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

Lin, Rongyu; Liu, Zhiyuan; Han, Peng; Lin, Ronghui; Lu, Yi; Cao, Haicheng; Tang, X.; Wang, Chuanju; Khandelwal, Vishal; Zhang, Xiangliang; Li, Xiaohang

doi:10.1039/d2tc02335k

Cited by 14 publications

(2 citation statements)

References 45 publications

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“…It is also noted that Professor Ohkawa has demonstrated the first 740-nm InGaN-based red LEDs in 2012 [125]. Related to photonics research, Professor Xiaohang Li's Advanced Semiconductor Laboratory [https://cemse.kaust.edu.sa/semiconductor] focused on the formation of ultrawide bandgap semiconductor for ultraviolet optoelectronic devices, notably in group-III-nitrides and group-III-oxides deep ultraviolet light-emitters [126], boron-containing nitrides [127], and Ga2O3 membrane [128]. The team has also reported > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < significant work in semiconductor heterostructures design and deep-ultraviolet photodetectors [129], [130].…”

Section: Saudi Arabiamentioning

confidence: 99%

Globalization in Photonics Research and Development

Ng,

Rjeb,

Cox

et al. 2024

IEEE Photonics J.

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Section: Saudi Arabiamentioning

confidence: 99%

Globalization in Photonics Research and Development

Ng,

Rjeb,

Cox

et al. 2024

IEEE Photonics J.

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“…Porosity is a common defect caused by a highly focused laser beam's rapid heating and cooling of a given material (Liao et al, 2020;Niu et al, 2022;Zhuang et al, 2022). Porosity can be analyzed using experiments, modeling and machine learning (Lin et al, 2020(Lin et al, , 2021(Lin et al, , 2022.…”

Section: Introductionmentioning

confidence: 99%

Printed layers height calibration curve and porosity in laser melting deposition of Ti6Al4V combining experiments, mathematical modelling and deep neural network

Mahmood,

Diana,

Sajjad

et al. 2023

RPJ

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Purpose Porosity is a commonly analyzed defect in the laser-based additive manufacturing processes owing to the enormous thermal gradient caused by repeated melting and solidification. Currently, the porosity estimation is limited to powder bed fusion. The porosity estimation needs to be explored in the laser melting deposition (LMD) process, particularly analytical models that provide cost- and time-effective solutions compared to finite element analysis. For this purpose, this study aims to formulate two mathematical models for deposited layer dimensions and corresponding porosity in the LMD process. Design/methodology/approach In this study, analytical models have been proposed. Initially, deposited layer dimensions, including layer height, width and depth, were calculated based on the operating parameters. These outputs were introduced in the second model to estimate the part porosity. The models were validated with experimental data for Ti6Al4V depositions on Ti6Al4V substrate. A calibration curve (CC) was also developed for Ti6Al4V material and characterized using X-ray computed tomography. The models were also validated with the experimental results adopted from literature. The validated models were linked with the deep neural network (DNN) for its training and testing using a total of 6,703 computations with 1,500 iterations. Here, laser power, laser scanning speed and powder feeding rate were selected inputs, whereas porosity was set as an output. Findings The computations indicate that owing to the simultaneous inclusion of powder particulates, the powder elements use a substantial percentage of the laser beam energy for their melting, resulting in laser beam energy attenuation and reducing thermal value at the substrate. The primary operating parameters are directly correlated with the number of layers and total height in CC. Through X-ray computed tomography analyses, the number of layers showed a straightforward correlation with mean sphericity, while a converse relation was identified with the number, mean volume and mean diameter of pores. DNN and analytical models showed 2%–3% and 7%–9% mean absolute deviations, respectively, compared to the experimental results. Originality/value This research provides a unique solution for LMD porosity estimation by linking the developed analytical computational models with artificial neural networking. The presented framework predicts the porosity in the LMD-ed parts efficiently.

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Inconel-718 processing windows by directed energy deposition: a framework combining computational fluid dynamics and machine learning models with experimental validation

Mahmood,

Ishfaq,

Khraisheh

2024

Int J Adv Manuf Technol

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

Abstract: Aluminium gallium nitride (AlGaN)-based deep ultraviolet (DUV) light-emitting diodes (LEDs) suffer from low internal quantum efficiency (IQE) and serious efficiency droop. One reason for this is the electron leakage and...

Cited by 14 publications

References 45 publications

Globalization in Photonics Research and Development

Globalization in Photonics Research and Development

Printed layers height calibration curve and porosity in laser melting deposition of Ti6Al4V combining experiments, mathematical modelling and deep neural network

Inconel-718 processing windows by directed energy deposition: a framework combining computational fluid dynamics and machine learning models with experimental validation

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