Handbook of plasma processing technology: Fundamentals, etching, deposition and surface interactions

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.

Section: Introductionmentioning

confidence: 99%

Physics-separating artificial neural networks for predicting initial stages of Al sputtering and thin film deposition in Ar plasma discharges

Gergs

Mussenbrock²,

Trieschmann³

2023

“…thin film sputter deposition, catalysis), plasma-facing surfaces and plasma-surface interaction (PSI; e.g. growth, sputtering, surface chemical reactions) are involved [1][2][3][4]. An issue that has been addressed by machine learning (ML).…”

Section: Introductionmentioning

confidence: 99%

Physics-separating artificial neural networks for predicting sputtering and thin film deposition of AlN in Ar/N₂ discharges on experimental timescales

Gergs¹,

Mussenbrock²,

Trieschmann³

2023

Understanding and modeling plasma-surface interactions frame a multi-scale as well as multi-physics problem. Scale-bridging machine learning surface surrogate models have been demonstrated to perceive the fundamental atomic fidelity for the physical vapor deposition of pure metals. However, the immense computational cost of the data-generating simulations render a practical application with predictions on relevant timescales impracticable. This issue is resolved in this work for the sputter deposition of AlN in Ar/N2 discharges by developing a scheme that populates the parameter spaces effectively. Hybrid reactive molecular dynamics / time-stamped force-bias Monte Carlo simulations of randomized plasma-surface interactions / diffusion processes are used to setup a physics-separating artificial neural network. The application of this generic machine learning model to a specific experimental reference case study enables the systematic analysis of the particle flux emission as well as underlying system state (e.g., composition, density, point defect structure) evolution within process times of up to 45 minutes.

“…Bombardment of such energetic particles prior to and during the initial stages of film formation may improve the film adherence to the substrate, i.e. by removing or reacting with contaminant surface layers [3,4]. Sugai found the main advantages of his method to be the low power consumption (2 W at 20 keV) and no need for external heating to obtain films.…”

Section: Introductionmentioning

confidence: 99%

Electrostatic powder impact deposition (EPID) of Ge on Si and Cu substrates, microstructure and morphology study

Svavarsson

Ólafsson

Hellgren

et al. 2000

Electrostatic powder impact deposition (EPID) is a novel method to deposit thin films by electrostatic acceleration of powder material between charged plates in vacuum. The EPID method has been used to deposit Ge films on Si and Cu substrates at room temperature. Surface morphology and microstructure as studied by SEM showed a very rough surface. XRD and RBS measurements revealed that the films were mostly nanocrystalline or amorphous Ge oxide. The grain size distribution of the Ge powder was measured before and after deposition. Initial distribution showed a median grain size of 32 µm and distribution width of 80 µm. After the deposition the median grain size had decreased to 16 µm and the width decreased to 55 µm. The grain size of the deposited film was less than 1 µm.