Defect cluster recognition system for fabricated semiconductor wafers

The International Technology Roadmap for Semiconductors (ITRS) identifies production test data as an essential element in improving design and technology in the manufacturing process feedback loop. One of the observations made from the high-volume production test data is that dies that fail due to a systematic failure have a tendency to form certain unique patterns that manifest as defect clusters at the wafer level. Identifying and categorising such clusters is a crucial step towards manufacturing yield improvement and implementation of real-time statistical process control. Addressing the semiconductor industry's needs, this research proposes an automatic defect cluster recognition system for semiconductor wafers that achieves up to 95% accuracy (depending on the product type).

[1]  Robert E. Schapire,et al.  Theoretical Views of Boosting and Applications , 1999, ALT.

[2]  Daoqiang Zhang,et al.  (2D)2PCA: Two-directional two-dimensional PCA for efficient face representation and recognition , 2005, Neurocomputing.

[3]  D. Donoho,et al.  Fast and accurate Polar Fourier transform , 2006 .

[4]  K. Preston White,et al.  AUTOMATED DEFECT PATTERN RECOGNITION: AN APPROACH TO DEFECT CLASSIFICATION AND LOT CHARACTERIZATION , 2002 .

[5]  L. Cui,et al.  Defect pattern recognition on nano/micro integrated circuits wafer , 2008, 2008 3rd IEEE International Conference on Nano/Micro Engineered and Molecular Systems.

[6]  Peter S. Pande,et al.  The Six Sigma Way: How GE, Motorola, and Other Top Companies are Honing Their Performance , 2000 .

[7]  R. Mukundan,et al.  Moment Functions in Image Analysis: Theory and Applications , 1998 .

[8]  Charles Ching-Hsiang Hsu,et al.  Novel assessment of process control monitor in advanced semiconductor manufacturing: a complete set of addressable failure site test structures (AFS-TS) , 1999, 1999 IEEE International Symposium on Semiconductor Manufacturing Conference Proceedings (Cat No.99CH36314).

[9]  Igor Kononenko,et al.  Machine Learning and Data Mining: Introduction to Principles and Algorithms , 2007 .

[10]  Yoav Freund,et al.  An Adaptive Version of the Boost by Majority Algorithm , 1999, COLT.

[11]  Y. Freund,et al.  Discussion of the Paper \additive Logistic Regression: a Statistical View of Boosting" By , 2000 .

[12]  Eric Bauer,et al.  An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.

[13]  Lindsay Kleeman,et al.  Getting More From the Semiconductor Test: Data Mining With Defect-Cluster Extraction , 2011, IEEE Transactions on Instrumentation and Measurement.

[14]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[15]  Yoav Freund,et al.  The Alternating Decision Tree Learning Algorithm , 1999, ICML.

[16]  R. Polikar,et al.  Ensemble based systems in decision making , 2006, IEEE Circuits and Systems Magazine.

[17]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[18]  Yoav Freund,et al.  Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.

[19]  Robert E. Schapire,et al.  The strength of weak learnability , 1990, Mach. Learn..

[20]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Jan Flusser,et al.  On the independence of rotation moment invariants , 2000, Pattern Recognit..

[22]  J. Ross Quinlan,et al.  Bagging, Boosting, and C4.5 , 1996, AAAI/IAAI, Vol. 1.

[23]  Keisuke Kameyama,et al.  Semiconductor defect classification using hyperellipsoid clustering neural networks and model switching , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[24]  Robert E. Schapire The Strength of Weak Learnability , 1989, COLT 1989.

[25]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[26]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[27]  Jan Flusser,et al.  Rotation Moment Invariants for Recognition of Symmetric Objects , 2006, IEEE Transactions on Image Processing.

[28]  Robert E. Schapire,et al.  How boosting the margin can also boost classifier complexity , 2006, ICML.

[29]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[30]  Daoqiang Zhang,et al.  ( 2 D ) 2 PCA : 2-Directional 2-Dimensional PCA for Efficient Face Representation and Recognition , 2005 .

[31]  Rémi Gilleron,et al.  Learning Multi-label Alternating Decision Trees from Texts and Data , 2003, MLDM.

[32]  Chih-Hsuan Wang,et al.  Recognition of semiconductor defect patterns using spectral clustering , 2007, 2007 IEEE International Conference on Industrial Engineering and Engineering Management.

[33]  K.P. White,et al.  Classification of Defect Clusters on Semiconductor Wafers Via the Hough Transformation , 2008, IEEE Transactions on Semiconductor Manufacturing.

[34]  Bian Zhi-xin,et al.  Analyzing the Lithography Part of the International Technology Roadmap for Semiconductors (2005 Edition) , 2006 .

[35]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .