ChipSecure: A Reconfigurable Analog eFlash-Based PUF with Machine Learning Attack Resiliency in 55nm CMOS

We exploit randomness in static I-V characteristics and reconfigurability of embedded flash memories to design very efficient physically unclonable function. Leakage current and subthreshold slope variations, nonlinearity, nondeterministic tuning error, and sneak path current in the redesigned commercial flash memory arrays are exploited to create a unique digital fingerprint. A time-multiplexed architecture is designed to enhance the security and expand the challenge-response pair space to 10
²¹¹
. Experimental results demonstrate 50.3% average uniformity, 49.99% average diffuseness, and native <; 5% bit error rate. The analysis of the measured data also shows strong resilience against machine learning attacks and possibility for extremely energy efficient, 0.56 pJ/b operation.