Re4PUF: A Reliable, Reconfigurable ReRAM-based PUF Resilient to DNN and Side Channel Attacks
Ning Lin, Yi Li, Yangu He, Songqi Wang, Hegan Chen, Kwunhang Wong, C.X. Li, Jichang Yang, Yifei Yu, Meng Xu, Yongkang Han, Rui Chen, Xiaoming Chen, Xiaoxin Xu, Jianguo Yang, D. S. Shang, Zhongrui Wang · 2025
Resistive random-access memory (ReRAM) based Physical Unclonable Functions (PUFs) have emerged as an attractive hardware security primitive due to their low energy consumption and compact footprint. However, the reliability of existing ReRAM-based PUFs is challenged by read noise and temperature variations, as well as their resistance to Deep Neural Network (DNN) modeling attacks and Side Channel Attacks (SCAs). In this paper, we propose a novel 3T2R ReRAM-based reconfigurable PUF to address these challenges. By adopting the digital 3T2R voltage division cell design, we improve its reliability against ReRAM read noise and temperature variations, while the adjustable analog supply voltage of inverters enables quick, low-cost reconfigurability without reprogramming ReRAMs, effectively mitigating DNN modeling and SCA vulnerabilities. Our $\mathrm{Re}^{4}$ PUF chip has been experimentally validated, achieving a low Bit Error Rate (BER) of $1 \%$ at $85^{\circ} \mathrm{C}$, a 7.59 -fold reduction compared to existing ReRAM-based PUFs. It also demonstrates robust resistance to both DNN modeling attacks (MLP and Transformer) and SCAs, with success rates of approximately 50% and less than $70 \%$, respectively.