INVITED: A 0.26% BER, Machine-Learning Resistant 1028 Challenge-Response PUF in 14nm CMOS Featuring Stability-Aware Adversarial Challenge Selection

A 10 28 challenge-response strong-PUF circuit is fabricated in 14nm CMOS, demonstrating modeling attack resistance across 6-million training samples. Two-stage nonlinearly cascaded PUF array organization with adversarial challenge selection increases modeling complexity by 4×, limiting ML attack acc...

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Bibliographic Details
Published in2020 57th ACM/IEEE Design Automation Conference (DAC) pp. 1 - 3
Main Authors Suresh, Vikram B., Kumar, Raghavan, Mathew, Sanu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2020
Subjects
Challenge-Response PUF
Entropy
Limiting
Machine learning
Machine-Learning Attacks
ML-attack resistance
Organizations
Resistance
Semiconductor device modeling
Strong PUF
Training
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Summary:A 10 28 challenge-response strong-PUF circuit is fabricated in 14nm CMOS, demonstrating modeling attack resistance across 6-million training samples. Two-stage nonlinearly cascaded PUF array organization with adversarial challenge selection increases modeling complexity by 4×, limiting ML attack accuracy to ~50%. A configurable cross-coupled inverter-based entropy source with stability-aware challenge pruning enables 9.8× higher array density, linearly-scaling enrollment data and worst-case bit-error-rate of 0.26% measured across 650-850mV, 0-100°C operation.
DOI:10.1109/DAC18072.2020.9218720