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一种新型三轴向压电 MEMS 加速度计,采用折叠梁结构。

A Novel Tri-Axial Piezoelectric MEMS Accelerometer with Folded Beams.

机构信息

The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China.

出版信息

Sensors (Basel). 2021 Jan 11;21(2):453. doi: 10.3390/s21020453.

DOI:10.3390/s21020453
PMID:33440659
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7826644/
Abstract

Microelectromechanical (MEMS) piezoelectric accelerometers are diversely used in consumer electronics and handheld devices due to their low power consumption as well as simple reading circuit and good dynamic performance. In this paper, a tri-axial piezoelectric accelerometer with folded beams is presented. The four beam suspensions are located at two sides of the mass aligned with edges of the mass, and the thickness of the beams is the same as the thickness of the mass block. In order to realize the multi-axis detection, a total of 16 sensing elements are distributed at the end of the folded beams. The structural deformations, stress distribution, and output characteristics due to the acceleration in , , and -axis directions are theoretically analyzed and simulated. The proposed accelerometer is fabricated by MEMS processes to form Mo/AlN/ScAlN/Mo piezoelectric stacks as the sensing layer. Experiments show that the charge sensitivity along the , , and -axes could reach up to ~1.07 pC/g, ~0.66 pC/g, and ~3.35 pC/g. The new structure can provide inspiration for the design of tri-axial piezoelectric accelerometers with great sensitivity and linearity.

摘要

微机电系统(MEMS)压电加速度计由于功耗低、读取电路简单以及良好的动态性能,在消费电子产品和手持设备中得到了广泛应用。本文提出了一种具有折叠梁的三轴压电加速度计。四个梁悬挂在质量块的两侧,与质量块的边缘对齐,梁的厚度与质量块的厚度相同。为了实现多轴检测,总共在折叠梁的末端分布了 16 个传感元件。从理论上分析和模拟了由于在 x、y 和 z 轴方向上的加速度引起的结构变形、应力分布和输出特性。该加速度计采用 MEMS 工艺制造,形成 Mo/AlN/ScAlN/Mo 压电堆叠作为传感层。实验表明,沿 x、y 和 z 轴的电荷灵敏度可达约 1.07 pC/g、约 0.66 pC/g 和约 3.35 pC/g。这种新结构可为设计具有高灵敏度和线性度的三轴压电加速度计提供启示。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/956a/7826644/3493c0c99bcb/sensors-21-00453-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/956a/7826644/437d43921841/sensors-21-00453-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/956a/7826644/1c267c4ae345/sensors-21-00453-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/956a/7826644/9c77936eb466/sensors-21-00453-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/956a/7826644/f9ec45649d41/sensors-21-00453-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/956a/7826644/6eb0830c1aed/sensors-21-00453-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/956a/7826644/a1b9d221f373/sensors-21-00453-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/956a/7826644/3493c0c99bcb/sensors-21-00453-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/956a/7826644/437d43921841/sensors-21-00453-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/956a/7826644/1c267c4ae345/sensors-21-00453-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/956a/7826644/9c77936eb466/sensors-21-00453-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/956a/7826644/f9ec45649d41/sensors-21-00453-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/956a/7826644/6eb0830c1aed/sensors-21-00453-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/956a/7826644/a1b9d221f373/sensors-21-00453-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/956a/7826644/3493c0c99bcb/sensors-21-00453-g007.jpg

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Microsyst Nanoeng. 2023 Nov 29;9:151. doi: 10.1038/s41378-023-00628-7. eCollection 2023.
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Deep Learning Based Multiresponse Optimization Methodology for Dual-Axis MEMS Accelerometer.基于深度学习的双轴MEMS加速度计多响应优化方法
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