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

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.

Keywords

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