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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

ISBN: 978-989-758-352-0

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 orthogonality 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). (More)

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Paper citation in several formats:
Arima, S.; Nagata, T.; Bu, H. and Shimada, S. (2019). Applications of Sparse Modelling and Principle Component Analysis for the Virtual Metrology of Comprehensive Multi-dimensional Quality.In Proceedings of the 8th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-352-0, pages 354-361. DOI: 10.5220/0007385603540361

@conference{icores19,
author={Sumika Arima. and Takuya Nagata. and Huizhen Bu. and Satsuki Shimada.},
title={Applications of Sparse Modelling and Principle Component Analysis for the Virtual Metrology of Comprehensive Multi-dimensional Quality},
booktitle={Proceedings of the 8th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2019},
pages={354-361},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007385603540361},
isbn={978-989-758-352-0},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - Applications of Sparse Modelling and Principle Component Analysis for the Virtual Metrology of Comprehensive Multi-dimensional Quality
SN - 978-989-758-352-0
AU - Arima, S.
AU - Nagata, T.
AU - Bu, H.
AU - Shimada, S.
PY - 2019
SP - 354
EP - 361
DO - 10.5220/0007385603540361

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