1. Machine Learning Force Field for Thermal Oxidation of Silicon
- Author
-
Cvitkovich, Lukas, Fehringer, Franz, Wilhelmer, Christoph, Milardovich, Diego, Waldhör, Dominic, and Grasser, Tibor
- Subjects
Condensed Matter - Materials Science - Abstract
Looking back at seven decades of highly extensive application in the semiconductor industry, silicon and its native oxide SiO$_2$ are still at the heart of several technological developments. Recently, the fabrication of ultra-thin oxide layers has become essential for keeping up with trends in down-scaling of nanoelectronic devices and for the realization of novel device technologies. With this comes a need for better understanding of the atomic configuration at the Si/SiO$_2$ interface. Classical force fields offer flexible application and relatively low computational costs, however, suffer from limited accuracy. Ab-initio methods give much better results but are extremely costly. Machine learning force fields (MLFF) offer the possibility to combine the benefits of both worlds. We train a MLFF for the simulation of the dry thermal oxidation process of a Si substrate. The training data is generated by density functional theory calculations. The obtained structures are in line with ab-initio simulations as well as with experimental observations. Compared to a classical force field, the most recent reactive force field (reaxFF), the resulting configurations are vastly improved., Comment: 9 pages, 6 figures, 3 tables
- Published
- 2024
- Full Text
- View/download PDF