Machine vision inspection of early failure and line-shaped defects of blue InGaN/GaN light emitting diodes soaked in liquid nitrogen for cryogenic tests
•Cryogenic tests reveal spectral shifts in LEDs.•Line-shaped defects appear on GaN LED surface.•Machine vision detects early areas of degradation.•Reliability studies show promise for Space and Arctic applications. Cryogenic tests were conducted on blue InGaN/GaN light-emitting diodes (LEDs) by imme...
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| Published in | Results in physics Vol. 50; p. 106594 |
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| Main Authors | , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier B.V
01.07.2023
Elsevier |
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| Abstract | •Cryogenic tests reveal spectral shifts in LEDs.•Line-shaped defects appear on GaN LED surface.•Machine vision detects early areas of degradation.•Reliability studies show promise for Space and Arctic applications.
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. |
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| AbstractList | 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. •Cryogenic tests reveal spectral shifts in LEDs.•Line-shaped defects appear on GaN LED surface.•Machine vision detects early areas of degradation.•Reliability studies show promise for Space and Arctic applications. 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. |
| ArticleNumber | 106594 |
| Author | Yang, Chun-Yen Yang, Shao-Jui Hon, Mou-Tuong Lin, You-Li Yu, Chih-Yuan Kang, Hsuan-Chia Kuo, Yaw-Wen Chou, Hsin-Hung Zhang, Yi-Zong Han, Jung Chen, Hsiang Lin, Chia-Feng Li, Yung-Hui Wu, You-Lin |
| Author_xml | – sequence: 1 givenname: Chun-Yen surname: Yang fullname: Yang, Chun-Yen organization: Department of Electrical Engineering, National Chi Nan University, Nantou 54561, Taiwan – sequence: 2 givenname: Chih-Yuan surname: Yu fullname: Yu, Chih-Yuan organization: Department of Applied Materials and Optoelectronic Engineering, National Chi Nan University, Nantou 54561, Taiwan – sequence: 3 givenname: You-Li surname: Lin fullname: Lin, You-Li organization: Department of Applied Materials and Optoelectronic Engineering, National Chi Nan University, Nantou 54561, Taiwan – sequence: 4 givenname: Mou-Tuong surname: Hon fullname: Hon, Mou-Tuong organization: Department of Applied Materials and Optoelectronic Engineering, National Chi Nan University, Nantou 54561, Taiwan – sequence: 5 givenname: Shao-Jui surname: Yang fullname: Yang, Shao-Jui organization: Department of Applied Materials and Optoelectronic Engineering, National Chi Nan University, Nantou 54561, Taiwan – sequence: 6 givenname: Yi-Zong surname: Zhang fullname: Zhang, Yi-Zong organization: Department of Computer Science & Information Engineering, National Chi Nan University, Nantou 54561, Taiwan – sequence: 7 givenname: Hsuan-Chia surname: Kang fullname: Kang, Hsuan-Chia organization: Department of Computer Science & Information Engineering, National Chi Nan University, Nantou 54561, Taiwan – sequence: 8 givenname: Yaw-Wen surname: Kuo fullname: Kuo, Yaw-Wen organization: Department of Electrical Engineering, National Chi Nan University, Nantou 54561, Taiwan – sequence: 9 givenname: Chia-Feng surname: Lin fullname: Lin, Chia-Feng organization: Department of Materials Science and Engineering, National Chung Hsing University, Taichung 40227, Taiwan – sequence: 10 givenname: You-Lin surname: Wu fullname: Wu, You-Lin organization: Department of Electrical Engineering, National Chi Nan University, Nantou 54561, Taiwan – sequence: 11 givenname: Hsin-Hung surname: Chou fullname: Chou, Hsin-Hung email: chouhh@ncnu.edu.tw organization: Department of Computer Science & Information Engineering, National Chi Nan University, Nantou 54561, Taiwan – sequence: 12 givenname: Hsiang orcidid: 0000-0002-5325-9265 surname: Chen fullname: Chen, Hsiang email: hchen@ncnu.edu.tw organization: Department of Applied Materials and Optoelectronic Engineering, National Chi Nan University, Nantou 54561, Taiwan – sequence: 13 givenname: Yung-Hui surname: Li fullname: Li, Yung-Hui organization: AI Research Center, Hon Hai Research Institute, Taipei 11494, Taiwan – sequence: 14 givenname: Jung surname: Han fullname: Han, Jung organization: Department of Electrical Engineering, Yale University, New Haven, CT 06511, USA |
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| Keywords | InGaN/GaN blue LED Machine vision Line defects Lattice deformation Liquid nitrogen |
| Language | English |
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| SubjectTerms | InGaN/GaN blue LED Lattice deformation Line defects Liquid nitrogen Machine vision |
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| Title | Machine vision inspection of early failure and line-shaped defects of blue InGaN/GaN light emitting diodes soaked in liquid nitrogen for cryogenic tests |
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