• Jan 21, 2020 News! Vol.11, No.1- Vol.11, No.3 has been indexed by EI(Inspec)!   [Click]
  • Apr 27, 2020 News!Vol.12, No. 2 has been published with online version.   [Click]
  • Feb 08, 2017 News!Welcome Assoc. Prof. Lei Chen from China to join the Editorial board of IJET.   [Click]
General Information
    • ISSN: 1793-8236 (Online)
    • Abbreviated Title Int. J. Eng. Technol.
    • Frequency:  Quarterly 
    • DOI: 10.7763/IJET
    • Executive Editor: Ms.Yoyo Y. Zhou
    • Abstracting/ Indexing: Chemical Abstracts Services (CAS) EBSCO, Google Scholar, Ulrich Periodicals Directory, Crossref, ProQuest, Index CopernicusEI (INSPEC, IET).
    • E-mail: ijet@vip.163.com
Prof. T. Hikmet Karakoc
Anadolu University, Faculty of Aeronautics and Astronautics, Turkey

IJET 2016 Vol.8(4): 273-285 ISSN: 1793-8236
DOI: 10.7763/IJET.2016.V8.898

Predictive Models for Equipment Fault Detection in the Semiconductor Manufacturing Process

Sathyan Munirathinam and Balakrishnan Ramadoss
Abstract—Semiconductor manufacturing is one of the most technologically and highly complicated manufacturing processes. Traditional machine learning algorithms such as uni-variate and multivariate analyses have long been deployed as a tool for creating predictive model to detect faults. In the past decade major collaborative research projects have been undertaken between fab industries and academia in the areas of predictive modeling. In this paper we review some of these research areas and thus propose machine learning techniques to automatically generate an accurate predictive model to predict equipment faults during the wafer fabrication process of the semiconductor industries. This research paper aims at constructing a decision model to help detecting as quickly as possible any equipment faults in order to maintain high process yields in manufacturing. In this research, we use WEKA tool and R languages for implementing our proposed method and other five machine learning discovery techniques.

Index Terms—redictive model, semiconductor manufacturing process, machine learning, data classification, feature selection, R language, and python language.

Sathyan Munirathinam is with Micron Technology and PhD researcher at Bharathiar University, India (e-mail: Sathyan.Munirathinam@gmail.com).
Balakrishnan Ramadoss is with National Institute of Technology, Trichy, India (e-mail: brama@nitt.edu).


Cite: Sathyan Munirathinam and Balakrishnan Ramadoss, "Predictive Models for Equipment Fault Detection in the Semiconductor Manufacturing Process," International Journal of Engineering and Technology vol. 8, no. 4, pp. 273-285, 2016.

Copyright © 2008-2020. International Journal of Engineering and Technology. All rights reserved. 
E-mail: ijet@vip.163.com