DocumentCode :
1772876
Title :
Customer return detection with features selection
Author :
Bertoncelli, Domenico ; Caianiello, Pasquale
Author_Institution :
Dept. of Inf. Eng., Univ. of L´Aquila, L´Aquila, Italy
fYear :
2014
fDate :
23-25 April 2014
Firstpage :
268
Lastpage :
269
Abstract :
We address the semiconductor industry problem of detecting microchips that escape production tests but are returned by customers as non-functional. This problem deals with analyzing high dimensional unbalanced databases collecting only a very small number of customer return samples. We show how to construct a model for effectively discriminating, based on wafer probe test data, potential customer returns from other good chips at the cost of a low overkill, where a model is a pair consisting of a selected set of wafer probe tests with minimal redundancy and a 1-class-SVM (Support Vector Machine) with optimal kernel parameters. We report about an experimentation on real data from EWS (Electronic Wafer Sort) test and customer returns showing the capability of predicting customer returns at cost of a relatively low overkill.
Keywords :
customer satisfaction; production engineering computing; semiconductor industry; support vector machines; SVM; customer return detection; electronic wafer sort; optimal kernel parameters; semiconductor industry; support vector machine; wafer probe test data; Kernel; Mutual information; Prediction algorithms; Predictive models; Probes; Semiconductor device modeling; Support vector machines; customer return; feature selection; semiconductor; support vector machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Design and Diagnostics of Electronic Circuits & Systems, 17th International Symposium on
Conference_Location :
Warsaw
Print_ISBN :
978-1-4799-4560-3
Type :
conf
DOI :
10.1109/DDECS.2014.6868806
Filename :
6868806
Link To Document :
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