odours using polar plot of an integrated thick film sensor array

Microelectronics Journal 27 (1996) 531-537 Copyright © 1996 Elsevier Science Limited Printed in Great Britain. All rights reserved 0026-2692/96/$15.00

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0026-2692(95)00077-1

Discrimination of individual gases/ odours using polar plot of an integrated thick film sensor array V.K. Singh, R. Dwivedi and S.K. Srivastava Centrefor Research in Microelectronics,Department of ElectronicsEngineering, Institute of Technology, Banaras Hindu University, Varanasi-221 005, India

A simple discrimination technique for detection and identification of individual gases/odoursusing a polar plot based on response of an integrated sensor array is presented. The responses of an array of eight integrated sensors are used to generate the desired pattern. The required pattern is generated by taking the normalized response of the sensors (normalization with reference to the response of the first sensor). For the identificationofgases/odours,the.data set is generated by taking the opposite angle of adjacent sensors in training mode and coraparing with the data set obtained in the sniffingmode. The suggestedmethod has been validated using experimental data for butanol, methanol and propanol. Copyright © 1996 ElsevierScienceLtd.

1. Introduction ince the discovery by Brattain and Bardeen in 1953 that adsorption o f a gas on the surface o f a semiconductor can produce a significant change in electrical resistance o f the material [1], there have been sustained efforts

S

[2-5] to make use o f this change for the purpose o f detection ofgases/odours [6]. Discrimination techniques play an important role in detecting and identifying the gas/odour present in an environment. A procedure to develop rules for h o w to assign an observation to a certain class o f observation is called the discrimination technique [7] and must be matched to a specific classification problem. This is dealt with in the present paper. Different mathematical models, statistical methods and pattern recognition methods [818] such as partial model building (PMB) [8], the fast Fourier transform technique [10], principal component analysis (PCA) [16], transformed cluster analysis (TCA) [17] and the multiple regression m e t h o d [18], have been

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reported by many authors for the analysis of sensor response with different identification capability. The cluster method [14] was reported by Shurmer et al. for the identification of different brands of tobaccos and alcohols. However, the scatter of data in a cluster was found to be large. Nayak et al. [17] reported a TCA method for the identification of individual gases/odours using an integrated gas sensor array. In this case, the overlapping of clusters of different gases was reduced by the transformation of data using the mean and variance of the trained data set; but still the problem of overlapping of clusters of different gases/odours remains, and it takes more computational time and memory. In the present work, a simple, less time- and memory-consuming pattern recognition approach is suggested for the detection and identification of individual gases/odours using the response of an array of eight integrated gas sensors. In training mode, a standard pattern for a specific gas is generated by plotting the normalized response of individual sensors on a polar chart and then the opposite angle of the adjacent sensor is evaluated and stored. Similarly, in sniffing mode, the above process is repeated and the magnitudes of opposite angles of adjacent sensors are obtained and subsequently compared with the values of the training node. The decision of an unknown gas/odour (sniffing mode) to the known gas/odour (training mode) is made based on the minimum value obtained (difference of training mode and sniffing mode). 2. Method

For identification of an unknown gas/odour, firstly a data set is generated using known gases/ odours and stored, which is known as the training mode data set. Subsequently the

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response pattern of the unknown gas/odour is obtained using the same sensor array (sniffing mode) and compared with the stored data for identification. Since the response of each sensor of an array is concentration dependent, and may not have exactly the same transfer function, in the present study the data set that has been used is the opposite angle of the normalized response values of adjacent sensors of the array instead of response values of the sensors. Normalization is performed using the response value of the first sensor in the array. These opposite angle values of the adjacent sensors are referred a s ~i,j,k+l, where i is the number ofodours,j is the number of observations at different concentrations and k is the number of sensors (here, the response of the ( K + 1)th sensor is substituted by the response of the first sensor). Assuming the sensor's response is linear to the gas/odour exposed (which is practical at lower concentrations), the method has been developed as follows. Let N be the number of odours, P be the number of observations at different concentrations and R be the number of sensors. Then, the response value which is the percentage change in resistance of the sensors, is given by:

Si,j, k -

Xi,0,k - Xi,j,k Xi,o,k x 100

(1)

where i = 1 to N, j = 1 to P, k = 1 to R, Si,j,k is the percentage change in resistance of kth sensor for the ith gas at the jth concentration, Xi,o,k is the sensor response of the kth sensor for the ith gas at the zeroth concentration, and Xi,j,k is the sensor response of the kth sensor for the ith gas at thejth concentration. Equation (1), after normalization with respect to the first sensor of the array, can be written as:

Microelectronics Journal, Vol. 27, No. 6

s;,j,k

(2)

S2

ni,j, k - - Si,j,1

where ni,j,k is the normalized response of the kth sensor for the ith gas at thejth concentration.

n2

The normalized response values of an individual gas/odour can be represented by a polar plot. Figure 1 shows an illustrative example of such plot (in arbitrary milts) when an array of four sensors is exposed to a gas/odour.

n3 S3

n1

In Fig. 1, / A = ( 3 6 0 / R ) °, where R is the number of sensors, nl to n4 are the normalized response values, and 01 t o 04 are the opposite angles of adjacent sensor response value. Using the same approach of Fig. 1 and the triangle formula, the opposite angle (in radians) of the normalized response of adjacent sensors can be calculated and is given below for k sensors:

[cos{ni n_ O]

$1

8

Fig. 1. Illustrationof polarplot for a four-sensorarray.

Oi,j,k + 1 =

[(r/i,j,k+1)2 +

(ni,j,k) 2 -- 2

x rli,j,k+ 1 X rli,j, k

f'

X COS(A)]1/2 J

(3) Here, the response of the (k + 1)th sensor is substituted by the response of the first sensor as the (k + 1)th sensor does not exit. Oi,j,k+ 1 is the opposite angle of the normalized response of the (k+ 1)th sensor for the ith gas at the jth concentration and LA is the angle between the normalized response of adjacent sensors. The average (averaging by number of observations) values of angles obtained from eq. (3) is given by: (AO)i,k+l

=

~--~N,R + 1,P .q 1,1,1 t'i,k+l,j

p

(4)

In sniffing mode, the above process is to be carried out in the same manner. The opposite angle (in radians) of the normalized response of adjacent sensors for an unknown gas/odour is thus given by

~k+l

cOs-I {

Nk -- Nk~+1 x c°s(A~) !, x ]-~6] / (Nk+,)~ + (N~)* - 2 / VxNk+l x Nk x cos(A)J (5)

where ~k+l is the opposite angle of the normalized response of the (k + 1)th sensor and Nk is the normalized response of the kth sensor. Here also, the normalized response corresponding to NR+I is substituted by the normalized response of the first sensor.

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In the ideal situation, each response value of the data set under sniffing mode should match the data set stored under training mode for a given gas/odour; but there will always be some deviation as it is practically diflqcult to maintain the identical conditions between training mode and sniffing mode. This deviation will be much more if the sniffing mode data of an odour is compared with the training mode data which do not correspond to the same odour. Therefore, in order to identify the unknown gas/odour as the most probable candidate, the difference between training and sniffing mode data has to be a minimum, which is obtained as follows. The difference between training and sniffing mode data (i.e. D) is defined as

R Di= Z{(AO)i,k+l-

~k+l} 2

(6)

K=1

The m i n i m u m value gas/odour is present.

of Di decides which type of

3 Quantification

Once the type of an unknown gas/odour becomes known, its concentration in the given ambient can be calculated as follows.

4. Illustration

The method has been applied to the data set obtained in the laboratory. The data were obtained from the fabricated gas sensor array. An array of eight sensors was fabricated using SnO2 thick film paste doped with TiO2, Pd, CuO2, Pt, Cd, Ag, Z n O and SnO2 itself. In the first step of fabrication of an array of sensors, gold electrodes were printed and fired on an alumina substrate using gold conductor paste (ESL 8080). Above these electrodes, sensor materials were printed on each pair of electrode using the above-mentioned pastes. The fabricated sensors are designated as $1 (TiO2 doped), $2 (SnO2 itself), $3 (Pd doped), $4 (CuO2 doped), Ss (Pt doped), $6 (Cd doped), Sv (Ag doped) and $8 (ZnO doped). An integrated heater was fabricated on the back side of the substrate using ruthenium oxide (RuO2)-based resistor paste so as to get the desired temperature of the sensor. Figure 2 shows the fabricated integrated gas sensor array. The fabricated gas sensor array was tested for butanol, methanol and propanol. To illustrate the method, the data set was divided into a training mode data set and a sniffing mode data set. Sensor responses at four different concentrations of each gas (i.e. butanol, methaGold electrodes

In general, the characteristics of the sensors are non-linear and can be described as

Si,j

= Ai,j × [Cj] B''j

(7)

where Si, j is the percentage change in resistance of the ith sensor for the jth gas, Ai,j is the proportionality constant of the ith sensor for the jth gas and is obtained from the training data, Cj is the concentration o f t h e j t h gas, and Bi,j is the power index of the ith sensor for the jth gas. If Bij = 1, the relationship is linear, otherwise it is non-linear. By knowing the type of gas present and the values of the As and Bs from the training mode, its concentration can be calculated using eq. (7) for any sensor response.

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Tin o x i d e film

co o to

.L 2 m

mm

Alumina substrate

.-- 25.4 mm --, Fig. 2. Schematicdiagramof sensor array.

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nol and propanol) were used as the training m o d e data set and[ sensor responses at any concentration were taken as the sniffing m o d e data set, and are illustrated below: 4.1 Training mode T h e percentage changes in resistance o f different sensors w h e n exposed to butanol was calculated using eq. (1) and is shown in Table 1. (This is shown here to explain the method; the results are similar for other gases.) Taking the response o f the first sensor as the base and using eq. (2), t!he normalized responses o f the sensors were calculated for butanol, methanol and propanol. Tables 2, 3 and 4 show the normalized response values for butanol, methanol and propanol. Figure 3 shows the polar plot of butanol, methanol and propanol in terms of

the normalized responses at 0.199% concentration o f each gas. As in our case, an array of eight sensors was exposed to the three gases and t h u s / A (Fig. 3) is equal to (360/8) ° , i.e. 45 ° . Substituting the value of I A in eq. (3), the opposite angles for Fig. 3, are calculated. Similarly, the opposite angles for butanol, methanol and propanol at three different concentrations were calculated. T h e n using eq. (4) the average values of the opposite angle were calculated, and the result is shown in Table 5. 4.2 Sniffing mode

In sniffing mode, a fixed concentration of butanol was used and the data sets of sensor array responses was obtained.

TABLE 1 Per cent change in resistance of sensors for butanol at four different concentrations % Concentration

S1

S2

S3

S4

$5

S6

$7

$8

0.1990 0.3972 0.5946 0.7913

24.92 25.83 25.98 26.13

29.22 33.24 33.78 34.85

15.18 15.27 15.35 15.44

37.92 41.17 44.27 47.29

40.46 41.81 43.26 44.84

42.04 44.04 45.95 48.30

57.12 58.50 59.74 60.84

26.90 28.48 30.53 33.75

TABLE 2 Normalized response values of sensors for butanol at four different concentrations % Concentration

$1

$2

$3

$4

$5

$6

$7

$8

0.1990 0.3972 0.5946 0.7913

1 1 1 1

1.17 1.28 1.30 1.33

0.60 0.59 0.59 0.59

1.52 1.59 1.70 1.80

1.62 1.61 1.66 1.71

1.68 1.70 1.76 1.84

2.29 2.26 2.29 2.32

1.07 1.10 1.17 1.29

TABLE 3 Normalized response values of sensors for methanol at four different concentrations % Concentration

$1

$2

$3

$4

$5

$6

$7

$8

0.1990 0.3972 0.5946 0.7913

1 1 1 1

1.81 1.94 2.06 2.05

0.84 0.82 0.81 0.80

1.82 1.93 2.01 1.98

1.86 2.04 2.14 2.20

1.65 1.77 1.89 1.87

2.53 2.80 3.02 3.04

0.67 0.77 0.89 0.88

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V.K. Singh

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TABLE 4 Normalized response values of sensors for propanol at four different concentrations % Concentration

$1

$2

$3

$4

$5

$6

$7

$8

0.1990 0.3972 0.5946 0.7913

1 1 1 1

0.67 0.88 0.80 0.81

0.49 0.48 0.43 0.39

2.23 2.36 2.09 1.88

2.43 2.77 2.52 2.27

2.14 2.48 2.26 2.09

3.54 3.89 3.47 3.15

1.02 1.29 1.20 1.12

01 = 1.36 rad; 02=0.52 rad; 0 3 = 1.98 rad; 04 = 1.25 rad; 0s = 1.22 rad; 0 6 = 1.52 rad; 07 = 0.46 rad; and 08 = 1.08 rad.

$3

$4

2

~ \

/

,' ~ .

$5

Using eq. (6), the differences between training and sniffing modes for the ith gas are calculated and the results are: D1 = 3 . 1 3 x 10-2; D2 = 47.87 × 10-2; and D3 = 35.47 x 10 -2.

.... .

,", '.

$1

/,: ',, ~," ' . l; $6

~\ \

$7

\\\\ $8

Butanol .......

Methanol

....

Propanol

Fig. 3. Polar plot of butanol, methanol and propanol in terms of normalized responses at 0.199% concentration. The normalized responses and the opposite angle o f the normalized response o f the adjacent sensor were calculated as described earlier. The calculated normalized response values are shown in Table 2. The opposite angles o f the normalized response o f adjacent sensors are as follows:

From the above, D1 has a m i n i m u m difference value amongst Di ( i - - 1 , . . . , 3 ) values. Thus butanol is the most probable candidate, which is the same as the selected gas for sniffing mode. Similarly, the data set was taken for methanol and propanol at different concentrations and tested in sniffing mode experimentation. Sniffing mode data were generated for butanol, methanol and propanol at four different concentrations and the identification method was found to give reproducible results for each concentration, thereby the validity o f method was established.

5. Conclusion This paper describes a simple discrimination technique for detection and identification o f individual gases/odours. It has been observed that an integrated gas sensor array can be used to generate a specific pattern corresponding to a

TABLE 5 Averagevalue (in radians) of opposite angle for butanol, methanol and propanol Gas/odour

01

02

03

04

05

06

07

08

Butanol Methanol Propanol

1.45 1.84 0.90

0.46 0.40 0.60

2.02 1.94 2.18

1.17 1.25 1.36

1.24 1.01 1.04

1.49 1.67 1.66

0.50 0.24 0.29

1.00 1.43 1.00

536

Microelectronics Journal, Vol. 27, No. 6

gas/odour, and also gas/odour identification is possible by evaluating the m i n i m u m difference value between the training and sniffing m o d e parameters (in our case, the difference between the opposite angle o f adjacent sensors). T h e suggested m e t h o d has been illustrated successfully using experimental data for butanol, methanol and propanol. It is predicted that this m e t h o d , in association w i t h integrated gas sensor array and microprocessor-based systems, can be used for the development o f an applicationspecific gas sensor.

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