| In this thesis, an accurate and efficient multi-physics based computer aided design (CAD) modeling approach using artificial neural networks (ANNs) for radio frequency micromechanical systems (RF MEMS) devices is presented. In the proposed methodology, a finite element method (FEM) analysis is performed for characterization of the selected MEMS devices in different coupled energy domains, such as electromechanical and electromagnetic-thermal-stress quantities. The FEM simulations are also utilized for the creation of training and testing sets for the ANN model. Developed ANN models are then implemented in a circuit simulator environment. Therefore, instead of using memory and time demanding full-wave analysis or even more extensive and expensive design process using multiple fabrication cycles, a simple yet accurate electronic design automation (EDA) tools compatible modeling and optimization procedure is developed. Capabilities of the proposed methodology are demonstrated with several examples featuring capacitively actuated MEMS resonating structures and coplanar waveguide (CPW) shunt MEMS switches. Simulation, design, and optimization are then performed for single MEMS devices as well as their collaborative function in an RF circuit level configuration. |
