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