An Accurate Detailed Routing Routability Prediction Model in Placement

Quan Zhou,  Xueyan Wang,  Zhongdong Qi,  Zhuwei Chen,  Qiang Zhou,  Yici Cai
Tsinghua University


Routability is one of the primary objectives in placement. There have been many researches on forecasting routing problems and improving routability in placement but no perfect solution is found. Most traditional routability-driven placers aim to improve global routing result, but true routability lies in detailed routing. Predicting detailed routing congestion in placement is extremely difficult due to the complexity and uncertainty of routing. In this paper, we propose a new detailed routing congestion prediction model based on machine learning. By feature extraction and multivariate adaptive regression, most design rule violations after detailed routing can be foreseen in placement stage. Experiments show that our average prediction accuracy is 79.8%, which is comparable with other state-of-art routability estimation techniques.