Open AccessSimultaneous Prediction for Multiple Key Performance Indicators in Semiconductor Wafer Fabrication
Author(s) -
Cao Zhengcai,
Liu Xuelian,
Hao Jinghua,
Liu Min
Publication year - 2016
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2016.11.001
Subject(s) - key (lock) , performance indicator , computer science , artificial neural network , naive bayes classifier , classifier (uml) , artificial intelligence , machine learning , bayesian network , semiconductor device fabrication , data mining , bayes' theorem , wafer fabrication , bayesian probability , wafer , engineering , support vector machine , computer security , management , electrical engineering , economics
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.