Convolutional Neural Network-based Design of EBG Structures in High-Speed Packages and PCBs

被引：1
|
作者：
Sim, Seongbo [1 ]
Kim, Myunghoi [1 ]
机构：
[1] Hankyong Natl Univ, Sch ICT Robot & Mech Engn, Anseong, Gyeonggi Do, South Korea
来源：
2021 JOINT IEEE INTERNATIONAL SYMPOSIUM ON ELECTROMAGNETIC COMPATIBILITY, SIGNAL & POWER INTEGRITY, AND EMC EUROPE (EMC+SIPI AND EMC EUROPE) | 2021年
基金：
新加坡国家研究基金会;
关键词：
D O I：
10.1109/EMC/SI/PI/EMCEurope52599.2021.9559214
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
We propose a method of predicting a cutoff frequency band(Stopband) of electromagnetic band gap(EBG) structure that reduces noise using Convolutional Neural Network(CNN). The stopband is predicted by using the structures of ResNet and DenseNet, which are known CNN models. This is compared and analyzed with the results of the existing method.
引用
收藏
页码：422 / 422
页数：1
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