Artificial Intelligence-Based Reliability and Material Characterization of AlInGaP LEDs in Salty Water Environments

Deng Yi Wang, You Li Lin, Yu Tung Chen, En Ting He, Wei Cheng Chen, Yaw Wen Kuo, Wei Han Hsiao, Hsin Hung Chou, Chia Feng Lin, Yung Hui Li, Yewchung Sermon Wu, Wen Chang Huang*, Hsiang Chen*, Dong Sing Wuu, Jung Han

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

Abstract

Reliability investigations are crucial for the advancement of light-emitting diode (LED) technology. Salty water vapor exposure poses a significant risk to the performance and longevity of AlInGaP LEDs, accelerating degradation. This study integrates multiple methods, including electrical and material analysis, machine vision, artificial intelligence (AI) recognition, and circuit design, to monitor the real-time degradation of LEDs. Our experiments reveal that exposure to saltwater vapor induces structural damage, including the detachment of the epitaxial layer and crack formation, leading to diminished of LED performance. Within 30 minutes of saltwater exposure, the device showed significant degradation in materials, electrical properties, and luminous areas, especially, the luminous area dropped to below 50% of its original brightness. Additionally, AI-based damage identification is implemented to monitor the LED's state, triggering a protective circuit as the luminous area is less than 50%. This research offers valuable insights into LED degradation, real-time monitoring, and protective measures due to damage caused by saltwater vapor, and proposes a promising solution for early fault detection, paving the way for more robust LED-related systems in harsh environments.

Original languageEnglish
Pages (from-to)170936-170945
Number of pages10
JournalIEEE Access
Volume12
DOIs
StatePublished - 2024

Bibliographical note

Publisher Copyright:
© 2024 The Authors.

Keywords

  • AI recognition
  • AlInGaP LED
  • machine vision
  • protective circuit
  • salt water damage

Fingerprint

Dive into the research topics of 'Artificial Intelligence-Based Reliability and Material Characterization of AlInGaP LEDs in Salty Water Environments'. Together they form a unique fingerprint.

Cite this