Additive-Assisted Nucleation and Growth by Electrodeposition II. Mathematical Model and Comparison with Experimental Data
Additive-Assisted Nucleation and Growth by Electrodeposition II. Mathematical Model and Comparison with Experimental Data
复制标题
电镀辅助成核和生长 II。
DOI：
10.1149/1.3183505
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
R. Alkire
中科院分区：
文献类型：
--
作者：
R. Stephens;M. Willis;R. Alkire
采用“岛动力学”数值方法研究了添加剂存在下铜电沉积过程中的动力学限制形核和生长。该系统的几何形状由金属基板最初图案化与铜种子集群的正方形阵列。模拟使用的反应速率常数的估计值与添加剂系统组成的酸性硫酸盐电解质含有“加速剂”和“抑制剂”的物种。数值结果获得的最近邻距离和距离的核从种子集群的概率分布，并与本系列的第一部分中报道的可比实验数据进行了比较。数值结果与实验趋势定性一致，与添加剂组合物，阵列间距，和施加的电位的变化。有利于形成高成核密度的条件是高氯化物（Cl -）和高聚（乙二醇）（PEG）浓度;在这种情况下，人们发现，90%的Au表面被覆盖的铜通过0.1 mC/cm 2。有利于沉积到预先存在的种子集群的条件是低（Cl -）浓度，中等（PEG）浓度，和紧密间隔的集群。这里报告的结果提供了一个基础，开发改进的参数估计程序的基础上的优化方法。
The "island dynamics" numerical method was used to investigate kinetically limited nucleation and growth during copper electrodeposition in the presence of additives. The system geometry consisted of a metal substrate initially patterned with a square array of Cu seed clusters. The simulations used estimated values of the reaction rate constants associated with an additive system consisting of acid sulfate electrolyte containing "accelerator" and "suppressor" species. Numerical results were obtained for the probability distributions for nearest-neighbor distance and for distance of nuclei from the seed cluster, and were compared with comparable experimental data reported in Part I of this series. Numerical results were in qualitative agreement with experimental trends associated with variations in additive composition, array spacing, and applied potential. Conditions that favored formation of high nucleation density were high chloride (Cl - ) and high poly(ethylene glycol) (PEG) concentrations; in this case, it was found that 90% of the Au surface was covered by Cu upon passage of 0.1 mC/cm 2 . Conditions that favored deposition onto pre-existing seed clusters were low (Cl - ) concentration, moderate (PEG) concentration, and closely spaced clusters. The results reported here provide a foundation for developing improved parameter estimation procedures based on optimization methods.