Pattern recognition and fault detection in MEMS

Micro Electro Mechanical Systems will soon usher in a new technological renaissance. Just as ICs brought the pocket calculator, PC, and video games, MEMS will provide a new set of products and markets. Learn about the state of the art, from inertial sensors to microfluidic devices [1]. Over the last few years, considerable effort has gone into the study of the failure mechanisms and reliability of MEMS. Although still very incomplete, our knowledge of the reliability issues relevant to MEMS is growing. One of the major problems in MEMS production is fault detection. After fault diagnosis, hardware or software methods can be used to overcome it. Most of MEMS have nonlinear and complex models. So it is difficult or impossible to detect the faults by traditional methods, which are model-based. In this paper different Neural Networks are used to classify and recognize faults. Different faults are recognized whilst considered as different patterns. We use different Neural Networks to classify different faults and fault free data. Two RF MEMS, which are RF Low pass filter and RF Inter digital capacitor are simulated by EM3DS, a MEMS software simulator. At last the results are compared.