New and very simple CAD models for coplanar waveguide synthesis

Abstract

This paper presents new, very simple, accurate closed‐form formulas obtained by using the curve‐fitting technique for coplanar waveguide synthesis. The results achieved from this work are compared to the results obtained from the quasi‐static analysis and the synthesis formulas proposed by other researchers, as well as experimental works available in the literature. © 2004 Wiley Periodicals, Inc. Microwave Opt Technol Lett 41: 49–53, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.20043

Very accurate and simple CAD models based on neural networks for coplanar waveguide synthesis

“…This figure shows the contours of normalized central width S/h versus normalized slot width W/h for various characteristic impedance values for a given substrate material ( r ϭ 12.9 and h ϭ 200 m). Similar comparisons are made for the second neural model presented in this work; the synthesis formulas [14,17], quasi-static analysis, and obtained counters 4, the results of the neural models proposed here are in excellent agreement with those of the quasistatic analysis, whereas there is no good agreement between the results of quasi-static analysis and the synthesis CAD models [14,17], especially in the extreme ranges of the normalized central strip width S/h and normalized slot width W/h. A similar perfect agreement between the results of the neural models and quasi-static analysis are also achieved for all CPW structures to be designed with different electrical parameters and physical dimensions.…”

“…Comparisons among the results of the first neural model presented in this work -the synthesis formula proposed by Deng et al [14], the synthesis formula proposed by Yildiz and Turkmen [17], and the quasistatic analysis -are presented graphically in Figure 3. This figure shows the contours of normalized central width S/h versus normalized slot width W/h for various characteristic impedance values for a given substrate material ( r ϭ 12.9 and h ϭ 200 m).…”

“…In these comparisons, the results obtained from the first and second neural models are compared with three other approaches from the literature. These are the results of the respective quasi-static analyses, two CAD models which are known as the best accurate tools for CPW synthesis available in the literature [14,17], and experimental work reported in [36].…”

“…For this reason, some approximate formulas have been proposed for the calculation of elliptic integrals [18]. The fourth synthesis CAD model, proposed by the authors of the present article [17], is the simplest model in the literature, but does not have very good accuracy of the other three CAD synthesis models. Hence, these syn-thesis CAD models are not very attractive for CPW synthesis.…”

“…These neural models were trained using the LM [29,30], Bayesian regularisation (BR) [31], conjugate gradient of Fletcher-Powel (CGF) [32][33], backpropagation with momentum (BPM) [34], and quasi-Newton (QN) [35] learning algorithms to obtain better performance and faster convergence with a simpler structure. For the validation of the neural models presented in this work, the synthesis results obtained from these neural models have been compared with the quasi-static analysis results [3,4], the results of the synthesis formulas proposed by other researchers [14,17], and the experimental results [36] available in the literature.…”