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CMOS-MEMS/NEMS Devices and Sensors: Part B
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CMOS-MEMS/NEMS Devices and Sensors: Part B

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 3089

Special Issue Editor

Dr. Ching-Liang Dai

E-Mail Website
Guest Editor
Department of Mechanical Engineering, National Chung Hsing University, Taichung 402, Taiwan
Interests: CMOS-MEMS; microsensors; microactuators
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I am glad to introduce this Special Issue to you, the second part of our previous Special Issue “CMOS-MEMS/NEMS Devices and Sensors”, which included seven excellent papers in total.

Microelectromechanical system (MEMS) and nanoelectromechanical system (NEMS) devices fabricated using the complementary metal oxide semiconductor (CMOS) process are called CMOS-MEMS/NEMS devices. Several CMOS-MEMS/NEMS sensors and devices have been developed and commercialized; examples include pressure sensors, accelerometers, gyroscopes, microphones, optical sensors, magnetic sensors, flow sensors, thermal sensors, image sensors, ink jet heads, and digital micro-mirror devices. Micro/nano devices developed by CMOS-MEMS/NEMS technology have the potential for integration with integrated circuits (IC) on chip. The integrated devices with IC have the advantages of low interference and high performance. Various sensing circuits and actuation circuits are essential for MEMS/NEMS devices. This Special Issue aims to collect high-quality research on CMOS-MEMS/NEMS sensors and devices.

Welcome in this Special Issue are submissions related to novel designs, analyses, simulations, fabrications, packaging, developments and applications of various sensors, devices and circuits, including CMOS circuits, energy harvesters, chemical sensors, gas sensors, humidity sensors, biosensors, biodevices, mechanical sensors, force sensors, optical sensors, magnetic sensors, thermal sensors, acoustic sensors, microphones, actuators, mirrors, switches, resonators, microchanels, microfluidic devices and others, based on CMOS-MEMS/NEMS technology. Review articles and original research articles are equally welcome.

Dr. Ching-Liang Dai
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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • CMOS circuits for MEMS/NEMS devices
  • energy harvesters
  • chemical sensors
  • gas sensors
  • humidity sensors
  • biosensors
  • biodevices
  • mechnical sensors
  • force sensors
  • magnetic sensors
  • optical sensors
  • thermal sensors
  • flow sensors
  • acoustic sensors
  • microphones
  • actuators
  • mirrors
  • switches
  • resonators
  • microchanels
  • microfludic devices

Published Papers (1 paper)

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Research

12 pages, 573 KiB  
Article
Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine
by Omar Rodríguez-Abreo, Juvenal Rodríguez-Reséndiz, L. A. Montoya-Santiyanes and José Manuel Álvarez-Alvarado
Sensors 2022, 22(1), 130; https://doi.org/10.3390/s22010130 - 25 Dec 2021
Cited by 10 | Viewed by 2351
Abstract
Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in a system is through [...] Read more.
Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in a system is through preparation for them with a model. The accuracy of the model depends mainly on the type of model and the fitting that is attained. The non-linear model parameters can be complex to fit. Therefore, artificial intelligence is an option for performing this tuning. Within evolutionary computation, there are many optimization and tuning algorithms, the best known being genetic algorithms, but they contain many specific parameters. That is why algorithms such as the gray wolf optimizer (GWO) are alternatives for this tuning. There is a small number of mechanical applications in which the GWO algorithm has been implemented. Therefore, the GWO algorithm was used to fit non-linear regression models for vibration amplitude measurements in the radial direction in relation to the rotational frequency in a gas microturbine without considering temperature effects. RMSE and R2 were used as evaluation criteria. The results showed good agreement concerning the statistical analysis. The 2nd and 4th-order models, and the Gaussian and sinusoidal models, improved the fit. All models evaluated predicted the data with a high coefficient of determination (85–93%); the RMSE was between 0.19 and 0.22 for the worst proposed model. The proposed methodology can be used to optimize the estimated models with statistical tools. Full article
(This article belongs to the Special Issue CMOS-MEMS/NEMS Devices and Sensors: Part B)
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