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

作者：Yunfan Zuo, Yuyang Ye, Hongchao Zhang, Tinghuan Chen, Hao Yan, Longxing Shi · 年份：2024 · DOI：10.23919/date58400.2024.10546799 · 被引用次数：7 · 研究领域：Copper Interconnects and Reliability、Synthesis and properties of polymers、Electronic Packaging and Soldering Technologies

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.