Skip to Main Content
Increasing variability in modern manufacturing processes makes it important to predict the yields of chip designs at early design stage. In recent years, a number of statistical static timing analysis (SSTA) and statistical circuit optimization techniques have emerged to quickly estimate the design yield and perform robust optimization. These statistical methods often rely on the availability of statistical process variation models whose accuracy, however, is severely hampered by the limitations in test structure design, test time, and various sources of inaccuracy inevitably incurred in process characterization. To consider model characterization inaccuracy, we present an efficient importance sampling based optimization framework that can translate the uncertainty in process models to the uncertainty in circuit performance, thus offering the desired statistical best/worst case circuit analysis capability accounting for the unavoidable complexity in process characterization. Furthermore, our new technique provides valuable guidance to process characterization. Examples are included to demonstrate the application of our general analysis framework under the context of SSTA.