CAD modeling of coplanar waveguide interdigital capacitor (original) (raw)
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A new, simple method characteristic impedance calculating the conductor-backed coplanar waveguide, based on artificial neural networks, is presented. Three leaming algorithms, the backpropagation, the delta-bar-delta, and the extended-delta-bar-delta, are used to train the networks. The method can be used for a wide range of substrate thicknesses and permittivities, and is useful for the computer-aided desigr (CAD) of coplanar waveguides. The calculated characteristic impedance results are in very good agreement with the results available in the literature.
International Journal of Rf and Microwave Computer-aided Engineering, 2006
This article reports on the use of the particle swarm optimization (PSO) algorithm in the synthesis of the planar interdigital capacitor (IDC). The PSO algorithm is used to optimize the geometry parameters of the IDC in order to obtain a certain capacitance value. The capacitance value of the IDC is evaluated using an artificial neural network (ANN) model with the geometry parameters of the IDC as its inputs. Several design examples are presented that illustrate the use of the PSO algorithm, and the design goal in each example is easily achieved. Full-wave electromagnetic simulations are also performed for some of the studied IDC structures implemented using coplanar waveguide (CPW) technology. The simulation results are in good agreement with those obtained using the ANN/PSO algorithm.
A CAD neural analysis for edge coupled coplanar waveguides
2008 International Conference on Recent Advances in Microwave Theory and Applications, 2008
Artificial neural networks (ANNs) have been promising tools for many applications. In recent years, a computer-aided design approach based on ANNs has been introduced to microwave modelling, simulation and optimization. In this work, the characteristics parameters of edge coupled coplanar waveguides (CPWs) have been determined with the use of models. These neural models were trained with LM, DR, QN, CGF and SCG learning algorithms. The results have shown that the estimated characteristics parameters are in very good agreement with the computed results by using conformal mapping theory. The LM learning algorithm was found to be the best algorithm among all. As a result, ANN models presented in this work can be used easily, simply and accurately to determine the characteristics parameters of the edge coupled CPWs. Index terms-Artificial neural networks; Characteristics impedance; Coupling Coefficient; Effective dielectric permittivity; Edge coupled Coplanar waveguides.
In order to compute the characteristic impedance and the relative effective dielectric constant of an asymmetric coplanar waveguide with infinite or finite dielectric thickness, the use of artificial neural networks is valuable. The method of neural computing presented in this paper uses only one neural model for both parameters, for this specific waveguide type. The BFGS quasi-Newton back-propagation algorithm was used to train the developed neural network. Numerical results are given for several configurations along with comparisons with previously published data.
Neural Model for Circular-Shaped Microshield and Conductor-Backed Coplanar Waveguide
Progress In Electromagnetics Research M
A Computer Aided Design (CAD) approach based on Artiflcial Neural Networks (ANN's) is successfully introduced to determine the characteristic parameters of Circular-shaped Microshield and Conductor-backed Coplanar Waveguide (CMCB-CPW). ANN's have been promising tools for many applications and recently ANN has been introduced to microwave modeling, simulation and optimization. The Multi Layered Perceptron (MLP) neural network used in this work were trained with Levenberg-Marquart (LM), Bayesian regularization (BR), Quasi-Newton (QN), Scaled Conjugate gradient (SCG), Conjugate gradient of Fletcher-Powell (CGF) and Conjugate Gradient backpropagation with Polak-Ribiere (CGP) learning algorithms. This has facilitated the usage of ANN models. The notable beneflts are simplicity & accurate determination of the characteristic parameters of CMCBCPW's. The greatest advantage is lengthy formulas can be dispensed with.
2009
In recent years, Computer Aided Design (CAD)based on Artificial Neural Networks (ANNs) have been introduced for microwave modeling simulation and optimization. In this paper, the characteristic parameters of Broadside - Coupled Coplanar Waveguides (BSCCPWs) have been determined with the use of ANN model. Eight learning algorithms, Levenberg - Marquart(LM), Bayesian Regularization (BR),Quasi-Newton (QN), Scaled Conjugate Gradient (SCG), Conjugate Gradient of Fletcher - Powell(CGF) , Resilient Propagation (RP), Conjugate Gradient back- propagation with Polak-Ribiere (CGP) and Gradient Descent (GD) are used to train the Multi- Layer Perceptron Neural Networks (MLPNNs). The results of neural models presented in this paper are compared with the results of Conformal Mapping Technique (CMT). The neural results are in very good agreement with the CMT results. When the performances of neural models are compared with each other, the best results are obtained from the neural networks trained b...
Progress In Electromagnetics Research B, 2009
In recent years, Computer Aided Design (CAD) based on Artificial Neural Networks (ANNs) have been introduced for microwave modeling, simulation and optimization. In this paper, the characteristic parameters of edge coupled and conductor-backed edge coupled Coplanar Waveguides have been determined with the use of ANN model. Eight learning algorithms, Levenberg-Marquart (LM), Bayesian Regularization (BR), Quasi-Newton (QN), Scaled Conjugate Gradient (SCG), Conjugate Gradient of Fletcher-Powell (CGF), Resilient Propagation (RP), Conjugate Gradient back-propagation with Polak-Ribiere (CGP) and Gradient Descent (GD) are used to train the Multi-Layer Perceptron Neural Networks (MLPNNs). The results of neural models presented in this paper are compared with the results of Conformal Mapping Technique (CMT). The neural results are in very good agreement with the CMT results. When the performances of neural models are compared with each other, the best results are obtained from the neural networks trained by LM and BR algorithms.
Very accurate and simple CAD models based on neural networks for coplanar waveguide synthesis
… Journal of RF and Microwave Computer …, 2005
Very accurate and simple neural models for coplanar waveguide (CPW) synthesis are proposed. The results obtained from these neural models are compared with the results of quasi-static analysis, the other synthesis formulas, and other experimental works. The accuracy of the neural models is found to be better than 0.4% for 11,206 CPW samples.
Progress In Electromagnetics Research, 2008
In this paper, two different neural models are proposed for calculating the quasi-static parameters of multilayer cylindrical coplanar waveguides and strip lines. These models were basically developed by training the artificial neural networks with the numerical results of quasi-static analysis. Neural models were trained with four different learning algorithms to obtain better performance and faster convergence with simpler structure. When the performances of neural models are compared with each other, the best test results are obtained from the multilayered perceptrons trained by the Levenberg-Marquardt algorithm. The results obtained from the neural models are in very good agreements with the theoretical results available in the literature.
Characterization of coplanar waveguide open end capacitance-theory and experiment
IEEE Transactions on Microwave Theory and Techniques, 1994
The theory, numerical analysis, analytical approximate formula, measurement technique, and characteristic curves were presented in this paper for the characterization of coplanar waveguide open end capacitance. A novel variational equation was proposed in terms of the scalar potential on the slot aperture and was solved by applying the finite element method. With the available analytical Green's function and exact integration formulas in the space domain, this approach was found to be quite efficient and suitable for analyzing the coplanar waveguide discontinuity problemwven with more complicated geometrical configurations. Numerical results were compared to those obtained numerically and experimentally in previous literature, but did not correlate very well. An analytical formula under narrow-slot assumption was thus derived to render a verification of numerical results. Measurement by utilizing the resonance method were also made and the experimental data confirmed the validity of our theory. The relationship between the capacitance and the physical dimensions was also investigated. The characteristic curves of the open end capacitance were obtained. Also, an empirical formula was established for the open end structures with a thick substrate and narrow gap.