Title :
How to simultaneously reduce α and β error with SPC? A multivariate process control approach
Author :
Nasongkhla, Ruj ; Shanthikumar, J. George ; Nurani, Raman K. ; McIntyre, Mike
Author_Institution :
Dept. of Ind. Eng. & Oper. Res., California Univ., Berkeley, CA, USA
Abstract :
We describe the multivariate statistical process control approach which uses a weighted average metric as a metric plotted on a control chart. We show that the optimal weighted coefficient is a function of the mean-shift vector and covariance matrix of metrics of interest. The control chart constructed by this optimal weighted average metric will have the highest signal to noise ratio and the lowest α and β errors. A numerical example using actual data from a fab is also provided
Keywords :
covariance analysis; covariance matrices; error analysis; integrated circuit measurement; integrated circuit yield; multivariable control systems; numerical analysis; statistical process control; SPC; alpha error reduction; beta error reduction; control chart; covariance matrix; in-line metrics; mean-shift vector; multivariate process control; multivariate statistical process control; numerical analysis; optimal weighted average metric; optimal weighted coefficient; signal to noise ratio; wafer fab data; weighted average metric; yield-limiting excursions; Control charts; Covariance matrix; Error correction; Industrial engineering; Manufacturing processes; Operations research; Principal component analysis; Process control; Semiconductor device manufacture; Signal to noise ratio;
Conference_Titel :
Advanced Semiconductor Manufacturing Conference and Workshop, 1998. 1998 IEEE/SEMI
Conference_Location :
Boston, MA
Print_ISBN :
0-7803-4380-8
DOI :
10.1109/ASMC.1998.731373
