Physical unclonable functions (PUFs) are hardware security primitives that are used for device authentication and cryptographic key generation. Standard XOR PUFs typically contain multiple standard arbiter PUFs as components, and are more secure than standard arbiter PUFs or feed-forward (FF) arbiter PUFs (FF PUFs). This paper proposes design of feed-forward XOR PUFs (FFXOR PUFs) where each component PUF is a FF PUF. Various homogeneous and heterogeneous FFXOR PUFs are presented and evaluated in terms of four fundamental properties of PUFs: uniqueness, attack-resistance, reliability and randomness. Certain key issues pertaining to XOR PUFs such as their vulnerability to machine learning attacks and instability in responses are investigated. Other important challenges like the lack of uniqueness in FF PUFs and the asymmetry in FPGA arbiter PUFs are addressed and it is shown that FFXOR PUFs can naturally overcome these problems. It is shown that heterogeneous FFXOR PUFs (i.e., FFXOR PUFs with non-identical components) can be resilient to state-of-the-art machine learning attacks. We also present systematic reliability analysis of FFXOR PUFs and demonstrate that soft-response thresholding can be used as an effective countermeasure to overcome the degraded reliability bottleneck. Observations from simulations are further verified through hardware implementation of 64-bit FFXOR PUFs on Xilinx Artix-7 FPGA.

Physical unclonable functions (PUFs) can be used to generate unique signatures of integrated circuit (IC) chips. XOR arbiter PUFs (XOR PUFs), that typically contain multiple standard arbiter PUFs as their components, are more secure than standard arbiter PUFs. This paper proposes design of feed-forward XOR PUFs (FFXOR PUFs) where each component PUF is a feed-forward arbiter PUF (FF PUF). Arbiter PUFs suffer from two main drawbacks: vulnerability to modeling attacks and degraded reliability. It is shown that FFXOR PUFs cannot be accurately modeled if the number of component PUFs is more than 5 or 6. We also state that the number of machine learning runs required to learn a model using evolutionary strategies increases by a factor of N^2/2 if N-stage FF PUFs with one loop are used as components instead of standard arbiter PUFs. In general, for FF PUFs with k loops (one intermediate arbiter), it would increase by a factor of N \choose k+1. We also show that the use of a thresholding strategy can increase the reliability of FFXOR PUFs by about 30% for a 15% noise level.

This paper addresses the effect of aging on linear and non-linear MUX physical unclonable functions (PUFs). It is well known that a PUF response can be modeled in terms of the delay difference of MUX stages. In this paper, we show that the aging effects can be modeled in terms of variations in delay-difference and arbiter delay. Specifically, with aging, the percent delay-difference variation of each MUX stage can be modeled as a ratio of two correlated Gaussian random variables. This ratio distribution is shown to be approximately Gaussian with zero mean and variance increasing with time. In case of the arbiter, the ratio distribution is modeled as a Gaussian with positive mean. The paper makes three contributions: modeling the effect of aging in terms of percent variations in delay-difference of the MUX stages and arbiter delay, analysis of authentication accuracy with aging, and approaches to increase the PUF's lifetime by either recalibrating it to obtain new delay-difference parameters, or by tuning a threshold based on the total delay-difference. A general approach for selecting the threshold values is described in the paper. It is shown that the authentication accuracy of a PUF is significantly affected due to aging effects of the arbiter itself. Therefore, under the assumption that the variations in arbiter delay are considerably more than in delay-differences, the performance degradation in the case of aging alone is prominent compared to noise alone. We show that the authentication accuracy of a feed-forward PUF is more degraded compared to linear or modified feed-forward PUF. Metrics like Jenson-Shannon and Henze-Penrose divergence are also used to analyze the effect of aging.