Abstract
This study uses the artificial neural back-propagation network model to detect the outliers in semiconductor machines. The 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. Our experimental results show that three-month period of network training data possesses the best results. We suggest that the prediction and model training be around 3 months.
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Yang, KC., Huang, CH., Yang, C., Chao, PY., Shih, PH. (2014). Using Artificial Neural Back-Propagation Network Model to Detect the Outliers in Semiconductor Manufacturing Machines. In: Ali, M., Pan, JS., Chen, SM., Horng, MF. (eds) Modern Advances in Applied Intelligence. IEA/AIE 2014. Lecture Notes in Computer Science(), vol 8482. Springer, Cham. https://doi.org/10.1007/978-3-319-07467-2_26
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DOI: https://doi.org/10.1007/978-3-319-07467-2_26
Publisher Name: Springer, Cham
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