Data mining for cycle time key factor identification and prediction in semiconductor manufacturing

Y. Meidan; B. Lerner; M. Hassoun; G. Rabinowitz

doi:10.3182/20090603-3-RU-2001.0466

Data mining for cycle time key factor identification and prediction in semiconductor manufacturing

Y. Meidan, B. Lerner, M. Hassoun, G. Rabinowitz

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

We suggest a data-driven methodology to identify key factors of the cycle time (CT) in a semiconductor manufacturing plant and to predict its value. We first extract a data set from a simulated fab and describe each operation in the set using 182 features (factors). Then, we apply conditional mutual information maximization for feature selection and the selective naïve Bayesian classifier for further selection and CT prediction. Prediction accuracy of 72.6% is achieved by employing no more than 20 features. Similar results are obtained by neural networks and the C5.0 decision tree.

Original language	English
Title of host publication	Proceedings of the 13th IFAC Symposium on Information Control Problems in Manufacturing, INCOM'09
Publisher	IFAC Secretariat
Pages	217-222
Number of pages	6
Edition	4 PART 1
ISBN (Print)	9783902661432
DOIs	https://doi.org/10.3182/20090603-3-RU-2001.0466
State	Published - 2009
Externally published	Yes

Publication series

Name	IFAC Proceedings Volumes (IFAC-PapersOnline)
Number	4 PART 1
Volume	42
ISSN (Print)	1474-6670

Keywords

Industrial plant control
Machine learning
Naïve Bayesian classifier
Probabilistic models

Access to Document

10.3182/20090603-3-RU-2001.0466

Cite this

Meidan, Y., Lerner, B., Hassoun, M., & Rabinowitz, G. (2009). Data mining for cycle time key factor identification and prediction in semiconductor manufacturing. In Proceedings of the 13th IFAC Symposium on Information Control Problems in Manufacturing, INCOM'09 (4 PART 1 ed., pp. 217-222). (IFAC Proceedings Volumes (IFAC-PapersOnline); Vol. 42, No. 4 PART 1). IFAC Secretariat. https://doi.org/10.3182/20090603-3-RU-2001.0466

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title = "Data mining for cycle time key factor identification and prediction in semiconductor manufacturing",

abstract = "We suggest a data-driven methodology to identify key factors of the cycle time (CT) in a semiconductor manufacturing plant and to predict its value. We first extract a data set from a simulated fab and describe each operation in the set using 182 features (factors). Then, we apply conditional mutual information maximization for feature selection and the selective na{\"i}ve Bayesian classifier for further selection and CT prediction. Prediction accuracy of 72.6% is achieved by employing no more than 20 features. Similar results are obtained by neural networks and the C5.0 decision tree.",

keywords = "Industrial plant control, Machine learning, Na{\"i}ve Bayesian classifier, Probabilistic models",

author = "Y. Meidan and B. Lerner and M. Hassoun and G. Rabinowitz",

note = "Funding Information: Acknowledgment: This work was supported in part by the Paul Ivanier Center for Robotics and Production Management, Ben-Gurion University, Beer-Sheva, Israel.",

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Meidan, Y, Lerner, B, Hassoun, M & Rabinowitz, G 2009, Data mining for cycle time key factor identification and prediction in semiconductor manufacturing. in Proceedings of the 13th IFAC Symposium on Information Control Problems in Manufacturing, INCOM'09. 4 PART 1 edn, IFAC Proceedings Volumes (IFAC-PapersOnline), no. 4 PART 1, vol. 42, IFAC Secretariat, pp. 217-222. https://doi.org/10.3182/20090603-3-RU-2001.0466

Data mining for cycle time key factor identification and prediction in semiconductor manufacturing. / Meidan, Y.; Lerner, B.; Hassoun, M. et al.
Proceedings of the 13th IFAC Symposium on Information Control Problems in Manufacturing, INCOM'09. 4 PART 1. ed. IFAC Secretariat, 2009. p. 217-222 (IFAC Proceedings Volumes (IFAC-PapersOnline); Vol. 42, No. 4 PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Meidan Y, Lerner B, Hassoun M, Rabinowitz G. Data mining for cycle time key factor identification and prediction in semiconductor manufacturing. In Proceedings of the 13th IFAC Symposium on Information Control Problems in Manufacturing, INCOM'09. 4 PART 1 ed. IFAC Secretariat. 2009. p. 217-222. (IFAC Proceedings Volumes (IFAC-PapersOnline); 4 PART 1). doi: 10.3182/20090603-3-RU-2001.0466

Data mining for cycle time key factor identification and prediction in semiconductor manufacturing

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