How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

New microproducts need the utilization of a diversity of materials and have complicated three-dimensional (3D) microstructures with high aspect ratios. To date, many micromanufacturing processes have been developed but specific class of such processes are applicable for fabrication of functional and true 3D microcomponents/assemblies. The aptitude to process a broad range of materials and the ability to fabricate functional and geometrically complicated 3D microstructures provides the additive manufacturing (AM) processes some profits over traditional methods, such as lithography-based or micromachining approaches investigated widely in the past. In this paper, 3D micro-AM processes have been classified into three main groups, including scalable micro-AM systems, 3D direct writing, and hybrid processes, and the key processes have been reviewed comprehensively. Principle and recent progress of each 3D micro-AM process has been described, and the advantages and disadvantages of each process have been presented.

/pdf/a-review-on-3d-micro-additive-manufacturing-technologies-1kbpeb2xb5.pdf

A review on 3D micro-additive manufacturing technologies

Seeking meaning: A process approach to library and information services

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

Ethnography: Step By Step

Deep x-ray lithography (DXRL) with synchrotron radiation represents the technological core of the Lithographie, Galvanoformung, Abformung (LIGA) microfabrication process, thus defining the lateral shape and the accuracy of the final product. High aspect ratio microcomponents with a height of some micrometers up to several millimeters can be manufactured with submicron precision. We report on recently performed theoretical investigations on the structure transfer accuracy in the shadow printing process. Model calculations revealed the importance of photoelectron emission compared to Fresnel diffraction and beam divergence for typical DXRL conditions. The parameters used to model the effects correspond to the spectral distribution of the BESSY I wavelength shifter, Berlin (0.8 GeV, 5 T) equipped with several vacuum windows and a mask membrane made of beryllium with a thickness of 500 μm. A poly(methylmethacrylate) resist layer of 300 μm thickness with a bottom dose of 5 kJ cm−3 is assumed. The calculated do...

Recent developments in deep x-ray lithography

The typical approach for learned DBMS components is to capture the behavior by running a representative set of queries and use the observations to train a machine learning model. This workload-driven approach, however, has two major downsides. First, collecting the training data can be very expensive, since all queries need to be executed on potentially large databases. Second, training data has to be recollected when the workload or the database changes. To overcome these limitations, we take a different route and propose a new data-driven approach for learned DBMS components which directly supports changes of the workload and data without the need of retraining. Indeed, one may now expect that this comes at a price of lower accuracy since workload-driven approaches can make use of more information. However, this is not the case. The results of our empirical evaluation demonstrate that our data-driven approach not only provides better accuracy than state-ofthe- art learned components but also generalizes better to unseen queries.

/pdf/deepdb-learn-from-data-not-from-queries-4sn88chaho.pdf

DeepDB: learn from data, not from queries!

The typical approach for learned DBMS components is to capture the behavior by running a representative set of queries and use the observations to train a machine learning model. This workload-driven approach, however, has two major downsides. First, collecting the training data can be very expensive, since all queries need to be executed on potentially large databases. Second, training data has to be recollected when the workload and the data changes. To overcome these limitations, we take a different route: we propose to learn a pure data-driven model that can be used for different tasks such as query answering or cardinality estimation. This data-driven model also supports ad-hoc queries and updates of the data without the need of full retraining when the workload or data changes. Indeed, one may now expect that this comes at a price of lower accuracy since workload-driven models can make use of more information. However, this is not the case. The results of our empirical evaluation demonstrate that our data-driven approach not only provides better accuracy than state-of-the-art learned components but also generalizes better to unseen queries.

DeepDB: Learn from Data, not from Queries!

Deep x-ray lithography with synchrotron radiation is the key microfabrication process in the LIGA technology. Micro-components with a height of some up to several mm can be manufactured with sub- precision. The pattern transfer accuracy is governed by technological constraints like thermal deformation of the mask as well as by various physical effects, e.g. diffraction, emission of photo- and Auger electrons, fluorescence radiation, radiation scattering and divergence of the synchrotron radiation beam. A computer program has been developed to investigate the significance of these effects to the dose distribution in the resist material, which in turn determines the lateral structure resolution and the quality of the resist structures. In this paper the models used for the calculation and the calculation procedure are introduced and the weight of the different contributions with respect to transfer accuracy is investigated. It is shown that beam divergence and diffraction are much less important than the image blur caused by photoelectrons. Fluorescence radiation emitted from the mask membrane or the substrate contributes to the dose deposition in the resist if mask membrane or substrate consists of high-atomic-number material. Scattering of radiation is negligible for resist layers which are less than some mm thick. The calculations are compared with measurement results for different substrate materials, synchrotron radiation sources and resist heights. A good agreement was found between calculated dose distributions and measured resist side wall profiles. This allows a partial compensation of the above-mentioned accuracy limiting effects early in the mask design phase.

Calculation and experimental determination of the structure transfer accuracy in deep x-ray lithography

Object shape information is essential for robot manipulation tasks, in particular for grasp planning and collision-free motion planning. But in general a complete object model is not available, in particular when dealing with unknown objects. We propose a method for completing shapes that are only partially known, which is a common situation when a robot perceives a new object only from one direction. Our approach is based on the assumption that most objects used in service robotic setups have symmetries. We determine and rate symmetry plane candidates to estimate the hidden parts of the object. By finding possible supporting planes based on its immediate neighborhood, the search space for symmetry planes is restricted, and the bottom part of the object is added. Gaps along the sides in the direction of the view axis are closed by linear interpolation. We evaluate our approach with real-world experiments using the YCB object and model set [1].

Andreas Schmidt

Papers

Recent developments in deep x-ray lithography

DeepDB: learn from data, not from queries!

DeepDB: Learn from Data, not from Queries!

Calculation and experimental determination of the structure transfer accuracy in deep x-ray lithography

Heuristic 3D object shape completion based on symmetry and scene context