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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Proceedings ArticleDOI
EM-based surrogate modeling and design exploiting implicit, frequency and output space mappings
John W. Bandler,Qingsha S. Cheng,D.H. Gebre-Mariam,K. Madsen,Frank Pedersen,Jacob Søndergaard +5 more
TL;DR: A significant improvement to the novel implicit space mapping (ISM) concept for EM-based microwave modeling and design is presented, and for the first time also, frequency space mapping is implemented in an ISM framework.
Journal ArticleDOI
A review of global modeling of charge transport in semiconductors and full-wave electromagnetics
TL;DR: The physical consistency of the models is considered and some open computational challenges are reviewed in this article, where both hydrodynamic and ensemble Monte Carlo transport models are discussed and sample results are presented.
Journal ArticleDOI
Exact adjoint sensitivity analysis for neural-based microwave modeling and design
TL;DR: For the first time, an adjoint neural network method is introduced for sensitivity analysis in neural-based microwave modeling and design and allows the models to learn both the input/output behavior of the modeling problem and its derivative data simultaneously.
Journal ArticleDOI
Neural network-based adaptive beamforming for one- and two-dimensional antenna arrays
TL;DR: In the approach suggested in this paper, the computation of the optimum weights is accomplished using three-layer radial basis function neural networks (RBFNN).
Journal ArticleDOI
Space mapping optimization of microwave circuits exploiting surrogate models
TL;DR: A powerful new Aggressive Space Mapping (ASM) optimization algorithm is presented in this paper, which draws upon recent developments in both surrogate-based optimization and microwave device neuromodeling.