Machine learning approaches to potential generation for molecular dynamics (MD) simulations of low-temperature plasma-surface interactions could greatly extend the range of chemical systems that can be modeled. Empirical potentials are difficult to generalize to complex combinations of multiple elements with interactions that might include covalent, ionic, and metallic bonds. This work demonstrates that a specific machine learning approach, Deep Potential Molecular Dynamics (DeepMD), can generate potentials that provide a good model of plasma etching in the Si-Cl-Ar system. Comparisons are made between MD results using DeepMD models and empirical potentials, as well as experimental measurements. Pure Si properties predicted by the DeepMD model are in reasonable agreement with experimental results. Simulations of Si bombardment by Ar ions demonstrate the ability of the DeepMD method to predict sputtering yields as well as the depth of the amorphous-crystalline interface. Etch yields as a function of flux ratio and ion energy for simultaneous Cl and Ar impacts are in good agreement with previous simulation results and experiment. Predictions of etch yields and etch products during plasma-assisted atomic layer etching of Si-Cl -Ar are shown to be in good agreement with MD predictions using empirical potentials and with experiment. Finally, good agreement was also seen with measurements for the spontaneous etching of Si by Cl atoms at 300 K. The demonstration that DeepMD can reproduce results from MD simulations using empirical potentials is a necessary condition to future efforts to extend the method to a much wider range of systems for which empirical potentials may be difficult or impossible to obtain.
Skip Nav Destination
Deep potential molecular dynamics simulations of low-temperature plasma-surface interactions
CHORUS
Article navigation
January 2025
Research Article|
January 03 2025
Deep potential molecular dynamics simulations of low-temperature plasma-surface interactions
Special Collection:
Atomic Layer Etching (ALE)
Andreas Kounis-Melas
;
Andreas Kounis-Melas
(Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing)
1
Department of Chemical and Biological Engineering, Princeton University
, Princeton, New Jersey 08540
Search for other works by this author on:
Joseph R. Vella
;
Joseph R. Vella
(Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing)
2
Princeton Plasma Physics Laboratory
, Princeton, New Jersey 08540
Search for other works by this author on:
Athanassios Z. Panagiotopoulos
;
Athanassios Z. Panagiotopoulos
(Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Supervision, Writing – review & editing)
1
Department of Chemical and Biological Engineering, Princeton University
, Princeton, New Jersey 08540
Search for other works by this author on:
David B. Graves
David B. Graves
a)
(Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Supervision, Writing – review & editing)
1
Department of Chemical and Biological Engineering, Princeton University
, Princeton, New Jersey 085402
Princeton Plasma Physics Laboratory
, Princeton, New Jersey 08540
Search for other works by this author on:
a)
E-mail: [email protected]
J. Vac. Sci. Technol. A 43, 012603 (2025)
Article history
Received:
August 30 2024
Accepted:
December 04 2024
Citation
Andreas Kounis-Melas, Joseph R. Vella, Athanassios Z. Panagiotopoulos, David B. Graves; Deep potential molecular dynamics simulations of low-temperature plasma-surface interactions. J. Vac. Sci. Technol. A 1 January 2025; 43 (1): 012603. https://doi.org/10.1116/6.0004027
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
363
Views
Citing articles via
Surface passivation approaches for silicon, germanium, and III–V semiconductors
Roel J. Theeuwes, Wilhelmus M. M. Kessels, et al.
Low-temperature etching of silicon oxide and silicon nitride with hydrogen fluoride
Thorsten Lill, Mingmei Wang, et al.
Low-resistivity molybdenum obtained by atomic layer deposition
Kees van der Zouw, Bernhard Y. van der Wel, et al.
Related Content
Exciting DeePMD: Learning excited-state energies, forces, and non-adiabatic couplings
J. Chem. Phys. (October 2024)
Many-body interactions and deep neural network potentials for water
J. Chem. Phys. (April 2024)
Fluorine spillover for ceria- vs silica-supported palladium nanoparticles: A MD study using machine learning potentials
J. Chem. Phys. (July 2023)
Machine learning interatomic potential for molten TiZrHfNb
AIP Conf. Proc. (December 2020)