A Practical Split Manufacturing Framework for Trojan Prevention via Simultaneous Wire Lifting and Cell Insertion


The advancing of reverse engineering techniques has complicated the efforts in intellectual property protection. Proactive methods have been developed recently, among which layout-level integrated circuit camouflaging is the leading example. However, existing camouflaging methods are rarely supported by provably secure criteria, which further leads to an over-estimation of the security level when countering latest de-camouflaging attacks, e.g., the SAT-based attack. In this paper, a quantitative security criterion is proposed for de-camouflaging complexity measurements and formally analyzed through the demonstration of the equivalence between the existing de-camouflaging strategy and the active learning scheme. Supported by the new security criterion, two camouflaging techniques are proposed, including the low-overhead camouflaging cell generation strategy and the AND-tree camouflaging strategy, to help achieve exponentially increasing security levels at the cost of linearly increasing performance overhead on the circuit under protection. A provably secure camouflaging framework is then developed combining these two techniques. The experimental results using the security criterion show that camouflaged circuits with the proposed framework are of high resilience against different attack schemes with only negligible performance overhead.

In IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD)
Meng Li
Meng Li
Staff Research Scientist

I am currently a staff research scientist and tech lead in the Meta On-Device AI team with a focus on researching and productizing efficient AI algorithms and hardwares for next generation AR/VR devices. I received my Ph.D. degree in the Department of Electrical and Computer Engineering, University of Texas at Austin under the supervision of Prof. David Z. Pan and my bachelor degree in Peking University under the supervision of Prof. Ru Huang and Prof. Runsheng Wang. My research interests include efficient and secure AI algorithms and systems.

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