Chip-level mass detection for micro-LED displays based on regression analysis and deep learning Article Swipe
YOU?
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· 2024
· Open Access
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· DOI: https://doi.org/10.1364/oe.515688
Though micro-light-emitting diode (micro-LED) displays are regarded as the next-generation emerging display technology, challenges such as defects in LED's light output power and radiation patterns are critical to the commercialization success. Here we propose an electroluminescence mass detection method to examine the light output quality from the on-wafer LED arrays before they are transferred to the display substrate. The mass detection method consists of two stages. In the first stage, the luminescent image is captured by a camera by mounting an ITO (indium-tin oxide) transparent conducting glass on the LED wafer. Due to the resistance of the ITO contact pads and on-wafer n-type electrodes, we develop a calibration method based on the circuit model to predict the current flow on each LED. The light output power of each device is thus calibrated back by multi-variable regression analysis. The analysis results in an average variation as low as 6.89% for devices predicted from luminescent image capturing and actual optical power measurement. We also examine the defective or non-uniform micro-LED radiation profiles by constructing a 2-D convolutional neural network (CNN) model. The optimized model is determined among three different approaches. The CNN model can recognize 99.45% functioning LEDs, and show a precision of 96.29% for correctly predicting good devices.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1364/oe.515688
- OA Status
- gold
- Cited By
- 5
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391354039
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391354039Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1364/oe.515688Digital Object Identifier
- Title
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Chip-level mass detection for micro-LED displays based on regression analysis and deep learningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-30Full publication date if available
- Authors
-
Hung-Yi Chiang, Szu-An Chen, Jyun-Jhe Chou, Kuan‐Heng Lin, Yihong Chen, Chi‐Sheng Shih, Jian‐Jang HuangList of authors in order
- Landing page
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https://doi.org/10.1364/oe.515688Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1364/oe.515688Direct OA link when available
- Concepts
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Light-emitting diode, Materials science, Optoelectronics, Wafer, Diode, Calibration, Computer science, Indium tin oxide, Substrate (aquarium), Optics, Artificial intelligence, Nanotechnology, Physics, Thin film, Geology, Quantum mechanics, OceanographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
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2025: 4, 2024: 1Per-year citation counts (last 5 years)
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25Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| primary_location.source.display_name | Optics Express |
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| primary_location.source.host_organization_name | Optica Publishing Group |
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| primary_location.source.host_organization_lineage_names | Optica Publishing Group |
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| primary_location.raw_source_name | Optics Express |
| primary_location.landing_page_url | https://doi.org/10.1364/oe.515688 |
| publication_date | 2024-01-30 |
| publication_year | 2024 |
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