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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

In this article, an innovative GaN-based trench current-aperture vertical electron transistor (CAVET) with a stepped doping microstructure is proposed and studied using Silvaco-ATLAS. According to the simulation and analyzed characteristics, the best performance renders a remarkable Baliga’s figure of merit (FOM) of 4.767 GW·cm2 owing to the modulation of the electric-field distribution. By adjusting the size of the stepped doping microstructure and doping concentration in the GaN drift, the maximum optimized result can achieve a relatively high breakdown voltage (BV) of 2523 V with a very low specific on-resistance (Ron,sp) of 1.34 mΩ·cm2, or the BV can be improved to 3024 V with a specific on-resistance (Ron,sp) of 2.08 mΩ·cm2. Compared with the conventional superjunction GaN-based trench CAVET, the newly demonstrated structure can achieve a 43% reduction in Ron,sp and increase by almost 20% the original BV. These results indicate the superiority of using the stepped doping microstructure in a trench CAVET to improve the BV and decrease Ron,sp, providing a reference for further development of GaN-based CAVETs.

Details

Title
Study of High-Performance GaN-Based Trench CAVET with Stepped Doping Microstructure
Author
Li, Yuan 1   VIAFID ORCID Logo  ; Xu, Liang 2 ; Guo, Zhiyou 1 ; Sun, Huiqing 1 

 Institute of Semiconductor Science and Technology, South China Normal University, 55 Zhongshan Avenue, Tianhe District, Guangzhou 510631, China 
 Foshan NationStar Semiconductor Co., Ltd., Foshan 528226, China 
First page
1273
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
2072666X
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2706250462
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.