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Micromachines
Special Issues
Artificial Intelligence for Micro Inertial Sensors
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Artificial Intelligence for Micro Inertial Sensors

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "A：Physics".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 911

Special Issue Editors

Prof. Dr. Xudong Zou

E-Mail Website
Guest Editor
State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
Interests: MEMS inertial sensors; MEMS resonators; PNT system; sensor fusion; 3D microsystem integration
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chong Li

E-Mail Website
Guest Editor
Micro System and Precision Control Laboratory, Ocean University of China, Shandong 266100, China
Interests: MEMS inertial sensors; gyroscopes; precision control systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advanced micro inertial sensors are embracing the impact of the blossoming of artificial intelligence (AI) technology. AI has proved its great potential to improve the design efficiency, fabrication quality, and measurement accuracy of MEMS inertial sensors. This special issue invites original research papers exploring AI for micro inertial sensors. Topics include but are not limited to machine learning, deep learning, edge computing for micro inertial sensor design optimization/acceleration, micro-fabrication virtual metrology, device self-testing, calibration techniques, and pose/position estimation. 

Prof. Dr. Xudong Zou
Prof. Dr. Chong Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Micromachines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2100 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Prof. Dr. Xudong Zou
Prof. Dr. Chong Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Micromachines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2100 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • MEMS sensors
  • inertial sensors
  • machine learning
  • artificial intelligence

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Published Papers (2 papers)

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Research

16 pages, 3304 KiB  
Article
Full-System Simulation and Analysis of a Four-Mass Vibratory MEMS Gyroscope
by Chenguang Ouyang, Wenzheng He, Lu Jia, Peng Wang, Kaichun Zhao, Fei Xing and Zheng You
Micromachines 2025, 16(4), 414; https://doi.org/10.3390/mi16040414 - 30 Mar 2025
Viewed by 338
Abstract
This study presents a full-system simulation methodology for MEMS, addressing the critical need for reliable performance prediction in microsystem design. While existing digital tools have been widely adopted in related fields, current approaches often remain fragmented and focused on specific aspects of device [...] Read more.
This study presents a full-system simulation methodology for MEMS, addressing the critical need for reliable performance prediction in microsystem design. While existing digital tools have been widely adopted in related fields, current approaches often remain fragmented and focused on specific aspects of device behavior. In contrast, our proposed framework conducts comprehensive device physics-level analysis by integrating mechanical, thermal and electrical modeling with process simulation. The methodology features a streamlined workflow that enables direct implementation of simulation results into fabrication processes. We model a MEMS gyroscope as an example to verify our simulation approach. Multiphysics coupling is considered to capture real-world device behavior, followed by quantitative assessment of manufacturing variations through virtual prototyping and experimental validation demonstrating the method’s accuracy and practicality. The proposed approach not only improves design efficiency but also provides a robust framework for MEMS gyroscope development. With its ability to predict device performance, this methodology is expected to become an essential tool in microsensor research and development. Full article
(This article belongs to the Special Issue Artificial Intelligence for Micro Inertial Sensors)
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22 pages, 1604 KiB  
Article
Maximum Correntropy Criterion Kalman/Allan Variance-Assisted FIR Integrated Filter for Indoor Localization
by Manman Li, Lei Deng, Yide Zhang, Yuan Xu and Yanli Gao
Micromachines 2025, 16(3), 303; https://doi.org/10.3390/mi16030303 - 4 Mar 2025
Viewed by 460
Abstract
To obtain more accurate information on using an inertial navigation system (INS)-based integrated localization system, an integrated filter with maximum correntropy criterion Kalman filter (mccKF) and finite impulse response (FIR) is proposed for the fusion of INS-based multisource sensor data in this work. [...] Read more.
To obtain more accurate information on using an inertial navigation system (INS)-based integrated localization system, an integrated filter with maximum correntropy criterion Kalman filter (mccKF) and finite impulse response (FIR) is proposed for the fusion of INS-based multisource sensor data in this work. In the realm of medical applications, precise localization is crucial for various aspects, such as tracking the movement of a medical instrument within the human body or monitoring its position in the human body during procedures. This study uses ultra-wideband (UWB) technology to rectify the position errors of the INS. In this method, the difference between the positions of the INS and UWB is used as the measurement of the filter. The main data fusion filter in this study is the mccKF, which utilizes the maximum correntropy criterion (mcc) method to enhance the robustness of the Kalman filter (KF). This filter is used for fusing data from multiple sources, including the INS. Moreover, we use the Mahalanobis distance to verify the performance of the mccKF. If the performance of the mccKF is lower than the preset threshold, the Allan Variance-assisted FIR filter is used to replace the mccKF, which is designed in this work. This adaptive approach ensures the resilience of the system in demanding medical environments. Two practical experiments were performed to evaluate the effectiveness of the proposed approach. The findings indicate that the mccKF/FIR integrated method reduces the localization error by approximately 32.43% and 37.5% compared with the KF and mccKF, respectively. These results highlight the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Artificial Intelligence for Micro Inertial Sensors)
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