Journal ArticleDOI
EM-Based Monte Carlo Analysis and Yield Prediction of Microwave Circuits Using Linear-Input Neural-Output Space Mapping
Jose E. Rayas-Sanchez,Vladimir Gutiérrez-Ayala +1 more
- Vol. 54, Iss: 12, pp 4528-4537
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TLDR
The accuracy and efficiency of the technique is illustrated through the design and statistical analysis of a classical synthetic problem and a microstrip notch filter with mitered bends.Abstract:
A computationally efficient method for highly accurate electromagnetics-based statistical analysis and yield estimation of RF and microwave circuits is described in this paper. The statistical analysis is realized around a space-mapped nominal solution. Our method consists of applying a constrained Broyden-based linear input space-mapping approach to design, followed by an output neural space-mapping modeling process in which not only the responses, but the design parameters and independent variable are used as inputs to the output neural network. The output neural network is trained using reduced sets of training and testing data generated around the space-mapped nominal solution. We illustrate the accuracy and efficiency of our technique through the design and statistical analysis of a classical synthetic problem and a microstrip notch filter with mitered bendsread more
Citations
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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
Parametric Modeling of EM Behavior of Microwave Components Using Combined Neural Networks and Pole-Residue-Based Transfer Functions
TL;DR: An advanced technique to develop combined neural network and pole-residue-based transfer function models for parametric modeling of electromagnetic (EM) behavior of microwave components and can obtain better accuracy in challenging applications involving high dimension of geometric parameter space and large geometrical variations, compared with conventional modeling methods.
Journal ArticleDOI
Rapid Yield Estimation and Optimization of Microwave Structures Exploiting Feature-Based Statistical Analysis
Slawomir Koziel,John W. Bandler +1 more
TL;DR: A simple, yet reliable methodology to expedite yield estimation and optimization of microwave structures by exploiting the almost linear dependence of the feature points on the designable parameters of the structure.
Journal ArticleDOI
Shape-Preserving Response Prediction for Microwave Design Optimization
TL;DR: A shape-preserving response prediction methodology for microwave design optimization that has very good generalization capability and it is not based on any extractable parameters, which makes it easy to implement.
Journal ArticleDOI
Space Mapping With Adaptive Response Correction for Microwave Design Optimization
TL;DR: An adaptive response correction scheme is presented to work in conjunction with space-mapping optimization algorithms and is designed to alleviate the difficulties of the standard output space mapping by adaptive adjustment of the response correction term according to the changes of thespace-mapped coarse model response.
References
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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.
Book
Neural Networks for RF and Microwave Design
Qi-Jun Zhang,K.C. Gupta +1 more
TL;DR: This paper presents a meta-modelling framework for knowledge-based ANN models for design and training of Neural Networks for RF/Microwave Components and Circuit Analysis and Optimization.
Journal ArticleDOI
A neural network modeling approach to circuit optimization and statistical design
TL;DR: This paper presents a new approach to microwave circuit optimization and statistical design featuring neural network models at either device or circuit levels, which has the capability to handle high-dimensional and highly nonlinear problems.
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.
Nonlinear Statistical Modeling and Yield Estimation Technique for Use in Monte Carlo Simulations
Jan F. Swidzinski,Kai Chang +1 more
TL;DR: The proposed modeling approach, when applied to the database of extracted equivalent circuit parameters (ECPs) for a pseudomorphic high electron mobility transistor device, has proven that it can generate simulated ECPs, S-parameters, that are statistically indistinguishable from measured ones.