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Micromachines
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Advanced Wide Bandgap Semiconductor Materials and Devices
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Advanced Wide Bandgap Semiconductor Materials and Devices

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 2488

Special Issue Editor

Dr. Peng Li

E-Mail Website
Guest Editor
Key Laboratory of UV-Emitting Materials and Technology, Ministry of Education, Northeast Normal University, Changchun 130024, China
Interests: wide band gap semiconductors; optoelectronic devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleague,

Advancements in the field of ultra-wide-bandgap (UWBG) semiconductors are swiftly expanding the horizon of technological possibilities, opening up novel avenues for exploration in electronics, photonics, detection systems, and quantum technology. Semiconductors like gallium oxide, aluminum gallium oxide, diamond, cubic-boron nitride, and aluminum gallium nitride, which boast bandgaps significantly wider than those of gallium nitride (3.4 eV) and silicon carbide (3.3 eV), are leading the charge in cutting-edge material science and the physics of semiconductor devices.

We look forward to receiving your contributions.

Dr. Peng Li
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

  • AlN
  • GaN
  • SiC
  • ZnO
  • TiO2
  • Ga2O3
  • photodetectors
  • LEDs
  • HEMTs
  • IGBT

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Published Papers (3 papers)

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Research

11 pages, 4845 KiB  
Article
Deep Learning Method for Breakdown Voltage and Forward I-V Characteristic Prediction of Silicon Carbide Schottky Barrier Diodes
by Hao Zhou, Xiang Wang, Shulong Wang, Chenyu Liu, Dongliang Chen, Jiarui Li, Lan Ma and Guohao Zhang
Micromachines 2025, 16(5), 583; https://doi.org/10.3390/mi16050583 - 15 May 2025
Viewed by 128
Abstract
This work employs a deep learning method to develop a high-precision model for predicting the breakdown voltage (Vbr) and forward I-V characteristics of silicon carbide Schottky barrier diodes (SiC SBDs). The model significantly reduces the testing costs associated with destructive [...] Read more.
This work employs a deep learning method to develop a high-precision model for predicting the breakdown voltage (Vbr) and forward I-V characteristics of silicon carbide Schottky barrier diodes (SiC SBDs). The model significantly reduces the testing costs associated with destructive experiments, such as breakdown voltage testing. Although the model requires a certain amount of time to establish itself, it supports linear variations in related variables once developed. A predicted model for Vbr with an accuracy of up to 99% was successfully developed using 600 sets of input data after 200 epochs of training. After training for 1000 epochs, the deep learning-based model could predict not only point values like Vbr but also curves, such as forward I-V characteristics, with a mean squared error (MSE) of less than 10−3. Our research shows the applicability and high efficiency of introducing deep learning into device characteristic prediction. Full article
(This article belongs to the Special Issue Advanced Wide Bandgap Semiconductor Materials and Devices)
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9 pages, 3876 KiB  
Article
A 3.2–3.6 GHz GaN Doherty Power Amplifier Module Based on a Compact Low-Loss Combiner
by Xiyu Wang, Dehan Wang, Wenming Li, Xiaolin Lv, Kai Cui, Haijun Liu and Kai Kang
Micromachines 2025, 16(2), 220; https://doi.org/10.3390/mi16020220 - 15 Feb 2025
Viewed by 792
Abstract
In this paper, a 3.2–3.6 GHz two-stage Doherty power amplifier (PA) module is proposed for fifth-generation (5G) massive multiple-input multiple-output (MIMO) base stations. A detailed design method and procedure for a compact and low-loss combiner suitable for the Doherty PA module are introduced. [...] Read more.
In this paper, a 3.2–3.6 GHz two-stage Doherty power amplifier (PA) module is proposed for fifth-generation (5G) massive multiple-input multiple-output (MIMO) base stations. A detailed design method and procedure for a compact and low-loss combiner suitable for the Doherty PA module are introduced. Based on the proposed combiner, a Doherty PA module is implemented using gallium nitride (GaN) transistors and surface-mounted devices (SMDs) with a packaged size of 8 × 8 mm2. The proposed two-stage Doherty PA module achieves a 3 dB small-signal bandwidth of 3.1–3.9 GHz and a peak gain of 31.7 dB. From 3.2 to 3.6 GHz, the saturated output power is 40.4–41.1 dBm. Moreover, the measured saturated drain efficiency (DE) and 8 dB power back-off (PBO) DE reach 51–56.6% and 45.5–48.6%, respectively. Full article
(This article belongs to the Special Issue Advanced Wide Bandgap Semiconductor Materials and Devices)
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8 pages, 1945 KiB  
Article
High-Temperature Characterization of AlGaN Channel High Electron Mobility Transistor Based on Silicon Substrate
by Yinhe Wu, Xingchi Ma, Longyang Yu, Xin Feng, Shenglei Zhao, Weihang Zhang, Jincheng Zhang and Yue Hao
Micromachines 2024, 15(11), 1343; https://doi.org/10.3390/mi15111343 - 31 Oct 2024
Viewed by 1201
Abstract
In this paper, it is demonstrated that the AlGaN high electron mobility transistor (HEMT) based on silicon wafer exhibits excellent high-temperature performance. First, the output characteristics show that the ratio of on-resistance (RON) only reaches 1.55 when the working temperature [...] Read more.
In this paper, it is demonstrated that the AlGaN high electron mobility transistor (HEMT) based on silicon wafer exhibits excellent high-temperature performance. First, the output characteristics show that the ratio of on-resistance (RON) only reaches 1.55 when the working temperature increases from 25 °C to 150 °C. This increase in RON is caused by a reduction in optical phonon scattering-limited mobility (μOP) in the AlGaN material. Moreover, the device also displays great high-performance stability in that the variation of the threshold voltage (ΔVTH) is only 0.1 V, and the off-state leakage current (ID,off-state) is simply increased from 2.87 × 10−5 to 1.85 × 10−4 mA/mm, under the operating temperature variation from 25 °C to 200 °C. It is found that the two trap states are induced at high temperatures, and the trap state densities (DT) of 4.09 × 1012~5.95 × 1012 and 7.58 × 1012~1.53 × 1013 cm−2 eV−1 are located at ET in a range of 0.46~0.48 eV and 0.57~0.61 eV, respectively, which lead to the slight performance degeneration of AlGaN HEMT. Therefore, this work provides experimental and theoretical evidence of AlGaN HEMT for high-temperature applications, pushing the development of ultra-wide gap semiconductors greatly. Full article
(This article belongs to the Special Issue Advanced Wide Bandgap Semiconductor Materials and Devices)
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