Yield Prediction Using Support Vectors Based Under-Sampling in Semiconductor Process
It is important to predict yield in semiconductor test process in order to increase yield. In this study, yield prediction means finding out defective die, wafer or lot effectively. Semiconductor test process consists of some test steps and each test includes various test items. In other world, test data has a big and complicated characteristic. It also is disproportionably distributed as the number of data belonging to FAIL class is extremely low. For yield prediction, general data mining techniques have a limitation without any data preprocessing due to eigen properties of test data. Therefore, this study proposes an under-sampling method using support vector machine (SVM) to eliminate an imbalanced characteristic. For evaluating a performance, randomly under-sampling method is compared with the proposed method using actual semiconductor test data. As a result, sampling method using SVM is effective in generating robust model for yield prediction.
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IEEE, 1997, pp. 130-136.
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algorithms and representations for text categorization," in Proceedings
of the seventh international conference on Information and knowledge
management. ACM, 1998, pp. 148-155.
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with many relevant features," Machine learning: ECML-98, pp. 137-
142, 1998.
[13] B. Schlkopf and A. Smola, "Learning with kernels: Support vector
machines, regularization, optimization, and beyond," 2002.
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[1] K. Kim, C.-G. Hwang, and J. G. Lee, "Dram technology perspective for
gigabit era," Electron Devices, IEEE Transactions on, vol. 45, no. 3, pp.
598 -608, mar 1998.
[2] P. Burggraaf, "New roadmap unveiled-2000 begins with a revised
industry roadmap-a revised 1999 international technology roadmapfor
semiconductors identifies future challenges to the evolution of semiconductor,"
Solid State Technology, vol. 43, no. 1, pp. 31-48, 2000.
[3] B. Ciciani and G. Iazeolla, "A markov chain-based yield formula for
vlsi fault-tolerant chips," Computer-Aided Design of Integrated Circuits
and Systems, IEEE Transactions on, vol. 10, no. 2, pp. 252-259, 1991.
[4] D. An, H. Ko, T. Gulambar, J. Kim, J. Baek, and S. Kim, "A semiconductor
yields prediction using stepwise support vector machine," in
Assembly and Manufacturing, 2009. ISAM 2009. IEEE International
Symposium on. IEEE, 2009, pp. 130-136.
[5] G. Verdier and A. Ferreira, "Adaptive mahalanobis distance and¡ formula
formulatype=," Semiconductor Manufacturing, IEEE Transactions on,
vol. 24, no. 1, pp. 59-68, 2011.
[6] K. Kerdprasop and N. Kerdprasop, "A data mining approach to automate
fault detection model development in the semiconductor manufacturing
process."
[7] N. Chawla, K. Bowyer, L. Hall, and W. Kegelmeyer, "Smote: synthetic
minority over-sampling technique," arXiv preprint arXiv:1106.1813,
2011.
[8] X. Liu, J. Wu, and Z. Zhou, "Exploratory undersampling for classimbalance
learning," Systems, Man, and Cybernetics, Part B: Cybernetics,
IEEE Transactions on, vol. 39, no. 2, pp. 539-550, 2009.
[9] V. Vapnik, The nature of statistical learning theory. springer, 1999.
[10] E. Osuna, R. Freund, and F. Girosit, "Training support vector machines:
an application to face detection," in Computer Vision and Pattern Recognition,
1997. Proceedings., 1997 IEEE Computer Society Conference on.
IEEE, 1997, pp. 130-136.
[11] S. Dumais, J. Platt, D. Heckerman, and M. Sahami, "Inductive learning
algorithms and representations for text categorization," in Proceedings
of the seventh international conference on Information and knowledge
management. ACM, 1998, pp. 148-155.
[12] T. Joachims, "Text categorization with support vector machines: Learning
with many relevant features," Machine learning: ECML-98, pp. 137-
142, 1998.
[13] B. Schlkopf and A. Smola, "Learning with kernels: Support vector
machines, regularization, optimization, and beyond," 2002.
[14] R. Barandela, J. S'anchez, V. Garc'─▒a, and E. Rangel, "Strategies for
learning in class imbalance problems," Pattern Recognition, vol. 36,
no. 3, pp. 849-851, 2003.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:54710", author = "Sae-Rom Pak and Seung Hwan Park and Jeong Ho Cho and Daewoong An and Cheong-Sool Park and Jun Seok Kim and Jun-Geol Baek", title = "Yield Prediction Using Support Vectors Based Under-Sampling in Semiconductor Process", abstract = "It is important to predict yield in semiconductor test process in order to increase yield. In this study, yield prediction means finding out defective die, wafer or lot effectively. Semiconductor test process consists of some test steps and each test includes various test items. In other world, test data has a big and complicated characteristic. It also is disproportionably distributed as the number of data belonging to FAIL class is extremely low. For yield prediction, general data mining techniques have a limitation without any data preprocessing due to eigen properties of test data. Therefore, this study proposes an under-sampling method using support vector machine (SVM) to eliminate an imbalanced characteristic. For evaluating a performance, randomly under-sampling method is compared with the proposed method using actual semiconductor test data. As a result, sampling method using SVM is effective in generating robust model for yield prediction.
", keywords = "Yield Prediction, Semiconductor Test Process, Support
Vector Machine, Under Sampling", volume = "6", number = "12", pages = "2693-5", }
