EMGraph: Fast Learning-Based Electromigration Analysis for Multi-Segment Interconnect Using Graph Convolution Networks

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 gr...

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Vydáno v:2021 58th ACM/IEEE Design Automation Conference (DAC) s. 919 - 924
Hlavní autoři: Jin, Wentian, Chen, Liang, Sadiqbatcha, Sheriff, Peng, Shaoyi, Tan, Sheldon X.-D.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 05.12.2021
Témata:
Convolution
Electromigration
Electromigration (EM)
Generative adversarial networks
graph convolution network (GCN)
hydrostatic stress assessment
Image edge detection
Integrated circuit interconnections
multisegment interconnect
Predictive models
Very large scale integration
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Shrnutí: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 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, 4\times accuracy and 14\times speedup over the GAN-based method.
DOI:10.1109/DAC18074.2021.9586239