Machine Vision Observation, Artificial Intelligence Pattern Recognition, Protective Circuit Design, Characterization of Multiple Materials, and Nano-Structural Analysis for Investigating InGaN Green Light Emitting Diode Degradation in a Salty Water Vapor Environment

Cheng Shan Chen; Chun Yen Yang; Shao Jui Yang; Deng Yi Wang; Yaw Wen Kuo; Wei Han Hsiao; Hsin Hung Chou; Chia Feng Lin; Yung Hui Li; Yewchung Sermon Wu; Hsiang Chen; Jung Han

doi:10.1109/IRPS48228.2024.10529494

Machine Vision Observation, Artificial Intelligence Pattern Recognition, Protective Circuit Design, Characterization of Multiple Materials, and Nano-Structural Analysis for Investigating InGaN Green Light Emitting Diode Degradation in a Salty Water Vapor Environment

Cheng Shan Chen, Chun Yen Yang, Shao Jui Yang, Deng Yi Wang, Yaw Wen Kuo, Wei Han Hsiao, Hsin Hung Chou, Chia Feng Lin, Yung Hui Li, Yewchung Sermon Wu, Hsiang Chen^*, Jung Han

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This study delves into the degradation of GaN-based LEDs in saline environments, a relatively underexplored area of research. LEDs are known for their longevity, but face challenges under extreme conditions. Utilizing artificial intelligence, machine vision, and material analysis, this study detects early signs of LED degradation[1], [2]. The results highlight the impact of saline exposure on LED performance, with some LEDs continuing to function for up to 30 minutes before failure. Advanced circuits ensure uninterrupted operation. Encompassing electrical engineering, computer science, and materials science, this study provides a comprehensive approach to LED fault detection and protection.

Original language	English
Title of host publication	2024 IEEE International Reliability Physics Symposium, IRPS 2024 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350369762
DOIs	https://doi.org/10.1109/IRPS48228.2024.10529494
State	Published - 2024
Event	2024 IEEE International Reliability Physics Symposium, IRPS 2024 - Grapevine, United States Duration: 14 04 2024 → 18 04 2024

Publication series

Name	IEEE International Reliability Physics Symposium Proceedings
ISSN (Print)	1541-7026

Conference

Conference	2024 IEEE International Reliability Physics Symposium, IRPS 2024
Country/Territory	United States
City	Grapevine
Period	14/04/24 → 18/04/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

InGaN LED degradation
Machine vision and AI recognition
Material characterizations
Protective circuit design
Saline mist environment

Access to Document

10.1109/IRPS48228.2024.10529494

Fingerprint

Dive into the research topics of 'Machine Vision Observation, Artificial Intelligence Pattern Recognition, Protective Circuit Design, Characterization of Multiple Materials, and Nano-Structural Analysis for Investigating InGaN Green Light Emitting Diode Degradation in a Salty Water Vapor Environment'. Together they form a unique fingerprint.

Cite this

Chen, C. S., Yang, C. Y., Yang, S. J., Wang, D. Y., Kuo, Y. W., Hsiao, W. H., Chou, H. H., Lin, C. F., Li, Y. H., Wu, Y. S., Chen, H., & Han, J. (2024). Machine Vision Observation, Artificial Intelligence Pattern Recognition, Protective Circuit Design, Characterization of Multiple Materials, and Nano-Structural Analysis for Investigating InGaN Green Light Emitting Diode Degradation in a Salty Water Vapor Environment. In 2024 IEEE International Reliability Physics Symposium, IRPS 2024 - Proceedings (IEEE International Reliability Physics Symposium Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IRPS48228.2024.10529494

Chen, Cheng Shan ; Yang, Chun Yen ; Yang, Shao Jui et al. / Machine Vision Observation, Artificial Intelligence Pattern Recognition, Protective Circuit Design, Characterization of Multiple Materials, and Nano-Structural Analysis for Investigating InGaN Green Light Emitting Diode Degradation in a Salty Water Vapor Environment. 2024 IEEE International Reliability Physics Symposium, IRPS 2024 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2024. (IEEE International Reliability Physics Symposium Proceedings).

@inproceedings{7b1de6cd6b694f2cb3cf05078586bdaf,

title = "Machine Vision Observation, Artificial Intelligence Pattern Recognition, Protective Circuit Design, Characterization of Multiple Materials, and Nano-Structural Analysis for Investigating InGaN Green Light Emitting Diode Degradation in a Salty Water Vapor Environment",

abstract = "This study delves into the degradation of GaN-based LEDs in saline environments, a relatively underexplored area of research. LEDs are known for their longevity, but face challenges under extreme conditions. Utilizing artificial intelligence, machine vision, and material analysis, this study detects early signs of LED degradation[1], [2]. The results highlight the impact of saline exposure on LED performance, with some LEDs continuing to function for up to 30 minutes before failure. Advanced circuits ensure uninterrupted operation. Encompassing electrical engineering, computer science, and materials science, this study provides a comprehensive approach to LED fault detection and protection.",

keywords = "InGaN LED degradation, Machine vision and AI recognition, Material characterizations, Protective circuit design, Saline mist environment",

author = "Chen, {Cheng Shan} and Yang, {Chun Yen} and Yang, {Shao Jui} and Wang, {Deng Yi} and Kuo, {Yaw Wen} and Hsiao, {Wei Han} and Chou, {Hsin Hung} and Lin, {Chia Feng} and Li, {Yung Hui} and Wu, {Yewchung Sermon} and Hsiang Chen and Jung Han",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE International Reliability Physics Symposium, IRPS 2024 ; Conference date: 14-04-2024 Through 18-04-2024",

year = "2024",

doi = "10.1109/IRPS48228.2024.10529494",

language = "英语",

series = "IEEE International Reliability Physics Symposium Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2024 IEEE International Reliability Physics Symposium, IRPS 2024 - Proceedings",

address = "美国",

}

Chen, CS, Yang, CY, Yang, SJ, Wang, DY, Kuo, YW, Hsiao, WH, Chou, HH, Lin, CF, Li, YH, Wu, YS, Chen, H & Han, J 2024, Machine Vision Observation, Artificial Intelligence Pattern Recognition, Protective Circuit Design, Characterization of Multiple Materials, and Nano-Structural Analysis for Investigating InGaN Green Light Emitting Diode Degradation in a Salty Water Vapor Environment. in 2024 IEEE International Reliability Physics Symposium, IRPS 2024 - Proceedings. IEEE International Reliability Physics Symposium Proceedings, Institute of Electrical and Electronics Engineers Inc., 2024 IEEE International Reliability Physics Symposium, IRPS 2024, Grapevine, United States, 14/04/24. https://doi.org/10.1109/IRPS48228.2024.10529494

Machine Vision Observation, Artificial Intelligence Pattern Recognition, Protective Circuit Design, Characterization of Multiple Materials, and Nano-Structural Analysis for Investigating InGaN Green Light Emitting Diode Degradation in a Salty Water Vapor Environment. / Chen, Cheng Shan; Yang, Chun Yen; Yang, Shao Jui et al.
2024 IEEE International Reliability Physics Symposium, IRPS 2024 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2024. (IEEE International Reliability Physics Symposium Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Machine Vision Observation, Artificial Intelligence Pattern Recognition, Protective Circuit Design, Characterization of Multiple Materials, and Nano-Structural Analysis for Investigating InGaN Green Light Emitting Diode Degradation in a Salty Water Vapor Environment

AU - Chen, Cheng Shan

AU - Yang, Chun Yen

AU - Yang, Shao Jui

AU - Wang, Deng Yi

AU - Kuo, Yaw Wen

AU - Hsiao, Wei Han

AU - Chou, Hsin Hung

AU - Lin, Chia Feng

AU - Li, Yung Hui

AU - Wu, Yewchung Sermon

AU - Chen, Hsiang

AU - Han, Jung

PY - 2024

Y1 - 2024

N2 - This study delves into the degradation of GaN-based LEDs in saline environments, a relatively underexplored area of research. LEDs are known for their longevity, but face challenges under extreme conditions. Utilizing artificial intelligence, machine vision, and material analysis, this study detects early signs of LED degradation[1], [2]. The results highlight the impact of saline exposure on LED performance, with some LEDs continuing to function for up to 30 minutes before failure. Advanced circuits ensure uninterrupted operation. Encompassing electrical engineering, computer science, and materials science, this study provides a comprehensive approach to LED fault detection and protection.

AB - This study delves into the degradation of GaN-based LEDs in saline environments, a relatively underexplored area of research. LEDs are known for their longevity, but face challenges under extreme conditions. Utilizing artificial intelligence, machine vision, and material analysis, this study detects early signs of LED degradation[1], [2]. The results highlight the impact of saline exposure on LED performance, with some LEDs continuing to function for up to 30 minutes before failure. Advanced circuits ensure uninterrupted operation. Encompassing electrical engineering, computer science, and materials science, this study provides a comprehensive approach to LED fault detection and protection.

KW - InGaN LED degradation

KW - Machine vision and AI recognition

KW - Material characterizations

KW - Protective circuit design

KW - Saline mist environment

UR - http://www.scopus.com/inward/record.url?scp=85194091024&partnerID=8YFLogxK

U2 - 10.1109/IRPS48228.2024.10529494

DO - 10.1109/IRPS48228.2024.10529494

M3 - 会议稿件

AN - SCOPUS:85194091024

T3 - IEEE International Reliability Physics Symposium Proceedings

BT - 2024 IEEE International Reliability Physics Symposium, IRPS 2024 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2024 IEEE International Reliability Physics Symposium, IRPS 2024

Y2 - 14 April 2024 through 18 April 2024

ER -

Chen CS, Yang CY, Yang SJ, Wang DY, Kuo YW, Hsiao WH et al. Machine Vision Observation, Artificial Intelligence Pattern Recognition, Protective Circuit Design, Characterization of Multiple Materials, and Nano-Structural Analysis for Investigating InGaN Green Light Emitting Diode Degradation in a Salty Water Vapor Environment. In 2024 IEEE International Reliability Physics Symposium, IRPS 2024 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2024. (IEEE International Reliability Physics Symposium Proceedings). doi: 10.1109/IRPS48228.2024.10529494

Machine Vision Observation, Artificial Intelligence Pattern Recognition, Protective Circuit Design, Characterization of Multiple Materials, and Nano-Structural Analysis for Investigating InGaN Green Light Emitting Diode Degradation in a Salty Water Vapor Environment

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this