SUBMITTED TO IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS -PART C: APPLICATIONS AND REVIEWS 1
A survey of modeling and control techniques for
Micro-and Nano-electromechanical systems
Antoine Ferreira *
Sumeet S. Aphale
Abstract—In the current times, MEMS and NEMS form
a major inter-disciplinary area of research involving science,
engineering and technology. A lot of work has been reported
in the area of modeling and control of these devices, with
the aim of better understanding their behavior and improving
their performance. This work presents a review of the emerging
advances in the modeling and control of these micro-and nanoscale
devices and converges on the exciting research in on-
chip control, with a mechatronics and controls perspective and
concludes by projecting future trends.
Index Terms—MEMS, NEMS, lab-on-a-chip, modeling, control
I. INTRODUCTION
Though micro-electromechanical systems (MEMS) and
nano-electromechanical systems (NEMS) research has gained
tremendous popularity and momentum in the past two decades,
the potential of small micro-, nano-and even molecular
machines was recognized by researchers, especially physicists
and chemists almost half a century ago [1]. The race for miniaturizing
had begun and finally in 1974, the term Nanotechnology
was coined [2]. The development of ‘cluster’ science, [3],
and the invention of the Scanning Tunneling Microscope, [4],
in the early 1980s ushered the era of nanotechnology and the
first book on this subject appeared in 1986, [5].
It is generally accepted that an electrostatically excited
tuning fork employing field-effect transistor ”readout” was the
first operational MEMS device, [6]. Since then, the MEMS
technology has progressed rapidly and in recent years specialized
devices for applications such as blood cell separation and
analysis are constantly expanding the boundaries of MEMS
[7]. NEMS devices have also evolved since their first prototype
was successfully demonstrated by researchers at IBM, [8].
Research aimed at developing specific sensor ([9], [10], [11])
and actuator ([12], [13], [14], [15], [16], [17], [18], [19])
technologies for improved MEMS and NEMS devices is
ongoing. More details as to the current state-of-the-art for
sensors and actuators can be found in [20].
Models that can capture the dynamic behavior of these
devices can be of great help in understanding and improving
their design and ultimately, their performance. Additionally,
as with any dynamic system, a suitable control strategy could
Prof. Antoine Ferreira [Corresponding Author] is with the Institut PRISME,
ENSI Bourges, 88 Boulevard Lahitolle, 18000 Bourges, France
Dr. Sumeet S. Aphale is with the Centre for Applied Dynamics Research
align the actual performance of these MEMS/NEMS devices
closer to the desired objectives. Therefore, the two key avenues
of current engineering interest that have the potential to
significantly enhance MEMS/NEMS devices are: (i) modeling
and (ii) control. Modeling techniques that lead to a better understanding
of these miniature device dynamics are currently
being sought after. Accurate dynamic models could lead to
specialized control strategies that will in turn lead to major
improvements in device performances. In the recent years, a
lot of research has been reported in the area of modeling and
control of MEMS and NEMS. This paper presents an overview
of the emerging innovative modeling techniques applicable to
these miniature devices. Different models are presented for
system design and control associated with physical mechanisms,
geometry/scaling issues or computational aspect for
real-time control of MEMS with challenging issues in NEMS.
It also reviews the recent advances in the control of MEMS
and NEMS devices that have been inspired by the recent
innovations in sensors, actuators, modeling techniques and
control theory.
A. Organization
The remainder of this review is organized as follows.
Section II presents an overview of the various modeling
innovations that describe the behavior of MEMS and NEMS
devices. Complexity in modeling is reviewed with respect to
associated physical mechanisms, geometry/scaling issues and
computational aspect to minimize the real time control issues.
This section is further divided into two parts viz: (i) Modeling
for MEMS/NEMS design (subsection II-A), (ii) Modeling
for MEMS/NEMS control (subsection II-B). Section III will
review the various control technique implementations and is
divided in to (i) Open-loop control (subsection III-A), (ii)
Open-loop control with input pre-shaping (subsection III-B),
(iii) Closed-loop control (subsection III-C) and (iv) On-chip
control (subsection III-D). Finally, section IV will give the
concluding remarks of this review.
II. MODELS FOR SYSTEM DESIGN AND CONTROL
Today, an abundance of commercial circuit and system simulation
tools exist for electronic circuits and control system virtual
prototyping. Microelectromechanical systems have been
analyzed using the classical physical models or continuum
theories for the mechanical (elastostatic or elastodynamic),
(CADR), School of Engineering, University of Aberdeen, Aberdeen UK
the thermal (thermostatic), magnetic (magnetodynamics) and
Copyright (c) 2008 IEEE. Personal use of this material is permitted.
the electrical (electrostatic) energy domains [22]. Naturally,
However, permission to use this material for any other other purposes must be
obtained from the IEEE by sending a request to pubs-permissions@ieee.org the design of reliable actuating techniques requires simple but
SUBMITTED TO IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS -PART C: APPLICATIONS AND REVIEWS
2
(a)
(b) (c)
Fig. 1. Examples of mechanical nodal conventions. F and M are positive valued. (a) Beam in tension, Fx,a
=
-Fx,b
=
-F. (b) Beam accelerating in x.
Fx,a
=
Fx,b
=
F. (c) Moment bending beam with positive curvature in y. Mz,a
=
-Mz,b
=
-M, [21].
realistic dynamic models of the device, either in input/output
or in the state variable form. Accurate models lead towards optimal
system design, better performance, better understanding
of the device, short development time, and consequently, lower
cost of the device. Furthermore, due to the compact layout,
manufacturing tolerance, modeling errors, and environmental
changes, MEMS are subjected to parasitics and parameter
variations. In order to better guarantee their stability and a
certain level of performance, one must take into account these
factors in the design of MEMS control systems. This section
reviews the models for system design (subsection II-A) and
control subsection II-B associated with physical mechanisms,
geometry/scaling issues or computational aspect for real-time
control of MEMS with challenging issues in NEMS.
A.
Modeling for MEMS/NEMS design
1) Reduced-order Models: In higher level MEMS/NEMS
simulation applications, the computational complexity of
getting an output for a given input from the model is
simply too high. Thus, model reduction involves reducing
the computational complexity of the model by reducing the
number of parameters in the original model. If the original
model is described by linear ordinary differential equations
(ODE) then a typical approach is to write down the algebraic
relation in the frequency domain. Reduced order models
(ROM) are cheap in terms of memory and computational
time and are needed to perform fast and efficient system-level
composite circuit for MEMS on-chip development. For
practical implementation of feedback control design, the
models need to be finite-dimensional. In [25], a reduced
nonlinear model was linearized at multiple operating points
in order to design a PID-controller tuned via LMI-theory.
For MEMS, truncated low-order models can be established
this way, using a summation over only selected operating
points. In the presence of significant nonlinearities, which
often is the case for MEMS, the simple truncated models
tend to be too imprecise. However, the technique can be
enhanced, by combining structure of the model with finite
element analysis a novel way to perform unknown parameters
identification. New technique by combining the Taylor series
expansion with the Arnoldi method to automatically develop
reduced-order models for coupled energy domain nonlinear
microelectromechanical devices is given in [26]. Model
order reductions via Arnoldi algorithm applied directly to
ANSYS finite element models has also been reported [27].
In this work, the authors adopt a micro accelerometer as
an example to demonstrate the advantages of this approach.
An electrostatically actuated fixed-fixed beam structure with
squeeze-film damping effect was examined to illustrate the
model-order reduction method in [28]. Compared with the
linearized model, these works show that the reduced-order
nonlinear models can capture the device dynamic behavior
over a much larger range of MEMS operation but stability
preservation is not guaranteed and has a low accuracy away
from the expansion point. Based on differentiation of the
discretized Finite Element (FE) equations for parameterization
of MEMS macromodels (see, Figure 1) the authors in [21]
computed the governing system matrices as well as high
order derivatives (HOD) with regard to design parameters
by means of Automatic Differentiation (AD). While the
above formalisms were developed primarily for numerical
simulations, the possibility to create nonlinear parameterized
models based on Karhunen-Loeve decomposition is proposed
in [23]. This reduced order model is cheap in terms of
memory and computational time and compatible with fast and
efficient system-level composite circuit for on-chip feedback
control. In the presence of significant nonlinearities, which
often is the case for MEMS, the simple linear model order
reduction reported in this section tend to be rapidly imprecise
due to the vast amount of possible expressions of nonlinearity.
General approaches are formulated in the following section for
updating the parameters of systems governed by multiphysics
equations using advanced optimization techniques.
2) Macromodeling: Several computer algorithms based on
3-D Finite Element Analysis (FEA) have been coupled to 3D
design tool to simulate MEMS, [29]. In order to alleviate
the computational expense associated with the 3-D analyses,
considerable efforts have been devoted to the development of
reliable distributed reduced-order models (ROM) for MEMS,
[21], [23]. As an illustration, model order reductions via the
block Arnoldi algorithm with/without Taylor-series expansion
directly to ANSYS finite element models have been proposed
for MEMS accelerometers, [27], as well as electrostatically
actuated fixed-fixed beam structure with squeeze-film damping
effect, [30]. Furthermore, the authors in [31] demonstrated
that the resulting ROM can capture the static/dynamic behaviors
of the electrostatically actuated MEMS plate very
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Fig. 2. System level model of an electrostatic torsional actuator. Damping is included as a mixed-level model, (Courtesy of [23]).
(a) (b)
Fig. 3. (a) ANSYS/Multiphysics finite element model of electrostatic MEMS actuator and (b) SEM micrograph of a fabricated 2 ×
2 optical switch MEMS
chip based on electrostatic actuation. (Courtesy of [24]).
well. Taking the analogy to electronic circuit design further,
the next generation of MEMS system designers are starting
to use composable MEMS models (macromodeling) [32] as
the electrostatic torsional actuator shown in Fig. 2, [33]. It
shows a mixed level damping approach where the torsional
actuator dynamics is simulated by Navier-Stokes equation-
based finite element modeling and the squeeze film damping
by lumped-parameter modeling. The pioneering work in
forming composable MEMS models is SUGAR from UC
Berkeley [34], coventor ARCHITECT, [35], and NODAS from
Carnegie Mellon, [36]. ARCHITECT and NODAS use Analog
hardware description language (AHDL) descriptions, while
SUGAR has its models written in MATLAB. In the later
case, the performance of the tunneling MEMS sensor can be
estimated and improved based on mechanical-level analysis
by ANSYS and system-level analysis by MATLAB, [37]. A
feedback control system with one zero and two poles has been
synthesized, improving the dynamic range and the bandwidth
of the closed-loop system (around 15 kHz).
Recently, MEMS design engineers developed a practical
method that combines structure of the model with Finite
Element Analysis (FEA) in novel way to perform system
identification and identify the unknown parameters. The
result was a lumped dynamical model of a MEMS device
that can be used for the design of feedback control systems,
[39]. In principle, any lumped-constant model can be
described in this way, thus overcoming the most serious
limitation of the equivalent-circuit modeling technique
mentioned earlier. A likely reason for the popularity of this
technique is that it makes it possible to simulate MEMS
using ordinary circuit simulators. An another modeling
alternative is to use functional entities representing nanodevices
in an object-oriented fashion, termed macromodeling.
Macromodeling procedure for coupled-domain MEMS devices
with electrostatic and electrothermal effects have been widely
presented. Numerical simulation of the dynamics using
hybrid BEM/FEM (Boundary Element and Finite Element
Method) approach was presented in, [40]. Furthermore,
numerical models generated by three-dimensional (3D)
finite-element or the finite-difference (FEM/FDM) methods
of original coupled-domain MEMS models can be reduced
into low-order macromodels (e.g. heat transfer as well as
electro-thermal effects) with by Arnoldi-based technique or
Krylov-subspace methods [41]. Hybrid analytical/numerical
macromodels for the substructures with regular geometry
were generated by analytical method and the ones with odd
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Fig. 4. A table showing the type of physical models, their features and the applications that are most suitable for respective modeling techniques (Courtesy
of [38]).
geometry by numerical method [42]. These techniques were
tested on a generic MEMS device, a microtweezer. The
nonlinear tunneling mechanism and electrostatic actuation
were linearized using small-signal approximation. It must
be noted that exporting macromodels for MEMS simulation
requires the interfacing of various commercial tools for
CAD (e.g., SolidWorksTM
), FEA, simulation of electronic
circuits (e.g., AHDL/VHDL language), control systems
(e.g., Matlab/SimulinkTM
), multibody systems (e.g.,
ANSYS/MultiphysicsTM
) and also the microfabrication
processes. Figure 3(a) illustrates the microfabrication of
an electrostatic MEMS comb-drive actuator [24] capable
of moving the MEMS mirror 50 µm designed using
ANSYS/Multiphysics finite element model (Fig. 3(a)).
This simulation can solve coupled field problems such as
electrostatic forces acting on the mechanical structures, and is
also capable of performing contact analysis when the glider
contact the mechanical stop. There are definite drawbacks,
the simulation of HDL models or models written in other
high-level languages is usually considerably slower than
the simulation of equivalent models built into the simulator.
Furthermore, it is noteworthy to discuss macromodeling
applicability in conjunction with MEMS control design since
realtime feedback control issues are still unsolved.
3) Multiscale Models: The ability to design reliable
MEMS/NEMS devices demand new simulation capabilities
due to the length and time scaling effects at nanoscale
[43]. Combination of classical microforces phenomena with
quantum fields and molecular considerations become key
issues to the point that thermal fluctuation influences the
NEMS operation. Furthermore, the roles of surface and defects
become more dominant. Finally, the behavior of materials at
nanometer scale begins to be atomistic rather than continuous.
Taken together, it gives rise to anomalous and often nonlinear
effects, i.e., nanomechanics (Casimir effect, van der
Waals, charges quantization), nano-optics (charge transfer),
electrostatic-fluidics effects (dielectrophoresis, electro-welting,
electroosmosis), nanomagnetics (paramagnetism), and so on.
The challenge now faced by NEMS designers is to bridge
the different scales to a more general framework, which has
been coined as multiscale modeling [44]. Conceptually, two
categories of multiscale simulations can be envisioned: both
sequential and concurrent.
(i) Sequential multiscale simulations
The sequential methodology attempts to piece together a
hierarchy of computational approaches in which large-scales
models use the coarse-grained representations from more
detailed smaller-scale models. In doing so, the simulations
are running independently of each other and a complete
separation of both length and time scales are achieved
[80], [76]. Some examples of sequential coupling show
that to accurately model MEMS/NEMS devices at least
three length-scales need to be explored: mesoscopic at the
package level; microscopic at the actuator/sensor level and
nanoscopic at the material level. Reliability of packaged
polysilicon microelectromechanical systems involves the
computational study of environmental effects to predict the
long-term performance of MEMS packages at mesoscopic
and microscopic length scales. The authors in [75] present a
SUBMITTED TO IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS -PART C: APPLICATIONS AND REVIEWS 5
TABLE I
COMPARISON OF MODELING AND SIMULATION APPROACHES FOR MEMS AND NEMS DESIGN.
Length scale Modeling Key Ref. Time scale Computational
complexity (.)
Computational
error (%)
Modeling level
L =
10µm
Macroscopic
Classical Physics
Physical models
1s =
t =
10s O(n2
)
~
10-30 %
System-level
[45],[46],[47]
[48],[49],[50]
Dynamical State-Space model
[51],[52],[53]
Lumped Dynamical model [54], [55], [56]
High-Order Derivatives model [57],[58]
Composite circuit macromodels
VHDL/AHDL language
Mixed-level model
[34],[35],[36]
[42],[40],[59]
O(n2
)
~
20 % System-level
Continuum Models
Finite Element Methods
Boundary Element Methods
[60],[21]
[61],[62]
O(n3
)
~
15 % MEMS part-level
Model Order Reduction
Krylov algorithms
Arnoldi algorithms
[23],[63]
[26],[30],[64]
O(n2
)
~
5-10 % MEMS part-level
Mesoscopic
100nm =
l =
1µm
Stochastic Methods
Direct Monte Carlo Methods
Kinetic Monte Carlo
[65]
[66]
1µs =
t =
1ms O(n3
)
~
25% Functional-level
Functional-level
10nm =
l =
100nm
Molecular
Molecular Dynamics [67],[68],[69] 1ns =
t =
1µs O(n4
)
~
20-30 % Functional-level
Tight-Binding Molecular Dynamics [70] Functional-level
Coarse-Grained Molecular Dynamics [71] Functional-level
Stochastic Dynamics [72] Functional-level
Atomistic
1
°A =
l =
1nm Density Functional Theory
Hartree-Fock Approximations
[73]
[70]
1ps =
t =
1ns O(n5
)
~
15 % Atomic-level
Atomic-level
1
°A =
l =
100µm
Multiscale
Continuum/MD coupled models [74] 1ps =
t =
10s O(n5
)
~
5-10 % System-level
FEM/CGMD coupled models [71] MEMS part-level
FEM/MC coupled models [75][76],[77] System-level
continuum/MD/QM coupled models [78],[79] System-level
. where n is the number of features in environment.
multiscale finite element modeling (FEM) approach coupled
to Monte-Carlo (MC) analysis for MEMS failure prediction.
In a same way, a predictive-science-based multiscale modeling
and simulation platform is proposed in [81] to predict material
performance issues, such as radiation, thermo-mechanical
cycling and damage and fracture due to shocks. The
computational coupling of the atomic-scale description of
nanomaterials (Molecular Dynamics (MD) simulation) to
microscale actuators designs (traditional FDM/FEM) pose
severe challenges. MD simulation cannot simulate the whole
systems due to its prohibitive computational cost, whereas
continuum FEM/BEM scales poorly with system size and
only approximately account for effects at material interfaces.
To remedy these inadequacies, several authors coupled
FDM/BEM simulations to MD models whose underlying
physics are derived from nanomechanics theory [53], [82],
[83], nanoelectronic structure theory [62], nanofluidics theory
[84], and molecular biology [77]. In overall, the sequential
multiscale model showed good qualitative agreement with the
experimental measurements but requires more refinement to
achieve good quantitative agreement.
(ii) Concurrent multiscale simulations
The concurrent multiscale approach attempt to link methods
appropriate at each scale together in a combined model,
where the different scales of the system are considered
concurrently and communicate with a hand-shake procedure.
The literature contains numerous methods of concurrent
coupling; (i) the combined finite element atomistic method
(FEAt), (ii) the material point method (MPM), (iii) the local
quasicontinuum method (QC), (iv) the bridging scale method,
(v) the atomic-scale finite element method (AFEM), and (vi)
coarse grained molecular dynamics (CGMD) [85], [43].
Molecular dynamics simulations are commonly used to
investigate size-dependence of the elastic properties of the
nano-scale silicon cantilevers [69]. It reveals that continuum
mechanics modeling can still be used on nanoscale structures
provided that the dependence of elastic constants on
dimensional scaling is accounted for. At a larger scale
Coarse-Grained Molecular Dynamics (CGMD) modeling
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have been developed [71] to describe the behavior of the
mechanical components of MEMS down to the atomic scale.
It builds a generalized finite element formalism from the
underlying atomistic physics in order to ensure a smooth
coupling between regions governed by different length scales.
Various electrostatic models namely: the classical conductor
model [74], the semiclassical model [86], and the quantum-
mechanical model [53], are being used for electrostatic
analysis of NEMS at various length scales. The design
methodology facilitates, under restricted conditions, the
insertion of quantum corrections to nano-scale device models,
during simulation. In the case of NEMS-based electrostatic
actuation, Figure 4 shows the evolution of modeling theory
w.r.t. device length scale : from classical continuum models to
atomistic quantum mechanical models. In [38], a multiscale
method, seamlessly combining semiclassical, effective-mass
Schro¨dinger, and Tight-Binding Theories (TBT), is proposed
for electrostatic analysis of silicon nanoelectromechanical
systems. In [87], an integrated modeling methodology
for nano-scale electronic devices has been proposed. This
methodology includes domain-oriented approximations from
ab-initio modeling and the selection of quantum mechanical
compact models that can be integrated with basic electronic
circuit or non-electronic lumped-element models.
Finally, molecular dynamics (MD) and ab-initio quantum
mechanics(QM) coupled to virtual reality (VR) techniques
have been developed in [78], [79] for the prototyping of
biological NEMS. The operator can design and characterize
through molecular dynamics simulation, the behavior of
bio-nanorobotic components and structures through 3-D
visualization. In these works, the nonlinear continuum
elastic theory, with material properties extracted from
MD simulations, is combined with either the classical,
semiclassical, or the quantum-mechanical electrostatic model
and the continuum theory for the van der Waals energy
domain to compute the self-consistent electromechanical
behavior of biological NEMS.
From the point of view of control, the concurrent
coupling between the mechanical and the electrical energy
domains at nanoscale necessitates a proper understanding of
relevant physical theories for NEMS feedback control [88].
Actually, carbon nanotube-based NEMS devices (nanoswitch,
nanotweezers) are actuated using analytical energy-based
methods modeling (electrical capacitance model including
van der Waals forces as well as finite kinematics) to predict
the structural behavior and instability of the on/off states of
the nanoswitch, or the open/close states of nanotweezers [61].
Recently, the influence of control parameters on the stationary
oscillations of carbon nanotube-based oscillators via molecular
dynamics simulations have been conducted [67]. The control
of oscillator motion in the case of considerable fluctuations
through the control force has been rendered possible. The
methodologies reported here are completely general and as
such are expected to be useful in the optimal control of
nanotube-based NEMS devices. Table 1 gives a comparison
-PART C: APPLICATIONS AND REVIEWS
table of various modeling technologies with its pros and
cons. Methods used for simulation of several properties of
MEMS/NEMS differ in their level of accuracy and in the
computation time necessary to perform such calculations.
Accordingly, the time scales that each of these methods
can handle can be from a single total energy for the most
accurate calculations, to picoseconds for ab-initio molecular
dynamics simulations, and up to seconds for classical physics.
B. Modeling for MEMS/NEMS control
1) Physical Models: Physicals methods for determining
lumped dynamical models of thermal, piezoelectric, magnetic
or electrostatic MEMS and NEMS devices for purposes of
feedback control have been studied extensively in literature.
Current modeling works are mainly focused on the
empirical responses of the system dynamics, black-box
models, as a practical model for real-time control, but
offer minimal insight into the governing equations. System
identification based on measured sets of input and output
data obtained from exciting the system with pseudo random
binary data (PRBS) gives a good fit to the measured data.
The MEMS dynamics are dominated mainly by the first mode
which can be accurately modeled by a mass-spring-damper
second order-model, e.g. piezoelectric MEMS scanner [47],
polymer MEMS actuators [50], piezoelectric microrobot-onchip
[89] and electrostatic MEMS vibrational gyroscope [90].
However, when the number of parameters grows, it becomes
more difficult to span the complete parameter space, since
each parameter lets the number of possible variations grow in
an exponential way. As example, the fast dynamics of MEMS
systems require higher-order models leading to complicated
model-based controllers.
As a more detailed approach, the gray-box models are
developed for determining lumped dynamical models of
MEMS devices, [54], [55], [56], for purposes of feedback
control. A model consisting of millions of equations (e.g.,
a FEM model) is surely more difficult to handle and takes
more time to solve than an analytic expression based on a
simplified gray-box model. In [91], the authors determined a
dynamical state-space model for control of thermal MEMS
devices. The importance of temperature-dependent parameters
was emphasized for dynamical modeling for purposes
of feedback control. In [58], a computationally efficient
model was developed for investigating the dynamics of
the voltage-driven MEMS device embedded in a dielectric
fluid. However, these models were partly based on physical
principles while also relying on empirical results to define
complex physical processes. Due to the compact layout [92],
manufacturing tolerance [93], modeling errors [22], and
environmental changes (e.g., adhesive surface interactions,
and scale dependent material and thermal properties) [94],
MEMS devices are subjected to parasitics and parameter
variations. In order to better guarantee their stability and
a certain level of performance, one must take into account
SUBMITTED TO IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS
these factors in the design of MEMS control systems.
In the most complex form, white-box models with partial
differential equations (PDEs), e.g., [57],[58], attempt to
explain the underlying physics for the sensing and actuation
responses of MEMS and NEMS. Nonlinear models based
on finite-difference discretization of MEMS structures, e.g.
lateral electrostatically-actuated DC-contact MEMS [55],
and applying boundary conditions have been recently solved
using a Gauss-Seidel relaxation iteration scheme. More
efficiently and equally accurate during circuit simulation than
PDEs, Volterra-series-based modeling describes the frequency
dependence (e.g., the mechanical resonance) in combination
with the nonlinear behavior of the MEMS variable capacitor
[95]. As the complexity of such models involves model
reduction techniques, there is always a tradeoff between
accuracy of the model or possible range of application.
2) Advanced Modeling Algorithms: Recently, black-box
advanced modeling algorithms of non-electronic parts has been
introduced in MEMS modeling, so enabling radically faster
simulation without concurrent algorithms and parallel computation,
e.g. artificial neural networks (ANN), genetic algorithm
(GA) optimization, model prediction (MP), or fuzzy logic
algorithms (FL). In [96], a lumped model of the capacitive
transducer, being the part of a MEMS capacitive pressure
sensing system, was created using an ANN. The ANNs here
are considered universal approximators, convenient for black-
box device modeling. A general approach was formulated
in [31] for updating the parameters of systems governed by
multiphysics equations using an optimization technique based
on Genetic Algorithms (GAs). This approach was demonstrated
on a MEMS micromirror which was governed by
both structural and electrostatic physics. For systems with fast
dynamics such as those in MEMS, a hardware embedded real-
time implementation of model predictive control (MPC) has
been investigated in [97]. The results show that MPC would be
an appropriate controller implementation since the size and the
application precludes the use of a dedicated computer. Finally,
a method for reliability prediction was presented in [98], based
on a combined fuzzy-logic and physics-of-failure approach.
The specific case of a MEMS Fabry-Perot interferometer
was analyzed and the failure rate estimations are discussed.
Similar fuzzy logic control algorithms have been applied to
optimally charge the microbattery of on-board MEMS sensors
[99]. Recent manufacturing advances have opened the path
for the fabrication of micromechanical devices and electronic
subsystems under the same manufacturing and packaging
process, thereby opening the path for the use of advanced
modeling algorithms towards systems-on-chip applications.
III. CONTROL SCHEMES
Presence of sensor dynamics, fast high-frequency system
dynamics and extremely sensitive system parameters make
the control of MEMS devices a complex task. Over the
years, researchers all over the world have come up with
feasible control algorithms for MEMS devices. Based on
-PART C: APPLICATIONS AND REVIEWS
these results, control techniques for MEMS can be grouped
under three broad classes viz: open-loop control, open-loop
control with input pre-shaping and closed-loop control [100],
see Fig. 5. The choice of the control technique depends on
various factors such as application, needed electronic circuitry,
device dynamics, space constraints and sensor availability /
implementation. The following sub-sections will review the
various control strategies mentioned earlier. The section will
close with a review of the on-chip control strategies that have
been implemented by researchers so far, (Subsection III-D).
A. Open-loop control
During the infancy stages of MEMS technology, most
MEMS devices were controlled in open-loop by applying very
simple control inputs. This was mainly due to the relatively
high speed of actuation as well as the inability of the then
existing sensor technologies to procure noise-free sensory
information that was unbiased by the sensor dynamics.
Though advances in sensor and actuator technologies have
further pushed the boundaries of accurate sensing at the
micro-and nano-levels, successful integration of these
sensors in MEMS remains an ongoing challenge, [20]. Recent
advances have resulted in improving the traditional MEMS
designs to achieve better dynamic performance under open-
loop actuations, [28]. Open-loop control for large deflection
electrostatic actuators was reported in [17]. In this paper
the authors incorporated significant design improvements
to the existing comb-drives designs [101], [102]. These
improvements included reducing the actuator area by half,
redesigning comb-teeth and suspensions to reduce side
instability and using a launch and capture actuation scheme.
MEMS deformable mirrors have been popularly controlled in
open-loop, [103]. The open-loop scheme delivered accurate
tracking to within 3% error.
Wavelength-division multiplexed (WDM) routers with
analog micromirror arrays were shown to operate in open-
loop with excellent repeatability and stability, [104]. High
repeatability and long-term stability of a MEMS wavelength
selector switch in open-loop operation was demonstrated in
[105], though it lacked a 100% add / drop functionality. A
low-drift micromirror in open-loop control was demonstrated
in [106]. Open-loop control of a MEMS deformable mirror
using a nonlinearly constrained quadratic optimization
approach has also shown improvements in performance,
[107]. In this case, with an a-priori knowledge about the
aberrations in the target waveform, a quasi-steady state
control was obtained. Though simulated results reportedly
showed improved performance, practical implementation
of this rigorously mathematical technique may be quite
challenging. Recently, MEMS actuator designs are being
modified to give better open-loop performance, [108]. An
improved modeling technique that resulted in open-loop
control of a tunneling accelerometer for very high resolution
acceleration measurement was reported in [109]. In this
case the tunnelling accelerometer was modeled based on a
clamped micro-circular plate with a tunneling tip and the
SUBMITTED TO IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS -PART C: APPLICATIONS AND REVIEWS 8
(a) (b) (c)
Fig. 5. (a) Scanning electron micrograph of a MEMS variable optical attenuator (VOA). (b) Schematic of the experimental setup of the VOA(c) Open-loop
step responses for the simulated model and the actual VOA, (Courtesy of [100]).
classic Kirchhoff thin plate theory was used for deriving the
governing equations. A new hardware platform for tuning
a MEMS based gyroscope in open-loop by measuring the
frequency response of the device was reported in [110]. These
platforms tuned the gyroscopes based on an evolutionary
computational technique that improved the sensitivity of
the gyroscopes and also enabled closed-loop operation.
An open-loop technique to address the Sagnac effect in a
fiber-optic gyroscope based on MEMS/NEMS fabrication has
also been proposed in [25].
As problems such as inherent system nonlinearity, induced
vibrations and effects such as stiction and friction cannot be
completely addressed using open-loop control in many MEMS
devices, input pre-shaping was seen as the next logical s tep
in MEMS control.
B. Open-loop control with input pre-shaping
This technique relies on the fact that the static and dynamic
behavior of many MEMS devices can be accurately modeled
and in most cases, linearized. In this technique, the input
signals are made more complex by shaping them in a way
such that the adverse effects of the system dynamics are
minimized (Ex: Bandlimiting the trajectory signal such that
the natural frequencies/system resonances were not excited),
[112]. For input pre-shaping, an accurate dynamic model of
the system is of paramount importance, if any performance
improvement is expected. An open-loop method that predicted
control voltages generating prescribed surface shapes on a
MEMS deformable mirror was given in [113]. In this work,
an analytic elastic model was used for the mirror membrane
and an empirical electromechanical model was used for the
actuator dynamics. Open-loop control with input pre-shaping
has also been applied to control oscillations of MEMS based
gyroscopes. For accurate angular rate measurement, the drive
mode oscillation amplitude of the second mass has to be
kept constant. By approximating the gyroscope by a lumped
mass-spring-damper model and applying pre-computed
actuation voltages, the oscillation amplitude can be kept
constant as shown in , [114]. For MEMS devices that involve
multiple moving parts, such as MEMS mirror arrays, a
feed-forward based control has been patented, [115]. This
patent was specific to MEMS based, optical mirror arrays
where motion of an active mirror has an aerodynamically
disturbing effect on the neighboring static mirrors in the
array. In this technique, feed-forward control signals with a
normalized profile that minimized the aerodynamic coupling
between the static mirrors were employed to cancel the
induced disturbances. Feedforward control of a MEMS
optical switch was reported in [116]. In this implementation,
feed-forward was used to force the switch to reach the desired
position in a fast and accurate manner with minimal overshoot.
Very recently, a patent was awarded for a input shaping
actuation technique for MEMS devices, [117]. In this
patent, a filtered voltage signal shaping technique has been
demonstrated. This scheme is mainly useful in conjunction
with MEMS devices that have micro-cantilevers and other
vibrating elements whose natural resonances are minimally
damped. The patent is based on results obtained in [118] by
actuating a two-axis gimbal-less scanner using the open-loop
with input pre-shaping technique presented in the patent.
Filtering the input voltages may not always be feasible
as it adds to either the system or the computation cost.
Additionally, sub-optimal filtering may lead to unachievable
slew-rates and supply saturation. It is the property of
electrostatic MEMS actuators to generate a residual charge in
their insulating layer that results in sticking of the electrode
and increases response time. To prevent this sticking of
electrostatic MEMS actuators and generate fast actuator
response, an input pre-shaping technique was described
in [119]. The patented technique of Input Shaping was
demonstrated to potentially nullify unwanted vibrations in
MEMS devices such as MEMS optical switches in [120]. A
similar technique was used in [111], to drive a micromirror
to a desired tilt angle without residual vibrations, see Figure
6. The key advance in this input shaping technique was
the inclusion of nonlinear system behavior, thus making it
suitable for application in conjunction with a wide range of
MEMS systems.
The inherent reliance of the input-preshaping technique on
an accurate system model as well as a-priori information of
the system behaviour limits the adaptability and robustness that
SUBMITTED TO IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS -PART C: APPLICATIONS AND REVIEWS 9
(a) (b)
Fig. 6. (a) Controlled (using shaped signal -solid) and uncontrolled (using standard step commands -dashed) tilt angle responses achieved by the MEMS
micromirror. (b) Corresponding shaped (solid) and unshaped (dashed) actuation voltage signals, (Courtesy of [111]).
can be built into this particular control technique. Finally, the
combined advances in MEMS technology, sensor and actuator
designs, system analysis tools and the ever-present demand
to push the boundaries of performance in terms of speed,
reliability and accuracy have led to the MEMS system be
controlled by employing complicated closed-loop strategies
[121].
C. Closed-loop control
Standard control techniques such as PID have been
implemented on MEMS devices manufactured in bulk,
such as MEMS based sensors and switches [123], [124],
see Fig. 7. MEMS based sensors have been using closed-
loop control for quite some time. Hitachi demonstrated a
MEMS based closed-loop silicon accelerometer more than
a decade ago, [125]. Closed-loop control was used for a
MEMS micro-cantilever based pressure sensor [126]. In
this application, an electromagnetic beam integrated onto
a standard silicon pressure sensor diaphragm was driven
to resonance using closed-loop control. As the diaphragm
deflects under pressure, the stress in the beam caused a
change in its resonant frequency. This change was found
to be a highly sensitive measure of pressure [127]. This
device offered wide dynamic range, high sensitivity, and
high stability. It was also easy to be interfaced with digital
compensation circuitry. Another successful application of
closed-loop control in MEMS was reported in [128], where
improving measurement accuracy was the main objective.
Feedback control has been employed to accurately regulate
the gap distance in an electrostatic MEMS based Fabry-Perot
interferometer, [129]. In this implementation, a feedback
circuit capable of sensing the property of the active device
and providing an electrical stop when the minimum separation
distance was achieved was integrated.
Closed-loop feedback control has been a common strategy
to correct for machining imperfections in MEMS based
gyroscopes. [130], [114] proposed active nonlinear and
adaptive drive control approaches to compensate for errors
due to device imperfections. Closed-loop tuning of a MEMS
based gyroscope was reported in [110]. A custom-built
integrated circuit that manages the signal filtering and
provides real-time control for the JPL-Boeing manufactured
MEMS based gyroscopes was reported in [131]. This
technique used an ASIC that enabled the gyroscope to reject
vibration disturbances and damped the transfer function
by almost 40 dB. A US Patent for an application specific
integrated circuit capable of exciting a selected gyroscope
mode, induce damping and demodulate the signal containing
the angular rate information to in-phase and quadrature
components was issued, [132]. This circuit featured attractive
properties such as low power consumption as well as
ease of sensor integration. A dual-stage control algorithm
that provided on-site identification of imperfections based
on the dynamic response of the device and compensated
for it using nonlinear electrostatic parallel plate actuators
was proposed in [133]. In this paper, the authors first
showed that using feedback alone to compensate for large
structural imperfections (to the tune of 10%) would seriously
compromise the device performance. Consequently they
successfully employed a feedforward control loop to reduce
large imperfections and combined it with a feedback loop to
compensate for the device non-idealities and perturbations.
[134] presented a novel architecture for the digital control of
MEMS based gyros.
Digital control was also proposed for performance
optimization of a MEMS based gyroscope, [135]. In general
digital control was shown to offer more flexibility in terms
of algorithms as well as control parameters. FPGAs used in
these implementations significantly speed up the development
process due to their ease of programmability. Cross-coupling
SUBMITTED TO IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS -PART C: APPLICATIONS AND REVIEWS 10
(a)
(b)
Fig. 7. (a) Implementation of the Adaptive Gain Control (AGC) feedback system for the velocity-controlled gyroscope. (b) Magnitude of the driving signal
and envelope of the associated velocity signal under various vacuum conditions for open-loop (a) and closed-loop (b), (Courtesy of [122]).
and fabrication imperfections are the major performance
limiting factors in MEMS based gyroscopes. Adaptive control
based on velocity estimation has shown promise in alleviating
these problems and improve the overall performance of
the gyroscope by achieving larger operational bandwidth,
eliminating zero-rate output, enabling self-calibration and
deeming the gyroscope highly robust to parameter variations,
[136], [137]. To further improve the gyroscope performance
by accurately estimating the unknown angular velocity, sliding
mode control has also been formulated, [138], [139]. These
investigations proved that though computationally intensive,
both these nonlinear approaches could significantly enhance
the device performance. Furthermore, they also showed that
sliding mode control of the vibrating proof mass resulted in a
better estimate of the unknown angular velocity than that of
the model reference adaptive feedback controller. An active
disturbance rejection control scheme was proposed recently
to address issues such as mechanical-thermal noise, parameter
variations, quadrature errors and the mismatch of natural
frequencies between two axes, [140]. In this work the two
main control problems addressed were the vibrating modes
of the gyroscope axes and the time-varying rotation rate
estimation. These major issues can also be alleviated using
an adaptive control method based on Lyapunov functions,
as demonstrated in [141]. A discrete time observer-based
adaptive control algorithm for improved angular rate estimates
has also been reported, [142].
Performance enhancement for a probe-based data storage
system was reported in [143]. In this paper, the authors
proposed a position control system that resulted in accurate
positioning of the micro-cantilever probe over a particular
SUBMITTED TO IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS
sector of the data storage disc. Positive Position Feedback
(PPF) control was implemented successfully to provide
active damping to a piezoelectric MEMS acoustic sensor,
[144]. A detailed comparison between open-and closed-
loop control of a MEMS electrostatic comb drive was
given in [39]. Model Reference Adaptive Control (MRAC)
technique was formulated for tracking control of MEMS
based comb resonators, [145]. A real-time implementation
of this technique demonstrated its ability to handle multiple
uncertainties in device parameters that occur due to machining
imperfections. In [116], a linear feedback controller was used
to shape the system dynamics in a MEMS optical switch,
resulting in fast switching operation. Nonlinear control was
also used in manipulating MEMS based mirrors for high
tilt and pointing accuracy, [146]. In this application, digital
implementation of a full-state feedback was carried out
resulting in a substantial increase in the mirror’s angular
operation range and a reduction in the long-term angular
noise. Nonlinear sliding mode control applied to controlling
the position of a lateral comb resonator has been simulated
in [147]. A cooperative angle control scheme to reduce
the output stable control time in MEMS optical switches
was proposed in [148]. In one of the novel applications,
feedback control has been employed to provide accurate
input gains and implement signal up-modulation to a MEMS
based high-performance operational amplifier, [149]. In this
application, the input stage of the operational amplifier is a
MEMS based variable capacitor that converts low-frequency
input voltages to high-frequency AC currents, resulting in
reduced offsets and low-frequency noise.
In most closed-loop control of MEMS devices, the control
loop was implemented using external circuitry and computing
facilities. In many cases, even the sensors were independent
and not an integral part of the MEMS device. With improved
fabrication methods, component densities on a chip have
increased drastically and on-board sensors and power sources
have become the norm, [150], [151], [152], [153]. Thus, the
system-on-chip concept with on-chip control is now gaining
popularity [154], [155], [156], see Fig. 8.
D. On-chip feedback control: The current trend
The main advantages of on-chip feedback are: (1) improved
linearity, (2) improved signal-to-noise ratio and (3) improved
accuracy due to ease of compensation for interferences and
system dynamics. The vast improvements in MEMS design
and fabrication have led to real-time on-chip feedback control
being the current thrust of many research endeavors. One
of the main control issues for on-chip applications is power
generation. Electrical power is needed for the actuation
as well as sensory systems used in MEMS. Generating
this power efficiently with-in the given space constraints
of a MEMS chip, without causing insulation, dynamics or
interference problems is a major concern and research focus.
Some on-chip power sources have been reported and various
power generation schemes are being investigated, [152], [151].
-PART C: APPLICATIONS AND REVIEWS
Microfluidics is an area where real-time feedback has been
applied successfully, [158]. Precise handling of microfluidics
in continuous-flow by using a flow sensor to monitor and
on-chip pumps with feedback control to regulate the flowrate
was presented in [159]. Electrowetting-on-dielectric (EWOD)
has been proposed as a method of actuation for on-chip
droplet generation [160]. Due to its compatibility with
miniaturization, simple device configuration and fabrication,
capacity to generate large forces at microscale, and low
energy consumption EWOD has gained popularity in
microfluidic applications. To monitor droplet volume and
control applied voltages for on-chip droplet generation of
constant volumes real-time feedback control was necessary.
A successful feedback strategy that resulted in automated
volume-controlled on-chip generation of droplets was
reported in [161]. To account for the uncertainties during
droplet separation, an improved feedback scheme was
proposed in [162]. In this work the authors combined voltage
modulation, capacitance sensing and a discrete-time PID
controller to obtain significant improvements in droplet
volume uniformity when compared to open-loop and standard
closed-loop techniques.
On-chip control has also been applied to MEMS based
high-speed synchronous micromotors, [22]. In this work, on-
chip VLSI drivers are used for various signal processing,
filtering, computing, interfacing and amplification tasks and
the control of micromotors is achieved by applying the proper
phase voltages to the micromotor windings. The control
technique also incorporates robust tracking and disturbance
rejection. Nonlinear control of electrostatic MEMS using a
novel integrated charge and position sensor was reported in
[163]. This technique resulted in full gap operation and improved
transient performance. The control technique showed
on-chip implementation potential as it could use the local
integrated circuit components and the required sensor was
easy to fabricate, did not increase device footprint and had
negligible effect on the device dynamics. The design of an
on-chip CMOS potentiostat was reported recently, [164]. The
potentiostat was mainly developed for controlling the volume
of conjugated polymer film used in microactuators. This on-
chip mechanism was proposed for controlling microactuators
used in cell capture microsystems. With the advent of nanoelectromechanical
systems (NEMS), on-chip sensor technologies
are being revolutionized, [157], [165]. As a result, the
future is bright and holds exciting prospects for on-chip control
of MEMS devices.
IV. CONCLUDING REMARKS
This review tries to be complete in its general scope by
reviewing most major works in the field of MEMS/NEMS
modeling and control. Though it is impossible (due to the size
and time constraints) to list every single work in a review of
such a varied and dynamic field, the authors believe that this
paper provides the reader with the most up-to-date information
about the various advances that have taken place over the
years in the modeling and control of MEMS/NEMS devices.
SUBMITTED TO IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS -PART C: APPLICATIONS AND REVIEWS
12
(a)
(b) (c)
Fig. 8. (a) Scanning electron micrograph of the released microgrippers showing the on-chip photo detectors placed beneath the gripping sites. (b) Schematic
of the photo-detector circuit (c) Gripping of a polystyrene bead: before (top); after (bottom), (Courtesy of [157]).
With the advent of better, faster computing hardware and
dedicated software, it will be prudent to say that the field
of MEMS/NEMS is bound to see an even greater influx of
academic and industrial interest. As the fields of physics,
chemistry, biology and mathematics evolve and fuse together,
more realistic models that capture the behavior of these micro-
scale systems most accurately should be a key result. This, in
turn, will combine with the fast developments occurring in
the areas of very high device density chip fabrications and
flexible electronics to produce control techniques that will
make the desirable performance of a MEMS/NEMS device
easily realizable, robust and adaptive.
ACKNOWLEDGMENTS
This work was supported by the French National Agency
(ANR) PIANHO project ”Innovative Haptic Instrumental Platform
for 3D Nanomanipulation” under the project number
ANR-09-NANO-042-02.
REFERENCES
[1]
R. Feynman, “There’s plenty of room at the bottom,” Lecture at the
American Physical Society meeting at Caltech on 29th
December,
1959.
[2]
N. Taniguchi, “On the basic concept of ‘nano-technology’,” Proceedings
of the International Conference of Production Engineering
(Japan Society of Precision Engineering and International Institution
for Production Engineering Research), Tokyo Part II, 1974.
[3]
H. W. Kroto, J. R. Heath, S. C. O’Brien, R. F. Curl, and R. E. Smalley,
“C60
: Buckminsterfullerene,” Nature, vol. 318, pp. 162 – 163, 1985.
[4]
G. Binnig and H. Rohrer, “Scanning tunneling microscopy,” IBM
Journal of Research and Development, vol. 30, no. 4, 1986.
[5]
K. E. Drexler, Engines of Creation: The Coming Era of Nanotechnology.
Doubleday, 1986.
[6]
H. C. Nathanson, W. E. Newell, R. A. Wickstrom, and J. R. Davis, “The
resonant gate transistor,” IEEE Transactions on Electronic Devices,
vol. 14, pp. 117 – 133, 1967.
[7]
X. Chen, D. F. Cui, C. C. Liua, and H. Lia, “Microfluidic chip for blood
cell separation and collection based on crossflow filtration,” Sensors
and Actuators B: Chemical, vol. 130, pp. 216 – 221, 2008.
[8]
M. Despont, J. Brugger, U. Drechsler, U. Drig, W. Hberle,
M.
Lutwyche, H. Rothuizen, R. Stutz, R. Widmer, G. Binnig,
H. Rohrer, and P. Vettiger, “VLSI-NEMS chip for parallel afm data
storage,” Sensors and Actuators A: Physical, vol. 80 (2), pp. 100 –
107, 2000.
[9]
H. Yamaguchi, S. Miyashita, and Y. Hirayama, “InAs/AlGaSb heterostructure
displacement sensors for MEMS/NEMS applications,”
Proceedings of the International Conference on Molecular Beam
Epitaxy, vol. 251, pp. 556 – 559, 2003.
[10]
V. Gayathri and R. Geetha, “Carbon nanotube as NEMS sensor effect
of chirality and stone-wales defect intend,” Journal of Physics:
Conference Series, vol. 34, pp. 824 – 828, 2006.
[11]
X. Li, J. Han, H. Bao, and Z. Yang, “Integrated spm probes with NEMS
technology,” Sensors and Actuators A: Physical, vol. 133 (2), pp. 383
– 387, 2007.
[12]
J.-H. Park, C.-H. Lee, Y.-H. Park, Y.-D. Kim, C.-H. Ji, J. Bu, and H.-J.
Nam, “A fully wafer-level packaged RF MEMS switch with low actuation
voltage using a piezoelectric actuator,” Journal of Micromechanics
and Microengineering, vol. 16, pp. 2281 – 2286, 2006.
[13]
I. Kanno, S. Tsuda, and H. Kotera, “High-density piezoelectric actuator
array for MEMS deformable mirrors composed of PZT thin
films,” IEEE/LEOS International Conference on Optical MEMs and
Nanophotonics, (Freiburg, Germany) 11 -14 August, pp. 132 – 133,
2008.
[14]
P. Srinivasan and M. S. Spearing, “Optimal materials selection for
bimaterial piezoelectric microactuators,” IEEE Journal of Microelectromechanical
Systems, vol. 17, no. 2, pp. 462 – 472, April 2008.
[15]
S. Heo and Y. Y. Kim, “Optimal design and fabrication of MEMS rotary
thermal actuators,” Journal of Micromechanics and Microengineering,
vol. 17, pp. 2241 – 2247, 2007.
[16]
G.-K. Lau, J. F. L. Goosen, F. van Keulen, T. C. Duc, and P. M. Sarro,
“Polymeric thermal microactuator with embedded silicon skeleton:
Part idesign and analysis,” IEEE Journal of Microelectromechanical
Systems, vol. 17, no. 4, pp. 809 – 822, August 2008.
[17]
J. D. Grade, H. Jerman, and T. W. Kenny, “Design of large deflection
electrostatic actuators,” IEEE Journal of Microelectromechanical
Systems, vol. 12, pp. 335 – 343, 2003.
[18]
R. Kornbluh, “Dielectric elastomer actuators: muscles that morph,”
ICASE Lecture Series on Morphing (Hampton, VA: NASA Langley
Research Center), 2001.
[19]
T. Tanaka, K. Saeki, and K. Matsuki, “Dynamic characteristics of high
field electro-active silicone and acrylic elastomer actuator devices,”
IEEE Conference on Electrical Insulation and Dielectric Phenomena,
(Kansas City, Missouri, USA), 15 -18 October,, pp. 202 – 205, 2006.
[20]
D. J. Bell, T. J. Lu, N. A. Fleck, and S. M. Spearing, “Mems
actuators and sensors: observations on their performance and selection
SUBMITTED TO IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS -PART C: APPLICATIONS AND REVIEWS
13
for purpose,” Journal of Micromechanics and Microengineering, vol.
15 (2), pp. S152 – S164, 2005.
[21]
V. Kolchuzhin, J. E. Mehner, T. Gessner, and W. Doetzel, “Parametric
finite element analysis for reduced order modeling of MEMS,”
International Conference on Thermal, Mechanical and Multi-Physics
Simulation and Experiments in Microelectronics and Micro-Systems,
(20-23 April), pp. 1 – 6, 2008.
[22]
S. E. Lyshevski, “Modeling and control of MEMS with high speed
synchronous micromotors and controllers/drivers-on-VLSI-chip ICs,”
Energy Conversion and Management, vol. 44, pp. 667 – 679, 2003.
[23]
S. De and N. Aluru, “Physical and reduced-order dynamic analysis of
MEMS,” International Conference on Computer Aided Design, 9-13
Nov. 2003, pp. 270 – 273, 2003.
[24]
G. Wu, A. R. Mirza, S. K. Gamage, L. Ukrainczyk, N. Shashidhar,
G. Wruck, and M. Ruda, “Design and use of compact lensed fibers
for low cost packaging of optical MEMS components,” J. of Micromechanics
and Microengineering, vol. 14, pp. 1367 – 1375, 2004.
[25]
B. Zhang and M. T. E. Kahn, “Overview and improving fiber optic
gyroscope based on MEMS/NEMS fabrication,” Proceedings of the
International MEMS Conference, vol. 34, pp. 148 – 154, 2006.
[26]
T. Bechtold, E. B. Rudnyi, and J. G. Korvink, “Automatic order
reduction of thermo-electric models for mems: Arnoldi versus guyan,”
Fourth International Conference onAdvanced Semiconductor Devices
and Microsystems, (14-16 October), vol. 3, pp. 333 – 336, 2002.
[27]
J. S. Han, E. B. Rudnyi, and J. G. Korvnik, “Efficient optimization
of transient dynamic problems in MEMS devices using model order
reduction,” Journal of Dynamic Systems, Measurement and Control,
vol. 15, pp. 822 – 832, 2005.
[28]
C. Chen and C. Lee, “Design and modeling for comb drive actuator
with enlarged static displacement,” Sensors and Actuators A, vol. 115,
pp. 530 – 539, 2004.
[29]
W. Zhang, G. Meng, and H. Li, “Adaptive vibration control of micro-
cantilever beam with piezoelectric actuator in MEMS,” International
Journal of Advanced Manufacturing Technology, vol. 28, pp. 321 –
327, 2006.
[30]
J. Chen, S.-M. Kang, J. Zou, C. Liu, and J. E. Schutt-Aine, “Reducedorder
modeling of weakly nonlinear MEMS devices with taylor-series
expansion and arnoldi approach,” IEEE Journal of Microelectromechanical
Systems, vol. 13, no. 3, pp. 441 – 451, June 2004.
[31]
X. Lin, Z. Chen, and J. Ying, “Macromodeling of the electrostatically
actuated rectangle plate based on modal projection,” International
Conference on Mechatronics and Automation, (5-8 August), pp. 2741
– 2746, 2007.
[32]
G. K. Fedder, “Issues in MEMS macromodeling,” 2003 International
Workshop on Behavioral Modeling and Simulation, (7-8 October), pp.
64 – 69, 2003.
[33]
J. Mehner, J. Wibbeler, and F. Bennini, “Ansys multiphysics capabilities
for mems modeling and simulation -part 3: Exporting macromodels for
circuit and system simulation tools.” ANSYS Solutions, www.ansys.com,
2008.
[34]
G. Lorenz, A. Morris, and I. Lakkis, “A top-down design flow for
moems,” IEEE International Conference on Micro-Electo-Mechanical
Systems, Jan. 20-24, 2002, Las Vegas, NV, USA, pp. 204 – 209, 2002.
[35]
——, “A top-down design flow for MOEMS,” SPIE -4408, Design,
Test, Integration, and Packaging of MEMS/MOEMS, (25-27 April)
Cannes, France, pp. 126 – 137, 2001.
[36]
G. K. Fedder and Q. Jing, “A hierarchical circuit-level design methodology
for microelectromechanical systems,” IEEE Transactions on
Circuits and Systems-II, vol. 46, no. 10, pp. 1309 – 1315, September
1999.
[37]
W. Xue, J. Wang, and T. Cui, “Modeling and design of polymer-
based tunneling accelerometers by ANSYS/MATLAB,” IEEE/ASME
Transactions on Mechatronics, vol. 10, no. 2, pp. 468 – 472, April
2005.
[38]
Y. Xu and N. Aluru, “Multiscale electrostatic analysis of silicon nems
via heterogeneous quantum models,” Physical Review B,, vol. 77 (7),
p. 075313, 2008.
[39]
B. Borovic, C. Hong, X. M. Zhang, A. Q. Liu, and F. L. Lewis,
“Open vs. closed-loop control of the MEMS electrostatic comb drive,”
Proceesings of the 13th
Mediterranean Conference on Control and
Automation, 2005.
[40]
F. Shi, P. Ramesh, and S. Mukherjee, “Dynamic analysis of microelectro-
mechanical systems,” International Journal for Numerical
Methods in Engineering, vol. 39, no. 24, pp. 4119 – 4139, December
1998.
[41]
Y.-J. Yang, S.-Y. Cheng, and K.-Y. Shen, “Macromodeling of coupled-
domain MEMS devices with electrostatic and electrothermal effects,”
J. of Micromechanics and Microengineering, vol. 04, pp. 1190 – 1196,
2004.
[42]
J. Xu, W. Yuan, H. Chang, X. Lu, and Y. Yu, “Hybrid macromodels for
modeling and simulation of a z-axis micro accelerometer,” 3rd IEEE
International Conference on Nano/Micro Engineered and Molecular
Systems, (6-9 January), pp. 357 – 361, 2008.
[43]
N. M. Ghoniem, E. N. Busso, N. Kioussis, and H. Huang, “Multiscale
modelling of nanomechanics and micromechanics: an overview,”
Philosophical Magazine, vol. 83, pp. 3475 – 3528, 2003.
[44]
C.-H. Ke, H. D. Espinosa, and N. Pugno, “Numerical analysis of
nanotube based NEMS devices. part ii: Role of finite kinematics,
stretching and charge concentrations.” Journal of Applied Mechanics,
vol. 72, pp. 726 – 731, 2005.
[45]
W. W. Liou, F. Liu, and Y. Fang, “Navier stokes and dsmc simulations
of forced chaotic microflows,” 33rd AIAA Fluid Dynamics Conference
and Exhibit, pp. AIAA 2003–3583, 2003.
[46]
B.-Y. Cao, J. Sun, M. Chen, and Z.-Y. Guo, “Molecular momentum
transport at fluid-solid interfaces in MEMS/NEMS: A review,” International
Journal of Molecular Science, vol. 10, 2009.
[47]
A. Pantazi, A. Sebastian, G. Cherubini, M. Lantz, H. Posidis,
H. Rothuizen, and Eleftheriou, “Control of mems-based scanning
probe data-storage devices,” IEEE Transactions on Control Systems
Technology, vol. 15, no. 5, pp. 824 – 841, September 2007.
[48]
Desquenes, S. V. Rotkin, and N. R. Alaru, Nanotechnology, vol. 13,
no. 6, pp. 120 – 901, December 2002.
[49]
C. Ke, N. Pugno, B. Peng, and H. Espinosa, “Experiments and
modeling of carbon nanotube based nems device,” Journal of the
Mechanics and Physics of Solids, vol. 53, pp. 1314 – 1333, 2005.
[50]
J. W. L. Zhou, H.-Y. Chan, T. K. H. To, K. W. C. Lai, and W. J.
Li, “Polymer MEMS actuators for underwater micromanipulation,”
IEEE/ASME Transactions on Mechatronics, vol. 9, no. 2, pp. 334 –
342, June 2004.
[51]
N. Kacem, S. Hentz, D. Pinto, B. Reig, and V. Nguyen, “Nonlinear
dynamics of nanomechanical beam resonators: improving the performance
of nems-based sensors,” Nanotechnology, vol. 20, p. 275501,
2009.
[52]
C.-H. Ke and H. D. Espinosa, “Numerical analysis of nanotube based
nems devices. part i: Electrostatic charge distribution on multiwalled
nanotubes,” Journal of Applied Mechanics, vol. 72, pp. 721 – 725,
2005.
[53]
Z. Tang, Y. Xu, G. Li, and N. R. Aluru, “Physical models for coupled
electromechanical analysis of silicon nanoelectromechanical systems,”
Journal of Applied Physics, vol. 97, no. 11, pp. 114 304.1 – 114 304.13,
November 2005.
[54]
J. B. Muldavin and G. M. Rebeiz, “Nonlinear electro-mechanical
modeling of MEMS switches,” IEEE MTT-S International Microwave
Symposium Digest, (20-25 May), vol. 3, pp. 2119 – 2122, 2001.
[55]
A. Lazaro, D. Girbau, L. Pradell, and A. Nebot, “Non linear actuation
model for lateral electrostatically-actuated dc-contact rf MEMS series
switches,” Spanish Conference on Electron Devices, Jan. 31-Feb. 2, pp.
181 – 184, 2007.
[56]
A. Boni, G. Fontana, and F. Pianegiani, “Time-domain modeling and
characterization of capacitive mems switches,” IEEE Conference on
Instrumentation and Measurement Technology Conference, 16-19 May
2005, vol. 3, pp. 1808 – 1811, 2005.
[57]
J. A. Pelesko, “Mathematical modeling of electrostatics mems with
tailored dielectric properties,” SIAM Journal of Applied Mathematics,
vol. 62, no. 3, pp. 888 – 908, February 2002.
[58] Q.
Qi, “A computationally efficient model for analyzing an electrostatically
driven MEMS device embedded in a dielectric fluid,” 7th
International Conference on Electronic Packaging Technology, vol. 14,
no. 5, pp. 1 – 9, October 2006.
[59]
G. Schrag and G. Wachutka, “Physically-based modeling of squeeze
film damping by mixed level simulation”,,” Sensors and Actuators A.,
pp. 64 – 69, 2001.
[60]
E. S. Hung and S. D. Senturia, “Dynamical models for microelectromechanical
systems from a few finite-element simulation runs,”
IEEE/ASME Journal of Microelectromechanical Systems, vol. 8, no. 2,
pp. 280 – 289, September 1999.
[61]
C. Ke and H. D. Espinosa, “Nanoelectromechanical systems and modeling,”
Handbook of Theoretical and Computational Nanotechnology,
Vol.1, Chapter 121, pp. 1 – 38, 2005.
[62]
H. Reid and J. White, “Boundary-element modeling of nanoscale
device electrostatics,” In Sensors, Proceedings of the IEEE, vol. 3, pp.
1403 – 1406, 2009.
[63]
V. Kolchuzhin, W. Doetzel, and J. Mehner, “A derivatives-based method
for parameterization of MEMS reduced order models,” International
SUBMITTED TO IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS
Conference on Thermal, Mechanical and Multi-Physics Simulation and
Experiments in Microelectronics and Micro-Systems, 20-23 April 2008,
pp. 1 – 5, 2005.
[64]
M. I. Younis, E. M. Abdel-Rahman, and A. Nayfeh, “A reduced-
order model for electrically actuated microbeam-based MEMS,” IEEE
Journal of Microelectromechanical Systems, vol. 12, no. 5, pp. 672 –
680, October 2003.
[65]
E. S. Piekos and K. S. Breuer, “Numerical modeling of micromechanical
devices using the direct simulation monte carlo method,” Journal
of fluids engineering, vol. 118, pp. 464 – 469, 1996.
[66]
J. Xu, W. Yuan, H. Chang, X. Lu, and Y. Yu, “Octree-search kinetic
monte carlo algorithm for the simulation of complex 3d mems structures,”
Micro Electro Mechanical Systems, 2008. MEMS 2008. IEEE
21st International Conference on, pp. 323 – 326, 2008.
[67]
H. Somada, K. Hirahara, S. Akita, and Y. Nakayama, “A molecular
linear motor consisting of carbon nanotubes,” Nano Letters, vol. 9,
no. 1, pp. 62 – 65, 2009.
[68]
L. Dong, X. Tao, M. Hamdi, L. Zhang, X. Zhang, A. Ferreira, and B. J.
Nelson, “Nanotube boiler: Attogram copper evaporator driven by electric
current, joule heating, charge, and ionization,” IEEE Transactions
on Nanotechnology, vol. 8, pp. 565 – 568, 2009.
[69]
S. P. et al., “Molecular dynamic study on-size dependent elastic
properties of the silicon nanocantilevers,” Thin Solid Films, vol. 492,
pp. 285 – 289, 2005.
[70]
S. Zhang, W. Kim, and R. S. Ruoff, “Atomistic simulations of double-
walled carbon nanotubes as rotational bearings,” Nano Letters, vol. 4,
pp. 293 – 297, 2004.
[71]
R. E. Rudd, “Coarse-grained molecular dynamics and multiscale modeling
of NEMS resonators,” Technical Proceedings of the International
Conference on Computational Nanoscience and Nanotechnology, 2002.
[72]
K. Esfarjani and G. A. Mansoori, “Statistical mechanical modeling
and its application to nanosystems,” Handbook of Theoretical and
Computational Nanotechnology, vol. 1, pp. 1 – 45, 2005.
[73]
S. J. Tans, M. H. Devoret, H. Dal, A. Thess, R. Smalley, L. Geerligs,
and C. Dekker, “Single-wall carbon nanotube as quantum wires,”
Nature, vol. 386, pp. 474 – 477, 1997.
[74]
D. H. S. Maithripala, B. D. Kawade, I. Wickramasinghe, J. M. Berg,
and W. P. Dayawansa, “A passivity-based controller for an electrostatic
MEMS model in the presence of parasitics,” International Conference
on Industrial and Information Systems, (9-11 August), pp. 267 – 272,
2007.
[75]
S. Mariani, A. Ghisi, A. Corigliano, and S. Zerbini, “Modeling impact-
induced failure of polysilicon nems: A multi-scale approach,” Sensors,
vol. 9, pp. 556 – 567;, 2009.
[76]
N. Aluru, “Hierarchical physical models for analysis of electrostatic
nanoelectromechanical systems (nems),” Automatica, vol.
http://nanohub.org/resources/850, 2006.
[77]
M. Bernashi, S. Melchionna, S. Succi, M. Fyta, and E. Kaxiras, “Quantized
current blockade and hydrodynamic correlations in biopolymer
translocation through nanopores: Evidence from multiscale simulations,”
vol. 8, no. 4, pp. 1115 – 1119, 2008.
[78]
M. Hamdi and A. Ferreira, “Dna nanorobotics,” Microelectronics
Journal, vol. 39, pp. 1051 – 1059, 2008.
[79]
——, “Computational design and multiscale modeling of a nanoactuator
using dna actuation,” Nanotechnology, vol. 20, p. 485501, 2009.
[80]
T. Karakasidis and C. Charitidis, “Multiscale modeling in nanomaterials
science,” Materials Science and Engineering C, vol. 27, pp. 1082
– 1089, 2007.
[81]
A. C. T. et al., “Materials integrity in microsystems: a framework for a
petascale predictive-science-based multiscale modeling and simulation
system,” Comput. Mechanics, vol. 42, pp. 485 – 510, 2008.
[82]
M. Odegard, T. S. Gates, K. E. Wise, C. Park, and E. J. Siochi, “Effect
of nanotube functionalisation on the elastic properties of polyethylene
nanotube composites,” AIAA Journal, vol. 43, no. 8, pp. 1828 – 1835,
August 2005.
[83]
H. Kubo, M. Ciappa, T. Masunaga, and W. Fichtner, “Multiscale
simulation of aluminium thin films for the design of hihly-reliable
mems devices,” Microelectronics Reliability, vol. 49, pp. 1278 – 1282,
2009.
[84]
H. Adalsteinsson, B. J. Debusschere, K. R. Long, and H. N. Najm,
“Components for atomistic-to-continuum multiscale modeling of flow
in micro-and nanofluidic systems,” Sci. Program., vol. 16, no. 4, pp.
297 – 313, 2008.
[85]
G. Lu and E. Kaxiras, “Overview of multiscale simulations of materials,”
Handbook of Theoretical and Computational Nanotechnology,
vol. 1, pp. 1 – 33, 2005.
-PART C: APPLICATIONS AND REVIEWS
[86]
G. Li and N. R. Aluru, “Hybrid techniques for electrostatic analysis of
nanoelectromechanical systems,” Journal of Applied Physics, vol. 96,
pp. 2221 – 2231, 2004.
[87]
A. Lombo-Carrasquilla and G. Becerra-Forigua, “Integrated modeling
methodology for nanoscale electronic devices,” 3rd IEEE International
Conference on Nano/Micro Engineered and Molecular Systems, 6-9
Jan. 2008, pp. 357 – 361, 2008.
[88]
D. W. Wood and L. Bijnens, “Multiphysics modeling of axispherical
MEMS,” 7th AFRICON Conference in Africa, vol. 2, pp. 1017 – 1022,
2004.
[89]
A. Ferreira, J. Agnus, N. Chaillet, and J.-M. Breguet, “A smart
microrobot on chip: Design, identification and control,” IEEE/ASME
Transactions on Mechatronics, vol. 9, no. 3, pp. 508 – 519, September
2004.
[90]
R. Oboe, R. Antonello, E. Lasalandra, G. S. Durante, and L. Prandi,
“Control of a z-axis MEMS vibrational gyroscope,” IEEE/ASME Transactions
on Mechatronics, vol. 10, no. 4, pp. 364 – 370, August 2005.
[91]
B. Borovic, F. L. Lewis, D. Agonafer, E. S. Kolesar, M. M. Hossain,
and D. O. Popa, “Method for determining a dynamical state-space
model for control of thermal MEMS devices,” IEEE Journal of
Microelectromechanical Systems, vol. 14, no. 5, pp. 961 – 970, October
2005.
[92]
Y. C. Lee, P. B. Amir, J. A. Chiou, and S. Chen, “Packaging for
microelectromechanical and nanoelectromechanical systems,” IEEE
Transactions on Advanced Packaging, vol. 26, no. 3, pp. 217 – 226,
August 2002.
[93]
M. Lishchynska, N. Cordero, and O. Slattery, “State of the art in
prediction of mechanical behaviour of microsystems [mems],” 5th
International Conference on Thermal and Mechanical Simulation and
Experiments in Microelectronics and Microsystems, pp. 287 – 294,
2004.
[94]
J. Redmond, D. Reedy, M. Heinstein, M. de Boer, J. Knapp, E. Piekos,
C. Wong, and L. Holm, “Microscale modeling and simulation,”
SANDIA Report, SAND2001-3675, December 2001, pp. 287 – 294,
December 2001.
[95]
M. Innocent, P. Wambacq, S. Donnay, H. A. C. Tilmans, W. Sansen,
and H. D. Man, “An analytic volterra-series-based model for a MEMS
variable capacitor,” IEEE Transactions on Computer-Aided Design of
Integrated Circuits and Systems, vol. 22, no. 2, pp. 124 – 131, February
2003.
[96]
V. Litovski, M. Andrejevic, and M. Zwolinski, “Ann based modeling,
testing and diagnosis of MEMS: Capacitive pressure transducer
example,” 7th Seminar on Neural Network Applications in Electrical
Engineering, 23-25 Sept. 2004, pp. 183 – 188, 2004.
[97]
L. G. Bleris and M. V. Kothare, “Real-time implementation of model
predictive control,” American Control Conference, 8-10 June 2005,
vol. 6, pp. 4166 – 4171, 2005.
[98]
M. Bazu, C. Tibeica, L. Galateanu, and V. E. Ilian, “Fuzzy-logic
reliability predictions in microtechnologies,” International Conference
on Computational Intelligence for Modelling, Control and Automation
and International Conference on Intelligent Agents, Web Technologies
and Internet Commerce, vol. 1, pp. 89 – 93, 2005.
[99]
P. Singh, J. Rajagopalan, R. LaFollette, C. Fennie, and D. E. Reisner,
“Fuzzy logic-based microbattery controller for mems applications,”
35th Intersociety Energy Conversion Engineering Conference and
Exhibit, vol. 2, pp. 747 – 751, 2000.
[100]
B. Borovic, A. Q. Liu, D. Popa, H. Cai, and F. L. Lewis, “Openloop
versus closed-loop control of MEMS devices: choices and issues,”
IEEE Journal of Microelectromechanical Systems, vol. 15, pp. 1917 –
1924, 2005.
[101]
W. Tang, T. Nguyen, and R. Howe, “Laterally driven polysilicon
resonant microstructures,” Sensors and Actuators A, vol. 20, pp. 25
– 32, 1989.
[102]
W. Hofman, C. Lee, and N. MacDonald, “Monolithic three-dimensional
single-crystal silicon microelectromechanical systems,” Sensors and
Materials, vol. 10, pp. 337 – 350, 1998.
[103]
K. M. Morzinski, K. B. W. Harpsie, D. T. Gavel, and S. M. Ammons,
“The open-loop control of MEMS : Modeling and experimental results,”
Proceedings of SPIE, vol. 6467, pp. 64 670G.1 – 64 670G.10,
2007.
[104]
S. Huang, J. Tsai, D. Hah, H. Toshiyoshi, and M. C. Wu, “Open-loop
operation of MEMS WDM routers with analog micromirror array,”
Proceedings of the IEEE/LEOS International Conference on Optical
MEMs, pp. 179 – 180, 2002.
[105]
J. Tsai, S. Huang, D. Hah, H. Toshiyoshi, and M. Wu, “Open-loop
operation of MEMS-based 1N wavelength-selective switch with long
SUBMITTED TO IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS
term stability and repeatability,” IEEE Photonics Technology Letters,
vol. 16 (4), pp. 1041 – 1043, 2004.
[106]
B.-W. Yoo, J.-H. Park, Y.-H. Jang, and Y.-K. Kim, “A low-drift, open-
loop controlled, single crystalline silicon micromirror with floating
field-limiting shields,” Journal of Micromechanics and Microengineering,
vol. 18, p. 035031(8pp), 2008.
[107]
C. R. Vogel and Q. Yang, “Modeling, simulation, and open-loop control
of a continuous facesheet mems deformable mirror.” Journal of the
Optical Society of America A: Optics, Image Science and Vision, vol.
23 (5), pp. 1074 – 1081, 2006.
[108]
B. D. Jensen, S. Mutlu, S. Miller, K. Kurabayashi, and J. J. Allen,
“Shaped comb fingers for tailored electromechanical restoring force,”
IEEE Journal of Microelectromechanical Systems, vol. 12, pp. 373 –
383, 2003.
[109]
H. J. Kordlar and G. Rezazadeh, “Modeling open-loop MEMS tunneling
accelerometer based on circular plate,” Sensors & Transducers
Journal, vol. 78(4), pp. 1083 – 1092, 2007.
[110]
D. Keymeulen, M. I. Ferguson, B. Oks, C. Peay, R. Terrile, Y. Cheng,
D. Kim, E. MacDonald, and D. Foor, “Hardware platforms for MEMS
gyroscope tuning based on evolutionary computation using open-loop
and closed -loop frequency response.” Proceedings of the International
Conference on Evolvable Systems, Barcelona, Spain (September 1214),
2005.
[111]
M. F. Daqaq, C. K. Reddy, and A. H. Nayfeh, “Input-shaping control
of nonlinear MEMS,” Nonlinear Dynamics, vol. 54 (1-2), pp. 167 –
179, 2008.
[112]
D. O. Popa, B. H. Kang, J. T. Wen, H. E. Stephanou, G. Skidmore,
and A. Geisberger, “Dynamic modeling and input shaping of thermal
bimorph actuators,” IEEE International Conference on Robotics Automation
(Taipei, Taiwan, 1419 Sept.), 2003.
[113]
J. B. Stewart, A. Diouf, Y. Zhou, and T. G. Bifano, “Open-loop control
of a MEMS deformable mirror for large-amplitude wavefront control.”
Journal of the Optical Society of America A: Optics, Image Science
and Vision, vol. 24 (12), pp. 3827 – 3833, 2007.
[114]
C. Acar, S. Eler, and A. M. Shkel, “Concept, implementation, and
control of wide bandwidth MEMS gyroscopes,” Proceedings of the
American Control Conference (June 25 -27), pp. 1229 – 1234, 2001.
[115]
“System and method for canceling disturbance mems devices,” United
States Patent 6909819, June 21, 2005.
[116]
B. Borovic, C. Hong, A. Q. Liu, L. Xie, and F. L. Lewis, “Control of
a MEMS optical switch,” Proceedings of the 43rd IEEE Conference
on Decision and Control (December 14 -17), vol. 5, pp. 3039 – 3044,
2004.
[117]
“MEMS device control with filtered voltage signal shaping,” United
States Patent 7428353, September 23, 2008.
[118]
V. Milanovic and K. Castelino, “Sub -100 µs settling time and low
voltage operation for gimbal -less two -axis scanners,” Proceedings
of the IEEE/LEOS International Conference on Optical MEMS, 2004.
[119]
T. Fukushige, S. Hata, and A. Shimokhobe, “A new driving method for
electrostatic MEMS actuators to prevent sticking,” Proceedings of the
4th euspen International Conference ( Glasgow, Scotland, UK, May June),
pp. 1 – 2, 2004.
[120]
S. Jordan and E. Lawrence, “Vibration nullification of MEMS devices
using input shaping,” Proceedings of the SPIE Conference on Damping
and Isolation (March 3 -5), vol. 5052, pp. 326 – 334, 2003.
[121]
S. E. Lyshevski, MEMS and NEMS: structures, devices, and systems.
CRC Press, Boca Raton, FL, 2002.
[122]
W.-T. Sung, S. Sung, J.-Y. Lee, T. Kang, Y. J. Lee, and J. G. Lee, “Development
of a lateral velocity-controlled MEMS vibratory gyroscope
and its performance test,” IEEE Journal of Microelectromechanical
Systems, vol. 18, p. 055028(13pp), 2008.
[123]
H. Cai, J. H. Wu, J. L. Zhang, X. M. Wang, Y. X. Lu, and C. Liu,
“Optical MEMS switch control and packaging,” Proceedings of the
5TH Conference on Electronics Packaging Technology (December 10
-12), pp. 291 – 293, 2003.
[124]
M. Vagiaa, G. Nikolakopoulos, and A. Tzesa, “Design of a robust PID-
control switching scheme for an electrostatic micro-actuator,” Control
Engineering Practice, vol. 16 (11), pp. 1321 – 1328, 2008.
[125]
A. Koide, K. Sato, S. Suzuki, and M. Miki, “A multistep anisotropic
etching process for producing 3-d accelerometers,” In Technical Digest
of the 11th
Sensor Symposium, 1992.
[126]
K. Ikeda, H. Kuwayama, T. Kobayashi, T. Watanabe, T. Nishikawa,
T. Yoshida, and K. Harada, “Three-dimensional micromachining of
silicon pressure sensor integrating resonant strain gauge on diaphragm,”
Sensors and Actuators A, pp. A21 – A23: 1007 – 1009, 1990.
-PART C: APPLICATIONS AND REVIEWS
[127]
——, “Silicon pressure sensor integrates resonant strain gauge on
diaphragm,” Sensors and Actuators A, pp. A21 – A23: 146 – 150,
1990.
[128]
J. Bryzek, E. Abbott, A. Flannery, D. Cagle, and J.Maitan, “Control
issues for MEMS,” Proceedings of the 42nd
International Conference
Decision and Control, pp. 3039 – 3047, 2003.
[129]
“MEMS device with feedback control,” United States Patent 7061660,
June 13, 2006.
[130]
A. Shkel, R. Horowitz, A. Seshia, S. Park, and R. Howe, “Dynamics
and control of micromachined gyroscopes,” Proceedings of the American
Control Conference (June 2 -4), vol. 3, pp. 2119 – 2124, 1999.
[131]
Y. Chen, R. M’Closkey, T. Tran, and B. Blaes, “A control and
signal processing integrated circuit for the jpl-boeing micromachined
gyroscopes,” IEEE Trans. Control Systems Technology, vol. 13 (2), pp.
286 – 300, 2005.
[132]
“Integrated low power digital gyro control electronics,” United States
Patent 6915215, July 5, 2005.
[133]
C. C. Painter and A. M. Shkel, “Active structural error suppression in
MEMS vibratory rate integrating gyroscopes,” IEEE Sensors Journal,
vol. 3 (5), pp. 595 – 606, 2003.
[134]
H. Rodjegard, D. Sandstrom, P. Pelin, M. Carlsson, M. Bohman,
N. Hedenstierna, G. I. Andersson, and A. B. Imego, “A novel architecture
for digital control of MEMS gyros,” In Sensors, Proceedings
of the IEEE, vol. 3, pp. 1403 – 1406, 2004.
[135]
M. Looney, “Optimizing MEMS gyroscope performance with digital
control,” Analog Devices Application Note (AN-942).
[136]
S. Park, R. Horowitz, and C. woo Tan, “Adaptive control for MEMS
gyroscopes,” California PATH Research Report, vol. UCB-ITS-PRR2002-
11, 2002.
[137]
S. Park and R. Horowitz, “Adaptive control for the conventional mode
of operation of MEMS gyroscopes,” IEEE Journal of Microelectromechanical
Systems, vol. 12 (1), pp. 101 – 108, 2003.
[138]
C. Batur, T. Sreeramreddy, and Q. Khasawneh, “Sliding mode control
of a simulated MEMS gyroscope,” Proceedings of the American
Control Conference (8 -10 June), vol. 6, pp. 4160 – 4165, 2005.
[139]
J. Fei and C. Batur, “Robust adaptive control for a MEMS vibratory
gyroscope,” International Journal of Advanced Manufacturing Technology
(July 4), vol. 42, no. 2 –3, pp. 293 – 300, 2008.
[140] Q. Zheng, L. Dong, and Z. Gao, “A novel control system design for
vibrational MEMS gyroscopes,” Sensors and Actuators A, vol. 78, pp.
1073 – 1082, 2007.
[141]
R. P. Leland, “Adaptive control of a MEMS gyroscope using lyapunov
methods,” IEEE Trans. of Control Systems Technology, vol. 14 (2), pp.
278 – 283, 2006.
[142]
S. Park and R. Horowitz, “Discrete time adaptive control for a MEMS
gyroscope,” International Journal of Adaptive Control and Signal
Processing, vol. 19 (6), pp. 485 – 503, 2005.
[143]
M. S.-C. Lu and G. K. Fedder, “Position control of parallel-plate
microactuators for probe-based data storage,” IEEE Journal of Micro-
electromechanical Systems, vol. 13(5), pp. 759 – 769, 2004.
[144]
X. Wu, T. Ren, and L. Liu, “Active damping of a piezoelectric MEMS
acoustic sensor,” Integrated Ferroelectrics, vol. 80, pp. 317 – 329, 2006.
[145]
A. Izadian, L. Hornak, and P. Famouri, “Adaptive control of MEMS
devices,” Proceedings of the Conference for Intelligent Systems and
Control (August 14 -16), 2006.
[146]
P. Chu, I. Brener, P. Chuan, L. Shi-Sheng, J. I. Dadap, P. Sangtae,
K. Bergman, N. H. Bonadeo, T. Chau, C. Ming, R. Doran, R. Gibson,
R. Harel, J. J. Johnson, C. D. Lee, D. R. Peale, T. Bo, D. T. K. Tong,
T. Ming-Ju, W. Qi, W. Zhong, E. L. Goldstein, L. Y. Lin, and J. A.
Walker., “Design and nonlinear servo control of MEMS mirrors and
their performance in a large port-count optical switch,” IEEE Journal
of Microelectromechanical Systems, vol. 14 (2), pp. 261 – 273, 2005.
[147]
J. M. Dawson, J. Chen, K. S. Brown, P. Famouri, and L. A. Hornak,
“Through-wafer interrogation of microstructure motion for MEMS
feedback control,” SPIE Proceedings for the Conference on Miniaturized
systems with micro-optics and MEMS : ( Santa Clara CA, 20-22
September), vol. 3878, pp. 281 – 292, 1999.
[148]
T. Seki, M. Murakami, J. Yamaguchi, and K. Oda, “High speed mirror
control technique for 3D-MEMS optical switch,” IEICE Transactions
on Communications (Japanese Edition), vol. 189-B, pp. 1315 – 1317,
2006.
[149]
P. Song-Hee, A. Aina, T. Denison, and K. Lundberg, “Feedback
control for a MEMS-based high-performance operational amplifier,”
Proceedings of the American Control Conference (30 June -2 July),
vol. 1 (30), pp. 380 – 385, 2004.
SUBMITTED TO IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS
[150]
M. Afridi, A. Hefner, D. Berning, C. Ellenwood, A. Varma, B. Jacob,
and S. Semancik, “MEMS-based embedded sensor virtual components
for system-on-a-chip,” Solid-State Electronics, vol. 48, pp. 1777 –
1781, 2004.
[151]
M. Behnam, G. V. Kaigala, M. Khorasani, P. Marshall, C. J. Backhouse,
and D. G. Elliott, “An integrated CMOS high voltage supply for labon-
a-chip systems,” Lab On A Chip, vol. 8, pp. 1524 – 1529, 2008.
[152]
C. D. Garca, Y. Liu, P. Anderson, and C. S. Henry, “Versatile 3-channel
high-voltage power supply for microchip capillary electrophoresis,”
Lab On A Chip, vol. 3, pp. 324 – 328, 2003.
[153]
T. Fujimori, Y. Hanaoka, K. Fujisaki, N. Yokoyama, and H. Fukuda,
“On-chip MEMS capacitive pressure sensor fabricated using standard
CMOS back-end-of-line processes,” Technical Digest: Sensors Symposium,
vol. 22, pp. 224 – 227, 2005.
[154]
O. Brand and H. Baltes, “Microsensor packaging,” Microsystems technology,
vol. 7, pp. 205 – 208, 2000.
[155]
C. Hagleitner, A. Hierlemann, D. Lange, A. Kummer, N. Kerness,
O. Brand, and H. Baltes, “Smart single-chip gas sensor microsystem,”
Nature, vol. 414, pp. 293 – 296, 2001.
[156]
Y. Xu, C.-W. Chiu, F. Jiang, Q. Lin, and Y.-C. Tai, “A MEMS multi-
sensor chip for gas flow sensing,” Sensors and Actuators A: Physical,
vol. 121 (1), pp. 253 – 261, 2005.
[157]
M. S.-C. Lu, Z.-H. Wu, C.-E. Huang, S.-J. Hung, M.-H. Chen, and
Y.-C. King, “CMOS micromachined grippers with on-chip optical
detection,” IEEE Journal of Microelectromechanical Systems, vol. 17,
pp. 482 – 488, 2007.
[158]
T. Vestad, D. W. M. Marr, and J. Oakey, “Flow control for capillary-
pumped microfluidic systems,” Journal of Micromechanics and Micro-
engineering, vol. 14, pp. 1503 – 1506, 2004.
[159]
C. J. Easley, J. M. Karlinsey, J. M. Bienvenue, L. A. Legendre, M. G.
Roper, S. Feldman, M. A. Hughes, E. L. Hewlett, T. J. Merkel, J. P.
Ferrance, and J. P. Landers, “A fully integrated microfluidic genetic
analysis system with sample-inanswer-out capability,” Proceedings of
the National Academy of Sciences, U.S.A, vol. 103, pp. 19 272 – 19 277,
2006.
[160]
M. G. Pollack, R. B. Fair, and A. D. Shenderov, “Electrowettingbased
actuation of liquid droplet for microfluidic applications,” Applied
Physics Letters, vol. 77, pp. 1725 – 1726, 2000.
[161]
H. Ren, R. B. Fair, and M. G. Pollack, “Automated on-chip droplet
dispensing with volume control by electro-wetting actuation and capacitance
metering,” Sensors and Actuators, B, vol. 98, pp. 319 – 327,
2004.
[162]
J. Gong and C. J. Kim, “All-electronic droplet generation on-chip with
real-time feedback control for EWOD digital microfluidics,” Lab On
A Chip, vol. 8, pp. 898 – 906, 2008.
[163]
R. Anderson, B. Kawade, K. Ragulan, D. H. S. Maithripala, J. M. Berg,
R. O. Gale, and W. P. Dayawansa, “Integrated charge and position
sensing for feedback control of electrostatic MEMS,” Proceedings of
the Conference on Sensors and Smart Structures Technologies for Civil,
Mechanical, and Aerospace Systems (March 7 -10), vol. 5765, 2005.
[164]
S. B. Prakash, P. Abshire, M. Urdaneta, M. Christophersen, and
E. Smela, “A CMOS potentiostat for control of integrated MEMS actuators,”
Proceedings of the IEEE International Symposium on Circuits
and Systems (May 21 -24), pp. 5555 – 5558, 2006.
[165]
K. Jensen, K. Kim, and A. Zettl, “An atomic-resolution nanomechanical
mass sensor.” Nature Nanotechnology, vol. 3 (9), pp. 533 – 537, 2008.
-PART C: APPLICATIONS AND REVIEWS
Antoine Ferreira received the M.S. and Ph.D.
degrees in electrical and electronics engineering
from the University of Franche-Comte, Besancon,
France,in 1993 and 1996, respectively. In 1997, he
PLACE
was a Visiting Researcher at the ElectroTechnical
PHOTO
Laboratory (ETL), in Tsukuba, Japan. He is cur-
HERE
rently a Professor of Robotics Engineering at the
Institut PRISME, Ecole Nationale Superieure dIngenieurs
de Bourges (ENSI Bourges), Bourges, France.
His research interests include the design, modeling,
and control of micro and nanorobotic systems using
active materials, micro-nanomanipulation systems, biological nanosystems,
and bionanorobotics. He is an author of three books on micro and nanorobotics
and more than 100 journal and conference papers and book contributions.
Dr. Ferreira was the Guest Editor for different special issues in IEEE/ASME
TRANSACTIONS ON MECHATRONICS in 2009, International Journal of
Robotics Research in 2009, and the IEEE Nanotechnology Magazine in 2008.
PLACE
PHOTO
HERE
Sumeet S. Aphale received the Bachelors degree in
electrical engineering from the University of Pune,
Pune, India, in 1999, and the M.S. and Ph.D. degrees
in electrical engineering from the University of
Wyoming, Laramie, in May 2003 and August 2005,
respectively.
From 2000 to 2005, he was a member of the Hexapod
Research Laboratory where he was involved
in design and control of Gough-Stewart platform-
type robotic manipulators. He was a member of the
Australian Research Councils Center of Excellence
for Complex Dynamic Systems and Control housed at the University of
Newcastle, Australia from October 2005 to June 2008 and later moved to a
Research Fellow appointment with the Center for Applied Dynamics Research
at the University of Aberdeen, U.K. Since June 2009, he has been appointed to
the New Blood Lectureship position at the School of Engineering, University
of Aberdeen, Aberdeen, U.K. His current research interests include robot
kinematics and control, vibration control applications as well as design and
control of smart structures, nanopositioning systems and MEMS.