A Yields Prediction in the Semiconductor Manufacturing Process Using Stepwise Support Vector Machine
![]() |
An, Dae-Wong
(Hynix Semiconductor)
Ko, Hyo-Heon (Division of Information Management Engineering, Korea University) Kim, Ji-Hyun (School of Business Administration, Kwangwoon University) Baek, Jun-Geol (Division of Information Management Engineering, Korea University) Kim, Sung-Shick (Division of Information Management Engineering, Korea University) |
1 | Baek, D. H., Nam, J. G. (2002), Improved Semiconductor Yield System using Datamining, Spring Seminnual Conference of Korean Operations Research And Management Society, 298-305 |
2 | Ciciani, B. and Jazeolla,G. (1991),AMarkovChain-BasedYield Formula forVLSI Fault-Tolerant Chips, IEEETransactionsonComputer-AidedDesign, 10(2), 252-259 DOI ScienceOn |
3 | Crosier, R. B. (1988), Multivariate Generalizations of Cumulative Sum Qualitycontrol Schemes,Technometrics, 30, 539-549 DOI ScienceOn |
4 | Joachims, T. (1998), Text catergorization with support vectormachines,Proceedings of theEuropeanConferenceonMachineLearning, 10th European Conference onMachine Learning, 137-142 |
5 | KinamKimet al. (1998), DRAMTechnology Perspective for Gigabit Era, IEEE Trans. ElectronDevices, 45(3), 598-608 DOI ScienceOn |
6 | Tam, K. and Kiang, M. (1992), Managerial applications of neural networks, Management Science, 38(7), 926-947 DOI ScienceOn |
7 | Vapnik, V. (1995), TheNature of Statical Learning Theory,Springer,New York |
8 | Kang, B. S., Lee, J.H., Shin, C.K., Yu, S. J., and Park, S. C. (1998),Hybridmachine learning system for integrated yield management in semiconductor manufacturing, Expert SystemswithApplications, 15, 123-132 DOI ScienceOn |
9 | Osuna, E., Freund, R., andGirosi, F. (1997), Training support vectormachines : an application to face detection, Proceedings of Computer Vision and Pattern Recognition, 130-136 |
10 | Weiss, S. and Kulikowski, C. (1991), Computer Systems That Learn, Morgan Kaufmann Publishers, Inc |
11 | Pieter Pete B. (2000), 2000 begins with a revised industry roadmap, Solid State Technology, 31-44 |
12 | Meyer,D., Leisch, F., andHornik, K. (2003), The support vectormachine under test,Neurocomputing, 55, 169-186 DOI ScienceOn |
13 | Kim, T. S., Bae, G. J. (1995), Research of TEST Trend forHigh densitymemory product,The Instituteof Electronics Engineers ofKorea |
14 | Uzsoy, R., Lee, C., and Martin-Vega, L. A. (1992), A Review of Production Planning and Scheduling models in the semiconductor industry PART I:Systemcharacteristics, Performance Evaluation and Production Planning, IIE Transactions, 24(4), 47-60 DOI ScienceOn |
15 | Odom, M. and Sharda, R. (1990), A neural network model for bankruptcy prediction, Proceedings of the International Joint Conference on Neural networks, II-163-II-168 |
16 | Hearst, M. A., Dumais, S. T., Osman, E., Platt, J., and Scholkopf, B. (1998), Support vector machines, IEEE Intelligent System, 13(4), 18-28 DOI ScienceOn |
![]() |