A Low-Power and Reliable RRAM-Based Configurable RO PUF With Aging Resilience
Emerging nano-device resistive random access memories (RRAMs) have become a promising primitive for PUF designs due to their non-volatility, high density, and low power, breaking through the physical limitations. A ring oscillator based physical unclonable function (RO PUF) is one of the most widely studied PUF designs due to its resilience against noise impacts and flexibility of implementation, but its reliability is susceptible to environmental variation and device aging. Present solutions to improve RO PUF reliability either require complicated RO selection algorithms or require discarding a large number of unstable challenge-response pairs (CRPs). This paper presents a highly reliable RRAM-based configurable RO PUF (RCRO-PUF). The proposed RCRO-PUF utilizes the intrinsic variations of RRAMs as the randomness source and applies the resistance variations of RRAMs to the frequency difference of current-starved (CS) ROs. By operating CS inverters in the subthreshold region, the RCRO-PUF achieves low power as well as high reliability. In addition, a reliability enhancement scheme is proposed to eliminate the effects of environmental variations and device aging. Based on Monte Carlo simulations of a 65 nm CMOS process, the proposed RCRO-PUF consumes only 16.18% of the hardware overhead for a regular RO PUF and has only 7.43 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu W$</tex-math></inline-formula> per CRP generation. The reliability of the RCRO-PUF is 99.51% over a broad range of temperatures from -50 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula>C to 150 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula>C and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula>20% supply voltage variations. It is also 4.7× more resilient to aging than state-of-the-art aging-resilient RO PUF.
Authors
- Yijun Cui (ORCID: https://orcid.org/0000-0002-6262-2329)
- Weiqiang Liu (ORCID: https://orcid.org/0000-0001-8398-8648)
- Chenghua Wang (ORCID: https://orcid.org/0000-0001-8415-0548)
- Chongyan Gu (ORCID: https://orcid.org/0000-0002-3028-8004)
- Jiang Li (ORCID: https://orcid.org/0000-0002-2792-3951)
Institutions
- Queen's University Belfast (GB)
- Nanjing University of Aeronautics and Astronautics (CN)
Publication Details
- Journal
- IEEE Transactions on Nanotechnology
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/tnano.2025.3569071
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.73