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
Space mapping optimization of waveguide filters using finite element and mode-matching electromagnetic simulators
TL;DR: For the first time, aggressive space mapping is applied to automatically align electromagnetic models based on hybrid mode-matching/network theory simulations with modelsBased on finite-element (FEM) simulations in design optimization of microwave circuits.
Proceedings ArticleDOI
Neuro-Space Mapping technique for nonlinear device modeling and large signal simulation
TL;DR: A new Neuro-Space Mapping (Neuro-SM) approach is presented enabling the space mapping (SM) concept to be applied to nonlinear device modeling and large signal circuit simulation.
Journal ArticleDOI
A large-signal characterization of an HEMT using a multilayered neural network
TL;DR: An approach to describe the large-signal behavior of a high electron-mobility transistor (HEMT) by using a multilayered neural network that shows excellent accuracy and generates good extrapolations is proposed.
Journal ArticleDOI
Neural Inverse Space Mapping (NISM) Optimization for EM-Based Microwave Design
TL;DR: The inverse of the mapping from the fine to the coarse model parameter spaces is exploited for the first time in a space mapping algorithm and the inverse is approximated by a neural network whose generalization performance is controlled through a network growing strategy.
Journal ArticleDOI
Yield-driven electromagnetic optimization via multilevel multidimensional models
TL;DR: The authors present the foundation of a sophisticated hierarchical multidimensional response surface modeling system for efficient yield-driven design that makes it possible, for the first time, to perform direct gradient-based yield optimization of circuits with components or subcircuits simulated by an electromagnetic simulator.