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Electronics
Special Issues
Microelectronic Devices and Materials
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Microelectronic Devices and Materials

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electronic Materials, Devices and Applications".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 600

Special Issue Editors

Dr. Milija Sarajlic

E-Mail Website
Guest Editor
Institute of Chemistry, Technology and Metallurgy, University of Belgrade, Njegoševa 12, 11000 Belgrade, Serbia
Interests: chemical sensors; microelectronics; system on chip; temperature sensors; X-ray detectors
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Danijela Randjelovic

E-Mail Website
Guest Editor
Department of Microelectronic Technologies, Institute of Chemistry, Technology and Metallurgy, University of Belgrade, Njegoševa 12, 11000 Belgrade, Serbia
Interests: micromachined thermal sensors; gas sensors; vacuum sensors; flow sensors; atomic force microscopy; microbial fuel cells; analytical modeling; numerical modeling; intelligent transmitters
Special Issues, Collections and Topics in MDPI journals
Dr. Predrag Poljak

E-Mail Website
Guest Editor
Institute of Chemistry, Technology and Metallurgy (ICTM), National Institute of the Republic of Serbia, University of Belgrade, Njegoševa 12, 11000 Belgrade, Serbia
Interests: digital signal processing; design and analysis of the virtual instrumentation; automotive and power system applications

Special Issue Information

Dear Colleagues,

The field of microelectronics is at the forefront of technological innovation, driving advancements in various sectors such as computing, communications, healthcare, and energy. This Special Issue aims to showcase the latest research and developments in microelectronic devices and materials, with a focus on novel device architectures, advanced fabrication techniques, and innovative material applications. Topics of interest include, but are not limited to, semiconductor devices, sensors, thin-film technologies, nanomaterials, MEMS/NEMS, flexible substrates, and integration strategies for enhanced performance and reliability. Contributions that address challenges in scaling, energy efficiency, and sustainability are particularly encouraged. By bringing together researchers and practitioners from academia and industry, this Special Issue seeks to foster collaboration and the exchange of knowledge, paving the way for next-generation microelectronic technologies.

Dr. Milija Sarajlic
Prof. Dr. Danijela Randjelovic
Dr. Predrag Poljak
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. Electronics 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 2400 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

  • microelectronic devices
  • semiconductor materials
  • thin-film technologies
  • MEMS/NEMS
  • nanomaterials
  • flexible substrates
  • sensors
  • device fabrication
  • integration techniques

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Published Papers (1 paper)

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Research

20 pages, 3260 KB  
Article
Lifetime Prediction of GaN Power Devices Based on COMSOL Simulations and Long Short-Term Memory (LSTM) Networks
by Yunfeng Qiu, Zenghang Zhang and Zehong Li
Electronics 2025, 14(19), 3883; https://doi.org/10.3390/electronics14193883 - 30 Sep 2025
Viewed by 399
Abstract
Gallium nitride (GaN) power devices have attracted extensive attention due to their superior performance in high-frequency and high-power applications. However, the reliability and lifetime prediction of these devices under various operating conditions remain critical challenges. In this study, a hybrid approach combining finite [...] Read more.
Gallium nitride (GaN) power devices have attracted extensive attention due to their superior performance in high-frequency and high-power applications. However, the reliability and lifetime prediction of these devices under various operating conditions remain critical challenges. In this study, a hybrid approach combining finite element simulation and deep learning is proposed to predict the lifetime of GaN power devices. COMSOL Multiphysics (V6.3) is employed to simulate the thermal and mechanical stress behavior of GaN devices under different power and frequency conditions, while capturing key degradation indicators such as temperature cycles and stress concentrations. The variation in temperature over time can reflect the degradation of the device and also reveal the fatigue damage caused by the long-term accumulation of thermal stress on the chip. LSTM performs exceptionally well in extracting features from time series data, effectively capturing the long-term and short-term dependencies within the time series. By using simulation data to establish a connection between the chip temperature and its service life, the temperature data and the lifespan data are combined into a dataset, and the LSTM neural network is used to explore the impact of temperature changes over time on the lifespan. The method mentioned in this paper can make preliminary predictions of the results when sufficient experimental data cannot be obtained in a short period of time. The prediction results have a certain degree of reliability. Full article
(This article belongs to the Special Issue Microelectronic Devices and Materials)
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