# A Robust and  Low-Complexity Gas Recognition Technique for On-Chip Tin-Oxide Gas Sensor Array

Authors: Farid Flitti; Aicha Far; Bin Guo; Amine Bermak
Journal: Journal of Sensors
 (2008)

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## Abstract

Gas recognition is a new emerging research area with many civil, military, and industrial applications. The success
of any gas recognition system depends on its computational
complexity and its robustness. In this work, we propose a
new low-complexity recognition method which is tested and
successfully validated for tin-oxide gas sensor array chip. The
recognition system is based on a vector angle similarity measure
between the query gas and the representatives of the different gas
classes. The latter are obtained using a clustering algorithm based
on the same measure within the training data set. Experimented results on our in-house gas sensors array show more than98%
of correct recognition. The robustness of the proposed method
is tested by recognizing gas measurements with simulated drift.
Less than 1% of performance degradation is noted at the worst case
scenario which represents a significant improvement when
compared to the current state-of-the-art.


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## Body

## 1. Introduction

The detection and discrimination of gases  using
microelectronic gas sensor array are required in
various industry and domestic applications, such as automobiles, safety, indoor air quality,  medicine and food industry [1, 2]. SnO2-based gas sensing film technology is commonly used for such
applications because of a number of advantages including cost effectiveness,
high sensitivity to various gases, and relative compatibility with standard
CMOS fabrication processes [3]. The SnO2-based sensors are operated at high temperature (typically 300C°) obtained using integrated microhotplates (MHPs). SnO2-based gas sensing film shows high response to a large variety of
target gases but low level of selectivity to a given target gas. This
phenomenon is widely exhibited in the animal and human biological olfactory
systems [4]. Besides
low selectivity, other shortcomings such as high sensitivity to humidity,
nonlinearities of the sensor's response, drift and slow response are associated
with electronic gas sensors in general. Poor selectivity towards the monitored
gas, or cross sensitivity towards other gases makes a sensor's output
unreliable. Long exposure cycles of the sensors as well as aging factors and
poor stability cause a sensor's calibration curve
drift with time [5].
The drift can be explained as a random temporal variation of the sensor
response when exposed to the same gases under identical conditions. These
drifts are due to unknown dynamic processes in the sensor system (e.g.,
poisoning or aging of sensors) or environmental changes (e.g., temperature and
pressure conditions). Many pattern recognition techniques were proposed in the
literature to deal with electronic nose low selectivity and good overviews
could be found in [2, 6]. However, the processing remains generally quite
complex involving many preprocessing operations as well as complex pattern
recognition algorithms especially with large number of sensors. In addition,
most of the proposed techniques do not address the drift problem and those who
deal with this issue require the retraining of the proposed systems using
simulated drift observations.Gas measurements issued from our sensor array are vectors inR16.
Usually, recognition in multidimensional space requires a sufficient number of
observations to estimate the parameters of the underlaying model. This number
increases rapidly with the dimension therefore recognition becomes inaccurate in practice even each dimension often brings additive information. This is
the well-known curse of dimensionality phenomenon [7]. One alternative to overcome
this problem is to use a dimensionality reduction technique by either feature
selection or feature extraction to find a more compact representation of the
data keeping only relevant information. Pursuit projection (PP) [8] is a dimensionality
reduction technique finding iteratively projection axis that
maximizes a given criterion called projection index
(PI). Principal component analysis (PCA) is a PP with the variance as PI,
linear discriminant analysis (LDA) is a PP with the classes separability
(classes separability is usually measured as the
ratio of the within covariance matrix to the between covariance matrix)
as PI and independent component analysis (ICA) is a PP with the non-Gaussianity
as PI [9, 10]. In this paper, we use a
low complexity yet efficient reduction technique based on the vector angle
between observations vectors in ℝ16 as a measure of their closeness. The proposed
recognition system uses the vector angle similarity measure between the
investigated gas and the different gas class representatives obtained using a
clustering algorithm with the same similarity measure. This modelling allowed
us to reach high recognition rate and has shown
high robustness against the drift phenomenon.The paper is organized as follows. The Sensor array is
described in Section2. In Section 3, the recognition system is detailed and
experiments on real gas measurements are presented. The robustness of the
proposed technique is investigated in Section 4. Finally, a conclusion is
presented in Section 5.


## 2. Sensor Array Characterization

The monolithic4×4 tin-oxide gas sensor array was designed and
fabricated using our in-house 5 μm 1-metal, 1-poly CMOS process [3]. The cross-sectional view
of the gas sensor is shown in Figure 1(a). The top view of the fabricated
sensor element with the micro heater and the electrodes is shown in Figure
1(b).(a) The structure of the sensor element. (b)
The SEM picture of the sensor element.
(a)(b)The MHP is at
the center of the sensor element and has a dimension of190×190μm2. A 2.8
μm air gap is formed between the hotplate membrane and the
substrate using a surface micromachining process. The SnO2 sensing film is deposited onto the MHP using a sputtering method. The
sensor signal is measured from the resistance variation across the two Pt
electrodes. Different posttreatment combinations were performed on the sensors
within the array, including metal catalysts (Pt, Pd, and Au) in 3 columns and
ion implantations (B, P, and H) in 3 rows, which results in a response
variation across the 16 sensors.Vapors were injected into the gas chamber, with a
diameter of90mm and a reaction volume of 100cm3,
at a flow rate determined by the mass flow controllers
(MFCs). The gas concentrations in the sensor
chamber are adjusted by selecting the correct flow rate for different gases.
Input signals generated by the data acquisition board and used to control the
MFC are pulse signals corresponding to different concentrations. The output is
then processed by the data acquisition board (DAQ). In this measurement, all
the MHPs on the chip were heated to 300C°, resulting in a total power consumption
of 352 mW.The test gases are relevant for a number of
applications mainly in the area of toxic and combustible gas identification.CO,CH4, and Ethanol are commonly found in mines and
daily life, which are the potential causes for
explosion or poisoning coal mine accidents. Furthermore, the mixture of gases
is more dangerous than single gas which is the case in real situations. Three
binary mixtures of CO–CH4,CH4–Ethanol,  and CO–Ethanol are selected as analyte gases and recognized
as new gases.The gas sensors array is characterized using a
periodicexposure-cleaning operating mode. Gases are injected over time in a periodical manner and each
gas injection is followed by a cleaning phase which can be done in some cases
by injecting dry air. After the cleaning phase, the signal
will reach the base-line which is taken as the reference measured before gas
injection.


## 3. The Proposed Gas Recognition Algorithm

### 3.1. Vector Angle Similarity Measure

The measurement
obtained from the 16-sensors array can be represented by asixteen-dimensional vector.  In this
representation, vector angle can therefore be used as similarity measure. Let A and B be two vectors belonging to ℝ16.
The vector angle θAB is given by (1)θAB=arccos(〈A,B〉∥A∥∥B∥), where 〈⋅,⋅〉 is the inner product between two vectors, and ∥⋅∥ is the magnitude of a vector. This is a simple
way to measure vectors similarity, that is, the closeness of their orientation
in ℝ16 space, regardless of their magnitudes. Indeed,
vectors belonging to the same category most likely occupy the same region in
the high-dimensionality space and therefore the angle between them is expected
to be small. Despite its simplicity, this measure has proven its effectiveness
with hyper and multispectral images for remote sensing [11, 12], where it is called “spectral angle" due to the
origin of the observation vectors, which represent the spectral reflectance of
the soil in the observed area. This measure has also been effectively used in
color image segmentation [13, 14]. In addition, using the vector angle measure reduces
substantially the problem complexity and represents an efficient dimensionality
reduction technique. In fact, dealing with data in high-dimensional space gives
rise to the well-known curse of dimensionality problem also known as Hughes
phenomenon [7].
Usually the model complexity increases with dimensionality, consequently, the
number of parameters increases and the size of the training data set required
for a reliable estimation becomes large. In practice, the training data set is
limited and the space becomes more and more empty when dimensionality grows,
which implies a lack in parameters estimation accuracy and therefore a
degradation in recognition performance. Using the vector angle is a simple way
to overcome this problem.


### 3.2. Vector Angle Approximation

The ultimate
goal of our research work is to integrate the sensor array and the pattern
recognition system in CMOS technology. Typically, thearccos(⋅),
which is a nonlinear function, is implemented in
hardware using a look-up table. High accuracy requires a large size of the
look-up table which is a real drawback for our proposed pattern recognition
system. To overcome this problem, we propose to use an approximation based on
Taylor's series expansion. The expansion of the arccos(⋅) is given by (2)arccos(x)=π2−∑n=0∞(2n)!4n(n!)2(2n+1)x2n+1for|x|<1. Extensive simulation results
show that a first-order approximation is sufficient to keep the same
recognition performances as the full expansion. Thus, the following formula is
used for the computation of the arccos(⋅): (3)arccos(x)=π2−x−16x3for|x|<1. In the next section, this
measure is used to design the gas recognition system.


### 3.3. The Recognition System

The K-means
algorithm [9] is used
to recover the potential local clusters in the data structure of each gas
class. The choice of the number of clusters is a model selection problem
motivated by the Occam's razor principle, which stipulates that the simplest
model that explains data is the one to be preferred [9]. Since no parametric model
is associated with the K-means algorithm, model selection techniques like
Akaike information criterion and Bayesian information criterion are not
applicable. Thus, a large number of clusters are
used at the beginning then small clusters are eliminated during the clustering
process.The centroids of the obtained clusters are treated as
representatives, which are compared to the candidate gas. The decision follows
the simple rule outlined as: “a candidate gas belongs to the class whose one
of the representatives is the closest to the candidate (Figure2)".Figure 2
The gas recognition
system. On the left, the offline learning: the classes data sets are
partitioned into clusters using the vector angle similarity measure. Each cluster is then represented by its
centroids. On the right, the online
recognition: the minimum angles between the investigated gas and classes
centroides are computed, then the class with the minimum angle is chosen.LetΦj={Fij}i=1,…,Kj be the centroids of the class j,
and C be a candidate gas. The minimum angles γj between C and the sets Φj are computed as follows: (4)γj=mini|θFijC|. The decision rule is
then (5)h(C)=argminj|γj|. The performances of this
recognition system is investigated in the next section.


### 3.4. Experiments

To experiment
the proposed recognition system, a large data set of measurements was
generated. Different concentrations of 6 categories of gases, namely: CO,CH4,
Ethanol, CH4–CO,
Ethanol–CO, and Ethanol–CH4,
were used with totally 432 vector measurements data set. The three last
mixtures are considered as separate gases. This
data set is partitioned into two parts: 90% for the learning and 10% for the test. The technique proposed in this
work is compared with different classical recognition techniques. All these
techniques are preceded by a dimensionality reduction step using PCA, while in
our case dimensionality reduction is inherently embedded into the classifier,
which constitutes a significant advantage. Table 1 reports the obtained average
accuracy with an increasing number of the retained projection axis of the PCA.
Despite its low complexity, the proposed system performs well compared to the
other techniques. In addition, the use of the vector angle-based classifier
also enables dimensionality reduction from the original space to 1D space
without any further preprocessing. Only one cluster per gas category is used in
our experiments, therefore only one representative is needed for each category
which is a very low complexity yet highly efficient recognition system. In
other words, our recognition system presents an embedded dimensionality
procedure which is very interesting since reduction and classification are
performed simultaneously reducing the complexity of the proposed system.
On-chip implementation of the proposed classifier will therefore take advantage
of the small number of parameters needed to be stored by the system while the
computation requirements are kept very low.Table 1
Comparison of gas recognition results: PCA is
used with a number of classical recognition techniques and compared to the proposed
method. The recognition accuracy is reported in%.
No. of PCA's axis23456789KNN95.4598.1899.3299.7799.7799.7799.7799.77MLP97.2797.9510099.7799.77100100100RBF96.3696.5998.1898.4198.4198.4198.4198.41GMM98.4999.55100100100100100100No. of PCA's axis10111213141516KNN99.7799.7799.7799.7799.7799.7799.77MLP100100100100100100100RBF98.6498.6498.6498.6498.6498.6498.64GMM100100100100100100100The proposed technique98.02


## 3.1. Vector Angle Similarity Measure

The measurement
obtained from the 16-sensors array can be represented by asixteen-dimensional vector.  In this
representation, vector angle can therefore be used as similarity measure. Let A and B be two vectors belonging to ℝ16.
The vector angle θAB is given by (1)θAB=arccos(〈A,B〉∥A∥∥B∥), where 〈⋅,⋅〉 is the inner product between two vectors, and ∥⋅∥ is the magnitude of a vector. This is a simple
way to measure vectors similarity, that is, the closeness of their orientation
in ℝ16 space, regardless of their magnitudes. Indeed,
vectors belonging to the same category most likely occupy the same region in
the high-dimensionality space and therefore the angle between them is expected
to be small. Despite its simplicity, this measure has proven its effectiveness
with hyper and multispectral images for remote sensing [11, 12], where it is called “spectral angle" due to the
origin of the observation vectors, which represent the spectral reflectance of
the soil in the observed area. This measure has also been effectively used in
color image segmentation [13, 14]. In addition, using the vector angle measure reduces
substantially the problem complexity and represents an efficient dimensionality
reduction technique. In fact, dealing with data in high-dimensional space gives
rise to the well-known curse of dimensionality problem also known as Hughes
phenomenon [7].
Usually the model complexity increases with dimensionality, consequently, the
number of parameters increases and the size of the training data set required
for a reliable estimation becomes large. In practice, the training data set is
limited and the space becomes more and more empty when dimensionality grows,
which implies a lack in parameters estimation accuracy and therefore a
degradation in recognition performance. Using the vector angle is a simple way
to overcome this problem.


## 3.2. Vector Angle Approximation

The ultimate
goal of our research work is to integrate the sensor array and the pattern
recognition system in CMOS technology. Typically, thearccos(⋅),
which is a nonlinear function, is implemented in
hardware using a look-up table. High accuracy requires a large size of the
look-up table which is a real drawback for our proposed pattern recognition
system. To overcome this problem, we propose to use an approximation based on
Taylor's series expansion. The expansion of the arccos(⋅) is given by (2)arccos(x)=π2−∑n=0∞(2n)!4n(n!)2(2n+1)x2n+1for|x|<1. Extensive simulation results
show that a first-order approximation is sufficient to keep the same
recognition performances as the full expansion. Thus, the following formula is
used for the computation of the arccos(⋅): (3)arccos(x)=π2−x−16x3for|x|<1. In the next section, this
measure is used to design the gas recognition system.


## 3.3. The Recognition System

The K-means
algorithm [9] is used
to recover the potential local clusters in the data structure of each gas
class. The choice of the number of clusters is a model selection problem
motivated by the Occam's razor principle, which stipulates that the simplest
model that explains data is the one to be preferred [9]. Since no parametric model
is associated with the K-means algorithm, model selection techniques like
Akaike information criterion and Bayesian information criterion are not
applicable. Thus, a large number of clusters are
used at the beginning then small clusters are eliminated during the clustering
process.The centroids of the obtained clusters are treated as
representatives, which are compared to the candidate gas. The decision follows
the simple rule outlined as: “a candidate gas belongs to the class whose one
of the representatives is the closest to the candidate (Figure2)".Figure 2
The gas recognition
system. On the left, the offline learning: the classes data sets are
partitioned into clusters using the vector angle similarity measure. Each cluster is then represented by its
centroids. On the right, the online
recognition: the minimum angles between the investigated gas and classes
centroides are computed, then the class with the minimum angle is chosen.LetΦj={Fij}i=1,…,Kj be the centroids of the class j,
and C be a candidate gas. The minimum angles γj between C and the sets Φj are computed as follows: (4)γj=mini|θFijC|. The decision rule is
then (5)h(C)=argminj|γj|. The performances of this
recognition system is investigated in the next section.


## 3.4. Experiments

To experiment
the proposed recognition system, a large data set of measurements was
generated. Different concentrations of 6 categories of gases, namely: CO,CH4,
Ethanol, CH4–CO,
Ethanol–CO, and Ethanol–CH4,
were used with totally 432 vector measurements data set. The three last
mixtures are considered as separate gases. This
data set is partitioned into two parts: 90% for the learning and 10% for the test. The technique proposed in this
work is compared with different classical recognition techniques. All these
techniques are preceded by a dimensionality reduction step using PCA, while in
our case dimensionality reduction is inherently embedded into the classifier,
which constitutes a significant advantage. Table 1 reports the obtained average
accuracy with an increasing number of the retained projection axis of the PCA.
Despite its low complexity, the proposed system performs well compared to the
other techniques. In addition, the use of the vector angle-based classifier
also enables dimensionality reduction from the original space to 1D space
without any further preprocessing. Only one cluster per gas category is used in
our experiments, therefore only one representative is needed for each category
which is a very low complexity yet highly efficient recognition system. In
other words, our recognition system presents an embedded dimensionality
procedure which is very interesting since reduction and classification are
performed simultaneously reducing the complexity of the proposed system.
On-chip implementation of the proposed classifier will therefore take advantage
of the small number of parameters needed to be stored by the system while the
computation requirements are kept very low.Table 1
Comparison of gas recognition results: PCA is
used with a number of classical recognition techniques and compared to the proposed
method. The recognition accuracy is reported in%.
No. of PCA's axis23456789KNN95.4598.1899.3299.7799.7799.7799.7799.77MLP97.2797.9510099.7799.77100100100RBF96.3696.5998.1898.4198.4198.4198.4198.41GMM98.4999.55100100100100100100No. of PCA's axis10111213141516KNN99.7799.7799.7799.7799.7799.7799.77MLP100100100100100100100RBF98.6498.6498.6498.6498.6498.6498.64GMM100100100100100100100The proposed technique98.02


## 4. Robustness Study

The drift phenomenon can be interpreted as a temporal
variation of the pattern distribution in the feature space, which can cause a
noxious robustness issue to the recognition system. Therefore, it is generally
necessary to retrain the entire recognition system using measurements affected
by the drift to enhance the performance of the electronic noise.Figure3 illustrates an example of an additive drift
in which we have reported the response of the sensor as function of the
concentration of gases periodically injected into a gas chamber in which the
sensors are being placed. We can note that the baseline response of the sensor
is shifted, which complicates the classification problem even further since the
learned behavior of the sensor is varying with time. The drift has been
modelled as vd(t)=v(1+αt), where v is the sensor output before the drift
experiment and αtmax was chosen randomly for each sensor [16]. Drift varying between 0
and 30% has been artificially generated. The
performance of the recognition was evaluated over the drifted measurements. The
identification results of the drifted measurements using the system trained on
a nondrifted measurements are shown in Figure 4 and compared to the recognition
performances obtained in [15].
The proposed technique shows a higher accuracy than the one achieved in
[15] with and without
retraining the system. It is also important to note the significant invariance
of our performance when increasing the drift. In the worst case, more than 97.89% of recognition accuracy is obtained. Less than 1% of performance degradation is noted with a
drift figure of 30%.
The robustness of the proposed technique shows that using the angle between the
query gas and the class representatives tends to be more robust than using
other similarity measures. In our case, the class representatives are
characteristic signatures of the corresponding gases, therefore even in the
presence of drift effects, the measurement vector of a given query gas is still
closer to its class signature than to the other
signatures.Figure 3
The drift phenomenon. The sensor response varies along with exposure time.Figure 4
Robustness Comparison: recognition accuracy as a function of drift’s
rate. In continuous line, the results of the proposed recognition system without
retraining. In dashed line, the results obtained in [15] with retraining. In
dashed-dot line, the results obtained in [15] without retraining. The proposed
method outperforms the performances in [15] and shows a quite invariance
of the recognition accuracy of the drifted measurements, even the system is
trained on a nondrifted measurements.


## 5. Conclusion

In this paper,
a novel gas recognition algorithm based on a vector angle similarity measure is
proposed. The K-means algorithm is adapted to recover the local data structures
in different classes training sets by starting with a large number of clusters
and eliminating small-size clusters during the clustering process.The obtained results show the effectiveness of the
proposed technique compared to the existing methods even though it does not
require any preprocessing. In fact, the embedded dimensionality reduction
performed using the vector angle similarity measure avoids additional
operations of the preprocessing required by other methods.In addition, the proposed technique shows a very high
robustness against the drift phenomenon without any additional retraining. The
small number of needed parameters and low complexity of the required
computations suggest efficient on-chip hardware
implementation of the recognition system. Work is underway to integrate the
proposed algorithms with the sensor array enabling single chip electronic nose
microsystem.



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*Source: 465209-2008-10-16.xml*