Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.0
3.0
Journals
Micromachines
Special Issues
Advanced Wide Bandgap Semiconductor Materials and Devices
share announcement

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 January 2026 | Viewed by 8679

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 250 words) can be sent to the Editorial Office for assessment.

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

14 pages, 2389 KB  
Article
Neural Synaptic Simulation Based on ZnAlSnO Thin-Film Transistors
by Yang Zhao, Chao Wang, Laizhe Ku, Liang Guo, Xuefeng Chu, Fan Yang, Jieyang Wang, Chunlei Zhao, Yaodan Chi and Xiaotian Yang
Micromachines 2025, 16(9), 1025; https://doi.org/10.3390/mi16091025 - 7 Sep 2025
Viewed by 841
Abstract
In the era of artificial intelligence, neuromorphic devices that simulate brain functions have received increasingly widespread attention. In this paper, an artificial neural synapse device based on ZnAlSnO thin-film transistors was fabricated, and its electrical properties were tested: the current-switching ratio was 1.18 [...] Read more.
In the era of artificial intelligence, neuromorphic devices that simulate brain functions have received increasingly widespread attention. In this paper, an artificial neural synapse device based on ZnAlSnO thin-film transistors was fabricated, and its electrical properties were tested: the current-switching ratio was 1.18 × 107, the subthreshold oscillation was 1.48 V/decade, the mobility was 2.51 cm2V−1s−1, and the threshold voltage was −9.40 V. Stimulating artificial synaptic devices with optical signals has the advantages of fast response speed and good anti-interference ability. The basic biological synaptic characteristics of the devices were tested under 365 nm light stimulation, including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), short-term plasticity (STP), and long-term plasticity (LTP). This device shows good synaptic plasticity. In addition, by changing the gate voltage, the excitatory postsynaptic current of the device at different gate voltages was tested, two different logical operations of “AND” and “OR” were achieved, and the influence of different synaptic states on memory was simulated. This work verifies the application potential of the device in the integrated memory and computing architecture, which is of great significance for promoting the high-quality development of neuromorphic computing hardware. Full article
(This article belongs to the Special Issue Advanced Wide Bandgap Semiconductor Materials and Devices)
►▼ Show Figures

Figure 1

17 pages, 4004 KB  
Article
Research on Switching Current Model of GaN HEMT Based on Neural Network
by Xiang Wang, Zhihui Zhao, Huikai Chen, Xueqi Sun, Shulong Wang and Guohao Zhang
Micromachines 2025, 16(8), 915; https://doi.org/10.3390/mi16080915 - 7 Aug 2025
Viewed by 1205
Abstract
The switching characteristics of GaN HEMT devices exhibit a very complex dynamic nonlinear behavior and multi-physics coupling characteristics, and traditional switching current models based on physical mechanisms have significant limitations. This article adopts a hybrid architecture of convolutional neural network and long short-term [...] Read more.
The switching characteristics of GaN HEMT devices exhibit a very complex dynamic nonlinear behavior and multi-physics coupling characteristics, and traditional switching current models based on physical mechanisms have significant limitations. This article adopts a hybrid architecture of convolutional neural network and long short-term memory network (CNN-LSTM). In the 1D-CNN layer, the one-dimensional convolutional neural network can automatically learn and extract local transient features of time series data by sliding convolution operations on time series data through its convolution kernel, making these local transient features present a specific form in the local time window. In the double-layer LSTM layer, the neural network model captures the transient characteristics of switch current through the gating mechanism and state transfer. The hybrid architecture of the constructed model has significant advantages in accuracy, with metrics such as root mean square error (RMSE) and mean absolute error (MAE) significantly reduced, compared to traditional switch current models, solving the problem of insufficient accuracy in traditional models. The neural network model has good fitting performance at both room and high temperatures, with an average coefficient close to 1. The new neural network hybrid architecture has short running time and low computational resource consumption, meeting the needs of practical applications. Full article
(This article belongs to the Special Issue Advanced Wide Bandgap Semiconductor Materials and Devices)
►▼ Show Figures

Figure 1

10 pages, 3553 KB  
Article
A Trench Heterojunction Diode-Integrated 4H-SiC LDMOS with Enhanced Reverse Recovery Characteristics
by Yanjuan Liu, Fangfei Bai and Junpeng Fang
Micromachines 2025, 16(8), 909; https://doi.org/10.3390/mi16080909 - 4 Aug 2025
Cited by 1 | Viewed by 712
Abstract
In this paper, a novel 4H-SiC LDMOS structure with a trench heterojunction in the source (referred as to THD-LDMOS) is proposed and investigated for the first time, to enhance the reverse recovery performance of its parasitic diode. Compared with 4H-SiC, silicon has a [...] Read more.
In this paper, a novel 4H-SiC LDMOS structure with a trench heterojunction in the source (referred as to THD-LDMOS) is proposed and investigated for the first time, to enhance the reverse recovery performance of its parasitic diode. Compared with 4H-SiC, silicon has a smaller band energy, which results in a lower built-in potential for the junction formed by P+ polysilicon and a 4N-SiC N-drift region. A trench P+ polysilicon is introduced in the source side, forming a heterojunction with the N-drift region, and this heterojunction is unipolar and connected in parallel with the body PiN diode. When the LDMOS operates as a freewheeling diode, the trench heterojunction conducts first, preventing the parasitic PiN from turning on and thereby significantly reducing the number of carriers in the N-drift region. Consequently, THD-LDMOS exhibits superior reverse recovery characteristics. The simulation results indicate that the reverse recovery peak current and reverse recovery charge of THD-LDMOS are reduced by 55.5% and 77.6%, respectively, while the other basic electrical characteristics remains unaffected. Full article
(This article belongs to the Special Issue Advanced Wide Bandgap Semiconductor Materials and Devices)
►▼ Show Figures

Figure 1

11 pages, 4845 KB  
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 1330
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)
►▼ Show Figures

Figure 1

9 pages, 3876 KB  
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 1897
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)
►▼ Show Figures

Figure 1

8 pages, 1945 KB  
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
Cited by 1 | Viewed by 1957
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop