Advances in Artificial Neural Networks for Electromagnetic Parameterized Modeling

被引：0
|
作者：
Zhang, Qi-Jun [1 ]
Feng, Feng [2 ]
Na, Weicong [3 ]
机构：
[1] Carleton Univ, Dept Elect, Ottawa, ON, Canada
[2] Tianjin Univ, Sch Microelect, Tianjin, Peoples R China
[3] Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
来源：
2021 13TH GLOBAL SYMPOSIUM ON MILLIMETER-WAVES & TERAHERTZ (GSMM) | 2021年
基金：
中国国家自然科学基金;
关键词：
artificial neural networks; deep neural networks; EM parameterized modeling; forward modeling; inverse modeling; knowledge-based neural networks; neuro-transfer functions;
D O I：
10.1109/GSMM53250.2021.9512007
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：
Electromagnetic (EM) parameterized modeling is important for EM repetitive analysis, such as EM optimization, what if analysis, and yield optimization. An overview of advances in artificial neural networks (ANNs) for EM parameterized modeling is presented in this paper, covering forward/inverse modeling, deep neural networks, knowledge-based neural networks, neuro-transfer functions, and applications for fast EM modeling with varying values in geometrical parameters.
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页数：3
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