Efficient Non-Monte-Carlo Yield Estimation

Parametric yield estimation is a critical component in the Integrated Circuit design flow. We propose an efficient non-Monte-Carlo yield estimation method. Key is the use of sensitivity information efficiently obtained with the nominal circuit response. Based on Taylor expansion, the circuit performance can be approximated with a multivariate Gaussian distribution. Combining this with the circuit performance specifications, the yield can be estimated efficiently by repeatedly sampling from the obtained Gaussian distribution. Also proposed is an efficient method to identify impactful factors leading to yield loss (e.g., the most or least sensitive process variables; the tightest performance specifications) based on a multi-dimensional Venn Diagram. Circuit examples demonstrate that the proposed method can well estimate the yield while significantly reducing the number of circuit simulations. Moreover, the proposed yield analysis method can provide useful hints for yield enhancement.