Progress in computational understanding of ferroelectric mechanisms in HfO2

• Published in: npj computational materials 2024-08, Vol.10 (1), p.188-18
• Main Authors: Zhu, Tianyuan, Ma, Liyang, Deng, Shiqing, Liu, Shi
• Format: Article
• Language: English
• Subjects: 639/301/1034/1035; 639/766/119/996; Characterization and Evaluation of Materials; Chemical composition; Chemistry and Materials Science; Coercivity; Computational Intelligence; Computer applications; Density functional theory; Domain walls; Ferroelectric materials; Ferroelectricity; First principles; Go/no-go discrimination learning; Hafnium oxide; Machine learning; Materials Science; Mathematical and Computational Engineering; Mathematical and Computational Physics; Mathematical Modeling and Industrial Mathematics; Molecular dynamics; Perovskites; Review Article; Theoretical; Thin films
• ISSN: 2057-3960
• DOI: 10.1038/s41524-024-01352-0

Since the first report of ferroelectricity in nanoscale HfO 2 -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 2 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 2 is intimately related to the complex interplay among diverse structural polymorphs, dopants and their charge-compensating oxygen vacancies, and unconventional switching mechanisms of domains and domain walls, which can sometimes yield conflicting theoretical predictions and theoretical-experimental discrepancies. We also discuss opportunities enabled by machine-learning-assisted molecular dynamics and phase-field simulations to go beyond DFT modeling, probing the dynamical properties of ferroelectric HfO 2 and tackling pressing issues such as high coercive fields.