Simultaneous Prediction for Multiple Key Performance Indicators in Semiconductor Wafer Fabrication

CAO Zhengcai; LIU Xuelian; HAO Jinghua; LIU Min

doi:10.1049/cje.2016.11.001

Volume 25 Issue 6

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CAO Zhengcai, LIU Xuelian, HAO Jinghua, et al., “Simultaneous Prediction for Multiple Key Performance Indicators in Semiconductor Wafer Fabrication,” Chinese Journal of Electronics, vol. 25, no. 6, pp. 1159-1165, 2016, doi: 10.1049/cje.2016.11.001

Citation:

CAO Zhengcai, LIU Xuelian, HAO Jinghua, et al., “Simultaneous Prediction for Multiple Key Performance Indicators in Semiconductor Wafer Fabrication,” Chinese Journal of Electronics, vol. 25, no. 6, pp. 1159-1165, 2016, doi: 10.1049/cje.2016.11.001

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Simultaneous Prediction for Multiple Key Performance Indicators in Semiconductor Wafer Fabrication

doi: 10.1049/cje.2016.11.001

1.
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China;
2.
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China;
3.
Department of Automation, Tsinghua University, Beijing 100084, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61104172, No.51375038), the Doctoral Fund of Ministry of Education of China (No.20130010110009), Beijing Municipal Natural Science Foundation (No.4162046), the Open Research Project from SKLMCCS (No.20120104), and the Open Project Program of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University (No.93K172014K05).

Received Date: 2014-08-26
Rev Recd Date: 2014-10-22
Publish Date: 2016-11-10

Abstract

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.
- Prediction for MKPI,
- Cycle time,
- Equipment utilization,
- BNN,
- Key factors identification

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References(11)

References

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