ANOVA Method for Variance Component Decomposition and Diagnosis in Batch Manufacturing Processes

Abstract

In batch manufacturing processes, the total process variation is generally decomposed into batch-by-batch variation and within-batch variation. Since different variation components may be caused by different sources, separation, testing, and estimation of each variance component are essential to the process improvement. Most of the previous SPC research emphasized reducing variations due to assignable causes by implementing control charts for process monitoring. Different from this focus, this article aims to analyze and reduce inherent natural process variations by applying the ANOVA method. The key issue of using the ANOVA method is how to develop appropriate statistical models for all variation components of interest. The article provides a generic framework for decomposition of three typical variation components in batch manufacturing processes. For the purpose of variation root causes diagnosis, the corresponding linear contrasts are defined to represent the possible site variation patterns and the statistical nested effect models are developed accordingly. The article shows that the use of a full factor decomposition model can expedite the determination of the number of nested effect models and the model structure. Finally, an example is given for the variation reduction in the screening conductive gridline printing process for solar battery fabrication.