Applying Artificial Neural Network to Predict Semiconductor Machine Outliers
Joint Authors
Yang, Keng-Chieh
Yang, Conna
Chao, Pei-Yao
Shih, Po-Hong
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-11-25
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Advanced semiconductor processes are produced by very sophisticated and complex machines.
The demand of higher precision for the monitoring system is becoming more vital when the devices are shrunk into smaller sizes.
The high quality and high solution checking mechanism must rely on the advanced information systems, such as fault detection and classification (FDC).
FDC can timely detect the deviations of the machine parameters when the parameters deviate from the original value and exceed the range of the specification.
This study adopts backpropagation neural network model and gray relational analysis as tools to analyze the data.
This study uses FDC data to detect the semiconductor machine outliers.
Data collected for network training are in three different intervals: 6-month period, 3-month period, and one-month period.
The results demonstrate that 3-month period has the best result.
However, 6-month period has the worst result.
The findings indicate that machine deteriorates quickly after continuous use for 6 months.
The equipment engineers and managers can take care of this phenomenon and make the production yield better.
American Psychological Association (APA)
Yang, Keng-Chieh& Yang, Conna& Chao, Pei-Yao& Shih, Po-Hong. 2013. Applying Artificial Neural Network to Predict Semiconductor Machine Outliers. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-1008709
Modern Language Association (MLA)
Yang, Keng-Chieh…[et al.]. Applying Artificial Neural Network to Predict Semiconductor Machine Outliers. Mathematical Problems in Engineering No. 2013 (2013), pp.1-10.
https://search.emarefa.net/detail/BIM-1008709
American Medical Association (AMA)
Yang, Keng-Chieh& Yang, Conna& Chao, Pei-Yao& Shih, Po-Hong. Applying Artificial Neural Network to Predict Semiconductor Machine Outliers. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-1008709
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1008709