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Temperature modulation in semiconductor gas sensing
Sensors and Actuators B 60 Ž1999. 35–42 www.elsevier.nlrlocatersensorb
Temperature modulation in semiconductor gas sensing Andrew P. Lee ) , Brian J. Reedy School of Applied Science, UniÕersity of Tasmania, P.O. Box 1214 Launceston, Tasmania 7250, Australia Received 18 January 1999; received in revised form 13 April 1999; accepted 14 April 1999
Abstract A review of semiconductor gas sensor literature pertaining to the use of temperature modulation techniques is presented. The temperature dependence of sensor conductance is discussed, along with transient and cyclic modulation techniques for improving sensitivity and selectivity of sensors in the analysis of single gases and multi-component gas mixtures. q 1999 Elsevier Science S.A. All rights reserved. Keywords: Semiconductor gas sensor; Tin oxide; Temperature modulation; Dynamic response; Thermal cycling; Multi-component gas analysis
1. Introduction Selectivity in semiconductor gas sensors can be obtained through a variety of methods which can be classified into four main groups: Ži. the use of filters or chromatographic columns to discriminate between gases on the basis of molecular size or other physical properties; Žii. the use of catalysts and promoters or more specific surface additives; Žiii. the physical preparation of the sensor material; and Živ. the analysis of transient sensor responses to changes in analyte concentration or sensor temperature. Of the last category, the most commonly employed technique involves controlling the temperature of the semiconductor surface, whether by selecting a fixed temperature to maximise sensitivity to a particular analyte gas, or by programming or modulating the temperature. In a review of semiconductor gas sensor selectivity in 1987, Morrison w1x, concentrating on the use of catalysts, stated that ‘‘temperature programming of the sensor is not a common technique used for selectivity’’. However, more recent work has demonstrated that temperature programming or modulation of a single sensor can achieve the type of selectivity that would otherwise require arrays of variously doped fixed temperature sensors. The purpose of the current paper is to review the use of temperature control of semiconductor gas sensors in single- and multi-component gas analysis, and to explore possible future directions in this promising field. More general reviews on recent devel)
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opments in semiconductor Žparticularly SnO 2 . gas sensor technology can be found elsewhere w2–7x. 2. Temperature dependence of gas sensor conductance Although this is not intended to be a review of the phenomenon of the temperature-dependence of semiconductor sensor conductance Žor resistance., a brief survey of some of the relevant literature is probably warranted as an introduction to methods involving temperature-modulation. Moseley and Williams w8x have reviewed work on the characterisation of adsorbed oxygen species on the surface of metal oxide semiconductors. It is widely accepted that the key process in the response of the semiconductor to a reducing gas involves the modulation of the concentration y of adsorbed oxygen species such as Oy or O 2y. By 2, O withdrawing electron density from the semiconductor surface, adsorbed oxygen gives rise to Schottky potential barriers at grain boundaries, and thus increases the resistance of the sensor surface. Reducing gases decrease the surface oxygen concentration and thus decrease the sensor resistance. The temperature dependence of this process arises in part from the differing stabilities of the surface oxygen species over different temperature ranges. While the identity of the surface oxygen species remains slightly more controversial w1,8–10x, it is clear that different gases have characteristic optimum oxidation temperatures, and therefore give rise to characteristic conductance–temperature profiles, which can be modified by doping the semiconductor with noble metals or other catalytic materials w3,4,7,9–20x. Fig. 1 shows some typical responses of doped
0925-4005r99r$ - see front matter q 1999 Elsevier Science S.A. All rights reserved. PII: S 0 9 2 5 - 4 0 0 5 Ž 9 9 . 0 0 2 4 1 - 5
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Fig. 1. Sensitivity–temperature profiles for doped and undoped tin oxide sensors in the presence of various analyte gases. Note that ‘‘sensitivity’’ is defined as GrGo , that is, the ratio of the sensor conductance Ž G . in the presence of the analyte gas to the conductance in clean air Ž Go .. Gas concentrations in air are 0.8% H 2 , 0.5% CH 4 , 0.2% C 3 H 8 , 0.02% CO Žfrom Yamazoe and Miura w3x..
and undoped SnO 2 sensors to various analyte gases. In general, the temperature of the gas sensor surface is controlled by varying the voltage applied to the sensor heater. Other factors, both chemical and physical, which depend upon temperature and contribute to the sensor response have been identified by Mizsei w6x. These include rates of adsorption and desorption Žof oxygen and reducing gases, or of oxidation products., the rate of surface decomposition of reducing gases, the charge-carrier concentration and the Debye length in the semiconductor. This means that the true relationship between the conductance of a semiconductor sensor in the presence of reducing gases and the temperature of the sensor surface is very complex. Another complicating feature of gas sensor operation is that the chemical reactions that give rise to the sensor response are usually exothermic, and thus make uncontrolled contributions to the sensor temperature w9x. Furthermore, the actual measurement of the sensor conductance requires the flow of a small current through the sensor and thus causes Joule heating of the sensor, although the relative significance of this is questionable. In their technical information sheets, Figaro Žmanufacturer of the most commonly used sensors. w21x indicate that the use of a flow system in gas detection causes cooling of the sensor surface, and thus also influences its response. An important aspect of the temperature-dependence of gas sensor conductance is the hysteresis observed w9,11,22–24x when the sensor temperature is continuously elevated and subsequently cooled over short time periods. Clifford and Tuma w25x found that hysteresis effects in sensor response to O 2rN2 mixtures were minimised if the sensor temperature was varied slowly enough, that is, at around 20 K hy1 . This was done in an attempt to operate the sensor in a quasi-steady state at each temperature. Both Moseley and Williams w8x and Clifford and Tuma w11x attribute hysteresis effects to the slow attainment of equi-
librium in the transfer of charge between adsorbed oxygen species and the semiconductor. One shortcoming of many papers published so far on semiconductor gas sensors is that an actual measurement of the surface temperature of the sensor is not performed, is performed using an unspecified technique, or is only estimated or inferred from other work. This is understandable considering the difficulties of measuring the temperature of ever-diminishing sensor surfaces, but illustrates a problem that needs to be addressed if the sensing mechanisms are to be properly understood. Published methods for the measurement of temperature can be classified into two main categories: Ži. measurement using a temperature sensor Že.g., a thermocouple or a resistance thermometer. in direct contact with the semiconductor w26x; and Žii. measurement using an infrared thermometer w23,27x. Mielle w28x has claimed to have developed a new method for sensor temperature measurement which will be covered by a patent. One cause for concern is that there appears to be a discrepancy between the surface temperatures published by Figaro w21x and those published by various research groups using the same sensors at the same applied heater voltages. Other information published by Figaro which is relevant to temperature control of gas sensors concerns pre-heating of gas sensors before use Žup to 7 days in clean air, in an upright position., as the response of the sensor can change by as much as 30% over this time.
3. Temperature transient or pulsed techniques The simplest way to observe a temperature-dependent dynamic sensor response is literally to switch the sensor power supply on or off, with or without an analyte gas being present. This was the approach employed by Hi-
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ranaka et al. w26x using Taguchi-type Figaro SnO 2 sensors. Sensors pre-heated using an applied potential of 5 V were exposed to an analyte gas; once the sensor response had stabilised, the applied voltage was switched off, and the sensor response was monitored over a period of 60 s. This yielded a transient response which peaked at a time characteristic of the analyte gas and, to a lesser degree, its concentration. This transient response, recorded as the conductance of the sensor, and ‘‘compensated’’ for the clean-air response, was simply used to demonstrate the possibility of distinguishing between different analyte gases. Amamoto et al. w29x constructed a low-power SnO 2 sensor, which was then used in pulse-drive experiments where a 7.5-V heating pulse was applied for 8 ms every second ŽFig. 2.. The transient sensor response was observed to be characteristic of the analyte gases tested ŽCO, ethanol and hydrogen., but was not applied quantitatively — sensitivity vs. concentration measurements were made using only the point in the response curve which corresponded to the end of the heating pulse. In more recent work, Kato et al. w24x applied a 16-ms heating pulse and observed the hysteretic behaviour of the sensor output, measured in this case as the resistance. Again, the transient response of the sensor Žduring heating and subsequent cooling. was characteristic of the gas ŽCO, methane, n-butane and hydrogen. to which the sensor was exposed. A model for the temperature-dependence of the sensor resistance, using parameters based on kinetic considerations, was proposed. The potential to use some of these parameters for qualitative and quantitative analysis was discussed. Kelleter et al. w30x have used the magnitude of the response to voltage pulses applied to a semiconductor gas sensor to calibrate for the effects of sensor aging. All of these papers are preliminary in nature — none of them actually carries out a quantitative analysis based on the transient response of the sensor as its temperature
Fig. 2. Operation of a ‘‘pulse-drive’’ sensor: Ža. heater voltage; Žb. sensor temperature; Žc. sensor output voltage. Ethanol and CO concentrations are both 1000 ppm Žfrom Amamoto et al. w29x..
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varies. Temperature pulse techniques do show promise in this regard, but little work seems to have been done to exploit this, and it may be that more information can be obtained as easily from cyclic temperature modulation techniques.
4. Temperature modulation through oscillation of heater voltage Possibly, the most promising temperature modulation technique involves the application of a periodic heating voltage to the semiconductor gas sensor. Sears et al. w9,10x suggest several advantages that can arise from the application of an oscillating heater voltage. Firstly, because of the different rates of reaction of various analyte gases at different temperatures, a cyclic temperature variation can give a unique signature for each gas. Secondly, because low temperature operation can lead to the accumulation of incompletely oxidised contaminants, periodic shifts to higher temperatures Žobtained by raising the heater voltage as high as 6 V. may be required to clean the sensor surface. Third, thermal cycling can lead to improvements in sensitivity because for each gas there is usually a point Žtemperature or heater voltage. in the cycle which corresponds to a maximum in the conductance–temperature profile. Early patents for gas detection systems involving temperature cycling have been reviewed by Sears et al. w10x, so we summarise them only briefly here. A very early system for carbon monoxide detection utilised a periodic heater voltage that oscillated the sensor between a low temperature at which gas detection was performed and a high temperature at which the sensor was ‘‘purged’’ of adsorbed gases w31x. Eicker w32x followed on from this with a system which was able to distinguish carbon monoxide in a mixture with methane, again by oscillating the sensor between two fixed temperatures. Owen w33x patented another system which cycled the sensor between three fixed temperatures. Again, the sensor detected CO most sensitively at the lowest temperature. Advani et al. w34x used what was effectively a square wave heater voltage with a 30-s period, measuring and comparing the ratio of conductances at two points in the cycle in order to quantify a single gas ŽH 2 S.. Bukowiecki et al. w35x used a variety of different heater voltage waveforms Žtriangular, sawtooth, asymmetric square wave. in an attempt to distinguish between CO, methane, ammonia and hydrogen. Lantto and Romppainen w36x used a square wave heater voltage waveform to heat the sensor between ‘‘160’’ and ‘‘380’’8C to improve the sensitivity of H 2 S detection. Forster and Strassler w37x patented a system which used a variety of heater voltage waveforms in conjunction with a variety of mechanical means of creating an oscillating concentration of analyte gas in the sensor compartment.
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These two oscillating variables were applied simultaneously and an empirical relationship between sensor conductances at particular points in the cycle and gas concentration was developed. It appears that the first workers to thoroughly investigate the effects of using a sinusoidal heater voltage were Sears et al. w9,10x in 1989. Using a Figaro 812 ŽSnO 2 doped with palladium. gas sensor, they applied an offset sinusoidal voltage, which varied between 0.2 and 7.2 V with a period of 10–200 s. The reason given for the use of a sinusoidal waveform is that the sensor temperature can more closely follow the heater voltage. When a number of different gases Žethanol, propane, methane, CO, acetone and hydrogen were investigated. using this system, it was found that the resulting conductance vs. time curves had one or more distinct peaks ŽFig. 3.. The number and position of peaks and the shape of these features was found to depend upon the identity of the gas and its concentration as well as upon the amplitude and period of the applied sinusoidal heater voltage ŽFigs. 4 and 5.. The authors found that a period of 50 s represented a compromise between the needs for quick response times and the sharpening of characteristic features in the conductance vs. time curve. In these experiments, peaks in conductance occur as the sensor temperature reaches the optimum catalytic oxidation temperature for the analyte gas; this happens twice during a typical cycle — once as the temperature rises through the optimum, and again as it cools through this temperature. An important suggestion made in the first of these papers w9x is that it might be possible to apply a heater voltage waveform that compensates for the non-linearity of the voltage–temperature relationship, and thus enables linear control of the sensor temperature. In the second of the two papers, Sears et al. w10x developed algorithms that exploit the characteristic shapes of the conductance–time curves for different gases in order to distinguish between them. They also noted irreversible
Fig. 3. The response of a tin oxide gas sensor to various sinusoidal heater voltages in the presence of 200 ppm ethanol: As 5.0 V; B s6.2 V; C s 7.2 V; Ds normalised heater voltage waveform Žfrom Sears et al. w9x..
Fig. 4. The response of a tin oxide gas sensor to sinusoidal heater voltages with various periods of oscillation in the presence of 200 ppm ethanol. Values of T Žperiod. are As10 s, B s 25 s, C s 50 s, Ds100 s, Es 200 s; F s heater voltage waveform Ž6.2 V maximum. Žfrom Sears et al. w9x..
poisoning effects that occur when the sensor is exposed to high concentrations of strongly reducing gases such as hydrogen or CO. In a third paper, Sears et al. w38x developed a ‘‘restricted flow thermally cycled gas sensor’’ which exhibited enhanced selectivity to propane over other gases. By connecting a small diameter tube to the case and oscillating the temperature of a Figaro 813 sensor, they created a ‘‘breathing’’ gas sensor. As the sensor cooled, a small amount of gas was drawn into sensor case; upon heating, this gas was expelled, the primary effect being the rapid oxidation of the adsorbed gas, which yielded a large increase in sensor conductance. Propane, which adsorbs more quickly onto the sensor than other gases at low temperatures, is therefore present in higher amounts when its oxidation occurs at higher temperatures. Subsequent work by the same research group investigated fitting Gaussian functions to the peaks in the conduc-
Fig. 5. The response of a tin oxide gas sensor to a sinusoidal heater voltage in the presence of various reducing gases: As1000 ppm methane; B s 200 ppm propane; C s 500 ppm carbon monoxide; Ds heater voltage waveform Ž6.2 V maximum. Žfrom Sears et al. w9x..
A.P. Lee, B.J. Reedy r Sensors and Actuators B 60 (1999) 35–42
tance vs. time plot generated by thermally cycling a tin oxide sensor w12x. In this case, heating was achieved using a linear temperature ramp Žas suggested previously by this group w9x. over 60 s, with a 10-s cooling period at 0 V Žsee Fig. 6.. It was concluded that the number of Gaussian functions needed to fit the conductance–temperature profiles indicated the number of oxidation mechanisms for the analyte gas at the sensor surface. In two-component mixtures where the concentration of one component was known, it was possible to construct a power-law Žlog–log. calibration plot using the peak conductance of the primary Gaussian and thus determine unknown concentrations of the second gas. However, cross-sensitivities limit the use of Gaussian deconvolution techniques in quantitative gas analysis. In a series of papers beginning in 1991, Nakata et al. w23,39,41,42,44–46x and Nakata and Yoshikawa w40,43x have published results from experiments in which a sinusoidal heating voltage Ž0.02–0.04 Hz. was applied to a TGS 813 tin oxide gas sensor, and the resistance was measured as a function of time Žand therefore of temperature or voltage.. The resulting cyclic response ŽFig. 7. was transformed to the frequency domain by FFT Žfast Fourier transform. and the relative intensities of various real and imaginary components of the higher harmonics were used to distinguish between different analyte gases ŽFig. 8.. The authors attempted to correlate the behaviour of the higher harmonics in the FFT with molecular structural characteristics of the gases such as alkyl chain length and presence of double bonds. For example, the relative amplitudes of the first three real harmonics for saturated hydrocarbons were found to increase steadily with chain length, and it was possible to differentiate between n-butane and isobutane. Further, experiments by Nakata et al. w23x and Nakata and Yoshikawa w43x examined the dynamic response of the gas sensor to mixtures of two gases Žpropanerethylene and propanerCO. and demonstrated that a three dimensional plot of the concentrations of the two gas components vs. the intensity of an individual harmonic Žreal or imaginary.
Fig. 6. The response of a tin oxide gas sensor to a linear temperature ramp, operating between ambient temperature and a maximum of ‘‘350– 4008C’’: crossess response in pure air; dotss response in 50 ppm propane; solids heater voltage Žfrom Wlodek et al. w12x..
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Fig. 7. Conductance–temperature response of a tin oxide gas sensor in Ža. air and 1000 ppm of Žb. methane, Žc. ethane, Žd. propane, Že. n-butane, Žf. isobutane, Žg. ethylene, Žh. propylene and Ži. carbon monoxide. Heater voltage waveform was 3.5q1.5 cos 2p ft, f s 0.05 Hz Žfrom Nakata et al. w23x..
yielded distinctive surfaces that could be used to characterise the mixture ŽFig. 9.. A very recent publication by Nakata et al. w46x reports work involving the use of pairs of different sensors ŽTGS813 and TGS 830; TGS813 and TGS 826. to yield characteristic three-dimensional response plots Žwhere x s heater voltage and the y and z coordinates are the responses of the respective sensors. for mixtures of CO and propane. However, methods for the quantitative use of the data presented in these publications are not given, and the authors suggest that their results are best suited for analysis by neural network techniques. The idea of using neural networks following FFT to analyse data from temperature-modulated gas sensor experiments has been taken up by Heilig et al. w27x and Yea et al. w47x. Using sinusoidal heater voltage waveforms Ž0.05 Hz. on a micromachined sensor array, Heilig et al. have employed neural networks to identify and quantify
Fig. 8. Relative amplitudes of the real components of the n-th harmonics to the 0-th harmonics ŽDC components. of the FFTs of the sensor responses in Fig. 7. R n denotes the amplitude of the n-th harmonic. All gases are at 1000 ppm Žfrom Nakata et al. w23x..
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Fig. 9. Three-dimensional plot of the dependence of the FFT higher harmonics for mixtures of carbon monoxide and propane. Data for FFT analysis was collected in a similar fashion to that illustrated in Fig. 7 Žfrom Nakata et al. w23x..
CO and NO 2 in binary mixtures from the intensities of the FFT harmonics with some success. However, they qualify their results, pointing out that FFT is probably not the ideal method of extracting frequencies which can be related to the non-linear sensor surface reaction kinetics. Yea et al. applied a square wave heating voltage Ž0.0625 Hz, 3–5 V. to a TGS 813 sensor after introduction of the analyte gas Žtown gas, hydrogen, methane, propane or butane. and analysed the response with FFT. A neural network was used to identify each gas and fuzzy inference was used for concentration estimation. Another aspect of the above-mentioned publications by Nakata and Yoshikawa et al. has been attempts to model the dynamic response of the gas sensor conductance with sinusoidal heating voltages in the presence of analyte gases. Both Langmuir–Rideal Žanalyte gas not adsorbed. and Langmuir–Hinshelwood Žanalyte gas adsorbed. mechanisms for the surface reaction of the analyte gas with adsorbed oxygen species have been considered w23,46x. The relative importance of these two pathways will depend greatly on the identity of the analyte gas and the nature of its interaction Žor lack thereof. with Pd- or Pt-doped sensor surfaces. 5. Future directions and concluding remarks In summary, temperature modulation or control can be used to improve the selectivity and sensitivity of semicon-
ductor gas sensors. This is possible because each analyte gas has a characteristic conductance vs. temperature Žor applied heater voltage. profile for each sensor type. This means that if the response of one sensor is measured at n temperatures, the sensor becomes analogous to an array of n ‘‘sensors’’. Thus if m different actual sensors are used, m P n dimensional information is afforded for analysis w46x, and herein lies the great potential for these techniques. The main problem to be overcome, however, is still the non-linearity of the sensor response. In recent years, one of the most common ways to approach this problem has been the use of neural networks and soft-modelling techniques to recognise patterns of sensor response w2x. With the increased information afforded by temperature modulation techniques, the response patterns become more characteristic, although the complexity of the neural networks will increase concomitantly. A partial alternative to the pattern recognition approach might be to find a way of linearising the response data, at least for some analytes, thus permitting the use of more traditional multivariate analysis techniques. One way that this might be achieved is by finding systems of gases that interact in a similar manner with the sensor surface. For example, analyte gases that do not themselves adsorb to the sensor surface ŽLangmuir–Rideal mechanism. and which are members of a homologous series might actually give simpler, more predictable combined responses. In other words, the use of dopants to improve the selectivity of semiconductor gas sensors may
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actually be a major cause of non-linear behaviour. If such systems of gases can be found, their study would contribute greatly to the understanding of sensor response mechanisms. With regard to temperature control, there are also other potential contributions to the non-linearity or reproducibility of sensor response which can easily be eliminated, but which have not always been taken into account in the papers reviewed in this article. These include: Ži. adequate and consistent sensor pre-heating, Žii. effective and reproducible cleaning of the sensor surface Žusing, for example, sinusoidal heating cycles w23x., Žiii. accurate temperature control, Živ. and the use of sufficiently slow sensor heating so as to avoid losing resolution in conductance vs. temperature profiles.
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Acknowledgements
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A.P.L. acknowledges a postgraduate scholarship funded from an Australian Research Council Large Grant ŽFile No. A29703193..
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nonlinear responses of a semiconductor gas sensor: dependence on the range and frequency of a cyclic temperature change, Anal. Chim. Acta 361 Ž1998. 93–100. w46x S. Nakata, M. Nakasuji, N. Ojima, M. Kitora, Characteristic nonlinear responses for gas species on the surface of different semiconductor gas sensors, Appl. Surf. Sci. 135 Ž1998. 285–292. w47x B. Yea, T. Osaki, K. Sugahara, R. Konishi, The concentration-estimation of inflammable gases with a semiconductor gas sensor utilizing neural networks and fuzzy inference, Sens. Actuators, B 41 Ž1997. 121–129. Andrew Lee graduated with a Bachelor of Applied Science ŽHons.. from the University of Tasmania in 1997, and is currently studying for his PhD in the School of Applied Science at the same institution. His research project examines the applications of temperature modulation of semiconductor gas sensors to quantitative analysis of gas mixtures. Brian Reedy received his PhD in inorganic chemistry from the University of Sydney in 1991, and currently lectures in chemistry in the School of Applied Science at the University of Tasmania. His main areas of interest are vibrational spectroscopy, inorganic chemistry and semiconductor gas sensors Žtemperature modulation and characterisation of sensor surface chemistry..
Temperature modulation in semiconductor gas sensing Andrew P. Lee ) , Brian J. Reedy School of Applied Science, UniÕersity of Tasmania, P.O. Box 1214 Launceston, Tasmania 7250, Australia Received 18 January 1999; received in revised form 13 April 1999; accepted 14 April 1999
Abstract A review of semiconductor gas sensor literature pertaining to the use of temperature modulation techniques is presented. The temperature dependence of sensor conductance is discussed, along with transient and cyclic modulation techniques for improving sensitivity and selectivity of sensors in the analysis of single gases and multi-component gas mixtures. q 1999 Elsevier Science S.A. All rights reserved. Keywords: Semiconductor gas sensor; Tin oxide; Temperature modulation; Dynamic response; Thermal cycling; Multi-component gas analysis
1. Introduction Selectivity in semiconductor gas sensors can be obtained through a variety of methods which can be classified into four main groups: Ži. the use of filters or chromatographic columns to discriminate between gases on the basis of molecular size or other physical properties; Žii. the use of catalysts and promoters or more specific surface additives; Žiii. the physical preparation of the sensor material; and Živ. the analysis of transient sensor responses to changes in analyte concentration or sensor temperature. Of the last category, the most commonly employed technique involves controlling the temperature of the semiconductor surface, whether by selecting a fixed temperature to maximise sensitivity to a particular analyte gas, or by programming or modulating the temperature. In a review of semiconductor gas sensor selectivity in 1987, Morrison w1x, concentrating on the use of catalysts, stated that ‘‘temperature programming of the sensor is not a common technique used for selectivity’’. However, more recent work has demonstrated that temperature programming or modulation of a single sensor can achieve the type of selectivity that would otherwise require arrays of variously doped fixed temperature sensors. The purpose of the current paper is to review the use of temperature control of semiconductor gas sensors in single- and multi-component gas analysis, and to explore possible future directions in this promising field. More general reviews on recent devel)
Corresponding author. Tel.: q61-3-63243856; fax: q61-3-63243839; E-mail: [email protected]
opments in semiconductor Žparticularly SnO 2 . gas sensor technology can be found elsewhere w2–7x. 2. Temperature dependence of gas sensor conductance Although this is not intended to be a review of the phenomenon of the temperature-dependence of semiconductor sensor conductance Žor resistance., a brief survey of some of the relevant literature is probably warranted as an introduction to methods involving temperature-modulation. Moseley and Williams w8x have reviewed work on the characterisation of adsorbed oxygen species on the surface of metal oxide semiconductors. It is widely accepted that the key process in the response of the semiconductor to a reducing gas involves the modulation of the concentration y of adsorbed oxygen species such as Oy or O 2y. By 2, O withdrawing electron density from the semiconductor surface, adsorbed oxygen gives rise to Schottky potential barriers at grain boundaries, and thus increases the resistance of the sensor surface. Reducing gases decrease the surface oxygen concentration and thus decrease the sensor resistance. The temperature dependence of this process arises in part from the differing stabilities of the surface oxygen species over different temperature ranges. While the identity of the surface oxygen species remains slightly more controversial w1,8–10x, it is clear that different gases have characteristic optimum oxidation temperatures, and therefore give rise to characteristic conductance–temperature profiles, which can be modified by doping the semiconductor with noble metals or other catalytic materials w3,4,7,9–20x. Fig. 1 shows some typical responses of doped
0925-4005r99r$ - see front matter q 1999 Elsevier Science S.A. All rights reserved. PII: S 0 9 2 5 - 4 0 0 5 Ž 9 9 . 0 0 2 4 1 - 5
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Fig. 1. Sensitivity–temperature profiles for doped and undoped tin oxide sensors in the presence of various analyte gases. Note that ‘‘sensitivity’’ is defined as GrGo , that is, the ratio of the sensor conductance Ž G . in the presence of the analyte gas to the conductance in clean air Ž Go .. Gas concentrations in air are 0.8% H 2 , 0.5% CH 4 , 0.2% C 3 H 8 , 0.02% CO Žfrom Yamazoe and Miura w3x..
and undoped SnO 2 sensors to various analyte gases. In general, the temperature of the gas sensor surface is controlled by varying the voltage applied to the sensor heater. Other factors, both chemical and physical, which depend upon temperature and contribute to the sensor response have been identified by Mizsei w6x. These include rates of adsorption and desorption Žof oxygen and reducing gases, or of oxidation products., the rate of surface decomposition of reducing gases, the charge-carrier concentration and the Debye length in the semiconductor. This means that the true relationship between the conductance of a semiconductor sensor in the presence of reducing gases and the temperature of the sensor surface is very complex. Another complicating feature of gas sensor operation is that the chemical reactions that give rise to the sensor response are usually exothermic, and thus make uncontrolled contributions to the sensor temperature w9x. Furthermore, the actual measurement of the sensor conductance requires the flow of a small current through the sensor and thus causes Joule heating of the sensor, although the relative significance of this is questionable. In their technical information sheets, Figaro Žmanufacturer of the most commonly used sensors. w21x indicate that the use of a flow system in gas detection causes cooling of the sensor surface, and thus also influences its response. An important aspect of the temperature-dependence of gas sensor conductance is the hysteresis observed w9,11,22–24x when the sensor temperature is continuously elevated and subsequently cooled over short time periods. Clifford and Tuma w25x found that hysteresis effects in sensor response to O 2rN2 mixtures were minimised if the sensor temperature was varied slowly enough, that is, at around 20 K hy1 . This was done in an attempt to operate the sensor in a quasi-steady state at each temperature. Both Moseley and Williams w8x and Clifford and Tuma w11x attribute hysteresis effects to the slow attainment of equi-
librium in the transfer of charge between adsorbed oxygen species and the semiconductor. One shortcoming of many papers published so far on semiconductor gas sensors is that an actual measurement of the surface temperature of the sensor is not performed, is performed using an unspecified technique, or is only estimated or inferred from other work. This is understandable considering the difficulties of measuring the temperature of ever-diminishing sensor surfaces, but illustrates a problem that needs to be addressed if the sensing mechanisms are to be properly understood. Published methods for the measurement of temperature can be classified into two main categories: Ži. measurement using a temperature sensor Že.g., a thermocouple or a resistance thermometer. in direct contact with the semiconductor w26x; and Žii. measurement using an infrared thermometer w23,27x. Mielle w28x has claimed to have developed a new method for sensor temperature measurement which will be covered by a patent. One cause for concern is that there appears to be a discrepancy between the surface temperatures published by Figaro w21x and those published by various research groups using the same sensors at the same applied heater voltages. Other information published by Figaro which is relevant to temperature control of gas sensors concerns pre-heating of gas sensors before use Žup to 7 days in clean air, in an upright position., as the response of the sensor can change by as much as 30% over this time.
3. Temperature transient or pulsed techniques The simplest way to observe a temperature-dependent dynamic sensor response is literally to switch the sensor power supply on or off, with or without an analyte gas being present. This was the approach employed by Hi-
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ranaka et al. w26x using Taguchi-type Figaro SnO 2 sensors. Sensors pre-heated using an applied potential of 5 V were exposed to an analyte gas; once the sensor response had stabilised, the applied voltage was switched off, and the sensor response was monitored over a period of 60 s. This yielded a transient response which peaked at a time characteristic of the analyte gas and, to a lesser degree, its concentration. This transient response, recorded as the conductance of the sensor, and ‘‘compensated’’ for the clean-air response, was simply used to demonstrate the possibility of distinguishing between different analyte gases. Amamoto et al. w29x constructed a low-power SnO 2 sensor, which was then used in pulse-drive experiments where a 7.5-V heating pulse was applied for 8 ms every second ŽFig. 2.. The transient sensor response was observed to be characteristic of the analyte gases tested ŽCO, ethanol and hydrogen., but was not applied quantitatively — sensitivity vs. concentration measurements were made using only the point in the response curve which corresponded to the end of the heating pulse. In more recent work, Kato et al. w24x applied a 16-ms heating pulse and observed the hysteretic behaviour of the sensor output, measured in this case as the resistance. Again, the transient response of the sensor Žduring heating and subsequent cooling. was characteristic of the gas ŽCO, methane, n-butane and hydrogen. to which the sensor was exposed. A model for the temperature-dependence of the sensor resistance, using parameters based on kinetic considerations, was proposed. The potential to use some of these parameters for qualitative and quantitative analysis was discussed. Kelleter et al. w30x have used the magnitude of the response to voltage pulses applied to a semiconductor gas sensor to calibrate for the effects of sensor aging. All of these papers are preliminary in nature — none of them actually carries out a quantitative analysis based on the transient response of the sensor as its temperature
Fig. 2. Operation of a ‘‘pulse-drive’’ sensor: Ža. heater voltage; Žb. sensor temperature; Žc. sensor output voltage. Ethanol and CO concentrations are both 1000 ppm Žfrom Amamoto et al. w29x..
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varies. Temperature pulse techniques do show promise in this regard, but little work seems to have been done to exploit this, and it may be that more information can be obtained as easily from cyclic temperature modulation techniques.
4. Temperature modulation through oscillation of heater voltage Possibly, the most promising temperature modulation technique involves the application of a periodic heating voltage to the semiconductor gas sensor. Sears et al. w9,10x suggest several advantages that can arise from the application of an oscillating heater voltage. Firstly, because of the different rates of reaction of various analyte gases at different temperatures, a cyclic temperature variation can give a unique signature for each gas. Secondly, because low temperature operation can lead to the accumulation of incompletely oxidised contaminants, periodic shifts to higher temperatures Žobtained by raising the heater voltage as high as 6 V. may be required to clean the sensor surface. Third, thermal cycling can lead to improvements in sensitivity because for each gas there is usually a point Žtemperature or heater voltage. in the cycle which corresponds to a maximum in the conductance–temperature profile. Early patents for gas detection systems involving temperature cycling have been reviewed by Sears et al. w10x, so we summarise them only briefly here. A very early system for carbon monoxide detection utilised a periodic heater voltage that oscillated the sensor between a low temperature at which gas detection was performed and a high temperature at which the sensor was ‘‘purged’’ of adsorbed gases w31x. Eicker w32x followed on from this with a system which was able to distinguish carbon monoxide in a mixture with methane, again by oscillating the sensor between two fixed temperatures. Owen w33x patented another system which cycled the sensor between three fixed temperatures. Again, the sensor detected CO most sensitively at the lowest temperature. Advani et al. w34x used what was effectively a square wave heater voltage with a 30-s period, measuring and comparing the ratio of conductances at two points in the cycle in order to quantify a single gas ŽH 2 S.. Bukowiecki et al. w35x used a variety of different heater voltage waveforms Žtriangular, sawtooth, asymmetric square wave. in an attempt to distinguish between CO, methane, ammonia and hydrogen. Lantto and Romppainen w36x used a square wave heater voltage waveform to heat the sensor between ‘‘160’’ and ‘‘380’’8C to improve the sensitivity of H 2 S detection. Forster and Strassler w37x patented a system which used a variety of heater voltage waveforms in conjunction with a variety of mechanical means of creating an oscillating concentration of analyte gas in the sensor compartment.
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These two oscillating variables were applied simultaneously and an empirical relationship between sensor conductances at particular points in the cycle and gas concentration was developed. It appears that the first workers to thoroughly investigate the effects of using a sinusoidal heater voltage were Sears et al. w9,10x in 1989. Using a Figaro 812 ŽSnO 2 doped with palladium. gas sensor, they applied an offset sinusoidal voltage, which varied between 0.2 and 7.2 V with a period of 10–200 s. The reason given for the use of a sinusoidal waveform is that the sensor temperature can more closely follow the heater voltage. When a number of different gases Žethanol, propane, methane, CO, acetone and hydrogen were investigated. using this system, it was found that the resulting conductance vs. time curves had one or more distinct peaks ŽFig. 3.. The number and position of peaks and the shape of these features was found to depend upon the identity of the gas and its concentration as well as upon the amplitude and period of the applied sinusoidal heater voltage ŽFigs. 4 and 5.. The authors found that a period of 50 s represented a compromise between the needs for quick response times and the sharpening of characteristic features in the conductance vs. time curve. In these experiments, peaks in conductance occur as the sensor temperature reaches the optimum catalytic oxidation temperature for the analyte gas; this happens twice during a typical cycle — once as the temperature rises through the optimum, and again as it cools through this temperature. An important suggestion made in the first of these papers w9x is that it might be possible to apply a heater voltage waveform that compensates for the non-linearity of the voltage–temperature relationship, and thus enables linear control of the sensor temperature. In the second of the two papers, Sears et al. w10x developed algorithms that exploit the characteristic shapes of the conductance–time curves for different gases in order to distinguish between them. They also noted irreversible
Fig. 3. The response of a tin oxide gas sensor to various sinusoidal heater voltages in the presence of 200 ppm ethanol: As 5.0 V; B s6.2 V; C s 7.2 V; Ds normalised heater voltage waveform Žfrom Sears et al. w9x..
Fig. 4. The response of a tin oxide gas sensor to sinusoidal heater voltages with various periods of oscillation in the presence of 200 ppm ethanol. Values of T Žperiod. are As10 s, B s 25 s, C s 50 s, Ds100 s, Es 200 s; F s heater voltage waveform Ž6.2 V maximum. Žfrom Sears et al. w9x..
poisoning effects that occur when the sensor is exposed to high concentrations of strongly reducing gases such as hydrogen or CO. In a third paper, Sears et al. w38x developed a ‘‘restricted flow thermally cycled gas sensor’’ which exhibited enhanced selectivity to propane over other gases. By connecting a small diameter tube to the case and oscillating the temperature of a Figaro 813 sensor, they created a ‘‘breathing’’ gas sensor. As the sensor cooled, a small amount of gas was drawn into sensor case; upon heating, this gas was expelled, the primary effect being the rapid oxidation of the adsorbed gas, which yielded a large increase in sensor conductance. Propane, which adsorbs more quickly onto the sensor than other gases at low temperatures, is therefore present in higher amounts when its oxidation occurs at higher temperatures. Subsequent work by the same research group investigated fitting Gaussian functions to the peaks in the conduc-
Fig. 5. The response of a tin oxide gas sensor to a sinusoidal heater voltage in the presence of various reducing gases: As1000 ppm methane; B s 200 ppm propane; C s 500 ppm carbon monoxide; Ds heater voltage waveform Ž6.2 V maximum. Žfrom Sears et al. w9x..
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tance vs. time plot generated by thermally cycling a tin oxide sensor w12x. In this case, heating was achieved using a linear temperature ramp Žas suggested previously by this group w9x. over 60 s, with a 10-s cooling period at 0 V Žsee Fig. 6.. It was concluded that the number of Gaussian functions needed to fit the conductance–temperature profiles indicated the number of oxidation mechanisms for the analyte gas at the sensor surface. In two-component mixtures where the concentration of one component was known, it was possible to construct a power-law Žlog–log. calibration plot using the peak conductance of the primary Gaussian and thus determine unknown concentrations of the second gas. However, cross-sensitivities limit the use of Gaussian deconvolution techniques in quantitative gas analysis. In a series of papers beginning in 1991, Nakata et al. w23,39,41,42,44–46x and Nakata and Yoshikawa w40,43x have published results from experiments in which a sinusoidal heating voltage Ž0.02–0.04 Hz. was applied to a TGS 813 tin oxide gas sensor, and the resistance was measured as a function of time Žand therefore of temperature or voltage.. The resulting cyclic response ŽFig. 7. was transformed to the frequency domain by FFT Žfast Fourier transform. and the relative intensities of various real and imaginary components of the higher harmonics were used to distinguish between different analyte gases ŽFig. 8.. The authors attempted to correlate the behaviour of the higher harmonics in the FFT with molecular structural characteristics of the gases such as alkyl chain length and presence of double bonds. For example, the relative amplitudes of the first three real harmonics for saturated hydrocarbons were found to increase steadily with chain length, and it was possible to differentiate between n-butane and isobutane. Further, experiments by Nakata et al. w23x and Nakata and Yoshikawa w43x examined the dynamic response of the gas sensor to mixtures of two gases Žpropanerethylene and propanerCO. and demonstrated that a three dimensional plot of the concentrations of the two gas components vs. the intensity of an individual harmonic Žreal or imaginary.
Fig. 6. The response of a tin oxide gas sensor to a linear temperature ramp, operating between ambient temperature and a maximum of ‘‘350– 4008C’’: crossess response in pure air; dotss response in 50 ppm propane; solids heater voltage Žfrom Wlodek et al. w12x..
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Fig. 7. Conductance–temperature response of a tin oxide gas sensor in Ža. air and 1000 ppm of Žb. methane, Žc. ethane, Žd. propane, Že. n-butane, Žf. isobutane, Žg. ethylene, Žh. propylene and Ži. carbon monoxide. Heater voltage waveform was 3.5q1.5 cos 2p ft, f s 0.05 Hz Žfrom Nakata et al. w23x..
yielded distinctive surfaces that could be used to characterise the mixture ŽFig. 9.. A very recent publication by Nakata et al. w46x reports work involving the use of pairs of different sensors ŽTGS813 and TGS 830; TGS813 and TGS 826. to yield characteristic three-dimensional response plots Žwhere x s heater voltage and the y and z coordinates are the responses of the respective sensors. for mixtures of CO and propane. However, methods for the quantitative use of the data presented in these publications are not given, and the authors suggest that their results are best suited for analysis by neural network techniques. The idea of using neural networks following FFT to analyse data from temperature-modulated gas sensor experiments has been taken up by Heilig et al. w27x and Yea et al. w47x. Using sinusoidal heater voltage waveforms Ž0.05 Hz. on a micromachined sensor array, Heilig et al. have employed neural networks to identify and quantify
Fig. 8. Relative amplitudes of the real components of the n-th harmonics to the 0-th harmonics ŽDC components. of the FFTs of the sensor responses in Fig. 7. R n denotes the amplitude of the n-th harmonic. All gases are at 1000 ppm Žfrom Nakata et al. w23x..
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Fig. 9. Three-dimensional plot of the dependence of the FFT higher harmonics for mixtures of carbon monoxide and propane. Data for FFT analysis was collected in a similar fashion to that illustrated in Fig. 7 Žfrom Nakata et al. w23x..
CO and NO 2 in binary mixtures from the intensities of the FFT harmonics with some success. However, they qualify their results, pointing out that FFT is probably not the ideal method of extracting frequencies which can be related to the non-linear sensor surface reaction kinetics. Yea et al. applied a square wave heating voltage Ž0.0625 Hz, 3–5 V. to a TGS 813 sensor after introduction of the analyte gas Žtown gas, hydrogen, methane, propane or butane. and analysed the response with FFT. A neural network was used to identify each gas and fuzzy inference was used for concentration estimation. Another aspect of the above-mentioned publications by Nakata and Yoshikawa et al. has been attempts to model the dynamic response of the gas sensor conductance with sinusoidal heating voltages in the presence of analyte gases. Both Langmuir–Rideal Žanalyte gas not adsorbed. and Langmuir–Hinshelwood Žanalyte gas adsorbed. mechanisms for the surface reaction of the analyte gas with adsorbed oxygen species have been considered w23,46x. The relative importance of these two pathways will depend greatly on the identity of the analyte gas and the nature of its interaction Žor lack thereof. with Pd- or Pt-doped sensor surfaces. 5. Future directions and concluding remarks In summary, temperature modulation or control can be used to improve the selectivity and sensitivity of semicon-
ductor gas sensors. This is possible because each analyte gas has a characteristic conductance vs. temperature Žor applied heater voltage. profile for each sensor type. This means that if the response of one sensor is measured at n temperatures, the sensor becomes analogous to an array of n ‘‘sensors’’. Thus if m different actual sensors are used, m P n dimensional information is afforded for analysis w46x, and herein lies the great potential for these techniques. The main problem to be overcome, however, is still the non-linearity of the sensor response. In recent years, one of the most common ways to approach this problem has been the use of neural networks and soft-modelling techniques to recognise patterns of sensor response w2x. With the increased information afforded by temperature modulation techniques, the response patterns become more characteristic, although the complexity of the neural networks will increase concomitantly. A partial alternative to the pattern recognition approach might be to find a way of linearising the response data, at least for some analytes, thus permitting the use of more traditional multivariate analysis techniques. One way that this might be achieved is by finding systems of gases that interact in a similar manner with the sensor surface. For example, analyte gases that do not themselves adsorb to the sensor surface ŽLangmuir–Rideal mechanism. and which are members of a homologous series might actually give simpler, more predictable combined responses. In other words, the use of dopants to improve the selectivity of semiconductor gas sensors may
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actually be a major cause of non-linear behaviour. If such systems of gases can be found, their study would contribute greatly to the understanding of sensor response mechanisms. With regard to temperature control, there are also other potential contributions to the non-linearity or reproducibility of sensor response which can easily be eliminated, but which have not always been taken into account in the papers reviewed in this article. These include: Ži. adequate and consistent sensor pre-heating, Žii. effective and reproducible cleaning of the sensor surface Žusing, for example, sinusoidal heating cycles w23x., Žiii. accurate temperature control, Živ. and the use of sufficiently slow sensor heating so as to avoid losing resolution in conductance vs. temperature profiles.
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Acknowledgements
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A.P.L. acknowledges a postgraduate scholarship funded from an Australian Research Council Large Grant ŽFile No. A29703193..
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A.P. Lee, B.J. Reedy r Sensors and Actuators B 60 (1999) 35–42 reducing gases in a gas mixture, US Pat. 4627269, December 9, 1986. W.M. Sears, C. Colbow, R. Slamka, F. Consadori, A restricted flow thermally cycled gas sensor, Sens. Actuators, B 1 Ž1990. 62–67. S. Nakata, Y. Kaneda, H. Nakamura, K. Yoshikawa, Detection and quantification of CO gas based on the dynamic response of a ceramic sensor, Chem. Lett. Ž1991. 1505–1508. S. Nakata, K. Yoshikawa, Distinguishing gases based on the dynamic electrical properties of an SnO 2 ceramic sensor, J. Mol. Electron. 7 Ž1991. 101–104. S. Nakata, H. Nakamura, K. Yoshikawa, New strategy for the development of a gas sensor based on the dynamic characteristics: principle and preliminary experiment, Sens. Actuators, B 8 Ž1992. 187–189. S. Nakata, Y. Kaneda, K. Yoshikawa, Novel strategy to develop chemical sensors based on nonlinear dynamics-intelligent gas sensor, Sens. Mater. 4 Ž1992. 101–110. S. Nakata, K. Yoshikawa, Nonlinear dynamics in chemical assembly, Trends Chem. Phys. 4 Ž1996. 23–58. S. Nakata, Y. Kato, Y. Kaneda, K. Yoshikawa, Rhythmic chemical reaction of CO on the surface of a SnO 2 gas sensor, Appl. Surf. Sci. 103 Ž1996. 369–376. S. Nakata, E. Ozaki, N. Ojima, Gas sensing based on the dynamic
nonlinear responses of a semiconductor gas sensor: dependence on the range and frequency of a cyclic temperature change, Anal. Chim. Acta 361 Ž1998. 93–100. w46x S. Nakata, M. Nakasuji, N. Ojima, M. Kitora, Characteristic nonlinear responses for gas species on the surface of different semiconductor gas sensors, Appl. Surf. Sci. 135 Ž1998. 285–292. w47x B. Yea, T. Osaki, K. Sugahara, R. Konishi, The concentration-estimation of inflammable gases with a semiconductor gas sensor utilizing neural networks and fuzzy inference, Sens. Actuators, B 41 Ž1997. 121–129. Andrew Lee graduated with a Bachelor of Applied Science ŽHons.. from the University of Tasmania in 1997, and is currently studying for his PhD in the School of Applied Science at the same institution. His research project examines the applications of temperature modulation of semiconductor gas sensors to quantitative analysis of gas mixtures. Brian Reedy received his PhD in inorganic chemistry from the University of Sydney in 1991, and currently lectures in chemistry in the School of Applied Science at the University of Tasmania. His main areas of interest are vibrational spectroscopy, inorganic chemistry and semiconductor gas sensors Žtemperature modulation and characterisation of sensor surface chemistry..