Automatic Generation of Dynamic Macro-Models Using Quasi-Static  Simulations in Combination with Modal Analysis

Gabbay, L.D.; Senturia, S.D.

doi:10.31438/trf.hh1998.43

Cited by 10 publications

(3 citation statements)

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“…There have been several approaches to model reduction for the dynamic simulation of MEMS [1][2][3][4][5][6][7][8][9][10]. Previous MEMS macromodelling efforts have investigated lumped-parameter techniques [3].…”

Section: Introductionmentioning

confidence: 99%

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A neural-network-based method of model reduction for the dynamic simulation of MEMS

Liang

Lin

Lee

et al. 2001

J. Micromech. Microeng.

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This paper proposes a neuro-network-based method for model reduction that combines the generalized Hebbian algorithm (GHA) with the Galerkin procedure to perform the dynamic simulation and analysis of nonlinear microelectromechanical systems (MEMS). An unsupervised neural network is adopted to find the principal eigenvectors of a correlation matrix of snapshots. It has been shown that the extensive computer results of the principal component analysis using the neural network of GHA can extract an empirical basis from numerical or experimental data, which can be used to convert the original system into a lumped low-order macromodel. The macromodel can be employed to carry out the dynamic simulation of the original system resulting in a dramatic reduction of computation time while not losing flexibility and accuracy. Compared with other existing model reduction methods for the dynamic simulation of MEMS, the present method does not need to compute the input correlation matrix in advance. It needs only to find very few required basis functions, which can be learned directly from the input data, and this means that the method possesses potential advantages when the measured data are large. The method is evaluated to simulate the pull-in dynamics of a doubly-clamped microbeam subjected to different input voltage spectra of electrostatic actuation. The efficiency and the flexibility of the proposed method are examined by comparing the results with those of the fully meshed finite-difference method.

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Section: Introductionmentioning

confidence: 99%

“…However, it is difficult to construct lumpedelement models from continuous systems. Another model reduction approach uses linear modal analysis to generate normal modes which are used as basis functions for the model [4][5][6]. However, linear modes may not adequately capture all the features of nonlinear behaviour.…”

Section: Introductionmentioning

confidence: 99%

A neural-network-based method of model reduction for the dynamic simulation of MEMS

Liang

Lin

Lee

et al. 2001

J. Micromech. Microeng.

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“…In order to cover the position-dependent mechanical deflection (bending line) the modal superposition technique (see e.g. [21,22]) is applied. According to this, the mechanical deformation of clamped structures can be described by a superposition of their mechanical eigenmodes:…”

Section: System-level Model Of the Transducermentioning

confidence: 99%

Virtual design and optimization studies for industrial silicon microphones applying tailored system-level modeling

Kuenzig

Dehe

Krumbein

et al. 2018

J. Micromech. Microeng.

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Maxing out the technological limits in order to satisfy the customers’ demands and obtain the best performance of micro-devices and-systems is a challenge of today’s manufacturers. Dedicated system simulation is key to investigate the potential of device and system concepts in order to identify the best design w.r.t. the given requirements. We present a tailored, physics-based system-level modeling approach combining lumped with distributed models that provides detailed insight into the device and system operation at low computational expense. The resulting transparent, scalable (i.e. reusable) and modularly composed models explicitly contain the physical dependency on all relevant parameters, thus being well suited for dedicated investigation and optimization of MEMS devices and systems. This is demonstrated for an industrial capacitive silicon microphone. The performance of such microphones is determined by distributed effects like viscous damping and inhomogeneous capacitance variation across the membrane as well as by system-level phenomena like package-induced acoustic effects and the impact of the electronic circuitry for biasing and read-out. The here presented model covers all relevant figures of merit and, thus, enables to evaluate the optimization potential of silicon microphones towards high fidelity applications.

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System‐Level Modeling of MEMS Using Generalized Kirchhoffian Networks – Basic Principles

Schrag

Wachutka

2013

Advanced Micro and Nanosystems

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Automatic Generation of Dynamic Macro-Models Using Quasi-Static Simulations in Combination with Modal Analysis

Cited by 10 publications

References 0 publications

A neural-network-based method of model reduction for the dynamic simulation of MEMS

A neural-network-based method of model reduction for the dynamic simulation of MEMS

Virtual design and optimization studies for industrial silicon microphones applying tailored system-level modeling

System‐Level Modeling of MEMS Using Generalized Kirchhoffian Networks – Basic Principles

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