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Sensors 2017, 17(11), 2561; https://doi.org/10.3390/s17112561

A Generalized Polynomial Chaos-Based Approach to Analyze the Impacts of Process Deviations on MEMS Beams

Key Laboratory of MEMS of the Ministry of Education, Southeast University, Nanjing 210096, China
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Received: 2 October 2017 / Revised: 4 November 2017 / Accepted: 4 November 2017 / Published: 8 November 2017
(This article belongs to the Collection Modeling, Testing and Reliability Issues in MEMS Engineering)
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Abstract

A microstructure beam is one of the fundamental elements in MEMS devices like cantilever sensors, RF/optical switches, varactors, resonators, etc. It is still difficult to precisely predict the performance of MEMS beams with the current available simulators due to the inevitable process deviations. Feasible numerical methods are required and can be used to improve the yield and profits of the MEMS devices. In this work, process deviations are considered to be stochastic variables, and a newly-developed numerical method, i.e., generalized polynomial chaos (GPC), is applied for the simulation of the MEMS beam. The doubly-clamped polybeam has been utilized to verify the accuracy of GPC, compared with our Monte Carlo (MC) approaches. Performance predictions have been made on the residual stress by achieving its distributions in GaAs Monolithic Microwave Integrated Circuit (MMIC)-based MEMS beams. The results show that errors are within 1% for the results of GPC approximations compared with the MC simulations. Appropriate choices of the 4-order GPC expansions with orthogonal terms have also succeeded in reducing the MC simulation labor. The mean value of the residual stress, concluded from experimental tests, shares an error about 1.1% with that of the 4-order GPC method. It takes a probability around 54.3% for the 4-order GPC approximation to attain the mean test value of the residual stress. The corresponding yield occupies over 90 percent around the mean within the twofold standard deviations. View Full-Text
Keywords: MEMS beams; GaAs MMIC-based process; stochastic process deviations; GPC; MC MEMS beams; GaAs MMIC-based process; stochastic process deviations; GPC; MC
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Gao, L.; Zhou, Z.-F.; Huang, Q.-A. A Generalized Polynomial Chaos-Based Approach to Analyze the Impacts of Process Deviations on MEMS Beams. Sensors 2017, 17, 2561.

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