A Strong Subthreshold Current Array PUF Resilient to Machine Learning Attacks

摘要

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 2(65) 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 80C 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 10(4) training points, which shows negligible loss in PUF unpredictability and $sim 100imes $ higher resilience than the 65-bit arbiter PUF, 3-XOR PUF, and 3-XOR lightweight (LW) PUF.