@inproceedings{c98f59044bdf4eca88d7537f9373192e,
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 = "English",
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 = "United States",
}