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A CAD neural analysis for conductor backed asymmetric CPW with one lateral ground plane

Artificial Neural Networks (ANNs) have been used as a promising tools for many applications. In recent years, a computer-aided design approach based on ANNs has been introduced to microwave modeling, simulation and optimization. In this work, the characteristics parameters of the conductor-backed asymmetric coplanar waveguide (CB - ACPW) with one lateral ground plane have been determined with the use of neural models. ANNs were trained with six learning algorithms to obtain better performance and faster convergence with simpler structure. The best results were obtained with Bayesian Regularization and Levenberg - Marquardt algorithms. The quasi-static parameters of CB - ACPW with one lateral ground plane configurations can be calculated very accurately using the neural model proposed in this work. This has facilitated the usage of ANN models. The notable benefits are simplicity & accurate determination of the characteristic parameters of CB-ACPW's with one lateral ground plane. The greatest advantage is lengthy formulas can be dispensed with.