Physical unclonable functions (PUFs) leverage process variability to generate unique signatures in electronic devices. They are a strong alternative to conventional security mechanisms as they do not rely on non-volatile memories (NVMs) to store the secrets. However, PUFs can be influenced by external factors and may exhibit biased output distributions, leading to vulnerabilities that could compromise their uniqueness and resistance to cloning. The quality of a PUF is evaluated through a common set of metrics such as uniformity, bit-aliasing, uniqueness, and reliability. However, the lack of standardized methodologies hinders effective comparisons between diverse PUF designs, limiting the broader understanding of their performance. Besides, the underlying physics and mechanisms of PUFs makes them difficult to study and mitigate potential vulnerabilities and attacks. Overall, the quality metrics for PUFs are still evolving, and there is ongoing research to address these challenges and develop more robust and reliable PUFs. In this article, we demonstrate the limitations of current PUF evaluation metrics using experimental data and introduce a novel set of metrics that provide a more rigorous and comprehensive assessment.

Physical Unclonable Functions (PUFs) present a promising alternative to traditional cryptographic techniques for securing sensitive information in modern circuits. By exploiting inherent process variability, PUFs generate unique secrets dynamically, thus eliminating the need for data storage. However, a major challenge in PUF-based security is distinguishing valid PUFs from those that may have been tampered with or are invalid (i.e., not belonging to the original design). This paper proposes a data mining-based approach for detecting anomalies and identifying tampered or invalid PUFs. The proposed method mines a set of rules that describe the expected behavior of the PUF, with deviations from these rules signaling potential security issues and vulnerabilities. Experimental results demonstrate that the method effectively identifies invalid or tampered PUFs, showcasing its potential for enhancing PUF-based security systems.

Physical Unclonable Functions (PUFs) offer a promising alternative to conventional cryptographic techniques to secure sensitive information in modern circuits. PUFs leverage inherent process variability to dynamically generate unique secrets, eliminating the need for data storage. However, a significant challenge in PUF-based security is differentiating between valid PUFs and those that may have been tampered with or are invalid (i.e., not belonging to the original design). This paper presents an innovative data mining-based technique for detecting anomalies and identifying tampered or invalid PUFs. The proposed method extracts a set of rules that describe the expected behavior of the PUF, where deviations from these rules indicate potential security issues and vulnerabilities. Experimental results demonstrate that the method effectively detects invalid or tampered PUFs, highlighting its potential to strengthen PUF-based security systems.

Physical Unclonable Functions (PUFs) provide a promising security mechanism by leveraging inherent process variations to generate unique, hardware-bound secrets without requiring secure storage. However, ensuring PUF reliability and detecting potential tampering remain critical challenges. This paper presents a fuzzy logic-based classification system that determines the authenticity of PUF responses using three key metrics: Reliability, Stability, and Reliability Invariance. The system classifies PUF responses into three categories: Trustable, Tampered, and Undecided. This approach enhances the automatic detection of unreliable responses that may indicate tampering while ensuring the fidelity of PUF responses over time. By applying fuzzy inference rules, our method achieves high accuracy in distinguishing between trustworthy and compromised PUFs. Experimental results demonstrate the effectiveness of our approach, making it a valuable method and tool for hardware security applications.

In the contemporary security landscape, the incorporation of photonics has emerged as a transformative force, unlocking a spectrum of possibilities to enhance the resilience and effectiveness of security primitives. This integration represents more than a mere technological augmentation; it signifies a paradigm shift towards innovative approaches capable of delivering security primitives with key properties for low-power systems. This not only augments the robustness of security frameworks, but also paves the way for novel strategies that adapt to the evolving challenges of the digital age. This paper discusses the security layers and related services that will be developed, modeled, and evaluated within the Horizon Europe NEUROPULS project. These layers will exploit novel implementations for security primitives based on physical un-clonable functions (PUFs) using integrated photonics technology. Their objective is to provide a series of services to support the secure operation of a neuromorphic photonic accelerator for edge comnuting applications.

The need to achieve statistically relevant results in electrical simulations requires a large number of iterations under different operating conditions. Moreover, the nature of parametric simulations makes the collection and filtering of the results non-trivial. To tackle these issues, scripts are normally used to control all the parameters. Still, this approach is usually ad-hoc and platform dependent, making the whole procedure hardly reusable, scalable and versatile. We propose a generic, open-source framework to generate complex stimuli and parameters for electrical simulations, together with a programmable Spice- and Verilog-A-based module capable of observing and logging internal states of the circuit to facilitate further result analysis.

Physical Unclonable Functions (PUFs) allow generating an intrinsic signature in electronic device thanks to process variability. One of the most researched solutions for PUF implementation is the Ring Oscillator PUF (RO-PUF). This solution is based on the comparison of the frequency of 2 identically designed ROs in an IC. Ideally these 2 ROs would have the same frequency, however this in not the case in reality due to fabrication-induced process variability. By measuring and comparing their actual frequency, a 1-bit PUF response is generated. The RO-PUF has been demonstrated to satisfy the principal randomness requirements (uniformity and uniqueness) but it suffers from problems such as bitaliasing and unrepeatability (i.e. low reliability). In this paper we perform a thorough analysis of RO-PUF bitaliasing and reliability and propose a methodology for its analytical estimation based on the variability profile of the underling technology.

With the rapid growth of IoT and embedded devices, the development of low power, high density, high performance SoCs has pushed the embedded memories to their limits and opened the field to the development of emerging memory technologies. The Resistive Random Access Memory (ReRAM) has emerged as a promising choice for embedded memories due to its reduced read/write latency and high CMOS integration capability. Intrinsic properties of ReRAMs make them suitable for the implementation of basic security primitives such as Physically Unclonable Functions (PUFs) and True Random Number Generators (TRNGs). The studies to be carried out during this thesis will allow the creation of robust, low cost and reliable security primitives by exploiting the inner variability of memristive technologies.

In this paper we propose an on-line test methodology for RO-PUF reliability which enables high accuracy in the results since it is not based on predictive simplified models of the device variability and noise, but on actual technological electrical models and high versatility since it is not based on measurements extracted from a single technology.