Deep potential molecular dynamics simulations of ion-enhanced etching of silicon by atomic chlorine

Journal Article · Thu Oct 16 00:00:00 EDT 2025 · Journal of Vacuum Science and Technology A

DOI:https://doi.org/10.1116/6.0004867· OSTI ID:3001885

Kounis-Melas, Andreas ^[1]; Panagiotopoulos, Athanassios Z. ^[1]; Graves, David B. ^[1]

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1. Princeton Univ., NJ (United States)

The continued development of plasma-assisted processing techniques requires a fundamental understanding of plasma-surface interactions. Molecular dynamics (MD) simulations have been employed to complement experimental studies and better understand the properties of such systems. Recently, machine learning (ML) methods have enabled the development of ab initio-based interatomic potentials, which can be generalized to complex combinations of multiple atom types. In this work, we use ML potentials developed using the Deep Potential Molecular Dynamics (DeepMD) framework to provide a model of ion-enhanced etching of Si by Cl atoms. We demonstrate the importance of proper selection of the training data set to the accuracy of the DeepMD model and compare our results to MD results using empirical potentials, as well as to experimental measurements. Exposure of undoped Si at 300 K to thermal Cl atoms yields a steady-state Cl coverage of 1.25 monolayers, which is slightly lower than the value obtained in previous experimental studies. Predictions of Si etch yields by simultaneous Cl atom and Ar ⁺ ion impacts as a function of ion energy, neutral to ion flux ratio, and angle of incidence of the ions are in reasonably good agreement with classical MD results and experimental measurements. Finally, etch yields and SiCl _x mixed layer thicknesses during simultaneous bombardment of the Si(100) surface by Cl atoms and Cl ⁺ ions are in good agreement with experimental data. In conclusion, the present work is a necessary condition for the extension of the DeepMD procedure to more complex systems of interest in plasma-surface interactions.

• Research Organization: Princeton Plasma Physics Laboratory (PPPL), Princeton, NJ (United States)
• Sponsoring Organization: Onassis Foundation; Samsung Electronics; USDOE
• Grant/Contract Number: AC02-09CH11466
• OSTI ID: 3001885
• Journal Information: Journal of Vacuum Science and Technology A, Journal Name: Journal of Vacuum Science and Technology A Journal Issue: 6 Vol. 43; ISSN 0734-2101; ISSN 1520-8559
• Publisher: American Vacuum Society / AIP
• Country of Publication: United States
• Language: English

Related Subjects

Artificial neural networks
Atomic energy
Density functional theory
Interatomic potentials
Machine learning
Microfabrication
Molecular dynamics
Physical quantities
Plasma material interactions
Reactive force field