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Micromachines
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Research Progress of Advanced SiC Semiconductors
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Research Progress of Advanced SiC Semiconductors

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "D1: Semiconductor Devices".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 7036

Special Issue Editor

Prof. Dr. Xingfang Liu

E-Mail Website
Guest Editor
Key Laboratory of Semiconductor Materials Science, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
Interests: silicon carbide; epitaxy; chemical vapor deposition; power device
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Silicon carbide (SiC) semiconductor technology is becoming increasingly integral to developments in power electronics, automotive industries, aerospace, and many other critical sectors. Therefore, we are thrilled to announce the call for papers for this Special Issue titled "Research Progress of Advanced SiC Semiconductors". This Special Issue aims to explore and showcase the latest research findings and technological advancements in this field. We cordially invite scholars and professionals from around the globe who are engaged in SiC semiconductor research to submit their original research findings and review articles.

The scope of this call includes, but is not limited to, the growth techniques and characterization of SiC materials; the design, fabrication, and application of SiC semiconductor devices; the integration and optimization of SiC power electronic devices and systems; and thermal management solutions for high-performance SiC semiconductors. This initiative is a fantastic opportunity for researchers and industry practitioners to contribute to a rapidly advancing field, sharing innovations that push the boundaries of what is possible with SiC semiconductors. Contributions that highlight groundbreaking work and forward-thinking insights into the future of SiC technologies are especially welcome.

Prof. Dr. Xingfang Liu
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

  • 3C-SiC
  • 4H-SiC
  • bulk growth
  • epitaxial growth
  • oxidation
  • film characteristics
  • FET device
  • power device

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Published Papers (4 papers)

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Research

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14 pages, 3865 KiB  
Article
SiC MOSFET with Integrated SBD Device Performance Prediction Method Based on Neural Network
by Xiping Niu, Ling Sang, Xiaoling Duan, Shijie Gu, Peng Zhao, Tao Zhu, Kaixuan Xu, Yawei He, Zheyang Li, Jincheng Zhang and Rui Jin
Micromachines 2025, 16(1), 55; https://doi.org/10.3390/mi16010055 - 31 Dec 2024
Viewed by 1380
Abstract
The SiC MOSFET with an integrated SBD (SBD-MOSFET) exhibits excellent performance in power electronics. However, the static and dynamic characteristics of this device are influenced by a multitude of parameters, and traditional TCAD simulation methods are often characterized by their complexity. Due to [...] Read more.
The SiC MOSFET with an integrated SBD (SBD-MOSFET) exhibits excellent performance in power electronics. However, the static and dynamic characteristics of this device are influenced by a multitude of parameters, and traditional TCAD simulation methods are often characterized by their complexity. Due to the increasing research on neural networks in recent years, such as the application of neural networks to the prediction of GaN JBS and Finfet devices, this paper considers the application of neural networks to the performance prediction of SiC MOSFET devices with an integrated SBD. This study introduces a novel approach utilizing neural network machine learning to predict the static and dynamic characteristics of the SBD-MOSFET. In this research, SBD-MOSFET devices are modeled and simulated using Sentaurus TCAD(2017) software, resulting in the generation of 625 sets of device structure and sample data, which serve as the sample set for the neural network. These input variables are then fed into the neural network for prediction. The findings indicate that the mean square error (MSE) values for the threshold voltage (Vth), breakdown voltage (BV), specific on-resistance (Ron), and total switching power dissipation (E) are 0.0051, 0.0031, 0.0065, and 0.0220, respectively, demonstrating a high degree of accuracy in the predicted values. Meanwhile, in the comparison of convolutional neural networks and machine learning, the CNN accuracy is much higher than the machine learning methods. This method of predicting device performance via neural networks offers a rapid means of designing SBD-MOSFETs with specified performance targets, thereby presenting significant advantages in accelerating research on SBD-MOSFET performance prediction. Full article
(This article belongs to the Special Issue Research Progress of Advanced SiC Semiconductors)
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9 pages, 3672 KiB  
Article
Positive Bias Temperature Instability in SiC-Based Power MOSFETs
by Vladislav Volosov, Santina Bevilacqua, Laura Anoldo, Giuseppe Tosto, Enzo Fontana, Alfio-lip Russo, Claudio Fiegna, Enrico Sangiorgi and Andrea Natale Tallarico
Micromachines 2024, 15(7), 872; https://doi.org/10.3390/mi15070872 - 30 Jun 2024
Cited by 1 | Viewed by 2268
Abstract
This paper investigates the threshold voltage shift (ΔVTH) induced by positive bias temperature instability (PBTI) in silicon carbide (SiC) power MOSFETs. By analyzing ΔVTH under various gate stress voltages (VGstress) at 150 °C, distinct mechanisms are revealed: (i) [...] Read more.
This paper investigates the threshold voltage shift (ΔVTH) induced by positive bias temperature instability (PBTI) in silicon carbide (SiC) power MOSFETs. By analyzing ΔVTH under various gate stress voltages (VGstress) at 150 °C, distinct mechanisms are revealed: (i) trapping in the interface and/or border pre-existing defects and (ii) the creation of oxide defects and/or trapping in spatially deeper oxide states with an activation energy of ~80 meV. Notably, the adoption of different characterization methods highlights the distinct roles of these mechanisms. Moreover, the study demonstrates consistent behavior in permanent ΔVTH degradation across VGstress levels using a power law model. Overall, these findings deepen the understanding of PBTI in SiC MOSFETs, providing insights for reliability optimization. Full article
(This article belongs to the Special Issue Research Progress of Advanced SiC Semiconductors)
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10 pages, 2757 KiB  
Article
Influence of Growth Process on Suppression of Surface Morphological Defects in 4H-SiC Homoepitaxial Layers
by Yicheng Pei, Weilong Yuan, Yunkai Li, Ning Guo, Xiuhai Zhang and Xingfang Liu
Micromachines 2024, 15(6), 665; https://doi.org/10.3390/mi15060665 - 21 May 2024
Viewed by 1824
Abstract
To address surface morphological defects that have a destructive effect on the epitaxial wafer from the aspect of 4H-SiC epitaxial growth, this study thoroughly examined many key factors that affect the density of defects in 4H-SiC epitaxial wafer, including the ratio of carbon [...] Read more.
To address surface morphological defects that have a destructive effect on the epitaxial wafer from the aspect of 4H-SiC epitaxial growth, this study thoroughly examined many key factors that affect the density of defects in 4H-SiC epitaxial wafer, including the ratio of carbon to silicon, growth time, application of a buffer layer, hydrogen etching and other process parameters. Through systematic experimental verification and data analysis, it was verified that when the carbon–silicon ratio was accurately controlled at 0.72, the density of defects in the epitaxial wafer was the lowest, and its surface flatness showed the best state. In addition, it was found that the growth of the buffer layer under specific conditions could effectively reduce defects, especially surface morphology defects. This provides a new idea and method for improving the surface quality of epitaxial wafers. At the same time, we also studied the influence of hydrogen etching on the quality of epitaxial wafers. The experimental results show that proper hydrogen etching can optimize surface quality, but excessive etching may lead to the exposure of substrate defects. Therefore, it is necessary to carefully control the conditions of hydrogen etching in practical applications to avoid adverse effects. These findings have important guiding significance for optimizing the quality of epitaxial wafers. Full article
(This article belongs to the Special Issue Research Progress of Advanced SiC Semiconductors)
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Review

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55 pages, 13764 KiB  
Review
Progress in Polycrystalline SiC Growth by Low Pressure Chemical Vapor Deposition and Material Characterization
by Michail Gavalas, Yann Gallou, Didier Chaussende, Elisabeth Blanquet, Frédéric Mercier and Konstantinos Zekentes
Micromachines 2025, 16(3), 276; https://doi.org/10.3390/mi16030276 - 27 Feb 2025
Viewed by 937
Abstract
The purpose of this paper is to give a review on the state of the art of polycrystalline SiC material grown by low-pressure chemical vapor deposition (LPCVD). Nowadays, LPCVD is the main technique used for the deposition of polycrystalline SiC, both in academic [...] Read more.
The purpose of this paper is to give a review on the state of the art of polycrystalline SiC material grown by low-pressure chemical vapor deposition (LPCVD). Nowadays, LPCVD is the main technique used for the deposition of polycrystalline SiC, both in academic research and industry. Indeed, the LPCVD technique is today the most mature technique to grow high purity polycrystalline thin films with controlled thickness and structure over a large area (>50 cm) and/or 3D substrate. Its ability to have a high degree of modification on the growth conditions and the chosen precursor system allows the deposition of polycrystalline SiC films in various substrates with tailored properties according to the desired application. After a short introduction on the SiC material and its growth by the LPCVD technique, a review of theoretical studies (thermodynamics and kinetics) related to the CVD SiC growth process is given. A synthesis of the experimental studies is made focusing on the effect of the growth conditions on the properties of the deposited SiC polycrystalline material. Despite the numerous results, a full understanding of them is limited due to the complexity of the LPCVD process and the polycrystalline SiC structure. The conclusions show that the growth conditions, like temperature, chamber pressure, (C/Si)(g), (Cl/Si)(g), and doping have an impact on the microstructure and on the corresponding properties of the polycrystalline SiC films. Future perspectives are given in order to improve our understanding on the polycrystalline–SiC–LPCVD process and to enable the growth of tailor-made polycrystalline SiC films for future applications. Full article
(This article belongs to the Special Issue Research Progress of Advanced SiC Semiconductors)
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