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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| Published in: | 2021 58th ACM/IEEE Design Automation Conference (DAC) pp. 919 - 924 |
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| Format: | Conference Proceeding |
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05.12.2021
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Tan, Sheldon X.-D. Chen, Liang Jin, Wentian Sadiqbatcha, Sheriff Peng, Shaoyi |
| Author_xml | – sequence: 1 givenname: Wentian surname: Jin fullname: Jin, Wentian email: wjin018@ucr.edu organization: University of California,Department of Electrical and Computer Engineering,Riverside,CA,92521 – sequence: 2 givenname: Liang surname: Chen fullname: Chen, Liang email: liangch@ucr.edu organization: University of California,Department of Electrical and Computer Engineering,Riverside,CA,92521 – sequence: 3 givenname: Sheriff surname: Sadiqbatcha fullname: Sadiqbatcha, Sheriff email: ssadi003@ucr.edu organization: University of California,Department of Electrical and Computer Engineering,Riverside,CA,92521 – sequence: 4 givenname: Shaoyi surname: Peng fullname: Peng, Shaoyi email: speng004@ucr.edu organization: University of California,Department of Electrical and Computer Engineering,Riverside,CA,92521 – sequence: 5 givenname: Sheldon X.-D. surname: Tan fullname: Tan, Sheldon X.-D. email: sheldont@ucr.edu organization: University of California,Department of Electrical and Computer Engineering,Riverside,CA,92521 |
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| Snippet | Electromigration (EM) becomes a major concern for VLSI circuits as the technology advances in the nanometer regime. With Korhonen equations, EM assessment for... |
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| StartPage | 919 |
| SubjectTerms | 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 |
| Title | EMGraph: Fast Learning-Based Electromigration Analysis for Multi-Segment Interconnect Using Graph Convolution Networks |
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