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
A CAD environment for performance and yield driven circuit design employing electromagnetic field simulators
TL;DR: A CAD environment for performance and yield driven circuit design with electromagnetic (EM) field simulations employed within the optimization loop is described and microstrip structures are accurately simulated and their responses are incorporated into the overall circuit analysis.
Proceedings ArticleDOI
EM-ANN modeling of overlapping open-ends in multilayer microstrip lines for design of bandpass filters
C. Cho,K.C. Gupta +1 more
TL;DR: In this paper, an EM-ANN model has been developed successfully for reducing the design time of multilayer end-coupled band-pass filters, which is used for obtaining a wide bandwidth.
Journal ArticleDOI
A coupled FDTD‐artificial neural network technique for large‐signal analysis of microwave circuits
TL;DR: A first‐order global modeling approach of Monolithic Microwave Integrated Circuits (MMIC) is proposed by modeling the active device with a neural network based on a full hydrodynamic model that describes the nonlinearities of the equivalent circuit parameters of an MESFET implemented in an extended Finite Difference Time Domain mesh.
Proceedings ArticleDOI
Neural network processing for adaptive array antennas
TL;DR: Neural network-based array antennas do not suffer from the shortcoming of conventional beamformers, and due to their high-speed computational capability, can yield results in real-time.
Proceedings ArticleDOI
Neural network structures for EM/microwave modeling
TL;DR: This paper discusses various microwave-oriented neural network structures that can be used during EM/microwave design to provide instant answers.