A Monte Carlo simulation flow for SEU analysis of sequential circuits
Meng Li, Ye Wang, Michael Orshansky · 2016
An efficient methodology for soft error analysis of sequential circuits based on Monte Carlo sampling is proposed. It uses nested sampling for faster statistical convergence: it samples only from the workload space and statically evaluates the conditional probability over the subspace of particle strike and circuit parameters. A novel check on the stationarity of machine state sequence to reduce the number of samples to convergence is introduced. The flow combines logic simulation for latch-level error propagation and stationarity diagnostic and an improved combinational error simulator with a new masking model based on signal controllability.