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 : andesyoung.tw@gmail.com

Abstract

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.