Microwave Modeling and Design Optimization: The Legacy of John Bandler

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.

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Publication Details

Journal
IEEE Transactions on Microwave Theory and Techniques
Published
2024-08-12
DOI
https://doi.org/10.1109/tmtt.2024.3437198
Citations
17
Primary Topic
Radio Wave Propagation Studies
Type
article
Field-Weighted Citation Impact
22.08
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Microwave Modeling and Design Optimization: The Legacy of John Bandler

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Microwave Modeling and Design Optimization: The Legacy of John Bandler

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article en
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Abstract

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.

IEEE Transactions on Microwave Theory and TechniquesVol. 73(1)
Instituto Tecnológico y de Estudios Superiores de Occidente (MX), Universidad de Guadalajara (MX), University of Victoria (CA), Southern University of Science and Technology (CN), Carleton University (CA), McMaster University (CA)
Openalex Percentile: Top 1%
Radio Wave Propagation Studies
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