Kim Byungwhan, Park Min-Geun
Department of Electronic Engineering, Sejong University, 98, Goonja-Dong, Kwangjin-Gu, Seoul 143-747, Korea.
Appl Spectrosc. 2006 Oct;60(10):1192-7. doi: 10.1366/000370206778664554.
A new model for controlling plasma processes was constructed by combining atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS), and neural networks. The applicability of XPS to modeling etch rate was also investigated, as well as the impact of dc bias inclusion. The back-propagation neural network was used to find complex relationships between XPS and AFM data. This technique was evaluated with the etching data characterized by a 2(4) full factorial experiment. Five prediction models of surface roughness were constructed and compared. The Type I model refers to the model constructed with conventional process parameters. The Type II and III models were built with XPS and XPS plus dc bias data, respectively. The remaining Type IV and V models refer to those constructed with principal component analysis (PCA) reduced-XPS and PCA reduced-XPS plus dc bias, respectively. Mode prediction performance was evaluated as a function of training factor. In predicting the surface roughness, the Type II model yielded an improved prediction of 39% with respect to the Type IV model. The improvement was also demonstrated in modeling the etch rate. These results indicate that utilizing full XPS data is more effective for improving the model prediction performance. The advantage of XPS data was more conspicuous in constructing the surface roughness model.
通过结合原子力显微镜（AFM）、X射线光电子能谱（XPS）和神经网络构建了一种用于控制等离子体过程的新模型。还研究了XPS在蚀刻速率建模中的适用性，以及包含直流偏压的影响。使用反向传播神经网络来寻找XPS和AFM数据之间的复杂关系。该技术通过以2(4)全因子实验表征的蚀刻数据进行评估。构建并比较了五个表面粗糙度预测模型。I型模型是指用传统工艺参数构建的模型。II型和III型模型分别用XPS和XPS加直流偏压数据构建。其余的IV型和V型模型分别指用主成分分析（PCA）降维后的XPS和PCA降维后的XPS加直流偏压构建的模型。根据训练因子评估模型预测性能。在预测表面粗糙度时，II型模型相对于IV型模型的预测改进了39%。在蚀刻速率建模中也证明了这种改进。这些结果表明，利用完整的XPS数据对于提高模型预测性能更有效。XPS数据在构建表面粗糙度模型中的优势更为明显。
