Surrogate Modeling of High-Speed Links Based on GNN and RNN for Signal Integrity Applications
Author:
Affiliation:
1. State Key Laboratory of Radio Frequency Heterogeneous Integration, Shanghai Jiao Tong University, Shanghai, China
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Condensed Matter Physics,Radiation
Link
http://xplorestaging.ieee.org/ielx7/22/10241248/10066142.pdf?arnumber=10066142
Reference29 articles.
1. Comparative Study of Surrogate Modeling Methods for Signal Integrity and Microwave Circuit Applications
2. Support Vector Regression-Based Active Subspace (SVR-AS) Modeling of High-Speed Links for Fast and Accurate Sensitivity Analysis
3. Semi-Supervised Learning Based on Hybrid Neural Network for the Signal Integrity Analysis
4. High-Speed Channel Modeling With Machine Learning Methods for Signal Integrity Analysis
5. Modeling of eye diagram height in high-speed links via support vector machine
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1. Predicting the Characteristics of High-Speed Serial Links Based on a Deep Neural Network (DNN)—Transformer Cascaded Model;Electronics;2024-08-02
2. A Multi-scale Deep Neural Network Technique for Surrogate Modeling of Microwave Components;2024 Photonics & Electromagnetics Research Symposium (PIERS);2024-04-21
3. High-Speed Channel Transformer: A Scalable Transformer Network-Based Signal Integrity Simulator;IEEE Transactions on Electromagnetic Compatibility;2024
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