CAO Zhengcai, LIU Xuelian, HAO Jinghua, LIU Min. Simultaneous Prediction for Multiple Key Performance Indicators in Semiconductor Wafer Fabrication[J]. Chinese Journal of Electronics, 2016, 25(6): 1159-1165. doi: 10.1049/cje.2016.11.001
Citation: CAO Zhengcai, LIU Xuelian, HAO Jinghua, LIU Min. Simultaneous Prediction for Multiple Key Performance Indicators in Semiconductor Wafer Fabrication[J]. Chinese Journal of Electronics, 2016, 25(6): 1159-1165. doi: 10.1049/cje.2016.11.001

Simultaneous Prediction for Multiple Key Performance Indicators in Semiconductor Wafer Fabrication

doi: 10.1049/cje.2016.11.001
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
  • 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.
  • loading
  • Z.N. Mevawalla, G.S. May, M. Honjo, et al., "Neural network modeling of fabrication yield using manufacturing data", Advanced Semiconductor Manufacturing Conference, Saratoga Springs, NY, USA, pp.1-6, 2011.
    D. An, H.H. Ko, T. Gulambar, et al., "A semiconductor yields prediction using stepwise support vector machine", International Symposium on Assembly and Manufacturing, Suwon, Korea, pp.130-136, 2009.
    I. Tirkel, "Cycle time prediction in wafer fabrication line by applying data mining methods", Advanced Semiconductor Manufacturing Conference (ASMC), Saratoga Springs, NY, USA, pp.1-5, 2011.
    W. Praepattharapisut, W. Pengchan, T. Phetchakul, et al., "Yield analysis by poisson yield model based on the defect analysis with derivative method", IEEE 11th International Conference on Telecommunications and Information Technology, Nakhon Ratchasima, Thailand, pp.14-17, 2014.
    Y. Meidan, B. Lerner, G. Rabinowitz, et al., "Cycle-time key factor identification and prediction in semiconductor manufacturing using machine learning and data mining", IEEE Transactions on Semiconductor Manufacturing, Vol.24, No.2, pp.237-248, 2011.
    D.C. Krueger, D.C. Montgomery and C.M. Mastrangelo, "Application of generalized linear models to predict semiconductor yield using defect metrology data", IEEE Transactions on Semiconductor Manufacturing, Vol.24, No.1, pp.44-58, 2011.
    Alain Casali and Christian Ernst, "Discovering correlated parameters in semiconductor manufacturing processes: A data mining approach", IEEE Transactions on Semiconductor Manufacturing, Vol.25, No.1, pp.118-127, 2012.
    J.C. Ni, F. Qiao, L. Li, et al., "A memetic PSO based KNN regression method for cycle time prediction in a wafer fab", World Congress on Intelligent Control and Automation, Beijing, China, pp.474-478, 2012.
    Z.C. Cao, H.D. Zhao and Y.J. Wang, "Releasing control policy for semiconductor wafer fabrication based on fuzzy petri nets reasoning", Acta Electronca Sinica, Vol.39, No.7, pp.1545-1550, 2011.
    H.X. Li, X.H. Zhao, H.Y. Chi, et al., "Prediction and analysis of land subsidence based on improved BP neural network model", Journal of Tianjin University, Vol.42, No.1, pp.60-64, 2009.
    Z.T. Wang, Q.D. Wu and F. Qiao, "A lot dispatching strategy integrating WIP management and wafer start control", IEEE Transactions on Automation Science and Engineering, Vol.4, No.4, pp.579-583, 2007.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (215) PDF downloads(445) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return