Unveiling Analog Aging Trojans (ATs): Vulnerabilities and Detection Strategies

The increasing complexity and miniaturization of semiconductor devices, driven by the forces of globalization, have introduced new vulnerabilities to malicious attacks in the realm of analog integrated circuits (ICs). In this paper, we explore a new type of malicious attacks for analog ICs, namely Aging Trojan (ATs), which exploit the inherent physical degradation mechanisms present in analog ICs and pose a significant threat to the reliability, security, and integrity of analog ICs. We investigate the underlying mechanisms of ATs, detection methods, and mitigation strategies. The physical degradation phenomena, such as Negative Bias Temperature Instability (NBTI), that render analog circuits susceptible to accelerated aging effects were studied. We discuss unsupervised machine learning techniques for detecting ATs. By analyzing the complexities of ATs on analog ICs and offering insights into effective detection strategies, this paper aims to advance understanding and awareness of this emerging threat landscape. Experimental results show effectiveness of the proposed machine learning scheme for detecting analog ATs.