Rethinking split manufacturing: An information-theoretic approach with secure layout techniques
Rethinking split manufacturing: An information-theoretic approach with secure layout techniques
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重新思考分割制造：采用安全布局技术的信息论方法
DOI：
10.1109/iccad.2017.8203796
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
O. Sinanoglu
中科院分区：
文献类型：
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作者：
A. Sengupta;Satwik Patnaik;J. Knechtel;M. Ashraf;S. Garg;O. Sinanoglu
拆分制造是一种很有前途的技术，可以抵御基于晶圆厂的恶意活动，如IP盗版、过度构建和插入硬件木马。然而，由Wang等人（DAC'16）[1]提出的基于网络流的邻近攻击已经证明了关于分离制造的大多数现有技术是高度脆弱的。在这项工作中，我们提出了两个实用的布局技术，以安全的分裂制造：（i）门级图着色和（ii）集群的相同类型的门。我们的方法显示出对高级邻近攻击的有希望的结果，当在金属层M1、M2和M3处分裂时，与未受保护的布局相比，其成功率平均降低了5.27倍、3.19倍和1.73倍。此外，它在很大程度上优于以前的防御工作;我们观察到平均8倍高的弹性相比，有代表性的现有技术。同时，广泛的模拟ISCAS'85和MCNC基准显示，我们的技术产生可接受的布局开销。除了这个实证研究，我们提供了-第一次-一个理论框架，用于量化布局层面的弹性对任何接近引起的信息泄漏。为此，我们利用互信息的概念，并提供广泛的结果来验证我们的模型。
Split manufacturing is a promising technique to defend against fab-based malicious activities such as IP piracy, overbuilding, and insertion of hardware Trojans. However, a network flow-based proximity attack, proposed by Wang et al. (DAC'16) [1], has demonstrated that most prior art on split manufacturing is highly vulnerable. Here in this work, we present two practical layout techniques towards secure split manufacturing: (i) gate-level graph coloring and (ii) clustering of same-type gates. Our approach shows promising results against the advanced proximity attack, lowering its success rate by 5.27x, 3.19x, and 1.73x on average compared to the unprotected layouts when splitting at metal layers M1, M2, and M3, respectively. Also, it largely outperforms previous defense efforts; we observe on average 8x higher resilience when compared to representative prior art. At the same time, extensive simulations on ISCAS'85 and MCNC benchmarks reveal that our techniques incur an acceptable layout overhead. Apart from this empirical study, we provide — for the first time — a theoretical framework for quantifying the layout-level resilience against any proximity-induced information leakage. Towards this end, we leverage the notion of mutual information and provide extensive results to validate our model.