Cluster-Based Sequence Analysis of Complex Manufacturing Process

Wafer fabrication in the semiconductor industry is probably one of the most complex manufacturing processes. Maintaining high yields through the statistical process control as a sole monitoring method for quality control is obviously inefficient in such highly complicated operations. We thus present in this paper a sequence analysis method, which is one of the advanced data mining techniques, to identify and extract unique patterns from manufacturing data that can reveal and differentiate low performance processes from the normal ones. We also provide the program coding, implemented with the R language, for easy experimental repetition.

[1]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[2]  Zhiqiang Ge,et al.  Semiconductor Manufacturing Process Monitoring Based on Adaptive Substatistical PCA , 2010, IEEE Transactions on Semiconductor Manufacturing.

[3]  Sunita Sarawagi,et al.  Sequence Data Mining , 2005 .

[4]  Costas J. Spanos,et al.  Fundamentals of Semiconductor Manufacturing and Process Control , 2006 .

[5]  Costas J. Spanos,et al.  Fault diagnosis of plasma etch equipment , 1997, 1997 IEEE International Symposium on Semiconductor Manufacturing Conference Proceedings (Cat. No.97CH36023).

[6]  Jin Wang,et al.  Fault Detection Using the k-Nearest Neighbor Rule for Semiconductor Manufacturing Processes , 2007, IEEE Transactions on Semiconductor Manufacturing.

[7]  C. Schmidt,et al.  Fault detection for a via etch process using adaptive multivariate methods , 2005, IEEE Transactions on Semiconductor Manufacturing.

[8]  G Verdier,et al.  Adaptive Mahalanobis Distance and $k$ -Nearest Neighbor Rule for Fault Detection in Semiconductor Manufacturing , 2011, IEEE Transactions on Semiconductor Manufacturing.

[9]  Pramodita Sharma.  2012 , 2013, Les 25 ans de l’OMC: Une rétrospective en photos.

[10]  Gilbert Ritschard,et al.  Extracting and Rendering Representative Sequences , 2009, IC3K.

[11]  Roni Romano,et al.  Visual analysis of quality-related manufacturing data using fractal geometry , 2012, J. Intell. Manuf..

[12]  Mehrdad Saif,et al.  Application of combined support vector machines in process fault diagnosis , 2009, 2009 American Control Conference.

[13]  Lior Rokach,et al.  Mining manufacturing databases to discover the effect of operation sequence on the product quality , 2008, J. Intell. Manuf..

[14]  Anju Vyas.  Print , 2003 .

[15]  Duane S. Boning,et al.  Simultaneous Fault Detection and Classification for Semiconductor Manufacturing Tools , 2003 .

[16]  Yuhua Li,et al.  Causality Challenge: Benchmarking relevant signal components for effective monitoring and process control , 2008, NIPS Causality: Objectives and Assessment.