Abstract
In this paper, an encoder-decoder structured multi-layer perceptron-sequential (MLP-SEQ) networks are proposed to model high-speed differential vias for estimating differential insertion loss (IL) and return loss (RL). Sequential neural networks including recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU) are introduced as the decoder NN to treat frequency responses as sequences. The proposed models are validated by finite element method (FEM) simulation results. The accuracy and training times of MLP-RNN, MLP-LSTM, and MLP-GRU models are compared and analyzed. Based on the MLP-LSTM model, various design of experiments (DoEs) are conducted to enhance the reproducibility and reliability of the proposed model. In addition, to further improve accuracy, various methods to enhance long-term memory of the encoder's output feature node are investigated.
Recommended Citation
H. Park and S. Sawant and B. Sathvika and A. Chada and S. Singh and S. Pk and T. Shin and H. Suh and J. Park and B. Mutnury and D. Kim, "Differential Via Modeling using Multilayer Perceptron-Sequential (MLP-SEQ) Neural Network," IEEE International Symposium on Electromagnetic Compatibility, pp. 114 - 119, Institute of Electrical and Electronics Engineers, Jan 2025.
The definitive version is available at https://doi.org/10.1109/EMCSIPI52291.2025.11170270
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Differential via; Modeling; Sequential neural network
International Standard Serial Number (ISSN)
2158-1118; 1077-4076
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2025
