Design of atomically-thin-body field-effect sensors and pattern
recognition neural networks for ultra-sensitive and intelligent
trace explosive detection
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The significance of detecting trace concentrations of chemical
molecules in particular low-volatile explosives and
explosive-related compounds has intensively increased in recent
years for national security [1], environmental monitoring [2], and
military applications [3]. With the large amount of energy stored
in their chemical bonds, explosives such as trinitrotoluene (TNT),
octogen (HMX), RDX (1,3,5-Trinitro-1,3,5-triazacyclohexane),
pentaerythritol tetranitrate (PETN) usually result in great release
of heat and light when detonated. The accurate detection of these
explosive chemicals has become a multi-decade long challenge, in
large part due to the inherently low vapor pressures of these
analytes. For instance, TNT exhibits a low vapor pressure of only
5.8 × 10−6 Torr at 25 °C (less than 10 parts-per-billion, that is,
only a few TNT molecules in 1 billion molecules of air), while some
non-volatile explosives such as HMX have extremely low vapor
pressure down to a few parts-per-trillion (ppt) level (5 × 10−9
Torr) [4]. Great strides have been made on exploring trace
explosives detection strategies to date. Existing trace detection
methods include ion mobility spectrometry [5, 6], mass spectrometry
[7, 8], surface acoustic wave detectors [9, 10], chemiluminescence
[11, 12], electron capture detection [13, 14], and surface enhanced
Raman spectroscopy [15, 16]. While being highly sensitive, these
methods usually require bulky machines and swipe collection of
particulate substance which are very expensive. Due to these
Y Qiang et al
Design of atomically-thin-body field-effect sensors and pattern
recognition neural networks for ultra-sensitive and intelligent
trace explosive detection
Yi Qiang1,5 , Ao Ren1,5, Xianzhe Zhang1, Preyaa Patel4, Xun Han1,
Kyung Jin Seo1, Zhan Shi1, Yanzhi Wang1 and Hui Fang1,2,3,6
1 Department of Electrical and Computer Engineering, Northeastern
University, Boston, MA 02115, United States of America 2 Department
of Mechanical and Industrial Engineering, Northeastern University,
Boston, MA 02115, United States of America 3 Department of
Bioengineering, Northeastern University, Boston, MA 02115, United
States of America 4 Department of Electrical Engineering, Indian
Institute of Technology, Simrol, Madhya Pradesh, 453552 India 5
These authors contributed equally to this work. 6 Author to whom
any correspondence should be addressed.
E-mail:
[email protected]
Keywords: trace explosive sensor, atomically-thin body
semiconductors, pattern recognition, deep neural network
Supplementary material for this article is available online
Abstract There has been enormous demand for detecting trace
concentrations of chemical molecules and in particular low-volatile
explosives through electronic instrumentation, which however, still
faces significant shortcomings in both detectability and
selectivity to date. In this work, we propose a novel sensor
strategy that incorporates arrays of atomically-thin-body
field-effect sensors for highly-scalable, ultra-sensitive trace
explosive sensors with fast response to ultra-low analyte
concentrations. Sensor performance and functionalization are
theoretically simulated through system-level considerations using
various kinetic, electrostatic, quantum mechanics, and
drift-diffusion models. Moreover, by implementing custom-built
neural network models for pattern recognition, we successfully
achieved automatic, accurate detection of four different types of
analytes with concentrations down to 0.02 part per trillion. The
intelligent sensors have the capability to differentiate analyte
types with 100% accuracy and predict the concentration values with
~10% of relative error simultaneously. We envision the proposed
sensor platform, design metrics, deep learning methods and the
combination of these innovations will be a promising yet practical
solution towards ultra-sensitive trace explosive detection and can
be extended to a wide range of molecular sensing
applications.
PAPER 2019
Y Qiang et al
limitations, trained dogs are still one of the most widely deployed
mobile detection systems because they offer incomparable
sensitivity in addition to their mobility and directionality.
However, canine sensing effectiveness is a function of its
training, mood, physical activity, etc. These challenges motivate
researchers to develop systems that mimic dog nose.
Indeed, there have been enormous interests in the detection of
trace concentrations of chemical mol- ecules by means of electronic
instrumentation during the past thirty years, which has led to the
development and commercialization of so-called ‘electronic noses’,
typically comprising an array of partially selective sen- sors with
analog outputs and suitable pattern recogni- tion algorithm to
infer the type and concentration of volatile chemicals. In an ideal
scenario, to achieve suc- cessful trace explosive detection using
electronic sys- tems, the sensors need to possess the following
charac- teristics: (1) sensitive to detect and quantify the trace
amounts of airborne explosive molecules, to the ppt or sub-ppt
level, within minutes or even seconds; (2) able to differentiate
and provide orthogonality at large throughput to form multiplexed
arrays required for selective sensing; (3) scalable to large-scale
implemen- tation and manufacturing; (4) robust and stable with time
and environmental changes; (5) miniaturized and low power
consumption to be compatible with small form factor, handheld
devices. Despite more than three decades of research on electronic
noses around the world, there is currently no proven system that
offers all of these properties.
Most existing electronic noses have utilized arrays of bulk
complementary metal–oxide–semiconductor
(CMOS)/microelectromechanical systems (MEMS) sensors which absorb
volatile molecules on the sensor surface resulting in a change of
physical state of the sen- sor [17–19]. Such electronic noses have
already been adopted in many different industries, in areas such as
food and beverage, pharmaceutical, cosmetic and per- fumes, flavor,
and biomedical diagnostics [20–22]. However, currently there is no
related technologies with detection limit down to even sub-ppb
level. For example, the Cyranose® 320 electronic nose integrates 32
nanocomposite sensor arrays with online pattern recognition and
memory, but with only low ppm level detection limit. Recently,
sensors from amino-termi- nated, chemical-vapor-deposition (CVD)
deposited Si nanowire field-effect sensors demonstrated one of the
lowest detection limits for several explosive chemicals in aqueous
solutions down to ppq concentration range [23]. This result
encouragingly shows that miniaturized semiconducting channels are
ultra-sensitive to external electrostatic perturbations and allow
for sub-ppt-level chemical sensing. However, it may eventually pose
sig- nificant technical challenges in scaling up these bottom- up
Si nanowires towards both large-scale arrays and sys- tems in both
reliable and uniform fashion.
Herein, we propose atomically-thin-body (ATB) semiconductor based,
field-effect sensors to achieve
ultra-sensitive and fast-responsive explosive detec- tion with high
sensor scalability. The surface of ATB semiconductors will be
chemically functionalized with silane derivatives, to which the
explosives molecules can strongly bind due to the interactions
between elec- tron-deficient aromatic rings from the explosives and
electron-rich amino groups from the silane deriva- tives [4]. This
charge transfer process will form dipoles close to the ATB sensor
surface, eventually resulting in the field-gating effect and
explosive detection. Sen- sors with different functionalization
will subsequently form partially orthogonal arrays yielding unique
response pattern associated with different analytes which
subsequently contribute to high selectivity. Due to their extreme
level of miniaturization, ATB semi- conductors could achieve
extreme level of field-effect sensitivity from each sensor.
Physical implementa- tion of these sensors can take advantage of
the exist- ing commercial ultra-thin body (UTB) silicon (Si)
fabrication platform, or the emerging manufacturing of many other
2-dimensional (2D) materials such as molybdenum disulfide (MoS2)
and graphene, which are both highly scalable.
In this work, we performed theoretical studies of the ATB
field-effect sensors through systematic model-based simulation to
predict individual sensor properties and neural networks (a 3-layer
neural net- work is only a shallow network, not a deep network)
based modelling to evaluate sensor array performance. The influence
of key sensor parameters including channel length, width, thickness
and doping concen- trations and impact of different
functionalization are holistically studied to optimize the sensor
design. Syn- thesized data from arrays of differentially
functional- ized sensors and multiple analytes was then used to
train the neural network, which generated distinctive pattern for
each explosive analyte. As a proof of con- cept, the developed
pattern recognition algorithm suc- cessfully achieved accurate
recognition of four differ- ent analytes with a range of
concentrations between 20 ppq to 1 ppt and were able to readout the
precise analyte concentrations simultaneously. These results reveal
the great potential of the ATB field-effect sen- sors to achieve
ultra-sensitive and selective explosive detection with high
scalability. We expect the proposed sensor design metrics and
simulation approaches as well as neural network models to be a
practical guide- line for developing highly scalable,
ultra-sensitive and intelligent trace explosive sensors targeting
at both detection and discrimination of ultra-low analyte con-
centrations.
2. Results and discussion
2D Mater. 6 (2019) 044002
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Y Qiang et al
electro-mechanical systems (MEMS), photonic crystals and
fluorescence polymers, etc. CMOS- compatible gas sensors developed
in recent years with various detection methods are summarized in SI
table 1 [23–36]. The sensing mechanism of the proposed ATB
field-effect sensors is the field-gating effect originating from
the charge transfer between analytes molecules and surface
functionalization layers, eventually forming dipoles close to the
sensor surface (figure 1(a)) [37]. In this work, we adopted ATB Si
as the sensing material, but the same design concept can be applied
to many other 2D semiconductors such as MoS2 with the same
compatibility with neural network algorithms. In the simulation
model using Synopsys Sentaurus TCAD platform, we defined the
surface dipole as 2 fixed elementary charges (that is, each charge
carries 1.602 × 10−19 C) with reversed signs (positive and
negative) located above the surface of an atomically-thin (less
than 5 nm thick) n-type Si layer (SI figure 1(a)
(stacks.iop.org/TDM/6/044002/ mmedia)). The molecular dipoles were
placed in air ambient (εr = 1) with the positive charges positioned
closer to the sensor surface to mimic that the surface
functionalization layer usually have electron-rich amino groups,
which forms positive charges during the charge transfer process
[4]. The simulation model does not include a gate oxide layer, but
the threshold voltage of the Si channel can still be tuned by
changing the doping concentration of the body. The source and drain
contacts are set to be ideal Ohmic contact. Key parameters that are
tuned in the present work are listed in SI figure 1(b). In this
simulation, Poisson equations coupled with electron, hole and
electron temperature continuity equations are self-consistently
solved. We also adopted quantum confinement model considering the
sub-5 nm semiconductor thickness. The formed surface dipoles induce
a change of electrostatic potential inside the 2D semiconductor
channel, which is visualized through the simulation software
(figure 1(b)). With the positive charges formed closer to the
channel, the electrostatic potential is increased throughout the
channel as plotted in figure 1(c), resulting in the modulation of
electron density inside the channel accordingly (figure 1(d)).
Eventually, the increased electron density contributes to a larger
output current of the sensor compared to the original state without
surface dipoles (figure 1(e)). The trend of Id-Vd curve is not
linear in this simulation due to the HighFieldSat model used, which
refers to the saturation of carrier velocity when electric field
increases to certain value. The results demonstrated in figure 1
are from representative simulation data to display the general
trend while with qualitative relevance. Meanwhile, we propose a
fabrication scheme to achieve the ATB Si field-effect sensor.
Realizing the atomically thin Si layer is the most significant step
in the fabrication process. Here we propose to adopt the thermal
oxidation process of silicon on insulator (SOI) wafer using a
dry-wet-dry process. The ultra-thin top
Si layer can then be achieved after the etching of the grown oxide
layer. Similar fabrication process has been validated by literature
to achieve 3.5 nm-thick silicon layer [33]. SI figure 2 illustrated
the key fabrication steps of this process.
2.2. Sensor response time and chemical binding kinetics When
detecting analytes with extremely low trace concentrations, the
surface binding process on the sensors usually saturate slowly due
to the low capture rate. To enable fast sensor responses, we
considered the transient sensor output by simulating the dynamic
molecule binding process. The binding kinetics between the
functionalization layer and analyte molecules forming the so-called
Meisenheimer complex [38, 39] is related to a number of parameters
including the dimensions of the sensing layers, analyte
concentrations and analyte types which determine the thermal
velocity at a given temperature. The analyte capture rate, which is
the number of analyte molecules bounded to the surface
functionalization layer for a given time, is assumed to be linearly
proportional to the analyte concentration. From molecular capturing
perspective, the capture rate R can be expressed as R = σ · C · v,
where σ is the capture cross section (L·W, where L and W stands for
the channel length and width, respectively), C is the analyte
concentration, and v is the thermal velocity. A dynamic molecular
binding process is illustrated in figure 2(a) as the number of
dipoles increase with the change of time. When the analyte
concentrations become extremely low (a few ppt or lower), it
usually takes more than a few minutes to reach the maximum amounts
of molecules that could be captured to certain functionalization
layers (saturation) [23]. Thus, to develop ultra-sensitive sensors
with fast responses (within seconds level), the transient behaviour
before the molecules binding saturation needs to be considered. As
a matter of fact, before reaching the molecules binding saturation,
there should be a continuous change of drain current as the number
of dipoles generated on the sensor surface increases with time,
which could be used for sensing. To understand the general trend
and derive average values, we assumed that formed dipoles evenly
distributed on the sensor surface. The ATB Si layer used for this
simulation is 120 nm long, 100 nm wide and 2 nm thick. The n-type
doping concentration adopted here is 1 × 1016 cm−3. The increase of
drain current as a function of time was then observed by comparing
the Id − Vd curves with different time (figure 2(b)).
Interestingly, a highly close-to-linear increase of the drain
current before saturation was observed, which motivated us to adopt
metrics associated with the average current increase rate (δI′) as
the alternative standard to characterize the sensitivities (figure
2(c)). Detailed electrostatic potential and electron density
changes as a function of time (0–5 s) are available in SI figures 3
and 4, respectively. The design
2D Mater. 6 (2019) 044002
metric regarding the transient sensor output change, instead of
considering the sensor status at saturation, will significantly
shorten the sensor response time particularly when detecting
ultra-low analyte concentrations. Notably, the time-window of the
close-to-linear current increase is related to the analyte
concentrations. Here we observed a window of linear- increasing of
~25 s for 1 ppt concentration, however,
with a much higher concentration such as 1 ppb, the time -window
will be shortened to ~25 ms. Here, we focus on small sub-ppb level
concentrations since they are currently beyond the detection limit
of conventional technologies. For higher concentrations, one can
use sensing metrics associated with the saturation current or
combine with existing technologies, which will be the subject of
subsequent studies.
Figure 1. Concept of ATB field-effect sensor. (a) Illustration of
ATB field-effect sensor. (b) Electrostatic potential distribution
of ATB field-effect sensor. Upper: without surface dipoles; bottom:
with surface dipoles; dotted box: ATB channel area. (c)
Electrostatic potential at the surface of an ATB channel. Black:
without surface dipoles; red: with surface dipoles. (d) Electron
density at the surface of the ATB channel. Black: without surface
dipoles; red: with surface dipoles. (e) Drain current of ATB field
effect sensor. Black: without surface dipoles; red: with surface
dipoles. Note: figures (c) and (d) do not have scales and units to
just show the general trend of field gating effect while with
qualitative relevance.
Figure 2. Sensor response time and chemical binding kinetics. (a)
Illustration of the dynamic molecules binding process. (b) Drain
current (Id) versus drain voltage (Vd) curves with time = 0, 1, 2,
3, 4, 5 s. Inset: zoomed-in view of the multiple Id − Vd curves.
(c) Drain current of ATB field-effect sensor as a function of time
(Vd = 0.1 V). Inset: electrostatic potential distribution at time =
1 and 2 s.
2D Mater. 6 (2019) 044002
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Y Qiang et al
Generally, a higher δI′ for a given analyte of a given
concentration should usually correspond to a larger detectability.
However, using δI′ as a standalone metric will misguide the sensor
design. For example, a large W of the ATB channel in the sensor can
always guarantee a high δI′ without true improvement of the field
effect caused by the dipole formation upon the channel, while W
might be strictly controlled for footprint and power consumption
requirements. Moreover, using δI′ alone does not capture the noise
level in the initial current (I0), which is highly relevant for the
detection of δI′ itself. It has been reported that the drain
thermal noise current constitutes a major noise source of field-
effect devices [40], and this noise is proportional to the drain
current, which is dominated by I0 at the begin- ning of the
molecules binding process if assuming the δI′ to be fairly small.
We therefore propose to use the dimensionless, relative current
change per second (δI′/I0) when characterizing the detectability.
Using this metric will allow us to reflect the true field effect to
the channel and embody the noise level.
2.3. Detectability considerations and design There are many
important parameters substantially affecting the sensor
detectability, which should be investigated for rational sensor
designs. The sensor detectability could vary with different
lengths, widths, thicknesses as well as doping concentrations of
the ATB semiconductor channel. Here we specifically characterized
δI′/I0 by simulating the dynamic molecules binding process from 0
to 5 s and averaging the current increase in the first 5 s. From
the perspective of control experiments, we maintained the other
parameters unchanged while tuning one of the selected ones. The
positive charges in the dipoles were positioned at 2 nm high from
Si surface where the nitro groups in the analyte molecules could be
located as described in literature [4]. The distance between the 2
fixed single charges was set to be 0.3 nm, derived from the
reported intermolecular length between analyte molecules and amino
groups [41]. As discussed before, the increasing rate of drain
current is positive with introduced dipoles, which is because the
n-type Si channel works in the accumulation mode while the surface
dipoles increase the major carrier densities (electrons) in the
channel. Dimensions used for different simulations are labeled in
each figure in figure 3 for a clear view.
We first examined the effect of different channel widths. As
expected, the initial drain current I0 is lin- early proportional
to the channel width (figure 3(a)). Meanwhile, as discussed in the
previous section, the molecule capture rate also increases linearly
with the channel width, which results in a direct increase of the
δI′. The net result is that we observed minimal change of the
δI′/I0 while tuning the channel width from 100 to 400 nm (figure
3(b)). The result also indi- cates that the channel width can be a
scale factor for sensor designs as the output current can be tuned
by
the channel width without compromising the device
detectability.
We then studied the dependence of the channel thickness and length.
To conclude the simulation results generally, smaller thicknesses
lead to higher δI′/I0, which is expected due to a smaller initial
cur- rent but with the same molecule capture rate. This trend is
universal as we clearly observed from the δI′/I0 values as a
function of thicknesses with different channel lengths and doping
concentrations (figures 3(c) and (d)). Unlike the effects from
channel width and thickness, the impact of channel length on δI′/I0
is more complicated and intriguing. We observed that with an
increase of channel length, there is an optimal channel length
resulting in maximum δI′/I0 per given doping concentration. This
phenomenon can be explained by the trade-off between the single-
dipole response and the number of dipoles generated with the change
of channel lengths. Each single dipole formed on the sensor surface
induces the change of electrostatic potential throughout the
channel (SI figure 5(a)). We found that for different chan- nel
length, the induced potential changes by a single dipole are
similar (SI figure 5(b)). However, with a longer channel, the same
gating volt age contributes to less current, indicating a lower
response to a single dipole when having longer channels. Meanwhile,
the molecule capture rate increases due to a larger sur- face area
(W × L), resulting in a larger number of formed dipoles per given
time. This trade-off along with the variation of initial current
makes the trend of δI′/I0 unpredictable without systematic simula-
tions. Taking advantage of the simulation approach, we observed
that the optimal channel length varies with different doping
concentrations (figure 3(e)). For example, the maximum δI′/I0
appears at 180 nm length for 1 × 1016 cm−3 doping concentration
while at 100 nm length for 5 × 1016 cm−3 doping concen-
tration.
Lastly, we studied the dependence of doping con- centrations.
Generally, the sensors benefit from a lower doping concentration,
but not the lower the better (figure 3(f)). In other words, there’s
an optimal dop- ing concentration for each channel length and
thick- ness, though the value is usually extremely low. For
example, the optimal concentration for a 100 nm-long device is 5 ×
1014 cm−3, which is not realistic for sen- sor deigns due to an
ultra-low initial current compara- ble to the environmental noises
in terms of amplitude. We simulated and listed the optimal channel
lengths for different doping concentrations in the table shown in
SI table 2. Besides the initial current, proper sen- sor size is
also necessary for rational sensor design. As mentioned previously,
the channel width can be tuned without compromising the δI′/I0.
Here we also show the channel width needed for achieving an initial
cur- rent of 1 µA with the optimal channel length selected, as a
guideline for optimizing sensor designs within the boundary
conditions.
2D Mater. 6 (2019) 044002
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Y Qiang et al
When the dipoles are formed further from the sensor surface, the
field-gating effect becomes weaker as the induced electrostatic
change are smaller. This expectation has been validated by
simulation results. Figure 4(a) shows the time δI′/I0 as a function
of dipoles heights ranging from 2 to 10 nm which are realistic
molecule heights from literature [4]. The sim- ulation with dipoles
having different inter-molecular length from 0.2 to 0.6 nm (which
leads to different dipole moments) also verified our intuition that
the Si sensor is less sensitive to dipoles with smaller dipole
moment, resulting from a closer to neutral com- bined interaction
from the positive and negative fixed charges (figure 4(b)). So far,
we have simulated differ- ent dipole conditions where the dipoles
are vertically aligned, however, the dipoles can form different
angles in real settings. Here we simulated dipoles with 0° (ver-
tically aligned), ±45°, ±90° to show the δI′/I0 varia- tion induced
by different dipole angles (figure 4(c)),
with how the dipoles are paired illustrated in the insets using
simulation visualization results. Dipoles verti- cally aligned lead
to the largest δI′/I0 compared to other angles while dipoles with
−90° even resulted in a slight decrease of drain current as the
positive and negative charges are now placed at the same height
from the sensor surface. Because we placed the dipoles equally
distributed on the top of channel, placing them with positive and
negative 90° (reversed charge orientation) caused the relative
change of each dipole’s position, eventually resulting in the
difference sensor responses.
Besides the dipole positions, analytes with different molecular
weights and concentrations have consider- able influence on the
sensor detectability as well. To probe into the details, we first
calculated the capture rate under different analyte concentrations
from 0.2 to 5 ppt and placed different number of dipoles on the
sensor surface accordingly. Consistent with the exper- imental
results from references, higher concentrations
Figure 3. Detectability dependence. (a) Left: drain current
increase rate (δI′), Right: initial drain current (I0) as a
function of channel widths. (b) Relative drain current increase
rate (δI′/I0) as a function of channel widths. (c) Relative drain
current increase rate (δI′/I0) as a function of channel thickness
with different doping concentrations. (d) Relative drain current
increase rate (δI′/I0) as a function of channel thickness with
different channel length. (e) Relative drain current increase rate
(δI′/I0) as a function of channel length with different doping
concentrations. (f) Relative drain current increase rate (δI′/I0)
as a function of doping concentrations with channel length. Labels
in figures: device parameters used for different simulation.
2D Mater. 6 (2019) 044002
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Y Qiang et al
contribute to stronger sensor responses [23]. Notably, with 0.2 ppt
analyte concentration, proposed field- effect sensor still
possesses δI′/I0 of ~2.38% s−1 from simulation results, revealing
an ultra-low detection limit (below ppt level). From simulation, it
is difficult to determine the formed dipole conditions strictly
fol- lowing the realistic cases. However, the different cap- ture
rates can still be differentiated from molecular weights. For
example, the molecular weights of com- mon explosive molecules like
TNT, HMX, RDX are 227.13, 296.155, 222.12 g mol−1 respectively. We
show a considerable change in δI′/I0 only resulting from the
molecular weight differences as an inset of figure 4(d). When
different types of analytes bind to the surface functionalization
layers, the variation of dipole con- ditions and capture rate will
result in the δI′/I0 differ- ence, subsequently leading to the
selectivity of sensors. The choice of different set of
functionalization layers according to the analyte chemistry can
therefore form the sensor array to achieve selective sensing. While
we assume a single layer of dipoles for all the simulations, the
chemical molecules are actually complex with mul- tiple layers of
charges which can induce channel mod- ulation. Here we simulated
the effect of multiple layers of charges by stacking 1–3 layers of
dipoles on top of the silicon channel. Distance between each dipole
layer was set to be 1 nm. With more layers of dipoles placed, the
sensor response increases with a nearly linear trend (SI figure 6).
Further analysis will include more com- plicated dipoles
arrangement.
Notably, the formation of native oxide at the sur- face could be
present for Si. Here we added a 1 nm- thick SiO2 layer on top of
the Si channel and adjusted the dipole distance from the device
surface accordingly to study the influence of the oxide layer.
Following the strategies mentioned above, we studied the
detectabil- ity dependence on different dipole heights, channel
lengths and doping concentrations. While we observed decreased
sensitivities due to the capacitive oxide layer applied, the
general trend of dependences on channel length and doping
concentrations remains the same, indicating that the existence of
the native oxide layer does not change the detectability dependence
(SI fig- ure 7).
As stated before, the proposed sensor design met- rics and the
simulation approach can be applied to any other channel materials.
Here within the capabil- ity of Sentaurus TCAD software, we
demonstrated the simulation results using Gallium Nitride (GaN) as
the channel material. Sensitivities of GaN sensors with dif- ferent
dipole positions were derived. With a higher car- rier mobility,
ATB GaN sensor has better detectability performance than Si with
the same dimensions and physical conditions (SI figure 8).
2.4. Pattern recognition algorithm based on neural networks To
enable fast, accurate, and automatic recognition of explosive
analytes, machine learning can greatly enhance the efficiency in
analyte recognition. Machine
Figure 4. Selectivity considerations. (a) Relative drain current
increase rate (δI′/I0) as a function of dipoles distance from the
sensor surface h. Inset: illustrations of h. (b) Relative drain
current increase rate (δI′/I0) as a function of intermolecular
length d. Inset: illustrations of d. (c) Relative drain current
increase rate (δI′/I0) as a function of angles of dipoles. Inset:
simulation results of different dipole angles. (d) Relative drain
current increase rate (δI′/I0) as a function of analyte
concentrations. Inset: δI′/I0 versus analyte molecule weight.
2D Mater. 6 (2019) 044002
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Y Qiang et al
learning based pattern recognition algorithms, have shown excellent
performance in many application domains [42–49] (SI table 3). There
are already many great efforts on combining gas sensors with
machine learning algorithms to enhance the sensor performance. SI
table 4 shows recently developed gas sensors utilizing state-of-the
art machine learning methods [50–56]. More existing work can be
found in the [57].
As a branch of machine learning, neural networks have achieved
great success in various fields, such as computer vision [56],
speech recognition [58] and natural language processing [59], due
to its remarkable ability in feature extraction and recognition.
Since the explosive detection task is also based on the recogni-
tion to the patterns formed by the sensor measurement results, we
believe that neural network is a promising candidate for
discovering the intrinsic features of the formed patterns.
For the neural network training purpose, we demonstrate an ATB
field-effect sensor array consist- ing of six different sensors
each with a different type of functionalization layer formed on the
sensor sur- face (figure 5(a)). Sensors all include a 120 nm long,
100 nm wide and 2 nm thick Si layer to ensure good detectability
results. We generated four different types of analytes with a
concentration range from 0.02 ppt to 1 ppt. Functionalization
layers and analytes used in this work are artificially designed
with different mol- ecules heights (1.5–5 nm), molecules angles
(0–180°), molecule weights (192, 227, 296, 316 g mol−1) as well as
the intermolecular length (0.1–0.5 nm). All the numbers used for
different sensor-analyte interfaces
were derived from literature to simulate the interac- tion between
surface functionalization and explosive analytes reasonably to
mimic the real-world com- plexity. Specifically, the parameters for
analytes were based on properties of explosive chemicals including
TNT, PETN, HMX, RDX. The surface functionaliza- tions designed in
this work were based on properties of silane derivatives with
electron-rich amino groups (i.e. APTES, APDMES, en-APTAS, etc) [4,
23]. The distance between the positive and negative charges were
selected using the reported intermolecular length between
nitroaromatic explosives and amine com- plexes [41]. Generated
δI′/I0 data forms distinctive pattern for each analyte. Generally,
the shapes of the generated patterns are independent of analyte
con- centrations, although patterns generated from ultra- low
concentrations will have severe variations and even negative values
due to small numbers of formed dipoles (figure 5(b)). To better
mimic the application environment in the real world, the
fluctuation of input data (that is, the variance of molecules
heights, dipole moments, analyte concentrations) was created by
changing the input data to 80% ~120% of their origi- nal value.
Having this range of fluctuation is reasona- ble in the
experimental conditions and is also required for the neural network
training purpose. We will train the neural network with
experimental data in the future studies. Besides, a large amount of
simulation data was generated, because neural networks generally
require rich data for training to achieve the state-of- the-art
recognition performance.
The neural networks built in this work were then trained and tested
with the synthesized data generated
Figure 5. Pattern recognition algorithm based on neural networks.
(a) Demonstration of the intelligent ATB field-effect sensor array.
(b) Fingerprinting patterns generated by simulation data (δI′/I0)
for 4 different analytes with 6 sensors. Upper row: analyte
concentration = 1 ppt; Bottom row: analyte concentration = 0.02
ppt. (c) Relative prediction error of 0.1 ppt data with 2, 3, 4
analytes using 3, 4, 5, 6 sensors, respectively. Red dotted line:
10% relative error bar. (d) Relative prediction error of 0.1, 0.4,
0.8 ppt data using 3, 4, 5, 6 sensors, respectively. (e) Analyte
type and concentration recognition results by training 0.02–1 ppt
data.
2D Mater. 6 (2019) 044002
9
Y Qiang et al
as described before. Detailed information about the self-built
neural network model is available in the Methods section. Each set
of data was composed of input data, which was sent to the inputs of
the neural networks, and label data, which was used to compare with
the outputs of the neural networks to evaluate their performance.
Input data were composed of the sensors measurement results. For
example, if 6 sen- sors are utilized to perform the detection, then
the input data will contain 6 numbers while the corre- sponding
output will consist of two numbers, one of which indicates the type
of the analyte, and the other one presents the predicted
concentration of the ana- lyte. In this work, 4 types of analytes
and their concen- trations are detected and quantized with the
neural networks. The neural networks are trained with com-
binations of data of 4 analyte types and multiple con- centrations
(0.02 ppt–1 ppt) to introduce high diversi- ties to the training
data and thus enable the models to accurately differentiate
different types of analytes and predict their concentrations. And
the trained mod- els are tested with data of concentrations that
are not included in the training data, to evaluate the generality
of the models. For the prediction of analyte concen- trations, we
investigated several approaches including (1) training lower
concentrations data for predicting higher ones, (2) training higher
concentrations data for predicting lower ones and (3) training
certain con- centration nodes for predicting values in the range.
The training and testing results validated that only the third
approach could be achieved with low prediction errors and the range
needs to be carefully determined (SI table 5). As it can be
observed from SI table 6, the analyte types detection accuracy can
reach 100% and the concentration prediction errors are remarkably
low (<7% relative error), if the neural networks are trained
with the combined 0.05, 0.2, 0.6, 1 ppt data sets and tested with
individual 0.1, 0.4, 0.8 ppt data. Nota- bly, with the more numbers
of analytes included, the relative detection error increases
simultaneously (fig- ure 5(c)). One way to control the detection
error is to increase the numbers of sensors. With 6 sensors used,
the relative error appears to be the smallest compared to others
(figure 5(d)), indicating that we can deter- mine the minimum
numbers of sensors required for detecting certain numbers of
analytes by simply setting a maximum error bar. For instance, if we
set the largest error accepted to be ~10%, as shown in figure 5(c),
the numbers of sensors needed for detecting 4 different analytes
are 5 with the given error. Detecting 3 ana- lytes within the same
error needs 4 different sensors and only 3 sensors are required for
recognizing 2 ana- lytes. When using the combined 0.02, 0.2, 0.6,
0.1 ppt data sets for training, the type detection accuracy can
still reach 100% with more than four sensors used for measurements
and the relative error increased but still lower than ~13% with 6
sensors (figure 5(e)). The acc- uracy degradation of the latter
combination is incurred by the low differentiability of 0.02 ppt as
the patterns
become severely distorted at this low concentration, but it can be
mitigated with more sensors being used. As a proof-of-concept, we
successfully achieved the recognition and concentration prediction
of 4 types of analytes with 0.02–1 ppt range. The results have dem-
onstrated detection limit down to 20 ppq, superior to the
state-of-the-art sensor performance. While highly promising,
however this conclusion is only prelimi- nary currently as we have
not conducted experimental validations of the device. Our future
work will include the validation and optimization of our ATB
field-effect sensor designs through fabricating and characterizing
the real devices. By using this neural network model, with more
sensors included and appropriate concen- tration nodes selected, we
envision that we can achieve the detection of a large quantity of
analytes with a con- tinuous range of concentrations down to tens
of ppq level, to form intelligent explosive sensors with the
capability of recognizing analyte types and quantizing the trace
concentrations in real time.
3. Conclusion
In this work, we proposed highly scalable ATB field- effect sensors
for fast-responsive, ultra-sensitive, and intelligent explosives
detection. We validated the concept by demonstrating the
theoretical results through system-level, model-based simulation
based on the field-gating effect. The proposed sensor design
benefits from the atomic-layer semiconductor materials and has the
potential to achieve ultra- low detection limit (down to tens of
ppq level) as well as fast response (within a few seconds). Through
systematic simulation with different sensor dimensions and doping
concentrations, we proposed the basic design rules for optimizing
the ATB field- effect sensors with tunable initial current and
sensor sizes. We also demonstrated the compatibility of the
proposed sensor designs with the dedicated neural network
algorithms and successfully achieved the recognition of various
types of analytes with a large, continuous concentration range. We
envision that the sensors design metrics, simulation approaches and
neural network models can be applicable to other 2D semiconductor
materials, facilitating the development of fast-response,
ultra-sensitive trace explosive sensors for a wide range of
molecules sensing applications. Future work should focus on the
large- scale implementation of the ATB field-effect sensor system
using nano-fabrication technologies and its validation in the
real-world environment.
4. Methods
4.1. Synopsys TCAD simulation All the simulation results are
computed using Synopsis Sentaurus TCAD (Version M-2016.12).
Simulation models were defined using Sentaurus Structure Editor and
device simulation was
2D Mater. 6 (2019) 044002
10
Y Qiang et al
implemented using Sentaurus Device in this work. While mesh size of
non-important region of air ambient was set to be around 10 to 20
nm, that of the Si and region with dipoles was set to be smaller
than 0.1 nm. This meshing approach is to manage a balance between
computation time and accuracy of the results. Carrier transport in
devices was simulated by solving Poisson continuity equations
coupled with electrons, holes and electrons temperature continuity
equations self-consistently (Hydrodynamic model). Quantum
confinement effects related to nano-scale devices were considered
using the density-gradient quantization model. The Slotboom and
Graaff bandgap narrowing model was used during the simulation to be
consistent with the density-gradient model. Additionally,
doping-dependent Shockley– Reed–Hall recombination model was
included with the band-band tunneling model. The simulation models
used are similar to previous studies of ultra- thin silicon devices
[60]. Data analysis including the plotting of drain current,
electrostatic potential and electrons densities was enabled by the
Sentaurus SVisual software.
4.2. Neural network training Each layer of a fully-connected neural
network calculates xi+1 = h(W i × xi + bi), where xi and xi+1 are
the input and output vector of the ith layer, respectively, W i and
bi are the weight parameter matrix and bias vector of the ith
layer, and h(·) denotes the activation function, which introduces
nonlinearities into the neural network. In the training phase, the
outputs of the output layer are compared with labels (i.e. the
expected correct outputs) to generate losses, then the losses are
back-propagated to update the weight and bias parameters through
stochastic gradient descent [61]. In the inference phase, the
weight and bias parameters are fixed, and the outputs at the output
layer are the predictions of the model.
In this work, 3-layer fully-connected neu- ral networks are built
with the configuration of (input_size, 70)− (70, 70)− (70,
output_size), where the first number in a pair (·, ·) denotes the
input size of that layer, and the second number denotes the number
of neurons in that layer. input_size is deter- mined by the data
sent to the first layer, which is the number of sensors that are
used to perform the detec- tion task. And output_size = analyte
types + 1, the added ‘1’ is due to the capability of our model not
only recognizing the types of the explosive analytes, but also
detecting their concentrations. The activation func- tion used in
the neural networks is the rectified linear unit (ReLU), which
calculates f (x) = max(x, 0) [62] (SI figure 9).
Acknowledgments
Author contributions
YQ and AR contributed equally to this work. YQ, AR, YW, and HF
designed the research; YQ, XZ, PP performed the Sentaurus TCAD
simulation; AR, YQ, carried out the neural network training and
pattern recognition studies; YQ, AR, XH, KJS performed the data
analysis. YQ, AR, YW, and HF co-wrote the manuscript.
ORCID iDs
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2D Mater. 6 (2019) 044002
Abstract
2.3. Detectability considerations and design
2.4. Pattern recognition algorithm based on neural networks
3. Conclusion
4. Methods