Supervised learning methods in sort yield modeling

ASMC (Advanced Semiconductor Manufacturing Conference) Proceedings

Volumn , Issue , 2009, Pages 133-136

  Hu, Helen ^a  

^a IBM MICROELECTRONICS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION AND REGRESSION TREE; DATA RELATIONSHIPS; ELECTRICAL PARAMETER; MACHINE LEARNING TECHNIQUES; MULTINOMIAL LOGISTIC REGRESSION; PROCESS EQUIPMENTS; RANDOM FORESTS; SEMICONDUCTOR DATA; SUPERVISED LEARNING METHODS; YIELD MODELING; YIELD PREDICTION MODELS;

LEARNING ALGORITHMS; MATHEMATICAL MODELS; SUPERVISED LEARNING;

REGRESSION ANALYSIS;

EID: 70350241092     PISSN: 10788743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ASMC.2009.5155961     Document Type: Conference Paper

References (8)

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3. C. Fan, R. Guo, A. Chen, K. Hsu, and C. Wei, "Data Mining and Fault Diagnosis based on Wafer Acceptance Test Data and In-line Manufacturing Data," IEEE ISSM, pp. 171-174, 2001.
4. A. Borisov, I. Chikalov, V. Eruhimov, and E. Tub, "Performance and Scalability Analysis of Tree-Based Models in Large-Scale Data-Mining Problems," Intel Technology Journal, Vol. 09, Issue 02, 2005.
5. T. Utlaut and K. Anderson, "On the Use of Machine Learning in the Semiconductor Industry: Examples and Case Studies," Joint Statistical Meetings, 2004.
6. T. Hastie, R. Tibshrirani, and J. Friedman, "The Elements of Statistical Learning," Springer, New York, 2001.
7. http://www.math.usu.edu/~adele/forests.
8. L. Breiman, "Random Forests," Machine Learning 45 (1), 5-32, 2001.