Control design and genetic algorithm optimization for electrostatic MEMS

This dissertation discusses structural-electrostatic modeling techniques, genetic algorithm based optimization and control design for electrostatic micro devices. First, an alternative modeling technique, the interpolated force model, for electrostatic micro devices is discussed. The method provides improved computational efficiency relative to a benchmark model, as well as improved accuracy for irregular electrode configurations relative to a common approximate model, the parallel plate approximation model. For the configuration most similar to two parallel plates, expected to be the best case scenario for the approximate model, both the parallel plate approximation model and the interpolated force model maintained less than 2.2% error in static deflection compared to the benchmark model. For the configuration expected to be the worst case scenario for the parallel plate approximation model, the interpolated force model maintained less than 2.9% error in static deflection while the parallel plate approximation model is incapable of handling the configuration. Second, genetic algorithm based optimization is shown to improve the design of an electrostatic micro sensor. The design space is enlarged from published design spaces to include the configuration of both sensing and actuation electrodes, material distribution, actuation voltage and other geometric dimensions. For a small population, the design was improved by approximately a factor of 6 over 15 generations to a fitness value of 3.2 fF. For a larger population seeded with the best configurations of the previous optimization, the design was improved by another 7% in 5 generations to a fitness value of 3.0 fF. Third, a learning control algorithm is presented that reduces the closing time of a radiofrequency microelectromechanical systems switch by minimizing bounce while maintaining robustness to fabrication variability. Electrostatic actuation of the plate causes pull-in with high impact velocities, which are difficult to control due to parameter variations from part to part. A single degree-of-freedom model was utilized to design a learning control algorithm that shapes the actuation voltage based on the open/closed state of the switch. Experiments on 3 test switches show that after 5-10 iterations, the learning algorithm lands the switch with an impact velocity not exceeding 0.2 m/s, eliminating bounce

A simple learning control to eliminate RF-MEMS switch bounce

A learning control algorithm is presented that reduces the closing time of a radio-frequency microelectromechanical systems switch by minimizing bounce while maintaining robustness to fabrication variability. The switch consists of a plate supported by folded-beam springs. Electrostatic actuation of the plate causes pull-in with high impact velocities, which are difficult to control due to parameter variations from part to part. A single degree-of-freedom model was utilized to design a simple learning control algorithm that shapes the actuation voltage based on the open/closed state of the switch. Experiments on three different test switches show that after 5-10 iterations, the learning algorithm lands the switch plate with an impact velocity not exceeding 0.20 m/s, eliminating bounce. Simulations show that robustness to parameter variation is directly related to the number of required iterations for the device to learn the input for a bounce-free closure. © 2009 IEEE

Fiscal Year 2008

This report summarizes the functional, model validation, and technology readiness testing of the Sandia MEMS Passive Shock Sensor in FY08. Functional testing of a large number of revision 4 parts showed robust and consistent performance. Model validation testing helped tune the models to match data well and identified several areas for future investigation related to high frequency sensitivity and thermal effects. Finally, technology readiness testing demonstrated the integrated elements of the sensor under realistic environments

Fiscal Year 2007

This report describes activities conducted in FY07 to mature the MEMS passive shock sensor. The first chapter of the report provides motivation and background on activities that are described in detail in later chapters. The second chapter discusses concepts that are important for integrating the MEMS passive shock sensor into a system. Following these two introductory chapters, the report details modeling and design efforts, packaging, failure analysis and testing and validation. At the end of FY07, the MEMS passive shock sensor was at TRL 4