A machine-learning interatomic potential to study dry/wet oxidation process of silicon

Abstract

We developed an accurate and efficient machine learning potential with DFT accuracy and applied it to the silicon dry/wet oxidation process to investigate the underlying physics of thermal oxidation of silicon (001) surfaces. The accuracy of the potential was verified by comparing the melting point and structural properties of silicon, the structural properties of a-SiO2, and the adsorption properties on the silicon surface with experiment and DFT data. In subsequent thermal oxidation simulations, we successfully reproduced the accelerated growth phenomenon of the wet oxidation in the experiment, discussed the oxide growth process in detail, and elucidated that the accelerated growth is due to hydrogen in the system that both enhances the adsorption of oxygen on the silicon surface and promotes the migration of oxygen atoms. Finally, we annealed the oxidized structure, counted the defect information in the structure before and after annealing, and analyzed the defect evolution behavior during the annealing process.