Evidence map›Paper›PMID 40044637›Full record

ArticleMicrosystems & nanoengineering2025

A miniaturized MEMS accelerometer with anti-spring mechanism for enhancing sensitivity.

Ruihong Xiong, Xuankai Xu, Yushuai Liu, Shihao Du, Lihui Jin, Fang Chen, Tao Wu

Abstract read
In one paragraph

Article in Microsystems & nanoengineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ruihong XiongSchool of Information Science and Technology, ShanghaiTech University, 201210, Shanghai, China.
Xuankai XuSchool of Information Science and Technology, ShanghaiTech University, 201210, Shanghai, China.
Yushuai LiuSchool of Information Science and Technology, ShanghaiTech University, 201210, Shanghai, China.
Shihao DuState Key Lab of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, 200050, Shanghai, China.
Lihui JinSchool of Information Science and Technology, ShanghaiTech University, 201210, Shanghai, China.
Fang ChenState Key Lab of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, 200050, Shanghai, China. fangchen@mail.sim.ac.cn.ORCID http://orcid.org/0000-0002-7961-6232
Tao WuSchool of Information Science and Technology, ShanghaiTech University, 201210, Shanghai, China. wutao@shanghaitech.edu.cn.ORCID http://orcid.org/0000-0003-1563-2994

Funding

National Natural Science Foundation of China (National Science Foundation of China) 61974156
6 · The paper itself

Abstract

Anti-spring mechanisms are widely used for improving the noise performance of MEMS accelerometers due to their stiffness softening effect. However, the existing mechanisms typically require large bias force and displacement for achieving stiffness softening, leading to large device dimensions. Here, we propose a novel anti-spring mechanism composed of two pre-shaped curved beams connected in a parallel configuration, which can achieve stiffness softening without requiring large bias force and displacement. The stiffness softening effect of the mechanism is verified through theoretical modeling and finite element method (FEM) simulation. After that, the mechanism is implemented in a 4.2 mm × 4.9 mm MEMS capacitive accelerometer prototype. The experimental results reveal that the sensitivity of the accelerometer increases by 10.4% compared to the initial sensitivity; at the same time, the noise floor and bias instability decrease by 10.5% and 4.2%. The sensitivity, nonlinearity, bias instability, and noise floor after biasing are 51.1 mV/g, 0.99%, 0.24 mg, and 21.3

Identifiers

PMID40044637
PMCPMC11883025

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.