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dc.contributor.supervisorShafai, Cyrus (Electrical & Computer Engineering)en_US
dc.contributor.authorRoy, Mark
dc.date.accessioned2012-09-24T17:14:05Z
dc.date.available2012-09-24T17:14:05Z
dc.date.issued2012-09-24
dc.identifier.urihttp://hdl.handle.net/1993/8920
dc.description.abstractThis 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.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectMEMSen_US
dc.subjectGenetic Algorithmen_US
dc.subjectOptimizationen_US
dc.subjectSensorsen_US
dc.subjectEvolutionary Computingen_US
dc.subjectFinite Element Analysisen_US
dc.subjectElectric Field Measurementen_US
dc.titleDesign optimization of a microelectromechanical electric field sensor using genetic algorithmsen_US
dc.typeinfo:eu-repo/semantics/masterThesis
dc.typemaster thesisen_US
dc.degree.disciplineElectrical and Computer Engineeringen_US
dc.contributor.examiningcommitteeMcLeod, Robert D. (Electrical & Computer Engineering); Wu, Christine (Mechanical & Manufacturing Engineering)en_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.noteOctober 2012en_US


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