Fast Prediction of Process Variation Band through Machine Learning Models

Fast computation of process variation band (PVB) is critical for several lithography applications such as yield estimation, hotspot detection, mask optimization, and etc. Conventionally, PVB is computed by lithography simulation that is very slow and can only be applied for a small part of a chip. These small parts of a chip are identified through a pattern matching process, where unseen patterns are often missed. We explore conditional generative adversarial networks (cGANs), a couple of machine learning models, for predicting PVB with high speed and sufficient accuracy. In our proposed method, we divide a full-chip into several small clips and then predict PVB for a small region of interest at the center of each clip. Experiments show that our proposed method can successfully predict PVB for more than 98% of the patterns with an average accuracy, and speedup of 86%, and 500 times, respectively, compared to the rigorous lithography simulation.