AI-Enabled Hardware Security Approach for Aging Classification and Manufacturer Identification of SRAM PUFs

Semiconductor microelectronics integrated circuits (ICs) are increasingly integrated into modern life-critical applications, from intelligent infrastructure and consumer electronics to the Internet of Things (IoT) and advanced military and medical systems. Unfortunately, these applications are vulnerable to new hardware security attacks, including microelectronics counterfeits and hardware modification attacks. Physical Unclonable Functions (PUFs) are state-of-the-art hardware security solutions that utilize process variations of integrated circuits for device authentication, secret key generation, and microelectronics counterfeit detection. The negative impact of aging on Static Random \linebreak Access Memory Physical Unclonable Functions (SRAM PUFs) has significant consequences for microelectronics authentication, security, and reliability. This research thoroughly \linebreak examines the effect of aging on the reliability of SRAM PUFs used for secure and trusted microelectronics integrated circuit applications. It initially provides an overview of SRAM PUFs, highlighting their significance and essential features while addressing encountered challenges. The study then covers mitigation techniques, including multi-modal PUFs, that already exist to boost the resilience of SRAM PUFs against aging impacts, highlighting their advantages and the gap in the research addressed in this research. This work proposes a novel AI-enabled security for reliable SRAM PUFs. The proposed approach aims to study and countermeasure the impact of aging on SRAM PUF by analyzing data samples, including Bias Temperature Instability (BTI), Bit Flips, Accelerated aging, and Hot Carrier Injection (HCI) and to study their effects on SRAM PUF cell properties and output. Accelerated aging is a direct result of a change in the environmental temperature and voltage for a few hours. We aim to mitigate the impact of accelerated aging on the reliability authentication and encryption keys of SRAM PUFs. Further, AI-assisted approach is used to analyze SRAM PUF stability under accelerated aging operating conditions. Illegal memory chips from shady companies around the world have compromised the safety and reliability of electronic devices. Detecting these manufacturer details is crucial to detecting illegal and substandard manufacturing prior to their integration into key systems. This paper presents a novel approach designed specifically to analyze the SRAM PUF manufacturers. The suggested technique assigns a distinct process variation to every manufacturer, \linebreak facilitating the classification of the manufacturer, without the need for elaborate registration or verification protocols.The experimental results show the reliability and stability factors of SRAM PUF, including its influence on environmental conditions and the associated effects of aging. Our findings stress the importance of reliable and trustworthy PUF-based hardware security to reliably classify older microelectronic devices from new ones. Using 345 FPGA tested chips, the results show that using different ML models, we can efficiently classify these chips to correctly distinguish the manufacturer, aging impacts, and component, with F1 scores of 96\% and 98\%.