Using Profile Monitoring Techniques for a Data‐rich Environment with Huge Sample Size

In-process sensors with huge sample size are becoming popular in the modern manufacturing industry, due to the increasing complexity of processes and products and the availability of advanced sensing technology. Under such a data-rich environment, a sample with huge size usually violates the assumption of homogeneity and degrades the detection performance of a conventional control chart. Instead of charting summary statistics such as the mean and standard deviation of observations that assume homogeneity within a sample, this paper proposes charting schemes based on the quantile–quantile (Q–Q) plot and profile monitoring techniques to improve the performance. Different monitoring schemes are studied based on various shift patterns in a huge sample and compared via simulation. Guidelines are provided for applying the proposed schemes to similar industrial applications in a data-rich environment. Copyright © 2005 John Wiley & Sons, Ltd.

[1]  Beat Kleiner,et al.  Graphical Methods for Data Analysis , 1983 .

[2]  Ralph B. D'Agostino,et al.  Goodness-of-Fit-Techniques , 2020 .

[3]  Mahmoud A. Mahmoud,et al.  On the Monitoring of Linear Profiles , 2003 .

[4]  Steven A. Yourstone,et al.  Non‐Normality and the Design of Control Charts for Averages* , 1992 .

[5]  William H. Woodall,et al.  Controversies and Contradictions in Statistical Process Control , 2000 .

[6]  S. W. Butler,et al.  Supervisory run-to-run control of polysilicon gate etch using in situ ellipsometry , 1994 .

[7]  Horng-Chih Lin,et al.  Evaluation of plasma charging damage in ultrathin gate oxides , 1998, IEEE Electron Device Letters.

[8]  Douglas C. Montgomery,et al.  Using Control Charts to Monitor Process and Product Quality Profiles , 2004 .

[9]  Stephen V. Crowder,et al.  An EWMA for Monitoring a Process Standard Deviation , 1992 .

[10]  S. P. Wright,et al.  Comparing Curves Using Additive Models , 2002 .

[11]  Lan Kang,et al.  On-Line Monitoring When the Process Yields a Linear Profile , 2000 .

[12]  Gudrun Freitag,et al.  Guidelines on Dissolution Profile Comparison , 2001 .

[13]  Rudolf G. Kittlitz,et al.  Detection of Multiple Change Points from Clustering Individual Observations , 2003 .

[14]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[15]  Anthony J. Walton,et al.  Automating and sequencing C-V measurements for process fault diagnosis using a pattern-recognition approach (MOS test structure) , 1989, Proceedings of the 1989 International Conference on Microelectronic Test Structures.

[16]  J. Cai,et al.  Monitoring interface traps by DCIV method , 1999 .

[17]  Youn Min Chou,et al.  Transforming Non-Normal Data to Normality in Statistical Process Control , 1998 .

[18]  Maurice G. Kendall.  The advanced theory of statistics , 1958 .

[19]  M. Kendall,et al.  Kendall's advanced theory of statistics , 1995 .

[20]  Michael J. A. Berry,et al.  Mastering Data Mining: The Art and Science of Customer Relationship Management , 1999 .