A novel neural Smith chart for use in microwave circuitry

Abstract

In this article, briefly the Smith chart is mapped with an artificial neural network (ANN) covering its whole details to be exploited CAD of the microwave circuitry. Thus, relative to the similar works in the existing literature, this article provides the continuous Smith chart domain to facilitate the ''Smith chart'' methodology in solving the highly nonlinear transformation equations between the rectangular impedance and polar reflection planes for an infinite number of passive impedance to be used in design tasks of the microwave circuits. Data ensembles for the training and testing processes are obtained from the systematically selected locations on the Smith chart with the adaptive radius sampling algorithm. The ANN architecture is also simple, which consists of the two simple multilayer perceptron (MLP) modules with the common inputs which are the termination Z S 5 R S 1 jX S , line {', Z 0 } operation bandwidth B between the defined f min , f max and the dielectric e. Briefly, the outputs of these ANN modules are the standing waves and the impedance transformation, which are the characteristic features of the transmission line circuits. Activation of the hidden layers of the modules are performed by the tangential-sigmoid type of function while the output layers are activated linearly. Furthermore, the neural unit element (NUE) is defined by the two independent neural networks as problems in the forward and reverse directions to be incorporated into the analysis and design algorithms of the unit element (UE). This can also be considered as solving the simultaneous nonlinear equation set for (', Z 0 ) parameters of the required impedance transformations Z OUT (x) 5 R OUT (x) 1 X OUT (x) from the given complex termination Z S 5 R S 1 jX S . Applications of the Neural Smith chart are given by the numerous examples with the proved accuracy. Thus it has been verified that this neural Smith chart can be exploited for the whole classical transmission line theory including impedance matching.

Artificial Neural Network Modeling of a CMOS Differential Low-Noise Amplifier Using the Bayesian Regularization Algorithm

“…In [39], an ANN model was introduced to address LNA impedance matching using the Smith chart. However, a complete ANN model for the entire LNA is still pending development.…”