silicon-compatible binary oxide has quickly garnered intense interest in academia and industry, and continues to do so. Despite its deceivingly simple chemical composition, the ferroelectric physics supported by HfO is remarkably complex, arguably rivaling that of perovskite ferroelectrics. Computational investigations, especially those utilizing first-principles density functional theory (DFT), have significantly advanced our understanding of the nature of ferroelectricity in these thin films. In this review, we provide an in-depth discussion of the computational efforts to understand ferroelectric hafnia, comparing various metastable polar phases and examining the critical factors necessary for their stabilization. The intricate nature of HfO is intimately related to the complex…
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Taxonomy
TopicsFerroelectric and Negative Capacitance Devices · Machine Learning in Materials Science · Semiconductor materials and devices
