Research Of SAW Formaldehyde Gas Sensor

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 BaTiO₃ 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.