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Proceedings Paper

Structure design of a high-performance aluminum nitride differential resonant accelerometer
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Paper Abstract

A high-performance aluminum nitride (AlN) differential resonant accelerometer is proposed. The inertia force of the proof mass is amplified to improve the sensitivity by two-stage microlever; the cross sensitivity is reduced by I-shape supporting beam; and the differential frequency detection scheme is used to decrease the effect of temperature common mode error. The accelerometer is mainly composed of proof mass, supporting beam, two-stage microlever and resonator, and its structural parameters are optimized by theoretical analysis and finite element simulation. The modal analysis shows that the fundamental frequencies of the two resonators are approximately 373.3 kHz, and the frequency differences from the interferential modes are about 9.4 kHz, which effectively achieves mode isolation. According to the simulation results of sensitivity, the sensitivity, linearity and cross sensitivity of AlN differential resonator accelerometer are 64.6 Hz/g, 0.787% and 0.0033 Hz/g, respectively. The simulation results of thermal stress show that the temperature sensitivity of a single resonator is about 490 Hz/°C, and the temperature sensitivity of output differential frequency is - 0.83 Hz/°C, which demonstrate that the differential frequency detection scheme can reduce the influence of temperature common mode error. All the above simulation results prove that this structural design of the accelerometer is feasible.

Paper Details

Date Published: 15 November 2017
PDF: 10 pages
Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 1060519 (15 November 2017); doi: 10.1117/12.2287159
Show Author Affiliations
Qiang Lei, Southwest Univ. of Science and Technology (China)
Yang Gao, China Academy of Engineering Physics (China)
Institute of High Energy Physics (China)
Da-peng Zhang, Southwest Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10605:
LIDAR Imaging Detection and Target Recognition 2017
Yueguang Lv; Weimin Bao; Weibiao Chen; Zelin Shi; Jianzhong Su; Jindong Fei; Wei Gong; Shensheng Han; Weiqi Jin; Jian Yang, Editor(s)

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