System-level Modeling of MEMS

1. Auflage 2013
ISBN: 978-3-527-31903-9
Verlag: Wiley VCH Verlag GmbH

Buch, Englisch, Band 10, 496 Seiten, Format (B × H): 175 mm x 249 mm, Gewicht: 1323 g

Reihe: Advanced Micro and Nanosystems

System-level modeling of MEMS - microelectromechanical systems - comprises integrated approaches to simulate, understand, and optimize the performance of sensors, actuators, and microsystems, taking into account the intricacies of the interplay between mechanical and electrical properties, circuitry, packaging, and design considerations. Thereby, system-level modeling overcomes the limitations inherent to methods that focus only on one of these aspects and do not incorporate their mutual dependencies.

The book addresses the two most important approaches of system-level modeling, namely physics-based modeling with lumped elements and mathematical modeling employing model order reduction methods, with an emphasis on combining single device models to entire systems. At a clearly understandable and sufficiently detailed level the readers are made familiar with the physical and mathematical underpinnings of MEMS modeling. This enables them to choose the adequate methods for the respective application needs.

This work is an invaluable resource for all materials scientists, electrical engineers, scientists working in the semiconductor and/or sensor
industry, physicists, and physical chemists.

Autoren/Hrsg.

Bechtold, Tamara

Schrag, Gabriele

Feng, Lihong

Brand, Oliver

Feng, Li-Hong

Weitere Mitwirkende

Brand, Oliver

Fedder, Gary K.

Hierold, Christofer

Korvink, Jan G.

Tabata, Osamu

Fachgebiete

• Technische Wissenschaften Maschinenbau | Werkstoffkunde Maschinenbau Mechatronik, Mikrosysteme (MEMS), Nanosysteme
• Technische Wissenschaften Technik Allgemein Konstruktionslehre und -technik

Inhaltsverzeichnis

PHYSICAL AND MATHEMATICAL FUNDAMENTALS FOR COMPACT MODELING OF MEMS System-Level Modeling of MEMS by Lumped-Elements - Physical Background System-Level Modeling of MEMS by Means of Model Order Reduction - Mathematical Background Modal Reduction - Mathematical Background Issues in MEMS Macromodeling APPLICATIONS OF MODEL REDUCTION BASED SYSTEM LEVEL MODELING OF MEMS Application of Parametric Model Reduction for MEMS System-Level Simulation and Design Application of Nonlinear Model Order Reduction for MEMS System-Level Simulation Model Order Reduction for Circuit Level Simulation of RF MEMS Frequency Selective Devices A Reduced-Order Model for Electrically Actuated Microplates Combination of Analytical Models and Order Reduction Methods for System Level Modeling of Gyroscopes APPLICATIONS OF LUMPED ELEMENT BASED SYSTEM LEVEL MODELING OF MEMS System-level Modeling of Energy-Harvesting modules Intertial MEMS Design with Higher Order Sigma-Delta Control Circuits Macro-Modeling of Systems Including Free-Space Optical MEMS A System-Level Model for a Silicon Thermal Flow Sensor System-Level Modeling and Simulation of Force-Balance MEMS Accelerometers System-Level Simulation of a Micromachined Electrometer using a Time-Domain Variable Capacitor Circuit Model Modeling and System-Level Simulation of a CMOS Convective Accelerometer System-Level Modeling of MEMS Based on SABER PlatformsVHDL Implementation of a communication interface for integrated MEMS Heterogenous (Optics, Fluidics) System-Level Design ENABLING TECHNIQUES FOR SYSTEM LEVEL MODELING OF MEMSManufacturable and EDA Compatible MEMS Design via 3D Parametric libraries MEMS Related Design Optimizing of SiP Efficient Optimization of Transient Dynamic Problems in MEMS Modeling and Synthesis Tools for Analog Circuit Design

PART I: PHYSICAL AND MATHEMATICAL FUNDAMENTALSINTRODUCTION: ISSUES IN MICROSYSTEMS MODELINGThe Need for System-Level Models for MicrosystemsCoupled Multiphysics MicrosystemsMultiscale Modeling and SimulationSystem-Level Model TerminologyAutomated Model Order Reduction MethodsHandling Complexity: Following the VLSI ParadigmAnalog Hardware Description LanguagesGeneral Attributes of System-Level ModelsAHDL Simulation CapabilitiesComposable Model LibrariesParameter Extraction, Model Verification, and Model ValidationConclusionsSYSTEM-LEVEL MODELING OF MEMS USING GENERALIZED KIRCHHOFFIAN NETWORKS - BASIC PRINCIPLES Introduction and Motivation Generalized Kirchhoffian Networks for the Tailored System-Level Modeling of Microsystems Application 1: Physics-Based Electrofluidic Compact Model of an Electrostatically Actuated Micropump Application 2: Electrostatically Actuated RF MEMS Switch SYSTEM-LEVEL MODELING OF MEMS BY MEANS OF MODEL ORDER REDUCTION (MATHEMATICAL APPROXIMATIONS) - MATHEMATICAL BACKGROUND Introduction Brief Overview Mathematical Preliminaries Numerical Algorithms Linear System Theory Basic Idea of Model Order Reduction Moment-Matching Model Order Reduction Gramian-Based Model Order Reduction Stability, Passivity, and Error Estimation of the Reduced Model Dealing with Nonzero Initial Condition MOR for Second-Order, Nonlinear, Parametric systems Conclusion and Outlook ALGORITHMIC APPROACHES FOR SYSTEM-LEVEL SIMULATION OF MEMS AND ASPECTS OF COSIMULATION Introduction Mathematical Structure of MEMS Models General Approaches for System-Level Model Description Numerical Methods for System-Level Simulation Emerging Problems and Advanced Simulation Techniques Conclusion PART II: LUMPED ELEMENT MODELING METHOD FOR MEMS DEVICES SYSTEM-LEVEL MODELING OF SURFACE MICROMACHINED BEAMLIKE ELECTROTHERMAL MICROACTUATORS Introduction Classification and Problem Description Modeling Solving Case Study Conclusion and Outlook SYSTEM-LEVEL MODELING OF PACKAGING EFFECTS OF MEMS DEVICES Introduction Packaging Effects of MEMS and Their Impact on Typical MEMS Devices System-Level Modeling Conclusion and Outlook MIXED-LEVEL APPROACH FOR THE MODELING OF DISTRIBUTED EFFECTS IN MICROSYSTEMS General Concept of Finite Networks and Mixed-Level Models Approaches for the Modeling of Squeeze Film Damping in MEMS Mixed-Level Modeling of Squeeze Film Damping in MEMS Evaluation Conclusion COMPACT MODELING OF RF-MEMS DEVICESIntroduction Brief Description of the MEMS Compact Modeling Approach RF-MEMS Multistate Attenuator Parallel SectionRF-MEMS Multistate Attenuator Series Section Whole RF-MEMS Multistate Attenuator Network Conclusions PART III: MATHEMATICAL MODEL ORDER REDUCTION FOR MEMS DEVICES MOMENT-MATCHING-BASED LINEAR MODEL ORDER REDUCTION FOR NONPARAMETRIC AND PARAMETRIC ELECTROTHERMAL MEMS MODELS Introduction Methodology for Applying Model Order Reduction to Electrothermal MEMS Models: Review of Achieved Results and Open Issues MEMS Case Study - Silicon-Based Microhotplate Application of the Reduced-Order Model for the Parameterization of the Controller Application of Parametric Reduced-Order Model to the Extraction of Thin-Film Thermal Parameters Conclusion and Outlook PROJECTION-BASED NONLINEAR MODEL ORDER REDUCTION Introduction Problem Specification Projection Principle and Evaluation Cost for Nonlinear Systems Taylor Series Expansions Trajectory Piecewise-Linear Method Discrete Empirical Interpolation method A Comparative Case Study of an MEMS Switch Summary and Outlook LINEAR AND NONLINEAR MODEL ORDER REDUCTION FOR MEMS ELECTROSTATIC ACTUATORS Introduction The Variable Gap Parallel Plate Capacitor Model Order Reduction Methods Example 1: IBM Scanning-Probe Data Storage Device Example 2: Electrostatic Micropump Diaphragm Results and Discussion Conclusions MODAL-SUPERPOSITION-BASED NONLINEAR MODEL ORDER REDUCTION FOR MEMS GYROSCOPES Introduction Model Order Reduction via Modal Superposition MEMS Testcase: Vibratory Gyroscope Flow Chart of the Nonlinear Model Order Reduction ProcedureTheoretical Background of Modal Superposition Technologies Specific Algorithms of the Reduced Order Model Generation Pass System Simulations of MEMS Based on Modal Superposition Conclusion and Outlook PART IV: MODELING OF ENTIRE MICROSYSTEMS TOWARDS SYSTEM-LEVEL SIMULATION OF ENERGY HARVESTING MODULES Introduction Design and Fabrication of the Piezoelectric Generator Experimental Results Modeling and Simulation Maximum Power Point for the Piezoelectric HarvesterConclusions and Outlook APPLICATION OF REDUCED ORDER MODELS IN CIRCUIT-LEVEL DESIGN FOR RF MEMS DEVICES Model Equations for RF MEMS Devices Extraction of the Reduced Order Model Application Examples Conclusion and Outlook SYSTEMC AMS AND COSIMULATION ASPECTS Introduction Heterogeneous Modeling with SystemC AMS Case Study: Detection of Seismic Perturbations Using the AccelerometerConclusion SYSTEM LEVEL MODELING OF ELECTROMECHANICAL SIGMA?DELTA MODULATORS FOR INERTIAL MEMS SENSORS PART V: SOFTWARE IMPLEMENTATIONS 3D PARAMETRIC-LIBRARY-BASED MEMS/IC DESIGNAbout Schematic-Driven MEMS Modeling Toward Manufacturable MEMS Designs Micromirror Array Design Example Conclusions MOR FOR ANSYS Introduction Practice-Oriented Research during the Development of MOR for ANSYS Programming Issues Open Problems Conclusion SUGAR: A SPICE FOR MEMS Introduction SUGAR SUGAR-Based Applications Integration of SUGAR + COMSOL + SPICE + SIMULINK Conclusion MODEL ORDER REDUCTION IMPLEMENTATIONS IN COMMERCIAL MEMS DESIGN ENVIRONMENTIntroduction IntelliSense's Design Methodology Implementation of System Model Extraction in IntelliSuite Benchmarks Summary REDUCED ORDER MODELING OF MEMS AND IC SYSTEMS - A PRACTICAL APPROACH Introduction The MEMS Development Environment Modeling Requirements and Implementation within SoftMEMS Simulation Environment Applications Conclusions and Outlook A WEB-BASED COMMUNITY FOR MODELING AND DESIGN OF MEMS Introduction The MEMS Modeling and Design Landscape Leveraging Web-Based Communities MEMS Modeling and Design Online Encoding MEMS Behavioral Models Conclusions and Outlook INDEX

Über Autor(innen)

Schrag, Gabriele
Gabriele Schrag heads a research group in the field of MEMS modeling with a focus on methodologies for the virtual prototyping of microdevices and microsystems at the Technical University of Munich, Germany. In her diploma and doctoral studies she worked on modeling methods for electromechanical microdevices and microsystems with an emphasis on fluid-structure interaction and viscous damping effects, including coupled effects on the device and system level.

Feng, Lihong
Lihong Feng is a team leader in the research group of Computational Methods in Systems and Control theory headed by Professor Peter Benner, Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany. After her PhD from Fudan University in Shanghai, China, she joined the faculty of the State Key Laboratory of Application-Specific Integrated Circuits (ASIC) & System, Fudan University, Shanghai, China. From 2007 to 2008 she was a Humboldt research fellow in the working group of Mathematics in Industry and Technology at the Technical University of Chemnitz, Germany. In 2009-2010, she worked in the Laboratory for Microsystem Simulation, Department of Microsystems Engineering, University of Freiburg, Germany. Her research interests are in the field of reduced order modelling and fast numerical algorithms for control and optimization in Chemical Engineering, MEMS simulation, and circuit simulation.

Bechtold, Tamara
Tamara Bechtold is post-doctoral researcher at Philips/NXP Research Laboratories in the Netherlands. She obtained her PhD from the University of Freiburg, Germany, with a thesis on microsystems simulation conducted at the Institute of Microsystems Technology in the group of Jan Korvink. She is the author of one book and many scientific publications. As of 2009, Tamara Bechtold has more than ten years of experience in modeling and simulation of MEMS.

Tamara Bechtold is post-doctoral researcher at Philips/NXP Research Laboratories in the Netherlands. She obtained her PhD from the University of Freiburg, Germany, with a thesis on microsystems simulation conducted at the Institute of Microsystems Technology in the group of Jan Korvink. She is the author of one book and many scientific publications. As of 2009, Tamara Bechtold has more than ten years of experience in modeling and simulation of MEMS. Gabriele Schrag is currently heading a research group at the Munich University of Technology, Germany, working in the field of MEMS modeling with a focus on virtual prototyping and predictive simulation methodologies, parameter extraction, and model verification for microdevices and microsystems. She studied physics at the University of Stuttgart and received her doctorate (with honors) from the Munich University of Technology in 2002, her thesis covering the 'Modeling of Coupled Effects in Microsystems' with a special emphasis on fluid-structure interaction and viscous damping effects. Gabriele Schrag authored and co-authored more than 70 publications in technical journals and conference proceedings.Lihong Feng is a team leader in the research group of Computational Methods in Systems and Control theory headed by Professor Peter Benner, Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany. After her PhD from Fudan University in Shanghai, China, she joined the faculty of the State Key Laboratory of Application-Specific Integrated Circuits (ASIC) & System, Fudan University, Shanghai, China. From 2007 to 2008 she was a Humboldt research fellow in the working group of Mathematics in Industry and Technology at the Technical University of Chemnitz, Germany. In 2009-2010, she worked in the Laboratory for Microsystem Simulation, Department of Microsystems Engineering, University of Freiburg, Germany. Her research interests are in the field of reduced order modelling and fast numerical algorithms for control and optimization in Chemical Engineering, MEMS simulation, and circuit simulation.