A Graph-Learning-Driven Prediction Method for Combined Electromigration and Thermomigration Stress on Multi-Segment Interconnects

As technology advances, the temperature gradient in the interconnects becomes more significant, which causes serious thermomigration. Simulating the coupling effects of thermomigration (TM) and electromigration (EM) on large-scale circuits is very time-consuming caused by a substantial increase in computational complexity. Recently, some researchers utilized graph learning-based methods to predict EM stress in medium-scale cases. Unfortunately, these works overlooked the effects of TM. To predict the EM - TM stress of large-scale interconnects accurately and efficiently, we propose a framework based on Graph Attention Networks (GATs) with a customized alternating aggregation method for collecting information in junctions and branches of interconnects jointly. The experimental results show that our work achieves an average relative error of less than 1 % compared to the commercial software COMSOL for inter-connects consisting of fewer than 200 segments. Furthermore, our method also achieves 9037 x speedup in predicting the OpenROAD test circuit with a maximum segment number reaching 10807.