Evidence map›Paper›PMID 41986304›Full record

ArticleMicrosystems & nanoengineering2026

A capacitive-piezoelectric hybrid MEMS microphone with signal fusion for enhancing signal-to-noise ratio.

Yangyang Guan, Michael Schneider, Dongsheng Li, Hemin Zhang, Jing Mi, Alexander Bertrand, Sina Sadeghpour, Chen Wang, Huicong Liu, Christ Glorieux and 1 more

Abstract read
In one paragraph

Article in Microsystems & nanoengineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Yangyang GuanESAT-MNS, KU Leuven, Leuven, Belgium.
Michael SchneiderInstitute of Sensor and Actuator Systems, TU Wien, Vienna, Austria.ORCID http://orcid.org/0000-0001-9846-7132
Dongsheng LiSchool of Mechanical and Electrical Engineering, Jiangsu Key Laboratory of Embodied Intelligence Robot Technology, Soochow University, Suzhou, China. dsli@suda.edu.cn.
Hemin ZhangMinistry of Education, Key Laboratory of Micro and Nano Systems for Aerospace, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China. zhanghm@nwpu.edu.cn.ORCID http://orcid.org/0000-0002-1067-1137
Jing MiESAT-MNS, KU Leuven, Leuven, Belgium.
Alexander BertrandESAT-STADIUS, KU Leuven, Leuven, Belgium.
Sina SadeghpourESAT-MNS, KU Leuven, Leuven, Belgium.
Chen WangESAT-MNS, KU Leuven, Leuven, Belgium. chen.wang@kuleuven.be.ORCID http://orcid.org/0000-0003-3476-3658
Huicong LiuSchool of Mechanical and Electrical Engineering, Jiangsu Key Laboratory of Embodied Intelligence Robot Technology, Soochow University, Suzhou, China.ORCID http://orcid.org/0000-0003-3651-9114
Christ GlorieuxSoft Matter and Biophysics Unit, Department of Physics and Astronomy, KU Leuven, Leuven, Belgium.
Michael KraftESAT-MNS, KU Leuven, Leuven, Belgium.

Funding

China Scholarship Council (CSC) CSC No. 202006320040National Natural Science Foundation of China (National Science Foundation of China) NSFC 62401385Natural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation) BK20240803
6 · The paper itself

Abstract

This work describes a hybrid micro-electro-mechanical-systems (MEMS) microphone integrating capacitive and piezoelectric transduction mechanisms for signal-to-noise ratio (SNR) improvement. A theoretical system-level model was established to characterize the hybrid device, which was fabricated using a silicon-on-insulator (SOI) wafer-based process. The piezoelectric transduction component employs a Si/SiO2/Au/AlN/Pt material stack, while the capacitive transduction part consists of a variable capacitor formed by a silicon handle layer and a silicon device layer. Experimental results at 1 kHz show that the hybrid MEMS microphone achieves sensitivities of -64.3 dB (re: 1 V/Pa) in piezoelectric mode, -54.9 dB (re: 1 V/Pa) in capacitive mode, and -52.4 dB (re: 1 V/Pa) in hybrid mode, with corresponding SNR values of 65.7 dB, 59.8 dB, and 62.2 dB, respectively. To enhance the overall SNR of the hybrid MEMS microphone, a signal fusion technique is applied to the dual synchronized signals, resulting in an enhanced SNR of 66.7 dB, exhibiting a 14.1 dB improvement compared to the 52.6 dB reported in previous work.

Identifiers

PMID41986304
PMCPMC13084049

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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.