By Topic

Optimizing automatic defect classification feature and classifier performance for post-fab yield analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Hunt, M.A. ; nLine Corp., Austin, TX, USA ; Karnowski, Thomas P. ; Kiest, C. ; Villalobos, L.

In this paper we present a methodology for enhanced automatic defect classification (ADC) of defects optically detected during post fab inspection and present results from production wafers. We have developed a unique approach to statistical feature calculation that enables the selection of four possible input intensity bands (gray, edge, hue, saturation), three image types (defect, reference, difference), and three defect masks (interior, edge, surround). To achieve the greatest separation between defect classes the optimum subset of features for a given training set must be determined. We propose an approach for feature ranking based on a feature evaluation index (FEI). The final step in optimizing the ADC performance is the selection and training of a pattern classification algorithm. We have evaluated three nonparametric, supervised classifiers including the k-nearest neighbor (KNN), fuzzy KNN, and radial basis function (RBF). The described approach is applied to several sets of defects detected with Electroglas' QuickSilverTM post-fab inspection system. The test results from this library were very good with the optimum accuracy of 84% (on testing set that was not seen during training). This level of performance was also seen in several other smaller libraries used during the development of the underlying algorithms. We believe that this approach of mask based descriptive feature calculation, feature ranking and nonparametric classifiers will enable reliable ADC in the post-fab environment. This post-fab ADC approach is complementary to in-line ADC and enables a more complete yield analysis process

Published in:

Advanced Semiconductor Manufacturing Conference and Workshop, 2000 IEEE/SEMI

Date of Conference:

2000