Fault Detection Based on Statistical Multivariate Analysis and Microarray Visualization

• Published in: IEEE transactions on industrial informatics Vol. 6; no. 1; pp. 18 - 24
• Main Authors: Ming-Da Ma, Wong, D.S.-H., Shi-Shang Jang, Sheng-Tsaing Tseng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.02.2010; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Control charts; Deoxyribonucleic acid; Discrimination; Etching; Fault detection; Manufacturing processes; microarray; Monitoring; Multistage; Principal component analysis; Process control; quality improvement; Regression; Regression analysis; Semiconductor device manufacture; semiconductor manufacturing; Statistical analysis; Visualization; Wilcoxon rank-sum test
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2009.2030793

In this work, a statistical method is proposed to mine out key variables from a large set of variables recorded in a limited number of runs through a multistage multistep manufacturing process. The method employed well-known single variable or multivariable techniques of discrimination and regression but also presented a synopsis of analysis results in a colored map of p-values very similar to a DNA microarray. This framework provides a systematic method of drawing inferences from the available evidence without interrupting the normal process operation. The proposed concept is illustrated by two industrial examples.