A Strong Subthreshold Current Array PUF Resilient to Machine Learning Attacks

Abstract

This paper presents a strong silicon physical unclonable function (PUF) resistant to machine learning (ML) attacks. The PUF, termed the subthreshold current array PUF (SCA-PUF), consists of a pair of two-dimensional transistor arrays and a low-offset comparator. The proposed 65-bit SCA-PUF is fabricated in a 130nm process and allows 265 challenge-response pairs (CRPs). It consumes 68nW and 11pJ/bit while exhibiting high uniqueness, uniformity, and randomness. It achieves bit error rate (BER) of 5.8% for the temperature range of −20 to 80°C and supply voltage variation of ±10%. The calibration-based CRP selection method improves BER to 0.4% with a 42% loss of CRPs. When subjected to ML attacks, the prediction error stays over 40% on 104 training points, which shows negligible loss in PUF unpredictability and $\sim 100\times $ higher resilience than the 65-bit arbiter PUF, 3-XOR PUF, and 3-XOR lightweight (LW) PUF.