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

Author:

Lin Rongyu1ORCID,Liu Zhiyuan1,Han Peng2,Lin Ronghui1,Lu Yi1,Cao Haicheng1,Tang Xiao1ORCID,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

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