Authors:
Sumika Arima
1
;
Takuya Nagata
2
;
Huizhen Bu
2
and
Satsuki Shimada
2
Affiliations:
1
Faculty of Engineering, Information, and Systems, University of Tsukuba, Tsukuba, Ibaraki Pref. and Japan
;
2
Graduate School of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki Pref. and Japan
Keyword(s):
Virtual Metrology (VM), PCA (Principal Component Analysis), LASSO (Least Absolute Shrinkage and Selection Operator), Support Vector Machines (SVM), Multi-class Discrimination, Variable Extractions.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Automation of Operations
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Health Engineering and Technology Applications
;
Knowledge-Based Systems
;
Operational Research
;
Pattern Recognition
;
Software Engineering
;
Symbolic Systems
Abstract:
This paper discussed the virtual metrology (VM) modelling of multi-class quality to describe the relationship between the variables of a production machine's condition and the estimated/forecasted product quality soon after finishing the machine processing. Applications of PCA and LASSO technique of the Sparse modelling were introduced to define the multi-dimensional quality. Because the high accuracy and quick computations are required for the VM modelling, in this study, the PCA-LASSO combination was applied before building the VM models based on the kernel SVM (kSVM), particularly the linear kernel for real-time use. As the result of evaluation of a CVD (Chemical vapor deposition) process in an actual semiconductor factory, LASSO and linear-SVM could reduce the scale of the machine variable's set and calculation time by almost 57% and 95% without deterioration of accuracy even without PCA. In addition, as the PCA-LASSO, the multi-dimensional quality was rotated to the orthogonalit
y space by PCA to summarize the extracted variables responding to the primary independent hyperspace. As the result of the PCA-LASSO combination, the scale of machine variables extracted was improved by 83%, besides the accuracy of the linear-SVM is 98%. It is also effective as the pre-process of Partial Least Square (PLS).
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