Physical Unclonable Functions (PUFs) based on Non-Volatile Memory (NVM) technology have emerged as a promising solution for secure authentication and cryptographic applications. By leveraging the multi-level cell (MLC) characteristic of NVMs, these PUFs can generate a wide range of unique responses, enhancing their resilience to machine learning (ML) modeling attacks. However, a significant issue with NVM-based PUFs is their endurance problem; frequent write operations lead to wear and degradation over time, reducing the reliability and lifespan of the PUF. This paper addresses these issues by offering a comprehensive model to predict and analyze the effects of endurance changes on NVM PUFs. This model provides insights into how wear impacts the PUF's quality and helps in designing more robust PUFs. Building on this model, we present a novel design for NVM PUFs that significantly improves endurance. Our design approach incorporates advanced techniques to distribute write operations more evenly and reduce stress on individual cells. The result is an NVM PUF that demonstrates a $62\times$ improvement in endurance compared to current state-of-the-art solutions while maintaining protection against learning-based attacks.

翻译：基于非易失性存储器（NVM）技术的物理不可克隆函数（PUF）已成为安全认证与密码学应用领域一种前景广阔的解决方案。通过利用NVM的多级单元（MLC）特性，此类PUF能够生成范围广泛的独特响应，从而增强其抵御机器学习（ML）建模攻击的能力。然而，基于NVM的PUF存在一个显著问题——其耐久性问题：频繁的写入操作会导致器件随时间推移发生磨损和性能退化，从而降低PUF的可靠性与使用寿命。本文通过提出一个综合模型来预测和分析耐久性变化对NVM PUF的影响，以解决上述问题。该模型揭示了磨损如何影响PUF的质量，并为设计更稳健的PUF提供了理论依据。基于此模型，我们提出了一种显著提升耐久性的新型NVM PUF设计方案。该设计方法采用先进技术实现写入操作更均匀的分布，并降低对单个存储单元的应力。最终实现的NVM PUF在保持抵御基于学习攻击能力的同时，其耐久性较当前最先进方案提升了$62\times$。