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Data Science, Quality & Reliability

Prediction of highly imbalanced semiconductor chip-level defects using uncertainty-based adaptive margin learning

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Pages 147-155 | Received 22 Mar 2021, Accepted 06 Dec 2021, Published online: 19 Jan 2022

References

  • Batuwita, R. and Palade, V. (2013) Class imbalance learning methods for support vector machines, in Imbalanced Learning: Foundations, Algorithms, and Applications, Wiley, Piscataway, NJ, pp. 83–99.
  • Brodersen, K.H., Ong, C.S., Stephan, K.E. and Buhmann, J.M. (2010) The balanced accuracy and its posterior distribution, in 2010 20th International Conference on Pattern Recognition, IEEE Press, Piscataway, NJ, pp. 3121–3124.
  • Bullag, R.F., Ortega, R.C. and Bullag, S.B. (2014) Adaptive trimming test approach—the efficient way on trimming analog trimmed devices at wafer sort, in 36th International Electronics Manufacturing Technology Conference, IEEE Press, Piscataway, NJ, pp. 1–4.
  • Chawla, N.V., Bowyer, K.W., Hall, L.O. and Kegelmeyer, W.P. (2002) Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357.
  • Chien, C.-F., Hsu, S.-C. and Chen, Y.-J. (2013) A system for online detection and classification of wafer bin map defect patterns for manufacturing intelligence. International Journal of Production Research, 51(8), 2324–2338.
  • Ezzat, A.A., Liu, S., Hochbaum, D.S. and Ding, Y. (2021) A graph-theoretic approach for spatial filtering and its impact on mixed-type spatial pattern recognition in wafer bin maps. IEEE Transactions on Semiconductor Manufacturing, 34(2), 194–206.
  • Gal, Y. (2016) Uncertainty in deep learning. PhD thesis, University of Cambridge, Cambridge, UK.
  • Gal, Y. and Ghahramani, Z. (2016) Dropout as a Bayesian approximation: Representing model uncertainty in deep learning, in International Conference on Machine Learning, New York, NY, pp. 1050–1059.
  • Hsu, S.-C. and Chien, C.-F. (2007) Hybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor manufacturing. International Journal of Production Economics, 107(1), 88–103.
  • Huang, C., Li, Y., Loy, C.C. and Tang, X. (2016) Learning deep representation for imbalanced classification, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE Press, Piscataway, NJ, pp. 5375–5384.
  • Hwang, J. and Kim, H. (2020) Variational deep clustering of wafer map patterns. IEEE Transactions on Semiconductor Manufacturing, 33(3), 466–475.
  • Hyun, Y. and Kim, H. (2020) Memory-augmented convolutional neural networks with triplet loss for imbalanced wafer defect pattern classification. IEEE Transactions on Semiconductor Manufacturing, 33(4), 622–634.
  • Jin, C.H., Na, H.J., Piao, M., Pok, G. and Ryu, K.H. (2019) A novel dbscan-based defect pattern detection and classification framework for wafer bin map. IEEE Transactions on Semiconductor Manufacturing, 32(3), 286–292.
  • Kang, S., Cho, S., An, D. and Rim, J. (2015) Using wafer map features to better predict die-level failures in final test. IEEE Transactions on Semiconductor Manufacturing, 28(3), 431–437.
  • Khan, S., Hayat, M., Zamir, S.W., Shen, J. and Shao, L. (2019) Striking the right balance with uncertainty, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE Press, Piscataway, NJ, pp. 103–112.
  • Khan, S.H., Hayat, M., Bennamoun, M., Sohel, F.A. and Togneri, R. (2017) Cost-sensitive learning of deep feature representations from imbalanced data. IEEE Transactions on Neural Networks and Learning Systems, 29(8), 3573–3587.
  • Kim, J., Lee, Y. and Kim, H. (2018) Detection and clustering of mixed-type defect patterns in wafer bin maps. IISE Transactions, 50(2), 99–111.
  • Kim, S., Kim, H. and Namkoong, Y. (2016) Ordinal classification of imbalanced data with application in emergency and disaster information services. IEEE Intelligent Systems, 31(5), 50–56.
  • Kubat, M. and Matwin, S. (1997) Addressing the curse of imbalanced training sets: one-sided selection, in Proceedings of the 14th International Conference on Machine Learning, Nashville, TN, pp. 179–186.
  • Kyeong, K. and Kim, H. (2018) Classification of mixed-type defect patterns in wafer bin maps using convolutional neural networks. IEEE Transactions on Semiconductor Manufacturing, 31(3), 395–402.
  • Lee, H. and Kim, H. (2020) Semi-supervised multi-label learning for classification of wafer bin maps with mixed-type defect patterns. IEEE Transactions on Semiconductor Manufacturing, 33(4), 653–662.
  • Liu, W., Wen, Y., Yu, Z. and Yang, M. (2016) Large-margin softmax loss for convolutional neural networks, in International Conference on Machine Learning, Volume 2, New York, NY, pp. 507–516.
  • Menon, A.K., Jayasumana, S., Rawat, A.S., Jain, H., Veit, A. and Kumar, S. (2021) Long-tail learning via logit adjustment, in International Conference on Learning Representations.
  • Munirathinam, S. and Ramadoss, B. (2016) Predictive models for equipment fault detection in the semiconductor manufacturing process. IACSIT International Journal of Engineering and Technology, 8(4), 273–285.
  • Park, S.H., Park, C.-S., Kim, J.S., Kim, S.-S., Baek, J.-G. and An, D. (2013) Data mining approaches for packaging yield prediction in the post-fabrication process, in 2013 IEEE International Congress on Big Data, IEEE Press, Piscataway, NJ, pp. 363–368.
  • Shapiro, S.S. and Francia, R. (1972) An approximate analysis of variance test for normality. Journal of the American Statistical Association, 67(337), 215–216.
  • Shin, C.K. and Park, S.C. (2000) A machine learning approach to yield management in semiconductor manufacturing. International Journal of Production Research, 38(17), 4261–4271.
  • Su, C.-T., Yang, T. and Ke, C.-M. (2002) A neural-network approach for semiconductor wafer post-sawing inspection. IEEE Transactions on Semiconductor Manufacturing, 15(2), 260–266.
  • Wang, S., Liu, W., Wu, J., Cao, L., Meng, Q. and Kennedy, P.J. (2016) Training deep neural networks on imbalanced data sets, in 2016 International Joint Conference on Neural Networks (IJCNN), IEEE Press, Piscataway, NJ, pp. 4368–4374.
  • Weiss, S.M., Dhurandhar, A. and Baseman, R.J. (2013) Improving quality control by early prediction of manufacturing outcomes, in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, pp. 1258–1266.
  • Wilson, D.L. (1972) Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man, and Cybernetics, 2(3), 408–421.
  • Wu, L. and Zhang, J. (2010) Fuzzy neural network based yield prediction model for semiconductor manufacturing system. International Journal of Production Research, 48(11), 3225–3243.

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