A Robust Area-Efficient Physically Unclonable Function with High Machine Learning Attack Resilience in 28-nm CMOS
Journal
IEEE Transactions on Circuits and Systems I: Regular Papers
Journal Volume
69
Journal Issue
1
Pages
347-355
Date Issued
2022
Author(s)
Abstract
Strong physically unclonable function (PUF) offers a promising solution to low-cost hardware identification and authentication for Internet of Things (IoT) applications. The continuous advancement of machine learning (ML) technology makes the PUF resilience to ML attacks a major design priority. This paper presents a robust and area-efficient strong PUF design with high ML attack resilience. The proposed PUF architecture based on inverter amplifiers operating in the subthreshold region achieves both low energy consumption and high supply and temperature scalability. The proposed nonlinearity topology effectively enhances PUF resilience to various ML attacks with low implementation area and cost. The proposed strong PUF design was designed and implemented using a 28-nm CMOS process. The measurement results show that the proposed PUF design achieves a nearly ideal ML attack resilience of 49.96 % with a small area of 239,857 F2, and demonstrates a stable operation across a wide range of supply voltage from 0.5-1.4 V and temperature from -40-100 °C. This represents $3\times $ improvement in area efficiency, $2.25\times $ and $1.08\times $ improvement in operating voltage and temperature range, respectively, compared to the state-of-the-art results. ? 2004-2012 IEEE.
Subjects
Authentication
hardware security
identification
Internet of things (IoT)
machine learning (ML)
ML resilience
physically unclonable function (PUF)
process variation
strong PUF
CMOS integrated circuits
Energy utilization
Internet of things
Machine learning
Architecture-based
Attack resiliences
Internet of Things (IOT)
Inverter amplifiers
Low energy consumption
Physically unclonable functions
Sub-threshold regions
Temperature range
Integrated circuit design
SDGs
Type
journal article