An increasing impact of micromechanically governed uncertainties is nowadays foreseen
due to the trend of progressively reducing the footprint of MEMS (microelectromechanical systems)
devices. For polysilicon MEMS, the two major sources of uncertainties, as resulting from the microfabrication
process, are linked to the polycrystalline morphology and to the etching. In this review, we
summarize some of our recent results related to the statistical assessment of the aforementioned
sources, on the basis of experimental data acquired via an on-chip testing device specifically designed
to enhance such effects. Through standard electrostatic actuation and readout, the scattering in the
response of a series of nominally identical cantilever structures is analyzed to determine characteristic
features of etching defects, and of the overall stiffness of the polysilicon film constituting the movable
parts of the tested devices.
Keywords: Polysilicon MEMS, micromechanical uncertainties, system identification, particle filtering, genetic algorithm,
transitional Markov Chain Monte Carlo method.
Rights & PermissionsPrintExport