Etching-to-deposition transition in SiO${}_2$/Si${}_3$N${}_4$ using CH${}_x$F${}_y$ ion-based plasma etching: An atomistic study with neural network potentials

Abstract

Plasma etching, a critical process in semiconductor fabrication, utilizes hydrofluorocarbons both as etchants and as precursors for carbon film formation, where precise control over film growth is essential for achieving high SiO${}_2$/Si${}_3$N${}_4$ selectivity and enabling atomic layer etching. In this work, we develop neural network potentials (NNPs) to gain atomistic insights into the surface evolution of SiO${}_2$ and Si${}_3$N${}_4$ under hydrofluorocarbon ion bombardment. To efficiently sample diverse local configurations without exhaustive enumeration of ion-substrate combinations, we propose a vapor-to-surface sampling approach using high-temperature, low-density molecular dynamics simulations, supplemented with baseline reference structures. The NNPs, refined through iterative training, yield etching characteristics in MD simulations that show good agreement with experimental results. Further analysis reveals distinct mechanisms of carbon layer formation in SiO${}_2$ and Si${}_3$N${}_4$, driven by the higher volatility of carbon-oxygen byproducts in SiO${}_2$ and the suppressed formation of volatile carbon-nitrogen species in Si${}_3$N${}_4$. This computational framework enables quantitative predictions of atomistic surface modifications under plasma exposure and provides a foundation for integration with multiscale process modeling, offering insights into semiconductor fabrication processes.