Evidence map›Paper›PMID 40240336›Full record

ArticleMicrosystems & nanoengineering2025

A MEMS electro-mechanical co-optimization platform featuring freeform geometry optimization based on a genetic algorithm.

Chen Wang, Xinyu Wu, Sina Sadeghpour, Milad Shojaeian, Linlin Wang, Bernardo Pereira Madeira, Yangyang Guan, Huafeng Liu, Yuan Wang, Pan Zhang and 1 more

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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 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

11 authors.

Chen WangESAT-MNS, University of Leuven, Leuven, 3001, Belgium.
Xinyu WuESAT-MNS, University of Leuven, Leuven, 3001, Belgium.
Sina SadeghpourESAT-MNS, University of Leuven, Leuven, 3001, Belgium.
Milad ShojaeianESAT-MNS, University of Leuven, Leuven, 3001, Belgium.
Linlin WangESAT-MNS, University of Leuven, Leuven, 3001, Belgium.
Bernardo Pereira MadeiraESAT-MNS, University of Leuven, Leuven, 3001, Belgium.
Yangyang GuanESAT-MNS, University of Leuven, Leuven, 3001, Belgium.
Huafeng LiuPGMF and School of Physics, Huazhong University of Science and Technology, Wuhan, 430074, PR China. huafengliu@hust.edu.cn.ORCID http://orcid.org/0000-0003-0757-2362
Yuan WangInstitute of Microelectronics State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau, Macau, China. yuanwang@um.edu.mo.ORCID http://orcid.org/0000-0003-3814-4520
Pan ZhangSchool of Integrated Circuits, Peking University, Beijing, 100871, China.
Pui-In MakInstitute of Microelectronics State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau, Macau, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper describes a novel, system-level design methodology based on a genetic algorithm (GA) using freeform geometries for microelectromechanical systems (MEMS) devices. The proposed method can concurrently design and co-optimize the electronic and mechanical parts of a MEMS device comprising freeform geometries to achieve a better system performance, i.e., a high sensitivity, a good system stability, and large fabrication tolerances. Also, the introduction of freeform geometries allows higher degrees of freedom in the design process, improving the diversity and potentially the performance of the MEMS devices. A MEMS accelerometer comprising a freeform mechanical motion preamplifier in a closed-loop control system is presented to demonstrate the effectiveness of the design approach. The optimization process shows the main figure-of-merit (FOM) is improved by 195%. In the mechanical component alone (open-loop system), the product of sensitivity and bandwidth has improved by 151%, with sensitivity increasing by 276%. For closed-loop performance, there is an improvement of 120% for the ratio of open and closed-loop displacements. The product of sensitivity and bandwidth is improved by 27% in the closed-loop system. Excellent immunities to fabrication errors and parameter mismatch are achieved. Experiments show that the displacement of the MEMS accelerometer in the closed-loop system decreased by 86% with 4.85 V feedback voltage compared with that in the open-loop system under a 1 g 100 Hz acceleration input. The static and dynamic nonlinearities in the closed-loop system are improved by 64% and 61%, respectively, compared with those in the open-loop system, in the ±1 g acceleration input range. Besides, the closed-loop system improves the cross-axis sensitivity by 18.43%, compared with that in the open-loop system. It is the first time a closed-loop system for a MEMS accelerometer comprising a mechanical motion preamplifier is successfully implemented experimentally.

Identifiers

PMID40240336
PMCPMC12003626

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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.