Exploiting multiple mahalanobis distance metrics to screen outliers from analog product manufacturing test responses
article
Stringent quality requirements on final electronic products are continuously forcing semiconductor industries, especially the automobile industry, to insert additional reliability tests in their production flow. The problem with linear regressions models for outlier identification in analog and RF devices is that they do not generally account for manufacturing variability and test measurement shifts. Test measurements have less predictability and lead to less certainty in the population mean of the measurements. This affects the distance metric so that marginal outliers have a higher chance of being undetected. The error distribution of the fit will be close to a normal distribution since the linear regression fit follows a least square fit. The sensitivity of a functional test to the outcome of the MRC is a suitable metric to re-condition the pass-fail boundary of a device. Determining those sets of tests that are sensitive to the results of the MRC is one of the problems that have to be addressed.
TNO Identifier
500684
ISSN
21682356
Source
IEEE Design and Test, 30(3), pp. 18-24.
Publisher
IEEE Computer Society
Article nr.
6227532
Pages
18-24
Files
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