Statistical Soft Error Rate (SSER) Analysis for Scaled CMOS Designs
This article re-examines the soft error effect caused by radiation-induced particles beyond the deep submicron regime. Considering the impact of process variations, voltage pulse widths of transient faults are found no longer monotonically diminishing after propagation, as they were formerly. As a result, the soft error rates in scaled electronic designs escape traditional static analysis and are seriously underestimated. In this article we formulate the statistical soft error rate (SSER) problem and present two frameworks to cope with the aforementioned sophisticated issues. The table-lookup framework captures the change of transient-fault distributions implicitly by using a Monte-Carlo approach, whereas the SVR-learning framework does the task explicitly by using statistical learning theory. Experimental results show that both frameworks can more accurately estimate SERs than static approaches do. Meanwhile, the SVR-learning framework outperforms the table-lookup framework in both SER accuracy and runtime.
soft error transient fault statistical learning algorithms performance monte carlo method support vector machine