A Wafer Bin Map “Relaxed” Clustering Algorithm for Improving Semiconductor Production Yield

Crescenzio Gallo 1  and Vito Capozzi 2
  • 1 Università di Foggia, Dipartimento di Medicina Clinica e Sperimentale, Foggia, Italy
  • 2 Università di Foggia, Dipartimento di Medicina Clinica e Sperimentale, Istituto Nazionale di Fisica Nucleare – Sezione di Bari, , Foggia, Italy


The semiconductor manufacturing process involves long and complex activities, with intensive use of resources. Producers compete through the introduction of new technologies for increasing yield and reducing costs. So, yield improvement is becoming increasingly important since advanced production technologies are complex and interrelated. In particular, Wafer Bin Maps (WBMs) presenting specific fault models provide crucial information to keep track of process problems in semiconductor manufacturing. Production control is often based on the “judgement” of expert engineers who, however, carry out the analysis of map templates through simple visual exploration. In this way, existing studies are subjective, time consuming, and are also limited by the capacity of human recognition. This study proposes a network-based data mining approach, which integrates correlation graphs with clustering analysis to quickly extract patterns from WBMs and then bind them to manufacturing defects. An empirical study has been conducted on real production data for validating the proposed clustering algorithm, which showed a perfect correspondence between the malfunction patterns found by the algorithm and those discovered by human experts, so confirming the validity of our approach in its ability of correctly identifying actual defective patterns to help improving production yield.

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  • [1] Chien C.F., Hsu C.Y., Chang K.H., Overall Wafer Effectiveness (OWE): A novel industry standard for semiconductor ecosystem as a whole, Computers & Industrial Engineering, 65, 2013, 117– 127, 10.1016.j.cie.2011.11.024

  • [2] Gardner R., Bieker J., Elwell S., Solving tough semiconductor manufacturing problems using data mining., IEEE/SEMI Advanced Semiconductor Manufacturing Conference 2000., 2000

  • [3] Soenjaya J., Hsu W., Lee M.L., Lee T., Mining wafer fabrication: framework and challenges, Next Generation of Data-Mining Application, John Wiley & Sons, New York, 2005, 17–40

  • [4] Chien C.F., Hsu S.C., Chen Y.J., A system for online detection and classification of wafer bin map defect patterns for manufacturing intelligence, International Journal of Production Research, 51(8), 2013, 2324–2338, http://dx.doi.org/10.1080/00207543.2012.737943

  • [5] Taam W., Hamada M., Detecting spatial effects from factorial experiments: an application from integrated-circuit manufacturing., Technometrics, 35(2), 1993, 149–160

  • [6] Kang S., Cho S., An D., Rim J., Using wafer map features to better predict die-level failures in final test, IEEE Transactions on Semiconductor Manufacturing, 28(3), 2015, 431–437

  • [7] Leachman R., Hodges D., Benchmarking semiconductor manufacturing., IEEE Transactions on Semiconductor Manufacturing, 9(2), 1996, 158–169

  • [8] Stapper C., LSI yield modeling and process monitoring., IBM Journal of Research and Development, 44(2), 2000, 112–118

  • [9] Montgomery D.C., Introduction to statistical quality control, John Wiley & Sons, 2007

  • [10] Myers J., Well A., Research Design and Statistical Analysis (2nd ed.), Lawrence Erlbaum, 2003

  • [11] Arnold N.N.A., Wafer defect prediction with statistical machine learning, Ph.D. thesis, Massachusetts Institute of Technology, 2016

  • [12] Jang S.J., Lee J.H., Kim T.W., Kim J.S., Lee H.J., Lee J.B., A wafer map yield model based on deep learning for wafer productivity enhancement, in SEMI Advanced Semiconductor Manufacturing Conference (ASMC), 2018 29th Annual, IEEE, 2018, 29–34

  • [13] Palma F.D., Nicolao G.D., Miraglia G., Donzelli O.M., Process diagnosis via electrical-wafer-sorting maps classification., in Proceedings of the Fifth IEEE International Conference on Data Mining., 2005

  • [14] Gan G., Ma C., Wu J., Data clustering: theory, algorithms, and applications, volume 20, Siam, 2007

  • [15] Praveen P., Rama B., An empirical comparison of Clustering using hierarchical methods and K-means, in 2016 2nd International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), IEEE, 2016, 445– 449

  • [16] Dabhi D.P., Patel M.R., Extensive survey on hierarchical clustering methods in data mining, International Research Journal of Engineering and Technology (IRJET), 3, 2016, 659–665

  • [17] Xu R., Wunsch D.C., II, Clustering. Hoboken, NJ: Wiley/IEEE Press, 6, 2009, 583–617

  • [18] Yin H., Benson A.R., Leskovec J., Gleich D.F., Local higher-order graph clustering, in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017, 555–564

  • [19] Thinsungnoena T., Kaoungkub N., Durongdumronchaib P., Kerdprasopb K., Kerdprasopb N., The clustering validity with silhouette and sum of squared errors, learning, 3(7), 2015

  • [20] Eckmann J., Moses E., Curvature of co-links uncovers hidden thematic layers in the world wide web., in Proc NatL Acad Sci USA, volume 99, 2002, 5825–5829

  • [21] Kim J., Lee Y., Kim H., Detection and clustering of mixed-type defect patterns in wafer bin maps, IISE Transactions, 50(2), 2018, 99–111

  • [22] Hsu C.Y., Clustering ensemble for identifying defective wafer bin map in semiconductor manufacturing, Mathematical Problems in Engineering, 2015, 2015

  • [23] Dong H., Chen N., Wang K., Wafer yield prediction using derived spatial variables, Quality and Reliability Engineering International, 33(8), 2017, 2327–2342

  • [24] Liukkonen M., Hiltunen Y., Recognition of Systematic Spatial Patterns in Silicon Wafers Based on SOM and K-means, IFACPapersOnLine, 51(2), 2018, 439–444

  • [25] Leachman R.C., Ding S., Excursion yield loss and cycle time reduction in semiconductor manufacturing, IEEE Transactions on Automation science and engineering, 8(1), 2011, 112–117

  • [26] Peng C., Chien C., Data value development to enhance yield and maintain competitive advantage for semiconductor manufacturing., International Journal of Service Technology and Management, 4(6), 2003, 365–383

  • [27] Cunningham S., Spanos C., Voros K., Semiconductor yield improvement: Results and best practices., IEEE Transactions on Semiconductor Manufacturing, 8(2), 1995, 103–109

  • [28] Wang C.H., Separation of composite defect patterns on wafer bin map using support vector clustering, Expert Systems with Applications, 36, 2009, 2554–2561, 10.1016/j.eswa.2008.01.057

  • [29] Wang C.H., Recognition of semiconductor defect patterns using spatial filtering and spectral clustering, Expert Systems with Applications, 34, 2008, 1914–1923, 10.1016/j.eswa.2007.02.014

  • [30] Hsu S.C., Chien C.F., Hybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor manufacturing, Int. J. Production Economics, 107, 2007, 88–103, 10.1016/j.ijpe.2006.05.015

  • [31] Liu C.W., Chien C.F., An intelligent system for wafer bin map defect diagnosis: An empirical study for semiconductor manufacturing, Engineering Applications of Artificial Intelligence, 26, 2013, 1479– 1486, 10.1016/j.engappai.2012.11.009

  • [32] Chien C., Wang W., Cheng J., Data mining for yield enhancement in semiconductor manufacturing and an empirical study., Expert Systems with Applications, 33(1), 2007, 1–7

  • [33] Friedman D., Hansen M., Nair V., James D., Model-free estimation of defect clustering in integrated circuit fabrication., IEEE Transactions on Semiconductor Manufacturing, 10(3), 1997, 344–359

  • [34] Li T.S., Huang C.L., Defect spatial pattern recognition using a hybrid SOM–SVM approach in semiconductor manufacturing, Expert Systems with Applications, 36, 2009, 374–385, 10.1016/j.eswa.2007.09.023

  • [35] Zar J.H., Significance testing of the Spearman rank correlation coeflcient, Journal of the American Statistical Association, 67(339), 1972, 578–580

  • [36] Student, An experimental determination of the probable error of Dr Spearman’s correlation coeflcients, Biometrika, 1921, 263– 282

  • [37] Di Salle P., Colantuono C., Gallo C., Traini A., Frusciante L., Chiusano M., Modeling molecular pathways based on gene expression and social network analyses: an example from Arabidopsis Thaliana., in 56th Annual Congress Società Italiana di Genetica Agraria, Perugia, 2012

  • [38] Gallo C., Capozzi V., Clustering techniques for revealing gene expression patterns, in Encyclopedia of Information Science and Technology, Third Edition, IGI Global, 2015, 438–447

  • [39] Watts D., Strogatz S., Collective dynamics of ’small-world’ networks., Nature, 393, 1998, 440–442


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