Artificial neural network modeling of RF MEMS resonators

✍ Scribed by Yongjae Lee; Yonghwa Park; Feng Niu; Bonnie Bachman; K.C. Gupta; Dejan Filipovic

• Publisher: John Wiley and Sons
• Year: 2004
• Tongue: English
• Weight: 411 KB
• Volume: 14
• Category: Article
• ISSN: 1096-4290
• DOI: 10.1002/mmce.20017

✦ Synopsis

In this article, a novel and efficient approach for modeling radio-frequency microelectromechanical system (RF MEMS) resonators by using artificial neural network (ANN) modeling is presented. In the proposed methodology, the relationship between physical-input parameters and corresponding electrical-output parameters is obtained by combined circuit/full-wave/ANN modeling. More specifically, in order to predict the electrical responses from a resonator, an analytical representation of the electrical equivalent-network model (EENM) is developed from the well-known electromechanical analogs. Then, the reduced-order, nonlinear, dynamic macromodels from 3D finite-element method (FEM) simulations are generated to provide training, validating, and testing datasets for the ANN model. The developed ANN model provides an accurate prediction of an electrical response for various sets of driving parameters and it is suitable for integration with an RF/microwave circuit simulator. Although the proposed approach is demonstrated on a clamped-clamped (C-C) beam resonator, it can be readily adapted for the analysis of other micromechanical resonators.