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With the rapid growth in IC outsourcing in the semiconductors industry, concerns have increased about the weakening ICs security against hardware Trojan attacks. In this brief, an integrated hardware Trojan detection and localization methodology is presented by employing the proposed SAT-based test pattern generation scheme and the MUX-based debugging technique. The experimental results show that our methodology can effectively detect timing anomalies in the path-delays caused by hardware Trojans with node coverage around 97% as well as localizing all Trojan’s gates with a localization resolution around 99.6%. Moreover, all timing error sites are successfully identified with zero False Negative and 0.56% False Positive rates. [ABSTRACT FROM AUTHOR]

Good knowledge of internal circuit states are crucial for efficient post-silicon debug. Due to the area, power, routing, and bandwidth limitations trace buffer-based design for debug hardware provides execution traces of a limited number of state elements which are then usually expanded through signal restoration. In this paper, we propose to record combinational execution traces and show that combined with signal restoration better knowledge of internal circuit states are obtained. Our experiment with benchmark circuits show that restoration quality in terms of state restoration ratio is improved up to 48% on average. Extending the solution space by, including combinational gates drastically increases signal selection runtime. Using hybrid signal selection methods, we also provide good tradeoff between restoration quality and runtime. By dynamically updating some metrics in state-of-the-art hybrid selection method, we achieve 65% reduction in runtime with only 2% penalty in solution quality. Using the proposed signal selection algorithms, designers can better explore the circuit states and achieve more efficient post-silicon debug. [ABSTRACT FROM AUTHOR]

As the complexity of digital designs continuously increases, existing methods to ensure their correctness are facing more serious challenges. Although many studies have been provided to enhance the efficiency of debugging methods, they are still suffering from the lack of scalable automatic correction mechanisms. In this paper, we propose a method for correcting multiple design bugs in gate level circuits. To reduce the correction time, an incremental satisfiability-based mechanism is proposed which not only does not require a complete set of test patterns to produce a gate level implementation which does not exhibit erroneous behavior, but also will not reintroduce old bugs after fixing new bugs. The results show that our method can quickly and accurately suggest corrected gates even for large industrial circuits with many bugs. Average improvements in terms of the runtime and memory usage in comparison with existing methods are ${2.8 \times }$ and ${{6.5 \times }}$ , respectively. Also, the results show that our method compared to the state-of-the-art methods needs ${{2.6 \times }}$ less test patterns. [ABSTRACT FROM AUTHOR]

Having an automatic tool capable of completing all steps of debugging including correction specially for complex designs such as modern processors is ideal for every debugging engineer. Although in theory, quantified Boolean formulas (QBFs) can be used to formulate this class of problems effectively, current solvers are still too immature for accomplishing this goal. In this paper, we propose a scalable symbolic simulation-based method utilizing QBF to correct the candidate bug locations in abstract formal models. We assume that candidate bug locations are determined already, the bug is located in bit-level logic, and the input arguments are known. We define correcting functions and adjust their parameters by formulating the problem as QBF and solving it using symbolic simulation-based method. To further improve scalability, we propose dynamic pruning which breaks the main problem into several smaller subproblems. The efficiency of the proposed method is proved by solving problems for which existing methods are not capable of providing results. [ABSTRACT FROM AUTHOR]