通过表面间扩散探测生长前驱体扩散长度
Probing growth precursor diffusion lengths by inter-surface diffusion
AI总结：
本文提出一种通过基底与条带间表面间扩散拟合实验轮廓的方法，以确定生长前驱体扩散长度，并开发了理论模型、模拟策略和拟合程序，为薄膜生长提供见解。
摘要：
理解和优化薄膜合成需要测量吸附生长前驱体的扩散长度 $d_α$。尽管技术进步，但由于高温或反应性环境等严苛沉积条件，原位测量 $d_α$ 往往难以实现。在本文中，我们提出一种拟合方法，利用基底与（例如通过处理薄膜获得的）条带之间的表面间扩散，从实验数据确定 $d_α$。基底充当先驱体的源或汇，影响生长动力学并塑造条带的轮廓。通过将模拟轮廓拟合到给定轮廓，我们证明可以确定 $d_α$。为此，我们开发了理论生长模型、模拟策略和拟合程序。生长模型包含表面间扩散、吸附和解吸，生长与吸附前驱体浓度成正比。在我们的模拟中，节点链代表轮廓，生长通过节点位移捕获，同时保持节点密度近似恒定。对于远宽于 $d_α$ 的条带，缩放的前驱体浓度和 $d_α$ 是拟合参数，通过最小化模拟轮廓与给定轮廓之间适当定义的距离度量来确定。我们通过分析轮廓分辨率和噪声对拟合参数的影响来评估程序的鲁棒性。我们的方法可为薄膜生长过程（如等离子体增强化学气相沉积过程中发生的那些）提供有价值的见解。
英文摘要：
Understanding and optimizing thin-film synthesis requires measuring the diffusion length $d_α$ of adsorbed growth precursors. Despite technological advances, in-situ measurements of $d_α$ 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 $d_α$ 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 $d_α$ 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 $d_α$, a scaled precursor concentration and $d_α$ 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.
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