Performance Measurement of MEMS Elements for Information Security of G-Cloud Channels Assoc. Prof. Dr. Roumiana IlievaSilvia Bobeva
Indispensabilityof such research The evolving G-Cloud strategy enthusiasm worldwide needs enormous efforts to provide a reliable security of the information flow through Public Cloud channels. G-Cloud security includes a wide set of controls, technologies, and policies used to protect the associated infrastructure, applications, and data in the Public Cloud. One of these technologies is MEMS-based.
MEMS world MEMS is a high-tech field that combines microelectronics and micro-production technology for micro component integration, micro sensors and devices (Sanchez et al, 2010). In a common silicon substrate, micro-hotplates are mainly built on a thin dielectric membrane that is suspended over a hole in the substrate. The sensors consist of a sensor module, measuring element in this module and the membrane (Xian et al, 2010), (Semancik & Cavicchi, 1998).
Design and simulation of MEMS The main purpose of CAD is to allow creation of a prototype, which at the first real production could have defined characteristics, appearance, behavior, work and physical endurance (Beeby et al, 2004). The list of leading software companies in the last year that support products for engineering applications include great names as Coventor Inc., COMSOL, SoftMEMS, ANSYS and so on. Particular sensor was designed with “CoventorWare2010” that has free access for students in ECAD laboratory in Technical University of Sofia.
PZR pressure sensor design with the MultiMEMS Process in CoventorWare2010 The presented 3-D model of Piezoresistive (PZR) sensor was designed by using “CoventorWare2010” (Kolev et al, 2010). in a tutorial practical course lead by Europractice. The approach is combination of diaphragm FEM analysis using Analyzer and PZR modeling using Architect. The sensor is based on thin silicon diaphragm bending measurement. Substrate is Silicon <100>, epitaxial grown (EPI silicon diaphragm at 3.1µ thick), followed by anisotropic material wet etching process (399.1 µ) and mask offset of 15 microns.
To create diaphragm layout proper coordinates were set in the worksheet to form the membrane dimensions. External configuration defines overall dimensions of the sensor: 1200 microns in the X and Y directions. Internal configuration defines dimensions of the etch hole: it is 990 microns in the X and Y directions (including offset).
Generated Solid Mesh Model of the membraneextracted from CoventorWare - top andbottom view It is automatic by import the 2-D layout mask information. 3-D model has to be meshed with the mapped mesh. Partition coordinates are the same and form bottom and frame parts. Device’s bed is fixed and the diaphragm is movable (pressure goes in).
2-D model (on the left) and 3D model (on theright) of PZR membrane Model is under simulation that is presented in five deformation stages when pressure is applied. MemMesh undergo simulation, which calculates the diaphragm deformation under a varying pressure load. The MemMech results are automatically stored in the CoventorWare database. They can be visualized by either using the 3D Visualizer or accessing them directly in Architect.
Performance Measurementof MEMS Elements MEMS Element transforms input pressure/Fp,in/ into output electrical signals as it is shown on the model in the next figure. These outputs have an added useful value compared to their input. The electrical signals flow at the output of the MEMS, in its turn, can be divided into a flow of qualified signals /Fs,q/ and a flow of disqualified signals, waste and emissions /Fs,d/:
After a lot of transformations in (de Ron & Rooda, 2001) under some conclusions and approximations the following universal measure for the technical performance is achieved: where ηT is transformation factor, representing the ratio between the average quantity of qualified signal, obtained during the considered period T and the maximum quantity of qualified signal, that could be provided in an ideal situation during the same period; Fs,qm is the maximum output flow of qualified signal which can be achieved by the actual MEMS;
Interfering and/or confusing factors are those factors that reflect on the transformation process i.e. effectiveness of the MEMS which is defined by the ratio of the average real output flow of qualified signal and the average maximum output flow of qualified signal : is the ratio between the average effective service period and theconsidered period. Feedback reflects on the final conclusion aboutthe service performance.
Conclusions Following the analysis and testing procedures general conclusion is provided for improving the G-Cloud Services performance and preventing any further problems to occur. It focuses on security utilization, increase of the signal transformation factor, reliability, quality and effectiveness. Several measures should be taken to improve the MEMS performance. Focal point of overall research and development of the future generation sensors and MEMS devices should go on and open a prospect to achieve high level of safeness in general and public security.
References: Beeby, S., & Ensell, G., & Kraft, M., & White, N. (2004). MEMS Mechanical Sensors. ISBN 1-58053-536-4, Artech House, Inc. Kolev, G., & Denishev, K., & Bobeva, S. (2010). Design and Analyzing of Silicon Diaphragm for MEMS Pressure Sensors. Annual Journal of Electronics, Sofia 2010, Volume 4, Number 2, ISSN 1313-1842, p. 112. de Ron, A. J., & Rooda, J.E. (2001). Structuring performance measures. 1st IFIP Seminar on performance measures, Glasgow, United Kingdom, 2001, pp.25-31 Sanchez, J., & Schmitt, A., & Berger, F., & Mavon, C. (2010). Silicon-micromachined gas chromatographic columns for the development of portable detection device. J. Sens., doi:10.1155/2010/409687. Semancik, S., & Cavicchi, R. E. (1998). Kinetically-Controlled Chemical Sensing Using Micromachined Structures. Chemical Science and Technology Laboratory, NIST, Gaithersburg, MD. Xian, Y., & Lai, J., & Liang, H. (2010). Fabrication of a MEMS micro-hotplate. Journal of Physics: Conference Series 276, 012098, doi:10.1088/1742-6596/276/1/012098.
About the Authors Silvia Bobeva Roumiana Ilieva PhD student in “Microelecreonics” Associate Professor on “Automated Systems for Data at Technical University of Sofia Processing and Management” at the Technical (TU-Sofia). Her study is University of Sofia (TU-Sofia). She received an MSc in concentrated on research, design Engineering from the TU-Sofia, then a MA in and simulation of MEMS elements Economics from the University of Delaware, USA. Her and devices for automotive PhD is in Techniques on Dissertation: “Problems of industry applications that aim to Methodology in the Investigation of FMS Productivity”. achieve more secure and safe eco She specializes and teaches in the field of life on the planet. The PhD study is eGovernment at the Universities of Amsterdam and focused on hydrogen leak The Hague (2007), Lancaster (2008), Westminster and detection through sensor usage in UCL, London (2009, 2011), Southampton Solent and eco and hybrid vehicles. She has Portsmouth, UK (2010), "Space Challenges" several publications in this field (2010-2012). Her major areas of research and teaching and she has conducted a lot of are G-Cloud Performance Measurement, eGovernance laboratory experiments and ontologies, eServices virtual prototyping and simulation tutorials on “Automated Systems modeling, etc. She is author of over 70 scientific for Data Processing and publications; member of IEEE: Computer Society; Management” with leading tutor the Robotics and Automation Society; Systems, Man, and second author Assoc. Prof. Dr. Cybernetics Society; UDBC at USAID; Union of Roumiana Ilieva. Automation and Informatics (UAI); PC member of JeDEM and CeDEM11 etc.