Arbiter-based Physically Unclonable Functions (APUFs) utilize the variability in manufacturing to create distinct digital identifiers for integrated circuits (ICs). Essentially, the input-output functions / truth-tables / full set of "responses" to "challenges", serve as potential hardware security primitives. To fulfill this role, every APUF batch from the same design should exhibit specific features; two of the most important are the response bias and uniqueness. A faulty APUF batch with a μ-fault from the design phase fails to achieve desired uniqueness levels and sometimes exhibits undesired response bias as well, hence is unqualified for security purposes. Instead of discarding such faulty APUFs and re-designing, we present a novel method to salvage a faulty APUF batch with the presence of multiple μ-faults, so that the desired uniqueness and bias are restored. This is done by carefully selecting challenges that can mitigate the impact of the faults. Such a salvaging strategy via challenge selection is intrinsically difficult, due to the enormous size of the challenge set, the black-box nature of APUFs, and the need to perform such tasks efficiently. To overcome these problems, we propose a simple yet effective way to estimate the intensity of the multiple faults and use them to guide the challenge selection process. The proposed method can efficiently find large challenge sets that achieve the desired response bias and uniqueness, thus salvaging a faulty APUF batch in the post-production phase.

Arbiter Physically Unclonable Functions (APUFs) are low-cost hardware security primitives that may serve as unique digital fingerprints for ICs. To fulfill this role, it is critical for manufacturers to ensure that a batch of PUFs coming off the same design and production line have different truth tables, and uniqueness / inter-PUF-distance metrics have been defined to measure this. This paper points out that a widely-used uniqueness metric fails to capture some special cases, which we remedy by proposing a modified uniqueness metric. We then look at two fundamental APUF-native production line fault models that severely affect uniqueness: the$\mu$(abnormal mean of a delay difference element) and (abnormal variance of a delay difference element) faults. We propose test and diagnosis methods aimed at these two APUF production line faults, and show that these low-cost techniques can efficiently and effectively detect such faults, and pinpoint the element of abnormality, without the (costly) need to directly measure the uniqueness metric of a PUF batch.

Arbiter Physically Unclonable Functions (APUFs) are hardware security primitives that exploit manufacturing random-ness to generate unique digital fingerprints for ICs. This paper theoretically and numerically examines the impact of faults native to APUFs - mask parameter faults from the design phase, or process variation (PV) during the manufacturing phase. We model them statistically, and explain quantitatively how these faults affect the resulting APUF bias and uniqueness. On a single APUF instance, these faults manifest as some outlier delta elements in magnitude, thus we focus on such abnormal delta elements when addressing APUF faults. To detect such bad APUF instances and diagnose the abnormal delta elements, we propose a testing methodology which partitions a random set of challenges so that a specific delta element can be targeted, forming a perceivable bias in the responses over these sets. This low-cost approach is highly effective in detecting and diagnosing bad APUFs with abnormal delta element(s).

Deep learning has been used recently to learn error-correcting encoders and decoders which may improve upon previously known codes in certain regimes. The encoders and decoders are learned "black-boxes", and interpreting their behavior is of interest both for further applications and for incorporating this work into coding theory. Understanding these codes provides a compelling case study for Explainable Artificial Intelligence (XAI): since coding theory is a well-developed and quantitative field, the interpretability problems that arise differ from those traditionally considered. We develop post-hoc interpretability techniques to analyze the deep-learned, autoencoder-based encoders of TurboAE-binary codes, using influence heatmaps, mixed integer linear programming (MILP), Fourier analysis, and property testing. We compare the learned, interpretable encoders combined with BCJR decoders to the original black-box code.