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Logic locking and Integrated Circuit (IC) camouflaging are the most prevalent protection schemes that can thwart most hardware security threats. However, the state-of-the-art attacks, including Boolean Satisfiability (SAT) and approximation-based attacks, question the efficacy of the existing defense schemes. Recent obfuscation schemes have employed reconfigurable logic to secure designs against various hardware security threats. However, they have focused on specific design elements such as SAT hardness. Despite meeting the focused criterion such as security, obfuscation incurs additional overheads, which are not evaluated in the present works. This work provides an extensive analysis of Look-Up-Table (LUT)-based obfuscation by exploring several factors such as LUT technology, size, number of LUTs, and replacement strategy as they have a substantial influence on Power-Performance-Area (PPA) and Security (PPA/S) of the design. We show that using large LUT makes LUT-based obfuscation resilient to hardware security threats. However, it also results in enormous design overheads beyond practical limits. To make the reconfigurable logic obfuscation efficient in terms of design overheads, this work proposes a novel LUT architecture where the security provided by the proposed primitive is superior to that of the traditional LUT-based obfuscation. Moreover, we leverage the security-driven design flow, which uses off-the-shelf industrial EDA tools to mitigate the design overheads further while being non-disruptive to the current industrial physical design flow. We empirically evaluate the security of the LUTs against state-of-the-art obfuscation techniques in terms of design overheads and SAT-attack resiliency. Our findings show that the proposed primitive significantly reduces both area and power by a factor of 8 × and 2 ×, respectively, without compromising security.

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With the growth and globalization of IC design and development, there is an increase in the number of Designers and Design houses. As setting up a fabrication facility may easily cost upwards of $20 billion, costs for advanced nodes may be even greater. IC design houses that cannot produce their chips in-house have no option but to use external foundries that are often in other countries. Establishing trust with these external foundries can be a challenge, and these foundries are assumed to be untrusted. The use of these untrusted foundries in the global semiconductor supply chain has raised concerns about the security of the fabricated ICs targeted for sensitive applications. One of these security threats is the adversarial infestation of fabricated ICs with a Hardware Trojan (HT). An HT can be broadly described as a malicious modification to a circuit to control, modify, disable, or monitor its logic. Conventional VLSI manufacturing tests and verification methods fail to detect HT due to the different and un-modeled nature of these malicious modifications. Current state-of-the-art HT detection methods utilize statistical analysis of various side-channel information collected from ICs, such as power analysis, power supply transient analysis, regional supply current analysis, temperature analysis, wireless transmission power analysis, and delay analysis. To detect HTs, most methods require a Trojan-free reference golden IC. A signature from these golden ICs is extracted and used to detect ICs with HTs. However, access to a golden IC is not always feasible. Thus, a mechanism for HT detection is sought that does not require the golden IC. Machine Learning (ML) approaches have emerged to be extremely useful in helping eliminate the need for a golden IC. Recent works on utilizing ML for HT detection have been shown to be promising in achieving this goal. Thus, in this tutorial, we will explain utilizing ML as a solution to the challenge of HT detection. Additionally, we will describe the Electronic Design Automation (EDA) tool flow for automating ML-assisted HT detection. Moreover, to further discuss the benefits of ML-assisted HT detection solutions, we will demonstrate a Neural Network (NN)-assisted timing profiling method for HT detection. Finally, we will discuss the shortcomings and open challenges of ML-assisted HT detection methods.