&lt;title&gt;Model library and tool support for MEMS simulation&lt;/title&gt;

Schwarz, Peter; Schneider, Peter

doi:10.1117/12.425295

Cited by 14 publications

(6 citation statements)

References 19 publications

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“…Normally, finite element methods [2] are used to analyse the mechanical structures. If FE models are available, it is evident to use information which is provided by these models, equations as well as simulation results, to derive behavioral models for system simulation [3], preferably automatically.…”

Section: Introductionmentioning

confidence: 99%

System Level Modeling of the Relevant Physical Effects of Inertial Sensors Using Order Reduction Methods

Reitz

Doring

Bastian

et al. 2005

Analog Integr Circ Sig Process

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Sensor development in the field of microsystem technology is driven by steadily increasing demands on sensor resolution and performance and the need to reduce costs. Thus, interactions between mechanical and electronic components of a complex sensor system become more important and have to be considered in the design process, preferably already in early design steps. That is why a system simulation of the entire sensor system is necessary to be able to verify different design steps and to decrease the number of expensive prototyping cycles. Due to typical combinations of different physical domains (mechanics, electronics, thermodynamics, etc.) a simulation of the entire system based on partial differential equations using e.g. finite element methods (FEM) is often impossible or too expensive concerning computing time. Furthermore, many details of the component behavior calculated by FEM are not needed at system level. A successful approach to system simulation of sensor systems is to derive behavioral models from FEM descriptions and to combine them with system level models of electronics. In the following a method for automatic generation of behavioral models for micromechanical components using order reduction methods will be introduced.

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Section: Introductionmentioning

confidence: 99%

System Level Modeling of the Relevant Physical Effects of Inertial Sensors Using Order Reduction Methods

Reitz

Doring

Bastian

et al. 2005

Analog Integr Circ Sig Process

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“…The process of model generation for system level simulation is supported by various methods: parameter optimization, model order reduction, and approximation [7], [8]. These methods are combined with direct calculation of model parameters from PDE simulations.…”

Section: Methods For Computer-aided Model Generationmentioning

confidence: 99%

Towards a methodology for analysis of interconnect structures for 3D-integration of micro systems

Schneider

Reitz

Wilde

et al. 2008

Analog Integr Circ Sig Process

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Functional aspects as well as the influence of integration technology on the system behavior have to be considered in the 3D integration design process of micro systems. Therefore, information from different physical domains has to be provided to designers. Due to the variety of structures and effects of different physical domains, efficient modeling approaches and simulation algorithms have to be combined. The article describes a modular approach which covers detailed analysis with PDE solvers and model generation for system level simulation

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“…However, there have been a variety of disparate works that have implemented piezoelectric models in a lumped-parameter framework. For example, a MEMS library and airbag deployment example including piezoelectric elements were implemented in VHDL-AMS in [3] and [4], respectively. Lumpedparameter models of piezoelectric devices derived from high-order FEM models, are presented in [5], but are not Modelica-specific implementations.…”

Section: Related Workmentioning

confidence: 99%

Modeling and Simulation of a Linear Piezoelectric Stepper Motor in MapleSim

Soroka

Wright

Vahid

2012

Linköping Electronic Conference Proceedings

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Devices based on piezoelectric materials have traditionally been modeled in PDE simulation software. These simulations are expensive to create and run. In this paper it is shown that lumped-parameter models of such devices can provide good fidelity with low computational cost. Modelica models of supporting components, along with a system-level model of a linear piezoelectric stepper motor are presented. The simulation results show good agreement with published experimental results. Future research is proposed based on the components and model.

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<title>Model library and tool support for MEMS simulation</title>

Cited by 14 publications

References 19 publications

System Level Modeling of the Relevant Physical Effects of Inertial Sensors Using Order Reduction Methods

System Level Modeling of the Relevant Physical Effects of Inertial Sensors Using Order Reduction Methods

Towards a methodology for analysis of interconnect structures for 3D-integration of micro systems

Modeling and Simulation of a Linear Piezoelectric Stepper Motor in MapleSim

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