Abstract

Surface acoustic wave (SAW) devices have exhibited unique capabilities to meet the demands for many applications due to the inherent properties of SAW devices and piezoelectric materials. In particular, SAW devices have been adapted as sensors that can be configured to operate both passively and wirelessly. SAW sensors can be operated in harsh environmental extremes where typical sensor technologies are not able to operate. Because the sensors are passive, a radio transceiver is required to interrogate the sensor and receive the reflected response that has been modulated by the SAW device. This dissertation presents the design of a passive, wireless SAW OFC strain sensor and software defined radio (SDR) interrogator. A SAW strain sensor has been designed and tested using orthogonal frequency coding (OFC) on YZ-LiNbO3. OFC for SAW devices has been previously developed at UCF and provides both frequency and time diversity in the RFID code as well as providing processing gain to improve the sensor SNR. Strain effects in SAW devices are discussed and two sensor embodiments are developed. The first embodiment is a cantilever structure and provides insight on how strain effects the SAW device. The second embodiment bonds the SAW die directly to a test structure to measure the strain on the structure. A commercial wired foil strain gage provides a performance comparison and shows that the wireless SAW sensor performs comparably. A commercial-off-the-shelf SDR platform has been employed as the SAW sensor interrogator. The Universal Software Radio Peripheral (USRP) is available in many embodiments and is capable of operation of to 6GHz and up to 160MHz of bandwidth. In particular, the USRP B200 is utilized as the RF transceiver platform. Custom FPGA modifications are discussed to fully utilize the USRP B200 bandwidth (56MHz) and synchronize the transmit and receive chains. External hardware has also been introduced to the B200 to improve RF performance, all of which are incorporated into a custom enclosure. Post-processing of the SAW sensor response is accomplished in Python using a matched filter correlator routine to extract sensor information. The system is demonstrated by interrogating wireless OFC SAW temperature and strain sensors at 915MHz.

Notes

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Graduation Date

2016

Semester

Summer

Advisor

Malocha, Donald C.

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Electrical Engineering and Computer Engineering

Degree Program

Electrical Engineering

Format

application/pdf

Identifier

CFE0006329

URL

http://purl.fcla.edu/fcla/etd/CFE0006329

Language

English

Release Date

August 2016

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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