An accurate detailed routing routability prediction model in placement

Abstract

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 routability in placement is extremely difficult due to the complexity and uncertainty of routing. In this paper, we propose a new detailed routing routability prediction model based on supervised learning. After extracting key features in placement and detailed routing, multivariate adaptive regression is performed to train the connection between these two stages. Using a well-trained model, 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.