A Weak PUF-Assisted Strong PUF With Inherent Immunity to Modeling Attacks and Ultra-Low BER

This paper presents a weak PUF-assisted strong PUF that combines the metrics between weak and strong PUFs. Unlike the conventional strong PUFs that rely on the nonlinear combination of a large number of entropy cells for high modeling attacks resilience, the presented strong PUF utilizes unique key streams to bitwise encrypt the raw responses from the conventional strong PUF to facilitate an inherent immunity to modeling attacks thus fulfilling high-level security. A device-specific pseudo-random number generator (P-RNG) configured by a dedicated weak PUF array generates a unique key stream (K). Since the weak PUFs are inherently immune to machine learning or deep learning-based modeling attacks, the final encrypted responses of the proposed strong PUF are also inherently immune to modeling attacks. Moreover, we propose a two-to-one (2-to-1) selection scheme and the digitally-controlled-delay-line (DCDL)-based stability checker to suppress the bit-error-rate (BER) of the weak PUF array and improve the efficiency of the spatial majority voting (SMV)-based error correction scheme, thus achieving high stability for our proposed strong PUF. Fabricated in 65nm CMOS GP technology, the proposed weak PUF-assisted strong PUF shows a high energy efficiency of 3.05 pJ/bit at a 2M bit rate. Meanwhile, it demonstrates an ultra-low average worst-case BER of $8.9 \times 10^{-11}$ for the temperature range of −20°C to 120°C and a supply voltage variation of ±10% with the proposed stabilization schemes. The proposed strong PUF occupies a core area of 0.075mm2.