摘要
近几年来，我国传感技术正在蓬勃发展，应用领域已渗入到国民经济的各个部门以及人们的日常生活之中，因此对传感器新理论的探讨、新技术的应用、新材料和新工艺的研究已成为传感器总的发展方向。声表面波(Surface Acoustic Waves，SAW)气体传感器具有精度高、分辨率高、体积小，易于集成化等特点，成为国际上器件研究的新热点之一。本论文的主要内容是关于声表面波甲醛气体传感器的设计及其气湿敏特性和选择性的研究，最后采用BP神经网络(Artificial Neural Networks)识别混合气体。
首先基于声表面波谐振器(SAWR)，采用射频放大器和必要的LC移相网络，设计出输出频率稳定的振荡器。将未涂敷敏感膜的声表面波振荡器的输出作为混频器的本机振荡频率，和涂敷敏感膜的声表面波振荡器的输出进行混频，把频率降到可以使用单片机测量的范围。通过整形电路，使混频后的正弦信号变为方波信号。设计了基于单片机的数据采集系统对传感器输出频率进行采集和显示，并送入PC机中进行存储和作图等操作。
研究敏感膜的涂敷方法，最后选用旋涂法对敏感膜进行涂敷。研究敏感材料薄膜的厚度对灵敏度及响应时间的影响，综合考虑后选择180nm厚的敏感膜。选用乙基纤维素(ethyl cellulose)、聚异丁烯(Polyisobutene)和聚环氧氯丙烷(Polyepichlorohydrin)三种敏感材料，涂敷于声表面波传感器上对甲醛及干扰气体(乙醇、丙酮、甲苯)进行测量。使用涂敷NaCl和BaTiO_3敏感膜及未涂敷敏感膜的声表面波传感器对湿度进行测量。
针对传感器对各种气体的交叉敏感特性，采用BP神经网络的方法对混合气体的成分进行分析。网络为3层网络，输入层8个节点，隐形层10个节点，输出层4个节点，输入层数据来自8个声表面波气体传感器组成的传感器阵列。BP神经网络用传感器阵列输出的数据进行学习，直至网络的样本输出误差小于期望值。使用训练过的BP网络对一些保留样本进行预测。结果表明，使用这种方法可以成功地用现有选择性较差的气敏传感器元件，分析出甲醛、乙醇、丙酮、甲苯混合气体中各气体的成分和浓度。
In recent years, sensing technology is advancing rapidly. Sensor applications have been extended to people's daily life and various departments. Therefore, research in new theories, new materials and new technology of sensors has received more and more attention from scientists. SAW (Surface Acoustic Waves) gas sensor has many excellent properties such as high precision, high resolution, small size and easy integration. It has become one of the new focus of sensor technology. The main contents of this thesis are the design of SAW formaldehyde gas sensor, the property of gas and humidity responses, the selective property and the recognition of the gas mixture using a array of 8 sensors and a BP neural network.
In this work, a stable SAW oscillator based on SAWR (surface acoustic wave resonator) was designed. It contains an RF amplifier and a necessary LC phase shifter network. Then it was coated with sensitive film. A mixer was used to low down the frequency, so that the MCU (Micro Control Unit) can measure it. The SAW oscillator with sensitive film was the input of the mixer, and another SAW oscillator without sensitive film was taken as the reference input. The sine signal from the mixer was turned to the square signal by a reshape circuit. A data acquisition system based on MCU was designed to measure and display the frequency. It sends the data to a PC to draw the response curves and store the data.
Spin-coating method was used in coating films. The sensor achieved its optimal performance when the thickness of film was about 180nm. Three sensitive materials (ethyl cellulose, polyisobutylene, polyepichlorohydrin) were chosen to coating on the surface acoustic wave sensors, respectively. The concentration of formaldehyde and the interference gases (ethanol, acetone and toluene) were measured by the SAW gas sensors. Sensors composed of NaCl and BaTiO_3 were used to measure the humidity, respectively.
The cross-sensitivity characteristic of the sensor is a big trouble for formaldehyde gas detection. A three-level BP neural network was use to identify the gases and measure the concentrations. There were 8 nods in input layer, 10 nods in hide layer and 4 nods in output layer. A sensor array with 8 sensors was used to collect the data which were the input of the network. Then the network learned by itself based on the data circularly until the error of output was below that you set before. Then the network was ready to distinguish quantificational formaldehyde, ethanol, acetone, and toluene from mixed air.
引文
[1] 王君，凌振宝．传感器原理及检测技术．长春：吉林大学出版社，2003．
[2] WATSON G, STAPLES E. SAW resonators as vapor sensors. Proceedings IEEE, 1990,1:311-314.
[3] M. Penza, F. Antolini,M. V. Antisari. Carbon nanorubes as SAW chemical sensors materials. Sensors and Actuators, 2004,100:47-59.
[4] L. Rayleigh. On waves propagating along the plane surface of an elastic solid. Proc. London Math. Soc. 1885,7:4-11.
[5] R. M. White and F. M. Voltmer. Direct piezoelectric coupling to surface elastic waves. Appl. Phys. Lett, 1965,7:314-316.
[6] H. Wohltjen. Mechanism of operation and design considerations for surface acoustic wave device vapor sensors. Sensors and Actuators, 1984,5:307-325.
[7] 孙海山，余愚，蒋永华．浅谈SAW气敏传感器的发展．中国仪器仪，2006，6：38-42．
[8] D. Enguang, F. Guanping, H. Zhenhua, et al. Organic vaporsensors based on SAW resonator and organic films. IEEE Trans. Ultr. Ferr. and Freq. Con, 1997,44(2):309-314.
[9] 黄颖援，邹志勇，邓皓．一例住宅甲醛超标引起外周血贫血的分析报告．江苏环境科技，2007，20(6)：16-17．
[10] 崔九思，王钦源，王汉平．大气污染检测方法．北京：化学工业出版社，1997．
[11] 田林芹，张爱梅．动力学光度法测定痕量甲醛．分析测试学报，2003，22(5)：107-109．
[12] 安从俊，丁哨兵．室内空气环境中微(痕)量甲醛的主要分析方法．武汉大学学报(理学版)，2001，47(4)：433-437．
[13] Julian W. Garder, Vijay K. Varadan, Osama O. Awadelkarim, Microsensors MEMS and Smart Devices. Beijing:Tsinghua University Press, 2004.
[14] 肖鸣山．声表面波器件基础．济南：山东科学技术出版社，1983．
[15] 武以立．声表面波原理及其在电子技术中的应用，北京：国防工业出版社，1983．
[16] 陈明，范东远，李岁劳．声表面波传感器，西安：西北工业大学出版社，1997．
[17] H. Wohltjen, A. Snow,D. Ballantine. The Selective Detection of Vapors Using Surface Acoustic Wave Devices. U.S:Naval Research Laboratory, 1995.
[18] M.S. Nieuwenhuizen, A. W. Barendsz, E. Nieuwkoop, et al. Transduction Mechanisms in SAW Gas Sensors. Electron. Lett, 1986,22,188-185.
[19] 王利敏．声表面波气体传感器技术．舰船科学技术，2006，28(2)，92—95．
[20] P. A. Banda. Modelling aspects of surface acoustic wave gas sensors. Sensors and Actuators A, 1994:638-642.
[21] S. J. Martin and A. J. Ricco. Characterization of SH acoustic plate mode liquid sensors. Sensors and Actuators, 1989,253:268.
[22] 邹晓红，郭成海，史瑞雪．声表面波气体传感器的设计．化学传感器，2000，20(3)：6-11．
[23] Ron F. Schmitt, John W. Allen, Randy Wright. Rapid design of SAW oscillator electronics for sensor applications. Sensors and Actuators B, 2001,76:80-85.
[24] A.B. Williams, F.J. Taylor. Electronic Filter Design Handbook. New York:McGraw Hill,1995.
[25] Reinhold Ludwig, Pavel Bretchko．射频电路设计．北京：电子工业出版社，2002．
[26] 朱欣华．RS-232C、RS-423和RS-422标准的特点及其在微机化仪表中的应用．电测与仪表，1992，12：21-24．
[27] 刘义祥．气体传感器在气体泄漏事故处置中的应用．消防技术与产品信息，2002，2：36-38．
[28] 姜晓明．聚异丁烯生产技术及其进展．吉林石油化工，1992，11(2)：32～37．
[29] 郭希山，童基均，陈裕泉等．用于室内有毒气体快速检测的便携式CC／SAW电子鼻．传感技术学报，2006，19(1)：68-73．
[30] 黄雁，何希文，揣成智．乙基纤维素水分散体在缓控释薄膜包衣领域中的应用．塑料，2007，36(3)：37-40．
[31] 左伯莉，李伟，陈传治等．压电晶体微天平阵列传感器识别毒剂的研究．分析化学研究简报，2007，35(8)：1171-1174．
[32] 李伟，左伯莉，张天等．QCM传感器法定量检测空气中芥子气．环境污染与防治，2006，28(10)：796-798
[33] 栾桂东，张金铎，金欢阳．传感器与应用．陕西：西安电子科技大学出版社，2002．
[34] 骆如枋．涂膜SAW的气、湿敏传感器响应特性研究．郑州轻工业学院学报，2002，15(4)：90-94．
[35] 刘静波，王智民，郑春萍．镧掺杂钛酸钡纳米晶陶瓷元件的湿敏特性．功能材料与器件学报，2002，6(2)：106-110．
[36] Jing Wang, Gang Song. Mechanism analysis of BaTiO_3 and polymer QAR composite humidity sensor. Thin Solid Films, 2007,515:8776-8779.
[37] 李啸，闫卫平，郝应光．气体传感器阵列信号处理的混合神经网络．传感器技术，2000，19(4)：20-25
[38] 童敏明，张愉，齐美星．基于BP神经网络的可燃混合气体分析方法的研究．计量学报，2006，27(2)：169：171．
[39] 董长虹．Matlab神经网络与应用．北京：国防工业出版社，2005．
[40] 杨建刚．人工神经网络实用教程．浙江：浙江大学出版社，2001．
[41] 石春燕，王剑钢．人工神经网络在混合气体定量检测中的应用．仪器仪表与检测技术，2004，27(7)：45～48．
[42] 李来强，王树林，赵兵涛．BP神经网络的LM算法及其对颗粒碰撞振动阻尼的预测．上海理工大学学报，2006，28(4)：231-233．
[43] 徐冠，夏克文，徐乃勋．基于LM算法的神经网络在冠心病诊断中的应用．微电子学与计算机，2006，23(2)：189-193．
[44] 赵弘，周瑞祥，林廷圻．基于Levenberg-Marquardt算法的神经网络监督控制．西安交通大学学报，2002，36(5)：523～527．