Layout Optimization Through Robust Pattern Learning And Prediction In Sadp Gridded Designs

In this paper, we study the problem of placement-level layout optimization for designs built from cells with unidirectional self-aligned double patterning (SADP) metal-1 interconnect. Our goal is to minimize the number of potential bridging hotspots in design layouts using predictive, machine learning-based models and applying incremental placement adjustments. In the first part of the paper, we explain how to build layout pattern classification models using machine learning methods. Our support vector machine (SVM)-based model predicts a given layout clip as either robust or non-robust. In the second part of the paper, we apply the predictive models to placement-level optimization. Our algorithm identifies and eliminates potential hotspots in standard cell based layout by modifying local cell position.