简介:IncreasingICdensitiesnecessitatediagnosismethodologieswithenhanceddefectlocatingcapabilities.YetthecomputationaleffortexpendedinextractingdiagnosticinformationandthestringentstoragerequirementsconstitutemajorconcernsduetothetremendousnumberoffaultsintypicalICs.Inthispaper,weproposeanRT-leveldiagnosismethodologycapableofrespondingtothesechallenges.Intheproposedscheme,diagnosticinformationiscomputedonagroupedfaulteffectbasis,enhancingboththestorageandthecomputationalaspects.Thefaulteffectgroupingcriteriaareidentifiedbasedonamodulestructureanalysis,improvingthepropagationabilityofthediagnosticinformationthroughRTmodules.Experimentalresultsshowthattheproposedmethodologyprovidessuperiorspeed-upsandsignificantdiagnosticinformationcompressionatnosacrificeindiagnosticresolution,comparedtotheexistinggate-leveldiagnosisapproaches.
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