- Email: [email protected]
A novel instrument based upon extremely high Q-value surface acoustic wave resonator array and neural network
Sensors and Actuators B 66 Ž2000. 109–111 www.elsevier.nlrlocatersensorb
A novel instrument based upon extremely high Q-value surface acoustic wave resonator array and neural network Enguang Dai a,) , Guanping Feng b b
a Department of Electronics, Peking UniÕersity, 100871 Beijing, People’s Republic of China Department of Precision Instruments, Tsinghua UniÕersity, 100084 Beijing, People’s Republic of China
Received 30 July 1998; received in revised form 15 February 1999; accepted 14 January 2000
Abstract An instrument comprising an array of surface acoustic wave ŽSAW. resonator and a neural network system capable of recognizing odours was developed. The system can distinguish gases with five SAW resonators. To make the sensor noise low, high Q-value SAW resonators were fabricated. To our knowledge, seldom has paper reported that a SAW resonator with metal reflector in this frequency band has such a high Q-factor. q 2000 Elsevier Science S.A. All rights reserved. Keywords: Surface acoustic wave; Resonator; Sensor array; Neural network
1. Introduction Flavor and odour are currently measured using the combination of gas chromatography and mass spectrometry. However, this can be a complex and slow process for the discrimination of a specific kind of gases. Multisensor array is able to discriminate between complex mixtures. A compact and fast analytical instrumentation based upon multisensor may be more suitable for the classification of a specific kind of gas. Surface acoustic wave ŽSAW. gas sensors have the advantages of highly sensitivity, low cost and frequency signal output. Compared with SAW delay line, SAW micro-resonator w1x has higher Q-value, lower insertion loss, and consequently, the oscillator based on SAW resonator has higher short-time frequency stability. Lipid and polymers show a poor selectivity towards a specific kind of organic components. But, sensor arrays w2,3x in conjunction with pattern recognition techniques can be successfully applied in identification and analytical function. Principal component analysis, partial least squares and artificial neural networks ŽANN. are commonly used as pattern recognition. Recently, ANN is considered as a promising one because it can handle highly non-liner
)
Corresponding author. E-mail address: [email protected] ŽE. Dai.
problem and can tolerate considerable changes due to drift and aging. The instrument developed is a flavor and odour analyzer. It can be considered as an electric nose expected to help human olfactory and gustation system for monitoring tasks Že.g. detection of illicit materials., for environmental analysis, and for the determination of the quality of food. 2. The instrument 2.1. Two-port SAW resonator The vast majority of SAW sensors are fabricated in the delay line configuration, but SAW resonators have superior performance of high Q-value and low insertion loss. The sensor can detect gases at as low as sub-part-per-million concentration. Q-value is the most important factor for SAW sensors based upon SAW resonator w1x. To design extremely high Q-value SAW resonator with metal reflector, coupling-ofmodes method should be used to locate the two transducers in the appropriate position accurately. The SAW resonator Žmetal reflector. with loaded Q-value up to 15 000 Žthe unloaded Q-value up to 18 000., center frequency at 150 MHz was fabricated. The frequency response is shown in Fig. 1 and it is easy to seen that the resonator has high Q-value. A detailed description will be given in another paper.
0925-4005r00r$ - see front matter q 2000 Elsevier Science S.A. All rights reserved. PII: S 0 9 2 5 - 4 0 0 5 Ž 0 0 . 0 0 3 0 9 - 9
110
E. Dai, G. Feng r Sensors and Actuators B 66 (2000) 109–111
Fig. 4. The convergence of variable learning rate ŽVLR. and constant learning rate ŽCLR.. Fig. 1. Experimental SAWR frequency response Žno match. by HP8510C network analyzer.
Fig. 2. The radial geometry equipped with five SAW resonator.
The miniature oscillator was represented by a two-port SAW resonator connected into the feedback path of a radio-frequency MMIC amplifier.
2.2. The layout of the system The instrument shown in Fig. 2 is an array of five SAW resonators mounting in separate chambers to shield RF cross-talk between oscillators sufficiently. One device was used as a common reference element Žbare device.. The frequency output of one sensor was mixed with the reference device. Mixing circuit was also built in their separate room. Individual gas supplied by pressurized carrier gas ŽN2 . was controlled by programmable mass flow controllers by which an automated gas handler system was made. In the cover of the instrument, analyte gases were distributed and routed into five channels that contain each SAW resonator. The layout is shown in Fig. 1. Frequency output was sampled and recorded by a selfmade frequency signal acquisition modular inserted into a microcomputer. For subsequent processing, the acquisition unit can acquire signals from 10 signal channel, the highest acquisition frequency was 8.5 MHz and the frequency resolution was 0.5 Hz. The whole system is shown in Fig. 3. 2.3. Neural network The multilayer neural network consists of an input layer, hidden layers and an output layer. The learning rule
Fig. 3. The diagram of the analytical instrument.
E. Dai, G. Feng r Sensors and Actuators B 66 (2000) 109–111
adopted here is back-propagation learning algorithm. It works by adjusting the neurons’ weights in order to minimize the difference between the actual output and the desired output values. The two-layer network had four inputs in the input layer corresponding to the four sensors, 11 artificial neurons in the hidden layers and six neurons in the output layer. A different learning rate was adopted, Fig. 4 is the comparison of the times of iteration between different learning rates. It is discovered that the convergence speed based upon a Ž k . s a 0rln k is faster than that based on constant learning rate a 0 .
3. Experiments Polymer and lipid are commonly used as sensitive material for SAW chemical sensors w4x. Ethanol, n-butanol, methanol and ethylene glycol were used as samples. Each of the four self-made high Q-value SAW resonators was coated with PC Žphosphatidyl-choline., PE Žphosphatidylethanolamine., SA Žstearic acid. and PA, respectively. The selected lipids were dissolved in appropriate organic solvents, then, solutions of these lipids were sprayed onto each resonator listed in Table 1. A measuring cycle consisted of sampling and purging period. When signals arrived at a stable state, the data was selected as the input of ANN. The response time was in the range of 1 min, purging period last for 30 s. To enable comparison of one sensor response to the other, the responses were normalized. When interacting with different gases, the array would produce a unique fingerprint for each one. The experiments were repeated for twenty times, each of the four kinds of gas has been sampled for about the same times, and there would be 20 samples for the ANN. The network was trained on 10 of these samples with the last 10 patterns used to test the network. The learning rate was 0.15, momentum coefficient was equivalent to 0.75. The acceptable network error was 0.0005. The four response patterns of micro-sensor is shown in Fig. 5. It can be seen that each sensor is partially sensitive Table 1 The sensitive material Resonator Reference SAWR SAWR 1 SAWR 2 SAWR 3 SAWR 4 Film
No film
PC
SA
PA
PE
111
Fig. 5. The bar diagram of the response patterns of the four SAW sensors.
to the four kinds of gases. The discrimination was 90% successful in distinguishing these 10 test samples by the network. Changes in the actual vapor concentrations generated by the apparatus will not decrease the ability of pattern recognition.
4. Conclusions An analytical instrument with ANN has been developed for the analysis of odour. The array consisted of five SAW micro-resonators with a fundamental frequency at 150 MHz. It is confirmed that the sensor array based upon SAW resonator with very high Q-value has the advantages of frequency signal output Ži.e. easy to be acquisited., very high sensitivity, good linearity and robustness to the interference of outer environment. The technology can be further developed into a microsystem with SAW resonators and circuits on the same chip. In the end, if the technologies as stated above combine with the passive and coded technology depicted in paper w5x, a more sophisticated and state-of-the art passive telemetry array system can be built to fulfill the sensing system without any electric powers and any wires. A detailed description will be provided in the near future.
References w1x E. Dai, G. Feng, IEEE Trans. Ultrason. Ferroelectr. Freq. Control 44 Ž1997. March. w2x J.R. Vig, Proc. IEEE Int. Freq. Control Symp. Ž1995. 852–869. w3x M. Rapp, Proc. IEEE Ultrason. Symp. Ž1994. 619–622. w4x T. Ohnishi, Sens. Mater. 4 Ž1992. 53. w5x E. Dai, G. Feng, Proceeding of IEEE MTT-S International Microwave and Optoelectronics Conference 1997, Brazil.
A novel instrument based upon extremely high Q-value surface acoustic wave resonator array and neural network Enguang Dai a,) , Guanping Feng b b
a Department of Electronics, Peking UniÕersity, 100871 Beijing, People’s Republic of China Department of Precision Instruments, Tsinghua UniÕersity, 100084 Beijing, People’s Republic of China
Received 30 July 1998; received in revised form 15 February 1999; accepted 14 January 2000
Abstract An instrument comprising an array of surface acoustic wave ŽSAW. resonator and a neural network system capable of recognizing odours was developed. The system can distinguish gases with five SAW resonators. To make the sensor noise low, high Q-value SAW resonators were fabricated. To our knowledge, seldom has paper reported that a SAW resonator with metal reflector in this frequency band has such a high Q-factor. q 2000 Elsevier Science S.A. All rights reserved. Keywords: Surface acoustic wave; Resonator; Sensor array; Neural network
1. Introduction Flavor and odour are currently measured using the combination of gas chromatography and mass spectrometry. However, this can be a complex and slow process for the discrimination of a specific kind of gases. Multisensor array is able to discriminate between complex mixtures. A compact and fast analytical instrumentation based upon multisensor may be more suitable for the classification of a specific kind of gas. Surface acoustic wave ŽSAW. gas sensors have the advantages of highly sensitivity, low cost and frequency signal output. Compared with SAW delay line, SAW micro-resonator w1x has higher Q-value, lower insertion loss, and consequently, the oscillator based on SAW resonator has higher short-time frequency stability. Lipid and polymers show a poor selectivity towards a specific kind of organic components. But, sensor arrays w2,3x in conjunction with pattern recognition techniques can be successfully applied in identification and analytical function. Principal component analysis, partial least squares and artificial neural networks ŽANN. are commonly used as pattern recognition. Recently, ANN is considered as a promising one because it can handle highly non-liner
)
Corresponding author. E-mail address: [email protected] ŽE. Dai.
problem and can tolerate considerable changes due to drift and aging. The instrument developed is a flavor and odour analyzer. It can be considered as an electric nose expected to help human olfactory and gustation system for monitoring tasks Že.g. detection of illicit materials., for environmental analysis, and for the determination of the quality of food. 2. The instrument 2.1. Two-port SAW resonator The vast majority of SAW sensors are fabricated in the delay line configuration, but SAW resonators have superior performance of high Q-value and low insertion loss. The sensor can detect gases at as low as sub-part-per-million concentration. Q-value is the most important factor for SAW sensors based upon SAW resonator w1x. To design extremely high Q-value SAW resonator with metal reflector, coupling-ofmodes method should be used to locate the two transducers in the appropriate position accurately. The SAW resonator Žmetal reflector. with loaded Q-value up to 15 000 Žthe unloaded Q-value up to 18 000., center frequency at 150 MHz was fabricated. The frequency response is shown in Fig. 1 and it is easy to seen that the resonator has high Q-value. A detailed description will be given in another paper.
0925-4005r00r$ - see front matter q 2000 Elsevier Science S.A. All rights reserved. PII: S 0 9 2 5 - 4 0 0 5 Ž 0 0 . 0 0 3 0 9 - 9
110
E. Dai, G. Feng r Sensors and Actuators B 66 (2000) 109–111
Fig. 4. The convergence of variable learning rate ŽVLR. and constant learning rate ŽCLR.. Fig. 1. Experimental SAWR frequency response Žno match. by HP8510C network analyzer.
Fig. 2. The radial geometry equipped with five SAW resonator.
The miniature oscillator was represented by a two-port SAW resonator connected into the feedback path of a radio-frequency MMIC amplifier.
2.2. The layout of the system The instrument shown in Fig. 2 is an array of five SAW resonators mounting in separate chambers to shield RF cross-talk between oscillators sufficiently. One device was used as a common reference element Žbare device.. The frequency output of one sensor was mixed with the reference device. Mixing circuit was also built in their separate room. Individual gas supplied by pressurized carrier gas ŽN2 . was controlled by programmable mass flow controllers by which an automated gas handler system was made. In the cover of the instrument, analyte gases were distributed and routed into five channels that contain each SAW resonator. The layout is shown in Fig. 1. Frequency output was sampled and recorded by a selfmade frequency signal acquisition modular inserted into a microcomputer. For subsequent processing, the acquisition unit can acquire signals from 10 signal channel, the highest acquisition frequency was 8.5 MHz and the frequency resolution was 0.5 Hz. The whole system is shown in Fig. 3. 2.3. Neural network The multilayer neural network consists of an input layer, hidden layers and an output layer. The learning rule
Fig. 3. The diagram of the analytical instrument.
E. Dai, G. Feng r Sensors and Actuators B 66 (2000) 109–111
adopted here is back-propagation learning algorithm. It works by adjusting the neurons’ weights in order to minimize the difference between the actual output and the desired output values. The two-layer network had four inputs in the input layer corresponding to the four sensors, 11 artificial neurons in the hidden layers and six neurons in the output layer. A different learning rate was adopted, Fig. 4 is the comparison of the times of iteration between different learning rates. It is discovered that the convergence speed based upon a Ž k . s a 0rln k is faster than that based on constant learning rate a 0 .
3. Experiments Polymer and lipid are commonly used as sensitive material for SAW chemical sensors w4x. Ethanol, n-butanol, methanol and ethylene glycol were used as samples. Each of the four self-made high Q-value SAW resonators was coated with PC Žphosphatidyl-choline., PE Žphosphatidylethanolamine., SA Žstearic acid. and PA, respectively. The selected lipids were dissolved in appropriate organic solvents, then, solutions of these lipids were sprayed onto each resonator listed in Table 1. A measuring cycle consisted of sampling and purging period. When signals arrived at a stable state, the data was selected as the input of ANN. The response time was in the range of 1 min, purging period last for 30 s. To enable comparison of one sensor response to the other, the responses were normalized. When interacting with different gases, the array would produce a unique fingerprint for each one. The experiments were repeated for twenty times, each of the four kinds of gas has been sampled for about the same times, and there would be 20 samples for the ANN. The network was trained on 10 of these samples with the last 10 patterns used to test the network. The learning rate was 0.15, momentum coefficient was equivalent to 0.75. The acceptable network error was 0.0005. The four response patterns of micro-sensor is shown in Fig. 5. It can be seen that each sensor is partially sensitive Table 1 The sensitive material Resonator Reference SAWR SAWR 1 SAWR 2 SAWR 3 SAWR 4 Film
No film
PC
SA
PA
PE
111
Fig. 5. The bar diagram of the response patterns of the four SAW sensors.
to the four kinds of gases. The discrimination was 90% successful in distinguishing these 10 test samples by the network. Changes in the actual vapor concentrations generated by the apparatus will not decrease the ability of pattern recognition.
4. Conclusions An analytical instrument with ANN has been developed for the analysis of odour. The array consisted of five SAW micro-resonators with a fundamental frequency at 150 MHz. It is confirmed that the sensor array based upon SAW resonator with very high Q-value has the advantages of frequency signal output Ži.e. easy to be acquisited., very high sensitivity, good linearity and robustness to the interference of outer environment. The technology can be further developed into a microsystem with SAW resonators and circuits on the same chip. In the end, if the technologies as stated above combine with the passive and coded technology depicted in paper w5x, a more sophisticated and state-of-the art passive telemetry array system can be built to fulfill the sensing system without any electric powers and any wires. A detailed description will be provided in the near future.
References w1x E. Dai, G. Feng, IEEE Trans. Ultrason. Ferroelectr. Freq. Control 44 Ž1997. March. w2x J.R. Vig, Proc. IEEE Int. Freq. Control Symp. Ž1995. 852–869. w3x M. Rapp, Proc. IEEE Ultrason. Symp. Ž1994. 619–622. w4x T. Ohnishi, Sens. Mater. 4 Ž1992. 53. w5x E. Dai, G. Feng, Proceeding of IEEE MTT-S International Microwave and Optoelectronics Conference 1997, Brazil.