Handling partial correlations in yield prediction

In nanometer regime, IC designs have to consider the impact of process variations, which is often indicated by manufacturing/parametric yield. This paper investigates a yield model - the probability that the values of multiple manufacturing/circuit parameters meet certain target. This model can be applied to predict CMP (chemical-mechanical planarization) yield. We focus on the difficult cases which have large number of partially correlated variations. In order to predict the yield for these difficult cases efficiently, we propose two techniques: (1) application of orthogonal principle component analysis (OPCA); (2) hierarchical adaptive quadrisection (HAQ). Systematic variations are also included in our model. Compared to previous work, the OPCA based method can reduce the error on yield estimation from 17.1%-21.1% to 1.3%-2.8% with 4.6x speedup. The HAQ technique can reduce the error to 4.1%-5.6% with 6x-9.4x speedup.