Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/631794
Title: Simultaneous Prediction for Multiple Key Performance Indicators in Semiconductor Wafer Fabrication
Authors: Zhengcai Cao;Xuelian Liu;Jinghua Hao;Min Liu
subject: MKPI prediction model|selective naive Bayesian classifier|cycle time|multiple key performance indicators|BNN|Bayesian neural network|equipment utilization|Bayes theorem|weight analysis method|prediction methodology|semiconductor wafer fabrication|multiobjective optimization|ANN|closed-loop structure|SNBC|semiconductor manufacturing industry|artificial neural network
Year: 2016
Publisher: IEEE
Abstract: The prediction and key factors identification for lot Cycle time (CT) and Equipment utilization (EU) which remain the Key performance indicators (KPI) are vital for multi-objective optimization in semiconductor manufacturing industry. This paper proposes a prediction methodology which predicts CT and EU simultaneously and identifies their key factors. Bayesian neural network (BNN) is used to establish the simultaneous prediction model for Multiple key performance indicators (MKPI), and Bayes theorem is key solution in model complexity controlling. The closed-loop structure is built to keep the stability of MKPI prediction model and the weight analysis method is the basis of identifying the key factors for CT and EU. Compared with Artificial neural network (ANN) and Selective naive Bayesian classifier (SNBC), the simulation results of the prediction method of BNN are proved to be more feasible and effective. The prediction accuracy of BNN has been obviously improved than ANN and SNBC.
URI: http://localhost/handle/Hannan/183191
http://localhost/handle/Hannan/631794
ISSN: 1022-4653
2075-5597
volume: 25
issue: 6
Appears in Collections:2016

Files in This Item:
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Title: Simultaneous Prediction for Multiple Key Performance Indicators in Semiconductor Wafer Fabrication
Authors: Zhengcai Cao;Xuelian Liu;Jinghua Hao;Min Liu
subject: MKPI prediction model|selective naive Bayesian classifier|cycle time|multiple key performance indicators|BNN|Bayesian neural network|equipment utilization|Bayes theorem|weight analysis method|prediction methodology|semiconductor wafer fabrication|multiobjective optimization|ANN|closed-loop structure|SNBC|semiconductor manufacturing industry|artificial neural network
Year: 2016
Publisher: IEEE
Abstract: The prediction and key factors identification for lot Cycle time (CT) and Equipment utilization (EU) which remain the Key performance indicators (KPI) are vital for multi-objective optimization in semiconductor manufacturing industry. This paper proposes a prediction methodology which predicts CT and EU simultaneously and identifies their key factors. Bayesian neural network (BNN) is used to establish the simultaneous prediction model for Multiple key performance indicators (MKPI), and Bayes theorem is key solution in model complexity controlling. The closed-loop structure is built to keep the stability of MKPI prediction model and the weight analysis method is the basis of identifying the key factors for CT and EU. Compared with Artificial neural network (ANN) and Selective naive Bayesian classifier (SNBC), the simulation results of the prediction method of BNN are proved to be more feasible and effective. The prediction accuracy of BNN has been obviously improved than ANN and SNBC.
URI: http://localhost/handle/Hannan/183191
http://localhost/handle/Hannan/631794
ISSN: 1022-4653
2075-5597
volume: 25
issue: 6
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7763013.pdf473.84 kBAdobe PDFThumbnail
Preview File
Title: Simultaneous Prediction for Multiple Key Performance Indicators in Semiconductor Wafer Fabrication
Authors: Zhengcai Cao;Xuelian Liu;Jinghua Hao;Min Liu
subject: MKPI prediction model|selective naive Bayesian classifier|cycle time|multiple key performance indicators|BNN|Bayesian neural network|equipment utilization|Bayes theorem|weight analysis method|prediction methodology|semiconductor wafer fabrication|multiobjective optimization|ANN|closed-loop structure|SNBC|semiconductor manufacturing industry|artificial neural network
Year: 2016
Publisher: IEEE
Abstract: The prediction and key factors identification for lot Cycle time (CT) and Equipment utilization (EU) which remain the Key performance indicators (KPI) are vital for multi-objective optimization in semiconductor manufacturing industry. This paper proposes a prediction methodology which predicts CT and EU simultaneously and identifies their key factors. Bayesian neural network (BNN) is used to establish the simultaneous prediction model for Multiple key performance indicators (MKPI), and Bayes theorem is key solution in model complexity controlling. The closed-loop structure is built to keep the stability of MKPI prediction model and the weight analysis method is the basis of identifying the key factors for CT and EU. Compared with Artificial neural network (ANN) and Selective naive Bayesian classifier (SNBC), the simulation results of the prediction method of BNN are proved to be more feasible and effective. The prediction accuracy of BNN has been obviously improved than ANN and SNBC.
URI: http://localhost/handle/Hannan/183191
http://localhost/handle/Hannan/631794
ISSN: 1022-4653
2075-5597
volume: 25
issue: 6
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7763013.pdf473.84 kBAdobe PDFThumbnail
Preview File