4.7 Article Proceedings Paper

A Low Power Diode-Clamped Inverter-Based Strong Physical Unclonable Function for Robust and Lightweight Authentication

期刊

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSI.2018.2855061

关键词

Strong physical unclonable function; lightweight authentication; mono-stable circuit; low power

资金

  1. Singapore MOE Tier-2 [MOE2015-T2-2-013]
  2. National Natural Science Foundation of China [61601168]
  3. Fundamental Research Foundation of Shenzhen [JCYJ20170302151209762]

向作者/读者索取更多资源

Strong physical unclonable function (PUF) transcends the limitations of legacy secure key storage methods as emerging security primitive for cryptography and device identification/authentication. In this paper, a new low power and reliable mono-stable strong PUF is proposed. Its primary entropy is derived from the process variations of the parallel diode-clamped single inverter ring working in the subthreshold region. Due to manufacturing process variations, the mono-stable state of the output voltage of single inverter ring is Gaussian distributed around the half V-dd point. The spread of the Gaussian is broadened by mixing it with another Gaussian distributed trip point obtained from a diode-clamped parallel inverter stage. As the main entropy of the raw response bits is derived from a mono-stable circuit, it has greater immunity to perturbances introduced by operating environments. In addition, as the mono-stable state for the output voltage is a non-linear combination of individual inverter rings, the resilience against machine learning attacks can be improved. The prototype chip was fabricated using a commercial 40-nm CMOS technology. The measurement results show that the power consumption of the 64-bit mono-stable PUF is merely 3.85 mu W. The native bit error rate is <8% at 0.9 similar to 1.3 V and -40 similar to 90 degrees C, which can be further reduced to <1% using the proposed thresholding technique. The proposed PUF reduces the accuracy of support vector machine and reliability-based covariance matrix adaptation evolution strategy attacks by 36x and 75x, respectively, over that of arbiter PUF.

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