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Performance Measurement
            of MEMS Elements
         for Information Security
           of G-Cloud Channels


                Assoc. Prof. Dr. Roumiana Ilieva
Silvia Bobeva
Indispensability
of 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 membrane
extracted from CoventorWare - top and
bottom 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 the
right) 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 Measurement
of 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/:
Microelectromecanical System macro
model
   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 the
considered period. Feedback reflects on the final conclusion about
the 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.

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Performance Measurement of MEMS Elements

  • 1. Performance Measurement of MEMS Elements for Information Security of G-Cloud Channels Assoc. Prof. Dr. Roumiana Ilieva Silvia Bobeva
  • 2. Indispensability of 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.
  • 3. 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).
  • 4. 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.
  • 5. 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.
  • 6. 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).
  • 7. Generated Solid Mesh Model of the membrane extracted from CoventorWare - top and bottom 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).
  • 8. 2-D model (on the left) and 3D model (on the right) 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.
  • 9. Performance Measurement of 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/:
  • 11. 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;
  • 12. 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 the considered period. Feedback reflects on the final conclusion about the service performance.
  • 13. 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.
  • 14. 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.
  • 15. 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.