Part I: Background and Fundamentals Introduction, Mohamed Gad-el-Hak, University of Notre Dame Scaling of Micromechanical Devices, William Trimmer, Standard MEMS, Inc., and Robert H. Stroud, Aerospace Corporation Mechanical Properties of MEMS Materials, William N. Sharpe, Jr., Johns Hopkins University Flow Physics, Mohamed Gad-el-Hak, University of Notre Dame Integrated Simulation for MEMS: Coupling Flow-Structure-Thermal-Electrical Domains, Robert M. Kirby and George Em Karniadakis, Brown University, and Oleg Mikulchenko and Kartikeya Mayaram, Oregon State University Liquid Flows in Microchannels, Kendra V. Sharp and Ronald J. Adrian, University of Illinois at Urbana-Champaign, Juan G. Santiago and Joshua I. Molho, Stanford University Burnett Simulations of Flows in Microdevices, Ramesh K. Agarwal and Keon-Young Yun, Wichita State University Molecular-Based Microfluidic Simulation Models, Ali Beskok, Texas A&M University Lubrication in MEMS, Kenneth S. Breuer, Brown University Physics of Thin Liquid Films, Alexander Oron, Technion, Israel Bubble/Drop Transport in Microchannels, Hsueh-Chia Chang, University of Notre Dame Fundamentals of Control Theory, Bill Goodwine, University of Notre Dame Model-Based Flow Control for Distributed Architectures, Thomas R. Bewley, University of California, San Diego Soft Computing in Control, Mihir Sen and Bill Goodwine, University of Notre Dame Part II: Design and Fabrication Materials for Microelectromechanical Systems Christian A. Zorman and Mehran Mehregany, Case Western Reserve University MEMS Fabrication, Marc J. Madou, Nanogen, Inc. LIGA and Other Replication Techniques, Marc J. Madou, Nanogen, Inc. X-Ray-Based Fabrication, Todd Christenson, Sandia National Laboratories Electrochemical Fabrication (EFAB), Adam L. Cohen, MEMGen Corporation Fabrication and Characterization of Single-Crystal Silicon Carbide MEMS, Robert S. Okojie, NASA Glenn Research Center Deep Reactive Ion Etching for Bulk Micromachining of Silicon Carbide, Glenn M. Beheim, NASA Glenn Research Center Microfabricated Chemical Sensors for Aerospace Applications, Gary W. Hunter, NASA Glenn Research Center, Chung-Chiun Liu, Case Western Reserve University, and Darby B. Makel, Makel Engineering, Inc. Packaging of Harsh-Environment MEMS Devices, Liang-Yu Chen and Jih-Fen Lei, NASA Glenn Research Center Part III: Applications of MEMS Inertial Sensors, Paul L. Bergstrom, Michigan Technological University, and Gary G. Li, OMM, Inc. Micromachined Pressure Sensors, Jae-Sung Park, Chester Wilson, and Yogesh B. Gianchandani, University of Wisconsin-Madison Sensors and Actuators for Turbulent Flows. Lennart Loefdahl, Chalmers University of Technology, and Mohamed Gad-el-Hak, University of Notre Dame Surface-Micromachined Mechanisms, Andrew D. Oliver and David W. Plummer, Sandia National Laboratories Microrobotics Thorbjoern Ebefors and Goeran Stemme, Royal Institute of Technology, Sweden Microscale Vacuum Pumps, E. Phillip Muntz, University of Southern California, and Stephen E. Vargo, SiWave, Inc. Microdroplet Generators. Fan-Gang Tseng, National Tsing Hua University, Taiwan Micro Heat Pipes and Micro Heat Spreaders, G. P. "Bud" Peterson, Rensselaer Polytechnic Institute Microchannel Heat Sinks, Yitshak Zohar, Hong Kong University of Science and Technology Flow Control, Mohamed Gad-el-Hak, University of Notre Dame) Part IV: The Future Reactive Control for Skin-Friction Reduction, Haecheon Choi, Seoul National University Towards MEMS Autonomous Control of Free-Shear Flows, Ahmed Naguib, Michigan State University Fabrication Technologies for Nanoelectromechanical Systems, Gary H. Bernstein, Holly V. Goodson, and Gregory L. Snider, University of Notre Dame Index

The MEMS Handbook

Surface micromachining is characterized by the fabrication of micromechanical structures from deposited thin films. Originally employed for integrated circuits, films composed of materials such as low-pressure chemical-vapor-deposition polycrystalline silicon, silicon nitride, and silicon dioxides can be sequentially deposited and selectively removed to build or "machine" three-dimensional structures whose functionality typically requires that they be freed from the planar substrate. Although the process to accomplish this fabrication dates from the 1960's, its rapid extension over the past few years and its application to batch fabrication of micromechanisms and of monolithic microelectromechanical systems (MEMS) make a thorough review of surface micromachining appropriate at this time. Four central issues of consequence to the MEMS technologist are: (i) the understanding and control of the material properties of microstructural films, such as polycrystalline silicon, (ii) the release of the microstructure, for example, by wet etching silicon dioxide sacrificial films, followed by its drying and surface passivation, (iii) the constraints defined by the combination of micromachining and integrated-circuit technologies when fabricating monolithic sensor devices, and (iv) the methods, materials, and practices used when packaging the completed device. Last, recent developments of hinged structures for postrelease assembly, high-aspect-ratio fabrication of molded parts from deposited thin films, and the advent of deep anisotropic silicon etching hold promise to extend markedly the capabilities of surface-micromachining technologies.

/pdf/surface-micromachining-for-microelectromechanical-systems-13y44jiffs.pdf

Surface micromachining for microelectromechanical systems

https://www.cse.iitk.ac.in/users/isaha/Publications/Journals/NC10.pdf/

Artificial neural networks in hardware: A survey of two decades of progress

This article reviews the growing body of scientific work in artificial chemistry. First, common motivations and fundamental concepts are introduced. Second, current research activities are discusse...

/pdf/artificial-chemistries-a-review-2jvfmhq9oc.pdf

Artificial chemistries—a review

This paper presents an exercise in comparing the performance of microelectromechanical systems (MEMS) actuators and sensors as a function of operating principle. Data have been obtained from the literature for the mechanical performance characteristics of actuators, force sensors and displacement sensors. On-chip and off-chip actuators and sensors are each sub-grouped into families, classes and members according to their principle of operation. The performance of MEMS sharing common operating principles is compared with each other and with equivalent macroscopic devices. The data are used to construct performance maps showing the capability of existing actuators and sensors in terms of maximum force and displacement capability, resolution and frequency. These can also be used as a preliminary design tool, as shown in a case study on the design of an on-chip tensile test machine for materials in thin-film form.

/pdf/mems-actuators-and-sensors-observations-on-their-performance-1ce9ndvk3j.pdf

MEMS actuators and sensors: observations on their performance and selection for purpose

We report the fabrication of a wavelength tunable optical filter using surface micromachining technology. The center wavelength is 1.517 /spl mu/m and the transmission bandwidth is 5 nm. The device with a 50-/spl mu/m diameter aperture has an optical loss of about 5 dB. A continuous wavelength tuning of 60 nm has been demonstrated. This device may find applications in optical sensing and wavelength division multiplexing systems, and can be readily integrated with surface emitting lasers, modulators, and detectors.

Surface micromachined Fabry-Perot tunable filter

Surface micromachined fabry-perot tunable filter

Thin-film micro-electrochemical energy storage cells (MEESC) such as microbatteries and double-layer capacitors (DLC) are provided. The MEESC comprises two thin layer electrodes, an intermediate thin layer of a solid electrolyte and optionally, a fourth thin current collector layer; said layers being deposited in sequence on a surface of a substrate. The MEESC is characterized in that the substrate is provided with a plurality of through cavities of arbitrary shape, with high aspect ratio. By using the substrate volume, an increase in the total electrode area per volume is accomplished.

Micro electrochemical energy storage cells

An acoustic filter array of microelectromechanical beams each having a characteristic resonance frequency response to mechanical and/or acoustical vibration. The array divides incoming acoustic signals into a plurality of discrete spectral components, each of which may be separately detected and converted into corresponding electrical signals. The acoustic filter may be integrated onto a single crystal silicon substrate with electrical circuity for performing acoustic signal processing functions required for applications such as speech processing and simulating the physiological function of the ear.

Microelectromechanics-based frequency signature sensor

A rapidly reprogrammable neural network architecture with the possibility for a large synapse matrix is presented. The concept is based on the use of bacteriorhodopsin as a molecular computational element with electrooptical characteristics that are associated with a series of intermediates that are photochemically initiated. One of these states has been stabilized by several orders of magnitude with specific environmental conditions, and this allows the concentration of intermediates to be readily affected without the need for continuous holding illuminations. Thus, the photoelectrical characteristics at each synapse can readily be modulated, and a scheme has been devised to read the synaptic matrix without erasing the impressed synaptic strengths. Electrical measurements are presented to test specific aspects of the overall neural network implementation, and the results of these measurements are encouraging for the development of such a distinctive neural network device.

Dan Haronian

Papers

Surface micromachined Fabry-Perot tunable filter

Surface micromachined fabry-perot tunable filter

Micro electrochemical energy storage cells

Microelectromechanics-based frequency signature sensor

Elements of a unique bacteriorhodopsin neural network architecture.