No abstract

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 errors have become a critical reliability concern for advanced CMOS designs because of the continuous technology scaling. Therefore, it is necessary to develop an approach that correctly estimates soft error rates (SERs) and considers process variation during design sign-off. Because of computational inaccuracy in previous approaches, a fast-yet-accurate framework is proposed in this paper for statistical soft-error-rate (SSER) analysis. This approach consists of two components: (1) intensified learning with data reconstruction, and (2) automatic bounding-charge selection. Experimental results show that the proposed framework increases the SER computation speed by 10 7 X, with only 0.8% accuracy loss when compared to the Monte-Carlo SPICE simulation.Index Terms-soft error, process variation, SVM, Monte Carlo

No abstract

No abstract