Multi-step ART1 algorithm for recognition of defect patterns on semiconductor wafers

The integrated circuits (ICs) on wafers are highly vulnerable to defects generated during the semiconductor manufacturing process. The spatial patterns of locally clustered defects are likely to contain information related to the defect generating mechanism. For the purpose of yield management, we propose a multi-step adaptive resonance theory (ART1) algorithm in order to accurately recognise the defect patterns scattered over a wafer. The proposed algorithm consists of a new similarity measure, based on the p-norm ratio and run-length encoding technique and pre-processing procedure: the variable resolution array and zooming strategy. The performance of the algorithm is evaluated based on the statistical models for four types of simulated defect patterns, each of which typically occurs during fabrication of ICs: random patterns by a spatial homogeneous Poisson process, ellipsoid patterns by a multivariate normal, curvilinear patterns by a principal curve, and ring patterns by a spherical shell. Computational testing results show that the proposed algorithm provides high accuracy and robustness in detecting IC defects, regardless of the types of defect patterns residing on the wafer.

[1]  T. Ravindra Babu,et al.  Classification of run-length encoded binary data , 2007, Pattern Recognit..

[2]  S. Grossberg,et al.  ART 2: self-organization of stable category recognition codes for analog input patterns. , 1987, Applied optics.

[3]  Giuseppe De Nicolao,et al.  Unsupervised spatial pattern classification of electrical-wafer-sorting maps in semiconductor manufacturing , 2005, Pattern Recognit. Lett..

[4]  Stephen Grossberg,et al.  Art 2: Self-Organization Of Stable Category Recognition Codes For Analog Input Patterns , 1988, Other Conferences.

[5]  Stephen Grossberg,et al.  Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps , 1992, IEEE Trans. Neural Networks.

[6]  T. Hastie,et al.  Principal Curves , 2007 .

[7]  Chen-Fu Chien,et al.  Hybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor manufacturing , 2007, International Journal of Production Economics.

[8]  J. A. Cunningham.  The use and evaluation of yield models in integrated circuit manufacturing , 1990 .

[9]  G. De Nicolao,et al.  Unsupervised Spatial Pattern Classification of Electrical Failures in Semiconductor Manufacturing , 2003 .

[10]  Stephen Grossberg,et al.  Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system , 1991, Neural Networks.

[11]  Tao Yuan,et al.  Bayesian spatial defect pattern recognition in semiconductor fabrication using support vector clustering , 2010 .

[12]  Suk Joo Bae,et al.  Yield prediction via spatial modeling of clustered defect counts across a wafer map , 2007 .

[13]  R. Sadananda,et al.  ART1: Similarity Measures , 1997, Neural Processing Letters.

[14]  Sang-Chan Park,et al.  Design of intelligent data sampling methodology based on data mining , 2001, IEEE Trans. Robotics Autom..

[15]  S. Grossberg,et al.  Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors , 1976, Biological Cybernetics.

[16]  A. Raftery,et al.  Model-based Gaussian and non-Gaussian clustering , 1993 .

[17]  Stephen Grossberg,et al.  ARTMAP: supervised real-time learning and classification of nonstationary data by a self-organizing neural network , 1991, [1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering.

[18]  Kenji Murakami,et al.  A classification method to reduce the number of categories in ART1 , 2001, Systems and Computers in Japan.

[19]  GrossbergS..  Adaptive pattern classification and universal recoding , 1976 .

[20]  Chih-Hsuan Wang,et al.  Automatic identification of spatial defect patterns for semiconductor manufacturing , 2006 .

[21]  Taho Yang,et al.  A neural-network approach for semiconductor wafer post-sawing inspection , 2002 .

[22]  Fei-Long Chen,et al.  A neural-network approach to recognize defect spatial pattern in semiconductor fabrication , 2000 .

[23]  Way Kuo,et al.  Model-based clustering for integrated circuit yield enhancement , 2007, Eur. J. Oper. Res..

[24]  Way Kuo,et al.  A model-based clustering approach to the recognition of the spatial defect patterns produced during semiconductor fabrication , 2007 .

[25]  Scott MacKinnon,et al.  Statistical methods for visual defect metrology , 1998 .

[26]  Scott MacKinnon,et al.  Statistical methods for visual defect metrology , 1998 .

[27]  Zhaowei Zhong,et al.  Defect detection on semiconductor wafer surfaces , 2005 .

[28]  Xin Song,et al.  The Application and Use of an Automated Spatial Pattern Recognition (SPR) System in the Identification and Solving of Yield Issues in Semiconductor Manufacturing , 2007, 2007 IEEE/SEMI Advanced Semiconductor Manufacturing Conference.

[29]  Chi-Hyuck Jun,et al.  A simulation-based semiconductor chip yield model incorporating a new defect cluster index , 1999 .

[30]  Sang-Chan Park,et al.  A new intelligent SOFM-based sampling plan for advanced process control , 2001, Expert Syst. Appl..

[31]  Stephen Grossberg,et al.  Adaptive pattern classification and universal recoding: II. Feedback, expectation, olfaction, illusions , 1976, Biological Cybernetics.

[32]  Winson Taam,et al.  Detecting Spatial Effects From Factorial Experiments: An Application From Integrated-Circuit Manufacturing , 1993 .

[33]  Hichem Frigui,et al.  The fuzzy c spherical shells algorithm: A new approach , 1992, IEEE Trans. Neural Networks.

[34]  Way Kuo,et al.  Detection and classification of defect patterns on semiconductor wafers , 2006 .

[35]  Jürgen Symanzik,et al.  Statistical Analysis of Spatial Point Patterns , 2005, Technometrics.

[36]  Susan L. Albin,et al.  Clustered defects in IC fabrication: Impact on process control charts , 1991 .

[37]  S. F. Liu,et al.  Wafer bin map recognition using a neural network approach , 2002 .

[38]  Stephen Grossberg,et al.  A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..

[39]  Rajesh N. Davé,et al.  Generalized fuzzy c-shells clustering and detection of circular and elliptical boundaries , 1992, Pattern Recognit..

[40]  Stephen Grossberg,et al.  ART 3: Hierarchical search using chemical transmitters in self-organizing pattern recognition architectures , 1990, Neural Networks.

[41]  Kenneth W. Tobin,et al.  Rapid yield learning through optical defect and electrical test analysis , 1998, Advanced Lithography.