Microwave Modeling and Design Optimization: The Legacy of John Bandler

被引:2
|
作者
Rayas-Sanchez, Jose E. [1 ]
Zhang, Qi-Jun [2 ]
Rautio, James C. [3 ]
Nikolova, Natalia K. [4 ]
Boria, Vicente E. [5 ,6 ]
Cheng, Qingsha S. [7 ]
Yu, Ming [7 ]
Hoefer, Wolfgang J. R. [8 ]
机构
[1] Jesuit Univ Guadalajara, ITESO, Dept Elect Syst & Informat, Tlaquepaque 45604, Jalisco, Mexico
[2] Carleton Univ, Dept Elect, Ottawa, ON K1S 5B6, Canada
[3] Sonnet Software, Syracuse, NY 13212 USA
[4] McMaster Univ, Dept Elect & Comp Engn, Hamilton, ON L8S 4K1, Canada
[5] Univ Politecn Valencia, Dept Comunicac, Valencia 46022, Spain
[6] Univ Politecn Valencia, ITEAM, Valencia 46022, Spain
[7] Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen 518055, Peoples R China
[8] Univ Victoria, Dept Elect & Comp Engn, Victoria, BC V8W 2Y2, Canada
关键词
Adjoint sensitivities; artificial intelligence (AI); circuit optimization; electromagnetic (EM) optimization; design centering; design optimization; frequency scaling; machine learning (ML); microwave circuits; minimax; neural networks; parameter extraction; port tuning; sensitivities; space mapping (SM); surrogate modeling; statistical analysis; yield; ARBITRARY STATISTICAL DISTRIBUTIONS; DRIVEN ELECTROMAGNETIC OPTIMIZATION; SPACE-MAPPING OPTIMIZATION; ARTIFICIAL NEURAL-NETWORKS; ADJOINT VARIABLE METHOD; EM-BASED OPTIMIZATION; SENSITIVITY-ANALYSIS; YIELD OPTIMIZATION; SCATTERING PARAMETERS; MINIMAX DESIGN;
D O I
10.1109/TMTT.2024.3437198
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
In this article, we honor Prof. John W. Bandler and his legacy in RF and microwave modeling and automated design optimization. We showcase his pioneering breakthroughs in minimax optimization, pth norm formulations, yield optimization, and nonlinear circuit design optimization. We highlight advances in direct electromagnetic (EM) microwave optimization, circuit response sensitivities, and efficient S-parameters sensitivity calculations. We explore the port-tuning version of space mapping (SM) for EM-based analysis, techniques for industrial microwave design of satellite systems, and post-manufacture hardware tuning. The integration of artificial neural networks (ANNs) with SM for enhanced EM-based design optimization and yield prediction, cognition-driven microwave filter design, and parallels between SM and artificial intelligence (AI) is examined. Finally, we speculate on the future integration of cognitive science with engineering design, leveraging the synergy of AI, machine learning (ML), and SM.
引用
收藏
页码:87 / 101
页数:15
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