Abstract
The complexity of semiconductor manufacturing is increasing due to the smaller feature sizes, greater number of layers, and existing process reentry characteristics. As a result, it is difficult to manage and clarify responsibility for low yields in specific products. This paper presents a comprehensive data mining method for predicting and classifying the product yields in semiconductor manufacturing processes. A genetic programming (GP) approach, capable of constructing a yield prediction system and performing automatic discovery of the significant factors that might cause low yield, is presented. Comparison with the results then is performed using a decision tree induction algorithm. Moreover, this research illustrates the robustness and effectiveness of this method using a well-known DRAM fab’s real data set, with discussion of the results.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
M.J. Berry G. Linoff (1997) Data Mining Techniques Wiley New York
C.C. Bojarczuk H.S. Lopes A.A. Freitas E.L. Michalkiewicz (2004) ArticleTitleA constrained-syntax genetic programming system for discovering classification rules. Application to medical data sets Artificial Intelligence in Medicine 30 IssueID1 27–48 Occurrence Handle10.1016/j.artmed.2003.06.001
D. Braha A. Shmilovici (2002) ArticleTitleData mining for improving a cleaning process in the semiconductor industry IEEE Transactions on Semiconductor Manufacturing 15 IssueID1 91–101 Occurrence Handle10.1109/66.983448
D. Braha (2001) Data mining for design and manufacturing: Methods and applications Kluwer Academic Boston, MA
S.P. Cunningham C.J. Spanos K. Voros (1995) ArticleTitleSemiconductor yield improvement: results and best practices IEEE Transactions on Semiconductor Manufacturing 8 IssueID2 103–109 Occurrence Handle10.1109/66.382273
I. Falco ParticleDe A. Della Cioppa E. Tarantino (2002) ArticleTitleDiscovering interesting classification rules with genetic programming Applied Soft Computing 1 IssueID4 257–269
Fayyad U., Piatetsky-Shapiro G., & Smyth P. (1996). From data mining to knowledge discovery: An overview. In Advances in knowledge discovery and data mining. Cambridge, MA: MIT Press
Gardner, R., Bieker, J., & Elwell, S. (2002). Solving tough semiconductor manufacturing problems using data mining. Proceedings of IEEE/SEMI Advanced Semiconductor Manufacturing Conference 46–55
D. Goldberg (1989) Genetic algorithms in search, optimization and machine learning Addison-Wesley Reading MA
J. Han M. Kamber (2001) Data mining: concepts and techniques Morgan Kaufmann Publishers San Francisco, CA
J.H. Holland (1975) Adaption in natural and artificial systems The University of Michigan Press Ann Arbor
B.S. Kang J.H. Lee C.K. Shin S.J. Yu S.C. Park (1998) ArticleTitleHybrid machine learning system for integrated yield management in semiconductor manufacturing Expert Systems with Applications. 15 123–132 Occurrence Handle10.1016/S0957-4174(98)00017-7
J.R. Koza (1992) Genetic programming: On the programming of computers by means of natural selection The MIT Press MA
Lee F. (1997). Advanced yield enhancement integrated yield analysis. Proceedings of IEEE/SEMI Advanced Semiconductor Manufacturing Conference, 67–75
M. Mitchell (1996) An introduction to genetic algorithms MIT Press MA
T.M. Mitchell (1997) Machine Learning McGraw-Hill New York
J.R. Quinlan (1986) ArticleTitleInduction of decision trees Machine Learning 1 IssueID1 81–106
J.R. Quinlan (1993) C4.5: Programs for machine learning Morgan Kaufmann Publishers San Francisco, CA
P. Rastogi M.N. Kozicki (1993) ArticleTitleExPro- an expert system based process management system IEEE Transaction on Semiconductor Manufacturing 6 IssueID3 207–218
K.C. Tan Q. Yu C.M. Heng T.H. Lee (2003) ArticleTitleEvolutionary computing for knowledge discovery in medical diagnosis Artificial Intelligence in Medicine 27 IssueID2 129–154 Occurrence Handle10.1016/S0933-3657(03)00002-2
A. Teller M.P. Veloso (1996) A new learning architecture for object recognition K. Ikeuchi M. Veloso (Eds) Symbolic visual learning Oxford University Press Oxford 81–116
Wong, A.Y. (1996). A statistical parametric and probe yield analysis methodology. Proceedings of the IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems, 131–139
Author information
Authors and Affiliations
Corresponding author
Additional information
Received: November 2004 / Accepted: September 2005
Rights and permissions
About this article
Cite this article
Li, TS., Huang, CL. & Wu, ZY. Data Mining using Genetic Programming for Construction of a Semiconductor Manufacturing Yield Rate Prediction System. J Intell Manuf 17, 355–361 (2006). https://doi.org/10.1007/s10845-005-0008-7
Issue Date:
DOI: https://doi.org/10.1007/s10845-005-0008-7