逆向工程屏障的证明:隐蔽门的扫描电镜图像分析

Tasnuva Farheen, Ulbert J. Botero, Nitin Varshney, D. Woodard, M. Tehranipoor, Domenic Forte, Haoting Shen
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引用次数: 3

摘要

集成电路伪装被认为是一种很有前途的对抗恶意逆向工程的方法。伪装门包含多种功能器件结构,但在显微镜成像下呈现为单一布局,从而隐藏了真正的电路功能。最近的隐蔽门伪装设计具有显着降低的开销成本,允许在电路中进行许多伪装门,从而能够抵御各种侵入性和半侵入性攻击。设计中使用了假人输入,但在之前的工作中,扫描电镜成像分析仅对简化的假人接触结构进行了分析。扫描电镜成像过程中电子束是否会在不同的接触点上产生不同的电荷,从而进一步揭示不同的结构,还需要进一步的研究。在这项研究中,我们制作了各种结构的真实和虚拟触点,并进行了系统的扫描电镜成像分析,以研究可能的充电和由此产生的触点的无源电压对比。此外,基于机器学习的模式识别也被用来检查区分真实和虚拟接触的可能性。基于实验结果,我们发现在扫描电镜成像中真实接触和虚拟接触的差异不显著,这有效地防止了基于扫描电镜的逆向工程。索引术语-逆向工程,IC伪装,扫描电子显微镜,机器学习,对策。
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Proof of Reverse Engineering Barrier: SEM Image Analysis on Covert Gates
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.
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