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Authors = Christophe Rigaud ORCID = 0000-0003-0291-0078

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12 pages, 2957 KiB  
Article
Deep Level Transient Fourier Spectroscopy Investigation of Electron Traps on AlGaN/GaN-on-Si Power Diodes
by Florian Rigaud-Minet, Christophe Raynaud, Julien Buckley, Matthew Charles, Patricia Pimenta-Barros, Romain Gwoziecki, Charlotte Gillot, Véronique Sousa, Hervé Morel and Dominique Planson
Energies 2023, 16(2), 599; https://doi.org/10.3390/en16020599 - 4 Jan 2023
Cited by 1 | Viewed by 1999
Abstract
Many kinds of defects are present in AlGaN/GaN-on-Si based power electronics devices. Their identification is the first step to understand and improve device performance. Electron traps are investigated in AlGaN/GaN-on-Si power diodes using deep level transient Fourier spectroscopy (DLTFS) at different bias conditions [...] Read more.
Many kinds of defects are present in AlGaN/GaN-on-Si based power electronics devices. Their identification is the first step to understand and improve device performance. Electron traps are investigated in AlGaN/GaN-on-Si power diodes using deep level transient Fourier spectroscopy (DLTFS) at different bias conditions for two Schottky contact’s etching recipes. This study reveals seven different traps corresponding to point defects. Their energy level ET ranged from 0.4 eV to 0.57 eV below the conduction band. Among them, two new traps are reported and are etching-related: D3 (ET = 0.47–0.48 eV; σ ≈ 10−15 cm2) and D7 (ET = 0.57 eV; σ = 4.45 × 10−12 cm2). The possible origin of the other traps are discussed with respect to the GaN literature. They are proposed to be related to carbon and nitrogen vacancies or to carbon, such as CN-CGa. Some others are likely due to crystal surface recombination, native defects or a related complex, or to the nitrogen antisite: NGa. Full article
(This article belongs to the Section F3: Power Electronics)
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34 pages, 7516 KiB  
Article
Digital Comics Image Indexing Based on Deep Learning
by Nhu-Van Nguyen, Christophe Rigaud and Jean-Christophe Burie
J. Imaging 2018, 4(7), 89; https://doi.org/10.3390/jimaging4070089 - 2 Jul 2018
Cited by 55 | Viewed by 19374
Abstract
The digital comic book market is growing every year now, mixing digitized and digital-born comics. Digitized comics suffer from a limited automatic content understanding which restricts online content search and reading applications. This study shows how to combine state-of-the-art image analysis methods to [...] Read more.
The digital comic book market is growing every year now, mixing digitized and digital-born comics. Digitized comics suffer from a limited automatic content understanding which restricts online content search and reading applications. This study shows how to combine state-of-the-art image analysis methods to encode and index images into an XML-like text file. Content description file can then be used to automatically split comic book images into sub-images corresponding to panels easily indexable with relevant information about their respective content. This allows advanced search in keywords said by specific comic characters, action and scene retrieval using natural language processing. We get down to panel, balloon, text, comic character and face detection using traditional approaches and breakthrough deep learning models, and also text recognition using LSTM model. Evaluations on a dataset composed of online library content are presented, and a new public dataset is also proposed. Full article
(This article belongs to the Special Issue Image Based Information Retrieval from the Web)
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