Under-Constrained SEE Data: Implications for Estimating and Bounding SEE Rates

Increasingly scarce single-event effect (SEE) testing resources and rapid growth of the New Space sector have increased the prevalence of under-constrained SEE data. We develop Monte Carlo (MC) tools to assess implications for SEE rate estimation. We also show that Bayesian priors based on large datasets of single-event latchup (SEL) susceptible parts can augment under-constrained data and improve bounds on SEL rates. The resulting Bayesian priors are also useful for bounding system SEL risk.