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新型高介电常数介电材料的进化搜索:方法及其在氧化铪基氧化物中的应用

Evolutionary search for new high-k dielectric materials: methodology and applications to hafnia-based oxides.

作者信息

Zeng Qingfeng, Oganov Artem R, Lyakhov Andriy O, Xie Congwei, Zhang Xiaodong, Zhang Jin, Zhu Qiang, Wei Bingqing, Grigorenko Ilya, Zhang Litong, Cheng Laifei

机构信息

Science and Technology on Thermostructural Composite Materials Laboratory, and School of Materials Science and Engineering, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, People's Republic of China.

Department of Geosciences, Center for Materials by Design, and Institute for Advanced Computational Science, State University of New York, Stony Brook, NY 11794-2100, USA.

出版信息

Acta Crystallogr C Struct Chem. 2014 Feb;70(Pt 2):76-84. doi: 10.1107/S2053229613027861. Epub 2014 Jan 9.

DOI:10.1107/S2053229613027861
PMID:24508952
Abstract

High-k dielectric materials are important as gate oxides in microelectronics and as potential dielectrics for capacitors. In order to enable computational discovery of novel high-k dielectric materials, we propose a fitness model (energy storage density) that includes the dielectric constant, bandgap, and intrinsic breakdown field. This model, used as a fitness function in conjunction with first-principles calculations and the global optimization evolutionary algorithm USPEX, efficiently leads to practically important results. We found a number of high-fitness structures of SiO2 and HfO2, some of which correspond to known phases and some of which are new. The results allow us to propose characteristics (genes) common to high-fitness structures--these are the coordination polyhedra and their degree of distortion. Our variable-composition searches in the HfO2-SiO2 system uncovered several high-fitness states. This hybrid algorithm opens up a new avenue for discovering novel high-k dielectrics with both fixed and variable compositions, and will speed up the process of materials discovery.

摘要

高介电常数(high-k)介电材料在微电子领域作为栅极氧化物以及作为电容器的潜在电介质都很重要。为了实现新型高-k介电材料的计算发现,我们提出了一个适应度模型(储能密度),该模型包括介电常数、带隙和本征击穿场。该模型与第一性原理计算和全局优化进化算法USPEX一起用作适应度函数,有效地得出了实际重要的结果。我们发现了许多SiO₂和HfO₂的高适应度结构,其中一些对应于已知相,一些是新的。这些结果使我们能够提出高适应度结构共有的特征(基因)——这些是配位多面体及其畸变程度。我们在HfO₂-SiO₂系统中的可变成分搜索发现了几种高适应度状态。这种混合算法为发现具有固定和可变成分的新型高-k电介质开辟了一条新途径,并将加快材料发现的进程。

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Rational design of inorganic dielectric materials with expected permittivity.
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Sci Rep. 2015 Nov 30;5:16769. doi: 10.1038/srep16769.