Pre-Routing Timing Estimation Considering Power Delivery Network

In recent years, the demand for fast and accurate pre-routing timing prediction is increasing. However, existing machine learning (ML)-based methods for predicting pre-routing timing often overlook the impact of power delivery networks (PDNs), which are critical to IR drop and routing congestion, limiting the effectiveness of these approaches in real-world circuit designs. To address this challenge, we propose a multimodal model that integrates PDN, layout, and netlist data. Additionally, we incorporate auxiliary and contrastive learning techniques to capture the differences across various PDNs. Extensive experiments on open-source designs demonstrate the superior performance of our model.