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

摘要

High-k dielectric materials are important as gate oxides in microelectronicsand as potential dielectrics for capacitors. In order to enable computationaldiscovery of novel high-k dielectric materials, we propose a fitness model(energy storage density) that includes the dielectric constant, bandgap, andintrinsic breakdown field. This model, used as fitness function in conjunctionwith first-principles calculations and global optimization evolutionaryalgorithm USPEX, efficiently leads to practically important results. We found anumber of high-fitness structures of SiO2 and HfO2, some of which correspond toknown phases and some are new. The results allow us to propose characteristics(genes) common to high-fitness structures - these are the coordinationpolyhedra and their degree of distortion. Our variable-composition searches inthe HfO2-SiO2 system uncovered several high-fitness states. This hybridalgorithm opens up a new avenue of discovering novel high-k dielectrics withboth fixed and variable compositions, and will speed up the process ofmaterials discovery.