- Email: [email protected]
Evaluation of a multiple gas mixture with a simple MOSFET gas sensor array and pattern recognition
Sensorsand
Actuators,
B, 2 (1990) 115-123
115
Evaluation of a Multiple Gas Mixture with a Simple MOSFET Gas Sensor Array and Pat tern Recognition HANS SUNDGREN, INGEMAR LUNDSTROM and FREDRIK WINQUIST Laboratory of Applied Physics, Department S-581 83 Linktiping (Sweden)
of Physics and Measurement
Technology,
Linkiiping Institute of Technology,
INGRID LUKKARI Department
of Analytical Chemistry, Institute of Chemistry,
Ume8 University, 990187
Urn& (Sweden)
ROLF CARLSSON Department
of Organic Chemistry, Institute of Chemistry. urn& University, S-901 87 Umea (Sweden)
SVANTE WOLD Research Group for Chemometrics,
Institute of Chemistry,
Umea University, S-901 87 Umd
(Sweden)
(Received April 7, 1989; in revised form November 2, 1989; accepted December 13, 1989)
Abstract The properties of a gas sensor array can be improved by the use of pattern recognition (PARC) methods. A gas sensor array with three pairs of Pd-gate MOSFETs and P&gate MOSFETs is exposed to a multiple-component gas mixture. Each pair is operated at a different temperature. The signals from the six sensors are analysed with both linear and nonlinear PLS (partial least square) models. The calculations of the PLS models are based on sensor signals obtained from calibration experiments. By means of combining a pattern recognition method with a semiconductor-based gas sensor array, we show that hydrogen concentrations can be predicted well in the presence of three other interfering gases.
Introduction Efforts have been made to modify existing gas sensors and to find new kinds of sensors in order to improve properties like selectivity, accuracy, reliability, sensitivity, speed of response and reproducibility. Gas sensors often have sensitivities towards classes of gases, with a ‘selectivity’ that may depend on the operation temperature. In order to improve not only the selectivity but also ,some of the other parameters mentioned above, sensor arrays are being utilized together with pattern recognition methods [l--7]. The Pd-gate MOSFET (PdMOS) is one of the most sensitive sensors for hydrogen (H,). These sensors normally have a Pd gate 100-400 nm thick [8]. At a working temperature of 150 “c, the PdMOS has a large sensitivity mainly to hydrogen 0925-4005/90/$3.50
and hydrogen sulfide. At higher temperatures it is also sensitive to ethanol and ethylene [9, lo]. The PdMOS has virtually no direct sensitivity to ammonia. A P&gate MOSFET (PtMOS), with a thin discontinuous Pt gate 3-30 nm thick, is also sensitive to ammonia when operated at 150 “c [ 11-13 1. This sensitivity increases with temperature [14]. The PtMOS is also sensitive to ethylene and ethanol, with a sensitivity which increases rapidly at temperatures above 150 “C [IS]. The selectivity of PdMOS and PtMOS devices at three different temperatures is illustrated in Table 1. This Table indicates that PdMOS and PtMOS sensors operated at, e.g., 150 “C and used together should be able to estimate the concentration of hydrogen and ammonia when simultaneously exposed to them. By increasing the number of different sensors in a gas sensor array, more complicated gas mixtures should also be able to be determined. In this work we have chosen to use a six-element sensor array with three Pd- and three Pt-gate MOSFET sensors with working temperatures of 100, 150 and 200 “C, respectively, making it in principle possible to detect more than one or two species, as indicated in Table 1. A gas sensor array with sensors of different types and/or operating conditions is a very powerful source of information. However, prediction of the content of a gas mixture from the sensor signals requires a well fitting mathematical model. Zaromb and Stetter were among the first to consider the theoretical basis for the use of arrays of sensors with partly overlapping selectivities [ 161. A semiempirical way of finding such a model is implemented by means of pattern recognition (PARC) methods. These models may be derived as Taylor expressions of underlying fundamental models [ 171. The quality of such a model depends on the 0 Elsevier Sequoia/Printed
in The Netherlands
116 TABLE 1. Relative sensitivity towards different gases of PdMOS and PtMOS devices (in 20% oxygen) Gas (concentration
100 ppm)
Hydrogen
Ammonia
Ethylene
Ethanol
PdMOS 200 “C PdMOS 150°C PdMOS 100 “C
+++a +++ ++
0 0 0
0 0 0
+ 0 0
PtTMOS 200 “C PtTMOS 150 “C PtTMOS 100 “C
++ ++ ++
+++ +++ 4-k
++ + 0
+++ ++ +
8+++, >200 mV; ++, SO-200 mV; +, S-50 mV; 0, <5 mV.
quality of the sensor signals and the design of the experiments used to evaluate the model. It has been pointed out that the information potential of gas sensor arrays is very large and that the PARC method is a clever way to extract this information. Carey et al. [ 1,2] have shown the usefulness of PARC in characterizing and analysing surface acoustic wave and quartz crystal microbalance sensor arrays. Arrays with SnOZ gas sensors were analysed with particular emphasis on nonlinear modelling by Horner et al. [3] and Hierold and Miiller [7]. Arrays of up to four MOSFET gas sensors have been analysed by Miiller and coworkers in binary gas mixtures [5,6]. In the work by Killer et al., devices with thick Pd or Pt gates or Pd.gate devices covered with different types of zeolites were used. Several pattern recognition routines were also developed. In this paper we use partial least square (PLS) methods developed for the analysis of (chemical) data in general, namely the PLS2 method implemented in the SIMCA-3B software supplied by Sepanova AB [ 181 and a nonlinear PIS [ 191, the NPLS developed by Wold and Skagerberg [20], for an array of six MOSFET sensors with a documented difference in their sensitivity pattern towards given molecules in air. This difference occurs due to the use of two different types of metal gates, ‘thick Pd and ‘thin’ Pt, operated at three temperatures. PLS2 and NPLS represent generalized pattern recognition, denoted as level 3 and level 4 [17], which can be used to predict one or several dependent variables from independent variables such as sensor signals. In this use they are often called methods of multivariate calibration [21]. However, the sensor signals cannot be treated as continuous signals in the modelling and it is necessary to divide them into a number of discrete values. It is also important to preprocess the sensor signals correctly. For instance, it is necessary to calculate the voltage shift corresponding to the exposing gas pulse separated from any voltage shift due to long-term drift, temperature dependence etc. However, scaling and normalizing are not necessarily needed. As in any
modelling, it is important to have a proper experimental design of the calibration set of measurements. In principle, a complete ‘factorial design in two levels’, as described by Box et al. [22] for example, was used. The investigations presented here are intended to examine the possibility of employing available methods for generalized pattern recognition, such as PLS2 and NPLS, for increasing the efficiency of available gas sensors such as the Pt- and Pd-gate MOSFET sensors in a sensor array. Furthermore, the investigations hopefully illuminate the advantages and difficulties raised by the extraction of more information, with the help of modern methods for pattern recognition, than that immediately apparent from these sensors. Experimental and Computational Techniques
Two types of commercially available MOSFET gas sensors, with Pd and Pt gates respectively, were studied [23]. Three devices of each type were chosen in order to have a Pd-gate and a Pt-gate MOSFET pair at three different working temperatures, 100, 150 and 200 “C. Each sensor had its own circuit for temperature control and for biasing of the MOSFET. The bias was a constant current of 125 I.IA feeding the gate and drain terminals, which were connected together. The source terminal was grounded. The voltage drop across the MOSFET is then close to its threshold voltage. Exposure to gas causes a change in this voltage. The sensors were mounted in’ a sample cell with a small dead volume, as shown in Fig. 1. This array includes only two types of sensors with respect to fabrication technology, but six different sensors with respect to response characteristics (see Table 1). A gas-mixing system was used to generate the gas mixtures needed for calibration and test experiments. The total flow of the test gas was 100 ml/ min. The gas mixtures included a carrier gas consisting of 21.3% O2 in Ar and a four-component
117
as the difference between the voltage drop over the MOSFET before and at the end of exposure to the test gas. In the next step the maximum positive and negative values of the derivative with respect to time of the sensor signal are determined. Since there are six sensors in the sensor array and both the voltage shift and the two derivatives are measured, there are 18 values which are collected for every single experiment. The total experiment consisted of two different experimental sets, see Table 2. The calibration set with 31 different gas mixtures was used only to make the mathematical model for the response of the array, and the test set was used to evaluate that model by making predictions of the gas concentrations. Variable values acquired from the experiments are illustrated in Fig. 2 in a so-called axonometric plot. With a knowledge of the configuration of the gas mixtures together with the appearance of the variable plot, it is not obvious
TABLE 2. The gas mixture composition of the calibration set and the test set respectively. Concentrations in ppm with 21.3% oxygen in argon as carrier gas. One row represents one individual experiment
Fig. 1. The sample cell with six separate sensors, each mounted in a TO-18 socket. The drawing shows the test gas passage and the small dead volume. A cut view from the side (a) and top view (b) of the sample cell.
gas mixture. The test gas concentration was selected according to the experimental design in the range lo-500 ppm (hydrogen, ammonia, ethylene) and 16-300 ppm (ethanol). The gas sensor array was exposed to the desired mixture for a period of five minutes with a relaxation time of 25 minutes. To restore the active surface of the sensors and thus increase the reproducibility, the sensor array was exposed to a gas mixture consisting of 190 ppm hydrogen in the carrier gas, for five minutes with 25 minutes relaxation, before exposure to each new gas mixture. This was done to ensure that the catalytic activity of the metal gates was as similar as possible upon exposure to a gas mixture. The gas mixing was performed under computer control, allowing stable experimental conditions to be achieved. The sensor signals were sampled every other second by means of a personal computer equipped with a data acquisition board, with a resolution of 1 mV. Both data acquisition and signal conditioning have been performed with the Asystantt software [24]. In the first signal-processing stage the sensor signal, the voltage shift of the drain/ gate voltage characteristics (i.e., the change in the threshold voltage) caused by the test gas, is obtained
Calibration
set
Hz
NH3
Cd4
CzHsOH
Ha
NH3
CzH4
CzHsOH
190 190 27 27 190 190 190 190 190 27 190 27 27 27 27 27 70 70 500 11 70 70 70 70 70 70 70 70 70 70 70
27 190 190 190 27 27 190 190 27 27 190 190 27 190 27 27 69 69 69 69 11 69 69 69 69 69 69 499 69 69 69
27 27 27 190 190 190 27 190 27 27 190 190 27 27 190 190 70 70 70 70 70 499 70 70 70 70 10 70 70 70 70
33 144 144 33 33 144 33 33 144 144 144 144 33 33 144 33 70 70 70 70 70 70 70 70 70 16 70 70 70 300 70
190 190 190 190 190 249 190 190 27 190 99 190 300 29
27 27 27 27 27 249 190 190 190 27 298 190 41 30
190 190 190 190 190 40 190 27 190 190 24 27 61 251
33 33 33 33 33 40 33 33 33 33 25 33 40 25
Test set
In our case we have p = 18 independent variables, xi (sensor signals). The 31 calibration signals are plotted in a p-dimensional space (they represent a matrix with 31 rows and 18 columns). By looking at similarities in the data points, they can be projected on an m-dimensional space (hyperplane). The m-dimensional x-space is now used to predict the dependent variables, y, by the use of least square methods, hence the name PLS, which stands for ‘partial least square modelling with latent variables’. Two dependent variables represent a 31X4 matrix (in our case the four concentrations of the gas mixtures in the calibration set). Several criteria are used to obtain the best model dimension, m. The residual sum of squares Fig. 2. The sensor data from the calibration and test set in a three-dimensional axonometric plot. The independent variables (voltage shift and charging derivatives) in X, the experimental order (or gas composition) in Y and amplitude of the signals in 2. The discharge derivatives are not shown in the Figure, hence for each Y there are only 12 X values. Each division on the X-axis corresponds to a single variable. Each division on the Z-axis corresponds to 200 mV for the voltage shifts.
how to treat the variables to predict the composition of a gas mixture. However, once the calibration variable values have been measured, the calibration set is used for model calculations in PIS2 and NPLS. The response characteristics of the Pt- and Pd-gate MOSFETs are nonlinear. It has been shown that the response follows a Langmuir-like relationship due to the kinetics of adsorption, reaction and desorption of gaseous species on the surface of the sensors [8]. It was therefore interesting to use not only a linear PIS model to predict the concentrations of hydrogen, ammonia, ethanol and ethylene, but also different nonlinear modifications of it. First, a five-dimensional ordinary linear PLS model (1PIS) based upon 18 variables is considered. Thereafter, a modified linear PLS model (mPLS) with seven dimensions based upon the 18 variables, as in the first model, and additionally the square of the original variables to make a total of 36 variables, is treated. Finally, a two-dimensional quadratic model (nPLS) based upon 18 variables is calculated with the NPLS. In the linear models each axis in a projection plane is defined as a linear combination of the original variables xk. In the quadratic model terms like xixk are also included. The PLS methods are implemented in the ready to use SIMCA-3B software package and a nonlinear version, NPLS [ 18-201. The principles and the practice of the PLS method are given in detail elsewhere [ 16,2 1,251. The procedure for multivariate calibration and prediction of unknown samples (gas mixtures) can briefly be described as follows.
RSS, = W:arcureted - Y ;d2 (1) is an important parameter which should be minimized. The autoscaled values (y’) are used in the SIMCA3B programme to give all variables (Y) the same weight. Another important and stronger criterion is the so-called cross validation. In cross validation part of the objects (rows in the x matrix) is left out (say one quarter) in the estimation of the last PIS dimension. A new model is made and used to predict the left out y values from the corresponding row in x; we call the prediction of the left out values ypredr&ed. This is then repeated for another part of the data until all y values have been predicted once [26]. The predicted sum of squares PRESS, =
%&redicted
-
Y)td2
(2)
is formed. Figure 3 illustrates the calculated PRESS value versus the PLS dimension in some of our cases. If the ‘cross validation term’ PRESS,,,/ RSS,_i < 1, the model with the chosen dimension has predictive relevance. Examples of cross validation terms are given in Table 3. The model with the 150000
-
100000
-
3 E
50000
-
0-l . , . , . , . , . 1
2
3
4
5
PLS - dimension
(
6
.
,
7
.
,
.
6
[# ]
Fig. 3. The PRESS (predicted square of sums) value vs. PLS dimension. The PRESS value was calculated as the sum for the hydrogen and ammonia predictions.
119 TABLE 3. The cross validation term of the prediction of the dependent variables, hydrogen (Hz) and ammonia (NHs) respectively. The term is calculated for the linear (IPLS), modified (mPLS) and the nonlinear (nPLS) models for each dimension separately. The crossed terms represent values for a larger dimension with no predictive value PLS dimension
PLS model mPLS
1PLS
nPLS
H2
NH3
H2
NH3
H2
NH3
0.9141 0.8134 1.0103 1.0042 1.0172
0.97 10 0.8271 0.9564 0.9179 0.9598
0.9036 0.725 1 0.9924 0.9698 0.9965 0.8361 0.8986
0.9798
0.572 1.002
1.002 0.877
highest dimension, for which PRESS,/RSS,_r < 1, is in general chosen as the PARC model to be used. The relationship thus obtained between y and x is now used to predict the composition of a gas mixture from measured x-variables. The description above is intended only to give a feeling for the PLS methods; interested readers are referred elsewhere [17, 19,21,25]. The SIMCA-3B and the NPLS are developed for IBM-PC type computers and the file with the sensor signals has to be converted into the SIMCA3B format. The programmes are menu driven and the calculating algorithms are effective, which makes it fast and easy to use. All the calculations were made with cross validation to ensure that the best model dimension was chosen.
0.7888 0.9574 0.8630 0.9880 0.9769 0.9183
300
0
IPLS nPLs
---
250-
200-
150-
100.
50.
O:....,....,....,..._,--__,--~ 0 50 100
150
CONCENTRATION
200
250
300
H2 [ppm]
Results
Fig. 4. Predicted vs. actual hydrogen concentration in the test set for the lPLS, nPLS and mPLS model respectively.
Predictions were made with the IPLS, mPLS and nPLS models. The optimal dimension of the nPLS model (2) is less than that of the 1PI.S model (5) which possibly indicates that the quadratic model fits better to the sensor signals than the linear model. Furthermore, the dimension of the mPLS model (7) is greater than that of the lPI_S (S), indicating the significance of the additional variables. Figure 4 illustrates how the three separate models predict the hydrogen concentration. The 1PLS model fits well in the medium concentration range but not in the outer ranges. The curve is slightly bent, verifying the nonlinear response characteristics of MOSFET sensors. Consequently, the nPLS curve is a straight line as expected, but the slope is not sufficiently steep. The best-fitting model in this case is the mPLS model. This curve is fairly good considering that the hydrogen is only one out of four gases that the sensor array is exposed to.
A perfect prediction should give rise to data points on a 45’-line through the origin in Fig. 4. Figure 5 illustrates predicted results for ammonia. The 1PIS model represents the best fit and the curve is bent comparable to the corresponding hydrogen predictions. The predictions from the mPL.S and nPLS models are mainly too low. Other attempts to preprocess data have been made in order to fmd the best-fitting model with the least number of independent variables. The dimension of the different PLS models used was chosen from PRESS plots (Fig. 3) and cross validation terms (Table 3) but any effort to reduce the number of variables or to exclude the voltage shift or the derivatives resulted only in minor improvements of the predicting models. The mPI_S is one of the bestfitting models to hydrogen predictions and the 1PLS to the ammonia predictions. One of the problems
nPLS
---
mps
-----
can be expressed as a function of the components in the gas mixture. For a Pd-gate MOS device we could use, to a good approximation, for some gas mixtures in air (oxygen),
AVlWn,x
= (nYiPp2
1 - AV/AV,,,
CONCENTRATION
NH3
[ppm]
Fig. 5. Predicted vs. actual ammonia concentration in the test set for the lPLS, mPLS and nPLS models respectively.
AMMONIA mI
(3)
where Pi is the partial pressure of the ith component and the ors are temperature-dependent constants. AV is the steady-state sensor signal, i.e., the observed shift of the drain current-gate voltage characteristics along the voltage axis. AV,,, is the maximum obtainable voltage shift (which is in the order of 0.5 V). This type of relationship has been used by Armgarth ef al. [27] to analyse mixtures of hydrogen and ethanol with one PdMOS sensor operated at different temperatures. It was found that hydrogen and ethanol could be independently determined in this way, through a knowledge of the physics of the devices. In an analysis of a binary gas mixture, with two types of MOS gas sensors, Gall and Mtiller [6] use Sr =(~r[A]~*+br[B]“‘u)“~
(4)
Sa = (a* [A] mA t bz [B] mn)“2
(5)
for the signals from the two types of sensors, where [A] and [B] are the concentrations of the two components in the gas mixture. A set of transformations yr = S,i’“l I 1
TIME' I h I
3
ik-, = [A]“*;
;
y, = &1’“2
(6)
Xn = [B]“n
(7)
4
Fig. 6. The predicted ammonia concentration in the test set vs. time (or order of exposure) with predictions made from the linear PLS model, lPLS, with a gas mixture consisting of 27 ppm ammonia, 190 ppm hydrogen, 190 ppm ethylene and 33 ppm ethanol.
with
ammonia is actually caused by the sensor array itself, since it was found that the prediction of the ammonia concentrations could decrease with time as illustrated in Fig. 6. It is therefore
likely that the calibration set gave too large values of the variables (AV values) just for ammonia, which could explain some of the deviations between the predicted values and the actual gas concentration. Ethylene and ethanol could not be separated and the sum of their concentrations could not be predicted either.
There are several ways in which sensor signals can be analysed. In certain cases the sensor response
leads to a linear set of equations, which is used to evaluate the signals with normal linear methods. The parameters n,. n2. mA and ntg are determined from experiments. They found that with the method above, called TLS (transformed least square-method), it was possible to determine hydrogen rather accurately in the presence of methanol (CHaOH). Even if it should be possible in principle to use a similar approach to analyse the data from our sensor array, this is not straightforward in a complex gas mixture. The determination of the parameters becomes a tedious procedure. Furthermore, it is also necessary to consider terms of the form [A] [B] since there may be a certain interaction between the different components. For example, ammonia is not detected by the thick Pd-gate devices although ammonia molecules participate in the chemical reactions on the Pd surface. We have chosen to use pattern recognition routines developed for the analysis of chemical data in general, such as chromatograms, optical spectra etc. [21]. The scope of the paper was to investigate if and how such routines could be used to predict the composition of a gas mixture from the signals
121
obtained from an array of six MOSFET gas sensors. We have concentrated on a moderately difficult problem, namely to analyse a gas mixture in oxygen and argon containing hydrogen, ammonia, ethanol and ethylene. It was found that with the present sensor array, the hydrogen concentration could be rather accurately predicted in the presence of the three other components. Ammonia could also be identified in the mixture, but in general its concentration was predicted too low. It was found that for hydrogen a modified linear PLS model with dimension seven gave the best prediction, whereas for ammonia a linear PLS with dimension five gave the best result. This suggests that different pattern recognition routines should be employed to extract different components of a gas mixture. When analysing a gas mixture consisting of several different species, the situation might be rather complicated. First, chemical reactions between the gases can already occur in the mixture before it reaches the sensor array. Secondly, reactions between reaction intermediates from the different gases in the mixture which are adsorbed on the catalytically active surface of the sensor are also likely to occur. This will change reaction pathways and thus also the selectivity and sensitivity of the sensor array. In a gas mixture consisting of low concentrations (<300 ppm) of hydrogen, ammonia, ethylene, ethanol in oxygen/argon, chemical reactions are not likely to occur between the species in the gas phase before they reach the active surfaces of the sensors. When adsorbed on the palladium and the ultrathin layer of platinum, however, very reactive species will be formed which can react with each other or with chemisorbed oxygen. For example, chemisorbed nucleophilic ammonia may react with partly oxidized ethylene and/or ethanol, thus changing the selectivity and the sensitivity properties of the sensor array. Furthermore, the difficulties in separating ethylene and ethanol may be due to the fact that the detection mechanisms for the two molecules are too similar on the two metals. Ethanol may also be formed from ethylene by addition of a water molecule formed from the oxidation of hydrogen. These are some of the reasons why all of the species cannot be separated from each other. Knowledge of the chemical reactions occurring at the surfaces of the sensors is, however, not important, since the multivariate calibration will show whether the species can be separated or not. Another observation was that in certain gas mixtures the voltage shifts were very large, more than 0.8 V for some of the Pt-gate sensors. These sensors are then operated close to saturation, which could influence the possibility of obtaining an accurate prediction.
One obvious practical problem was encountered in the evaluation of the sensor array. It was observed that the predictions of a small concentration of ammonia decreased during the measurements (see Fig. 6). Actually P&gate devices operated at high temperatures have shown a bum-in phenomenon, which has been further investigated by Spetz et al. [ 141. The burn in leads to a decrease of the sensitivity to ammonia and may in part be the cause of the deviation between actual and predicted ammonia concentrations. We have in the present investigation tried to predict individual components in a gas mixture. It is, however, also possible to use pattern recognition to identify a given mixture. This will be enough in many situations of practical interest. The cross reactions discussed above will then in principle not make the identification impossible. Generalized pattern recognition methods, like PLS, used in connection with sensor arrays will be very powerful in the future. The many possibilities to tailor the selectivity and sensitivity of devices with catalytic metal gates make such devices particularly interesting for the development of a ‘synthetic olfactory sense’ which can be used in, e.g., industrial applications or environmental studies.
Acknowledgement This work has been supported by grants. from the National Swedish Board for Technical Development in the areas of ‘Electronic and Electra Optical Sensor Technology’ and ‘Micronics’.
W. P. Carey, K. R. Beebe, B. R. Kowalski, D. L. Illman and T. Hirschfeld, Selection of adsorbates for chemical sensor arrays by pattern recognition, Anal. Chem., 58 (1986) 149-153. W. P. Carey and B. R. Kowalski, Chemical piezoelectric sensor and sensor array characterization, Anal. Chem., 58 (1986) 3077-3084. G. Horner, E. Lange and R. Milller, Improvement of selectivity of a gas sensor system by the use of an array of sensors, Eurosensors ‘87, Cambridge, U.K., Sept. 22-24, 1987, Programme and Abstracts, pp. 40-41. D. Dalby and H. V. Shurmer, Pattern recognition systems for gas sensor arrays, Eurosensors ZZ, Enschede, The Netherlands, Nov. 2-4, 1988, Final programme. p. 147. R. Miiller and E. Lange, Multidimensional sensor for gas analysis, Sensors and Actuators, 9 (1986) 39-48. M. Gall and R. Milller, Investigation of gas mixtures with different MOSgas sensors with regard to pattern recognition, Eurosensors ZZ, Enschede, The Netherlands, Nov. 2-4.1988, Final programme, p. 2 16. C. Hierold and R. Mtier, Quantitative analysis of gas mixtures with non-selective gas sensors, Eurosensors ZZ,
122 Enschede, The Netherlands, programme, p. 2 17.
Nov.
2-4,
1988,
Final
8 I. Lundstrom,
9
10
11
12
13
14
15
16
17
18 19 20 21 22 23 24 25
M. Armgarth, A. Spetz and F. Winquist, Gas sensors based on catalytic metal-gate field-effect devices, SensorsandActuators, 10 (1986)399-421. U. Ackelid. F. Winquist and I. Lundstrom, Metal-oxidesemiconductor structures with thermally activated sensitivity to ethanol vapour and unsaturated hydrocarbons, Proc. 2nd Int. Meet. Chemical Sensors, Bordeaux, France, July 7-10.1986, pp. 395-398. U. Ackelid, M. Armgarth, A. Spetz and I. Lundstrom, Ethanol sensitivity of palladium-gate metal-oxide-semiconductor structures, IEEE Electron Device Lett., EDL-7 (1985) 353-355. A. Spetz, F. Winquist, C. Nylander and I. Lundstrom, Modified palladium gate MOS devices for ammonia gas detection, Proc. Int. Meet. Chemical Sensors, Fukuoka. Japan, 1983, pp. 419-487. A. Spetz, M. Armgarth and I. Lundstrom, Optimization of ammonia sensitive metal oxide semiconductor structures with platinum gates, Sensors and Actuators, I1 (1987) 349-365. A. Spetz, M. Armgarth and I. Lundstrom, Hydrogen and ammonia response of metal-silicon dioxide-silicon structures with ulatinum gates. J. Annl. Phvs., 64 (1988) 1274-1283. -A. Spetz, M. Armgarth and I. Lundstrom, Comparison between platinum and iridium as gate metals in ammonia sensitive metal-silicon dioxide-silicon structures, Sensors and Actuators, 4 (1988) 187-207. F. Winquist and I. Lundstrom, Thin metal film-oxidesemiconductor structures with temperature-dependent sensitivity for unsaturated hydrocarbons, Sensors and Actuators, 12 (1987) 255-261. S. Zaromb and J. R. Stetter, Theoretical basis for identification and measurement of air contaminants using an array of sensors having partly overlapping selectivities, Sensors and Actuators, 6 (1984) 225-243. S. Wold, C. Albano, W. Dunn, K. Esbensen, S. Hellberg, E. Johansson and M. Sjostrom, Pattern recognition: finding and using regularities in multivariate data, Food Research and Data Analysis, Applied Science Publishers, London, 1983, pp. 147-188. Sepanova AB, &trandsvPgen 14, S-12243 Enskede, Sweden. S. Wold, N. Kettaneh and B. Skagerberg, Nonlinear PLS modeling, Chemometrics Intelligent Lab. Syst., (1989) to be published. S. Wold and B. Skagerberg, Research Group for Chemometrics, Institute of Chemistry, UmeB University, Umeil, Sweden. H. A. Martens, Multivariate Calibration, Dissertation Thesis, Technical University of Norway, Trondheim, Norway, 1985. G. E. P. Box, W. G. Hunter and J. S. Hunter, Statistics for Experimenters, Wiley, New York, 1978, Ch. 10, pp. 306-309 and 520-521. Sensistor AB, Box 76, S-581 02 Linkoping, Sweden. Asyst Software Technologies, Inc., 100 Corporate Woods, Rochester, NY 14623, U.S.A. S. Wold, C. Albano, W. J. Dunn III, U. Edlund, K. Esbensen, P. Geladi, S. Hellberg, E. Johansson, W. Lmdberg and M. Sjostrom, Multivariate data analysis in chemistry, Proc. Nato Advanced Study Institute on
Chemometrics, Mathematics and Statistics in Chemistry, Cosensa, Italy, Sept. 1983, pp. 17-96. 26 S. Wold, Cross-validatory estimation of the number
of components in factor and principal components models, Technometrics, 20 (1918) 397-405. 27 M. Armgarth, U. Ackelid and I. Lundstrom, Enhanced selectivity of catalytic gate metal-oxide-semiconductor
field effect transistors by temperature scan, Tech. Digest. 4th Int. Conf Solid-State Sensors and Actuators, (Trans ducer’s 87), Tokyo, Japan, June 2-5. 1987, pp. 640.-
643.
Biographies Hans Sundgren became an engineer in 1969. He was employed by the Ericsson company in 1973 to develop computer systems and in 1976-1979, he developed both analogue and digital electronics at the SAAB-SCANIA company. Since 1979 he has been a senior research engineer at the Laboratory of Applied Physics at the University of Linkoping. His research interest is in developing sensor arrays and sensor systems with hardware and signal conditioning, especially for semiconductor-based gas sensors. Zngrid Lukkari received a B.Sc. in chemistry at the University of Umef, Sweden, in 1988. She is now a graduate student at the Department of Analytical Chemistry. Her research interest is in the application of chemometrical methods using multivariate calibration. Zngemar Lundstrtim received a Ph.D. in 1970, in electrical engineering (solid-state electronics) from Chalmers University of Technology, Cothenburg, Sweden. He was an assistant professor at the Research Laboratory of Electronics, Chalmers, until 1978, when he was appointed as a professor of applied physics at Linkoping Institute of Technology, Linkoping, Sweden, where he now conducts research in surface physics, on solid-state chemical sensors and in the area of biophysics and surface-orientated diagnostic methods. He heads an interdisciplinary research group consisting of microbiologists, biochemists, chemists, physicists and electrical engineers. Rolf Cmlsson received a Ph.D. on organic synthesis in 1978. He was a research associate in organic chemistry at Umell University until 1987. He was appointed as associate professor in 1983. Since 1986 he has been a research fellow at the Swedish National Science Research Council. His research treats problems on strategies and optimization in organic synthesis and he is now heading the ‘synthesis group’ at the Department of Organic Chemistry, Umefi University. Fredrik Winquist joined the Department of Pure and Applied Biochemistry at Lund as a research assistant in 1978. His main research area was to study analytical applications of immobilized enzymes. After receiving his M.Sc. degree in 1982, he is now at the Laboratory of Applied Physics at Linkoping, where his current research area is the
123
development of chemical sensors based on semiconductor technology and the applications of these sensors in bioanalysis. He received a Ph.D. in 1987. Suenfe Chemistry
WoZd joined the Department of Organic in UmeP as a research assistant in 1965,
where he got his Ph.D. in 1971 and became professor of chemometrics in 1986. His research concerns chemometrical methods development (design and multivariate analysis) and the application of these methods to chemical problems, e.g., as structure and property relations.
Actuators,
B, 2 (1990) 115-123
115
Evaluation of a Multiple Gas Mixture with a Simple MOSFET Gas Sensor Array and Pat tern Recognition HANS SUNDGREN, INGEMAR LUNDSTROM and FREDRIK WINQUIST Laboratory of Applied Physics, Department S-581 83 Linktiping (Sweden)
of Physics and Measurement
Technology,
Linkiiping Institute of Technology,
INGRID LUKKARI Department
of Analytical Chemistry, Institute of Chemistry,
Ume8 University, 990187
Urn& (Sweden)
ROLF CARLSSON Department
of Organic Chemistry, Institute of Chemistry. urn& University, S-901 87 Umea (Sweden)
SVANTE WOLD Research Group for Chemometrics,
Institute of Chemistry,
Umea University, S-901 87 Umd
(Sweden)
(Received April 7, 1989; in revised form November 2, 1989; accepted December 13, 1989)
Abstract The properties of a gas sensor array can be improved by the use of pattern recognition (PARC) methods. A gas sensor array with three pairs of Pd-gate MOSFETs and P&gate MOSFETs is exposed to a multiple-component gas mixture. Each pair is operated at a different temperature. The signals from the six sensors are analysed with both linear and nonlinear PLS (partial least square) models. The calculations of the PLS models are based on sensor signals obtained from calibration experiments. By means of combining a pattern recognition method with a semiconductor-based gas sensor array, we show that hydrogen concentrations can be predicted well in the presence of three other interfering gases.
Introduction Efforts have been made to modify existing gas sensors and to find new kinds of sensors in order to improve properties like selectivity, accuracy, reliability, sensitivity, speed of response and reproducibility. Gas sensors often have sensitivities towards classes of gases, with a ‘selectivity’ that may depend on the operation temperature. In order to improve not only the selectivity but also ,some of the other parameters mentioned above, sensor arrays are being utilized together with pattern recognition methods [l--7]. The Pd-gate MOSFET (PdMOS) is one of the most sensitive sensors for hydrogen (H,). These sensors normally have a Pd gate 100-400 nm thick [8]. At a working temperature of 150 “c, the PdMOS has a large sensitivity mainly to hydrogen 0925-4005/90/$3.50
and hydrogen sulfide. At higher temperatures it is also sensitive to ethanol and ethylene [9, lo]. The PdMOS has virtually no direct sensitivity to ammonia. A P&gate MOSFET (PtMOS), with a thin discontinuous Pt gate 3-30 nm thick, is also sensitive to ammonia when operated at 150 “c [ 11-13 1. This sensitivity increases with temperature [14]. The PtMOS is also sensitive to ethylene and ethanol, with a sensitivity which increases rapidly at temperatures above 150 “C [IS]. The selectivity of PdMOS and PtMOS devices at three different temperatures is illustrated in Table 1. This Table indicates that PdMOS and PtMOS sensors operated at, e.g., 150 “C and used together should be able to estimate the concentration of hydrogen and ammonia when simultaneously exposed to them. By increasing the number of different sensors in a gas sensor array, more complicated gas mixtures should also be able to be determined. In this work we have chosen to use a six-element sensor array with three Pd- and three Pt-gate MOSFET sensors with working temperatures of 100, 150 and 200 “C, respectively, making it in principle possible to detect more than one or two species, as indicated in Table 1. A gas sensor array with sensors of different types and/or operating conditions is a very powerful source of information. However, prediction of the content of a gas mixture from the sensor signals requires a well fitting mathematical model. Zaromb and Stetter were among the first to consider the theoretical basis for the use of arrays of sensors with partly overlapping selectivities [ 161. A semiempirical way of finding such a model is implemented by means of pattern recognition (PARC) methods. These models may be derived as Taylor expressions of underlying fundamental models [ 171. The quality of such a model depends on the 0 Elsevier Sequoia/Printed
in The Netherlands
116 TABLE 1. Relative sensitivity towards different gases of PdMOS and PtMOS devices (in 20% oxygen) Gas (concentration
100 ppm)
Hydrogen
Ammonia
Ethylene
Ethanol
PdMOS 200 “C PdMOS 150°C PdMOS 100 “C
+++a +++ ++
0 0 0
0 0 0
+ 0 0
PtTMOS 200 “C PtTMOS 150 “C PtTMOS 100 “C
++ ++ ++
+++ +++ 4-k
++ + 0
+++ ++ +
8+++, >200 mV; ++, SO-200 mV; +, S-50 mV; 0, <5 mV.
quality of the sensor signals and the design of the experiments used to evaluate the model. It has been pointed out that the information potential of gas sensor arrays is very large and that the PARC method is a clever way to extract this information. Carey et al. [ 1,2] have shown the usefulness of PARC in characterizing and analysing surface acoustic wave and quartz crystal microbalance sensor arrays. Arrays with SnOZ gas sensors were analysed with particular emphasis on nonlinear modelling by Horner et al. [3] and Hierold and Miiller [7]. Arrays of up to four MOSFET gas sensors have been analysed by Miiller and coworkers in binary gas mixtures [5,6]. In the work by Killer et al., devices with thick Pd or Pt gates or Pd.gate devices covered with different types of zeolites were used. Several pattern recognition routines were also developed. In this paper we use partial least square (PLS) methods developed for the analysis of (chemical) data in general, namely the PLS2 method implemented in the SIMCA-3B software supplied by Sepanova AB [ 181 and a nonlinear PIS [ 191, the NPLS developed by Wold and Skagerberg [20], for an array of six MOSFET sensors with a documented difference in their sensitivity pattern towards given molecules in air. This difference occurs due to the use of two different types of metal gates, ‘thick Pd and ‘thin’ Pt, operated at three temperatures. PLS2 and NPLS represent generalized pattern recognition, denoted as level 3 and level 4 [17], which can be used to predict one or several dependent variables from independent variables such as sensor signals. In this use they are often called methods of multivariate calibration [21]. However, the sensor signals cannot be treated as continuous signals in the modelling and it is necessary to divide them into a number of discrete values. It is also important to preprocess the sensor signals correctly. For instance, it is necessary to calculate the voltage shift corresponding to the exposing gas pulse separated from any voltage shift due to long-term drift, temperature dependence etc. However, scaling and normalizing are not necessarily needed. As in any
modelling, it is important to have a proper experimental design of the calibration set of measurements. In principle, a complete ‘factorial design in two levels’, as described by Box et al. [22] for example, was used. The investigations presented here are intended to examine the possibility of employing available methods for generalized pattern recognition, such as PLS2 and NPLS, for increasing the efficiency of available gas sensors such as the Pt- and Pd-gate MOSFET sensors in a sensor array. Furthermore, the investigations hopefully illuminate the advantages and difficulties raised by the extraction of more information, with the help of modern methods for pattern recognition, than that immediately apparent from these sensors. Experimental and Computational Techniques
Two types of commercially available MOSFET gas sensors, with Pd and Pt gates respectively, were studied [23]. Three devices of each type were chosen in order to have a Pd-gate and a Pt-gate MOSFET pair at three different working temperatures, 100, 150 and 200 “C. Each sensor had its own circuit for temperature control and for biasing of the MOSFET. The bias was a constant current of 125 I.IA feeding the gate and drain terminals, which were connected together. The source terminal was grounded. The voltage drop across the MOSFET is then close to its threshold voltage. Exposure to gas causes a change in this voltage. The sensors were mounted in’ a sample cell with a small dead volume, as shown in Fig. 1. This array includes only two types of sensors with respect to fabrication technology, but six different sensors with respect to response characteristics (see Table 1). A gas-mixing system was used to generate the gas mixtures needed for calibration and test experiments. The total flow of the test gas was 100 ml/ min. The gas mixtures included a carrier gas consisting of 21.3% O2 in Ar and a four-component
117
as the difference between the voltage drop over the MOSFET before and at the end of exposure to the test gas. In the next step the maximum positive and negative values of the derivative with respect to time of the sensor signal are determined. Since there are six sensors in the sensor array and both the voltage shift and the two derivatives are measured, there are 18 values which are collected for every single experiment. The total experiment consisted of two different experimental sets, see Table 2. The calibration set with 31 different gas mixtures was used only to make the mathematical model for the response of the array, and the test set was used to evaluate that model by making predictions of the gas concentrations. Variable values acquired from the experiments are illustrated in Fig. 2 in a so-called axonometric plot. With a knowledge of the configuration of the gas mixtures together with the appearance of the variable plot, it is not obvious
TABLE 2. The gas mixture composition of the calibration set and the test set respectively. Concentrations in ppm with 21.3% oxygen in argon as carrier gas. One row represents one individual experiment
Fig. 1. The sample cell with six separate sensors, each mounted in a TO-18 socket. The drawing shows the test gas passage and the small dead volume. A cut view from the side (a) and top view (b) of the sample cell.
gas mixture. The test gas concentration was selected according to the experimental design in the range lo-500 ppm (hydrogen, ammonia, ethylene) and 16-300 ppm (ethanol). The gas sensor array was exposed to the desired mixture for a period of five minutes with a relaxation time of 25 minutes. To restore the active surface of the sensors and thus increase the reproducibility, the sensor array was exposed to a gas mixture consisting of 190 ppm hydrogen in the carrier gas, for five minutes with 25 minutes relaxation, before exposure to each new gas mixture. This was done to ensure that the catalytic activity of the metal gates was as similar as possible upon exposure to a gas mixture. The gas mixing was performed under computer control, allowing stable experimental conditions to be achieved. The sensor signals were sampled every other second by means of a personal computer equipped with a data acquisition board, with a resolution of 1 mV. Both data acquisition and signal conditioning have been performed with the Asystantt software [24]. In the first signal-processing stage the sensor signal, the voltage shift of the drain/ gate voltage characteristics (i.e., the change in the threshold voltage) caused by the test gas, is obtained
Calibration
set
Hz
NH3
Cd4
CzHsOH
Ha
NH3
CzH4
CzHsOH
190 190 27 27 190 190 190 190 190 27 190 27 27 27 27 27 70 70 500 11 70 70 70 70 70 70 70 70 70 70 70
27 190 190 190 27 27 190 190 27 27 190 190 27 190 27 27 69 69 69 69 11 69 69 69 69 69 69 499 69 69 69
27 27 27 190 190 190 27 190 27 27 190 190 27 27 190 190 70 70 70 70 70 499 70 70 70 70 10 70 70 70 70
33 144 144 33 33 144 33 33 144 144 144 144 33 33 144 33 70 70 70 70 70 70 70 70 70 16 70 70 70 300 70
190 190 190 190 190 249 190 190 27 190 99 190 300 29
27 27 27 27 27 249 190 190 190 27 298 190 41 30
190 190 190 190 190 40 190 27 190 190 24 27 61 251
33 33 33 33 33 40 33 33 33 33 25 33 40 25
Test set
In our case we have p = 18 independent variables, xi (sensor signals). The 31 calibration signals are plotted in a p-dimensional space (they represent a matrix with 31 rows and 18 columns). By looking at similarities in the data points, they can be projected on an m-dimensional space (hyperplane). The m-dimensional x-space is now used to predict the dependent variables, y, by the use of least square methods, hence the name PLS, which stands for ‘partial least square modelling with latent variables’. Two dependent variables represent a 31X4 matrix (in our case the four concentrations of the gas mixtures in the calibration set). Several criteria are used to obtain the best model dimension, m. The residual sum of squares Fig. 2. The sensor data from the calibration and test set in a three-dimensional axonometric plot. The independent variables (voltage shift and charging derivatives) in X, the experimental order (or gas composition) in Y and amplitude of the signals in 2. The discharge derivatives are not shown in the Figure, hence for each Y there are only 12 X values. Each division on the X-axis corresponds to a single variable. Each division on the Z-axis corresponds to 200 mV for the voltage shifts.
how to treat the variables to predict the composition of a gas mixture. However, once the calibration variable values have been measured, the calibration set is used for model calculations in PIS2 and NPLS. The response characteristics of the Pt- and Pd-gate MOSFETs are nonlinear. It has been shown that the response follows a Langmuir-like relationship due to the kinetics of adsorption, reaction and desorption of gaseous species on the surface of the sensors [8]. It was therefore interesting to use not only a linear PIS model to predict the concentrations of hydrogen, ammonia, ethanol and ethylene, but also different nonlinear modifications of it. First, a five-dimensional ordinary linear PLS model (1PIS) based upon 18 variables is considered. Thereafter, a modified linear PLS model (mPLS) with seven dimensions based upon the 18 variables, as in the first model, and additionally the square of the original variables to make a total of 36 variables, is treated. Finally, a two-dimensional quadratic model (nPLS) based upon 18 variables is calculated with the NPLS. In the linear models each axis in a projection plane is defined as a linear combination of the original variables xk. In the quadratic model terms like xixk are also included. The PLS methods are implemented in the ready to use SIMCA-3B software package and a nonlinear version, NPLS [ 18-201. The principles and the practice of the PLS method are given in detail elsewhere [ 16,2 1,251. The procedure for multivariate calibration and prediction of unknown samples (gas mixtures) can briefly be described as follows.
RSS, = W:arcureted - Y ;d2 (1) is an important parameter which should be minimized. The autoscaled values (y’) are used in the SIMCA3B programme to give all variables (Y) the same weight. Another important and stronger criterion is the so-called cross validation. In cross validation part of the objects (rows in the x matrix) is left out (say one quarter) in the estimation of the last PIS dimension. A new model is made and used to predict the left out y values from the corresponding row in x; we call the prediction of the left out values ypredr&ed. This is then repeated for another part of the data until all y values have been predicted once [26]. The predicted sum of squares PRESS, =
%&redicted
-
Y)td2
(2)
is formed. Figure 3 illustrates the calculated PRESS value versus the PLS dimension in some of our cases. If the ‘cross validation term’ PRESS,,,/ RSS,_i < 1, the model with the chosen dimension has predictive relevance. Examples of cross validation terms are given in Table 3. The model with the 150000
-
100000
-
3 E
50000
-
0-l . , . , . , . , . 1
2
3
4
5
PLS - dimension
(
6
.
,
7
.
,
.
6
[# ]
Fig. 3. The PRESS (predicted square of sums) value vs. PLS dimension. The PRESS value was calculated as the sum for the hydrogen and ammonia predictions.
119 TABLE 3. The cross validation term of the prediction of the dependent variables, hydrogen (Hz) and ammonia (NHs) respectively. The term is calculated for the linear (IPLS), modified (mPLS) and the nonlinear (nPLS) models for each dimension separately. The crossed terms represent values for a larger dimension with no predictive value PLS dimension
PLS model mPLS
1PLS
nPLS
H2
NH3
H2
NH3
H2
NH3
0.9141 0.8134 1.0103 1.0042 1.0172
0.97 10 0.8271 0.9564 0.9179 0.9598
0.9036 0.725 1 0.9924 0.9698 0.9965 0.8361 0.8986
0.9798
0.572 1.002
1.002 0.877
highest dimension, for which PRESS,/RSS,_r < 1, is in general chosen as the PARC model to be used. The relationship thus obtained between y and x is now used to predict the composition of a gas mixture from measured x-variables. The description above is intended only to give a feeling for the PLS methods; interested readers are referred elsewhere [17, 19,21,25]. The SIMCA-3B and the NPLS are developed for IBM-PC type computers and the file with the sensor signals has to be converted into the SIMCA3B format. The programmes are menu driven and the calculating algorithms are effective, which makes it fast and easy to use. All the calculations were made with cross validation to ensure that the best model dimension was chosen.
0.7888 0.9574 0.8630 0.9880 0.9769 0.9183
300
0
IPLS nPLs
---
250-
200-
150-
100.
50.
O:....,....,....,..._,--__,--~ 0 50 100
150
CONCENTRATION
200
250
300
H2 [ppm]
Results
Fig. 4. Predicted vs. actual hydrogen concentration in the test set for the lPLS, nPLS and mPLS model respectively.
Predictions were made with the IPLS, mPLS and nPLS models. The optimal dimension of the nPLS model (2) is less than that of the 1PI.S model (5) which possibly indicates that the quadratic model fits better to the sensor signals than the linear model. Furthermore, the dimension of the mPLS model (7) is greater than that of the lPI_S (S), indicating the significance of the additional variables. Figure 4 illustrates how the three separate models predict the hydrogen concentration. The 1PLS model fits well in the medium concentration range but not in the outer ranges. The curve is slightly bent, verifying the nonlinear response characteristics of MOSFET sensors. Consequently, the nPLS curve is a straight line as expected, but the slope is not sufficiently steep. The best-fitting model in this case is the mPLS model. This curve is fairly good considering that the hydrogen is only one out of four gases that the sensor array is exposed to.
A perfect prediction should give rise to data points on a 45’-line through the origin in Fig. 4. Figure 5 illustrates predicted results for ammonia. The 1PIS model represents the best fit and the curve is bent comparable to the corresponding hydrogen predictions. The predictions from the mPL.S and nPLS models are mainly too low. Other attempts to preprocess data have been made in order to fmd the best-fitting model with the least number of independent variables. The dimension of the different PLS models used was chosen from PRESS plots (Fig. 3) and cross validation terms (Table 3) but any effort to reduce the number of variables or to exclude the voltage shift or the derivatives resulted only in minor improvements of the predicting models. The mPI_S is one of the bestfitting models to hydrogen predictions and the 1PLS to the ammonia predictions. One of the problems
nPLS
---
mps
-----
can be expressed as a function of the components in the gas mixture. For a Pd-gate MOS device we could use, to a good approximation, for some gas mixtures in air (oxygen),
AVlWn,x
= (nYiPp2
1 - AV/AV,,,
CONCENTRATION
NH3
[ppm]
Fig. 5. Predicted vs. actual ammonia concentration in the test set for the lPLS, mPLS and nPLS models respectively.
AMMONIA mI
(3)
where Pi is the partial pressure of the ith component and the ors are temperature-dependent constants. AV is the steady-state sensor signal, i.e., the observed shift of the drain current-gate voltage characteristics along the voltage axis. AV,,, is the maximum obtainable voltage shift (which is in the order of 0.5 V). This type of relationship has been used by Armgarth ef al. [27] to analyse mixtures of hydrogen and ethanol with one PdMOS sensor operated at different temperatures. It was found that hydrogen and ethanol could be independently determined in this way, through a knowledge of the physics of the devices. In an analysis of a binary gas mixture, with two types of MOS gas sensors, Gall and Mtiller [6] use Sr =(~r[A]~*+br[B]“‘u)“~
(4)
Sa = (a* [A] mA t bz [B] mn)“2
(5)
for the signals from the two types of sensors, where [A] and [B] are the concentrations of the two components in the gas mixture. A set of transformations yr = S,i’“l I 1
TIME' I h I
3
ik-, = [A]“*;
;
y, = &1’“2
(6)
Xn = [B]“n
(7)
4
Fig. 6. The predicted ammonia concentration in the test set vs. time (or order of exposure) with predictions made from the linear PLS model, lPLS, with a gas mixture consisting of 27 ppm ammonia, 190 ppm hydrogen, 190 ppm ethylene and 33 ppm ethanol.
with
ammonia is actually caused by the sensor array itself, since it was found that the prediction of the ammonia concentrations could decrease with time as illustrated in Fig. 6. It is therefore
likely that the calibration set gave too large values of the variables (AV values) just for ammonia, which could explain some of the deviations between the predicted values and the actual gas concentration. Ethylene and ethanol could not be separated and the sum of their concentrations could not be predicted either.
There are several ways in which sensor signals can be analysed. In certain cases the sensor response
leads to a linear set of equations, which is used to evaluate the signals with normal linear methods. The parameters n,. n2. mA and ntg are determined from experiments. They found that with the method above, called TLS (transformed least square-method), it was possible to determine hydrogen rather accurately in the presence of methanol (CHaOH). Even if it should be possible in principle to use a similar approach to analyse the data from our sensor array, this is not straightforward in a complex gas mixture. The determination of the parameters becomes a tedious procedure. Furthermore, it is also necessary to consider terms of the form [A] [B] since there may be a certain interaction between the different components. For example, ammonia is not detected by the thick Pd-gate devices although ammonia molecules participate in the chemical reactions on the Pd surface. We have chosen to use pattern recognition routines developed for the analysis of chemical data in general, such as chromatograms, optical spectra etc. [21]. The scope of the paper was to investigate if and how such routines could be used to predict the composition of a gas mixture from the signals
121
obtained from an array of six MOSFET gas sensors. We have concentrated on a moderately difficult problem, namely to analyse a gas mixture in oxygen and argon containing hydrogen, ammonia, ethanol and ethylene. It was found that with the present sensor array, the hydrogen concentration could be rather accurately predicted in the presence of the three other components. Ammonia could also be identified in the mixture, but in general its concentration was predicted too low. It was found that for hydrogen a modified linear PLS model with dimension seven gave the best prediction, whereas for ammonia a linear PLS with dimension five gave the best result. This suggests that different pattern recognition routines should be employed to extract different components of a gas mixture. When analysing a gas mixture consisting of several different species, the situation might be rather complicated. First, chemical reactions between the gases can already occur in the mixture before it reaches the sensor array. Secondly, reactions between reaction intermediates from the different gases in the mixture which are adsorbed on the catalytically active surface of the sensor are also likely to occur. This will change reaction pathways and thus also the selectivity and sensitivity of the sensor array. In a gas mixture consisting of low concentrations (<300 ppm) of hydrogen, ammonia, ethylene, ethanol in oxygen/argon, chemical reactions are not likely to occur between the species in the gas phase before they reach the active surfaces of the sensors. When adsorbed on the palladium and the ultrathin layer of platinum, however, very reactive species will be formed which can react with each other or with chemisorbed oxygen. For example, chemisorbed nucleophilic ammonia may react with partly oxidized ethylene and/or ethanol, thus changing the selectivity and the sensitivity properties of the sensor array. Furthermore, the difficulties in separating ethylene and ethanol may be due to the fact that the detection mechanisms for the two molecules are too similar on the two metals. Ethanol may also be formed from ethylene by addition of a water molecule formed from the oxidation of hydrogen. These are some of the reasons why all of the species cannot be separated from each other. Knowledge of the chemical reactions occurring at the surfaces of the sensors is, however, not important, since the multivariate calibration will show whether the species can be separated or not. Another observation was that in certain gas mixtures the voltage shifts were very large, more than 0.8 V for some of the Pt-gate sensors. These sensors are then operated close to saturation, which could influence the possibility of obtaining an accurate prediction.
One obvious practical problem was encountered in the evaluation of the sensor array. It was observed that the predictions of a small concentration of ammonia decreased during the measurements (see Fig. 6). Actually P&gate devices operated at high temperatures have shown a bum-in phenomenon, which has been further investigated by Spetz et al. [ 141. The burn in leads to a decrease of the sensitivity to ammonia and may in part be the cause of the deviation between actual and predicted ammonia concentrations. We have in the present investigation tried to predict individual components in a gas mixture. It is, however, also possible to use pattern recognition to identify a given mixture. This will be enough in many situations of practical interest. The cross reactions discussed above will then in principle not make the identification impossible. Generalized pattern recognition methods, like PLS, used in connection with sensor arrays will be very powerful in the future. The many possibilities to tailor the selectivity and sensitivity of devices with catalytic metal gates make such devices particularly interesting for the development of a ‘synthetic olfactory sense’ which can be used in, e.g., industrial applications or environmental studies.
Acknowledgement This work has been supported by grants. from the National Swedish Board for Technical Development in the areas of ‘Electronic and Electra Optical Sensor Technology’ and ‘Micronics’.
W. P. Carey, K. R. Beebe, B. R. Kowalski, D. L. Illman and T. Hirschfeld, Selection of adsorbates for chemical sensor arrays by pattern recognition, Anal. Chem., 58 (1986) 149-153. W. P. Carey and B. R. Kowalski, Chemical piezoelectric sensor and sensor array characterization, Anal. Chem., 58 (1986) 3077-3084. G. Horner, E. Lange and R. Milller, Improvement of selectivity of a gas sensor system by the use of an array of sensors, Eurosensors ‘87, Cambridge, U.K., Sept. 22-24, 1987, Programme and Abstracts, pp. 40-41. D. Dalby and H. V. Shurmer, Pattern recognition systems for gas sensor arrays, Eurosensors ZZ, Enschede, The Netherlands, Nov. 2-4, 1988, Final programme. p. 147. R. Miiller and E. Lange, Multidimensional sensor for gas analysis, Sensors and Actuators, 9 (1986) 39-48. M. Gall and R. Milller, Investigation of gas mixtures with different MOSgas sensors with regard to pattern recognition, Eurosensors ZZ, Enschede, The Netherlands, Nov. 2-4.1988, Final programme, p. 2 16. C. Hierold and R. Mtier, Quantitative analysis of gas mixtures with non-selective gas sensors, Eurosensors ZZ,
122 Enschede, The Netherlands, programme, p. 2 17.
Nov.
2-4,
1988,
Final
8 I. Lundstrom,
9
10
11
12
13
14
15
16
17
18 19 20 21 22 23 24 25
M. Armgarth, A. Spetz and F. Winquist, Gas sensors based on catalytic metal-gate field-effect devices, SensorsandActuators, 10 (1986)399-421. U. Ackelid. F. Winquist and I. Lundstrom, Metal-oxidesemiconductor structures with thermally activated sensitivity to ethanol vapour and unsaturated hydrocarbons, Proc. 2nd Int. Meet. Chemical Sensors, Bordeaux, France, July 7-10.1986, pp. 395-398. U. Ackelid, M. Armgarth, A. Spetz and I. Lundstrom, Ethanol sensitivity of palladium-gate metal-oxide-semiconductor structures, IEEE Electron Device Lett., EDL-7 (1985) 353-355. A. Spetz, F. Winquist, C. Nylander and I. Lundstrom, Modified palladium gate MOS devices for ammonia gas detection, Proc. Int. Meet. Chemical Sensors, Fukuoka. Japan, 1983, pp. 419-487. A. Spetz, M. Armgarth and I. Lundstrom, Optimization of ammonia sensitive metal oxide semiconductor structures with platinum gates, Sensors and Actuators, I1 (1987) 349-365. A. Spetz, M. Armgarth and I. Lundstrom, Hydrogen and ammonia response of metal-silicon dioxide-silicon structures with ulatinum gates. J. Annl. Phvs., 64 (1988) 1274-1283. -A. Spetz, M. Armgarth and I. Lundstrom, Comparison between platinum and iridium as gate metals in ammonia sensitive metal-silicon dioxide-silicon structures, Sensors and Actuators, 4 (1988) 187-207. F. Winquist and I. Lundstrom, Thin metal film-oxidesemiconductor structures with temperature-dependent sensitivity for unsaturated hydrocarbons, Sensors and Actuators, 12 (1987) 255-261. S. Zaromb and J. R. Stetter, Theoretical basis for identification and measurement of air contaminants using an array of sensors having partly overlapping selectivities, Sensors and Actuators, 6 (1984) 225-243. S. Wold, C. Albano, W. Dunn, K. Esbensen, S. Hellberg, E. Johansson and M. Sjostrom, Pattern recognition: finding and using regularities in multivariate data, Food Research and Data Analysis, Applied Science Publishers, London, 1983, pp. 147-188. Sepanova AB, &trandsvPgen 14, S-12243 Enskede, Sweden. S. Wold, N. Kettaneh and B. Skagerberg, Nonlinear PLS modeling, Chemometrics Intelligent Lab. Syst., (1989) to be published. S. Wold and B. Skagerberg, Research Group for Chemometrics, Institute of Chemistry, UmeB University, Umeil, Sweden. H. A. Martens, Multivariate Calibration, Dissertation Thesis, Technical University of Norway, Trondheim, Norway, 1985. G. E. P. Box, W. G. Hunter and J. S. Hunter, Statistics for Experimenters, Wiley, New York, 1978, Ch. 10, pp. 306-309 and 520-521. Sensistor AB, Box 76, S-581 02 Linkoping, Sweden. Asyst Software Technologies, Inc., 100 Corporate Woods, Rochester, NY 14623, U.S.A. S. Wold, C. Albano, W. J. Dunn III, U. Edlund, K. Esbensen, P. Geladi, S. Hellberg, E. Johansson, W. Lmdberg and M. Sjostrom, Multivariate data analysis in chemistry, Proc. Nato Advanced Study Institute on
Chemometrics, Mathematics and Statistics in Chemistry, Cosensa, Italy, Sept. 1983, pp. 17-96. 26 S. Wold, Cross-validatory estimation of the number
of components in factor and principal components models, Technometrics, 20 (1918) 397-405. 27 M. Armgarth, U. Ackelid and I. Lundstrom, Enhanced selectivity of catalytic gate metal-oxide-semiconductor
field effect transistors by temperature scan, Tech. Digest. 4th Int. Conf Solid-State Sensors and Actuators, (Trans ducer’s 87), Tokyo, Japan, June 2-5. 1987, pp. 640.-
643.
Biographies Hans Sundgren became an engineer in 1969. He was employed by the Ericsson company in 1973 to develop computer systems and in 1976-1979, he developed both analogue and digital electronics at the SAAB-SCANIA company. Since 1979 he has been a senior research engineer at the Laboratory of Applied Physics at the University of Linkoping. His research interest is in developing sensor arrays and sensor systems with hardware and signal conditioning, especially for semiconductor-based gas sensors. Zngrid Lukkari received a B.Sc. in chemistry at the University of Umef, Sweden, in 1988. She is now a graduate student at the Department of Analytical Chemistry. Her research interest is in the application of chemometrical methods using multivariate calibration. Zngemar Lundstrtim received a Ph.D. in 1970, in electrical engineering (solid-state electronics) from Chalmers University of Technology, Cothenburg, Sweden. He was an assistant professor at the Research Laboratory of Electronics, Chalmers, until 1978, when he was appointed as a professor of applied physics at Linkoping Institute of Technology, Linkoping, Sweden, where he now conducts research in surface physics, on solid-state chemical sensors and in the area of biophysics and surface-orientated diagnostic methods. He heads an interdisciplinary research group consisting of microbiologists, biochemists, chemists, physicists and electrical engineers. Rolf Cmlsson received a Ph.D. on organic synthesis in 1978. He was a research associate in organic chemistry at Umell University until 1987. He was appointed as associate professor in 1983. Since 1986 he has been a research fellow at the Swedish National Science Research Council. His research treats problems on strategies and optimization in organic synthesis and he is now heading the ‘synthesis group’ at the Department of Organic Chemistry, Umefi University. Fredrik Winquist joined the Department of Pure and Applied Biochemistry at Lund as a research assistant in 1978. His main research area was to study analytical applications of immobilized enzymes. After receiving his M.Sc. degree in 1982, he is now at the Laboratory of Applied Physics at Linkoping, where his current research area is the
123
development of chemical sensors based on semiconductor technology and the applications of these sensors in bioanalysis. He received a Ph.D. in 1987. Suenfe Chemistry
WoZd joined the Department of Organic in UmeP as a research assistant in 1965,
where he got his Ph.D. in 1971 and became professor of chemometrics in 1986. His research concerns chemometrical methods development (design and multivariate analysis) and the application of these methods to chemical problems, e.g., as structure and property relations.