ChronoTE: Crosstalk-Aware Timing Estimation for Routing Optimization via Edge-Enhanced GNNs

Le résumé fourni par la source

Accurate timing estimation during the routing stage is critical for modern VLSI design closure, especially under increasing crosstalk effects in advanced technology nodes. During the routing process, the crosstalk effect is usually modeled by predicting coupling capacitance with congestion information. However, such estimations are often overly pessimistic, as crosstalk-induced delay is influenced not only by coupling capacitance but also by the relative arrival times of signals. In this work, we propose ChronoTE, a novel edge-enhanced graph neural network (GNN) framework that performs crosstalk-aware net delay estimation by jointly modeling physical topology and timing characteristics. By embedding timing-window-aware features into edge representations, ChronoTE enables accurate delay prediction without requiring full routing or parasitic extraction. Experimental results on industrial-scale open-source designs demonstrate that ChronoTE, by delivering sign-off quality delay estimation in the early global routing stage, significantly accelerates design closure and contributes to area reduction.

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