Results 261 to 270 of about 339,375 (294)

Electrochemical lift-off of GaN films for GaN-on-GaN technology

Journal of Physics D: Applied Physics, 2023
Abstract Lifting off the native GaN substrate is an essential step in the fabrication of high-performance devices. In this study, we report a method to separate GaN thin films from GaN substrate through electrochemical (EC) lateral etching.
Yuzhen Liu, Meixin Feng, Shanshan Yang, Chuanjie Li, Yayu Dai, Shuming Zhang, Jianxun Liu, Jing Jin, Qian Sun, Hui Yang   +9 more
openaire   +1 more source

GaN-on-Diamond : The Next GaN

Microwave journal Internation. ed., 2014
Engineers in the defense community have long sought to replace the bulky and heavy traveling wave tubes (TWT) used in high power systems such as radar, communications satellite and electronic warfare (EW) with compact, lightweight, cheaper and more efficient semiconductor components. Diamond is also an excellent electrical insulator - a property needed
Ejeckam, F., Francis, D., Faili, F., Lowe, F., Wilman, J., Mollart, T., Dodson, J., Twitchen, D., Bolliger, B., Babić, Dubravko   +9 more
openaire   +2 more sources

Fabrication of GaN/Alumina/GaN Structure to Reduce Dislocations in GaN

Japanese Journal of Applied Physics, 2003
GaN/Alumina/GaN structures were fabricated to reduce dislocation density in GaN. Alumina films were deposited on GaN templates by electron cyclotron resonance plasma sputtering. GaN was regrown on the alumina films by metalorganic vapor phase epitaxy.
Masanobu Hiroki, Kazuhide Kumakura, Toshiki Makimato, Naoki Kobayashi, Takashi Kobayashi   +4 more
openaire   +1 more source

SE-GAN: A Swap Ensemble GAN Framework

2019 International Joint Conference on Neural Networks (IJCNN), 2019
In recent years, Generative adversial network(GAN) becomes prevalent in the research and was applied in various fields. Though GAN attracting great attention undoubtedly, it was still blamed for the difficulty of training. Training a GAN is very likely to get into undesirable results such as gradient vanishing, gradient explosion, model collapse.
Licheng Shen, Yan Yang 0001
openaire   +2 more sources

GAN-in-GAN for Monaural Speech Enhancement

IEEE Signal Processing Letters, 2023
Yicun Duan, Jianfeng Ren, Heng Yu 0001, Xudong Jiang 0001   +3 more
openaire   +1 more source

p-Channel GaN Transistor Based on p-GaN/AlGaN/GaN on Si

IEEE Electron Device Letters, 2019
In this letter, we demonstrate a recessed-gate p-channel MISFET using a p-GaN (70 nm)/Al 0.2Ga0.8N (15 nm)/GaN heterostructure grown by metalorganic chemical vapor deposition (MOCVD) on a Si substrate. A major benefit of such epi-structure is that it contains both a 2-dimensional electron gas (2-DEG) and a 2-dimensional hole gas (2-DHG), hence making ...
Chowdhury, Nadim, Lemettinen, Jori, Xie, Qingyun, Zhang, Yuhao, Rajput, Nitul S., Xiang, Peng, Cheng, Kai, Suihkonen, Sami, Then, Han Wui, Palacios, Tomas   +10 more
openaire   +3 more sources

Consistency-GAN: Training GANs with Consistency Model

Proceedings of the AAAI Conference on Artificial Intelligence
For generative learning tasks, there are three crucial criteria for generating samples from the models: quality, coverage/diversity, and sampling speed. Among the existing generative models, Generative adversarial networks (GANs) and diffusion models demonstrate outstanding quality performance while suffering from notable limitations.
Yunpeng Wang, Meng Pang, Shengbo Chen, Hong Rao   +3 more
openaire   +2 more sources

Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions

ACM Computing Surveys, 2022
Jiannong Cao, Divya Saxena
exaly  

GAN review: Models and medical image fusion applications

Information Fusion, 2023
Huiling Lu
exaly  

Cluster with GANs

Computer Vision and Image Understanding, 2022
Yuri Feigin, Hedva Spitzer, Raja Giryes
openaire   +2 more sources