Scalable Detection of Hardware Trojans using ATPG-based Activation of Rare Events

TLDR: Wang et al. as discussed by the authors proposed a scalable test generation framework for detecting hardware Trojans using Automated Test Pattern Generation (ATPG) based activation of rare events, which utilizes the complementary abilities of N-detection and maximal clique activation to generate efficient test patterns.

Abstract: Semiconductor supply chain vulnerability is a major concern in designing trustworthy systems. Malicious implants, popularly known as hardware Trojans, can get introduced at different stages in the System-on-Chip (SoC) design cycle. While there are promising test generation techniques for hardware Trojan detection, they have two practical limitations: (i) these approaches are designed to activate rare states while ignoring rare transitions, and (ii) these approaches are not scalable for large designs. In this paper, we propose a scalable test generation framework to address the above challenges. Our threat model assumes that an adversary may exploit rare events consisting of rare signals (states) as well as rare branches (transitions). We show that the rare branch coverage problem can be mapped to the rare signal coverage problem. We propose a scalable framework for detecting hardware Trojans using Automated Test Pattern Generation (ATPG) based activation of rare events. Specifically, we utilize the complementary abilities of N-detection and maximal clique activation of rare events to generate efficient test patterns. Experimental evaluation shows that our ATPG-based framework is scalable and significantly outperforms the state-of-the-art test generation based Trojan detection techniques.