ARTIFICAL NEURAL NETWORKS IN RF MEMS SWITCH MODELING

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 modeling tool providing similar accuracy as the standard commercial simulation packages. This paper gives an overview of the applications of artificial neural networks in modeling 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 given requirements for the switch characteristics

A Resonant Based Test Methodology for Capacitive MEMs

This work presents a test method for capacitive Micro-Electro-Mechanical Systems (MEMS). A major class of MEMS sensors operate based on the principle of capacitance variation.The proposed test method in this work utilizes a resonant circuit to detect structural defects of capacitive MEMS sensors. It is shown that a small variation of MEMS capacitance due to a defect alters the resonance frequency considerably. It is also shown that the variation of the output amplitude can be observed for fault detection if an inductor with a high quality factor is employed in the test circuit. Mathematical approach is taken and verified to prove the validity of this work. The effects of structural defects such as short, broken and missing fingers of the MEMS comb-drive on the equivalent circuit models have been determined through frequency domain simulations.Simulation results and experimental measurements using an implemented MEMS comb drive indicate that the proposed method can detect common faults such as missing, broken and short fingers

Behavioural Modelling, Simulation, Test and Diagnosis of MEMS using ANNs

The design of Micro-Electrical-Mechanical Systems requires that the entire system can be modelled and simulated. Additionally, behaviour under fault conditions must be simulated to determine test and diagnosis strategies. While the electrical parts of a system can be modelled at transistor, gate or behavioural levels, the mechanical parts are conventionally modelled in terms of partial differential equations (PDEs). Mixed-signal electrical simulations are possible, using e.g. VHDL-AMS, but simulations that include PDEs are prohibitively expensive. Here, we show that complex PDEs can be replaced by black-box functional models and, importantly, such models can be characterized automatically and rapidly using artificial neural networks (ANNs). We demonstrate a significant increase in simulation speed and show that test and diagnosis strategies can be derived using such models

Novi pristupi u modelovanju RF MEMS prekidača

Modeling of RF MEMS switches includes modeling of electromagnetic (EM) and mechanical characteristics, which is standardly performed in commercial EM (full-wave) and mechanical simulators. Although these methods provide necessary accuracy, they are generally limited to one analysis for a certain structure, also very computationally and time demanding, especially in the procedures of optimizing the dimensions of the considered switch structure. RF MEMS switch models often lack direct relationships between the geometric parameters of the switch and its EM/mechanical characteristics, which would be used to optimize the characteristics of the switch or circuits that contain them. Precisely for these reasons, this dissertation presents a research whose goal is to develop new approaches for reliable and efficient modeling of the characteristics of RF MEMS switches. Modeling of RF MEMS switches was performed using artificial neural networks. New approaches were developed for modeling EM and mechanical characteristics: scattering parameters, resonant frequencies, actuation voltages, as well as elements of the equivalent circuit depending on the geometric dimensions of RF MEMS switches. Application of the developed models for the analysis of the sensitivity of the characteristics of capacitive RF MEMS switches is presented, in order to observe the behavior of the switches, i.e. change of resonant frequency and actuation voltage with changes of bridge dimensions which are conditioned by dimensional deviations during switches fabrication. A new inverse modeling approach is presented, which significantly shortens the time required to optimize the characteristics of the switch, i.e. design of switches in accordance with the desired characteristics. The further developed models refer to the modeling of the equivalent circuit elements of an RF MEMS switch depending on the lateral dimensions of the switch bridge. Neural models have been developed for the optimization of circuit elements, i.e. calculation of the equivalent circuit elements for the given lateral dimensions of the switch. This new approach provides significant shortening of the time required to determine the equivalent circuit elements and characteristics of RF MEMS switches. Finally, hybrid inverse models have been developed, aimed for direct determination and optimization of the switch bridge dimensions and the values of the equivalent circuit elements for given characteristics of the switch