Artifical neural networks in RF MEMS switch modelling

Abstract

The increased growth of the applications of RF MEMS switches in modern communication systems has created an increased need for their accurate and efficient models. Artificial neural networks have appeared as a fast and efficient modelling tool providing similar accuracy as standard commercial simulation packages. This paper gives an overview of the applications of artificial neural networks in modelling of RF MEMS switches, in particular of the capacitive shunt switches, proposed by the authors of the paper. Models for the most important switch characteristics in electrical and mechanical domains are considered, as well as the inverse models aimed to determine the switch bridge dimensions for specified requirements for the switch characteristics.

Hybrid neural lumped element approach in inverse modeling of RF MEMS switches

“…The proposed approach is a hybrid approach combining neural modeling with a lumped element equivalent circuit. In other words, it is a combination of the black-box neural inverse modeling approach [19][20][21] and a modification of the scalable lumped element model proposed in [18]. Schematic diagram of proposed model is shown in Fig.…”

“…2. The aim of the first ANN (ANN 1) is to determine the length of the fingered part L f for the desired resonant frequency [19,20,22].…”

“…to perform the inverse modeling of the switch. The authors of this work proposed earlier a black-box inverse modeling of the RF MEMS capacitive switches where the bridge lateral dimensions were determined for given electrical or mechanical switches [19][20][21][22][23][24][25]. In this work, the neural based inverse modeling approach is extended in a way that the novel approach provides not only determination of the bridge dimensions but also the values of the corresponding lumped model elements, resulting in a lumped element model ready to be used for further simulations of the circuits containing the considered switch.…”

Comparative Analysis of Different Cad Methods for Extraction of the Hemt Noise Wave Model Parameters

“…The field of electrical engineering has proved to be a very suitable for the ANN applications [26][27][28][29][30][31][32][33][34][35]. By learning the dependence between two datasets, ANNs have the capability to approximate any nonlinear function, whereby the knowledge about the physical characteristics of the problem to be modeled is not needed [36].…”

A review on the artificial neural network applications for small‐signal modeling of microwave FETs

“…Artificial neural networks (ANNs) are a well‐established and very powerful mathematical tool, finding a variety of applications as a modeling tool in the field of RF and microwaves . One of the most attractive features of ANNs is their ability to learn and generalize from a set of training data, which is suitable to be exploited for building device models from the measured characteristics.…”