PATROL: An Evolutionary APproach to Automatic Test Pattern Generation for Hardware TROjan Detection Leveraging PSO-GA Hybrid Techniques

The global distribution of the semiconductor supply chain has heightened the risk of hardware Trojans (HTs), small malicious circuits that adversaries may embed during various stages of the system-on-chip (SoC) design process. Frequently implanted by untrusted third parties, these HTs can operate covertly and, when activated, pose a serious threat to the integrity, performance, and functionality of the system. Although there are promising test generation techniques for HT detection, they face two significant practical limitations: a lack of scalability for large designs and insufficient trigger coverage. The effective detection of HTs requires the application of appropriate test vectors. This paper introduces PATROL, a novel algorithm that combines Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) within a scalable framework for the detection of HTs. This framework employs Automated Test Pattern Generation (ATPG)-based activation to achieve high trigger coverage. This approach significantly accelerates the convergence towards a solution while substantially improving the solution accuracy. Our experimental results demonstrate that our proposed method is more than 43 times faster and achieves an average increase in trigger coverage of more than 34%, significantly outperforming state-of-the-art test generation techniques for Trojan detection.