Physics-separating artificial neural networks for predicting initial stages of Al sputtering and thin film deposition in Ar plasma discharges
This work addresses the need for improved plasma-surface interaction models in materials science simulations, offering a more accurate approach for predicting sputtering and thin film deposition processes, though it is incremental by building on prior machine learning methods with added physical details.
The paper tackled the challenge of accurately modeling plasma-surface interactions for Al thin film sputter deposition by introducing physics-separating artificial neural networks that incorporate evolving surface state and defect structures, resulting in a high-fidelity surrogate model that resolves inherent kinetics and can be applied to both simulation and experimental data.
Simulations of Al thin film sputter depositions rely on accurate plasma and surface interaction models. Establishing the latter commonly requires a higher level of abstraction and means to dismiss the fundamental atomic fidelity. Previous works on sputtering processes addressed this issue by establishing machine learning surrogate models, which include a basic surface state (i.e., stoichiometry) as static input. In this work, an evolving surface state and defect structure are introduced to jointly describe sputtering and growth with physics-separating artificial neural networks. The data describing the plasma-surface interactions stem from hybrid reactive molecular dynamics/time-stamped force bias Monte Carlo simulations of Al neutrals and Ar$^+$ ions impinging onto Al(001) surfaces. It is demonstrated that the fundamental processes are comprehensively described by taking the surface state as well as defect structure into account. Hence, a machine learning plasma-surface interaction surrogate model is established that resolves the inherent kinetics with high physical fidelity. The resulting model is not restricted to input from modeling and simulation, but may similarly be applied to experimental input data.