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dc.creatorPathak, Rajesh
dc.date.accessioned2020-09-03T21:15:36Z
dc.date.available2020-09-03T21:15:36Z
dc.date.issued1995
dc.identifier.urihttps://hdl.handle.net/1969.1/DISSERTATIONS-1562510
dc.descriptionVita.en
dc.description.abstractAutonomous Finite State Machines constructed from shift registers and EXOR gates only are attractive as test pattern generators for Built-in Self Test. Popular TPGs like LFSRs and CA are one such class of Autonomous Linear Finite State Machines. In this dissertation a general theory of maximal sequence ALFSMs is develped. It is shown that such ALFSMs can be realized in an alternate modular form with fixed automata and a variable hardware consisting of EXOR gates. This implementation leads to sharing of the fixed automata among different ALFSMs. Using the theory TPGs for exhaustive two pattern testing of stuck open/delay-y faults are developed with a possible extension to higher dimensions. A synthesis algorithm for constructing Generalized Cellular Automata is also presented. Finally a fast algorithm for bounding a parameter of interest called fault detection probability of stuck-at faults is also presented for its usefulness in indentifying hard-to-detect faults.en
dc.format.extentix, 86 leavesen
dc.format.mediumelectronicen
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.rightsThis thesis was part of a retrospective digitization project authorized by the Texas A&M University Libraries. Copyright remains vested with the author(s). It is the user's responsibility to secure permission from the copyright holder(s) for re-use of the work beyond the provision of Fair Use.en
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectMajor electrical engineeringen
dc.subject.classification1995 Dissertation P3793
dc.titleAutonomous linear finite state machines for built-in self test of digital circuits : theory and applicationsen
dc.typeThesisen
thesis.degree.grantorTexas A&M Universityen
thesis.degree.nameDoctor of Philosophyen
thesis.degree.namePh. Den
dc.type.genredissertationsen
dc.type.materialtexten
dc.format.digitalOriginreformatted digitalen
dc.publisher.digitalTexas A&M University. Libraries
dc.identifier.oclc35105954


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