CASSER: A closed-form analysis framework for statistical soft error rate

Austin C.C. Chang, Ryan H.M. Huang, Charles H.P. Wen

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


CMOS designs in the deep submicrometer era require statistical methods to accurately estimate the circuit soft error rate (SER). However, process variation increases the complexity of statistical characteristics related to transient faults, leading to considerable uncertainty in the behavior of soft errors. Regardless of the methods used, current statistical SER (SSER) frameworks invariably involve a tradeoff between accuracy and efficiency. This paper presents accurate cell models in first-order closed form to overcome this problem, thereby enabling the analysis of SSERs in a block-based fashion similar to statistical static timing analysis. These cell models are derived as a closed form in the proposed framework named CASSER, and remain precise under the assumption of a normal distribution for the process parameters. Experimental results demonstrate the efficiency >2-order times faster than the latest framework) and accuracy < 3% error of CASSER in estimating circuit SERs, when compared with the Monte Carlo SPICE simulation.

Original languageEnglish
Article number6341119
Pages (from-to)1837-1848
Number of pages12
JournalIEEE Transactions on Very Large Scale Integration (VLSI) Systems
Issue number10
StatePublished - 30 Sep 2013


  • Reliability
  • single event upset
  • statistical SER (SSER)
  • statistical static timing analysis (SSTA)
  • transient fault

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