Micro Gas Sensor Array With Neural Network for ... - IEEE Xplore

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IEEE SENSORS JOURNAL, VOL. 5, NO. 3, JUNE 2005

Micro Gas Sensor Array With Neural Network for Recognizing Combustible Leakage Gases Dae-Sik Lee, Sang-Woo Ban, Minho Lee, and Duk-Dong Lee

Abstract—A micro gas sensor array, consisting of four porous tin oxide thin films added with noble metal catalysts on a micro-hotplate, was designed and fabricated. The micro-hotplate was designed to obtain a uniform thermal distribution along with a low-power consumption and fast thermal response. The sensing properties of the sensors toward certain combustible gases, i.e., propane, butane, LPG, and carbon monoxide, were evaluated. A multilayer neural network was then used to classify the gas species. The results demonstrated that the proposed micro sensor array, plus multilayer neural network employing a backpropagation learning algorithm, was very effective in recognizing specific kinds and concentration levels of combustible gas below their respective threshold limit values.

Accordingly, in the current study, a micro gas sensor array, consisting of highly sensitive porous tin oxide thin films on a microhotplate, was designed and fabricated, where the micro-hotplate was suspended in air using Pt bonding wires. The sensor array was then used to recognize certain kinds of combustible gas (propane, butane, LPG, and carbon monoxide) and various concentration levels, below the respective TLVs, based on using a multilayer neural network with a backpropagation learning algorithm and principal component analysis (PCA).

Index Terms—Combustible leakage gas recognition, micro gas sensors, neural network.

Double-sided polished silicon (100) piece wafers, with about 600 m thickness, were used as the substrate. The thickness of some piece wafers was controlled to 150 m thickness by employing the chemical mechanical polishing (CMP) process, to reduce the thermal mass. A Si N (1500 )/SiO (3000 )/Si N (1500 ) (N/O/N) film was deposited using the LPCVD system to create an etch mask on both sides of the wafer. First, the insulating layers on the bottom of the Si wafer, allocated for the outskirts of heating diaphragm, were patterned by photolithography and etched down to the silicon using a reactive ion etching (RIE) system. To fabricate the Pt thin film heater on top of the Si wafer, 0.2- m-thick Pt was first deposited using a radio-frequency (RF) magnetron sputter with a 0.03- m-thick Ti thin layer as an adhesion layer. Then the thin film was patterned by photolithography and etched using the aqua regia, which is nitric acid:hydrochloric acid:water in a 1:7:8 ratio. The alignment of the patterns of the heater on top of the wafer with that of the heating diaphragm was accomplished by a bottom-side alignment using a mask aligner (MA6, SussMicroTec Co.). The silicon oxide (0.4 m) was deposited using the LPCVD system on top of the wafer and then patterned by photolithography and dry etched using the RIE system to the platinum electrode pads. The silicon oxide functions as an electrical insulating. To obtain highly sensitive membranes, porous thin films (1000 ) were prepared by the thermal evaporation of metallic tin granules with sputtered addition of Au and/or Pt (30 ) onto an electrically isolated membrane located on a hotplate employing two shadow masks. Then the thermal oxidation of the evaporated metal films at 700 C for 3 h in an O atmosphere was carried out. KOH (26 wt.%, 85 C) wet etching was employed as the separating process for the sensor arrays, so that the wafer dicing process with a diamond saw was not needed. To protect the sensing membrane on the micro-hotplate from the KOH solutions, for this process, a home-made etching manifold that completely sealed the top of the chip and only allowed the bottom of the silicon to be etched was used.

I. INTRODUCTION

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HE availability of portable devices that can monitor combustible gases on site has become a priority to avoid accidents due to leakage. As such, there is an urgent need for the development of miniaturized sensors and systems that can identify and quantify leakage gases within regulation ranges, i.e., below the lower explosion limits (LELs) or threshold limit value (TLVs). A sensor system for recognizing the combustible leakage gas types or concentrations have rarely been reported. Over the last decade, there have been several reports on miniaturized gas sensor arrays developed to selectively detect certain gas species [1]–[3]. However, various problems need to be overcome to obtain highly sensitive membranes at low levels and micro-hotplates with a suitable mechanical strength at elevated temperatures. Therefore, to reduce the power consumption at an elevated temperature, micro-hotplates have been fabricated using stubborn materials, like SiC [4], and different silicon micromachining techniques, resulting in closed-membrane-type [5]–[7] and suspended-membrane-type micro-hotplates [8]–[10]. However, these techniques have serious problems related to the fragility of the thin membrane during the heat cycle, difficulties in the membrane formation processes, poor compatibility with subsequent processes, and high costs, whereas in the case of using a stubborn material, like SiC, there are difficulties in the fabrication process. Manuscript received July 1, 2003; revised July 4, 2004. The associate editor coordinating the review of this paper and approving it for publication was Dr. Bahram Kermani. D.-S. Lee is with the Bio-MEMS Group, Electronics and Telecommunications Research Institute (ETRI), Daejon, Korea (e-mail: [email protected]). S.-W. Ban, M. Lee, and D.-D. Lee are with the School of Electronic and Electrical Engineering, Kyungpook National University, Taegu 702-701, Korea (e-mail: [email protected]). Digital Object Identifier 10.1109/JSEN.2005.845186

II. FABRICATION OF SENSOR ARRAY

1530-437X/$20.00 © 2005 IEEE

LEE et al.: MICRO GAS SENSOR ARRAY WITH NEURAL NETWORK

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Fig. 1. (a) Cross-sectional view and (b) photographs of the fabricated micro sensor array.

Fig. 3. SEM photographs of thin films in the sensor array with different additives. (a) None. (b) Pt. (c) Au.

Fig. 2. Temperature versus power consumption of the micro-hotplate.

Finally, the micro-hotplate (1.9 2.1 mm ) of a Si N SiO SiO N multilayer on silicon was totally suspended in air by Pt bonding wires. Through thermal isolation by J cm s K), air with a low thermal conductivity (2.85 and the use of 12 Pt or Al bonding wires (0.71 or 2.37 J cm s K, respectively), instead of a silicon supporter (1.48 J cm s K), the mechanical strength was enhanced and the thermal loss of the micro-hotplate was reduced at an elevated temperature. The resulting micro gas sensor array is shown in

Fig. 1. To reduce the power consumption, the thickness of the hotplate was controlled by employing the CMP process. The dependency of the temperature on the micro-hotplate relative to the power consumption is shown in Fig. 2. Using an infrared temperature-monitoring system, the heat on the micro-hotplate was monitored. The resulting power consumption was reduced according to the thickness, for example, about 100 mW at 400 C for a 150 m thickness [11]. The gas-sensing properties of the micro sensor were tested using the gas injection method, which involved a 3-L volume chamber with a small fan, injection syringe, and small headspaces containing standard hydrocarbon gases. After sampling a gas from a headspace using an injection syringe, the gas was injected into the closed chamber with an operating fan and dispersed in throughout the chamber. After allowing the gas to stabilize in the chamber for 15 min, the gas was purged by opening the door of the chamber within a hood for 3 min; during which time, the signal usually returned to the base line. Meanwhile, the sensor signals were monitored through the different processes using a personal computer, and a voltage detecting method was used to calculate the sensitivity of the sensor [12]. The sensitivity was defined , where and were as

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Fig. 4. AES depth profile of the SnO thin film.

the electrical resistances in combustible gas ambient and pure air, respectively. III. CHARACTERISTICS OF SnO -BASED GAS SENSORS TOWARD COMBUSTIBLE GASES A. Properties of Sensing Films To detect the leakage of combustible gases at levels lower than theirlowexplosionlimits(methane:5.3vol.%,propane:3.3vol.%, butane: 1.9 vol.%, LPG: 2.0 vol.%, and carbon monoxide: 12.5 vol.%) and threshold limit values (butane: 800 ppm, LPG: 1000 ppm, and carbon monoxide: 50 ppm), stable sensors are required withahighsensitivitytosuchgasesatlowconcentrations.Thesensitivity of a sensor can be enhanced based on certain parameters, such as the grain size, specific surface area, and crystalline structure[13].Therefore,themicrostructureofthesensormaterialswas investigated for its adaptability. The surface morphology of the thin films for the sensor array are shown in Fig. 3. All SnO -based thin films showed many protrusions caused by the volume expansion during the Sn oxidation and represent potential sorption sites for the gases to be detected. In particular, many pores and protrusions were observed on the Pt-added SnO thin film, as shown in Fig. 3(b), which indicates a high sensitivity to gases. Yet, there were fewer pores and protrusions on the Au-added SnO thin film compared with the pure SnO thin film, as shown in Fig. 3(c). Fig. 4 shows the surface and depth profiles of the SnO thin film oxidized at 700 C for 3 h, based on Auger electron spectroscopy (AES), which is an extremely sensitive and commonly used technique for a surface and depth chemical composition analysis. The chemical composition was surveyed by analyzing the Auger electrons emitted in the process of bombarding the target surface with a beam of high-energy ions. It was shown that the oxidized SnO film contained small amounts of carbon and silicon. The presence of carbon as a contaminant on chemically cleaned sufaces at atmospheric pressure is a common result and corresponded to less than 5% of the thin film composition in the current study, thereby demonstrating the high sensitivity of Auger electron spectroscopy to surface species [14]. Silicon appeared on the SnO surface due to grains in several layers of the

Fig. 5. Sensitivity of the SnO thin films to 2300-ppm concentration of butane as a function of operating temperature.

thin film stack; plus there were many pores on the SnO thin film, as shown in Fig. 3. In addition, the nonstoichiometric composition of Sn and O was revealed at a ratio of 1 to 1.42 for the whole film. And it contributes to the gas sensing properties of the SnO thin film, as the nonstoichiometric property of SnO is known to be the main cause of gas-sensing [15], [16]. As for the crystalline structure, X-ray diffraction (XRD) also showed that the film had a nonstoichiometric composition of Sn and O based on a tetragonal structure with SnO peaks (JCPDS card no. 21-1250) and weak SnO peaks (JCPDS card no. 6-395) [11]. B. Gas-Sensing Properties of Micro Sensor Array The response of the SnO thin film sensors in an array to butane (2300 ppm) as a function of the operating temperature (200 C–450 C) is shown in Fig. 5. Since unique sensitivity patterns were obtained as a function of the operating temperature, this enabled the discrimination of specific combustible gases as distinct patterns [4]. Furthermore, Pt was found to enhance the sensitivity of the SnO thin film to combustible gases, whereas Au deteriorated its sensitivity, and 400 C was determined as the optimal operating temperature for sensitive detection of the combustible gases.


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Fig. 8.

Long-term stability of the SnO /Pt sensor to 3000-ppm butane. TABLE I TEST COMBUSTIBLE GASES AND THE DESIGNED OUTPUT

Fig. 6. Sensitivity of the micro sensor array as a function of (a) LPG and (b) carbon monoxide concentration at an operating temperature of 400 C.

Fig. 7. Time response of the SnO thin film to 500 ppm of carbon monoxide gas at an operating temperature of 400 C.

The four porous sensors on the micro-hotplate exhibited a good sensitivity, plus rapid response and recovery for the tested combustible gases, i.e., butane, propane, LPG, and carbon monoxide, within a range of 50–3000 ppm, which is below the respective TLVs. For example, the sensitivity of the sensors to LPG and carbon monoxide is shown in Fig. 6. As anticipated from the surface morphology and microstructure analysis, the Pt additive had a sensitivity-enhancing effect on the sensing

membranes, which were highly sensitive to even low concentrations. Distinct sensing patterns were also observed for the test gases at different concentrations. The time response curve for the SnO thin film sensor in 500-ppm carbon monoxide, as shown in Fig. 7, revealed a fast s , rapid desorption when response to carbon monoxide the gas was vented, and good reproducibility. The sensor responses tended to drift significantly when the sensors were used over a long period of time, resulting in poor reproducibility and redundant pattern recognition. Therefore, the fabrication of more reliable and stable sensors, or the ability to adapt thepatternrecognitionroutineaccordingtovariationsinthesensor signal, is required. For example, Fig. 8 shows the long-term stabilityofthesensorwithporoustinoxidematerials,i.e.,theSnO /Pt sensor. In this case, the sensor exhibited long-term stability in air andin3000-ppmbutanefor500h.Althoughthesensitivityinitially increased, and then stabilized for the remainder of test period, the gases could be identified without making further adjustments to variations in the sensor response. IV. RECOGNITION OF COMBUSTIBLE GASES USING MICRO SENSOR ARRAY The feasibility of recognizing and quantifying a particular kind of combusible gas was examined using PCA and an error backpropagation neural network algorithm. First, the possibility of classifying various kinds and quantities of simple combustible gases could be achieved using a PCA that maps

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Fig. 9.

PCA of responses of a sensor array exposed to combination of four different kinds and four different concentrations of combustible gas.

Fig. 10. Multilayer neural network for the gas recognition system.

multidimensional data onto two- or three-dimensional axes with a minimal loss of information. The amounts of the test gases used were below the respective TLVs, whereas the target output values were from 1 to 16, as shown in Table I. The data shown in Fig. 6 were remeasured several times at critical concentrations of 250, 500, 1000, and 3000 for three combustible gases and at 50, 250, 1000, and 3000 for carbon monoxide considering its TLV of 50 ppm, and then they were transposed onto two principal components to obtain PCA results, as shown in Fig. 9. The variances for the first and second principal vectors were 89.6% and 7.8%, respectively. Furthermore, the relative importance of the sensors in the array was reviewed using coefficients of the principal vectors with the two largest eigenvalues,

TABLE II NUMBER OF REAL MEASUREMENT TIMES FOR TRAININGS AND FOR EACH CLASS TEST

which shows the loadings of the first principal components. We could find out that all sensors had some loading properly. The resulting PCA plot of the two principal components displayed 16 distinct groups of combustible gases, demonstrating the ability to cluster according to kind and quantity. However, it was difficult to clearly classify certain groups (e.g., 1000-ppm


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Fig. 11. Recognition results of the implemented gas recognition system for combustible gases: (a) 250-ppm propane, (b) 500-ppm LPG, (c) 1000-ppm butane, and (d) 3000-ppm carbon monoxide.

LPG and 3000-ppm propane), as they were located close to each other. Therefore, to precisely classify the clusters with similar characteristics and overcome statistical dispersion, a gas pattern recognizer based on a multilayer neural network was employed along with an error backpropagation learning algorithm. Using the sensitivity signals from the sensor array as multidimensional input patterns, a gas pattern recognizer based on a multilayer neural network with an error backpropagation learning algorithm was then implemented [17]. The neural network consisted of an input layer with four nodes, which received the data from the sensors in an array, a hidden layer with eight neurons, and an output layer with 16 nodes, as shown in Fig. 10. The initial weights for the neurons are randomly chosen with small values, and a sigmoid function was used as a transfer function of the hidden layer and the output layer. In the current study, the experimental data were totally measured 192 times for 250 (50)-, 500(250)-, 1000-, and 3000-ppm concentrations of the test gases, i.e., below their respective LELs and TLVs. The 192 samples from real experiments are used for training data of the neural network, which are divided into two groups: one for the training set, and the other for the testing set, for cross-validation to verify the generalization of the recognizer (Table II). Moreover, we have used a different number of hidden nodes from three to ten for complexity control of the neural network in each trial. As a result, one hidden layer was used with eight neurons, and the 16 nodes in the output layer represented the recognition results for four concentration levels of four kinds of combustible gas: propane, LPG, butane, and carbon monoxide. The learning process was repeated until the learning error finally reached 0.0001, after iterating the process as many as 600 000 times, as shown by the inset in Fig. 10.

Fig. 10 shows an example of the recognition results in the case of an injection of 500-ppm butane gas. The same gas recognition system was also implemented using a PC. After learning was finished successfully, the proposed gas recognition system produced an almost 98% recognition rate for training data. To verify the performance of the recognizer, the experiments were repeated about 50 times for each gas and each concentration level, and the measured sensing data were used as the input to the recognizer. As a result, the proposed recognizer exhibited a high recognition rate of about 95% for discriminating among four different gases with four concentration levels. For example, the specific recognition results for the proposed gas recognition system with 250-ppm propane, 500-ppm LPG, 1000-ppm butane, and 3000-ppm carbon monoxide are shown in Fig. 11. Consequently, the proposed combustible gas recognizer using a simple neural network demonstrated that it could effectively discriminate among the four test combustible gases and identify the four concentration levels of each combustible gas within a range of 50–3000 ppm.

V. CONCLUSION The current study presented a combustible gas recognition system, including a micro sensor array and neural network pattern recognizer. To recognize specific kinds and quantities of combustible gas below their low explosion limits, a micro gas sensor array (1.9 2.1 mm ) was fabricated that consisted of four highly sensitive porous tin oxide thin films with added noble metal catalysts on a micro-hotplate. The resulting microhotplate exhibited a uniform thermal distribution along with a low-power consumption and fast thermal response.

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The thermally oxidized SnO thin film revealed a highly porous surface morphology and nonstoichiometric composition of Sn and O (1:1.4). In addition, the films showed a good sensitivity to test combustible gases at their TLV levels. The effect of additives on the gas sensitivity was examined, and the addition of Pt was found to enhance the sensitivity, whereas the addition of Au made it worse. Thus, by controlling the additives in the SnO thin film, various kinds of sensing patterns were obtained according to the kind and amount of the test gas within a range of 50–3000 ppm at an operating temperature of 400 C. Furthermore, the sensors in the array exhibited a fast response to the test combustible gases and long-term stability. Thereafter, a multilayer neural network was employed to recognize the combustible gas species. The simulation and experimental results showed that the micro sensor array, plus multilayer neural network employing a backpropagation learning algorithm, was very good at identifying the specific kind and particular concentration level of combustible gas within a range of 50–3000 ppm, i.e., below TLVs. As such, the proposed gas recognition system demonstrated an almost 98% recognition rate for the training data and an almost 95% recognition rate for the real test data. With further enhancements to the reliability and sensitivity of the sensors in array, there is a good chance that the detecting gases will move to the lower level rapidly and precisely. Furthermore, by enhancing the data analysis to have generalization ability, the possiblity for practical applications, like the indoor atmosphere-related or healthcare-related point-of-care services, would be increased. REFERENCES [1] A. Hierlemann, U. Weimar, and H. Baltes, “Hand-held and palm-top chemical microsensor systems for gas analysis,” in Handbook of Machine Olfaction, T. C. Pearce, S. S. Schiffman, H. T. Nagle, and J. W. Gardner, Eds. Weinheim, Germany: Wiley-VCH, 2003. [2] J. W. Gardner and P. N. Bartlett, Electronic Noses: Principles and Applications. New York: Oxford Univ. Press, 1999, pp. 210–218. [3] C. Hagleitner, A. Hierlemann, D. Lange, A. Kummer, N. Kerness, O. Brand, and H. Baltes, “Smart single-chip gas sensor microsystem,” Nature, vol. 414, pp. 293–296, 2001. [4] F. Solzbacher, C. Imawan, H. Steffes, E. Obermeier, and M. Eickhoff, “A highly stable SiC based microhotplate NO gas-sensor,” Sens. Actuators B, vol. 78, pp. 216–220, 2001. [5] J. W. Lim, D. W. Kang, D. S. Lee, J. S. Huh, and D. D. Lee, “Heating power-controlled micro-gas sensor array,” Sens. Actuators B, vol. 77, pp. 139–144, 2001. [6] F. Calame, J. Baborowski, N. Ledermann, and P. Muralt, “Local growth of sol-gel films by means of microhotplates,” in Proc. Transducers, 2003, pp. 750–753. [7] P. Ruther, M. Ehmann, T. Lindemann, and O. Paul, “Dependence of the temperature distribution in micro hotplates on heater geometry and heating mode,” in Proc. Transducers, 2003, pp. 73–76. [8] M. Graf, D. Barrettino, P. Kaser, J. Cerda, A. Hierlemann, and H. Baltes, “Smart single-chip CMOS microhotplate array for metal-oxide-based gas sensors,” in Proc. Transducers, 2003, pp. 123–126. [9] S. Semancik, R. E. Cavicchi, M. C. Wheeler, J. E. Tiffany, G. E. Poirier, R. M. Walton, J. S. Suehle, B. Panchapaesan, and D. L. DeVoe, “Microhotplate platforms for chemical sensor research,” Sens. Actuaors B, vol. 77, pp. 579–591, 2001. [10] M. Y. Afridi, J. S. Suehle, M. E. Zaghloul, D. W. Berning, A. R. Hefner, R. E. Cavicchi, S. Semancik, C. B. Montgomery, and C. J. Taylor, “A monolithic CMOS microhotplate-based gas sensor system,” IEEE Sensors J., vol. 2, no. 6, pp. 644–655, Dec. 2002. [11] D. Lee, C. Shim, J. Lim, J. Huh, and D. Lee, “A novel micro sensor array with porous tin oxide thin films and micro-hotplate dangled by wires in air,” in Proc. Transducers, 2001, pp. 1704–1707.

[12] D. D. Lee and D. S. Lee, “Environmental gas sensors,” IEEE Sensors J., vol. 1, no. 3, pp. 214–224, Oct. 2001. [13] N. Yamazoe and N. Miura, “Some basic aspects of semiconductor gas sensors,” in Chemical Sensor Technology. Tokyo, Japan: Kodansha, Ltd., 1992. [14] M. Prutton, Introduction to Surface Physics. Oxford, U.K.: Oxford Science, 1994. [15] M. J. Madou and S. R. Morrison, Chemical Sensing With Solid State Device. New York: Academic, 1989. [16] G. Sberveglieri, G. Faglia, S. Groppelli, and P. Nelli, “RGTO; a new technique for preparing SnO sputtering thin film as gas sensors,” in Proc. Transducers, 1991, pp. 165–168. [17] S. Haykin, Neural Network: A Comprehensive Foundation. New York: Macmillan, 1994.

Dae-Sik Lee received the B.Eng., M.Eng., and Ph.D. degrees from Kyungpook National University, Taegu, Korea, in 1995, 1997, and 2000, respectively. Since 2000, he has been a Senior Researcher with the Bio-MEMS team, Basic Research Laboratory, Electronics and Telecommunication Research Institute (ETRI), Daegeon, Korea. His current research interests are electronic noses, Bio-MEMS, and microfluidics.

Sang-Woo Ban received the B.S. and M.S. degrees from the Department of Computer Science, Kyungpook National University, Taegu, Korea, in 1992 and 1995, respectively. He is currently pursuing the Ph.D. degree from the School of Electronic and Electrical Engineering, Kyungpook National University. He was a full-time Researcher with the Sensor Techonology Research Center, Kyungpook National University, from 1995 to 2001. His research interests include intelligent sensor system, neural networks, independent component analysis, pattern recognition techniques, and biologically motivated active vision system.

Minho Lee received the B.S. degree in electronics from Kyungpook National Univeristy, Taegu, Korea, and the M.S. and Ph.D. degrees in electrical engineering from the Korea Advanced Institute of Science and Technology, Daejeon, in 1988, 1992, and 1995, respectively. He is currently an Associate Professor with the School of Electronic and Electrical Engineering, Kyungpook National University. His main research interests include sensor systems using intelligent information technology, neural networks, active noise control, selective attention, independent component analysis, and active vision systems based on human eye movement.

Duk-Dong Lee was born in Taegu, Korea, in 1942. He received the B.S. degree in physics and the M.E. degree in electronics from Kyungpook National University, Taegu, Korea, and the Ph.D. degree from Yonsei University, Seoul, Korea, in 1966, 1974, and 1984, respectively. He is currently a Professor with the School of Electronic and Electrical Engineering, Kyungpook National University. He was the Dean of the College of Engineering, Kyungpook National University. He has performed research on semiconductor gas sensors since 1978 and has worked on microsensors, detecting systems, and the electronic nose system. Dr. Lee is the former president of the Korean Sensors Society.