{"title":"Improving diagnosis resolution of a fault detection test set","authors":"Andreas Riefert, M. Sauer, S. Reddy, B. Becker","doi":"10.1109/VTS.2015.7116269","DOIUrl":null,"url":null,"abstract":"Manufactured VLSI circuits using a new technology typically suffer from systematic defects that are process-dependent and at sub-nanometer feature sizes such defects may be even design-dependent. The root causes for systematic defects must be determined to ramp up yields. Volume diagnosis is becoming popular to identify root causes for systematic defects. Volume diagnosis uses logic diagnosis based on failing circuit responses to production tests of a large number of failing devices, followed by statistical analysis methods to determine the root cause(s) for yield limiters. Typically production tests use fault detection tests and hence may have limited diagnosis resolution. To improve diagnosis resolution diagnostic ATPGs can be used to generate test sets to distinguish all pairs of distinguishable faults in one or more fault models. The sizes of such tests tend to be considerably higher than fault detection test sets used as production tests. For this reason, generation of test sets that detect faults and also possess a high diagnosis resolution is important. In this work we present a method to improve the diagnosis resolution of a compact fault detection test set without increasing pattern count or decreasing fault coverage. The basic idea of the approach is to generate a SAT formula which enforces diagnosis and is solved by a MAX-SAT solver which is a SAT-based maximization tool. We believe this is the first time a method to improve diagnosis resolution of a test set of given size has been reported. Experimental results on ISCAS 89 circuits demonstrate the effectiveness of the proposed method.","PeriodicalId":187545,"journal":{"name":"2015 IEEE 33rd VLSI Test Symposium (VTS)","volume":"200 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 33rd VLSI Test Symposium (VTS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VTS.2015.7116269","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
Manufactured VLSI circuits using a new technology typically suffer from systematic defects that are process-dependent and at sub-nanometer feature sizes such defects may be even design-dependent. The root causes for systematic defects must be determined to ramp up yields. Volume diagnosis is becoming popular to identify root causes for systematic defects. Volume diagnosis uses logic diagnosis based on failing circuit responses to production tests of a large number of failing devices, followed by statistical analysis methods to determine the root cause(s) for yield limiters. Typically production tests use fault detection tests and hence may have limited diagnosis resolution. To improve diagnosis resolution diagnostic ATPGs can be used to generate test sets to distinguish all pairs of distinguishable faults in one or more fault models. The sizes of such tests tend to be considerably higher than fault detection test sets used as production tests. For this reason, generation of test sets that detect faults and also possess a high diagnosis resolution is important. In this work we present a method to improve the diagnosis resolution of a compact fault detection test set without increasing pattern count or decreasing fault coverage. The basic idea of the approach is to generate a SAT formula which enforces diagnosis and is solved by a MAX-SAT solver which is a SAT-based maximization tool. We believe this is the first time a method to improve diagnosis resolution of a test set of given size has been reported. Experimental results on ISCAS 89 circuits demonstrate the effectiveness of the proposed method.