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Micromachines
Special Issues
Inertial MEMS Devices
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Inertial MEMS Devices

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

Deadline for manuscript submissions: closed (15 February 2021) | Viewed by 6908

Special Issue Editor

Prof. Dr. Edmond Cretu

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Interests: MEMS; inertial sensors; microsystems; signal processing and control; microtechnologies; ultrasound; CMUT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Despite being considered one of the most mature applications of micro-electromechanical systems (MEMS), inertial sensors still have a steady growth rate, with their range of applications extending from the initial automotive market to smartphones and wearable sensors for various body monitoring functions. Recent advances in this context range from alternative microfabrication technologies, beyond silicon, for low-cost wearable sensors, to new operating principles that lead to higher sensitivity of MEMS inertial sensors while maintaining the downscaling trend. Such new techniques exploit new electromechanical interactions at microscale, such as operation on the stability border, parametric amplification or mode localized sensing, pushing the inertial sensing limits set by the thermomechanical and electronic noise sources. The higher sensitivity and stable operation targets, leading, for instance, to demanding sensors like gravimeters, require in most cases an integration with electronic feedback loops—the trend here is to emphasize digital control such as sigma-delta or sliding mode techniques, for an easy and robust integration with other electronic subsystems. At the application level, many new directions lead to a structured data fusion of several sensing channels for the reconstruction of multiple degrees-of-freedom (DoFs), for instance, through Kalman filtering and its various extensions.

We intend therefore to cover in this Special Issue some of these exciting topics, through papers addressing a wide range of inertial sensors research avenues, including, but not limited to:

  • modern microfabrication technologies for inertial MEMS sensors;
  • Advanced sensing alternatives for high-sensitivity or robust inertial sensing;
  • Modelling and simulation (information flow or energy flow) of inertial MEMS sensors;
  • Specific packaging solutions for long term robust operation in a varying environment;
  • Modern feedback control architectures dedicated to inertial sensors;
  • Low-power readout electronics dedicated to inertial sensors;
  • Self-calibrating techniques for a guaranteed accuracy in-the-field;
  • Innovative applications of inertial sensors.

Prof. Edmond Cretu
Guest Editor

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

  • inertial sensors
  • MEMS
  • accelerometer
  • gyroscope
  • inertial measurement unit
  • microfabrication
  • Kalman filtering
  • reduced order macromodelling

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Published Papers (2 papers)

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Research

14 pages, 3542 KiB  
Article
Research on Decomposition of Offset in MEMS Capacitive Accelerometer
by Xianshan Dong, Yun Huang, Ping Lai, Qinwen Huang, Wei Su, Shiyuan Li and Wei Xu
Micromachines 2021, 12(8), 1000; https://doi.org/10.3390/mi12081000 - 22 Aug 2021
Cited by 5 | Viewed by 3195
Abstract
In a MEMS capacitive accelerometer, there is an offset due to mechanical and electrical factors, and the offset would deteriorate the performance of the accelerometer. Reducing the offset from mechanism would benefit the improvement in performance. Yet, the compositions of the offset are [...] Read more.
In a MEMS capacitive accelerometer, there is an offset due to mechanical and electrical factors, and the offset would deteriorate the performance of the accelerometer. Reducing the offset from mechanism would benefit the improvement in performance. Yet, the compositions of the offset are complex and mix together, so it is difficult to decompose the offset to provide guidance for the reduction. In this work, a decomposition method of offset in a MEMS capacitive accelerometer was proposed. The compositions of the offset were first analyzed quantitatively, and methods of measuring key parameters were developed. Based on our proposed decomposition method, the experiment of offset decomposition with a closed-loop MEMS capacitive accelerometer was carried out. The results showed that the offset successfully decomposed, and the major source was from the fabricated gap mismatch in the MEMS sensor. This work provides a new way for analyzing the offset in a MEMS capacitive accelerometer, and it is helpful for purposefully taking steps to reduce the offset and improve accelerometer performance. Full article
(This article belongs to the Special Issue Inertial MEMS Devices)
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16 pages, 4537 KiB  
Article
A High-Precision Magnetic-Assisted Heading Angle Calculation Method Based on a 1D Convolutional Neural Network (CNN) in a Complicated Magnetic Environment
by Guanghui Hu, Hong Wan and Xinxin Li
Micromachines 2020, 11(7), 642; https://doi.org/10.3390/mi11070642 - 29 Jun 2020
Cited by 11 | Viewed by 2704
Abstract
Due to its widespread presence and independence from artificial signals, the application of geomagnetic field information in indoor pedestrian navigation systems has attracted extensive attention from researchers. However, for indoors environments, geomagnetic field signals can be severely disturbed by the complicated magnetic, leading [...] Read more.
Due to its widespread presence and independence from artificial signals, the application of geomagnetic field information in indoor pedestrian navigation systems has attracted extensive attention from researchers. However, for indoors environments, geomagnetic field signals can be severely disturbed by the complicated magnetic, leading to reduced positioning accuracy of magnetic-assisted navigation systems. Therefore, there is an urgent need for methods which screen out undisturbed geomagnetic field data for realizing the high accuracy pedestrian inertial navigation indoors. In this paper, we propose an algorithm based on a one-dimensional convolutional neural network (1D CNN) to screen magnetic field data. By encoding the magnetic data within a certain time window to a time series, a 1D CNN with two convolutional layers is designed to extract data features. In order to avoid errors arising from artificial labels, the feature vectors will be clustered in the feature space to classify the magnetic data using unsupervised methods. Our experimental results show that this method can distinguish the geomagnetic field data from indoors disturbed magnetic data well and further significantly improve the calculation accuracy of the heading angle. Our work provides a possible technical path for the realization of high-precision indoor pedestrian navigation systems. Full article
(This article belongs to the Special Issue Inertial MEMS Devices)
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