A 4T/Cell Amplifier-Chain-Based XOR PUF With Strong Machine Learning Attack Resilience

Abstract

This paper presents an amplifier-chain-based XOR physical unclonable function (AC-XOR PUF), with the process- and/or bias-dependent voltage and amplification information of two identical amplifier chains serving as the entropy sources. The current-biased PUF cell using only 4 NMOS transistors achieves a small area with reduced temperature and supply sensitivity. Optimization on both the stage gain and stage number can reduce the input-referred noise (IRN) and improve the PUF reliability. We further employ an XOR gate to process the amplifier-chain outputs for the final response to improve the energy efficiency and uniqueness. The process- and bias-dependent stage amplification and the nonlinear amplifier-chain multiplication, which can significantly increase the number of modeling parameters and introduce a complex decision boundary respectively, can effectively resist machine learning (ML) modeling attacks. Fabricated in standard 65nm CMOS, the proposed AC-XOR PUF occupies an active area of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6845\mu \text{m}^{2}$ </tex-math></inline-formula> . Without discarding any challenge-response pairs (CRPs), this work features a measured worst case bit error rate (BER) of 5.70% across <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.06\sim 1.55V$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$- 30\sim 125^{\circ }\text{C}$ </tex-math></inline-formula> , while demonstrating a reliability (intra-die HD) and uniqueness (inter-die HD) of 0.58% and 49.92%, respectively. It also achieves a ML prediction accuracy of 50.72% using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$80\times 80\times 80$ </tex-math></inline-formula> artificial neural network (ANN) with 1M CPRs as training set.