Abstract
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 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.
Highlights
In this paper, we investigate the stochastic effects of the microstructure of polysilicon films on the overall response of microelectromechanical systems (MEMS)
The advances in the field of integrated circuits and semiconductors had a direct impact on the emergence of a wide range of micro-sized devices, which we know today as microelectromechanical systems, or MEMS [1,2]
The analysis has been motivated by the continuous demand for further miniaturization in the MEMS industry
Summary
The advances in the field of integrated circuits and semiconductors had a direct impact on the emergence of a wide range of micro-sized devices, which we know today as microelectromechanical systems, or MEMS [1,2]. Due to the continuous growth in the industrial demand for MEMS applications, novel devices are designed to feature ever smaller dimensions, with characteristic sizes of some structural components on the order of 1 μm The success of these new MEMS critically hinges on their reliability and the predictability of their performances. The miniaturization pathway may increase issues related to the mechanical and/or geometrical properties of the fabricated devices [5]. This has forced a focus on assessing the effect of the underlying uncertainty sources at the microscale on the overall performance of the devices (see, e.g., [6])
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