- Email: [email protected]
Improvement in signal evaluation methods for semiconductor gas sensors
B ELSEVIER
Sensors and Actuators B 26-27 (1995) 267-270
CnEUlCAt.
Improvement in signal evaluation methods for semiconductor gas sensors Hanns-Erik Endres a, Wolfgang G6ttler a, Hildegard D. Jander a, Stephan M. Drost Hermann Sandmaier a, Giorgio Sberveglieri b, Guido Faglia b, Cesare Perego b
a,
~ Fraunho~er-Institute for Solid State Technology, Hansastrasse 27d, D-80686 Munich, Germany b Universita degli studi di Breschia, FacolM di Ingegneria, Diparcimento di Chimica e Fisica per i Materiali, Ida Valotti 9, 1-25133 Brescia, Ita~,
Abstract
Applied chemical sensor research focuses on sensor arrays and signal evaluation methods, to improve reliability, selectivity and other features of the single sensor. State-of-the-art is the use of self-adapting systems like artificial neural networks (ANNs), mostly used for classification purposes. Systems for the prediction of gas concentrations were seldom investigated, because one of the main problems for those signal processing systems is the enormous amount of training data and the time dependency of the sensor signal. This work uses an array of semiconductor sensors (RGTO method and commercial sensors) and a modified A N N method for signal processing. After a drift correction based on an empirical model, a feed forward network predicts gas concentrations more precisely. A new method, the dynamic test point distribution (DTPD) has been invented, which achieves a significant reduction in the calibration time, together with a high accuracy in calculating the gas concentration. Keywords: Time dependency; Semiconductor sensors; Artificial neural networks; Calibration time; RGTO technique; Dynamic test point distribution
1. Introduction
2. Sensors
The task for a gas sensing system is to measure one or more specified gases and to suppress any interference from other gases and humidity. A common way to suppress interference is to use multisensor systems together with a smart signal processing system. Foremost semiconducting sensors were used for such applications due to their easy modification with several dopants, working as catalysts. As the basic detection effects of gases (such as adsorption) are similar for other sensors, they also can easily be adapted to such a system. If there exists no mature theory of the sensor, selflearning systems like artificial neural networks (ANNs) are best suited methods for signal processing systems. A common disadvantage of these systems is the immense amount of training data needed for the calibration. This implies a sophisticated m e a s u r e m e n t technology to gather enough data and improved methods to handle the calibration data and training of such systems.
For this work, thick film semiconductor sensors (Umweltsensortechnik, Geraberg, type 1 and 2) and thin film sensors, prepared by the R G T O (rheotaxial growth and thermal oxidation) technique, were used [2]. The R G T O tin oxide thin films have been grown with an Alcatel magnetron sputtering plant (model SCM 450). This material has been deposited on polished alumina substrates 3 × 3 m m 2 in size. A m e a n d e r e d Pt thin film has been grown on the back side of the substrate to act as a heating element. The final sensors are mounted on standard TO-8 sockets. Owing to the special growing technique, the surface of the thin films is made up of many polycrystalline agglomerates connected by necks, and the surface roughness is very high. The material obtained shows a high sensitivity towards low concentrations of gases in air.
0925-4005/95/$09.50 © 1995 Elsevier Science S.A. All rights reserved SSDI 0925 -4005 ( 9 4 ) 0 1 5 9 9 - D
268
H.-E. Endres et al. / Sensors and Actuators B 26-27 (1995) 267-270 0
3. Measurement equipment
125
30 . . . .
Standards for measuring equipment for gas sensors have increased together with the demands of signal evaluation systems. The measurement system should be able to mix several test gases and to humidify the gas mixture with a high reproducibility and accuracy. A test chamber should be able to contain one or more sensor arrays, should have a small volume and a short purging time. A special necessity for measuring heated or gas consuming sensors (like semiconductor sensors) is the reduction of any mutual influence of the sensors. Also an easy handling of the total equipment (hardware and software) is necessary. The measurement equipment of IFT tries to find a good compromise of all demands [31. To achieve the training data for the signal processing system, calibration cycles with a duration of at least one week were performed. The gas combinations and concentrations were statistically ordered, to reduce the influence of hysteresis and memory effects of the sensors onto the ANN.
E O.
25
E
is
250
375
i
625
]
750 30 25
l
>-
-i
....
is
B
c 0
500
~ 10
~"
ff
L 0
.... 0
(a)
-
I
i ............ 125 250
,
375
J ........ 500 625
0 750
CO concentration [vpm]
30
0
125
250
750 30
E
25
25
[= 0
20
C
15 .......
lid
~
375
625
20
q
~ID----t~
500
)
~
B
15
I
5
cO C 0
ff Z
5
I)
--
w
F
0
4. Signal evaluation system
0
(b)
The task of the signal processing system for a gas sensor system is to classify the measured gases and to calculate their concentrations. Therefore, the signal processing of sensor signals is a twofold problem: to classify the gases and to calculate their concentration. The difference between the algorithms is that the classification system has a binary output (gas detected or not detected) while the calculation system for the gas concentration has an analog output, which implies a higher number of calibration data. In most cases the signal of a metal oxide sensor is a highly nonlinear problem, especially when coadsorption occurs. For a calculation of gas concentrations, an analytical relation of the sensor response with the gas concentration is a preferable choice. It combines a deeper physical and chemical understanding with a small computation time. An approximation formula was given by Schierbaum et al. [4].
Go
i
~Pi0)/
This formula is easy to linearize and gives a wellbehaved approximation for a gas mixture of up to three gases. However, classification of the gas type and calculation of the gas concentration gives a better result, when ANNs are used. Fig. 1 shows a comparison of the results of an analytical and a neural signal processing output. We are using feed forward networks for classification and calculation, provided by a common de-
................. 125 250
375
' ..... 500 625
0 750
CO concentration [vpm]
Fig. 1. Comparison of (a) an analytical and (b) a neural signal processing result.
velopment tool SNNS V. 3.2. [5]. A faster backpropagation algorithm [6] and the weight decay method [7] were added to this tool. The neural networks used consist of 3 to 5 input neurons, 6 to 12 hidden neurons and one output neuron for each gas. The number of hidden neurons was optimized empirically. Normally ANN need no data preprocessing with exception of a normalization of the input data. Additional data preprocessing methods may increase the accuracy of the ANN output. This preprocessing consists of a baseline correction (compensating the short time drift after switching on) and an averaging procedure (boxcar filtering with an optimized number of data points) of the sensor signal. The treatment of time dependent sensors signals is depicted elsewhere [1].
5. DTPD method and results
The metal oxide sensors were used to measure mixtures of CO and C3H7OH together with a moisture of 20% r.h. (at 298 K ambient temperature). Depending on the number and the type of the sensors, classification rates of more than 95% were achieved (see Table 1). This is in concordance with the classification rates in the literature, where such systems are described. The
H.-E. Endres et aL / Sensors and Actuators B 26-27 (1995) 267-270 Table 1 Classification rates
269
E J
.oo]
N u m b e r of sensors Classification rate (%)
2 78.0
3 98.1
4 98.9
300
......... :....... :....... :---y
.......
.:/:: ;
I
......... i....... i....... :......
......
...... \ ~ i .......i........i ........ ...... 8 200,o]U
8 =
=o
(D
6
lilii ,ok.IKH
,o
,JliiilI I 0 iiIili!0
0
Z
0
1% 20"~30I 40 25 35
"
100
200
300
400
500
CO concentration [vpm] CO TLV: 30 vpm
50 ~> 3o4°~ ""
'25
250
375
500 ==~"
m CO concentration [ vpm ]
ii...........
oo8 ........ ; 0
A:
linear data .i.! ..t distribution
20 .....
TLV Fig. 2. D T P D calibration points around the C O - T L V of 30 vpm.
calculation of the gas concentrations was performed with a single ANN sufficient for a mixture of only three gases. Depending on the number and type of the sensors in the array, an average accuracy of better than 3% was achieved. An essential result of our investigations is the strong dependence of the calculation performance on the number and distribution of the calibration points. This is caused by the interpolating properties of ANN. From a combinatorial viewpoint a high density of calibration points should be distributed in the two-dimensional input space (concentration of two gases), which causes an unacceptably long calibration time. The new DTPD method proposes a different and application-related distribution of calibration points: (i) high density near regions of interest (for example, TLV or LEL); (ii) modest density elsewhere. Fig. 2 gives an example of such a distribution around the CO-TLV of 30 vpm. Two ANN were trained with preprocessed calibration data. Network A was trained with a subset of the data, containing only CO concentration with a linear distribution (step width 125 vpm). Network B was trained with the full calibration data set with a high density of calibration points near the CO threshold limit value (TLV) of 30 vpm. The test data were not enclosed in the training data sets. Fig. 3 shows as result the predicted CO concentrations. Network A demonstrates a good result over the whole concentration range, but lacks near the CO-TLV. The DTPD trained network B calculates the CO-TLV with a high accuracy as well as the concentrations within the desired concentration range up to 500 vpm. Compared with a dense linear distribution of calibration points (CO stepwidth about 10 vpm), using the DTPD
!
10
20
30
40
50
COconcentration[vpm]
i so 40 ~o ~ ] 20
o8
CO TLV: 30 vpm
.
.... ! B: , ..... . ; DTPD ~ data ..... distribution around TLV
o 0
10 20 30 40 50 co concentration[vprn]
Fig. 3. ANN prediction result of network A (using linear calibration point distribution) and network B (using D T P D ) .
method results in a calibration time reduced to 20% of the linear distribution.
6. Conclusions It is evident that the distribution of calibration points in the training data affects the performance of ANN. A high and equal density of calibration points in the concentration space gives a higher quality of the network adaption. Accordingly, it is necessary to mix all gases in all concentrations and with all humidity contents and many concentrations. Assuming a time interval of about 10-20 min for each gas combination/concentration, suitable for most gas sensors, a complete measurement cycle (assuming three test gases plus humidity) needs about one week or longer. A more complex measurement problem implies such a large number of measurements that they cannot be performed within acceptable time span. Therefore, we propose and demonstrate in this work that the distribution of calibration points should be adapted to the application. This new strategy is the dynamic test point distribution (DTPD) method with a higher density in the region of interest (for example near the TLV value) and a modest density
270
H.-E. Endres et al. / Sensors and Actuators B 26-27 (1995) 267-270
elsewhere. The application of this method should give quite enough calibration points for high accuracy. The distribution of the calibration points has to be carefully chosen, to improve the network prediction abilities only within desired pattern space regions. The possibility for this method was shown in this work.
Acknowledgements This work was supported by the Commission of the EU (ESPRIT No. 6374-MMMGAS). The authors gratefully acknowledge the contributions of R. Hartinger and J. Albrecht to this work and thank H. Diehl for his suggestion of the statistically ordering of calibration points.
References [1] H.-E. Endres, W. G6ttler, H. Jander and S. Drost, A systematic investigation on the use of time dependent sensor signals in signal processing techniques, Sensors and Actuators B, 24-25 (1995) in press. [2] G. Sberveglieri, Classical and novel techniques for the preparation of SnO2 thin-film gas sensors, Sensors and Actuators B, 6 (1992) 239-247. [3] H.-E. Endres, H.D. Jander and W. G6ttler, A test system for gas sensors, paper presented at Eurosensors VIII, Toulouse, 21-24 Sept. 1994.
[4] K.D. Schierbaum, U. Weimar and W. G6pel, Multicomponent gas analysis: an analytical approach applied to SnO2 gas sensors, Sensors and Actuators B, 2 (1990) 71-78. [5] A. Zell et al., S N N S - Stuttgart Neural Network Simulator Ver. 3.Z, University of Stuttgart, Germany, 1994. [6] R. Salomon, Verbesserung konnektionistischer Lernverfahren, die nach der Gradientenmethode arbeiten, Ph.D. Thesis, Technical University of Berlin, 1991. [7] J. Hertz, A. Krogh and R.G. Palmer, Introduction to the Theory of Neural Computation, Addison-Wesley, Redwood City, 1992.
Sensors and Actuators B 26-27 (1995) 267-270
CnEUlCAt.
Improvement in signal evaluation methods for semiconductor gas sensors Hanns-Erik Endres a, Wolfgang G6ttler a, Hildegard D. Jander a, Stephan M. Drost Hermann Sandmaier a, Giorgio Sberveglieri b, Guido Faglia b, Cesare Perego b
a,
~ Fraunho~er-Institute for Solid State Technology, Hansastrasse 27d, D-80686 Munich, Germany b Universita degli studi di Breschia, FacolM di Ingegneria, Diparcimento di Chimica e Fisica per i Materiali, Ida Valotti 9, 1-25133 Brescia, Ita~,
Abstract
Applied chemical sensor research focuses on sensor arrays and signal evaluation methods, to improve reliability, selectivity and other features of the single sensor. State-of-the-art is the use of self-adapting systems like artificial neural networks (ANNs), mostly used for classification purposes. Systems for the prediction of gas concentrations were seldom investigated, because one of the main problems for those signal processing systems is the enormous amount of training data and the time dependency of the sensor signal. This work uses an array of semiconductor sensors (RGTO method and commercial sensors) and a modified A N N method for signal processing. After a drift correction based on an empirical model, a feed forward network predicts gas concentrations more precisely. A new method, the dynamic test point distribution (DTPD) has been invented, which achieves a significant reduction in the calibration time, together with a high accuracy in calculating the gas concentration. Keywords: Time dependency; Semiconductor sensors; Artificial neural networks; Calibration time; RGTO technique; Dynamic test point distribution
1. Introduction
2. Sensors
The task for a gas sensing system is to measure one or more specified gases and to suppress any interference from other gases and humidity. A common way to suppress interference is to use multisensor systems together with a smart signal processing system. Foremost semiconducting sensors were used for such applications due to their easy modification with several dopants, working as catalysts. As the basic detection effects of gases (such as adsorption) are similar for other sensors, they also can easily be adapted to such a system. If there exists no mature theory of the sensor, selflearning systems like artificial neural networks (ANNs) are best suited methods for signal processing systems. A common disadvantage of these systems is the immense amount of training data needed for the calibration. This implies a sophisticated m e a s u r e m e n t technology to gather enough data and improved methods to handle the calibration data and training of such systems.
For this work, thick film semiconductor sensors (Umweltsensortechnik, Geraberg, type 1 and 2) and thin film sensors, prepared by the R G T O (rheotaxial growth and thermal oxidation) technique, were used [2]. The R G T O tin oxide thin films have been grown with an Alcatel magnetron sputtering plant (model SCM 450). This material has been deposited on polished alumina substrates 3 × 3 m m 2 in size. A m e a n d e r e d Pt thin film has been grown on the back side of the substrate to act as a heating element. The final sensors are mounted on standard TO-8 sockets. Owing to the special growing technique, the surface of the thin films is made up of many polycrystalline agglomerates connected by necks, and the surface roughness is very high. The material obtained shows a high sensitivity towards low concentrations of gases in air.
0925-4005/95/$09.50 © 1995 Elsevier Science S.A. All rights reserved SSDI 0925 -4005 ( 9 4 ) 0 1 5 9 9 - D
268
H.-E. Endres et al. / Sensors and Actuators B 26-27 (1995) 267-270 0
3. Measurement equipment
125
30 . . . .
Standards for measuring equipment for gas sensors have increased together with the demands of signal evaluation systems. The measurement system should be able to mix several test gases and to humidify the gas mixture with a high reproducibility and accuracy. A test chamber should be able to contain one or more sensor arrays, should have a small volume and a short purging time. A special necessity for measuring heated or gas consuming sensors (like semiconductor sensors) is the reduction of any mutual influence of the sensors. Also an easy handling of the total equipment (hardware and software) is necessary. The measurement equipment of IFT tries to find a good compromise of all demands [31. To achieve the training data for the signal processing system, calibration cycles with a duration of at least one week were performed. The gas combinations and concentrations were statistically ordered, to reduce the influence of hysteresis and memory effects of the sensors onto the ANN.
E O.
25
E
is
250
375
i
625
]
750 30 25
l
>-
-i
....
is
B
c 0
500
~ 10
~"
ff
L 0
.... 0
(a)
-
I
i ............ 125 250
,
375
J ........ 500 625
0 750
CO concentration [vpm]
30
0
125
250
750 30
E
25
25
[= 0
20
C
15 .......
lid
~
375
625
20
q
~ID----t~
500
)
~
B
15
I
5
cO C 0
ff Z
5
I)
--
w
F
0
4. Signal evaluation system
0
(b)
The task of the signal processing system for a gas sensor system is to classify the measured gases and to calculate their concentrations. Therefore, the signal processing of sensor signals is a twofold problem: to classify the gases and to calculate their concentration. The difference between the algorithms is that the classification system has a binary output (gas detected or not detected) while the calculation system for the gas concentration has an analog output, which implies a higher number of calibration data. In most cases the signal of a metal oxide sensor is a highly nonlinear problem, especially when coadsorption occurs. For a calculation of gas concentrations, an analytical relation of the sensor response with the gas concentration is a preferable choice. It combines a deeper physical and chemical understanding with a small computation time. An approximation formula was given by Schierbaum et al. [4].
Go
i
~Pi0)/
This formula is easy to linearize and gives a wellbehaved approximation for a gas mixture of up to three gases. However, classification of the gas type and calculation of the gas concentration gives a better result, when ANNs are used. Fig. 1 shows a comparison of the results of an analytical and a neural signal processing output. We are using feed forward networks for classification and calculation, provided by a common de-
................. 125 250
375
' ..... 500 625
0 750
CO concentration [vpm]
Fig. 1. Comparison of (a) an analytical and (b) a neural signal processing result.
velopment tool SNNS V. 3.2. [5]. A faster backpropagation algorithm [6] and the weight decay method [7] were added to this tool. The neural networks used consist of 3 to 5 input neurons, 6 to 12 hidden neurons and one output neuron for each gas. The number of hidden neurons was optimized empirically. Normally ANN need no data preprocessing with exception of a normalization of the input data. Additional data preprocessing methods may increase the accuracy of the ANN output. This preprocessing consists of a baseline correction (compensating the short time drift after switching on) and an averaging procedure (boxcar filtering with an optimized number of data points) of the sensor signal. The treatment of time dependent sensors signals is depicted elsewhere [1].
5. DTPD method and results
The metal oxide sensors were used to measure mixtures of CO and C3H7OH together with a moisture of 20% r.h. (at 298 K ambient temperature). Depending on the number and the type of the sensors, classification rates of more than 95% were achieved (see Table 1). This is in concordance with the classification rates in the literature, where such systems are described. The
H.-E. Endres et aL / Sensors and Actuators B 26-27 (1995) 267-270 Table 1 Classification rates
269
E J
.oo]
N u m b e r of sensors Classification rate (%)
2 78.0
3 98.1
4 98.9
300
......... :....... :....... :---y
.......
.:/:: ;
I
......... i....... i....... :......
......
...... \ ~ i .......i........i ........ ...... 8 200,o]U
8 =
=o
(D
6
lilii ,ok.IKH
,o
,JliiilI I 0 iiIili!0
0
Z
0
1% 20"~30I 40 25 35
"
100
200
300
400
500
CO concentration [vpm] CO TLV: 30 vpm
50 ~> 3o4°~ ""
'25
250
375
500 ==~"
m CO concentration [ vpm ]
ii...........
oo8 ........ ; 0
A:
linear data .i.! ..t distribution
20 .....
TLV Fig. 2. D T P D calibration points around the C O - T L V of 30 vpm.
calculation of the gas concentrations was performed with a single ANN sufficient for a mixture of only three gases. Depending on the number and type of the sensors in the array, an average accuracy of better than 3% was achieved. An essential result of our investigations is the strong dependence of the calculation performance on the number and distribution of the calibration points. This is caused by the interpolating properties of ANN. From a combinatorial viewpoint a high density of calibration points should be distributed in the two-dimensional input space (concentration of two gases), which causes an unacceptably long calibration time. The new DTPD method proposes a different and application-related distribution of calibration points: (i) high density near regions of interest (for example, TLV or LEL); (ii) modest density elsewhere. Fig. 2 gives an example of such a distribution around the CO-TLV of 30 vpm. Two ANN were trained with preprocessed calibration data. Network A was trained with a subset of the data, containing only CO concentration with a linear distribution (step width 125 vpm). Network B was trained with the full calibration data set with a high density of calibration points near the CO threshold limit value (TLV) of 30 vpm. The test data were not enclosed in the training data sets. Fig. 3 shows as result the predicted CO concentrations. Network A demonstrates a good result over the whole concentration range, but lacks near the CO-TLV. The DTPD trained network B calculates the CO-TLV with a high accuracy as well as the concentrations within the desired concentration range up to 500 vpm. Compared with a dense linear distribution of calibration points (CO stepwidth about 10 vpm), using the DTPD
!
10
20
30
40
50
COconcentration[vpm]
i so 40 ~o ~ ] 20
o8
CO TLV: 30 vpm
.
.... ! B: , ..... . ; DTPD ~ data ..... distribution around TLV
o 0
10 20 30 40 50 co concentration[vprn]
Fig. 3. ANN prediction result of network A (using linear calibration point distribution) and network B (using D T P D ) .
method results in a calibration time reduced to 20% of the linear distribution.
6. Conclusions It is evident that the distribution of calibration points in the training data affects the performance of ANN. A high and equal density of calibration points in the concentration space gives a higher quality of the network adaption. Accordingly, it is necessary to mix all gases in all concentrations and with all humidity contents and many concentrations. Assuming a time interval of about 10-20 min for each gas combination/concentration, suitable for most gas sensors, a complete measurement cycle (assuming three test gases plus humidity) needs about one week or longer. A more complex measurement problem implies such a large number of measurements that they cannot be performed within acceptable time span. Therefore, we propose and demonstrate in this work that the distribution of calibration points should be adapted to the application. This new strategy is the dynamic test point distribution (DTPD) method with a higher density in the region of interest (for example near the TLV value) and a modest density
270
H.-E. Endres et al. / Sensors and Actuators B 26-27 (1995) 267-270
elsewhere. The application of this method should give quite enough calibration points for high accuracy. The distribution of the calibration points has to be carefully chosen, to improve the network prediction abilities only within desired pattern space regions. The possibility for this method was shown in this work.
Acknowledgements This work was supported by the Commission of the EU (ESPRIT No. 6374-MMMGAS). The authors gratefully acknowledge the contributions of R. Hartinger and J. Albrecht to this work and thank H. Diehl for his suggestion of the statistically ordering of calibration points.
References [1] H.-E. Endres, W. G6ttler, H. Jander and S. Drost, A systematic investigation on the use of time dependent sensor signals in signal processing techniques, Sensors and Actuators B, 24-25 (1995) in press. [2] G. Sberveglieri, Classical and novel techniques for the preparation of SnO2 thin-film gas sensors, Sensors and Actuators B, 6 (1992) 239-247. [3] H.-E. Endres, H.D. Jander and W. G6ttler, A test system for gas sensors, paper presented at Eurosensors VIII, Toulouse, 21-24 Sept. 1994.
[4] K.D. Schierbaum, U. Weimar and W. G6pel, Multicomponent gas analysis: an analytical approach applied to SnO2 gas sensors, Sensors and Actuators B, 2 (1990) 71-78. [5] A. Zell et al., S N N S - Stuttgart Neural Network Simulator Ver. 3.Z, University of Stuttgart, Germany, 1994. [6] R. Salomon, Verbesserung konnektionistischer Lernverfahren, die nach der Gradientenmethode arbeiten, Ph.D. Thesis, Technical University of Berlin, 1991. [7] J. Hertz, A. Krogh and R.G. Palmer, Introduction to the Theory of Neural Computation, Addison-Wesley, Redwood City, 1992.