0.3 pJ/Bit Machine Learning Resistant Strong PUF Using Subthreshold Voltage Divider Array

Abilash Venkatesh, Aishwarya Bahudhanam Venkatasubramaniyan, Xiaodan Xi, Arindam Sanyal

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

This brief presents a subthreshold voltage divider based strong physical unclonable function (PUF). The PUF derives its uniqueness from random mismatch in threshold voltage in an inverter with gate and drain shorted and biased in subthreshold region. The nonlinear current-voltage relationship in subthreshold region also makes the proposed PUF resistant to machine learning (ML) based attacks. Prediction accuracy of PUF response with logistic regression, support vector machine (SVM) and multi-layer perceptron (MLP) is close to 51%. A prototype PUF fabricated in 65nm consumes only 0.3pJ/bit, and achieves the best combination of energy efficiency and resistance to ML attacks. The measured inter and intra hamming distance (HD) for the PUF are 0.5026 and 0.0466 respectively.

Original languageEnglish (US)
Article number8846235
Pages (from-to)1394-1398
Number of pages5
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
Volume67
Issue number8
DOIs
StatePublished - Aug 2020
Externally publishedYes

Keywords

  • hardware security
  • machine learning
  • Physical unclonable function (PUF)
  • strong PUF

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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