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CARNEGIE MELLONDepartment of Electrical ~nd Computer Engineering__
Development of MEMSTesting Methodology
Abhijeet Kolpekwar
1998
Development of MEMS Testing Methodology
Abhijeet Kolpekwar
Department of Electrical Engineering & Computer Engineering
Carnegie Mellon University
Pittsburgh, PA 15213-3890
May 1998
Submitted in partial fulfillment of the requirements for the degree of Master of Science in
Electrical and Computer Engineering.
ABSTRACT
Microelectromechanical systems (MEMS) are miniature electromechanical sensor and actuator
systems developed from the mature batch-fabricated processes of VLSI technologies. Projected
growth in the MEMS market requires significant advances in CAD and manufacturing for MEMS.
These advances must be accompanied with testing methodologies that ensure both high quality and
reliability. Effectiveness of any testing methodology depends on the accuracy of the fault models
applied. So, the first step towards development of comprehensive testing methodology involves
generating accurate fault models for MEMS. Creating accurate fault models requires a complete
knowledge of all the possible failure mechanisms in MEMS. A systematic study of the impact of
these failure mechanisms on the final functionality of MEMS devices provides important guidelines
for MEMS fault model generation.
A CAD tool called CARAMEL (Contamination And Reliability Analysis of MicroElectromechanical
Layout) has been developed that can be used for fault model generation for MEMS. CARAMEL
is based on the IC contamination analysis tool CODEF and is capable of analyzing the impact
of contamination particles on the geometrical and material properties of microelectromechanical
systems. Output generated by CARAMEL indicates that a wide range of defective structures are
possible due to the presence of particulate contaminations. Moreover, electromechanical simula-
tions of CARAMEL’s mesh representations of defective layout has revealed that a wide variety of
faulty behaviors are associated with these defects.
Several hundred contamination simulations were performed using CAI~AMEL on the surface
micromachined comb-drive resonator. The results obtained were classified under three broad fault
classes: catastrophic, parametric, and harmless. The defect statistics generated by CARAMEL
indicates the comb drive as the most defect-prone region of the microresonator. Most of the
comb drive defects resulted in the catastrophic failures. Contaminations introduced during PolyO
deposition caused the maximum number of faults fotmd in the microresonator. Such defect-to-fault
mappings provide an essential tool for the MEMS fault model generation process. Simulation results
presented in this report confirm that CARAMEL can be used as an effective tool for development
of realistic fault models for MEMS.
ACKNOWLEDGMENTS
Several people at Carngie Mellon University have contributed towards this work. First and
foremost among these is my advisor, Shawn Blanton. I am very much grateful to him for his
invaluable support and guidance throughout this project. It would be difficult to find a more
understanding, patient and friendlier advisor. I would like to thank Dr. Gary Fedder for giving
useful advice and reviewing this report. I am also thankful to Christopher Kellen for his valuable
contribution. My thanks are due to Dr. Tamal Mukherjee and Sitaraman Iyer for providing useful
tips at every stage of the project.
This work would not have been possible without the help of all my friends at CMU: Kumar
Dwarkanath, Alok Jain, Srihari Cadambi, Piyush~ Singh, Vishnu Patankar, Anand Dixit, Anan-
dasivam Gopal, to name a few. A big thanks to all of you for an exciting and memorable stay in
Pittsburgh. My thanks are also due to Ashit Talukder and Srihari Cadambi who are always there
when I need their help.
I am deeply indebted to my parents and my fiancee Sonali for offering constant support and
encouragement.
Finally, I would like to thank my sponsors. This research is sponsored by the National Science
Foundation under grant MIP-9702678 and the Defense Research Projects Agency under Rome
Laboratory, Air Force Materiel Command, USAF, under grant F30602-97-2-0323,
III
Contents
1 Introduction 1
2 Related Work 11
2.1 Open Problems in MEMS Testing ............................ 11
2.2 Fault Modeling and Simulation of MEMS ........................ 13
2.3 Test Generation for MEMS ................................ 15
2.4 BIST and Fault Tolerance for MEMS ........................... 17
2.5 Summary .......................................... 21
3 CARAMEL 23
3.1 Micromachined Microresonator .............................. 23
3.2 Resonatcr Application: Accelerometer .......................... 26
3.3 CARAMEL Implementation ................................ 28
3.3.1 Process Simulation ................................. 28
3.3.2 Structure Extraction ................................ 31
Lis¢ of Figures
1.1 (a) A SEM and (b) top view of a surface-micromachined comb-drive microresonator.
1.2 Effect of stiction on surface-micromachined microresonator ............... 7
1.3 MEMS contamination analysis performed using CARAMEL .............. 8
2.1 (a) Cross section of piezoresistive accelerometer die and (b) motion of doubly
ported mass under acceleration. (Cap is not shown for clarity.) ............ 18
2.2 (a) Schematic of accelerometer with thermal self test and (b) deflection of seismic
mass due to thermal expansion of the actuator beam .................. 20
3.1 (a) Top view of the microresonator and pertinent dimensional parameters of the (b)
comb drive, (c) shuttle, and (d) folded-flexure ...................... 25
3.2 Accelerometer basics:(a) Sensing operation of a typical accelerometer. (b) Signal
sensing and processing circuitry for transducing a mass deflection into an output
voltage ............................................ 29
3.3 Abbreviated flow for MCNC’s MultiUser MEMS Process service. (a) Cross-sectional
view. (b) Top view (layout) ................................. 30
vi
3.4 Illustration of element splitting for mechanical mesh construction: (a) A set of regions
before split and (b) the same regions after x-y and z splits ............... 33
3.5 Three-dimensional representations of defective resonators and their corresponding
mechanical mesh for particle contaminations located (a) between comb fingers, (b)
on a beam, and (c) under the shuttle ........................... 36
3.6 Three-dimensional representations of defective resonators and their corresponding
mechanical mesh for particle contaminations located (a) outside of the resonator’s
active area and (b) on the shuttle ............................. 37
vii
List of Tables
3.1 Resonator parameters (and typical values) that define the modeled operation of the
microresonator 26
3.2 Five representative cases of defect analysis using CARAMEL .............. 37
3.3 Coarse fault categorization based on deviation in resonant frequency fx ........ 40
Defect categorization based on the MUMPs process step for which the contamination
was introduced ........................................
3.4
41
3.5 Spatial distribution of faults in the microresonator .................... 41
4.1 Appendix A - The complete MCNC MUMPs process described in the PREDITOR
format ............................................. 51
viii
Chapter 1
Introduction
Microelectromechanical systems (MEMS) are miniature electromechanicM sensor and actuator sys-
tems developed from the mature batch-fabricated processes of VLSI technologies. MEMS have wide
applications such as miniature inertial measurement units, biochemical analysis on a chip, arrayed
micromanipulation of parts, optical displays and micro-probes for neural recording. The current
and increasing success of MEMS stems from its promise of better performance, low manufacturing
cost, miniaturization and its capacity for integration with electronic circuits.
The low cost of MEMS, as with the integrated circuit (IC), is attributed to the amortization
of capital cost over millions of individual batch-fabricated devices. This has substantially reduced
the cost of sensors and actuators by several orders of magnitude. For example, Analog Devices’
MEMS accelerometer used extensively in airbag systems sells for about $5 a unit when sold in large
quantities. An accelerometer of similar quality manufactured from a competing technology sells for
more than $1000 [5].
The smaller and reduced-mass characteristics of MEMS structures allow higher operating fre-
quencies and lower power consumptions. These properties make MEMS ideal for many portable
and remote applications. For example, MEMS-based pressure sensors are being used to moni-
tor truck tire pressure in order to extend tire life, while optical gratings are being developed for
heads-up color displays [5].
The ability to use the same manufacturing technology to integrate MEMS and electronic systems
adds another dimension of utility. Single-chip systems of electronics and mechanical sensors are
being produced and envisioned for a variety of applications. Texas Instruments, for example, has
developed a micromirror projection display consisting of a two-dimensional array of aluminum
mirrors, each individually controlled by on-chip RAM cells.
By the early 1980s, progress in microelectronics had reduced the cost of a microprocessor to less
than that of a typical silicon sensor; today the peripheral functions of sensing and actuation con-
tinue to represent the principal bottleneck in the application of microelectronics to many emerging
systems, not only in terms of cost but also in terms of reliability and accuracy as well. It is thus
evident that continued progress in MEMS is likely to exert considerable leverage in the microelec-
tronics industry beyond the direct markets for sensors and actuators. Accelerometers [1], pressure
sensors [2] and thermal ink-jet printheads [3] are the most matured MEMS applications accounting
for almost all of the commercial MEMS market of around $1 billion in 1994. The MEMS market
is projected to reach between $12 and $14 billion by the year 2000 [5]. The technology trend is
towards higher degrees of integration that result in greater capabilities by MEMS-based products to
asses and manipulate their surrounding environment. Realizing these market projections requires
that the open problems in design methodology and process and package technology be confronted
and adequately addressed.
There is a desperate need for computer-aided design (CAD) tools that shorten the design and de-
velopment time for MEMS-based products involving physical interactions between mechanical, elec-
trostatic, magnetic, thermal, fluidic, and optical domains° Current design cycles for microsystems1
are measured in years [6]. Success in this area depends greatly on new design methodologies that
allow complex microsystems of mixed domains (mechanical, electrical, thermal, etc.) to be hier-
archically represented and simulated. Improvements in packaging and process technology will also
be required to support the mixed-system applications required by MEMS integration. Currently,
most MEMS are made from commonly used IC fabrication techniques. Process and equipment
development specifically for MEMS will expand the design space and result in more opportunities
for completely integrated systems.
Advances in the above areas alone will not fuel MEMS growth. Continued success for MEMS will
require cost-effective methods of manufacturing. Similar to digital ICs, success in this area must
include a testing methodology that allows products to be economically tested while ensuring high
quality and reliability. This is especially important in applications where MEMS are integral parts of
safety-critical systems such as automotive airbag systems that utilize Analog Devices’ ADXL-series
of accelerometer sensors. MEMS testing methodologies must be developed in concert with new
design, packaging, and process technologies. Most industrial practices focus on partially exercising
the functionality of MEMS by performing certain electrical measurements. However, there is a need
to create formal links between failure modes (i.e. electrical measurements to identify failures) and
the underlying physical causes. These correlations will allow accurate modeling of complex effects
that can be used in fault model generation, fault diagnosis and the development of efficient testing
1Microsystems is a generic term for all micro-sensors, micro-actuators and other self-contained~ miniaturizedsub-systems that possess additional processing capabilities.
combdrive
shuttle
movingcombs flexurefixed anchorscombs
(a) (b)Figure 1.1: (a) A 8EM and (b) top view of a surface-micromachined comb-drive microresonator.
techniques for MEMS. Such links are also critical for providing important guidelines for modifying
process control parameters that result in fast yield improvements.
We have chosen a folded-flexure comb-drive microresonator2 as our research vehicle because it
possesses many of the basic structures (beams, joints, springs, etc.) that many researchers believe
will form the core pri~niti~es of a MEMS design library [14, 15, 16]. The hope is that the analysis
performed with the resonator will be applicable to many classes of MEMS. The resonator structure
(see Figure 1.1a.) belongs to a class of MEMS known as surface-micromachined MEMS. Prototype
surface micromachining processes are available from MCNC (Multi-user MEMS Processes service
(MUMPs) [17]), Analog Devices’ iMEMS process, and from Sandia NationM Labs. We have selected
the MUMPs process for the contaminations simulations of the resonator due to its open availability.
In this process, thin films are sequentially deposited and patterned on top of the substrate. The
movable MEMS structure is created when a "release" step is used to etch away a sacrificial layer.
The seminal paper on the resonator is given in [18]. Analytic models of the resonator~s pertinent
2We will refer Go the folded-flexure comb-drive microresonator simply as the "resonator".
characteristics can be found in [19, 20, 21]. The resonator structure is a mature case study in the
design of "suspended MEMS’~ which are now used in commercial accelerometers [22, 23], gyro-
scopes (soon), and micromirror optical beam steering [29]. Future commercial applications are
resonator-based oscillators [26], IF mixers, high-Q IF filters for communications~ micromechanical
filters [25], micromachined resonant pressure sensors [24], and microstages for probe-based data
storage [30].
A detailed description of the structure and operation of the resonator is provided in chapter 3.
Here, we give a general overview of the resonator. A top view of the resonator is shown in Fig-
ure 1.lb. It is a mass-spring-damper system made only from the first and second polysilicon layers.
A majority of the resonator’s structure is suspended above the wafer surface. It is connected to
the wafer only at the four anchor locations shown. The shuttle ~nass is supported by symmetrical
folded flexures that are designed to be compliant in the x direction and rigid in the y direction.
Each comb drive is formed from interdigitated fingers on either side of the shuttle mass. Between
every pair of fixed comb fingers is a moving comb finger that is attached to the shuttle. Such an ar-
rangement of fixed and movable beams produces an array of differential parallel plate capacitances.
The resonator is primarily used for sensing acceleration. The shuttle mass moves under applied
acceleration (in the x direction) causing a change in the area of overlap between the fixed combs
and moving combs which in turn results in a capacitance change. The change of capacitance is
then sensed by signal processing circuitry connected to the resonator. The folded flexure provides
a restoring force to the shuttle movement.
Complexity of physical failures is a major obstacle in the extensive use of MEMS-based devices.
It is imperative to test these devices thoroughly for all the possible failures to achieve high quality.
Figure 1.2 illustrates an example of a complex failure called stiction. Stiction is defined as adhesion
of the microstructure to adjacent surfaces [42]. It is a result of surface tension forces (mostly due to
moisture) overpowering the stiffness of the comb fingers. Figure 1.2b shows the "stuck-down" beams
that lie flat on the substrate due to stiction. Figure 1.2c shows the "tipped" beams which get tilted
towards the die surface due to surface tension. Both, stuck-down and tipped beams result in loss
of resonator functionality. Complex failure mechanisms like these degrade the reliability of MEMS-
based devices. Such effects need to be modeled accurately for fault simulation and test generation.
In [8], we have identified a failure mode to differentiate between the above two physical failures.
The information obtained in this way is critical for MEMS testability and yield improvement.
Success of any testing methodology is highly dependent on the fault models employed. Fault mod-
els that do not "cover" real defective behavior can reduce defect coverage and degrade test quality.
The first step of our work towards development of a comprehensive MEMS testing methodology
focuses on fault model generation for MEMS. MEMS fault models, unlike their digital and analog
counterparts, must explicitly consider the impact of defects on the micromechanical structures. Our
approach centers on the inductive generation of the possible faulty behaviors from realistic con-
tamination simulations. Moreover, the simulations are performed on the microresonator, a generic
MEM structure that we believe contains the design primitives for many classes of MEMS.
Our experience is that a major cause of faulty behavior in MEMS is due to particulate contam-
inations that occur during various steps of fabrication. Contaminations can cause a significant
perturbation in the structural and material properties of the microstructure [7]. A formal assess-
ment of both the possible defective structures and the corresponding faulty behaviors on MEMS
design primitives will lead to the formation of effective MEMS fault models. Such fault models
6
Shuttle Mass
Normal Beam
Stuck-down beam
(b)
Tipped beam
(c)Figure 1.2: Effect of stiction on surface-micromachined microresonator.
1. Deposit Nitride2. Depesit.sw (poly0)3. Lithe.Spin on (po~y0-rnask)4. Lithe.expose (poty0-mask)5. Li~o.develop (poly0-mask)
Process recipe
Process Simulation
Structure Extraction
ElectromechanicalSimulation
Parameter Analysis
Contaminations~ Design Layout
CARAMEL
DefectiveStructure
’x-ysplit ~1
]zx
’~-------~ Mesh Model
Top view of mechanical mesh
Figure 1.3: MEMS contamination analysis performed using CARAMEL.
will undoubtedly lead to methods for fault grading, test generation, design for testability (DFT),
and design for fault avoidance. The fault modeling process can also be used to form links between
defects and faulty behaviors. Such links would aid in diagnosis by facilitating the identification of
process steps that are likely to produce the observed faulty behavior [11]. Thus, our approach to
developing a complete MEMS testing methodology relies on a fault inductive approach that :
1. Completely characterizes the faulty behavior of MEMS primitives.
2. Supports the generation of fault models for simulation and test generation.
3. Provides techniques for testable and reliable MEMS design.
We have developed a MEMS contamination analysis tool called CARAMEL (Contamination And
Reliability Analysis of MicroElectromechanical Layout) for analyzing the impact of contamination
particulates on the properties of microelectromechanical layout. The core of CARAMEL is based
on the IC contamination analysis tool CODEF [12]. CARAMEL is an integral component of our
MEMS fault model generation methodology (see Figure 1.3). CARAMEL requires three inputs:
1. Design definition: This is a layout of the design in the Caltech Intermediate Form (CIF).
2. Process definition: This includes a sequence of process steps with all the required details
such as deposition thickness, etching rate, etching time etc.
3. Contamination definition: This includes geometrical and material characteristics of the
particulate contamination, its location in the MEMS layout, and its process step of introduc-
tion.
CARAMEL performs process simulation and creates a three-dimensional representation of the
defective microelectromechanical layout. It then extracts a mesh representation from the defective
layout whose form is completely compatible with the mechanical simulator ABAQUS [13]. Me-
chanical simulation of the mesh then allows us to link the contamination of concern to a defective
structure and a faulty behavior. Observed faulty behaviors are then classified and used to form
models at the next level of abstraction. Monte Carlo iteration around the flow of Figure 1.3 provides
a mechanism for creating realistic fault models for MEMS.
The rest of the report is organized as follows. Chapter 2 presents prior work in the area of
MEMS testing. Chapter 3 describes our tool CARAMEL and illustrates its use in the fault model
generation flow illustrated in Figure 1.3. Finally, chapter 4 presents our conclusions and outlines
areas of future work.
10
Chapter 2
Related Work
Most research work in the MEMS area centers on design, technology, and packaging problems and
not testing. However, there have been a small number of researchers that are concerned with
MEMS testability. In this chapter, we present the open issues in MEMS testing and discuss the
important work done in this area.
2.1 Open Problems in MEMS Testing
The test problem is complicated by systems that contain a large number and large variety of
component or modules. Thus, MEMS inherently poses a new challenge to the testing community
due to its multi-domain operational characteristics. The testing problem associated with MEMS has
created serious limitations to the high-volume commercial production of microsystems. Described
below are the main difficulties in MEMS testing [38]:
11
¯ Accessibility, Controllability, and Observability: There is limited electrical access (I/O
connections) to most MEMS-based devices. This prevents internal nodes access which in turn
makes controlling certain internal parameters and observing test responses difficult.
¯ Diverse Function: Different modules within the microsystem (i.e. sensors, actuators, analog
or digital signal processing circuits) require completely new approaches to testing. Unlike
digital and analog circuits, there are no known fault models and test algorithms designated
especially for the microelectromechanical structures found in microsystems.
¯ Interference: In highly integrated microsystems, various sub-systems are in immediate prox-
imity and therefore, can adversely affect each other. For example, heat dissipation of a digital
signal processor may result in the thermal expansion of the microstructure used in the sensor
circuitry. Such a change in the sensor geometry results in faulty sensor output.
¯ Test Environment: A system under test typically requires some form of initialization. This
is inherently difficult for systems with sensors and actuators where special environmental
conditions are required.
¯ Packaging Influence: Packaging of a high-density multi-module system can cause me-
chanical stress that may change the MEMS functionality. For example, in case of a bulk-
micromachined pressure sensor, any stress developed on the diaphragm due to packaging can
create undesired voltage fluctuations at the output.
¯ Complex Failure Mechanisms: A comprehensive MEMS testing methodology requires
a complete knowledge of all the possible failure mechanisms. Problems such as damping,
stiction, cross-axis sensitivity, over-load protection and in-process survivability need to be
12
analyzed and understood. These effects are quite complex and difficult to model accurately
for test generation purposes.
The research performed so far in the area of MEMS testing addresses a subset of the above issues.
In the next few sections, we describe research activities related to various aspects of MEMS testing.
2.2 Fault Modeling and Simulation of MEMS
Fault models are assumptions about how physical defects affect the behavior of the unit under test.
In the case of MEMS, where physical failure mechanisms are much more complex due to presence
of mixed domains, developing fault models becomes a difficult challenge.
Lubraszewski et. al [35] provide an extensive overview of the issues and possible solutions for the
problems related to MEMS fault modeling, simulation, test generation, design for testability (DFT),
and built-in self test (BIST). They also described their MEMS testing environment termed CAT
(Compttter-Aided Testing). They use an electrical schematic to model MEMS structures. MEMS
defects are then modeled using the concept of mutants and saboteurs. Mutants use analog-like
faults that model non-electrical (mechanical, optical, etc.) defects. The modeled defects normally
cause parametric changes in the electrical model representation. Saboteurs, on the other hand, cause
components to be removed or added to the electrical model. They are used to represent defects
that add or remove components of the microstructure. However, the accuracy of such modeling is
very much dependent on the accuracy of the electrical representation of the system. Inaccuracies
in the fault model lead to poor-quality testing techniques. Moreover, experiments involving the
fabrication and analysis of real faulty microsystems is needed to validate the modeling accuracy of
13
mutants and saboteurs. The paper proposes to perform fault simulation by simply modifying the
fault-free microsystem description by instantiating mutant or saboteur descriptions of an HDL-A
fault model library. A drawback of such an approach is that there is no methodology for selecting
the probable mutant/saboteur faults.
Vermeiren et. al [37] stress the importance of the MEMS model and its relationship to defects
of the MEMS structure. They too use an electrical ~nodel to represent mechanical and electricM
components of MEMS. However, the model is constructed in a such way so as to allow the accurate
modeling of a wide variety of MEMS defects. They have focused on the construction of a simulation
model that allows fault injection in order to perform fault simulation. Experiments are performed on
two microsystems: a resonant silicon beam force sensor and a miniature opto-electric transformer.
Some of the requirements for fault models and fault simulation listed in [37] include:
Simultaneous development of the functional and behavioral models of microsystems at higher
levels of abstraction along with the development of simulation models for faulty MEMS be-
havior.
Meeting the requirement of underlying physical laws by simulation models in the presence of
injected faults.
Consideration of "side effects" while developing simulation models. These side effects may
become dominant under the influence of faults. For example, consider an accelerometer with
impeded shuttle movement. A voltage applied across the comb drives fails to move the shuttle
mass resulting in a heating side effect. This may lead to thermal expansion of the beams which
may affect beam stiffness and therefore, the resonant frequency of the resonator.
14
¯ Systematic methods for fault list reduction (similar to Analog fault simulation) through iden-
tification of equivalent or dominant faults.
Most of the on-going research in MEMS fault modeling and simulation builds upon the approaches
used in analog fault modeling and simulation. However, a large variety of physical, chemical
and other effects complicate fault model generation for MEMS, making a purely analog approach
inadequate.
2.3 Test Generation for MEMS
Most test generation approaches proposed for microsystems are similar to those applied to analog
circuits. Here, we discuss some important test generation approaches explored in the previous work.
The approach described in [35] is based on the sensitivity-guided search process originally de-
scribed in [36]. The sensitivity of an operational parameter to a given component is defined as the
ratio of change in the former to the change in the latter. The fault model applied here uses the
mutant/saboteur concept described earlier. Test generation begins with the selection of an initial
test stimulus that corresponds to the mid-range of input voltage values. A fault simulation is then
performed to estimate the fault coverage. Authors do not state how the estimate is obtained. The
undetected faults are re-simulated using test patterns that are higher and lower than the initial
voltage value. Faults which remain undetected by the new tests are partitioned into two new lists;
one containing faults for which the sensitivity of the measuring parameter has increased for higher
input stimuli, and ~nother containing those faults for which the sensitivity of the measuring pa-
rameters has increased for lower input stimuli. The p~per does not discuss the case in which the
15
sensitivity of the measuring parameter remains unchanged. The procedure is recursively applied
until all faults are detected. The rationale for using such a fault partitioning approach is not clear
from the paper. Moreover, the test set produced is not guaranteed to be optimal. Also, the use of
saboteurs can lead to highly complicated transfer functions for the system which in turn, may result
in an intense fault simulation and test generation process. It is necessary to examine the viabil-
ity of this approach through systematic analysis of the complexity of the operations implemented.
Finally, the use of saboteurs and mutants to model MEMS defects remains suspect.
Another test generation approach is presented by Olbrich and others in [38]. They also use an elec-
trical schematic to model both the electrical and mechanical components of a bulk-micromachined
accelerometer. Similar to digital fault modeling, they model defects using stuck-at, bridging, and
stuck-open faults. Carefully placed resistors are a~ided to their electrical schematic to model these
faults. An additional level of complexity is added by allowing the parameter values of these "fault"
resistors to vary. Exhaustive Hspice simulations are used to determine which voltage and current
nodes are good candidates for observing faulty behavior. These simulations require sweeping the
values of the fault resistors from 1K~ to 1GFt -- the extremes of the value range attempts to mimic
shorts and opens, respectively. Voltage and current are measured at various nodes in the circuit
for every value of the fault resistor. However, taking such measurements from a packaged sensor is
practically impossible. Measurement windows for ranges of voltage and current for which a circuit
is defined to be faulty or fault-free are established somewhat arbitrarily. It is not clear how many
fault-free simulations are conducted, i.e., whether it is fully exhaustive. The authors state that
fault masking may occur, that is, the combination of a fault and allowable resistor tolerances can
cause fault-free measurements. The boundaries that define faulty behavior are set "intuitively". A
"grey zone" is used to further push the fault boundaries out for safety reasons. This seems to be
16
counter intuitive because it appears that more "out-of-spec" behavior is tolerated when the fault
boundaries are extended. The authors claim that the faults under consideration come from the
analysis of accelerometer failures. Using statistics of field and manufacturing failures, they choose
which faults to simulate according to their actual occurrence in the field. From this information,
the location of the most sensitive nodes to monitor accelerometer temperature are identified. But,
the method used to monitor accelerometer temperature is not discussed by the authors. They have
also presented the voltage values at different nodes under the influence of faults. These values
facilitate identification of observable faulty behaviors at the node of concern. Unlike the testing
approaches given in [39, 41~ 40], this approach allows on-line monitoring. However, the paper does
not give any detailed explanation of why and how the faults under consideration are chosen.
Test generation for MEMS is not a straight forward extension of digital or analog test techniques.
Presence of multiple domains like mechanical, chemical, optical etc. complicates the test generation.
Also, the accuracy and effectiveness of test generation is highly subject to the accuracy of the fault
models applied.
2.4 BIST and Fault Tolerance for MEMS
In this section, we describe the research performed in the areas of built-in self test (BIST) and
fault tolerance for MEMS. Similar to MEMS fault simulation and fault modeling, ideas presented
in these areas also leverage techniques from analog test. There are two broad classes of analog
BIST that can be identified:
17
piezoresistors.__ framevoltage excitation /~ capfor self test / \ I I . , .
[ J ’---- shuttle mass
deflection directionshuttle ma.ss ~ piezoresistor
(b/Figure 2.1: (a) Cross section of piezoresistive accelerometer die and (b) motion of doubly supportedmass under acceleration. (Cap is not shown for clarity.)
1. Spatial Redundancy." These techniques require extra circuitry to perform the self test.
These include specific test/monitoring cells [43, 44], analog test buses and circuit duplication.
2. Analytical l:tedundancy: These include analytic redundancy techniques such as parametric
analysis, frequency chaxacteristics [46], reliability indicators [45], signal delay measurements,
etc.
Most BIST approaches for MEMS use a combination of the above two approaches. In [39], Allen
and his colleagues have described a BIST approach for a piezoresistive accelerometer. Figure 2.1a
shows a crossection of the piezoresistive accelerometer. The motion of the silicon mass under the
acceleration is depicted in Figure 2.lb. The following relation was used to compute the voltage
required to subject the silicon-mass to a desired acceleration.
eA V’~gelectro = 19.6mxo2 (2.1)
where g~tect, o is the silicon-mass acceleration, A is the electrode area, V is the applied voltage, m
is the mass of the shuttle, e is the dielectric constant of the damping media and Xo is the initial
position the movable silicon mass. The electrostatic force applied to set the desired acceleration
gelectro depends on voltage and geometry of the shuttle and is suitable for calibration purposes. The
above equation is valid only for small deflections (<3%) of the silicon mass. During test, an applied
18
voltage is varied in DC step from ÷20V to -20V and the sensor output is measured. At every
applied voltage, shuttle mass acceleration and the sensitivity (V/g) of the device is computed
verify whether the device is functional. Structural design modifications are suggested for protecting
the device from excessive force. The self-testing feature provides two benefits. The first is that the
user can confirm that the shuttle-mass is free to respond in critical operating conditions. Secondly,
the self-test is a force applied to the mass, which cannot be differentiated from an acceleration force.
Hence, using a known electrostatic force, the accelerometer can be calibrated over temperature.
Built-in self test approaches for micromachined accelerometers described in [39, 47] rely on ap-
plication of electrostatic forces to the silicon mass. However, electrostatic forces are fundamentally
weak. Therefore, very high voltages are required to achieve full-scale electrostatic actuation in ac-
celerometers. This is the main drawback in using electrostatic actuation for implementing built-in
self test for MEMS-based devices.
In [48], Pourahmadi and others have proposed a thermal self-test mechanism for accelerometers
(with 50G full-scale deflection). This technique uses a differential thermal expansion to provide
actuation. It is claimed that the BIST structure used is virtually insensitive to ambient temperature
variations and has no effect whatsoever on the thermal performance of the sensor. Figure 2.2a shows
a schematic of accelerometer with thermal self test. The self-test feature uses an actuation beam
that is thermally isolated from the seismic mass (suspended silicon) by a gap of 8 microns.
ion-implanted heating resistor is situated near the middle of the central beam. When the voltage
pulse is applied to the heat resistor, the temperature of the actuation beam increases. As a result,
it expands and creates a force on the seismic mass that causes a downward movement. (See
Figure 2.2b). The piezoresistive sense elements are built in such a way that they remain unaffected
19
downward deflectionof seismic massbuckledPiezoresistive Actuation Beam
sense ele/nent / actuator beam
Actuation heaterSeismic~ ~------~----~
(a) (b)
Figure 2.2: (a) Schematic of accelerometer with thermal self test and (b) deflection of seismic massdue to thermal expansion of the actuator beam.
by the voltage pulse and the temperature change at the actuation beam. The sense element detects
the downward motion of seismic mass as an acceleration signal. The mechanism, though simple
in theory, is difficult to implement. However, no other performance aspects of the sensor itself are
compromised by the actuator structure and mechanism. The authors do not comment about the
time required for thermal actuation of the seismic mass and also the the time required to restore
the mass to its normal position. We believe that this could be a potential performance issue in
such a BIST technique.
Most BIST approaches for MEMS-based inertial measurement devices use actuation of the me-
chanical part for test. None of the previous work provide an analysis of BIST structures’ potential
impact on performance. Also, all the practical BIST approaches are off-line, that is, they require
devices to switch to a test mode where normal operation must be suspended. Such mode-switching
may not be desirable in many MEMS applications. It should also be noted that most BIST ap-
proaches perform an indirect measurement of the mechanical movement of the microstructure. The
electrical/thermal circuitry involved in applying self test may hide (for instance, due to electrical
or thermal noise) or not uncover (due to inadequate self test) the non-electrical/mechanical faults
2O
in the system. The researchers in this area provide no discussion on this topic. Thus, the BIST
approaches described above may not guarantee 100% fault coverage.
2.5 Summary
In this chapter, we have presented an overview of the research in MEMS testing. The list of
references attached at the end of the report contains some more interesting papers in this area
[41, 44, 49, 50, 51]. Following are our observations :
1o Most testing techniques are highly design specific. As a result, they may work well with one
design but not with others. There is a need to develop a comprehensive testing methodology
which can be applicable to all instances of a class of MEMS structures like inertial sensors.
The long-term objective of our research work is to develop such a testing methodology.
2. The success of any testing technique depends on the fault models applied. None of the papers
described here have general guidelines for developing fault models. Fault models discussed
so far are highly design specific and are in general very difficult to apply. Also, they are not
guaranteed to cover all the possible defects in the design. We plan to address this concern by
developing realistic fault models that can be applied to a class of MEMS designs at a higher
level of abstraction.
3. Most BIST techniques estimate the mechanical movement of the microstructure by perform-
ing electrical measurements. In practice, such measurements are difficult, if not impossible~
to perform because of the presence of noise. Setting the range for fault-free behavior, as in
analog testing, is a difficult task. Mistakes in setting such thresholds may result in informa-
21
tion loss that degrades test quality. This problem can be tackled by analyzing various failure
mechanisms and the resulting deviations in the final functionality. Knowledge of such correla-
tions will lead to more effective BIST techniques (like modification of structural and material
properties, selection of better measurement techniques) and aid in MEMS fault diagnosis.
Therefore, our work in MEMS testing first centers on exploring a wide range of defects and
the faulty MEMS behaviors associated with them°
22
Chapter 3
CARAMEL
In this chapter, we first describe the normal operation of the surface micromachined microres-
onator. We then describe our CAD tool CARAMEL (Contamination And l~eliability Analysis of
MicroElectromechanical Layout) and its application to the development of realistic fault models for
MEMS. Finally, simulation results obtained using CARAMEL for the microresonator are discussed
in detail.
3.1 Micromachined Microresonator
The folded-flexure comb-drive microresonator that we consider for our analysis can be fabricated
using a simplified version of the three-layer polysilicon MUMPs process. (Appendix A provides
complete listing of the MUMPs process steps.) The top view of a resonator shown in Figure 1.1b
is repeated in Figure 3. la. The resonator is a mass-spring-damper system made only from the first
and second polysilicon layers. A majority of the resonator’s structure is suspended above the wafer
23
surface. It is connected to the wafer only at the four anchor locations shown. The shuttle mass
is supported by symmetrical folded flexures that are designed to be compliant in the x direction
and rigid in the y direction. The shuttle can be electrostatically actuated in the x direction by
applying a voltage across the comb drives as shown in Figure 3.1a. Each comb drive is formed from
interdigitated fingers on either side of the shuttle mass.
Lateral motion in the x and y directions are modeled by the second order equations:
Fe,x = rnx~ + Bx~ + kxx (3.1)
Fe,y = my~) + Bye] + kyy (3.2)
where F~,~ and F~,y are the electrostatic forces generated by the comb drives in the x and y
directions, respectively. The parameter values for the effective masses (rex and my), damping coef-
ficients (Bx and By), and spring constants (ks and ky) are calculated from the material properties
and geometries of the resonator°
The parameters [32] that define the resonator’s operation are described in Table 3.1 and illustrated
in Figures 3. lb, c, and d. These parameters along with properties of the materials are used to model
the operation of the resonator. The effective masses rn~ and my are given by
1 12= + + (3.3)8my = m~ + ~mt + mb (3.4)
where ms is the shuttle mass, and mt and mb are the total masses for the all the truss sections and
long beams~ respectively. ~om [20], the damping coefficiem in the x direction is
24
combdrive
movingcombsfixedcombs
shuRlemass
x
folded
anchors
(a)
(d)
Figure 3.1: (a) Top view of the microresonator and pertinent dimensional parameters of the (b)comb drive, (c) shuttle, and (d) folded-flexure.
where # is the viscosity of air, d is the spacer gap, 5 is the penetration depth of airflow above the
resonator structure, g is the gap between comb fingers, and As, At, Ab, and Ac are the surface
areas for the shuttle, truss beams, flexure beams and comb-finger sidewalls, respectively.
The linear equations for the flexure spring constants for the x and y directions are taken from
[19]
kz = L~ 4L2t + 41o~LtLb + 36(~2L~ (3.6)
2Etwat 8L~t + 8aLtLb + a2L~ky = L~ 4L~ + lOc~LtLb + 5(~2L~ (3.7)
where E is Young’s modulus for polysilicon, t is the polysilicon thickness and c~ = (Wt/Wb)3. For
the special case of wc = g = t = d, we have from [21] the force generated by each of the comb drives
Fe,z ~ 1.12eoNt-V2 (3.8)g
25
Parameter Parametername description
Lb length of flexure beam
Wb
LtWt
Lsy
Wsa
Wsy
LcgXo
TE
PN
ms
mb
m$
width of flexure beamlength of truss beamwidth of truss beamlength of shuttle yokewidth of shuttle axlewidth of shuttle yokelength of comb fingersgap between comb fingerscomb finger overlapthickness of resonatorYoung’s modulus of polydensity of polynumber of comb fingersshuttle masslong beam masstruss section mass
Typical
value82 ttm2 t~m12 #m4 #m50 #m18 #m11 #m20 t~m2 #m10 #m2 #m165 GPa2330 Kg/m3
151.61e-11 Kg6.11e-12 Kg3.73e-13 Kg
Table 3.1: Resonator parameters (and typical values) that define the modeled operation of the
microresonator.
where eo is the permitivity of air and V is the voltage applied to the N-finger comb drives. The
above relationships define the resonator’s maximum deflection Xmax and resonant frequency Ix [32].
Xmax - (3.10)
However, it should be noted that the above mathematical models have limited ~ccuracy because
they do not account for beam compression and axle bending effects.
3.2 Resonator Application: Accelerometer
In this section, we describe the use of resonator in designing an accelerometero Figure 3.2a shows
the basic sensing operation of a typical accelerometer. It consists of a shuttle mass which is designed
26
to move with respect to its package under applied acceleration in the y direction (See Figure 3.2).
There is an array of parallel comb fingers attached to both sides of the shuttle mass1. Between
every pair of fixed combs is a moving comb. Such an arrangement of fixed and movable combs
produces an array of differential parallel-plate capacitors° The capacitance C, for every pair of
moving-fixed parallel beams is given by the simple equation :
AeC- d (3.11)
where A is the area of parallel plates (i.e. beam overlap), e is the permitivity of the spacer between
the combs, and d is the distance between adjacent combs.
Thus, when the shuttle moves in y-direction as shown in Figure 3.2a, the distance d between
the parallel beams changes, resulting in a corresponding change in the capacitance. Observe in
Figure 3.2a, the capacitance for one pair of parallel combs increases to C + AC and decreases
for the other pair to C - AC. Similarly, if the shuttle moves in the x-direction, overlap area A
of the comb fingers which again results in a change of capacitance. Thus, shuttle movement can
be converted to an equivalent change in capacitance. A simple circuit for sensing the capacitance
change is shown in Figure 3.2b. This circuit can sense capacitance changes and produce a linear
change in the output voltage. Two pulses of the same amplitude but 180° out of phase are applied
to the potential divider arrangement. Initially, when the shuttle is stationary, the opposite voltages
applied to the fixed beams produce an output of zero volts. However, with the slightest shuttle
movement, the capacitance balance is disturbed and a non-zero output voltage Vb results. A
demodulator is used to produce a DC voltage Vclem representing the magnitude and direction (+ or
-) of the acceleration. One of the driving signals fed to the potential divider arrangement is used
1For illustration purpose we have shown only one pair of differential capacitance in the Figure 3.2.
27
by the demodulator as a reference voltage.
The shuttle senses acceleration and converts it to a corresponding change in the differential
capacitance. Sensing circuitry then converts this capacitance change to a corresponding linear
change in output voltage. This is the basic principle behind accelerometer operation. As shown
in Figure 3.2, a typical accelerometer consists of a microresonator and some surrounding electrical
circuitry. There are three distinct regions identified on the microresonator. The shuttle is a proof
mass that moves under acceleration, combs are the parallel beams (fixed and movable) that form
differential capacitances, and the tether beams are the long, thin beams that apply a restoring
force to the displaced shuttle displacement.
The microresonator is also used in other applications such as micromachined resonant pressure
sensors [24], microelectromechanical filters [25], oscillators [26], and various signal processing cir-
cuits [27, 28].
3.3 CARAMEL Implementation
CARAMEL is a process simulator that maps particle contaminations to defective microstructures.
It is built around the tool CODEF, which is a contamination-to-defect-to-fault mapper for pure
electrical layouts [12]. Described next are the three phases of CARAMEL’s operation.
3.3.1 Process Simulation
This phase of CARAMEL maps spot contaminations to layout defects and is implemented using
a modified version of CODEF. CODEF determines the impact of a particle on the contaminated
region of the IC layout. It accepts layout information, ~ process description, and contamination
28
Shuttle Mass
Shuttle at rest
Tether beams AppliedFixed beam ,~ "~ Acceleration
~m Moving beam ~~
~ Anchors ~~ c-~c
acceleration
Demodulator DC Voltage
(b)Figure 3.2: Accelerometer basics: (a) Sensing operation of a typical accelerometer. (b) Signal sensingand processing circuitry for transducing a mass deflection into an output voltage.
29
PolyO silicon nitride
PSG--~
PolylL
micromechanicalstructure
anchor
polyO
anchor cut
polylstructure
releasedstructure
(b)
Figure 3.3: Abbreviated flow for MCNC’s MultiUser MEMS Process service. (a) Cross-sectional
view. (b) Top view (layout).
statistics for each processing step of interest. CODEF allows for the exact characteristics of the
contamination particle to be simulated including the particle’s size, density, conductivity, and fab-
rication step of introduction. Given the contamination parameters, it simulates all the fabrication
steps and creates a three-dimensional (3D) structure of the defective device. CODEF, in its unmod-
ified form, is used to analyze the effects of particles on an electrical IC layout. It utilizes a circuit
extractor which traverses the 3D structure to create a SPICE netlist. Defective circuit behavior
resulting from the contaminations can then be analyzed through SPICE simulations.
To make use of CODEF for MEMS contamination analysis, we have defined a complete MUMPs
fabrication process as a sequence of steps in the PREDITOR format [31]. The MUMPs process
is used to form micromechanical structures composed of thin films formed on the surface of the
substrate. These thin-film microstructures are called surface micromachined MEMS. Because the
resonator is a single-polysilicon structure, fabrication of the resonator utilizes only a subset of
3O
the complete three-polysilicon MUMPs process. This simplified version of the MUMPs process is
described in [32] and is shown in Figure 3.3. First, a low-stress silicon nitride is deposited on the sil-
icon substrate to provide electrical isolation between microstructureso An electrical interconnection
layer of polysilicon is then deposited and patterned. Next, a 2 #m-thick layer of phosphosilicate
glass (PSG) is deposited. PSG acts as sacrificial spacer layer for the microstructures. After con-
tact cuts are made in PSG, a 2 ttm-thick layer of polysilicon is deposited and patterned to form
a microstructure. A final wet etch in hydrofluoric acid (HF) dissolves the PSG and releases the
microstructure so that it is free to move. Contact cuts in the PSG are anchor points that fix the
microstructure to the substrate.
In CARAMEL, we have modified CODEF to handle the MUMPs "release" step (i.e. the HF etch
of PSG) as described above. Adding this step, which is not part of any standard CMOS process,
allows us to perform MUMPs process simulations using realistic contaminations.
3.3.2 Structure Extraction
The structure extraction phase produces a three-dimensional (3D) mesh representation of the de-
fective structure generated by CODEF. Mechanical simulation of the mesh (using finite element
analysis (FEA) tools like ABAQUS [13]) allows detailed analysis of the effects of contaminations
on the mechanical structure.
The process simulator creates a 3D representation of the resonator structure consisting of hierar-
chically connected layers of m~terial known ~s the Chip Data Base (CDB) [33]. CDB is created from
the MUMPS process flow and the CIF layout of the resonator. In addition, contamination particles
may be introduced at random locations in the process flow~ under control of the process simulator,
31
which can alter the final 3D structure of the resonator structure. CARAMEL creates a separate
"mesh database" from the CDB to represent the MEMS structure as a set of three-dimensional
(3D) corner-stitched rectangular regions [34]. Each region of the mesh consists of a set of material
elements. However, the mesh database differs from a traditional corner-stitched database because
elements in the former are allowed to be less than maximal width. This is a necessary feature since
the mesh used for mechanical simulation requires that each region have a single neighbor or no
neighbor along each of its edges. Each element also has associated data that describes its material
characteristics, its position in the z direction, and a flag that indicates if the base of the element is
anchored.
The MEMS meshing phase:
1. Creates a corner-stitched database containing the regions to be simulated.
2. Determines the connectivity of the regions.
3. "Splits" the mesh database to ensure that each region has only one or zero neighbors in the
x, y, and z directions.
4. Identifies the elements and generate node numbers for all the vertices of every region. (This
is required by the FEA tool for mechanical simulation.)
5. Generates a final mesh input model for FEA tool.
Mesh Database Creation
The mesh database for the resonator consists only of polysilicon one (POLY1) and defect material,
i.e., the material of the particle contamination. The mesh is created by examining every region
32
edge~--~
(b)
z split
Figure 3.4: Illustration of element splitting for mechanical mesh construction: (a) A set of regionsbefore split and (b) the same regions after x-y and z splits.
of CODEF’s CDB. Each element of POLY1 or defect material is added to the mesh database
along with a flag indicating if the element is fixed to some other material or contains free space
underneath. The flag information is used later to determine which elements are free to move.
Determining Element Connectivity
Understanding the behavior of the resonator requires mechanical simulation of its moving parts.
The moving parts of the resonator includes the mass shuttle and everything connected to the shuttle.
Because a particle contamination can alter the topography of the resonator, the resulting defective
structure must be derived from the CDB of elements. In this phase, CARAMEL determines all the
connected elements from a a user-specified element on the resonator’s layout. A simple algorithm is
employed that marks the user-specified element and continues to mark neighboring elements until
all connected regions are marked.
Element Splitting
The mechanical mesh required by FEA tools must be constructed so that adjacent regions meet
only at region vertices (nodes). Caramel must meet this adjacency constraint not only in the x and
y directions but also in the z-direction.
33
x-y splitting: In a standard corner-stitched database, a region edge can have multiple
neighboring regions or a partial neighbor as shown in Figure 3.4a. Such a situation violates
the constraint described above for the mechanical mesh. The x-y splitting operation modifies
an element so that every region edge has a single neighboring element or no neighbor at all.
Splitting is performed by analyzing the edge neighbors of every region in the CDB. For each
edge that violates the constraint, the current region is split at the neighbor’s edge. (See
Figure 3.4.)
¯ z splitting: In an analogous way, elements may need to be split in the z direction as well.
In this case, adjacent regions that have elements of differing heights must be split in the
z direction so that the resulting elements share nodes. This is accomplished using a simple
algorithm that compares the top and bottom coordinates of an element with all its neighboring
elements. If an adjacent element is contained in the current element, the current element is
split. (See Figure 3.4.) There is one exception to the z-split operation when the element
concern is fixed. Typically, split elements inherit identical properties (material, conductivity,
etc.) from the original element. In the case of fixed elements, only the newly-created bottom-
half element is fixed.
The result of CARAMEL’s extraction phase is a mechanical mesh that is directly compatible
with the FEA tool ABAQUS. The resulting mechanical mesh captures the impact of the particle
contamination on both the electrical and mechanical properties of the resonator. It should be
noted here that CARAMEL is a general tool and is therefore not restricted to the resonator but is
applicable to any generic MEMS layout. It is therefore possible to analyze the impact of particle
contaminations on a wide variety of MEMS topologies. In the next section, we examine the impact
34
of 721 contaminations on the structure and behavior of the resonator.
3.4 Simulation Results
CARAMEL was used to perform 721 unique contamination simulations of the surface microma-
chined comb-drive resonator shown in Figure 1.1. We analyzed contaminations of various sizes and
material properties. In addition, contaminations were placed at many different resonator locations
and were introduced at many different steps of the MUMPs process. Out of 721 contaminations, 263
were physically in contact with the resonator structure. The effect of each of these 263 contamina-
tions was observed at each phase of CARAMEL’s operation. The mechanical simulator ABAQUS
was also used to compute the resonant frequency of each of the mechanical meshes generated by
CARAMEL. The resonant frequency is analyzed because it is one of the crucial parameters for
determining if the resonator is functioning properly [8]. In Table 3.2, we provide details of five
representative cases of contamination analysis. Note that the first entry of Table 3.2 gives the
resonant frequency for the defect-free resonator. For each of the five defects considered, we provide
the location of the contamination (Table 3.2), the resulting 3D defective structure (Figures 3.5a,
b, c and Figures 3.6a, b), top view of the mechanical mesh (Figures 3.5a, b, c and Figures 3.6a, b),
and the resulting resonant frequency reported by ABAQUS (Table 3.2).
Comb Defect: Figure 3.5a shows the impact of a contamination located between adjacent
comb fingers. The process simulation phase of CARAMEL reveals that the contamination
welds together the two normally-moving fingers. The fixed fingers transforms the two comb
drives into a single structure thereby changing the mesh model required for mechanical simu-
lation. Mechanical simulation of a defect-free resonator requires only the shuttle and flexure.
35
Fixed combs are also simulated
(a)
Mesh changes on the flexure
(b)
Additional anchor
Figure 3.5: Three-dimensional representations of defective resonators and their corresponding me-chanical mesh for particle contaminations located (a) between comb fingers, (b) on a beam, (c) under the shuttle.
36
Contamination Diameter Density Added after Resonant % change inlocation size (#m) (kg/m °) step # frequency fz (Hz)
NoneCombBeamShuttle-1OutsideShuttle-2
2.41.52.02.01.5
23302330233023302330
Polyl depositionPSG depositionPoty0 PR StripPoly0 depositionPSG deposition
697729011668646883286977269721
+29:2%-1.61%+26.6%o%0%
Table 3.2: Five representative cases of defect analysis using CARAMEL.
Mesh remains unchanged
(a)
Mesh changes on the shuttle
(b)
Figure 3.6: Three-dimensional representations of defective resonators and their corresponding me-
chanical mesh for particle contaminations located (a) outside of the resonator’s active area and (b)
on the shuttle.
37
The result obtained from mechanical simulation shows a 33% increase in resonant frequency;
an indication that this defect has caused a catastrophic failure.
¯ Beam Defect: The impact of a defect affecting a beam of the folded-flexure is illustrated in
Figure 3.5b. Such a contamination results in a slightly heavier beam. The increased mass of
the beam decreases the resonant frequency by 1.61% . Note the meshing complexity of the
area surrounding the defect’s location. The increased "meshing" is a reflection of the number
of splits required to accurately represent the region containing the defect.
¯ Shuttle-1 Defect: A contamination that becomes lodged between the resonator and the
substrate acts as an anchor. CARAMEL handles such cases by defining an anchor element at
the contamination location during the extraction phase. The meshing phase discovers that
the contaminant is connected to the suspended structure and the substrate surface. The
resulting mesh used in the mechanical simulation therefore has an extra anchor. The impact
of such a defect is shown in Figure 3.5c. Simulation of the corresponding mesh indicates that
the resonant frequency has increased which is quite intuitive since the resonator is now fixed
to the substrate at an additional location.
¯ Outside Defect: Contaminations lying outside the structure are treated like the others,
but during the "connectivity" phase of meshing, it is determined that the contaminant is
not connected to the structure of interest. Meshes for such defects are generated but are
not simulated. For these cases the nominal values of resonant frequency are reported. An
example of this type of harmless defect is illustrated in Figure 3.6a.
¯ Shuttle-2 Defect: Figure 3.6b shows another interesting case where the contamination
becomes totally encapsulated by the shuttle mass. Process simulation indicates that a small
38
bump is formed on the shuttle surface. The creation of such a bump changes the meshing of
the affected region evidenced by the dense meshing surrounding the defect area. Mechanical
simulation reveals a very small (almost none) decrease in resonant frequency due to the
additional mass.
The five analyzed cases demonstrate CARAMEL’s ability to model and simulate a variety of
contamination effects on the MEMS microstructure. The tool can be effectively used to generate
several thousand contamination simulations under a Monte-Carlo mode of operation. In Monte
Carlo mode, contaminations are introduced into the process at random steps, with random sizes,
and at random locations in the layout. Such analysis can produce the full spectrum of MEMS
failure modes. The observed deviations in behavior can then be systematically categorized under
various fault classes. These fault classes will then be mapped to appropriate fault models at the
higher level of abstraction. In other words, such an analysis provides a technique for formulating
behavioral models for defective beams, gaps, shuttles, etc.. We have performed 721 simulations
using CARAMEL to illustrate its Monte-Carlo mode of operation. Process simulation showed that
only 263 contaminants were in physical contact with the microstructure. Mechanical simulation
showed that 65 of the 263 simulations produced defective structures that significantly altered the
resonant frequency of the resonator.
Table 3.3 presents a coarse fault categorization of the 263 defects. The three broad fault classes
are identified as catastrophic, parametric, and harmless. Catastrophic defects are those defects
that cause more than a 30% change in the resonant frequency fx. Defects that cause a frequency
deviation between 5% and 30% are termed parametric. Defects are termed harmless if they have
none or negligible affect on
39
Number of DeviationsDefect type occurrences in f~ CommentsCatastrophic 47 Af:~ > 30% Resonator functionality is
completely destroyed.Parametric 18 5% _< Af~ _< 30% Resonator still performs but
with large deviations fromthe desired functionality.
Harmless 198 ~f~ < 5% No impact on the functionality.
Table 3.3: Coarse fault categorization based on deviation in resonant frequency fx.
More details about the defects are provided in Table 3.4. It shows the occurrences of defects with
respect to its process step of introduction. This information indicates which MUMPs process steps
are most vulnerable to particle contaminations. It can be seen that step 8 (i.e. Etch Poly0 resist)
is highly prone to catastrophic failures. This result is quite intuitive. Poly0 forms the base of the
resonator while the "suspended’ structure is built from Polyl. Hence, any contamination occurring
on Poly0 can bind the two layers and impede resonator movement. This situation often leads to
catastrophic failures. Thus, the Poly0 deposition and development phases of fabrication are quite
vulnerable to particle contaminations. As indicated in Table 3.4, contaminations introduced during
this phase (steps 3 to 12) lead to over 60% of the catastrophic failures observed.
Table 3.5 shows the spatial distribution of the contaminations over the resonator layout that
caused catastrophic and parametric failures. It can be observed that the comb fingers are the most
defect-prone region in the resonator causing around 43% of the total catastrophic faults. Defects
affecting the combs have resulted in significant changes in the resonant frequency. The shuttle, due
to its relatively large area, is quite susceptible to being "hit" by a contamination; however for the
same reason the changes in the shuttle do not affect the resonator operation. The smaller surface
area of the folded-flexure beams, on the other hand, is less likely to be affected. However, when
the beams are impacted, their behavior is significantly disturbed.
4O
Process Number Of % of occurrences Catastrophic Parametricstep occurrences causing faults faults faults
131 (Initialize)2 (Nitride deposit)3 (Poly0 deposit)4 (Litho spinon Poly0)5 (Litho expose Poly0)6 (Litho develop Poly0)7 (Etch Poly0)8 (Etch Poly0 resist)9 (PSG deposit)12 (Litho develop PSG)13 (Etch PSG)19 (Etch Anchorl resist)20 (Polyl deposit)21 (Litho spinon Polyl)22 (Litho expose Polyl)23 (Litho develop Polyl)24 (Etch Polyl)25 (Etch Polyl resist)48 (PSG release)
916334414221331292622193824
30.844.468.833.366.710025.085.71000.00.00.012.913.811.64.610.515.84.2
41122
4431
Total defects 263 -- 47 18
Table 3.4: Defect categorization based on the MUMPs process step for which the contamination
was introduced.
ContaminationlocationShuttleComb fingersFolded flexure
Catastrophicfaults (%)31.942.625.5
Parametricfaults (%)33.316.750.0
Totalfaults (%)32.335.432.3
Table 3.5: Spatial distribution of faults in the microresonator.
41
It is also important to note that all the catastrophic failures due to shuttle contaminations occur
before deposition of the Polyl layer. These conta~ninations are lodged between the Polyl and Poly0
layers creating an anchor effect. Conversely, contaminations occurring on the shuttle after/during
Polyl deposition do not cause appreciable change in the resonant frequency. Thus, contamination
impact on resonator behavior is a function of both the location and process step in which it is
introduced.
Following are the main observations that can be drawn from the simulation results :
1. Particle contaminations have a large impact of the resonator functionality and the spectrum
of faulty behaviors can be discovered through contamination and FEM simulations.
2. A systematic classification of the faulty behavior can be made by mapping various fault
classifications to a higher level of abstraction.
3. Impact of particle contamination is a function of both the location and process step in which
it is introduced.
4. The comb drive is the most defect prone region of the resonator and the Poly0 deposition
process is the most vulnerable to a particle contamination that leads to catastrophic faults.
42
Chapter 4
Conclusions and Future Work
Our long term goal is to develop a comprehensive MEMS testing methodology. The first step in
achieving this goal requires the generation of effective and accurate fault models for MEMS. Our
approach towards developing realistic fault models has resulted in a systematic analysis of a large
spectrum of faulty MEMS behaviors that can be further mapped to fault models at a higher level
of abstraction° This was accomplished by:
Performing process contamination simulations to produce a large variety of defective three-
dimensional microstructures.
Mapping the effects of structural damages to faulty MEMS behavior.
Performing systematic classification of faulty MEMS behavior.
We have automated the contamination analysis process for microelectromechanical layout using
our tool CARAMEL. CARAMEL can be effectively used to directly investigate the faulty behavior
of defective MEMS structures. Here, we have illustrated CARAMEL’s use on a folded-flexure res-
43
onator. Simulation of 263 different defects indicates that spot contaminations can have a significant
impact on the behavior of the resonator. Moreover, the results show that the impact of a particular
defect is highly dependent upon where it is located in the resonator’s layout and when it is intro-
duced into the manufacturing process. The results obtained were classified under three broad fault
classes: catastrophic, parametric, and harmless. The simulation runs show that CARAMEL can
be effectively used to generate fault models for MEMS.
We plan to conduct a large number of random process simulations using real contamination
data at every step of the manufacturing process in order to produce a large spectrum of defective
MEMS structures. Low-level mechanical simulations will be performed to categorize these defective
structures into a smaller set of faulty behavior classes. These fault classes will form the basis of
MEMS fault models and serve as our first step in developing a comprehensive testing methodology
for such systems.
There are several open research areas that we plan to address concerning the development of a
MEMS testing methodology.
Process Simulation: CARAMEL uses the process simulator CODEF to simulate MEMS
designs. The accuracy of the process simulation is somewhat limited since it does not account
for the manufacturing characteristics inherent to MEMS processes. The preditor format used
for defining the process sequence should be modified to capture the manufacturing details crit-
ical to micromachining. For example, the type of etching (isotropic, directional, anisotropic,
etc.), the variation of etching rates with respect to plane orientation, selectivity, and as-
pect ratios are quite crucial for bulk micromachining and need to be accounted for. What
is required is a process simulator that can simulate both bulk- and surface-micromachining
44
processes accurately. The new process simulator should be able to identify and simulate the
effects from stiction, buckling, etc. that are present in the microstructures, in addition to the
problems caused by particulates.
¯ Structure extraction: Our testing methodology depends on gaining a comprehensive un-
derstanding of the effects of spot contaminations. Process simulations of random contamina-
tions will create a large variety of perturbed structures. In order to classify these defective
structures into an appropriate fault model, we have developed CARAMEL that automatically
derives (when possible) the structural and material parameters that define behavior. Cur-
rently, we are addressing the following issues to improve CARAMEL for MEMS fault model
generation:
1. Mesh Optimization: Presently, the mechanical mesh generated by CARAMEL is not
optimized in any way. It contains many artifacts from the original process simulation
that add to the size and complexity of the mesh. We plan to optimize the mesh in order
to accelerate the mechanical simulation phase.
2. Automatic Fault Categorization: We plan to have CARAMEL sort through the
large amount of simulated data and extract the unique faults. This will reduce the
amount of mechanical simulation required for fault categorization.
3. Simulation Complexity: We are currently simulating static structures that do not
change during the course of simulation. Static structures do not capture a number of
interesting and possibly important defects. For example, a particle located between but
not touching a comb finger may limit the motion of the resonator. This and other types
of interactions will require more knowledge of the material properties of the contaminants
45
but can be modeled using our approach.
¯ Fault Model Verification: We plan to verify the accuracy of the developed fault models
using actual fabricated systems. Our approach will use defective devices to measure and
compare actual and predicted faulty behaviors. For example, a number of contaminations
can be introduces during the structure release process and their impact can be observed by
measuring the device parameters. Also, the surface micromachined microstructures can be
manually pressed down to cause stiction. The effect of stiction can then be measured by
evaluating the faulty device.
¯ Test Methodology Grading: Given an accurate fault model, one can measure the effective-
ness of any given test methodology. Presumably, the fault model will provide an enumeration
of possible faulty behaviors along with their likelihood of occurrence. Coverage figures for
current MEMS testing methods can then be determined through systematic fault simulations.
¯ Test Methodology Development: Any possible shortcomings in current testing method-
ologies will be exposed by test methodology grading. Even if shortcomings do not exist,
knowledge about MEMS faulty behavior may lead to more effective test or design techniques
that reduce cost and increase quality.
¯ Design For Testability Techniques: Results obtained by performing contamination sim-
ulations indicate the most defect-prone parts of the MEMS design. Such guidelines can be
used to make appropriate structural design changes to make defects less likely or less harmful.
For example, a comb finger gap can be made wider to reduce the effect of contaminations
trapped between fixed and moving comb fingers. CARAMEL can also be used to compare
and contrast different MEMS designs based on process contamination simulations.
46
¯ Analytical Fault Model Generation: We obtain a large spectrum of possible defects
and corresponding faulty MEMS behavior using CARAMEL. These observations can then be
related to analytical models to define the faults at a higher level of abstraction. For example,
the shuttle-2 defect shown in Table 3.2 can be defined as a small increase in shuttle mass ms
and therefore in ro, x as shown in equation a.a. The change in mz will in turn cause change in
resonant frequency fx according to equation 3.9. Thus, shuttle-2 defect can be represented
by equation 3.9 with increased rex. Such an analysis will be complicated for the defects like
comb defects where the analytical model itself needs to be changed.
¯ MEMS Diagnosis: Finally, more effective testing methodologies will undoubtedly lead to
better diagnosis. We plan to create formal links between observed faulty behavior, testing
methods, and process contaminations for diagnostic purposes.
47
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5O
Step ~ Name Description
34567891011121314151617181920212223242526272829303132333435363738394O4142434445464748
InitializeNitride Deposit
Deposit Poly0Litho SpinOnLitho ExposeLitho DevelopEtch Poly0Etch Poly0 ResistDeposit OxidelLitho SpinOnLitho ExposeLitho DevelopEtch OxidelEtch Dimplel ResistLitho SpinOnLitho ExposeLitho DevelopEtch OxidelEtch Anchorl ResistDeposit PolylLitho SpinOnLitho ExposeLitho DevelopEtch PolylEtch Polyl ResistDeposit Oxide2Litho SpinOnLitho ExposeLitho DevelopEtch Oxide2Etch Oxide2 ResistLitho SpinOnLitho ExposeLitho DevelopEtch Oxidel2Etch Anchor2 ResistDeposit Poly2Litho SpinOnLitho ExposeLitho DevelopEtch Poly2Etch Poly2 ResistDeposit MetalLitho SpinOnLitho ExposeLitho DevelopEtch Metal ResistRelease
Heavy doping of wafer surface using phoshous (POC13)Deposition of 600 nm silicon nitride layer using a low stress,low pressure chemical vapor deposition (LPCVD)Deposition of 500 nm polysilicon film (poly0) using LPCVDCoating of wafer with photoresist (PR)Exposure of PR with CPZ maskPR developmentEtching of poly0 layer with reactive ion etch (RIE)PR stripDeposition of phosphosilicate glass (PSG), oxide1 by LPCVDCoating of wafer with PRExposure of PR with COS maskPR developmentEtching of PSG layer with RIE to form DIMPLESPR stripCoating of wafer with the PRExposure of PR with COF maskPR developmentEtching of PSG layer in RIE to form ANCHORS (anchor1)PR stripDeposition of 2 #m of polysilicon blanket (polyl) using LPCVDCoating of wafer with PRExposure of PR with CPS maskPR developmentEtching of Polyl layer in RIEPR stripDeposition of 0.75/zm of PSGCoating of wafer with PRExposure of PR with COT maskPR developmentEtching of oxide2 with RIE to form polyl-poly2 viasPR stripCoating of wafer with PRExposure of PR with COL maskPR developmentEtching of oxide1 and oxide2 with RIE to form ANCHORS (anchor2)PR stripDeposition of 1.5 #m of polysilicon blanket (poly2)Coating of wafer with PRExposure of PR with CPT maskPR developmentEtching of Poly2 with RIEPR stripDeposition of metal (gold with adhesion layer)Coating of wafer with PRExposure of PR with CCM maskPR developmentRemoving the unwanted metal and PR in a solvent bathRelease of sacrificial PSG layer by immersing in 49% HF solution
Table 4.1: Appendix A - The complete MCNC MUMPs process described in the PREDITORformat. 51
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