2025/07/14 by Joel Saucedo, Saucedo, Joel, Uday Lamba +3
Earth and Planetary Sciences · Engineering · Materials Science · #Copper Interconnects and Reliability #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci) #Plasma Diagnostics and Applications #Plasma Physics (physics.plasm-ph) #Statistical Mechanics (cond-mat.stat-mech) #nanoparticles nucleation surface interactions
paper · pdf · doi:10.48550/arxiv.2507.10645
openalex publication_date 2025/07/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Connecting plasma processing parameters to the resultant film microstructure remains a fundamental challenge in materials synthesis, one that has largely confined process design to empirical approaches. To bridge this gap, we develop a predictive analysis of coupling by applying a renormalization group (RG) analysis to an effective Hamiltonian for the stochastic dynamics of the plasma-surface interface, derived systematically from microscopic principles. The central result from this formalism is the system's exhibition of asymptotic freedom; the effective dimensionless coupling, g, between the plasma and the growing surface is found to weaken systematically at macroscopic length scales, a finding that provides a rigorous justification for the success of continuum-level models in describing large-scale film evolution. The RG framework yields a non-perturbative scaling relation for the mean grain area, ⟨ A ⟩ ∝ exp(κ/g), where g itself is defined by fundamental parameters such as ion flux (Φ) and ion collision time (τion). This relation reveals the origin of widely-observed empirical power-law scaling, showing it to be an effective behavior limited to specific process regimes. Crucially, the model furnishes sharp, testable predictions, including the pressure-independence of grain size within collision-dominated plasmas and a parameter-free criterion, Λc = 1/(n2-1), for the onset of morphological instability and faceting based on crystal symmetry. This work establishes a quantitative, parameter-sparse engine for predicting and ultimately controlling microstructural outcomes in thin film synthesis.