References
- An, D., Ko, H-H., Baek, J., and Kim, S.-S. (2009), A Final Test Yields Prediction Methodology in the Semiconductor Manufacturing Process Using Stepwise Support Vector Machine, Journal of the Korean Institute of Industrial Engineers, 1-8.
- An, D., Ko, H.-H., Kim, J., Baek, J., and Kim, S.-S. (2009), A Yields Prediction in the Semiconductor Manufacturing Process Using Stepwise Support Vector Machine, IE interfaces, 22(3), 252-262.
- Anon, Kim, T. S. and Bae, G. J. (1995), Research of TEST Trend for High density memory product, The Institute of Electronics Engineers of Korea.
- Baddeley, A. (2008), Analysing spatial point patterns in R, Technical report, CSIRO, 2010, Version 4.
- Cleveland, W. S. (1979), Robust locally weighted regression and smoothing scatterplots, Journal of the American statistical association, 74(368), 829-836. https://doi.org/10.1080/01621459.1979.10481038
- 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. https://doi.org/10.1016/j.ijpe.2006.05.015
- Hwang, S. H., Kim, J. H., Yoo, C., Jung, S. W., and Lee, J. H. (2010), Characteristics of Inter-monthly Climatic Change Appeared in Long-term Seoul Rainfall, Journal of the Korean Society of Civil Engineers, 30(1), 1-11.
- 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 Institute of Industrial Engineers, 38(1), 46-56. https://doi.org/10.7232/JKIIE.2012.38.1.046
- Kim, K.-H. and Baek, J. (2014), A Prediction of Chip Quality using OPTICS(Ordering Points to Identify the Clustering Structure)-based Feature Extraction at the Cell Level, Journal of the Korean Institute of Industrial Engineers, 40(3), 257-266. https://doi.org/10.7232/JKIIE.2014.40.3.257
- Li, T. S. and Huang, C. L. (2009), Defect spatial pattern recognition using a hybrid SOM-SVM approach in semiconductor manufacturing, Expert Systems with Applications, 36(1), 374-385. https://doi.org/10.1016/j.eswa.2007.09.023
- Liu, S. F., Chen, F. L., and Lu, W. B. (2002), Wafer bin map recognition using a neural network approach, International Journal of production research, 40(10), 2207-2223. https://doi.org/10.1080/00207540210122275
- Nurani, R. K., Strojwas, A. J., Maly, W. P., Ouyang, C., Shindo, W., Akella, R., and Derrett, J. (1998), In-line yield prediction methodologies using patterned wafer inspection information, Semiconductor Manufacturing, IEEE Transactions on, 11(1), 40-47. https://doi.org/10.1109/66.661283
- Park, S-R., Kim, J. S., Park, C-S., Park, S. H., and Baek, J.-G. (2014), Under Sampling for Imbalanced Data using Minor Class based SVM(MCSVM) in Semiconductor Process, Journal of the Korean Institute of Industrial Engineers, 40(4), 404-414. https://doi.org/10.7232/JKIIE.2014.40.4.404
- Ripley, B. D. (1996), Pattern recognition and neural networks, Cambridge university press.
- 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. https://doi.org/10.1080/07408179208964233
- Wang, C. H., Kuo, W., and Bensmail, H. (2006), Detection and classification of defect patterns on semiconductor wafers, IIE Transactions, 38(12), 1059-1068. https://doi.org/10.1080/07408170600733236
- Wang, C. H. (2008), Recognition of semiconductor defect patterns using spatial filtering and spectral clustering, Expert Systems with Applications, 34(3), 1914-1923. https://doi.org/10.1016/j.eswa.2007.02.014