Evidence map›Paper›PMID 40731724›Full record

ArticleMicromachines2025

Design and Electromagnetic Performance Optimization of a MEMS Miniature Outer-Rotor Permanent Magnet Motor.

Kaibo Lei, Haiwang Li, Shijia Li, Tiantong Xu

Abstract read
In one paragraph

Article in Micromachines, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

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

4 authors.

Kaibo LeiNational Key Laboratory of Science and Technology on Aero-Engine Aero-Thermodynamics, Beihang University, Beijing 100191, China.
Haiwang LiNational Key Laboratory of Science and Technology on Aero-Engine Aero-Thermodynamics, Beihang University, Beijing 100191, China.
Shijia LiNational Key Laboratory of Science and Technology on Aero-Engine Aero-Thermodynamics, Beihang University, Beijing 100191, China.
Tiantong XuNational Key Laboratory of Science and Technology on Aero-Engine Aero-Thermodynamics, Beihang University, Beijing 100191, China.ORCID 0000-0002-0840-6692

Funding

Advanced Aerodynamics Innovation Workstation HKCX2020-02-015
6 · The paper itself

Abstract

In this study, we present the design and electromagnetic performance optimization of a micro-electromechanical system (MEMS) miniature outer-rotor permanent magnet motor. With increased attention towards higher torque density and lower torque pulsations in MEMS micromotor designs, an adaptation of an external rotor can be highly attractive. However, with the design complexity involved in such high-performance MEMS outer-rotor motor designs, the ultra-miniature 3D coil structures and the thin-film topology surrounding the air gap have been one of the main challenges. In this study, an ultra-thin outer-rotor motor with 3D MEMS silicon-based coils and a MEMS-compatible manufacturing method for the 3D coils is presented. Additionally, finite element simulations are conducted for the thin-film topology around the air gap to optimize performance characteristics such as torque developed, torque pulsations, and back electromotive force amplitude. Ultimately, the average magnetic flux density increased by 37.1%, from 0.361 T to 0.495 T. The root mean square (RMS) value of the back EMF per phase rises by 14.4%. Notably, the average torque is improved by 11.3%, while the torque ripple is significantly reduced from 1.281 mNm to 0.74 mNm, corresponding to a reduction of 49.9% in torque ripple percentage.

Indexed as

air gap flux densityback EMFcogging torqueelectromagnetic performance optimizationMEMS motorouter-rotor permanent magnet motortorque

Identifiers

PMID40731724
PMCPMC12299092

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.