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arXiv 0905.3370cond-mat.mtrl-scicond-mat.dis-nn
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薄膜生长动力学中的网络行为

Network Behavior in Thin Film Growth Dynamics

Tansel Karabacak, Hasan Guclu, Murat Yuksel

摘要:

我们提出了一种新的网络建模方法,用于各种薄膜生长技术,包括重新发射的颗粒,由于非统一的粘附系数。我们将粒子从一个表面位置到另一个表面位置的再发射模拟为网络链接,并生成对应于薄膜生长的网络模型。蒙特卡罗模拟用于生长薄膜和动态跟踪再发射粒子的轨迹。我们进行了模拟法向入射,斜角,和化学气相沉积(CVD)技术。每种沉积方法导致不同的动态演变的表面形态,由于不同的粘附系数所涉及的和不同的强度的阴影效应起源于倾斜入射粒子。传统的表面形貌动态标度分析不能指出任何普遍的行为。另一方面,我们详细的网络分析表明,度分布,加权平均度与度,距离分布独立于所使用的粘附系数,有时甚至独立于增长技术存在普遍的行为。我们还观察到,在高粘附系数CVD和斜角沉积的网络交通主要发生在所形成的柱状结构的边缘之间,而小粘附系数CVD和法向角沉积,产生更光滑的表面的山丘和山谷之间的网络交通是更均匀和短距离的。

英文摘要:

We present a new network modeling approach for various thin film growth techniques that incorporates re-emitted particles due to the non-unity sticking coefficients. We model re-emission of a particle from one surface site to another one as a network link, and generate a network model corresponding to the thin film growth. Monte Carlo simulations are used to grow films and dynamically track the trajectories of re-emitted particles. We performed simulations for normal incidence, oblique angle, and chemical vapor deposition (CVD) techniques. Each deposition method leads to a different dynamic evolution of surface morphology due to different sticking coefficients involved and different strength of shadowing effect originating from the obliquely incident particles. Traditional dynamic scaling analysis on surface morphology cannot point to any universal behavior. On the other hand, our detailed network analysis reveals that there exist universal behaviors in degree distributions, weighted average degree versus degree, and distance distributions independent of the sticking coefficient used and sometimes even independent of the growth technique. We also observe that network traffic during high sticking coefficient CVD and oblique angle deposition occurs mainly among edges of the columnar structures formed, while it is more uniform and short-range among hills and valleys of small sticking coefficient CVD and normal angle depositions that produce smoother surfaces.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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