System-level Modeling of MEMS (Advanced Micro and Nanosystems)

System-level Modeling of MEMS (Advanced Micro and Nanosystems)

By: Tamara Bechtold (editor), Gabriele Schrag (editor), Lihong Feng (editor), Osamu Tabata (series_editor), Jan G. Korvink (series_editor), Oliver Brand (series_editor), Christofer Hierold (series_editor), Gary K. Fedder (series_editor)Hardback

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Description

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.

About Author

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 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. 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.

Contents

About the Editors XIX Series Editor Preface XXI Volume Editors Preface XXIII List of Contributors XXVII Part I Physical and Mathematical Fundamentals 1 1 Introduction: Issues in Microsystems Modeling 3 Gary K. Fedder and Tamal Mukherjee 1.1 The Need for System-Level Models for Microsystems 3 1.2 Coupled Multiphysics Microsystems 4 1.3 Multiscale Modeling and Simulation 6 1.4 System-Level Model Terminology 7 1.5 Automated Model Order Reduction Methods 9 1.6 Handling Complexity: Following the VLSI Paradigm 10 1.7 Analog Hardware Description Languages 11 1.8 General Attributes of System-Level Models 12 1.9 AHDL Simulation Capabilities 13 1.10 Composable Model Libraries 14 1.11 Parameter Extraction, Model Verification, and Model Validation 15 1.12 Conclusions 16 References 17 2 System-Level Modeling of MEMS Using Generalized Kirchhoffian Networks Basic Principles 19 Gabriele Schrag and Gerhard Wachutka 2.1 Introduction and Motivation 19 2.2 Generalized Kirchhoffian Networks for the Tailored System-Level Modeling of Microsystems 20 2.3 Application 1: Physics-Based Electrofluidic Compact Model of an Electrostatically Actuated Micropump 32 2.4 Application 2: Electrostatically Actuated RF MEMS Switch 41 References 48 3 System-Level Modeling of MEMS by Means of Model Order Reduction (Mathematical Approximations) Mathematical Background 53 Lihong Feng, Peter Benner, and Jan G. Korvink 3.1 Introduction 53 3.2 Brief Overview 55 3.3 Mathematical Preliminaries 56 3.4 Numerical Algorithms 63 3.5 Linear System Theory 66 3.6 Basic Idea of Model Order Reduction 71 3.7 Moment-Matching Model Order Reduction 73 3.8 Gramian-Based Model Order Reduction 77 3.9 Stability, Passivity, and Error Estimation of the Reduced Model 84 3.10 Dealing with Nonzero Initial Condition 85 3.11 MOR for Second-Order, Nonlinear, Parametric systems 86 3.12 Conclusion and Outlook 86 References 87 4 Algorithmic Approaches for System-Level Simulation of MEMS and Aspects of Cosimulation 95 Peter Schneider, Christoph Clauss, Ulrich Donath, Gunter Elst, Olaf Enge-Rosenblatt, and Thomas Uhle 4.1 Introduction 95 4.2 Mathematical Structure of MEMS Models 96 4.3 General Approaches for System-Level Model Description 104 4.4 Numerical Methods for System-Level Simulation 107 4.5 Emerging Problems and Advanced Simulation Techniques 113 4.6 Conclusion 118 References 118 Part II Lumped Element Modeling Method for MEMS Devices 123 5 System-Level Modeling of Surface Micromachined Beamlike Electrothermal Microactuators 125 Ren-Gang Li and Qing-An Huang 5.1 Introduction 125 5.2 Classification and Problem Description 127 5.3 Modeling 131 5.4 Solving 136 5.5 Case Study 139 5.6 Conclusion and Outlook 142 References 143 6 System-Level Modeling of Packaging Effects of MEMS Devices 147 Jing Song and Qing-An Huang 6.1 Introduction 147 6.2 Packaging Effects of MEMS and Their Impact on Typical MEMS Devices 148 6.3 System-Level Modeling 150 6.4 Conclusion and Outlook 160 References 160 7 Mixed-Level Approach for the Modeling of Distributed Effects in Microsystems 163 Martin Niessner and Gabriele Schrag 7.1 General Concept of Finite Networks and Mixed-Level Models 163 7.2 Approaches for the Modeling of Squeeze Film Damping in MEMS 165 7.3 Mixed-Level Modeling of Squeeze Film Damping in MEMS 169 7.4 Evaluation 179 7.5 Conclusion 186 References 187 8 Compact Modeling of RF-MEMS Devices 191 Jacopo Iannacci 8.1 Introduction 191 8.2 Brief Description of the MEMS Compact Modeling Approach 192 8.3 RF-MEMS Multistate Attenuator Parallel Section 194 8.4 RF-MEMS Multistate Attenuator Series Section 202 8.5 Whole RF-MEMS Multistate Attenuator Network 205 8.6 Conclusions 207 References 208 Part III Mathematical Model Order Reduction for MEMS Devices 211 9 Moment-Matching-Based Linear Model Order Reduction for Nonparametric and Parametric Electrothermal MEMS Models 213 Tamara Bechtold, Dennis Hohlfeld, Evgenii B. Rudnyi, and Jan G. Korvink 9.1 Introduction 213 9.2 Methodology for Applying Model Order Reduction to Electrothermal MEMS Models: Review of Achieved Results and Open Issues 213 9.3 MEMS Case Study Silicon-Based Microhotplate 220 9.4 Application of the Reduced-Order Model for the Parameterization of the Controller 223 9.5 Application of Parametric Reduced-Order Model to the Extraction of Thin-Film Thermal Parameters 227 9.6 Conclusion and Outlook 232 References 234 10 Projection-Based Nonlinear Model Order Reduction 237 Amit Hochman, Dmitry M. Vasilyev, Micha J. Rewienski, and Jacob K. White 10.1 Introduction 237 10.2 Problem Specification 238 10.3 Projection Principle and Evaluation Cost for Nonlinear Systems 239 10.4 Taylor Series Expansions 240 10.5 Trajectory Piecewise-Linear Method 245 10.6 Discrete Empirical Interpolation method 250 10.7 A Comparative Case Study of an MEMS Switch 255 10.8 Summary and Outlook 260 Acknowledgment 260 References 261 11 Linear and Nonlinear Model Order Reduction for MEMS Electrostatic Actuators 263 Jan Lienemann, Emanuele Bertarelli, Andreas Greiner, and Jan G. Korvink 11.1 Introduction 263 11.2 The Variable Gap Parallel Plate Capacitor 264 11.3 Model Order Reduction Methods 269 11.4 Example 1: IBM Scanning-Probe Data Storage Device 275 11.5 Example 2: Electrostatic Micropump Diaphragm 281 11.6 Results and Discussion 285 11.7 Conclusions 286 Acknowledgments 287 References 287 12 Modal-Superposition-Based Nonlinear Model Order Reduction for MEMS Gyroscopes 291 Jan Mehner 12.1 Introduction 291 12.2 Model Order Reduction via Modal Superposition 292 12.3 MEMS Testcase: Vibratory Gyroscope 293 12.4 Flow Chart of the Nonlinear Model Order Reduction Procedure 294 12.5 Theoretical Background of Modal Superposition Technologies 295 12.6 Specific Algorithms of the Reduced Order Model Generation Pass 299 12.7 System Simulations of MEMS Based on Modal Superposition 304 12.8 Conclusion and Outlook 307 References 308 Part IV Modeling of Entire Microsystems 311 13 Towards System-Level Simulation of Energy Harvesting Modules 313 Dennis Hohlfeld, Tamara Bechtold, Evgenii B. Rudnyi, Bert Op het Veld, and Rob van Schaijk 13.1 Introduction 313 13.2 Design and Fabrication of the Piezoelectric Generator 317 13.3 Experimental Results 318 13.4 Modeling and Simulation 318 13.5 Maximum Power Point for the Piezoelectric Harvester 327 13.6 Conclusions and Outlook 332 References 333 14 Application of Reduced Order Models in Circuit-Level Design for RF MEMS Devices 335 Laura Del Tin, Evgenii B. Rudnyi, and Jan G. Korvink 14.1 Model Equations for RF MEMS Devices 337 14.2 Extraction of the Reduced Order Model 340 14.3 Application Examples 345 14.4 Conclusion and Outlook 354 References 355 15 SystemC AMS and Cosimulation Aspects 357 Francois Pecheux, Marie-Minerve Louerat, and Karsten Einwich 15.1 Introduction 357 15.2 Heterogeneous Modeling with SystemC AMS 358 15.3 Case Study: Detection of Seismic Perturbations Using the Accelerometer 363 15.4 Conclusion 370 Appendix 371 References 374 16 System Level Modeling of Electromechanical Sigma Delta Modulators for Inertial MEMS Sensors 377 Michael Kraft 16.1 Introduction and Motivation 377 16.2 Second Order Electromechanical M for a MEMS Accelerometer 380 16.3 Higher Order Electromechanical M for MEMS Accelerometer 391 16.4 Higher Order Electromechanical M for MEMS Gyroscopes 397 16.5 Concluding Remarks 400 References 401 Part V Software Implementations 405 17 3D Parametric-Library-Based MEMS/IC Design 407 Gunar Lorenz and Gerold Schroepfer 17.1 About Schematic-Driven MEMS Modeling 407 17.2 A 3D Parametric Library for MEMS Design MEMS+ (R) 409 17.3 Toward Manufacturable MEMS Designs 415 17.4 Micromirror Array Design Example 419 17.5 Conclusions 422 References 423 18 MOR for ANSYS 425 Evgenii B. Rudnyi 18.1 Introduction 425 18.2 Practice-Oriented Research during the Development of MOR for ANSYS 426 18.3 Programming Issues 429 18.4 Open Problems 432 18.5 Conclusion 436 References 437 19 SUGAR: A SPICE for MEMS 439 Jason V. Clark 19.1 Introduction 439 19.2 SUGAR 439 19.3 SUGAR-Based Applications 444 19.4 Integration of SUGAR + COMSOL + SPICE + SIMULINK 454 19.5 Conclusion 457 References 458 20 Model Order Reduction Implementations in Commercial MEMS Design Environment 461 Sandeep Akkaraju 20.1 Introduction 461 20.2 IntelliSense s Design Methodology 467 20.3 Implementation of System Model Extraction in IntelliSuite 470 20.4 Benchmarks 474 20.5 Summary 480 References 481 21 Reduced Order Modeling of MEMS and IC Systems A Practical Approach 483 Sebastien Cases and Mary-Ann Maher 21.1 Introduction 483 21.2 The MEMS Development Environment 484 21.3 Modeling Requirements and Implementation within SoftMEMS Simulation Environment 485 21.4 Applications 494 21.5 Conclusions and Outlook 498 References 498 22 A Web-Based Community for Modeling and Design of MEMS 501 Peter J. Gilgunn, Jason V. Clark, Narayan Aluru, Tamal Mukherjee, and Gary K. Fedder 22.1 Introduction 501 22.2 The MEMS Modeling and Design Landscape 501 22.3 Leveraging Web-Based Communities 502 22.4 MEMS Modeling and Design Online 505 22.5 Encoding MEMS Behavioral Models 508 22.6 Conclusions and Outlook 515 References 515 Index 519

Product Details

  • ISBN13: 9783527319039
  • Format: Hardback
  • Number Of Pages: 562
  • ID: 9783527319039
  • weight: 1324
  • ISBN10: 3527319034

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