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A novel method for determining machine subgroups and backups with an empirical study for semiconductor manufacturing

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Abstract

Wafer fabrication for semiconductor manufacturing consists of multiple layers, in which the displacements (i.e., overlay errors) between layers should be reduced to enhance the yield. Although it can reduce variance between layers by fixing the exposure machine (i.e. steeper or scanner), it is not practical to expose the wafer on the same machine from layer to layer for the lengthy fabrication process in real setting. Thus, there is a critical need to determine the similarity machine subgroups, in which appreciate backups for unexpected machine down can be also prioritized. This study aims to develop a novel methodology to fill this gap based on the proposed similarity measurement of systematic overlay errors and residuals. The proposed methodology was validated via empirical study in a wafer fab and the results showed practical viability of this approach.

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References

  • K.S. Al-Sultan (1997) ArticleTitleHard clustering approach to the part family formation problem Production Planning and Control 8 IssueID3 231–236 Occurrence Handle10.1080/095372897235280

    Article  Google Scholar 

  • K.S. Al-Sultan C.A. Fedjki (1997) ArticleTitleGenetic algorithm for the part family formation problem Production Planning and Control 8 IssueID8 788–796 Occurrence Handle10.1080/095372897234687

    Article  Google Scholar 

  • M.R. Anderberg (1973) Cluster analysis for applications Academic Press New York

    Google Scholar 

  • W.H. Arnold (1983) ArticleTitleImage placement differences between 1:1 projection aligners and 10:1 reduction wafer steppers Proceedings of SPIE: Optical Microlithography 394 87–98

    Google Scholar 

  • D. Ben-Arieh E. Traintaphyllou (1992) ArticleTitleQuantifying data for group technology with weighted fuzzy features International Journal of Production Research 30 IssueID6 1285–1299

    Google Scholar 

  • H.M. Chan D.A. Milner (1982) ArticleTitleDirect clustering algorithm for group formation in cellular manufacturing Journal of Manufacturing systems 1 IssueID1 65–74

    Google Scholar 

  • M.P. Chandrasekharan R. RajaGopalan (1986) ArticleTitleAn ideal seed non-hierarchical clustering algorithm for cellular manufacturing International Journal of Production Research 24 IssueID2 451–464

    Google Scholar 

  • C. Chien S. Hsu J. Deng (2001) ArticleTitleA cutting algorithm for optimizing the wafer exposure pattern IEEE Transactions on Semiconductor Manufacturing 14 IssueID2 157–162 Occurrence Handle10.1109/66.920727

    Article  Google Scholar 

  • C. Chien D. Lin Q. Liu C. Peng C. Hsu C. Huang (2002) ArticleTitleDeveloping a data mining method for wafer binmap clustering and an empirical study in a semiconductor manufacturing fab Journal of the Chinese Institute of Industrial Engineers 19 IssueID2 23–38 Occurrence Handle10.1080/10170660209509189

    Article  Google Scholar 

  • C. Chien K. Chang C. Chen (2003) ArticleTitleDesign of a sampling strategy for measuring and compensating for overlay errors in semiconductor manufacturing International Journal of Production Research 41 IssueID11 2547–2561 Occurrence Handle10.1080/0020754031000087256

    Article  Google Scholar 

  • C. Chien J. Wu (2003) ArticleTitleAnalyzing repair decisions in the site imbalance problem of semiconductor test machines IEEE Transactions on Semiconductor Manufacturing 16 IssueID4 704–711 Occurrence Handle10.1109/TSM.2003.818955

    Article  Google Scholar 

  • Johnson, R. A., Wichern, D. W. (1992). applied multivariate statistical analysis, 3rd edn. Englewood Cliffs, New Jersey: Prentice Hall.

  • S. Kamal L.I. Burke (1996) ArticleTitleFACT: a new neural network-based clustering algorithm for group technology International Journal of Production Research 34 IssueID4 919–946

    Google Scholar 

  • M.Y. Kiang U.R. Kulkarni K.Y. Tam (1995) ArticleTitleSelf-organizing map network as an interactive clustering tool: an application to group technology Decision Support Systems 15 351–374 Occurrence Handle10.1016/0167-9236(94)00046-1

    Article  Google Scholar 

  • J.R. King (1980) ArticleTitleMachine-component group formation in production flow analysis: an approach using a rank order clustering algorithm International Journal of Production Research 18 IssueID2 213–232

    Google Scholar 

  • A. Kusiak (1985) ArticleTitleThe part families problem in flexible manufacturing systems Annals of Operational Research 25 561–569

    Google Scholar 

  • A. Kusiak (1987) ArticleTitleThe generalized group technology concept International Journal of Production Research 25 IssueID4 561–569

    Google Scholar 

  • A. Kusiak M. Cho (1992) ArticleTitleSimilarity coefficient algorithms for solving the group technology problem International Journal of Production Research 30 IssueID11 2633–2646

    Google Scholar 

  • Z. Lin W. Wu (1999) ArticleTitleMultiple linear regression analysis of the overlay accuracy model IEEE Transaction on Semiconductor Manufacturing 12 229–237 Occurrence Handle10.1109/66.762881

    Article  Google Scholar 

  • D. MacMillen W.D. Ryden (1982) ArticleTitleAnalysis of image field placement deviations of a 5 ×  microlithographic reduction lens Proceedings of SPIE: Optical Microlithography-Technology 334 78–89

    Google Scholar 

  • J. McAuley (1972) ArticleTitleMachine grouping for efficient production The Production Engineering 52 53–57 Occurrence Handle10.1049/tpe.1972.0006

    Article  Google Scholar 

  • Y.B. Moon S.C. Chi (1992) ArticleTitleGeneralized part family formation using neural network techniques Journal of Manufacturing Systems 11 IssueID3 149–159

    Google Scholar 

  • D.S. Perloff (1978) ArticleTitleA four-point electrical measurement technique for characterizing mask superposition errors on semiconductor wafers IEEE Journal of Solid State Circuits 13 IssueID4 436–444 Occurrence Handle10.1109/JSSC.1978.1051074

    Article  Google Scholar 

  • H. Seiffodoni P.M. Wolfe (1986) ArticleTitleApplication of similarity coefficient method in group technology IIE Transactions 18 IssueID13 271–277

    Google Scholar 

  • G. Srinivasan T.T. Narendran B. Mahadevan (1990) ArticleTitleAn assignment model for the part-families problem in group technology International Journal of Production Research 28 IssueID1 145–152

    Google Scholar 

  • S. Subhash (1996) Applied multivariate techniques Wiley New York

    Google Scholar 

  • K.Y. Tam (1990) ArticleTitleAn operation sequence based similarity coefficient for part families formations Journal of Manufacturing Systems 9 IssueID1 55–68 Occurrence Handle10.1016/0278-6125(90)90069-T

    Article  Google Scholar 

  • M.A. Brink Particlevan den C.G.M. DeMol R.A. George (1988) ArticleTitleMatching performance for multiple wafer steppers using an advanced metrology procedure Proceedings SPIE: Integrated Circuit Metrology, Inspection, and Process Control II, 921 180–197

    Google Scholar 

  • A. Vannelli K. Kumar (1986) ArticleTitleA method for finding minimal bottle-neck for grouping part-machine families International Journal of Production Research 24 IssueID2 387–400

    Google Scholar 

  • G.U. Yule (1900) ArticleTitleOn the association of attribute in statistics: with illustration from the material of the childhood society Philosophical Transactions of the Royal Society of London Series A 194 257–319

    Google Scholar 

Download references

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Correspondence to Chen-Fu Chien.

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Received: May 2005 / Accepted: December 2005

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Chien, CF., Hsu, CY. A novel method for determining machine subgroups and backups with an empirical study for semiconductor manufacturing. J Intell Manuf 17, 429–439 (2006). https://doi.org/10.1007/s10845-005-0016-7

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  • DOI: https://doi.org/10.1007/s10845-005-0016-7

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