MoM - a process variation aware statistical capacitance extractor

With the advent of newer technologies, the underlying manufacturing processes are becoming more complicated. This results in substantial variations between mask and actual fabricated geometries of the conductors. These variations in geometries can lead to significant differences in the interconnect capacitances computed using mask and actual geometries. The sources for these variations could be many and the variations could be independent or correlated. We present a novel extension to the statistical Monte-Carlo capacitance extractor to take into account the statistical models of these variations. The variations are handled by an additional Monte-Carlo sampling; hence termed Monte-Carlo over Monte-Carlo (MoM). Our technique reports the expected value and the expected spread of the capacitances along with the co-variance among the different capacitances. We demonstrate the correctness, accuracy and scalability of our technique analytically and experimentally.