{"title":"An early prediction methodology for aging sensor insertion to assure safe circuit operation due to NBTI aging","authors":"Andres F. Gomez, L. Poehls, F. Vargas, V. Champac","doi":"10.1109/VTS.2015.7116290","DOIUrl":null,"url":null,"abstract":"This paper proposes an early resilience methodology to identify circuit output nodes where aging sensors should be inserted for an error prediction framework. The methodology is based in a pre-layout statistical estimation of the signal paths likely to become critical due to NBTI and/or Process Variations. To handle the fact that spatial correlation information is not available at early steps of the design flow, a statistical approach maximizing critical paths coverage is proposed. The results obtained with the early prediction methodology are compared with those obtained with spatial correlation information. The proposed methodology provides a good prediction of the set of critical paths to be monitored. Furthermore, location and number of aging sensors required to be inserted at critical paths output nodes are closely predicted.","PeriodicalId":187545,"journal":{"name":"2015 IEEE 33rd VLSI Test Symposium (VTS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 33rd VLSI Test Symposium (VTS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VTS.2015.7116290","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
This paper proposes an early resilience methodology to identify circuit output nodes where aging sensors should be inserted for an error prediction framework. The methodology is based in a pre-layout statistical estimation of the signal paths likely to become critical due to NBTI and/or Process Variations. To handle the fact that spatial correlation information is not available at early steps of the design flow, a statistical approach maximizing critical paths coverage is proposed. The results obtained with the early prediction methodology are compared with those obtained with spatial correlation information. The proposed methodology provides a good prediction of the set of critical paths to be monitored. Furthermore, location and number of aging sensors required to be inserted at critical paths output nodes are closely predicted.