Electronic nose system with micro gas sensor array

B ELSEVIER

Sensors and Actuators B 35-36 (1996) 338-341

Electronic nose system with micro gas sensor array H y u n g - K i H o n g ", H y u n W o o Shin,, D o n g H y u n Y u n '~, S e u n g - R y e o l K i m a, C h u l H a n K w o w , K y u c h u n g Lee a.*, T o y o s a k a M o r i i z u m i b aLG Etectronics Research Center, 16 Woomyeon-dong. Se~n:ho.gu, Seou1137-140, South Korea bDepartment of Electrical and Electronic Engineering, Tokyo Institute of Technology. Tokyo 152. Japan

Abstract We have fabricated an electronic nose system using a thin film oxide semiconductor micro gas sensor array which shows only 65 mW of power consumption at an operating temperature of 300°C. Principal component analysis and neural network pattern recog. nition analysis were used to identify 12 gas samples (CH3SH, (CH3)3N, C2HsOH and CO gases in the concentration range of 0.1100 ppm) or six flavor samples (carrot, green onion, woman's perfume (eau de cologne), man's perfume (eau de toilette), 25% liquor (Korean soju) and 40% liquor (whisky)). Good separation among the gases with different concentrations or flavor samples was obtained using the principal component analysis. The recognition probability of the neural network was 100% for each of the 5 trials of 12 gas samples and 93% for each of 10 trials of 6 flavor samples.

£eywords: Gas sensor array; Pattern recognition; Back-rmpagation; Electronic nose system 1, Introduction It is well known that the use of gas sensor array and pattern recognition analysis gives advantages for the identification of odors or gases because of poor selectivity of many gas sensors [ I-3]. Recently, considerable interest has arisen in silicon-based micro gas sensors fabricated by thin-film deposition and micromachining techniques [4-6] because they meet the main requirements for gas sensors such as high sensitivity, good selectivity, short response time, long-term stability and low power consumption, and have such additional advantages as accurate temperature controllability, small size, low cost due to automatic and batch production, easy realization of sensor array and possibility of on-chip integration with microelectronivs. The objective of this work is to discriminate among 12 gas samples (CH3SH, (CH3)3N, C2HsOH and CO gases in the concentration range of 0.1100 ppm) or six flavor samples (carrot, green onion, woman's perfume, man's perfume, 25% liquor and 40% liquor) by an electronic nose system using a silicon-based micro gas sensor array which shows only 65 mW of * Conesponding author.

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power consumption at 300°C along with principal component analysis and neural network pattern recognition analysis.

2. Experimental As seen in Fig. ia, the micro gas sensor array basically consists of stacked layers: the gas-sensing layers, electrode + temperature sensor, insulating layer and heater formed on the membrane. Ten masks were used for the fabrtcauon of the sensor array. The processing steps are: (i) deposition of membrane layer on a silicon substrate; (ii) formation of heater; (iii) deposition of an insulating layer; (iv) opening contact holes; (v) formation of heater pads: (vi) formation of electrodes and temperature sensor; (vii) formation of sensing layers (4 different materials); (viii) backside etching; (ix) packaging: slicing/mounting/bonding. The detailed fabrication processes have been described elsewhere [6]. As the gas-sensitive materials, we selected 1 wt.% Pd-doped SnO 2, 6 wt.% Al203-doped ZnO, WO 3 and ZnO. Each sensing material was deposited by r.f.

H.-K. Hong el al. I Sensors and Actuators B35-36 (1996) 338-341

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magnetron sputtering and annealed at 550°C for 120 min in air ambient. Table 1 shows the deposition conditions of four different sensing materials. A lift-off process was used to pattern the sensing layers after the deposition of each sensing material. After deposition of the sensing materials, the windows for the backside etching were made by reactive ton etching. The water was then backside-etched anisotropically in KOH, thereby leaving behind the thin layers of sensing element and membrane. The sensing layer was protected during the etching process. The chips were mounted on 16 lead side braze packages as shown in Fig lb. The electronic nose system for the measurement and identification of gases is shown in Fig. 2. It consists of Teflon line tubes, two solenoid valves, a sensor cell, a pressure buffer for suppressing the pt'essure fluctuation due to the change of flow rate, a suction pump, interface cards containing CPU, AID and D/A converters, and a personal computer. The personal computer was used to read out sensor outputs through the AID converter, to control solenoid valves and heater power through the D/A converter, and to perform pattern recognition by means of principal component analysis and neural network analy-

sis. The sensor array was installed in the sensor cell. The target gas and dry air were alternately switched on by solenoid valves. The inner volume of the sensor cell was 30 cm 3 and the flow rate of gas and air was 500 cm3 rain -j each. Sensitivity was defined as RglRa, where Ra and Rg are the resistances of the sensor in air and upon exposure to the gas to be measured, respectively. The figure for RglRa accordingly decreases below unity as the gas concentration increases. The operating temperature of the sensor was about 300°C and the corresponding heater power was about 65 mW. 3. Results and discussion Figs. 3-6 show the sensitivity (S30m= (Rg.x0/Ra)m) versus four different gases with different gas concentrations at the operating temperature of 300°C, where S3omis the mean value of the sensitivities $30 of 5 trials for each target gas after 30 s from the onset of target gases. Upon exposure to target gases, the sensors exhibited resistance changes usable for subsequent data processing and the sensitivity strongly depended on the sensing layer materials. WO3 showed better sensitivity than any other materials for all four gases and ZnO revealed good sensitivity for CH3SH gas. The 6 wt.% AI203-doped ZnO displayed better sensitivity for CH3SH and (CH3)3N than for C2HsOH and CO gases. The sensitivities ($30 = Rg3olRa) of 5 trials for each target gas were used as input parameters for principal component analysis. Fig. 7 exhibits the result of principal component analysis for the 12 gas samples. Good separation is observed

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tt.-K, Hong et al, / Sensors and Actuators B35-36 (1996) 338-341

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Fig. 4. SensitivityversusCO concentration. among the g~ses with different concentrations although the pattern separation; between CO I ppm and (CEI3)3N 0. I ppm is not sharp due to the poor sensitivity in the low concentration range. Fig. 8 shows the result of principal component analysis for the six flavor samples. In this case Ss (Ss: RgsIRa)were used as input parameters, where Ss are the sensitivities of 10 trials for each flavor sample after 5 s from the onset of target flavor sample. The data pattern of 25% liquor is slightly overlapped with that of both 40% liquor and woman's perfume. But the results suggest that it is promising to discriminate the 12 gas or 6 flavor samples using the neural network analysis. The neural network, having a three layer structure made up of 4 input, 8 hidden and 12 output units, was used to discriminate the 12 gas samples, and the neural network consisting of 4 input, 6 hidden and 6 output units was used to identify the 6 flavor samples. The back-propagation algorithm was applied as a supervised learning rule. The sensitivity values S3o of each sensor were used as input layer of the network for the 12 gas samples and Ss were used as input layer for the 6 flavor samples. The network was ~rained using data so that the desired outputs could be obtained. The connections between hidden and both input and output layers were optimized after 10000 times training for our 12 gas samples, while the connections were optimized after 20 000 times training for our 6 flavor samples, The recognition probability of the neural network analysis, defined as the ratio of number of right answers to that of total trials was 100% for each of 5 trials of the 12 gas samples and 93% for each of 10 trials of the 6 flavor samples, respectively. Achieving the excellent real-time separation among the samples would require

further improvement in sensitivity and selectivity, espec'ally in the low concentration ranges for the gas samples and between liquor and perfume for the flavor samples. 4. Conclusions

The identification of CH3SH, (CH3)3N, C2HsOH and CO gases with different concentrations or six flavor samples (carrot, green onion, woman's perfume, man's perfume, 25% liquor and 40% liquor) has been successfully demonstrated by an electronic nose system using a thin film oxide semiconductor micro gas sensor array and neural network pattern recognition technique. The sensing materials of 1 wt.% Pal-doped SnO2, 6 I,~z Ir/.~%

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H.-K. Hanget al. /Sensors and Actuators 1335-36(1996)338-341

wt.% Al203-doped ZnO, WO 3 and ZnO were used for the gas sensor array whose power consumption was only 65 mW at 300°C, and the back-propagation algorithm was applied as the supervised learning rule. The recognition probability of the neural network was 100% for the 12 gas samples and 93% for the 6 flavor samples used in the work. References [I] K. Persaud and G. Dodd, Analysis of discrimination mechanisms in the mammali,'molfactory system using a model nose, Nature, 299 (1982) 352-355. [2] W.P. Carey, K.R. Beebe and B.R. Kowalski, Selection of adsorbates for chemical sensor arrays by pattern recognition, Anal. Chum., 58 (1986) 149-153. [3] 14. Sundgren, !. Lundstmmand F. Winquist, Evaluationof a multiple gas mixture with a simple MOSFET gas sensor array and pattern recognition, Sensar.vand Actualars B, 2 (1990) 115-123. [4] P. Krebs and A. Grisel, A low power iategrated catalytic gas sensor, Sensors and Aauatars 13, 13-14 (1993) 155-158. [5] X. Wang, S. Yee and P. Carey, An integrated array of multiple thin-film metal oxide sensors for quantification of individual components in organic vapor mixtures, Sensors and Actuators B, 13-14 (1993) 458--46!. [6] H.S. Park, H.W. Shin, D.H. Yun, H.-K. l-long,C.H. Kwon, K. Lee and S.-T. Kim, Tin oxide microelectroniegas sensor for detecting CH3SI-I, Tech. Digest, Fifth Int. Mtg Chemical Sensors, Rome, 1994, pp. 612--615.

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Biographies Hyung-Ki Hang received his M.S. in electrical engineering from Korea University in 1991. He joined LG Electronic Research Center( LG-ERC ) in 1991. Hyun Woo Shin received his M.S. in metallurgical engineering from Seoul National University in 1989. He joined LG-ERC in 1989. Dang Hyun Yun received his M.S. in electronic materials engineering from Korea Advanced Institute of Science and Technology in 1993. He joined LG-ERC in 1993. Seung-Ryeol Kim received his Ph.D. in chemical engineering from Korea Advanced Institute of Science and Technology in 1996. He join in LG-ERC in 1996. Chul Hart Kwon received his M.S. in ceramic engineering from Yonsei Uaiversity in 1989. He joined LGERC in 1989. Kyuchung Lee is the project leader of the gas sensor group. He received his M.S. in electrical engineering from Georgia Institute of Technology in 1990. He joined LG-ERC in 1990. Toyosaka Moriizumi received his Ph.D. in electronic engineering from Tokyo Institute of Technology in 1969. He is a Professor at the Department of Electrical and Electronic Engineering, Tokyo Institute of Technology.