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Microsystem Technologies

, Volume 25, Issue 1, pp 351–359 | Cite as

Design of SAW sensor for longitudinal strain measurement with improved sensitivity

  • Juan Ren
  • Kanat Anurakparadorn
  • Hairong Gu
  • Minghui Zhao
  • Xueyong WeiEmail author
Technical Paper
  • 71 Downloads

Abstract

This paper presents the design of a highly sensitive surface acoustic wave (SAW)-based sensor with novel structure for the longitudinal strain measurement. The sensor utilizes thin lithium niobate (LiNbO3) diaphragm as the sensing element rather than the bulk substrate. The application of the diaphragm effectively decreases the cross-sectional area of the strain sensitive element, and meanwhile reduces the resistance between the sensor and the specimen. The newly designed strain sensor is to operate around a frequency of 50 MHz. The insertion loss of − 12 dB and quality factor of 63 are obtained analytically from impulse-response model. The sensor performance with tensile testing of the steel beam is predicted by the finite element method. The prestressed eigenfrequency analysis is conducted with the COMSOL commercial software. The simulation shows the resonance frequency of the sensor shifts linearly with the strain induced in the testing beam. For the SAW sensor with traditional configuration applying 1 mm thick substrate, the strain sensitivity is obtained as 0.41 ppm/με. For the sensor with the novel design employing thin diaphragm with the thickness of 200 μm, the strain sensitivity is increased to 0.83 ppm/με. With the availability of the bulk micromachining of LiNbO3, the application of the piezoelectric diaphragm as sensing element in SAW strain sensor can be an alternative way to enhance the sensor sensitivity.

Notes

Acknowledgement

This work was financially supported by the National Natural Science Foundation of China (51575439), Natural Science Foundation of Shaanxi Province (2014jm2-5054) and the open fund from the State Key Laboratory for Manufacturing Systems Engineering (sklms2017006).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Construction MachineryChang’an UniveristyXi’anChina
  2. 2.State Key Laboratory for Manufacturing Systems EngineeringXi’an Jiaotong UniversityXi’anChina

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