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Micromachines 2017, 8(8), 248;

Uncertainty Quantification of Microstructure—Governed Properties of Polysilicon MEMS

Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, Italy
Author to whom correspondence should be addressed.
Received: 3 July 2017 / Revised: 4 August 2017 / Accepted: 9 August 2017 / Published: 12 August 2017
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In this paper, we investigate the stochastic effects of the microstructure of polysilicon films on the overall response of microelectromechanical systems (MEMS). A device for on-chip testing has been purposely designed so as to maximize, in compliance with the production process, its sensitivity to fluctuations of the microstructural properties; as a side effect, its sensitivity to geometrical imperfections linked to the etching process has also been enhanced. A reduced-order, coupled electromechanical model of the device is developed and an identification procedure, based on a genetic algorithm, is finally adopted to tune the parameters ruling microstructural and geometrical uncertainties. Besides an initial geometrical imperfection that can be considered specimen-dependent due to its scattering, the proposed procedure has allowed identifying an average value of the effective polysilicon Young’s modulus amounting to 140 GPa, and of the over-etch depth with respect to the target geometry layout amounting to O = 0.09 μ m. The procedure has been therefore shown to be able to assess how the studied stochastic effects are linked to the scattering of the measured input–output transfer function of the device under standard working conditions. With a continuous trend in miniaturization induced by the mass production of MEMS, this study can provide information on how to handle the foreseen growth of such scattering. View Full-Text
Keywords: microelectromechanical systems (MEMS); polysilicon; coupled electromechanical analysis; stochastic effects; parameter identification microelectromechanical systems (MEMS); polysilicon; coupled electromechanical analysis; stochastic effects; parameter identification

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Mirzazadeh, R.; Mariani, S. Uncertainty Quantification of Microstructure—Governed Properties of Polysilicon MEMS. Micromachines 2017, 8, 248.

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