Journal ArticleDOI
EM-based optimization of microwave circuits using artificial neural networks: the state-of-the-art
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
Gradient evaluation for neural-networks-based electromagnetic optimization procedures
Giulio Antonini,Antonio Orlandi +1 more
TL;DR: This paper extends the use of a neural network approximating a function, to the evaluation of the gradient of the same function, without any extra training of the network.
Proceedings ArticleDOI
Accurate and efficient small-signal modeling of active devices using artificial neural networks
TL;DR: Artificial neural networks (ANNs) are presented for the accurate and efficient small-signal modeling of active devices and provides substantial data storage reduction over previously used modeling techniques without loss of accuracy.
Proceedings ArticleDOI
Creating neural network based microwave circuit models for analysis and synthesis
TL;DR: A systematic approach to convert conventional circuit models into neural network models for reverse modeling process and the development of an HBT amplifier model and its applications are demonstrated.
Proceedings ArticleDOI
A global modeling approach using artificial neural network
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, which shows excellent accuracy and dramatically reduces the computational time in comparison with the hydrod dynamic model.
Proceedings ArticleDOI
New directions in model development for RF/microwave components utilizing artificial neural networks and space mapping
TL;DR: This paper presents advances in model development for RF/microwave components exploiting two powerful technologies: artificial neural networks (ANN) and space mapping (SM), and shows how SM based neuromodels decrease the cost of training, improve generalization ability and reduce the complexity of the ANN topology w.r.t. the classical neurmodeling approach.