Locating disturbances in semiconductor manufacturing with stepwise regression

The ability to locate disturbances in semiconductor manufacturing processes is critical to developing and maintaining a high yield. Analysis of variance (ANOVA), the best current practice for this problem, consists of conducting a series of hypothesis tests at each individual processing step. This a...

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Bibliographic Details
Published in:IEEE transactions on semiconductor manufacturing 2005-08, Vol.18 (3), p.458-468
Main Authors: McCray, A.T., McNames, J., Abercrombie, D.
Format: Article
Language:English
Subjects:
Analysis of variance
Analysis of variance (ANOVA)
Applied sciences
Circuit testing
Computer simulation
Disturbances
Electric variables measurement
Electronics
Exact sciences and technology
False alarms
fault isolation
Large scale integration
Logic
Manufacturing processes
Microelectronic fabrication (materials and surfaces technology)
Monitoring
Monte Carlo methods
Positioning
Process control
Regression
Scheduling algorithm
Semiconductor device manufacture
Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices
semiconductor manufacturing
Semiconductors
statistical process control and monitoring
stepwise regression
variance reduction
variance source isolation
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Summary:The ability to locate disturbances in semiconductor manufacturing processes is critical to developing and maintaining a high yield. Analysis of variance (ANOVA), the best current practice for this problem, consists of conducting a series of hypothesis tests at each individual processing step. This approach can lead to excessive false alarms and limited sensitivity when the process contains more than one disturbance. We describe how this problem can be framed as a subset selection problem and propose two new methods based on stepwise regression. Results of over 90 000 Monte Carlo simulations suggest that these new SWR methods locate disturbances with fewer false positives and false negatives than ANOVA. This means process engineers will spend less time responding to false alarms and will be able to locate real disturbances more often.
ISSN:0894-6507
1558-2345
DOI:10.1109/TSM.2005.852118