Journal ArticleDOI
EM-based optimization of microwave circuits using artificial neural networks: the state-of-the-art
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Abstract:
This paper reviews the current state-of-the-art in electromagnetic (EM)-based design and optimization of microwave circuits using artificial neural networks (ANNs). Measurement-based design of microwave circuits using ANNs is also reviewed. The conventional microwave neural optimization approach is surveyed, along with typical enhancing techniques, such as segmentation, decomposition, hierarchy, design of experiments, and clusterization. Innovative strategies for ANN-based design exploiting microwave knowledge are reviewed, including neural space-mapping methods. The problem of developing synthesis neural networks is treated. EM-based statistical analysis and yield optimization using neural networks is reviewed. The key issues in transient EM-based design using neural networks are summarized. The use of ANNs to speed up "global modeling" for EM-based design of monolithic microwave integrated circuits is briefly described. Future directions in ANN techniques to microwave design are suggested.read more
Citations
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Journal ArticleDOI
Space mapping: the state of the art
John W. Bandler,Qingsha S. Cheng,S.A. Dakroury,Ahmed S.A. Mohamed,Mohamed H. Bakr,Kaj Madsen,Jacob Søndergaard +6 more
TL;DR: For the first time, a mathematical motivation is presented and SM is placed into the context of classical optimization to achieve a satisfactory solution with a minimal number of computationally expensive "fine" model evaluations.
Journal Article
Space mapping
TL;DR: A generic space-mapping optimization algorithm is formulated, explained step-by-step using a simple microstrip filter example, and its robustness is demonstrated through the fast design of an interdigital filter.
Journal ArticleDOI
A Space-Mapping Framework for Engineering Optimization—Theory and Implementation
TL;DR: A comprehensive approach to engineering design optimization exploiting space mapping (SM) using a new generalization of implicit SM to minimize the misalignment between the coarse and fine models of the optimized object over a region of interest.
Book ChapterDOI
Surrogate-Based Methods
TL;DR: This chapter briefly describes the basics of surrogate-based optimization, various ways of creating surrogate models, as well as several examples of surrogate -based optimization techniques.
Journal ArticleDOI
Deep neural networks for the evaluation and design of photonic devices
TL;DR: In this paper, the authors show how deep neural networks, configured as discriminative networks, can learn from training sets and operate as high-speed surrogate electromagnetic solvers, inverse modelling tools and global device optimizers, and how deep generative networks can learn geometric features in device distributions and even be configured to serve as robust global optimizers.
References
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Journal ArticleDOI
Neural space-mapping optimization for EM-based design
TL;DR: In this article, a neural space-mapping (NSM) optimization for electromagnetic-based design is proposed, where the initial mapping is established by performing upfront fine-model analyses at a reduced number of base points.
Journal ArticleDOI
A new macromodeling approach for nonlinear microwave circuits based on recurrent neural networks
TL;DR: A new macromodeling approach is developed in which a recurrent neural network (RNN) is trained to learn the dynamic responses of nonlinear microwave circuits to provide fast prediction of the full analog behavior of the original circuit.
Neural space mapping optimization for EM-based design of RF and microwave circuits
TL;DR: This work proposes, for the first time, neural space-mapping (NSM) optimization for electromagnetic based design and proposes a novel procedure that does not require troublesome parameter extraction to predict the next point.
Proceedings ArticleDOI
A trust region aggressive space mapping algorithm for EM optimization
TL;DR: In this paper, a robust algorithm for EM optimization of microwave circuits is presented, which integrates a trust region methodology with aggressive space mapping (ASM), and a new automated multipoint parameter extraction process is implemented.
Proceedings ArticleDOI
Development of knowledge based artificial neural network models for microwave components
TL;DR: This paper addresses the use of prior knowledge (or existing models) for reducing the complexity of the input/output relationships that an ANN has to learn and demonstrates two simple methods of incorporating prior knowledge into ANN training.