Sensitivity Improvement of Micro Thermal Convective Accelerometer with Structure Optimization: Theoretical and Experimental Studies

Xiaoyi Wang, Gyuha Lim, Wei Xu, Yi Kuen Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

For the first time, we conduct the structure optimization of the micro thermal convective accelerometer (MTCA). Firstly, the effects of sensor size (L), the location of detectors (D) and the cover height (H) on the performance are deeply analyzed by means of the proposed theoretical model. Six types of micro thermal convective accelerometers with parallel-stack detectors are fabricated by means of the CMOS compatible fabrication process to enhance the sensitivity. Both the theoretical and experimental results demonstrated that larger sensor size and cover height could improve the sensitivity of MTCA. Additionally, increasing sensor size would lead to a shift of the normalized optimal distance to the heater side. By means of the structure optimization and the parallel-stack method with three pairs of detectors, a MTCA with the sensor length of 1600μm is achieved with an outstanding sensitivity of 7075μV/g (gain=1) and normalized sensitivity/power (S/P) ratio of 201.4 μV/g/mW, which is twenty-fold larger than the state of art.

Original languageEnglish
Title of host publication2019 IEEE Sensors, SENSORS 2019 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728116341
DOIs
Publication statusPublished - Oct 2019
Externally publishedYes
Event18th IEEE Sensors, SENSORS 2019 - Montreal, Canada
Duration: 27 Oct 201930 Oct 2019

Publication series

NameProceedings of IEEE Sensors
Volume2019-October
ISSN (Print)1930-0395
ISSN (Electronic)2168-9229

Conference

Conference18th IEEE Sensors, SENSORS 2019
Country/TerritoryCanada
CityMontreal
Period27/10/1930/10/19

Keywords

  • Micro thermal convective accelerometer
  • Structure optimization
  • sensitivity improvement
  • theoretical model

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