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Alternate Test Generation for Detection of Parametric Faults

Tests for detecting faults in analog and mixed-signal circuits have been traditionally
derived from the datasheet speci and #64257;cations. Although these speci and #64257;cations describe important
aspects of the device, in many cases these application oriented tests are costly to implement
and are inefficient in determining product quality. Increasingly, the gap between speci and #64257;cation test requirements and the capabilities of test equipment has been widening.
In this work, a systematic method to generate and evaluate alternate tests for detecting parametric faults is proposed. We recognize that certain aspects of analog test generation problem are not amenable to automation. Additionally, functional features of analog circuits are widely varied and cannot be assumed by the test generator. To overcome these problems, an extended device under test (DUT) model is developed that encapsulates the DUT and the DUT speci and #64257;c tasks. The interface of this model provides a well de and #64257;ned and uniform view of a large class of devices. This permits several simpli and #64257;cations in the test generator. The
test generator is uses a search-based procedure that requires evaluation of a large number
of candidate tests. Test evaluation is expensive because of complex fault models and slow
fault simulation techniques. A tester-resident test evaluation technique is developed to
address this issue. This method is not limited by simulation complexity nor does it require
an explicit fault model. Making use of these two developments, an efficient and automated
test generation method is developed. Theoretical development and a number of examples
are used to illustrate various concepts that are presented in this thesis.

Identiferoai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/5285
Date26 November 2003
CreatorsGomes, Alfred Vincent
PublisherGeorgia Institute of Technology
Source SetsGeorgia Tech Electronic Thesis and Dissertation Archive
Languageen_US
Detected LanguageEnglish
TypeDissertation
Format2671089 bytes, application/pdf

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