Abstract

The 3-D NAND flash memory has become an integral part of the cyber-physical systems to cope with the huge data explosion in this era of Internet of Things (IoT). Moreover, hardware security primitives such as physical unclonable function (PUF) have become indispensable in the functional circuits of these cyber-physical systems for protection against security vulnerabilities and adversary attacks. Therefore, in this paper, for the first time, we propose a PUF exploiting the intrinsic variability in the string current of the ubiquitous 3-D NAND flash memory owing to the process variations and the inherent material imperfections such as grain boundaries and the associated traps. With the aid of the Monte Carlo simulations utilizing a calibrated compact model for 3-D nand flash memory, we demonstrate that the proposed PUF exhibits excellent performance metrics such as uniformity (UF) (50%), diffuseness (DF) (50%), and uniqueness (UQ) (50.08%) and is resilient to the machine learning attacks. The ultradense 3-D NAND flash memory array also enables a significantly large set of challenge-response pairs (CRPs) for a strong PUF action.

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