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Empirical high cycle fatigue assessment model of MEMS devices
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Empirical high cycle fatigue assessment model of MEMS devices

Chia-Cheng ChangKuo-Ning Chiang
Journal of Electronic Packaging, Transactions of the ASME, 卷.142(1), 011005
03/2020

摘要

Dynamic simulation Electrostatic force Finite element method (FEM) Frequency effect High cycle fatigue Microelectromechanical systems (MEMS) Silicon on insulator (SOI) Electronic Optical and Magnetic Materials Mechanics of Materials Computer Science Applications Electrical and Electronic Engineering
Many microelectromechanical systems (MEMS) devices generate large deflections and stresses under some severe loads, and this cumulative stress often causes structural fatigue when applying high cyclic loads. It affects the reliability and quality of the product and can easily damage structures in harsh environments. For example, accelerometers and gyroscopes in motion sensors always combine a movable mass component with a bending or torsion spring, which can severely deform their structure at some large accelerations or angular accelerations. After this high cycle of deformation, the spring structure will be damaged and cause device failure, we call this failure mode high cycle fatigue (HCF). This is very common for MEMS products, but it is difficult for designers to predict it at design stage. To prevent early fatigue problems and reduce product development time, we developed a simulation process and empirical prediction model to help designers predict HCF and improve fatigue strength of moving structures used in MEMS devices. In this study, a bending and a torsion beam are used as test vehicles. A combination of dynamic and electromechanical coupling simulations was developed and applied to analyze the effects of frequency under HCF testing. The test structures were fabricated using silicon-on-insulator (SOI) MEMS technique and these test structures were used to validate empirical life prediction equation developed in this research. The frequency effect of HCF was also to be included in the developed prediction model. Based on the test results, the accuracy of the empirical prediction equation is improved by including frequency effect.

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