Forward prediction based on wafer sort data - A case study

This paper studies the potential of using wafer probe tests to predict the outcome of future tests. The study is carried out using test data based on an SoC design for the automotive market. Given a set of known failing parts, there are two possible approaches to learn. First a single binary classif...

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Bibliographic Details
Published in:	2011 IEEE International Test Conference pp. 1 - 10
Main Authors:	Sumikawa, N., Drmanac, D. G., Wang, Li-C, Winemberg, L., Abadir, M. S.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-09-2011
Subjects:	Mathematical model Predictive models Principal component analysis Probes Semiconductor device modeling Support vector machines Training
Online Access:	Get full text
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Summary:	This paper studies the potential of using wafer probe tests to predict the outcome of future tests. The study is carried out using test data based on an SoC design for the automotive market. Given a set of known failing parts, there are two possible approaches to learn. First a single binary classification model can be learned to model all failing parts. We show that this approach can be effective if the failing parts are compatible in learning. Second, an individual outlier model can be learned for each failing part. We show that this approach is suitable for learning failing parts such as customer returns, where each may have a unique failing behavior. We also show that with Principal Component Analysis (PCA), a learning model can be visualized in two or three dimensional PC space, which facilitates an engineer to manually select or adjust the model.
ISBN:	9781457701535 1457701537
ISSN:	1089-3539 2378-2250
DOI:	10.1109/TEST.2011.6139174