Science-based MEMS reliability methodology

In cases where device numbers are limited, large statistical studies to verify reliability are impractical. Instead, an approach incorporating a solid base of modelling, simulation, and material science into a standard reliability methodology makes more sense and leads to a science-based reliability...

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Veröffentlicht in:Microelectronics and reliability 2007-09, Vol.47 (9), p.1806-1811
Hauptverfasser: Tanner, D.M., Parson, T.B., Corwin, A.D., Walraven, J.A., Wittwer, J.W., Boyce, B.L., Winzer, S.R.
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container_end_page 1811
container_issue 9
container_start_page 1806
container_title Microelectronics and reliability
container_volume 47
creator Tanner, D.M.
Parson, T.B.
Corwin, A.D.
Walraven, J.A.
Wittwer, J.W.
Boyce, B.L.
Winzer, S.R.
description In cases where device numbers are limited, large statistical studies to verify reliability are impractical. Instead, an approach incorporating a solid base of modelling, simulation, and material science into a standard reliability methodology makes more sense and leads to a science-based reliability methodology. The basic reliability method is (a) design, model and fabricate, (b) test structures and devices, (c) identify failure modes and mechanisms, (d) develop predictive reliability models (accelerated aging), and (e) develop qualification methods. At various points in these steps technical data is required on MEMS material properties (residual stress, fracture strength, fatigue, etc.), MEMS surface characterization (stiction, friction, adhesion, role of coatings, etc.) or MEMS modelling and simulation (finite element, analysis, uncertainty analysis, etc.). This methodology is discussed as it relates to reliability testing of a micro-mirror array consisting of 144-piston mirrors. In this case, 140 mirrors were cycled full stroke (1.5 μm) 26 billion times with no failure. Using our technical science base, fatigue of the springs was eliminated as a mechanism of concern. Eliminating this wear-out mechanism allowed use of the exponential statistical model to predict lower bound confidence levels for failure rate in a “no-fail” condition.
doi_str_mv 10.1016/j.microrel.2007.07.061
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subjects Applied sciences
Electronics
Exact sciences and technology
Materials
Micro- and nanoelectromechanical devices (mems/nems)
Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices
Testing, measurement, noise and reliability
title Science-based MEMS reliability methodology
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