Optimizating Emerging Nonvolatile Memories for Dual-Mode Applications: Data Storage and Key Generator

Abstract

Memory-based physical unclonable functions (PUFs) have been studied and developed as powerful primitives to generate device-specific random keys, which can be used for various security applications. However, the existing memory-based PUFs need to safely buffer the data bits in the memory before it is used to produce random bits, resulting in additional area/energy consumption and potential data security issues. In this paper, we propose a new memory-based PUF that exploits the nonvolatility and random variability of emerging memory technologies to produce random bits. Unlike conventional implementations, the random bit generation process of our proposed PUF does not disturb the data bits already stored in the memory. To satisfy the quality requirements for both memory and PUF applications, we also propose a general method to find the optimal design point of emerging nonvolatile memory (eNVM)-based PUF. An illustrative design using spin-transfer torque magnetic RAM exhibits desirable results using our method. Compared to the conventional types of memory-based PUFs, eNVM-based PUFs features enhanced security as cryptographic primitives and lower area and energy cost as data storage.