Fault Detection of Unbalanced Cycle Signal Data Using SOM-based Feature Signal Extraction Method

In this paper, a feature signal extraction method is proposed in order to enhance the low performance of fault detection caused by unbalanced data which denotes the situations when severe disparity exists between the numbers of class instances. Most of the cyclic signals gathered during the process are recognized as normal, while only a few signals are regarded as fault; the majorities of cyclic signals data are unbalanced data. SOM(Self-Organizing Map)-based feature signal extraction method is considered to fix the adverse effects caused by unbalanced data. The weight neurons, mapped to the every node of SOM grid, are extracted as the feature signals of both class data which are used as a reference data set for fault detection. kNN(k-Nearest Neighbor) and SVM(Support Vector Machine) are considered to make fault detection models with comparisons to Hotelling`s Control Chart, the most widely used method for fault detection. Experiments are conducted by using simulated process signals which resembles the frequent cyclic signals in semiconductor manufacturing.

[1]  Fred Spiring,et al.  Introduction to Statistical Quality Control , 2007, Technometrics.

[2]  Nitesh V. Chawla,et al.  SMOTEBoost: Improving Prediction of the Minority Class in Boosting , 2003, PKDD.

[3]  Jionghua Jin,et al.  Diagnostic Feature Extraction From Stamping Tonnage Signals Based on Design of Experiments , 2000 .

[4]  P. Mahalanobis.  On the generalized distance in statistics , 1936 .

[5]  Jun-Geol Baek,et al.  Spline regression based feature extraction for semiconductor process fault detection using support vector machine , 2011, Expert Syst. Appl..

[6]  Reha Uzsoy,et al.  A REVIEW OF PRODUCTION PLANNING AND SCHEDULING MODELS IN THE SEMICONDUCTOR INDUSTRY PART I: SYSTEM CHARACTERISTICS, PERFORMANCE EVALUATION AND PRODUCTION PLANNING , 1992 .

[7]  Nathalie Japkowicz,et al.  The Class Imbalance Problem: Significance and Strategies , 2000 .

[8]  Douglas C. Montgomery,et al.  Introduction to Statistical Quality Control , 1986 .

[9]  Sungzoon Cho,et al.  EUS SVMs: Ensemble of Under-Sampled SVMs for Data Imbalance Problems , 2006, ICONIP.

[10]  Jorma Laurikkala,et al.  Improving Identification of Difficult Small Classes by Balancing Class Distribution , 2001, AIME.

[11]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[12]  Yang Wang,et al.  Cost-sensitive boosting for classification of imbalanced data , 2007, Pattern Recognit..

[13]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[14]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..