Fast and Accurate Machine Learning Inverse Lithography Using Physics Based Feature Maps and Specially Designed DCNN

摘要

To achieve full chip inverse lithography technology (ILT) solution, we proposed a hybrid approach in this study by combining first few physics based feature maps as model input with a specially designed DCNN structure to learn the rigorous ILT algorithm. Our results show that this approach can make machine learning ILT easy, fast and more accurate.