A Low-Complexity Method to Address Process Variability in True Random Number Generators based on Digital Nonlinear Oscillators

We discuss a monitoring system aiming to select, among a set of integrated entropy sources affected by process variability, the source guarantying the highest worst-case entropy. The approach is particularly suitable when considering True Random Number Generators based on Digital Nonlinear Oscillators, since multiple instances of the entropy sources can be implemented at a reduced hardware cost. In general, the approach can be applied for TRNGs based on parametric systems, thus offering entropy tuning capabilities. The original theoretical results have been validated with experiments.