Atomistic simulation of HF diffusion on ammonium fluorosilicate surface using neural network potential

Abstract Plasma etching is an essential technology in semiconductor fabrication, enabling precise nanoscale patterning. For high-aspect-ratio channel hole etching in 3D NAND flash memory, cryogenic etching using hydrogen fluoride (HF) gas shows great potential. This process often leads to the deposition of ammonium hexafluorosilicate (AFS) on sidewalls, which critically impacts surface diffusion. Understanding such phenomena requires accurate atomistic modeling, and while density functional theory (DFT) provides reliable and accurate results, its significant computational cost makes it challenging to apply to large-scale or dynamic simulations. As a promising alternative, neural network potentials (NNPs) provide DFT-level accuracy at a fraction of the computational cost. In this study, we develop a fine-tuned NNP based on the pretrained SevenNet-0 model to simulate HF diffusion on AFS surfaces. Although SevenNet-0 is trained on a broad chemical space and exhibits great generalization capabilities, it requires further refinement to accurately capture the complex energy landscape occurring during cryogenic etching, particularly for configurations far from equilibrium. To address this, we fine-tune the SevenNet-0 model using a minimal DFT dataset. The resulting fine-tuned NNP demonstrates superior accuracy and stability in molecular dynamics simulations compared to both the NNP trained from scratch and the SevenNet-0 model. Our analysis reveals that the additive gas IF 5 enhances HF diffusivity by reducing chain formation and lowering the diffusion barrier. This work underscores the potential of fine-tuned NNPs for simulating complex etching processes, offering valuable insights for advancing semiconductor manufacturing.