A Strong Subthreshold Current Array PUF Resilient to Machine Learning Attacks

• Published in: IEEE transactions on circuits and systems. I, Regular papers Vol. 67; no. 1; pp. 135 - 144
• Main Authors: Zhuang, Haoyu, Xi, Xiaodan, Sun, Nan, Orshansky, Michael
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Arrays; Bit error rate; Computer architecture; Delays; Machine learning; machine learning attacks; nonlinearity; Physical unclonable functions (PUFs); Security; Subthreshold current; subthreshold operation; Transistors
• ISSN: 1549-8328; 1558-0806
• DOI: 10.1109/TCSI.2019.2945247

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 10 4 training points, which shows negligible loss in PUF unpredictability and ~100× higher resilience than the 65-bit arbiter PUF, 3-XOR PUF, and 3-XOR lightweight (LW) PUF.