Tasnuva Farheen, Ulbert J. Botero, Nitin Varshney, D. Woodard, M. Tehranipoor, Domenic Forte, Haoting Shen
{"title":"Proof of Reverse Engineering Barrier: SEM Image Analysis on Covert Gates","authors":"Tasnuva Farheen, Ulbert J. Botero, Nitin Varshney, D. Woodard, M. Tehranipoor, Domenic Forte, Haoting Shen","doi":"10.31399/asm.cp.istfa2021p0179","DOIUrl":null,"url":null,"abstract":"\n IC camouflaging has been proposed as a promising countermeasure against malicious reverse engineering. Camouflaged gates contain multiple functional device structures, but appear as one single layout under microscope imaging, thereby hiding the real circuit functionality from adversaries. The recent covert gate camouflaging design comes with a significantly reduced overhead cost, allowing numerous camouflaged gates in circuits and thus being resilient against various invasive and semi-invasive attacks. Dummy inputs are used in the design, but SEM imaging analysis was only performed on simplified dummy contact structures in prior work. Whether the e-beam during SEM imaging will charge differently on different contacts and further reveal the different structures or not requires extended research. In this study, we fabricated real and dummy contacts in various structures and performed a systematic SEM imaging analysis to investigate the possible charging and the consequent passive voltage contrast on contacts. In addition, machine-learning based pattern recognition was also employed to examine the possibility of differentiating real and dummy contacts. Based on our experimental results, we found that the difference between real and dummy contacts is insignificant in SEM imaging, which effectively prevents adversarial SEM-based reverse engineering.\n Index Terms—Reverse Engineering, IC Camouflaging, Scanning Electron Microscopy, Machine Learning, Countermeasure.","PeriodicalId":188323,"journal":{"name":"ISTFA 2021: Conference Proceedings from the 47th International Symposium for Testing and Failure Analysis","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ISTFA 2021: Conference Proceedings from the 47th International Symposium for Testing and Failure Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31399/asm.cp.istfa2021p0179","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
IC camouflaging has been proposed as a promising countermeasure against malicious reverse engineering. Camouflaged gates contain multiple functional device structures, but appear as one single layout under microscope imaging, thereby hiding the real circuit functionality from adversaries. The recent covert gate camouflaging design comes with a significantly reduced overhead cost, allowing numerous camouflaged gates in circuits and thus being resilient against various invasive and semi-invasive attacks. Dummy inputs are used in the design, but SEM imaging analysis was only performed on simplified dummy contact structures in prior work. Whether the e-beam during SEM imaging will charge differently on different contacts and further reveal the different structures or not requires extended research. In this study, we fabricated real and dummy contacts in various structures and performed a systematic SEM imaging analysis to investigate the possible charging and the consequent passive voltage contrast on contacts. In addition, machine-learning based pattern recognition was also employed to examine the possibility of differentiating real and dummy contacts. Based on our experimental results, we found that the difference between real and dummy contacts is insignificant in SEM imaging, which effectively prevents adversarial SEM-based reverse engineering.
Index Terms—Reverse Engineering, IC Camouflaging, Scanning Electron Microscopy, Machine Learning, Countermeasure.