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ALUMINUM NITRIDE MEMS
RESONANT THERMAL BIOSENSORS
A Thesis Presented
by
Raul Vyas
to
The Department of Electrical and Computer Engineering
in partial fulfillment of the requirements
for the degree of Master of Science
in
Electrical Engineering
in the field of
Microsystems, Materials and Devices
Northeastern University, Boston, MA
August 2014
© Copyright 2014 by Raul Vyas
All Rights Reserved
To my family
Acknowledgements
First I would like to express my most sincere gratitude to my advisor Prof. Matteo Rinaldi
for providing me such a precious opportunity to work in the field of MEMS/NEMS design and his
unwavering support towards my research, and belief in my efforts. His practical and hands-on
approach to tackle important research problems set an example for me of what a truly dynamic
and successful engineer should be.
I also wish to thank my colleagues and group members, especially Yu Hui and Zhenyun
Qian, who shared many helpful experiences and knowledge with me, and whose response to my
queries would always be positive and readily available. They always set an example, a benchmark
that I and others in our research group would try to emulate.
I am very grateful to the staff of the George J. Kostas Nanoscale Technology and
Manufacturing Research Center at Northeastern University, especially Scott Mcnamara, where the
devices reported in this thesis were fabricated.
Most of all, I would like to express immense gratitude to my family who, even if living far
away, made me feel their unconditional love and support always present and helped me overcome
many professional and personal issues that I encountered on this fantastic journey.
ABSTRACT
Calorimetry is a very effective technique employed for analyzing biochemical reactions
(glucose and urea sensing, DNA detection and biodefense). Most of the commercial micro-
calorimetric sensors available in the market don’t have either a simple operational configuration
(amperometric sensors which rely on a detection of ions in a solution based on changes in electric
current), or can only detect temperature changes in the range of 0.1-0.2 K. The most important
performance metrics that ought to be considered for the design and optimization of micro-
calorimetric biochemical sensors are the thermal detection capabilities of the sensing element and
the thermal coupling between the biochemical reaction and the thermal detector. All these
fundamental challenges are addressed in this thesis by taking advantage of advanced material
properties and innovative device engineering, the result of which are high temperature resolution
(994.5 µK/Hz1/2 in a 50 Hz measurement bandwidth and 534.355 µK/Hz1/2 in a 200 Hz
measurement bandwidth) micro-calorimetric sensors based on high frequency (134.5 MHz and
116.67 MHz) Aluminum Nitride (AlN) nano-plate resonators (NPR), overlapped by a freestanding
reaction chamber separated by a micro-scale air gap (~50 m). High sensitivity (~8.66 ppm/K and
~22.2 ppm/K) and low noise performance (~1.16 Hz/Hz1/2 in a 50 Hz bandwidth) are achieved by
scaling the overall volume of the resonant structure and by taking advantage of two high quality
factor, Q (~882 and ~985), resonant systems. Efficient thermal coupling between the biochemical
reaction and the resonant thermal detector is achieved by reducing to ~50 m the air gap between
the resonator and the freestanding reaction chamber. The non – contact measurement also reduces
the degradation of performance metrics like mass loading effects.
The unique thermal detection capabilities of the AlN NPR calorimetric sensors enabled the
monitoring of Urea and Glucose enzymatic reactions, as well as exothermic Acid-base
neutralization reactions using Hydrochloric Acid, Ammonium Hydroxide and Sodium Hydroxide.
The effectiveness of the fabricated micro-calorimetric sensor prototype for monitoring chemical
reactions was characterized by sequentially placing in the reaction chamber different
concentrations of Acid and Base and monitoring the resonator frequency shift induced by the heat
generated by the exothermic reaction at each concentration. As expected, saturation was reached
when the concentration ratio between the two chemicals was 1:1, because maximum heat is
generated at equal concentrations, resulting in a maximum frequency shift. For the biochemical
enzymatic reactions, sensitivities of 9.4815 kHz/M for urea and 2.583 kHz/M for glucose were
obtained. Detection limits for urea and glucose measurement were calculated to be 61.22 M and
535.803 M respectively. The experimental results also demonstrate the great potential of the
proposed technology for the implementation of a new class of high temperature resolution and low
noise AlN NPR thermal detection based biochemical sensors.
Contents
1. INTRODUCTION 1
2. CALORIMETRIC BIO-SENSORS 3
2.1 Parameters of Calorimetric Biosensors 6
3. ALUMINIUM NITRIDE NANO PLATE RESONANT (AlN-NPR)
THERMAL DETECTORS 8
3.1 Motivation 8
3.2 Aluminum nitride nano plate resonators 11
3.3 Important Parameters of an AlN nano plate resonant thermal detector 19
3.4 Initial design of the Thermal detector 23
4. FABRICATION & ASSEMBLY: AlN NPR THERMAL
DETECTOR 26
5. RESULTS 39
5.1 Temperature Sensitivity 39
5.2 Finite Element Method (FEM) Simulation 41
5.3 Acid-Base Neutralization Reaction 45
5.4 Enzymatic Hydrolysis of Urea 49
6. NOISE PERFORMANCE 52
6.1 Peak to Peak Noise 52
6.2 Root Mean Square (RMS) Noise 52
6.3 Noise Spectral Density 53
6.4 Detection Resolution 53
7. PROTOTYPE II – GLUCOSE SENSING 54
8. CONCLUSIONS 59
8.1 Summary 59
8.2 Future Work 61
9. REFERENCES 63
1
1. INTRODUCTION
In recent years, Micro and Nano Electro Mechanical Systems (MEMS/NEMS) resonators
have been widely used for multiple sensing applications thanks to the unique combination of
extremely high sensitivity to external perturbations and ultra-low noise performance. Of all the
MEMS/NEMS resonant sensors that are in the market or under development, the AlN nano plate
resonant sensor (NPR-S) technology [1], which involves exciting high frequency (100 MHz to 10
GHz) bulk acoustic waves in piezoelectric nano plates (thickness < 1 μm) developed using
Aluminum Nitride, can be seen as a frontrunner for realizing highly sensitive, miniaturized and
low power chemical sensors [2], thermal detectors [3], and magnetic field sensors [4]. The reduced
mass and high frequency of operation of the nanomechanical resonant elements combined with
their high Q factor values make the AlN NPR-S capable to achieve unprecedented values of limit
of detection and detection speed [2-5].
The work presented in this thesis explores the potential of using the Aluminum Nitride
Nano Plate resonator technology for the development of high performance micro-calorimetric
biochemical sensors. For the first time, the unique scaling capabilities and the excellent
piezoelectric transduction properties of Aluminum Nitride, characterized by high values of Quality
factor and temperature sensitivity are utilized to construct Thermal Biosensors. By placing the
biochemical reaction chamber out of the resonant body of the device (but suspended over it), the
electromechanical performance of the resonator is unaffected by the chamber and the materials
used to implement it. Efficient heat transfer from the reaction chamber to the resonator is achieved
by scaling the air gap between them.
The thesis is organized in the following chapters:
2
Chapter 2 explores the idea of a biosensor and details the benefits of using calorimetry for
biosensing applications. It further describes the various performance metrics and characteristics of
a biosensor.
In Chapter 3, the motivations behind using an AlN MEMS Resonator for thermal detection
are explored. After reviewing the mechanism and fundamental parameters of the Aluminum
Nitride Nano Plate Resonator (AlN NPR), the analysis and optimization of the thermal detection
capabilities of Aluminum Nitride Nano Plate Resonant Sensor (AlN NPR-S) are introduced. It
further explores the idea of using AlN-NPR as a thermal detector through a Finite Element Method
(FEM) Simulation, and describes the salient features of such a detector.
In Chapter 4, the design and fabrication solutions of the proposed Aluminum Nitride Nano
Plate Resonator (AlN-NPR) thermal detector as well as simulation verification using Finite
Element Method (FEM) are presented. Measured Temperature Coefficient of Frequency (TCF) of
the AlN NPR is reported and discussed. At the end of chapter 3, there is a detailed account of the
thermal detector assembly.
Chapter 5 presents the experimental demonstration of a prototype of AlN-NPR based
thermal detector, and its ultimate utilization as a biosensor. The effectiveness of the fabricated
prototype is verified by monitoring exothermic acid-base neutralization reactions, and is
characterized as a biosensor by monitoring the catalytic hydrolysis of urea in the presence of
urease.
Chapter 6 discusses the Noise performance of the biosensor described in chapter 5.
In Chapter 7, another prototype of AlN-NPR based thermal biosensor is presented and
characterized by monitoring the catalytic oxidation of glucose in the presence of glucose oxidase.
3
2. CALORIMETRIC BIO-SENSORS
An analytical device which functions to analyze a sample for the presence of a specific
compound is known as sensor. A sensor which utilizes biological material to specifically interact
with an analyte is known as biosensor. An analyte refers to the compound which has to be ‘sensed’
or the presence of which has to be determined. The interaction of analyte and biosensor is measured
and converted to signals, which are again amplified and displayed. A biosensor thus involves
converting a chemical flow of information into electrical signals. The biological materials used in
biosensors are mostly enzymes, antibodies, nucleic acids, lectins and even a cell as a whole.
A Biosensor mainly consists of two major components, as shown in figure 1. The
Biological component constitutes of enzymes, antibodies and other biological materials which
mainly interacts with the analyte particles and induces a physical change in these particles. The
Transducer component collects information from the biological part, converts, amplifies and
displays them.
There are several different types of biosensors based on different interpretations of the
results of the analyte – bioreceptor interaction and the transduction principle, as shown in figure
2, like Potentiometry – where an electric potential is produced, Amperometry – where the
analyte/biological material interaction induces a redox reaction resulting in a movement of
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electrons, Optometry – where light is released, and Calorimetry. A calorimeter is a device used
for calorimetry, the science associated with determining the changes in energy of a system by
measuring the heat exchanged with the surroundings. A calorimetric biosensor uses calorimetry as
the basic transduction principle by measuring the heat of a reaction on the sensor surface.
Calorimetric sensors are perfect as an application of thermal sensors because they can be used to
determine physical properties, including the enthalpy of chemical reaction, biochemical
transformation, and the heat capacity of a material, from measurements of a temperature difference
across a thermal resistance through which heat flows. Calorimetry is a very effective technique
employed for analyzing biochemical reactions, and offers distinctive advantages over other
measurement techniques for biomolecular characterization. Moreover, enzyme-catalyzed
reactions show high selectivity and typically involve an enthalpy change, which makes them
highly suitable for configuring them as thermal biosensors for a broad range of applications. Most
of these enzyme-catalyzed reactions are biological in nature and are exothermic, enabling detection
by calorimetric analysis. Calorimetry allows direct determination of thermodynamic properties,
and is universally applicable to a wide variety of biomolecules in that almost all reactions are
thermally active. Additionally, calorimetric measurements occur in solution phase without
requiring the biomolecule to be attached to solid surfaces. It is label-free in that it does not require
biomolecules to be labeled with a radioactive, enzymatic or fluorescent labeling groups to report
molecular binding or conformational changes.
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Figure 1: Typical Components of Biosensors
6
1.1 Parameters of Calorimetric Biosensors
There are several static as well as dynamic parameters that can be used to characterize the
performance of calorimetric biosensors. The following list explains these parameters in detail.
Sensitivity: It refers to a change in the measurement signal per concentration
unit/temperature variation of the analyte, depending on whether the sensor is characterized
as a biosensor or a thermal sensor. Essentially, it is the slope of a calibration graph.
Detection Limit: The lowest value which can be detected by the sensor in question, under
definite conditions. Procedures for evaluation of the detection limit depend on the kind of
sensor considered.
Selectivity: An expression of whether a sensor responds selectively to a group of analytes
or even specifically to a single analyte.
Resolution: The lowest concentration/temperature difference which can be distinguished
when the composition is varied continuously.
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This parameter is important chiefly for detectors in flowing streams/liquid-phase biochemical
reactions.
Response Time: The time required for a sensor to change from a zero value to a step
change. Usually specified as the time to rise to a definite ratio of the final value. The time
which has elapsed until 63 percent of the final value is reached is called the time constant.
Heat Transfer Efficiency: It is defined as the ratio between the temperatures of the detector
element and the source of heat.
Some other parameters like Dynamic Range, Selectivity, Linearity, and stability are also
important for characterizing the performance of a biosensor, but are usually only applicable for
commercial products.
8
3. ALUMINIUM NITRIDE NANO PLATE
RESONATOR (AlN-NPR) THERMAL
DETECTORS
3.1 MOTIVATION
The demand for highly miniaturized sensors capable of detecting minuscule concentrations
of multiple liquid phase and gaseous analytes has grown in recent years, and the necessity to detect
such small concentrations requires reliably measuring extremely small variations in the sensor
output signal like voltage and frequency. In this perspective, optimal sensor performance is
facilitated by synthesizing a transducer that occupies a large area (which, in turn, facilitates
efficient transduction) and is very thin (which allows fabricating low mass devices with ultra-high
sensitivity). Suspended membranes with thickness in the nanometer range are therefore desirable
for sensing applications. Aluminum Nitride Nano Plate Resonators (AlN-NPR), first proposed by
Prof. Matteo Rinaldi are a particularly representative example of high performance bulk mode
acoustic NEMS contour-mode resonant sensors which involve exciting high frequency bulk
acoustic waves in a piezoelectric nano-plate (thickness 50 ~ 500 nm) made of Aluminum Nitride
(AlN). Such AlN Piezoelectric Nano-Plate Resonant Sensor (NPR-S) technology is not only
characterized by high values of sensitivity, due to the reduced mass and high frequency of
operation of the nanomechanical resonant element, but is also associated with low noise
performance, due to the combination of high quality factor, Q, and power handling capability of
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the bulk acoustic wave NEMS resonators [6]. Concurrently, the device is very well isolated from
the heat sink (high thermal resistance) and has a very small volume (small thermal mass) which
are all desired parameters for a thermal sensor. In addition, the AlN nano-plate composing the
NPR-S can be efficiently actuated and sensed piezoelectrically on-chip solving the fundamental
transduction issues associated with electrostatically transduced NEMS resonators.
For a thermal detector, a vital parameter to consider is the Temperature/Thermal
Sensitivity. In case of a resonator, it is given by the following equation –
𝑇ℎ𝑒𝑟𝑚𝑎𝑙 𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 = Δ𝑓 × 106
𝑓0 × ΔT 𝑝𝑝𝑚/𝐾
Where, Δ𝑓 is the shift in the resonance frequency of the resonator, 𝑓0 is the fundamental resonance
frequency and ΔT is the change in temperature. This sensitivity is characterized by the Temperature
Coefficient of Frequency (TCF), i.e., higher the TCF, higher is the sensitivity. Now,
miniaturization of the AlN-NPRs results in a higher TCF. This is due to the effect of the electrodes,
whose thickness becomes a considerable fraction of the AlN film in the resonator implementation,
where the Young’s modulus of the metal electrodes is considered in the measurement of equivalent
TCF [7]. Nevertheless, the sensitivity cannot be considered the only important parameter for the
design of a high performance resonator based thermal detector. In fact, the detection resolution of
the sensor, described as the smallest change in a parameter (temperature in case of a thermal
detector and concentration in case of a biosensor) in a given measurement bandwidth is given as-
𝐷𝑒𝑡𝑒𝑐𝑡𝑖𝑜𝑛 𝑅𝑒𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛 ∝Δ𝑓
𝑚𝑖𝑛
𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦
Where Δ𝑓𝑚𝑖𝑛 is the minimum noise-induced frequency fluctuation, which is proportional to –
10
Δ𝑓𝑚𝑖𝑛 ∝ √𝑘𝐵𝑇0𝐵
𝑃.𝑓0𝑄
Where kB
is Boltzmann constant, T0
is temperature, B is the measurement bandwidth, P is the
driving power and Q is the quality factor. Δ𝑓𝑚𝑖𝑛 is directly related to the noise performance of the
AlN-NPR, and should be as low as possible. Hence, for a better noise performance, Q has to be
higher, which an important factor behind choosing the AlN Nano Plate Resonator for thermal
sensing.
The potential of using MEMS resonators for the development of high performance IR
thermal detectors has been recently demonstrated through the implementation of highly
temperature sensitive gallium nitride [8] and Y-cut quartz [9] MEMS resonators. Also, MEMS
technology has been currently used in the market by manufacturers like Analog Devices and
Omron for the development of Thermal Sensors. However, most of these sensor technologies are
bulky, costly and suffer from fundamental challenges like mass loading and low thermal
sensitivities. In this thesis, the unique scaling capabilities and the excellent piezoelectric
transduction properties of Aluminum Nitride are exploited for the fabrication of a high frequency
and small volume resonant structure characterized by high values of Quality factor and temperature
sensitivity, which is then developed into a Thermal Biosensor. Furthermore, by placing the
biochemical reaction chamber out of the resonant body of the device (but suspended over it), the
electromechanical performance of the resonator is unaffected by the chamber and the materials
used to implement it. Efficient heat transfer from the reaction chamber to the resonator is achieved
by scaling the air gap between them.
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3.2 ALUMINUM NITRIDE NANO PLATE
RESONATORS
The Physical properties of AlN are desirable to both MEMS designers and large research
community. An III–V semiconductor, many of its material properties are a result of its close–
packed tetrahedral diamond-like crystal structure. Under ambient conditions, the
thermodynamically–stable structure (Figure 3) of AlN is hexagonal wurtzite (a hexagonal closed
packed structure), and exhibits direct piezoelectric effect.
Figure 3: Wurtzite hexagonal close-packed structure of AlN with Nitrogen atoms in white circles and Al
atoms in black circles.
12
A solid is defined as piezoelectric if it becomes electrically polarized when subjected to a
mechanical stress. When an applied stress creates a strain in the structure of a crystal, there is a
small change in the bond lengths between the atoms, resulting in a shift in the positions or
directions of the individual dipoles. In most crystals, the constituent atoms of the crystal are
distributed such that the sum of the individual dipoles between all of the atoms is zero. However,
in the case of AlN, the stress results in the formation of a nonzero dipole—a manifestation of
piezoelectricity. The piezoelectric effect can be expressed mathematically using equations that
describe the electric and structural behaviors of a material. One of them, the electric displacement
is defined as –
𝐷 = 𝜀𝐸 (1)
Where 𝜀 is the permittivity matrix of the material and 𝐸 is the applied electric field. Similarly,
Hooke’s law for mechanical strain is given by –
𝑆 = 𝑠𝜎 (2)
Where 𝑠 is the elastic compliance tensor of the material and 𝜎 is the stress. However, if we take
into account the electromechanical coupling, the above equations can be written as –
S = 𝑠𝜎 + 𝑑𝜎𝐸 (3)
D = 𝑑𝜎 + 𝜀𝐸 (4)
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Where 𝑑𝜎 is the transpose of the strain-charge form (d-form) piezoelectric coefficient.
Equation 4 expresses the direct piezoelectric effect, whereas equation 3 expresses converse
piezoelectric effect, where an applied voltage induces a stress in the material. The converse effect
is often used to determine the piezoelectric coefficients. The equations of state corresponding to
Wurzite-type-structure Aluminium Nitride can be expressed in the following matrix form, based
on above equations –
[ 𝑆1𝑆2𝑆3𝑆4𝑆5𝑆6]
=
[ 𝑠11 𝑠12 𝑠13 0 0 0𝑠21 𝑠22 𝑠23 0 0 0𝑠31 𝑠32 𝑠33 0 0 00 0 0 𝑠44 0 00 0 0 0 𝑠44 00 0 0 0 0 2(𝑠11 − 𝑠12)]
.
[ 𝜎1𝜎2𝜎3𝜎4𝜎5𝜎6]
+
[ 0 0 𝑑310 0 𝑑310 0 𝑑330 𝑑15 0𝑑15 0 00 0 0 ]
.[𝐸1𝐸2𝐸3
] (5)
[𝐷1𝐷2𝐷3
] = [
0 0 0 0 𝑑15 00 0 0 𝑑15 0 0𝑑31 𝑑31 𝑑33 0 0 0
].
[ 𝜎1𝜎2𝜎3𝜎4𝜎5𝜎6]
+ [𝜀11 0 00 𝜀11 00 0 𝜀33
]. [𝐸1𝐸2𝐸3
] (6)
The piezoelectric coefficient 𝑑𝑖𝑗is the ratio of the strain in the j-axis to the electric field
applied along i-axis, when all external stresses are held constant. The 𝑑33 coefficient has been
exploited in film bulk acoustic wave resonators (FBARs), which employ thickness-extensional mode
of vibration, for duplexer applications [4-5], but their resonant frequency is set by the thickness of a
thin film piezoelectric material such as AlN, which is not suitable for single chip multi-frequency
operation. A comparison of different modes of vibration in AlN is given in Table 1.
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Mode of Vibration Frequency Equation Uses
Flexural
𝑓0 ∝ 𝑇
𝐿2√𝐸
𝜌
[10KHz – 10MHz]
Suitable for High Q and low frequency
operations.
Contour-Mode/Lamb-
Wave
𝑓0 ∝ 1
2𝑊√𝐸
𝜌
[10MHz – 10GHz]
Suitable for high frequency operations.
Multiple devices on a single chip can have
different frequencies.
Thickness Extensional
𝑓0 ∝ 1
2𝑇√𝐸
𝜌
[500MHz – 20GHz]
Used in Duplexers.
Shear Mode
𝑓0 ∝ 1
2𝑇√𝐺
𝜌
[800MHz – 2GHz]
Have a high stiffness and can operate at a
high frequency.
Table 1: Shows a comparison between different modes of vibration in AlN resonators.
By using the 𝑑31 piezoelectric coefficient and applying an AC electric field in the direction of
thickness, in-plane displacement or lateral vibration can be excited in the MEMS structure (Figure 4).
Such lateral-extensional mode of vibration is employed for the piezoelectric Contour-mode AlN NPRs
presented in this work.
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Figure 4: Schematic representation of the dihexagonal structure of AlN. The fundamental X(1), Y(2) and
Z(3) directions are indicated. As shown, the anisotropic nature of the film permits the excitation of contour
mode shapes through the d31 coefficient.
A conventional AlN NPR is composed of an AlN film sandwiched between two metal
electrodes (Figure 5). When an AC voltage is applied to the interdigital electrode a contour-extensional
mode of vibration is excited through the equivalent 𝑑31 piezoelectric coefficient of AlN [6].
Figure 5: Schematic representation of a conventional AlN Nano Plate Resonator. The inset shows a FEM
Simulation of the device mode of vibration.
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Given the equivalent mass density, ρeq, and Young’s modulus, Eeq
, of the material stack
(AlN and electrodes) that forms the resonator, the center frequency, f0, of this laterally vibrating
mechanical structure, is univocally set by the pitch, W0, of the metal electrode patterned on the
AlN nano plate. The resonance frequency of the device can be expressed as –
𝑓0 = 1
2𝑊0√𝐸𝑒𝑞
𝜌𝑒𝑞
Several of these unitary cells of width, W0 (known as fingers), are arrayed together and
excited in an alternating fashion (two adjacent fingers are excited 180° out of phase with respect
to each other) so as to form an equivalent symmetric lamb wave [12] in the AlN nano plate (Figure
6).
Figure 6: Schematic representation of a three-finger AlN Nano Plate Resonator
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The other two geometrical dimensions, thickness, T, and length, L, set the equivalent
electrical impedance of the resonator and can be designed independently of the desired resonance
frequency. The number of fingers, n, their length, L, (also known as aperture of the transducer)
and the film thickness, T, are used to set the equivalent resonator electrical capacitance, C0, and its
motional resistance, Rm –
𝑅𝑚 ∝ 𝑇
𝑛𝐿
𝐶0 ∝ 𝑛𝐿𝑊0𝑇
For defining a resonator in terms of its equivalent circuit parameters, a Butterworth-Van
Dyke (BVD) Equivalent Model is used. It is a common lumped element circuit model to simplify
the transcendental functions that completely characterize the resonator. As shown in figure 5, it is
divided into two parts, where the right branch, called the static branch contains capacitances
representing the resonator capacitance and external connection capacitances, and the left branch,
called the motional branch represents the acoustic resonances in AlN and its load. These two
branches of the BVD model interact with each other via piezoelectric transduction. Also, at MEMS
scale (given the reduced size of the device), the external elements that provide for physical support
(i.e. the silicon) and routing of the electrodes need to be taken into account via a parasitic series
Rs, and a parallel resistance R0.
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Figure 7: Modified Butterworth Van Dyke (BVD) Model of an AlN Resonator
Qualitatively, the R-L-C branch determines the “series” resonance, where the impedance
drops sharply to a minimum value of R at a frequency where the series inductance and capacitance
cancel each other out. At some higher frequency, the loop reactance hits zero and causes a
“parallel” resonance where most of the current will travel around the loop instead of past it. The
only thing preventing the series and parallel resonance impedances from going to zero and infinity,
respectively, is the motional resistance RM.
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3.3 IMPORTANT PARAMETERS OF AN AlN
NANO-PLATE RESONANT THERMAL
DETECTOR
A thermal sensor is essentially a temperature sensor or a thermometer that has been
characterized to result in maximum temperature change upon exposure to a heat source. An ideal
thermal sensor is schematically illustrated in figure 8. A detector element of thermal resistance H
(J/K) is coupled to a heat sink at a constant temperature T0 (K) by a thermal conductance G (W/K)
[9].
Figure 8: Illustration of a Thermal Sensor
20
Upon absorption of a power Q (W) by the detector element, its temperature T can be
calculated from the equation:
𝑄 = 𝐻 (𝑑(𝑇 − 𝑇0)
𝑑𝑡) + 𝐺(𝑇 − 𝑇0)
Where t (sec) is the time. For a sinusoidal power input, 𝑄 = 𝑄0𝑒𝑖𝜔𝑡, the above equation can be
solved to yield:
𝛥𝑇 = 𝑇 − 𝑇0 = 𝑄0𝑒
𝑖𝜔𝑡
√𝐺2 +𝜔2𝐻2
For a thermal detector to exhibit high sensitivity, 𝛥𝑇 must be adequately large to ensure
that the heat lost to the surroundings is nominal [9], which, according to the above equation, can
be achieved by making G as small as possible and 𝜔 sufficiently low such that 𝐺/𝜔𝐻 ≫ 1. In
other words, both the thermal heat capacity of the detector element and its thermal coupling to the
surroundings should be as small as possible [9].
A major parameter that should be considered for the design and optimization of the
sensitivity of a micro-calorimetric thermal sensor is the thermal coupling between the heat source
and the thermal detector (in this case, the AlN-NPR). For ensuring an efficient thermal coupling,
the thermal resistance between the heat source and the detector should be as small as possible,
21
since the heat flow takes the path of lowest thermal resistance. In other words, the thermal
conductance between the source and the detector should be as high as possible.
For a thermal sensor to exhibit high values of sensitivity, it should not only have effective
thermal coupling, it should also have an efficient thermal detection capability of the sensor
element. In AlN Resonators, it is defined by the Temperature Coefficient of Frequency (TCF) [6].
It is also a measure of their thermal stability. It is generally expressed in ppm/K and is given by–
𝑇𝐶𝐹 = 1
𝑓
𝜕𝑓
𝜕𝑇= −
1
𝑎
𝜕𝑎
𝜕𝑇+
1
2
1
𝐸𝑃
𝜕𝐸𝑃
𝜕𝑇−
1
2
1
𝜌
𝜕𝜌
𝜕𝑇
Where a is the fundamental geometrical parameter that sets the center frequency and T
denotes temperature. In general, for contour-extension mode resonators, this equation can be
written as –
𝑇𝐶𝐹 = −𝛼1 + 1
2
𝜕𝑙𝑛𝐸𝑃𝜕𝑇
− 1
2(2𝛼1 + 𝛼3)
Where 𝛼1and 𝛼3are the linear coefficients of thermal expansion for AlN in the 1 (in the
plane of the film) and 3 (out of plane) directions, respectively; 𝜕𝑙𝑛𝐸𝑃/𝜕𝑇 is a coefficient that
describes the temperature variation of the in-plane modulus of elasticity [3]. For AlN, the largest
contribution to the TCF comes from the equivalent in-plane modulus of elasticity, whereas the
22
contribution of 𝛼1and 𝛼3 is less as compared to the young’s modulus [6]. If temperature stability
of center frequency is desired, lowering the TCF should be the main goal. But, for thermal
detection applications, the primary mode of detection is the change in center frequency, since
frequency is a quantity that can be monitored with the highest level of accuracy. For this purpose,
TCF should be sufficiently large to detect temperature variations.
23
3.4 INITIAL DESIGN OF THE THERMAL
DETECTOR
Taking into account the excellent thermal detection capabilities of an Aluminium Nitride
Nano Plate Resonator as well as a high quality factor, the design of a thermal detector has to allow
for temperature variation experiments in close proximity to the resonator to maintain efficient
thermal coupling. Keeping in mind the important characteristics and parameters of such a sensor,
a prototype thermal detector was conceptualized, as shown in figure 9.
Figure 9: Prototype of a Thermal Detector, with the AlN-NPR and Heat Source separated by an Air Gap.
24
Finite Element Method Simulations
The most common numerical method used for finding approximate solutions to boundary
value problems for differential equations is the Finite element method (FEM). It is a means of
predicting (and optimizing) the behavior of various complex objects or systems that are often
connected - without having to rely on physically existing models, prototypes or measurements.
The FEM is a stiffness based method in which the entire problem domain is divided into
subdomains called elements. It involves numerically dividing the system being analyzed into a
large number of small (finite) volumes, calculating the physical states (stresses, field strengths,
temperatures, etc.) of each individual cell and using an iterative process to approximate the
neighboring cells until a physically practical solution is obtained for the overall system. A variety
of specializations such as aeronautical, biomechanical, and automotive industries commonly use
integrated FEM in design and development of their products. Several modern FEM packages
include specific components such as thermal, electromagnetic, fluid, and structural working
environments.
As a proof of concept, a 3D finite element method (FEM) simulation was performed using
CMOSOL Multiphysics to investigate the temperature variation in a simplified sensor prototype
(without electrodes). The model chosen was heat transfer in solids. COMSOL provides a wide
range of simulation options for controlling the complexity of both modeling and analysis for the
system. The purpose of the simulation was to ensure that there was an effective heat transfer
between the heat source and AlN layer through a micro air gap of 0.2 m. The results of the
simulation are shown in figures 10 and 11. An efficiency of 97.635% was obtained for the micro
air gap, indicating its possible development into an effective thermal detector.
25
Figure 10: Temporal profile of a Thermal Detector. Inset shows the cross-section of the structure with a
0.2 m air gap.
Figure 11: Evolution of temperature with time for different layers of a Thermal Detector, with an air gap
of 0.2 m.
26
4. FABRICATION AND ASSEMBLY: AlN-
NPR THERMAL DETECTOR
In order to properly design the device and optimize its performance, an equivalent thermal
circuit shown in Figure 12 was considered. The power coming from a heat source at the top surface
is dissipated into device increasing both the top surface temperature, TS, and the resonator
temperature TR. This dissipation is through RRC1 (thermal resistance between the heat source and
the heat sink through the length of the top surface, which is the source of heat), and the series
combination of RRC2 (thermal resistance associated with the thickness of the top surface), Rair
(thermal resistance between the heat source and the resonator through the air gap), and RR (thermal
resistance associated with the length of the resonator). To achieve optimal device performance, it
is necessary to have both the source and the detector at almost equivalent temperatures, hence the
thermal resistance of the resonator has to be greater than the combined thermal resistance
associated with the air gap and the top surface thickness [3]. This translates in reducing the
thickness of the top surface (to reduce RS2), the air gap (to reduce Rair) and the thickness of the
AlN resonant plate (to increase RR). Another important factor to consider is that the vertical scaling
of the AlN plate causes an increase of the equivalent thermal resistance of the device, Rth, (hence
the overall sensitivity) and a reduction of its thermal capacitance, Cth, maintaining its thermal time
27
constant, , unchanged ( = Rth.Cth). In any case, lower values of thermal time constant can be
achieved by scaling the overall volume of the device (vertical and lateral dimensions). By reducing
the thickness of the AlN plate (i.e. implementing a nano-plate), the lateral dimensions of the device
can be scaled maintaining high transduction efficiency [17].
Figure 12: Equivalent Thermal Circuit of the micro-calorimetric AlN-NPR thermal detector.
28
The first step in the design of the AlN-NPR thermal detector was the Resonator itself.
According to what is described in chapter 2.3, a prototype of AlN-NPR with enhanced thermal
detection capabilities was designed and fabricated. A three dimensional schematic representation
of such a prototype of AlN-NPR, used as a reference for its fabrication, is shown in figure 13.
Figure 13: 3D schematic representation of the prototype of AlN-NPR.
In this prototype device, a lateral field excitation with floating top electrode (LFE-F) is
employed to excite a higher order contour-extensional mode of vibration in the AlN nano-
structures.
The LFE-F involves depositing the 500 nm AlN film (forming the resonant nano plate) on
top of an interdigital bottom electrode (100 nm) employed to excite the higher order lateral-
29
extensional mode of vibration. The electrically floating top electrode is instead used to confine the
excitation electric field across the thickness of the piezoelectric layer. It is worth noting that
without the electrically conductive top electrode the excitation electric field would not be
effectively confined across the thickness, T, of the device, hence the electromechanical coupling
coefficient, kt
2, of the nanomechanical structure would be approaching ~0 [10] (Figure 14) and it
would not be possible to excite the high frequency contour-extensional mode of vibration in such
ultra-thin (500 nm) AlN nano plate.
Figure 14: A schematic representation of Electric field in AlN Nano Plate.
The effective device area of this first prototype of AlN-NPR was designed to be 75 μm (W)
× 200 μm (L), the pitch, W0, of bottom Platinum (Pt) finger electrode was set to be 20 μm and the
thickness of AlN - nano plate, the top Au and bottom Pt electrode are 500 nm, 100 nm and 100 nm
30
respectively, resulting in a high order contour-extensional mode resonator working at a high
resonance frequency of approximately 135 MHz.
A 2D finite element method (FEM) simulation was performed using COMSOL
Multiphysics to investigate vibration mode, operation frequency and expected sensitivity for the
AlN Nano Plate Resonator. The model chosen was Piezoelectric Devices. Given to the designed
dimensions, the resonant frequency was found to be 133 MHz for AlN-NPR (Figure 15). And the
vibration mode of AlN-NPR was confirmed as contour-extensional mode (Figure 16).
Figure 15: Admittance plot from a 2D finite element method (FEM) simulation of the AlN-NPR.
31
Figure 16: Vibration mode from 2D finite element method (FEM) simulation of AlN-NPR shows in-plane
compression and extension.
32
Figure 17 shows the schematic illustration of the cross section of process flow for the
fabrication of AlN-NPR. As depicted, a 5-mask fabrication technique was used. A high resistivity
(>104
Ω·cm) Silicon (Si) wafer was used as substrate.
Figure 17: Microfabrication process: (a) Sputter deposition and lift-off of Pt bottom electrode; (b)
Sputter deposition of AlN, wet etch to open vias and dry etch to define device lateral dimensions; (c)
Sputter deposition and lift-off of top Au probing pads; (d) sputter deposition and liftoff of top Au
electrode; (e) Device release using XeF2 dry etch.
33
A 100 nm thick Platinum (Pt) film was sputter-deposited and patterned by lift-off on top
of the Si substrate to define the bottom interdigital electrode. Then, the 500 nm AlN film (stress
60 MPa and FWHM 2.2O) was sputter-deposited and then etched by Inductively Coupled Plasma
(ICP) etching in Cl2 based chemistry using photoresist as a mask to define the in-plane dimensions
of the resonant nano plate. Vias to access the bottom electrode were etched by H3PO
4. Optical
lithography was performed for the definition of both the electrode contact pads and the alignment
marks for the subsequent electron-beam lithography step. Then, a 100 nm thick gold (Au) film was
sputter-deposited and patterned by lift-off to form the probing pads. The MEMS die was then
sliced into multiple chips for further miniaturization and accessibility for the subsequent
measurement assembly. In the end, the device was released from the silicon substrate by isotropic
dry etching in XeF2. The fabricated prototype of AlN-NPR is shown in an optical microscope
image below (Figure 18).
Figure 18: An optical microscope image of an AlN-NPR.
34
The fabricated AlN-NPR was tested at room temperature and atmospheric pressure in a RF
probe station and its electrical response was measured by an Agilent E5071C network analyzer
after performing a short–open–load calibration on a reference substrate. The electromechanical
performance of the device was extracted by Butterworth-Van Dyke (BVD) model fitting (Figure
19).
Figure 19: Measured admittance and BVD fitting of the fabricated AlN-NPR. The extracted value of the
device mechanical quality factor, Qm, does not include the losses in the series Resistance, Rs.
35
The Temperature Coefficient of Frequency (TCF) of the fabricated AlN-NPR was
measured and found to be −26.2 ppm/K (Figure 20). This value is comparable to what is typically
measured in the case of conventional AlN-based MEMS resonators [6], in which the TCF is
dominated by the temperature-dependent Young's modulus of AlN.
Figure 20: Temperature coefficient of frequency (TCF) measurement of the AlN-NPR.
36
The first step in the assembly of the thermal sensor was wire bonding the AlN-NPR to a
Printed Circuit Board (PCB). The MEMS chip was attached to a custom designed PCB, designed
using Altium Designer (as shown in Figure 21) and fabricated by Advanced Circuits, Inc. The size
of each component on the PCB and the traces were chosen to maximize performance and cost
effectiveness. Gerber and NC drill files were then generated and submitted for fabrication.
Figure 21: PCB designed using Altium Designer Tool. The two tracks on either sides of the chip connect
to an SMA connector soldered underneath the PCB. Ground and Signal Pads were designed for a larger
wire bonding area and better electrical performance with lower parasitic capacitances.
The resonator was then wire-bonded to the PCB using a K & S 4523 Wire Bonder, and
SMA connector mounted and soldered underneath the PCB using an X-Tronic 9000 series
soldering station.
37
The next step was creating a reaction chamber to facilitate biochemical reactions in close
proximity to the thermal detector. Materials chosen were 140 m thick borosilicate glass and
PDMS, both of which have a high chemical resistance, essential in this application. The PDMS
acts as a sidewall, providing depth to the reaction chamber. An Anatech SP-100 plasma system
was used to attach the PDMS to the glass coverslips. Oxygen plasma converts the hydrophobic
PDMS surface to hydrophilic, which makes it easier to adhere to the glass surface. A schematic
representation of the process is shown in Figure 22.
Figure 22: Reaction chamber comprising of PDMS bonded to Glass using an O2 plasma.
The final step in the process was the entire assembly of the Thermal Sensor, where the
reaction chamber was placed on top of the AlN-NPR - attached PCB. The resonator and the
chamber were separated by a micro-air gap using a stainless steel spacer (Figure 23). The electrical
response of the sensor when tested initially was very poor, which was due to the spacer and the
track connecting the signal pad to the SMA connector touching each other. An incision was made
38
on the spacer along the line of the track which led to a significant improvement in electrical
response. Figure 24 depicts the electromechanical response of the sensor fitted to a BVD model.
Figure 23: Picture of the fabricated sensor. The inset shows a cross-sectional view of the overall
structure.
Figure 24: Measured admittance and BVD fitting of the wire bonded AlN-NPR.
39
5. RESULTS:
5.1 TEMPERATURE SENSITIVITY
The temperature sensitivity of the resonant frequency of fabricated and assembled micro-
calorimetric sensor was characterized by sequentially placing in the reaction chamber 50 μL
samples of deionized water pre-heated at different temperatures and monitoring the induced
resonant frequency shift for each temperature. It was measured for different air-gaps – 50, 200 and
300 µm – made possible by using steel spacers of varying thicknesses. The air gaps were estimated
using a Finn Tools & Instruments micrometer wafer measuring equipment, by placing the needle
at the chip, the reaction chamber and the steel spacers, and measuring the difference between them.
The resonator was excited at a single frequency (134.6788 MHz), where the slope of admittance
curve versus frequency is maximum, and the temperature induced admittance variation was
monitored over time. Figure 25 depicts the various temperature responses for different Air Gaps,
with an expected linear increase in temperature sensitivity with falling air-gap.
40
Figure 25: Temperature Response of the micro-calorimetric sensor for different air gaps. (a), (c) and (e)
depict response of the sensor in terms of an admittance variation for different temperatures for different
air gaps. (b), (d) and (f) show the measured Temperature Sensitivities of the sensor in terms of kHz/0C,
which amount to 8.66 ppm/K, 8.02 ppm/K and 6.32 ppm/K for the air gaps of 50, 200 and 300 µm
respectively, clearly indicating a marked rise in sensitivity with the lowering of air gap.
41
5.2 FINITE ELEMENT METHOD (FEM)
SIMULATION
To verify and optimize the proposed design solutions, a 3-Dimensional Finite Element
Method (3D FEM) simulation using COMOL Multiphysics was performed (Figure 26). The
thermal conductivity of AlN was set to 80 W/m.K [11]. A fixed temperature of 20°C was set as
the boundary condition for the edges of the device. Heat transfer in solids module was used for
generating a temporal profile.
Figure26: 3D finite element method (FEM) simulated temperature profile of the micro-calorimetric
sensor. A 1mW thermal power was applied to the top glass plate in the simulation.
42
The graphs in Figure 27 compares the rise in temperature of the reaction chamber and that
of the resonator for different air gaps.
Figure 27: A comparison of Temperature Rise for the reaction chamber, the air gap and the AlN Nano-
Plate resonator for different air gaps when a 1 mW power is applied over the reaction chamber surface.
43
The effect of air gap dimensions on the device performance was studied in terms of heat transfer
efficiency, which is defined by the ratio between the temperature of AlN resonator TR and one of
the reaction chamber TS. Figure 28 shows that smaller air gaps guarantee significantly more
efficient heat transfer.
Figure 28: Simulated Heat Transfer Efficiency for different Air Gaps.
A comparison between the simulated and actual heat transfer efficiency is given in Table
2. The measured values of the efficiency confirm that effective heat transfer from the reaction
chamber to the thermal detector is achieved by minimizing the air gap between them.
44
Air Gap 50 µm 200 µm 300 µm
Simulated Efficiency 26.66% 17.8% 17.045%
Experimental Efficiency 33% 31.3% 24.15%
Table 2: Simulated and experimental values of heat transfer efficiency for different air gaps. The device
efficiency extracted by FEM simulation was found to be slightly lower than the experimental result. This
is attributed to the fact that only heat conduction was considered for the simulation (heat transfers via
radiation and convection were neglected).
45
5.3 ACID-BASE NEUTRALIZATION
REACTION
To initialize and demonstrate the capability of the thermal sensor as a micro-calorimetric
biosensor, the exothermic reactions of hydrochloric acid with ammonium hydroxide and sodium
hydroxide were chosen to be monitored. The neutralization reactions can be expressed as –
𝐻𝐶𝑙 + 𝑁𝐻4𝑂𝐻 𝐻= −51.5 𝐾𝐽/𝑚𝑜𝑙→ 𝑁𝐻4𝐶𝑙 + 𝐻2𝑂
And
𝐻𝐶𝑙 + 𝑁𝑎𝑂𝐻 𝐻= −57.1 𝐾𝐽/𝑚𝑜𝑙→ 𝑁𝑎𝐶𝑙 + 𝐻2𝑂
The enthalpy of the reaction for the chemical reactions are -51.5 and -57.1 KJ/mol,
respectively. The effectiveness of the fabricated micro-calorimetric sensor prototype for
monitoring chemical reactions was characterized by sequentially placing in the reaction chamber
different concentrations of Hydrochloric Acid and Ammonium Hydroxide for the first reaction
(Figure 29) and Hydrochloric Acid and Sodium Hydroxide for the second reaction (Figure 30) and
monitoring the resonator frequency shift induced by the heat generated by the exothermic reaction
at each concentration. The HCl, NH4OH and NaOH solutions were prepared by diluting stock
46
solutions from the manufacturer (Doe & Ingalls) with DI water in the range of 1 M to 5 M. Prior
to the test, the sensor was connected to a Network Analyzer and allowed to stabilize for 30 minutes.
The reaction chamber was aligned on top of the sensor, with 100 l of the first reactant placed in
it and allowed to stabilize for 5 minutes. A 100 l of the second reactant was thereafter added. The
resonator was excited at a single frequency (134.6788 MHz), where the slope of the admittance
curve versus frequency is maximum, and the reaction induced admittance variation was monitored
over time. As expected, saturation levels were reached when the concentration ratio between the
two reactants for both the reactions was 1:1, because maximum heat is generated at equal
concentrations, resulting in a maximum frequency shift.
47
Figure 29: Response of the AlN NPR to the reactions between NH4OH and HCl. (a) NH4OH with varying
concentrations (1M to 5M) was mixed with 3M HCl in the reaction chamber. (c) HCl with varying
concentrations (1M to 5M) was mixed with 3M NH4OH. (b) & (d) show the corresponding resonance
frequency shifts induced by the reaction for different concentations of the reactants.
48
Figure 30: Response of the AlN NPR to the reactions between NaOH and HCl. (a) NaOH with varying
concentrations (1M to 5M) was mixed with 3M HCl in the reaction chamber. (c) HCl with varying
concentrations (1M to 5M) was mixed with 3M NaOH. (b) & (d) show the corresponding resonance
frequency shifts induced by the reaction for different concentations of the reactants.
An important point here is that the frequency variations shown in figures 29 and 30 (b &
d), which were supposed to be constant beyond the 1:1 concentrations are not exactly constant but
do show some variations because of pipetting errors. This results in a slight inaccuracy in the
volume of the reactants dispensed over the reaction chamber. This issue can be solved by using a
more accurate chemical dispensing mechanism.
Concentration of a reactant in a given acid-base reaction can be determined based on the
linear region of curves in Figures 29 and 30 (b & d). But there are limitations to this approach.
This technique can only be used in cases where the reactant whose concentrations need to be
determined has a concentration that is lower than or equal to the other reactant whose concentration
is known.
49
5.4 ENZYMATIC HYDROLYSIS OF UREA
Urea is widely distributed in nature and its analysis is of considerable interest in clinical
chemistry, agro-food chemistry, and environmental monitoring. The handling of Urea by the
kidneys is a vital part of human metabolism. Many methods of urea determination are based on its
hydrolysis in the presence of the catalyst urease and successive measurement of ions consumed or
produced:
𝐶𝑂(𝑁𝐻2)2 + 2𝐻2𝑂 𝑈𝑟𝑒𝑎𝑠𝑒,−61 𝐾𝐽/𝑚𝑜𝑙→ 2𝑁𝐻4
+ + 𝐶𝑂32+
The enthalpy change of the reaction is -61 kJ/mol. The possibility of electrochemical
detection led to the development of a large number of urea biosensors based on conductometric
[18, 19], potentiometric [20-22] and Amperometric [23, 24] transducers. However, they suffer
from slow responses, vulnerability to the interference of other ions in sample solution and
relatively high detection limits. The micro-calorimetric NPR based biosensor presented in this
work could potentially lead to a better sensitivity and lower detection limits.
For the experiment, 0.1, 0.2 and 0.3 M solutions of urea were prepared in a phosphate
buffer saline (PBS, pH 7.0) solution. Since 1 unit of urease can hydrolyze 0.5 mol of urea,
250U/ml urease solution in PBS was prepared which has sufficient enzymes to react with urea for
all concentrations in the experiment. Initially, 200 l of 0.1 M urea solution was placed into the
50
reaction chamber. Then the reaction chamber was stabilized in air for 5 minutes following which
50 l of urease solution was added into the urea solution in the reaction chamber. The experiment
data for hydrolysis of urea are plotted in Figure 31 for 50, 200 and 300 m air gaps. The data
exhibit a linear dependence of the peak sensor output as a function of the concentration of urea.
Using the data in Figure 31, concentration of Urea in an unknown sample can be determined.
51
Figure 31: Response of the AlN-NPR to the catalytic hydrolysis of urea in the presence of urease. (a), (c)
and (e) depict response of the sensor in terms of an admittance variation for varying concentrations
(0.1 M to 0.3 M) of urea for different air gaps in the presence of urease in the reaction chamber. (b),
(d) and (f) show the corresponding resonance frequency shifts induced by the reaction for different
concentations of the reactants.
The slopes of Figure 31 - (b), (d) and (f) depict Concentration Sensitivities of the sensor in
terms of kHz/M for the air gaps of 50, 200 and 300 µm respectively, clearly indicating a marked
rise in sensitivity with the lowering of air gap.
Given the slope for different air gaps, concentration of urea in an unknown sample can be
determined by dividing the measured variation in frequency with the value of the slope of the
Frequency – concentration curve when the reaction is performed.
52
6. NOISE PERFORMANCE
6.1 PEAK TO PEAK NOISE
Given a temperature induced admittance variation, the peak to peak noise was calculated
and is shown in figure 32.
Figure 32: Peak to Peak noise is calculated for a given temperature induced admittance variation.
6.2 ROOT MEAN SQUARE (RMS) NOISE
The RMS noise is calculated using the following equation –
𝑁𝑅𝑀𝑆 = 𝑁𝑃−𝑃𝜎
=0.004
6.6= 0.00060606 𝑑𝐵
Where σ is the conversion ratio, where percentage of the time Noise exceeding the nominal
Peak-to-Peak value is the lowest. One of the important factors while considering the RMS noise
is Thermomechanical Noise in the sensor system.
53
6.3 NOISE SPECTRAL DENSITY
Noise Spectral Density describes how power of the noise signal is distributed over different
frequencies. It is calculated using the following equation –
𝑁𝑜𝑖𝑠𝑒 𝑆𝑝𝑒𝑐𝑡𝑟𝑎𝑙 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 = 𝑁𝑅𝑀𝑆
√𝐵=0.0006
√50= 8.57 × 10−5 𝑑𝐵/√𝐻𝑧
Where B is the measurement Bandwidth.
6.4 DETECTION RESOLUTION
The temperature resolution of the micro-calorimetric sensor was calculated to be 994.5
µK/Hz1/2 in a 50 Hz measurement bandwidth.
The concentration resolution for the hydrolysis of urea was calculated for different air gaps
in a 200 Hz Bandwidth and is given in Table 3.
Air Gap 50 µm 200 µm 300 µm
Detection Resolution of Concentration
61.22 µM/√𝐻𝑧 209.049 µM/√𝐻𝑧 267.84 µM/√𝐻𝑧
Table 3: Lists the calculated Concentration Resolution of Urea for 50, 200 and 300 µm air gaps.
54
7. PROTOTYPE II – GLUCOSE SENSING
To monitor the effect of miniaturization on Temperature Coefficient of Frequency (TCF)
and temperature sensitivity of the AlN NPR thermal detector, a resonator with thinner Aluminum
Nitride layer (250 nm) was fabricated using the same 4 mask technique, with electrodes (Au and
Pt) of comparable thickness (100 nm and 50 nm respectively). The electromechanical performance
of the device extracted by Butterworth-Van Dyke (BVD) model fitting is given in figure 33.
Figure 33: Measured admittance and BVD fitting of the fabricated AlN-NPR II Prototype.
55
The thermal capability of the device measured through Temperature Coefficient of
Frequency (TCF) of the fabricated AlN-NPR II prototype was evaluated and found to be −51.35
ppm/K (Figure 34), which is an improvement over the previous prototype of the device. The higher
value of the TCF is because of comparable thicknesses of the electrodes (Au and Pt) and AlN
layer. As a result, the Young’s modulus of these electrodes factors into the value of TCF.
Figure 34: Temperature coefficient of frequency (TCF) measurement of the fabricated AlN-NPR II
Prototype.
56
The temperature sensitivity of the resonant frequency of the prototype II - fabricated and
assembled micro-calorimetric sensor was characterized by sequentially placing in the reaction
chamber 50 μL samples of deionized water pre-heated at different temperatures and monitoring
the induced resonant frequency shift for each temperature. It was measured for a 50 µm Air Gap.
The resonator was excited at a single frequency (116.840688 MHz), where the slope of
admittance curve versus frequency is maximum, and the temperature induced admittance variation
was monitored over time. Figure 35 (a) depicts the various temperature responses for an air gap of
50 µm. The high value of TCF results in a higher value of the temperature sensitivity of the device,
shown in Figure 35 (b).
Figure 35: Temperature Response of the micro-calorimetric sensor. (a) Depicts response of the sensor in
terms of an admittance variation for different temperatures. (b) Shows the measured Temperature
Sensitivity of the sensor in terms of kHz/0C, which amounts to 22.199 ppm/K for the air gap of 50 µm.
The increase in TCF and temperature sensitivity indicates an even higher efficiency over
the first prototype of the device, at 43.23%.
57
Glucose is a ubiquitous fuel source in biological systems. It is a simple sugar which is a
permanent and immediate primary source of energy in most organisms, from bacteria to humans.
The ability to monitor the concentration of glucose is of critical importance for sustained biological
operations. One method for quantitative determination of glucose is with the use of enzymatic
methods as outlined below (Figure 36). Glucose oxidase is known to oxidize glucose to gluconic
acid with the concomitant release of hydrogen peroxide.
Figure 36: Catalytic Oxidation of Glucose in the presence of Glucose oxidase.
The enthalpy change of the reaction is -80 kJ/mol. For the experiment, 0.1, 0.2 and 0.3 M
solutions of Glucose were prepared in a phosphate buffer saline (PBS, pH 7.0) solution. Since 1
unit of Glucose Oxidase can catalyze 1 mol of glucose solution at 25 0C, 260 U/ml Glucose
Oxidase solution in PBS was prepared which has sufficient enzymes to react with glucose for all
concentrations in the experiment. Initially, 200 l of 0.1 M Glucose solution was placed into the
reaction chamber. Then the reaction chamber was stabilized in air for 5 minutes following which
50 l of glucose oxidase solution was added into the glucose solution in the reaction chamber. The
experiment data for oxidation of glucose in the presence of oxygen are plotted in Figure 37 for a
50 m air gap.
58
Figure 37: Response of the AlN-NPR to the catalytic oxidation of glucose in the presence of glucose
oxidase (GOD). (a) depicts the response of the sensor in terms of an admittance variation for varying
concentrations (0.1 M to 0.3 M) of glucose for a 50 µm air gap in the presence of GOD in the reaction
chamber. (b) shows the corresponding resonance frequency shifts induced by the reaction for different
concentations of the reactants.
The slope of Figure 37 (b) depicts Concentration Sensitivities of the sensor in terms of
kHz/M for the air gap of 50 µm.
Given the value of the slope, concentration of glucose in an unknown sample can be
determined by dividing the measured variation in frequency with the value of the slope of the
Frequency – concentration curve.
The temperature resolution of the micro-calorimetric sensor and the concentration
resolution for the oxidation of glucose were calculated to be 534.355 µK/Hz1/2 and 535.803
µM/Hz1/2 respectively, in a 200 Hz measurement bandwidth.
59
8. CONCLUSIONS
8.1 SUMMARY
This thesis presents two high temperature resolution (994.5 µK/Hz1/2 in a 50 Hz
measurement bandwidth and 534.355 µK/Hz1/2 in a 200 Hz measurement bandwidth) micro-
calorimetric sensor prototypes based on two different high frequency (134.5 MHz, and 116.67
MHz) Aluminum Nitride (AlN) nano-plate resonators (NPR) overlapped by a freestanding reaction
chamber separated by a micro-scale air gap (~50 m). For the first time, the unique thermal
detection capabilities of the AlN NPR technology are exploited to devise calorimetric sensors with
superior performance. Efficient heat transfer from the reaction chamber to the thermal detector is
achieved by minimizing the air gap between them. By taking advantage of the large thermal
resistance (2.64 × 104 K/W) of the AlN NPR and the reduced air gap, high heat transfer efficiencies
(ratio between the temperature of the resonator and the one of the reaction chamber) of 33% for
Prototype I and 43.23% for Prototype II were achieved. Using a finite element method (FEM)
simulation, a remarkable agreement between the simulated and experimentally measured temporal
evolution profile of the sensor was obtained. The effectiveness of the fabricated prototype is
verified by monitoring exothermic reactions between Hydrochloric Acid and Ammonium
Hydroxide, and between Hydrochloric Acid and Sodium Hydroxide. Prototype I was used as a
micro-calorimetric biosensor for the hydrolysis of urea in presence of urease, and Prototype II was
used for the oxidation of Glucose in the presence of Glucose Oxidase. From the signal to noise
ratio analysis of the urea and glucose sensors, < 61.22 µM urea sensitivity and < 535.803 µM
glucose sensitivity could be obtained, respectively.
60
Table 4 shows the comparison between the performance parameters of the fabricated
micro-calorimetric sensor Prototypes and Y-cut Quartz resonant sensor technology. The sensor
technology described in this thesis is superior in terms of sensitivity and heat transfer efficiency,
two very important parameters for characterizing a sensor for biochemical sensing applications.
AlN-NPR micro-
Calorimetric Biosensor I
Y-cut Quartz Resonator based
biosensor
AlN-NPR micro-Calorimetric Biosensor II
Temperature Sensitivity of the
resonator technology
4.035 kHz/0C 7.32 kHz/0C 5.991 kHz/0C
Temperature Sensitivity of the
sensor 1.165 kHz/0C 1.942 kHz/0C 2.59 kHz/0C
Air Gap 50 µm 200 µm 50 µm
Heat Transfer Efficiency
33% 26.5% 43.23%
Urea Sensitivity 9.8415 kHz/M 5.75 kHz/M
Table 4a: A comparison between performance parameters of sensor technologies.
The reduced mass and volume, the increased frequency of operation, the high temperature
sensitivity, the high heat transfer efficiency, very low detection resolution and superior noise
performance all demonstrate the great potential of the proposed technology for the implementation
of a new class of micro-calorimetric biosensors capable of achieving unprecedented detection
capabilities.
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8.2 FUTURE WORK
The research work presented in this thesis has set a pathway for the development of a new class
of ultra-sensitive and low noise AlN micro-calorimetric biosensors. The current work presented in this
thesis achieves a high heat transfer efficiency, but even higher efficiency values can be obtained by
further scaling the air gap in 100 s of nm. Figure 37 presents the efficiency curve for the values of air
gap used in this thesis and the future direction of this work. Also, higher values of temperature
sensitivity can be obtained by further scaling the thickness of AlN layer to 10 s of nm.
Figure 37: Heat Transfer Efficiency for different Air Gaps.
62
Such low air gaps can be obtained by system-level implementations of chip-scale sensing
platforms using encapsulation to fabricate the reaction chamber directly on top of the resonator, as
shown in figure 38.
Figure 38: 8 mask fabrication process for encapsulating and constructing a device-level micro-calorimetric
sensor : (a) deposition of bottom Pt electrode, thin AlN layer and top gold electrode; (b) dry etching of
AlN; (c) deposition and patterning of polysilicon sacrificial layer; (d) deposition and patterning of SiO2
capping layer; (e) release holes etching in SiO2; (f ) XeF2 dry etch of sacrificial layer and release of AlN
resonator; (g) deposition and patterning of SiO2 to refill and seal the released holes.
63
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