1 |
An, D., Ko, H. H., Gulambar, T., Kim, J., Baek, J. G., and Kim, S. S. (2009), A semiconductor yields prediction using stepwise support vector machine, In Assembly and Manufacturing, ISAM 2009, IEEE International Symposium, 130-136.
|
2 |
Baek, D. H. and Han, C. H. (2003), Application of data mining for improving and predicting yield in wafer fabrication system. KIISS, 9(1), 157-177.
|
3 |
Breiman, L. (2001), Random forests. Machine learning, 45(1), 5-32.
DOI
|
4 |
Chandola, V., Banerjee, A., and Kumar, V. (2009), Anomaly detection : A survey. ACM computing surveys (CSUR), 41(3), 15.
|
5 |
Chang, Y. J., Kang, Y., Hsu, C. L., Chang, C. T., and Chan, T. Y. (2006), Virtual metrology technique for semiconductor manufacturing, In Neural Networks, IJCNN 2006. International Joint Conference on, IEEE, 5289-5293.
|
6 |
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.
DOI
|
7 |
Chen, C., Liaw, A., and Breiman, L. (2004), Using random forest to learn imbalanced data, University of California, Berkeley.
|
8 |
Chen, P., Wu, S., Lin, J., Ko, F., Lo, H., Wang, J., and Liang, M. (2005), Virtual metrology : a solution for wafer to wafer advanced process control, In Semiconductor Manufacturing, ISSM 2005, IEEE International Symposium, 155-157.
|
9 |
Chen, Y.-T., Yang, H.-C., and Cheng, F.-T. (2006), Multivariate Simulation Assessment for Virtual Metrology, Proc. IEEE Int. Conf. on Robotics and Automation(ICRA 2006), 1048-1053.
|
10 |
Chien, C. F., Wang, W. C., and Cheng, J. C. (2007), Data mining for yield enhancement in semiconductor manufacturing and an empirical study. Expert Systems with Applications, 33(1), 192-198.
DOI
|
11 |
Cunningham, S. P., Spanos, C. J., and Voros, K. (1995), Semiconductor yield improvement: results and best practices. Semiconductor Manufacturing, IEEE Transactions on, 8(2), 103-109.
DOI
|
12 |
Ferreira, A., Roussy, A., and Conde, L. (2009), Virtual metrology models for predicting physical measurement in semiconductor manufacturing, In Advanced Semiconductor Manufacturing Conference, ASMC 2009, IEEE/SEMI, 149-154.
|
13 |
Hosmer, D. and Lemeshow, W. (1989), Applied Logistic Regression, Ed. John Wolfley and Sons, 8-20.
|
14 |
Kang, P. and Cho, S. (2006), EUS SVM : Ensemble of under-sampled SVMs for data imbalance problems, Proceedings in Korean Industrial Engineering Conference, 291-298.
|
15 |
Kang, P. and Cho, S. (2006), EUS SVMs : Ensemble of under-sampled SVMs for data imbalance problems, In Neural Information Processing, Springer Berlin Heidelberg, 837-846.
|
16 |
Kang, P., Kim, D., Lee, H. J., Doh, S., and Cho, S. (2011), Virtual metrology for run-to-run control in semiconductor manufacturing. Expert Systems with Applications, 38(3), 2508-2522.
DOI
|
17 |
Kang, P., Kim, D., Lee, S. K., Doh, S., and Cho, S. (2012), Estimating the reliability of virtual metrology predictions in semiconductor manufacturing : A novelty detection-based approach. Journal of the Korean Institue of Industrial Engineers, 38(1), 46-56.
DOI
|
18 |
Kohavi, R. (1995), A study of cross-validation and bootstrap for accuracy estimation and model selection. In IJCAI, 14(2), 1137-1145.
|
19 |
Kang, P., Lee, H. J., Cho, S., Kim, D., Park, J., Park, C. K., and Doh, S. (2009), A virtual metrology system for semiconductor manufacturing. Expert Systems with Applications, 36(10), 12554-12561.
DOI
|
20 |
Khan, A. A., Moyne, J. R., and Tilbury, D. M. (2008), Virtual metrology and feedback control for semiconductor manufacturing processes using recursive partial least squares. Journal of Process Control, 18(10), 961-974.
DOI
|
21 |
Kubat, M., Holte, R. C., and Matwin, S. (1998), Machine learning for the detection of oil spills in satellite radar images. Machine learning, 30(2/3), 195-215.
DOI
|
22 |
Kumar, N., Kennedy, K., Gildersleeve, K., Abelson, R., Mastrangelo, C. M., and Montgomery, D. C. (2006), A review of yield modelling techniques for semiconductor manufacturing. International Journal of Production Research, 44(23), 5019-5036.
DOI
|
23 |
Li, T. S., Huang, C. L., and Wu, Z. Y. (2006), Data mining using genetic programming for construction of a semiconductor manufacturing yield rate prediction system. Journal of Intelligent Manufacturing, 17(3), 355-361.
DOI
|
24 |
Liaw, A. and Wiener, M. (2002), Classification and Regression by randomForest. R news, 2(3), 18-22.
|
25 |
Lynn, S., Ringwood, J., and MacGearailt, N. (2012), Global and local virtual metrology models for a plasma etch process, Semiconductor Manufacturing, IEEE Transactions, 25(1), 94-103.
DOI
|
26 |
Murphy, B. T. (1964), Cost-size optima of monolithic integrated circuits. Proceedings of the IEEE, 52(12), 1537-1545.
DOI
|
27 |
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.
DOI
|
28 |
Park, K. S., Jun, C. H., and Kim, S. Y. (1997), The comparison and use of yield models in semiconductor manufacturing. IE interfaces, 10(1), 79-93.
|
29 |
Quinlan, J. R. (1986), Induction of decision trees. Machine learning, 1(1), 81-106.
DOI
|
30 |
Rosenblatt, F. (1958), The perceptron : a probabilistic model for information storage and organization in the brain. Psychological review, 65(6), 386.
DOI
|
31 |
Uzsoy, R., Lee, C. Y., and Martin-Vega, L. A. (1992), A review of production planning and scheduling models in the semiconductor industry part I : system characteristics, performance evaluation and production planning. IIE Transactions, 24(4), 47-60.
DOI
|
32 |
Vapnik, V. (2000), The Nature of Statistical Learning Theory, Springer.
|