Defect site prediction based upon statistical analysis of fault signatures
dc.contributor.advisor | Mercer, M. Ray | |
dc.creator | Trinka, Michael Robert | |
dc.date.accessioned | 2004-09-30T01:42:04Z | |
dc.date.available | 2004-09-30T01:42:04Z | |
dc.date.created | 2003-08 | |
dc.date.issued | 2004-09-30 | |
dc.identifier.uri | https://hdl.handle.net/1969.1/95 | |
dc.description.abstract | Good failure analysis is the ability to determine the site of a circuit defect quickly and accurately. We propose a method for defect site prediction that is based on a site's probability of excitation, making no assumptions about the type of defect being analyzed. We do this by analyzing fault signatures and comparing them to the defect signature. We use this information to construct an ordered list of sites that are likely to be the site of the defect. | en |
dc.format.extent | 213991 bytes | en |
dc.format.extent | 32875 bytes | en |
dc.format.medium | electronic | en |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Texas A&M University | |
dc.subject | defect | en |
dc.subject | testing | en |
dc.subject | circuit | en |
dc.subject | fault | en |
dc.subject | diagnosis | en |
dc.title | Defect site prediction based upon statistical analysis of fault signatures | en |
dc.type | Book | en |
dc.type | Thesis | en |
thesis.degree.department | Electrical Engineering | en |
thesis.degree.discipline | Computer Engineering | en |
thesis.degree.grantor | Texas A&M University | en |
thesis.degree.name | Master of Science | en |
thesis.degree.level | Masters | en |
dc.contributor.committeeMember | Reddy, A. L. Narasimha | |
dc.contributor.committeeMember | Childs, S. Bart | |
dc.type.genre | Electronic Thesis | en |
dc.type.material | text | en |
dc.format.digitalOrigin | born digital | en |
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Electronic Theses, Dissertations, and Records of Study (2002– )
Texas A&M University Theses, Dissertations, and Records of Study (2002– )