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Monte Carlo general sample classification for rare circuit events using Random Forest

Yield estimation is becoming a challenging task for circuits that are replicated in millions of instances on a large design (High Replication Circuits, HRC) such as SRAMs and flip flops. This is because a rare event in a circuit cell may have a large impact on the system yield. To achieve high yield in HRC, the failure probability of the individual cell is requested to be very small. Thus the number of Monte Carlo simulations needed to detect a rare event is very large and no longer practical. The statistical blockade has been proposed to decrease the number of Monte Carlo simulations needed using classification of tail points and simulating these points only. The Support Vector Machine (SVM) was used in the classification of tail points. Kernel functions for SVM classifier, linear or radial, were chosen according to the data complexity. In this paper, Random Forest (RF) classifier is used as a general purpose classifier irrespective of the complexity of the data. It is shown that RF classifier provides the same accuracy or improves it without having to know the relationship between the input parameters.