Breakdown of dynamic scaling in surface growth under shadowing

Area of Science:

• Materials Science
• Surface Physics
• Thin Film Growth

Background:

• Thin film deposition techniques like sputter deposition and chemical vapor deposition are crucial for various applications.
• Surface morphology evolution during deposition is often described by dynamic scaling theory, assuming self-affinity.
• However, experimental observations sometimes deviate from these predictions, indicating the need for refined models.

Purpose of the Study:

• To investigate the formation and temporal evolution of mound structures in thin films.
• To differentiate the scaling behavior of the mound separation wavelength (lambda) from the mound size (lateral correlation length, xi).
• To elucidate the role of nonlocal growth effects, such as shadowing, in mound formation.

Main Methods:

• Utilizing Monte Carlo simulations to model thin film growth processes.
• Comparing simulation results with experimental data from sputter deposition and chemical vapor deposition.
• Analyzing the temporal evolution of characteristic length scales like wavelength and mound size.

Main Results:

• A distinct mound structure with a characteristic separation wavelength (lambda) is formed during thin film deposition.
• The temporal evolution of lambda follows a power law, lambda ~ t^p, with p approximately 0.5, independent of growth conditions.
• The mound size (xi) evolves as xi ~ t^(1-z), where 1/z is dependent on specific growth conditions, showing distinct scaling from lambda.
• Nonlocal effects like shadowing break the self-affinity assumption of traditional dynamic scaling theory.

Conclusions:

• Mound formation in thin films is governed by nonlocal growth mechanisms, leading to a characteristic wavelength.
• The distinct temporal scaling of wavelength and mound size necessitates a departure from simple self-affine dynamic scaling models.
• The findings provide a deeper understanding of surface morphology evolution in thin film deposition, relevant for optimizing material properties.