Accurately predicting routing congestion caused by netlist topology is essential as circuit designs become increasingly complex. To correctly predict routing congestion, the use of graph neural networks (GNNs) has gained great attention. However, existing GNN-based methods have limitations in capturing crucial netlist information and effectively representing complex topologies. In this work, we propose a novel approach, ClusterNet, to predict routing congestion caused by netlist topology. Our approach leverages netlist clustering to overcome these limitations. We first divide the netlist into highly connected clusters using the Leiden algorithm, enabling an analysis of the local netlist topology. We then predict routing congestion by exploiting GNNs to generate cluster embeddings that capture the detailed netlist topology. In addition, we introduce a cluster padding method that utilizes the trained model to mitigate routing congestion. By applying the proposed ClusterNet, we can accurately predict and optimize routing congestion from specific cluster topologies. Our experimental results demonstrated improved prediction performance, with a mean absolute error of 0.056 and an R2 score of 0.669. Furthermore, routing congestion optimization significantly improved the total negative slack and reduced the number of failing endpoints by 14.5% and 9.9%, respectively.

Machine learning (ML) techniques have been widely studied to predict routability in early-stage. To reduce the design turn-around time during the placement and routing iterations, it is crucial to predict the design rule violation (DRV) hotspots precisely before actual detailed routing. However, complex network architectures of ML make it challenging for humans to understand how ML generates predictions and to identify the factors that significantly influence the predictions. This black-box nature of ML limits the efficient integration of the prediction techniques into an optimization process. Explainable artificial intelligence enables the interpretation of decision rationales in the ML model and brings us the reasons underlying the prediction of the model. In this paper, we propose a routability optimization framework that analyzes the input features relevant to the predicted DRV hotspots using an explainable model and selects the most suitable optimization methods. The proposed framework comprises three steps - (1) predicting DRV hotspots in the early-global routing stage, (2) calculating how much each input feature contributes to the predictions and (3) applying a proper optimization method to improve the routability. We reduced the number of DRVs by 78% on average in 16 design layouts without degrading the design Quality.