Machine vision inspection of early failure and line-shaped defects of blue InGaN/GaN light emitting diodes soaked in liquid nitrogen for cryogenic tests

• Published in: Results in Physics
• Main Authors: Chun-Yen Yang, Chih-Yuan Yu, You-Li Lin, Mou-Tuong Hon, Shao-Jui Yang, Yi-Zong Zhang, Hsuan-Chia Kang, Yaw-Wen Kuo, Chia-Feng Lin, You-Lin Wu, Hsin-Hung Chou, Hsiang Chen, Yung-Hui Li, Jung Han
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier 2023
• Subjects: InGaN/GaN blue LED; Liquid nitrogen; Machine vision; Line defects; Lattice deformation; Physics; QC1-999; Arctic
• Online Access: https://doi.org/10.1016/j.rinp.2023.106594; https://doaj.org/article/55e0b0b5a5d04e1c8d8458d8df67ad43

Cryogenic tests were conducted on blue InGaN/GaN light-emitting diodes (LEDs) by immersing the device in liquid nitrogen (LN2). To study the degradation of the LEDs, a combination of optical, electrical, and material characterizations were performed on the LN2-soaked device. The results indicate that noticeable emission spectral shifts were observed during the LED immersion due to lattice deformation. Moreover, clustering of cracks was generated in the cross-section, and line-shaped defects appeared on the surface of the GaN LED, which was different from the defects on AlInGaP LEDs after LN2 immersion. To track the trend of device damage, computer-assisted OpenCV-Python software was used to zoom in on nuanced variations in certain areas of the emission images. Early degradation of certain areas can be detected by computer-assisted machine vision, which has the potential to provide an early warning and protective circuits for the system consisting of the initial degraded LEDs. Reliability studies on LN2-soaked InGaN/GaN LEDs show promise in studying the reliability issues of GaN device applications in Arctic regions and space exploration.