Probing growth precursor diffusion lengths by inter-surface diffusion

TL;DR

This paper introduces a fitting method to measure the diffusion length of growth precursors in thin-film deposition by analyzing inter-surface diffusion profiles, enabling in-situ characterization under challenging conditions.

Contribution

It develops a theoretical growth model and a simulation-based fitting procedure to determine precursor diffusion lengths from experimental profiles, addressing measurement challenges in harsh environments.

Findings

The method accurately estimates diffusion lengths from simulated profiles.

Robustness of the fitting procedure against profile noise and resolution.

Application potential for plasma-enhanced chemical vapor deposition processes.

Abstract

Understanding and optimizing thin-film synthesis requires measuring the diffusion length $d_\alpha$ of adsorbed growth precursors. Despite technological advances, in-situ measurements of $d_\alpha$ are often unachievable due to harsh deposition conditions, such as high temperatures or reactive environments. In this paper, we propose a fitting approach to determine $d_\alpha$ from experimental data by leveraging inter-surface diffusion between a substrate and a strip obtained by, for example, processing a film. The substrate serves as a source or sink of precursors, influencing the growth dynamics and shaping the profile of the strip. By fitting simulated profiles to given profiles, we demonstrate that $d_\alpha$ can be determined. To achieve this, we develop a theoretical growth model, a simulation strategy, and a fitting procedure. The growth model incorporates inter-surface diffusion, adsorption, and desorption of growth precursors, with growth being proportional to the concentration of adsorbed precursors. In our simulations, a chain of nodes represents a profile, and growth is captured by the displacement of those nodes, while keeping the node density approximately constant. For strips significantly wider than $d_\alpha$, a scaled precursor concentration and $d_\alpha$ are the fitting parameters that are determined by minimizing a suitably defined measure of the distance between simulated and given profiles. We evaluate the robustness of our procedure by analyzing the effect of profile resolution and noise on the fitted parameters. Our approach can offer valuable insights into thin-film growth processes, such as those occurring during plasma-enhanced chemical vapor deposition.