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dc.contributor.supervisor Shafai, Cyrus (Electrical & Computer Engineering) en_US
dc.contributor.author Roy, Mark
dc.date.accessioned 2012-09-24T17:14:05Z
dc.date.available 2012-09-24T17:14:05Z
dc.date.issued 2012-09-24
dc.identifier.uri http://hdl.handle.net/1993/8920
dc.description.abstract This thesis studies the application of a multi-objective niched Pareto genetic algorithm on the design optimization of an electric field mill sensor. The original sensor requires resonant operation. The objective of the algorithm presented is to optimize the geometry eliminating the need for resonant operation which can be difficult to maintain in the presence of an unpredictable changing environment. The algorithm evaluates each design using finite element simulations. A population of sensor designs is evolved towards an optimal Pareto frontier of solutions. Several candidate solutions are selected that offer superior displacement, frequency, and stress concentrations. These designs were modified for fabrication using the PolyMUMPs abrication process but failed to operate due to the process. In order to fabricate the sensors in-house with a silicon-on-glass process, an anodic bonding apparatus has been designed, built, and tested. en_US
dc.subject MEMS en_US
dc.subject Genetic Algorithm en_US
dc.subject Optimization en_US
dc.subject Sensors en_US
dc.subject Evolutionary Computing en_US
dc.subject Finite Element Analysis en_US
dc.subject Electric Field Measurement en_US
dc.title Design optimization of a microelectromechanical electric field sensor using genetic algorithms en_US
dc.degree.discipline Electrical and Computer Engineering en_US
dc.contributor.examiningcommittee McLeod, Robert D. (Electrical & Computer Engineering); Wu, Christine (Mechanical & Manufacturing Engineering) en_US
dc.degree.level Master of Science (M.Sc.) en_US
dc.description.note October 2012 en_US


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