A Worst-Case-Aware Design Methodology for Noise-Tolerant Oscillator-Based True Random Number Generator With Stochastic Behavior Modeling

This paper presents a worst-case-aware design methodology for an oscillator-based true random number generator (TRNG) that produces highly random bit streams even under deterministic noise. We propose a stochastic behavior model to efficiently determine design parameters, and identify a class of deterministic noise under which the randomness gets the worst. They can be used to directly estimate the worst chi value of a poker test under deterministic noise without generating bit streams, which enables efficient exploration of design space and guarantees sufficient randomness in a hostile environment. The proposed model is validated by measuring prototype TRNGs fabricated with a 65-nm CMOS process.