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Electromigration (EM) becomes a major concern for VLSI circuits as
the technology advances in the nanometer regime. With Korhonen
equations, EM assessment for VLSI circuits remains challenged due to
the increasing integrated density. VLSI multisegment interconnect
trees can be naturally viewed as graphs. Based on this observation,
we propose a new graph convolution network (GCN) model, which is
called {\it EMGraph} considering both node and edge embedding
features, to estimate the transient EM stress of interconnect trees.
Compared with recently proposed generative adversarial network (GAN)
based stress image-generation method, EMGraph model can learn more
transferable knowledge to predict stress distributions on new graphs
without retraining via inductive learning. Trained on the large
dataset, the model shows less than 1.5% averaged error compared to
the ground truth results and is orders of magnitude faster than both
COMSOL and state-of-the-art method. It also achieves smaller model
size, 4X accuracy and 14X speedup over the GAN-based
method.
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