CAPP: context analyzer and printability predictor

Layout context plays a very significant role in printability of layout shapes, and hence it is extremely critical to include layout context information while performing printability checks. In this paper, we are proposing a unique approach of analyzing layout context geometries and use Machine Learning (ML) technique to predict lithography hotspots. Our method uses past lithography simulation results to evaluate geometry margins and profile them in simple geometry rules. The markers of these rules then analyzed by our unique context analyzer and generate data set for train Arterial Neural Network (ANN). Later this trained ANN model used for predictions on new input designs. In this paper, we will also present results to highlight how our approach is better in the accuracy of lithography hotspots detection in comparison to previous work related to pattern matching and machine-learning techniques.