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 178

Special Issue Editors


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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

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

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

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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
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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