Cumulative correspondence analysis of ordered categorical data from industrial experiments

Abstract

Most studies of quality improvement deal with ordered categorical data from industrial experiments. Accounting for the ordering of such data plays an important role in effectively determining the optimal factor level of combination. This paper utilizes the correspondence analysis to develop a procedure to improve the ordered categorical response in a multifactor state system based on Taguchi's statistic. Users may find the proposed procedure in this paper to be attractive because we suggest a simple and also popular statistical tool for graphically identifying the really important factors and determining the levels to improve process quality. A case study for optimizing the polysilicon deposition process in a very large-scale integrated circuit is provided to demonstrate the effectiveness of the proposed procedure.

Keywords:

• ordered categories
• correspondence analysis
• quality engineering
• experimental design
• Taguchi's statistic

Acknowledgements

The participation of Biagio Simonetti was funded by a FIRB 2003 grant held by a local research unit of Benevento lead by Prof Filippo Bencardino. The authors are grateful to Shelemyahu Zacks, Keith M. Jones, the Editor, and the two anonymous referees for their very helpful comments that contributed significantly to the improvement of this paper.