SRAM Physical Unclonable Functions (PUFs) serve as security primitives and can be used to generate random and unique identifiers, which makes their reliability crucial. The reliability is affected by aging and in particular Bias Temperature Instability (BTI), which in turn affects the PUF responses over time typically measured by the Hamming distance (HD). In this work, we model the BTI impact on SRAM PUF reliability for 14 nm FinFET technology and evaluate the reliability of SRAM PUFs using both simulation and silicon measurements. Additionally, we explore the effectiveness of an anti-aging technique on SRAM PUF reliability. Our simulation model and results (which include process variation and circuit noise) are validated with silicon measurements. From them we conclude the following: 1) there exists a direct correlation between BTI and the Hamming distance of an SRAM PUF, where its reliability decreases with 6% over a 6-month period due to aging, and 2) applying anti-aging patterns improves the Hamming distance and hence the reliability with 3% over a 6-month period.

Semi-invasive fault injection attacks are powerful techniques well-known by attackers and secure embedded system designers. When performing such attacks, the selection of the fault injection parameters is of utmost importance and usually based on the experience of the attacker. Surprisingly, there exists no formal and general approach to characterize the target behavior under attack. In this work, we present a novel methodology to perform a fast characterization of the fault injection impact on a target, depending on the possible attack parameters. We experimentally show our methodology to be a successful one when targeting different algorithms such as DES and AES encryption and then extend to the full characterization with the help of deep learning. Finally, we show how the characterization results are transferable between different targets.