A machine learning study on superlattice electron blocking layer design for AlGaN deep ultraviolet light-emitting diodes using the stacked XGBoost/LightGBM algorithm-Reference-Cited by-同舟云学术

A machine learning study on superlattice electron blocking layer design for AlGaN deep ultraviolet light-emitting diodes using the stacked XGBoost/LightGBM algorithm

Published:2022 Issue:46 Volume:10 Page:17602-17610
ISSN:2050-7526
Container-title:Journal of Materials Chemistry C
language:en
Short-container-title:J. Mater. Chem. C

Author:

Lin Rongyu¹^ORCID,Liu Zhiyuan¹,Han Peng²,Lin Ronghui¹,Lu Yi¹,Cao Haicheng¹,Tang Xiao¹^ORCID,Wang Chuanju¹,Khandelwal Vishal¹,Zhang Xiangliang²³,Li Xiaohang¹

Affiliation:

1. Advanced Semiconductor Laboratory, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia

2. Laboratory Machine, Intelligence and Knowledge Engineering (MINE), Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia

3. Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA

Abstract

A stacked XGBoost/LightGBM model was developed to predict and systematically investigate various high-performance SL-EBLs and to suggest a simpler and experimentally realizable low Al-content SL-EBL design.

Funder

King Abdullah University of Science and Technology

Publisher

Royal Society of Chemistry (RSC)

Subject

Materials Chemistry,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/TC/D2TC02335K

Reference45 articles.

1. The emergence and prospects of deep-ultraviolet light-emitting diode technologies

2. The 2020 UV emitter roadmap

3. Investigation of Light Extraction Efficiency in AlGaN Deep-Ultraviolet Light-Emitting Diodes