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

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

Research output: Contribution to journalArticlepeer-review

49 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)76-84
Number of pages9
JournalActa Crystallographica Section C: Structural Chemistry
Volume70
Issue number2
DOIs
Publication statusPublished - Feb 2014
Externally publishedYes

Keywords

  • computational materials discovery
  • dielectric materials
  • hafnia-based oxides

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