Accurate statistical soft error rate (SSER) analysis using a quasi-Monte Carlo framework with quality cell models

For CMOS designs in sub 90 nm technologies, statistical methods are necessary to accurately estimate circuit SER considering process variations. However, due to the lack of quality statistical models, current statistical SER (SSER) frameworks have not yet achieved satisfactory accuracy. In this work, we present accurate table-based cell models, based on which a Monte Carlo SSER analysis framework is built. We further propose a heuristic to customize the use of quasirandom sequences, which successfully speeds up the convergence of simulation error and hence shortens the runtime. Experimental results show that this framework is capable of more precisely estimating circuit SSERs with reasonable speed.