4.5.12, Lightning Talks, Main Hall: Performance Measurement of MEMS Elements for Information Security of G-Cloud Channels (Roumiana Ilieva, Silvia Bobeva) #CeDEM12
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.