Reliability Investigations, Machine Vision Recognition, and Artificial Intelligence-Driven Protective Circuit Design of Extreme Temperature Fluctuations on Orange AlInGaP Light-Emitting Diodes

This study investigates the stability and degradation of AlInGaP-based yellow light-emitting diodes (LEDs) subjected to extreme temperature cycling between $106~^{\circ }$ C and $- 196~^{\circ }$ C. Over 120 min, significant changes in electrical and optical properties were observed, including current surges, emission wavelength shifts, and the emergence of surface defects. Furthermore, multiple morphological and material analyses revealed that cracks, elemental diffusion, and nanostructural damage were found within the LED. To monitor and protect the system, including LED devices from total failure, two protective circuit designs were developed: one using resistance-based detection and another leveraging artificial intelligence (AI)-driven image recognition. Both methods automatically redirect current to backup LEDs upon detecting anomalies. This comprehensive approach integrates materials science, optics, AI, and machine vision, contributing to the reliability of optoelectronic devices in extreme environments.