System-level Modeling of MEMS: (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.

Table of Contents:
PART I: PHYSICAL AND MATHEMATICAL FUNDAMENTALS INTRODUCTION: ISSUES IN MICROSYSTEMS MODELING The Need for System-Level Models for Microsystems Coupled Multiphysics Microsystems Multiscale Modeling and Simulation System-Level Model Terminology Automated Model Order Reduction Methods Handling Complexity: Following the VLSI Paradigm Analog Hardware Description Languages General Attributes of System-Level Models AHDL Simulation Capabilities Composable Model Libraries Parameter Extraction, Model Verification, and Model Validation Conclusions SYSTEM-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 DEVICES Introduction Brief Description of the MEMS Compact Modeling Approach RF-MEMS Multistate Attenuator Parallel Section RF-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 Procedure Theoretical 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 Harvester Conclusions 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 Accelerometer Conclusion SYSTEM LEVEL MODELING OF ELECTROMECHANICAL SIGMA?DELTA MODULATORS FOR INERTIAL MEMS SENSORS PART V: SOFTWARE IMPLEMENTATIONS 3D PARAMETRIC-LIBRARY-BASED MEMS/IC DESIGN About 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 ENVIRONMENT Introduction 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

About the 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.