MATEC Web Conf.
Volume 119, 2017The Fifth International Multi-Conference on Engineering and Technology Innovation 2016 (IMETI 2016)
|Number of page(s)||8|
|Published online||04 August 2017|
Do peak/slack seasons influence semiconductor machine outliers? A back propagation neural network analysis
1 Department of Information Management, Hwa Hsia University of Technology, New Taipei City 235, Taiwan
2 Department of Business Administration, National Taipei University of Business, Taipei City 100, Taiwan
3 Institute of Information Management, National Chiao Tung University, Hsinchu 300, Taiwan
a Corresponding author : email@example.com
The semiconductor industry is often affected by the economy impact, which also influences the production schedule planning. The back propagation neural network model has the advantages of great precision and effectiveness. This research uses Novellus Vector Machine and its Remote Process Controller (RPC) function to collect the data. This study detects the gas transmission pressure of chamber. We uses fault detection and classification (FDC) to analyze the model. FDC can 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 back propagation neural network model and gray relational analysis as tools to analyze the data and detect the semiconductor machine outliers. The findings indicate that peak seasons have less outliers than slack seasons.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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