Neural Based Lumped Element Model of Capacitive RF MEMS Switches

Abstract

In this paper a lumped element model of RF MEMS capacitive switches which is scalable with the lateral dimensions of the bridge is proposed. The dependence of the elements of the model on the bridge dimensions is introduced by using one or more artificial neural networks to model the relationship between the bridge dimensions and the inductive and resistive elements of the lumped element model. The achieved results show that the developed models have a good accuracy over the whole considered range of the bridge dimension values.

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.…”

“…The second ANN (ANN 2) is used for modeling the relationship between the resistance and the bridge lateral dimensions L s and L f . Unlike the model considered in [18] where the inductance dependence on the dimensions is modeled also by the ANN, having in mind that in the considered case the resonant frequency is known, it is possible to calculate the inductance by using the Eq. 3, assuming that the capacitance, which is constant and does not depend on the bridge lateral dimensions, has been determined previously.…”

“…However, if differently sized bridges are analyzed, the procedures for obtaining the equivalent circuit elements have to be repeated, which is a time-consuming process. To make the lumped element model scalable with the dimensions, artificial neural networks (ANNs) were proposed to model the dependence of the lumped element model on the switch bridge dimensions [18]. The switch bridge dimensions determine the electromagnetic characteristics of the switch.…”

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.…”

Characterization and Neural Modeling of a Microwave Gas Sensor for Oxygen Detection Aimed at Healthcare Applications

“…To develop the model, artificial neural networks (ANNs) have been chosen as the modelling tool. Owing to their ability to learn the relationship between an input–output set of data, ANNs have found a variety of applications in different research fields, such as microwave device modelling [ 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 ] and gas sensing purposes [ 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 ]. As far as microwave device modelling is concerned, the ANNs have often been applied to model the device’s electrical characteristics versus different operating and ambient conditions, as well as versus device dimensions making the model scalable.…”