Defect Detection on LED Chips Based on Position Pre-Estimation and Feature Enhancement

Defect Detection on LED Chips Based on Position Pre-Estimation and Feature Enhancement

Light-emitting diode (LED) chips have disordered arrangement and defects with characteristics of low contrast, for which traditional segmentation methods cannot classify surface defects effectively. In this paper, a chip segmentation method based on position pre-estimation and a modified Normalized...

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Bibliographic Details
Published in:Applied sciences Vol. 12; no. 3; p. 1265
Main Authors: Xu, Lu, Hu, Xuejuan, He, Ting, Hu, Kai, Zhang, Jaming
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-02-2022
Subjects:
Accuracy
accurate segmentation
Algorithms
Correlation coefficient
Correlation coefficients
Decomposition
defect identification
Defects
Efficiency
Inspection
LED chip
Light emitting diodes
Methods
Noise
position pre-estimation
Registration
Segmentation
Semiconductors
Wavelet transforms
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Summary:Light-emitting diode (LED) chips have disordered arrangement and defects with characteristics of low contrast, for which traditional segmentation methods cannot classify surface defects effectively. In this paper, a chip segmentation method based on position pre-estimation and a modified Normalized Correlation Coefficient (NCC) matching algorithm, as well as feature enhancement methods are proposed. The position pre-estimation method is used to avoid the interference introduced by the disordered chip arrangement and the large missing area. By modifying the NCC algorithm, matching speed is improved by eight times compared to traditional NCC while matching result is not affected by brightness change. Furthermore, feature enhancement schemes with higher speed and accuracy were designed to identify low-contrast defects. The experimental results showed that the average accuracy reached 99.54%, improved by 0.66% compared to the state-of-the-art method while the inspection missing rate was 0.03%. In addition, the detection time of a single chip was approximately 1.098 ms, which meets the requirements of online detection, and the smallest defect that could be detected was 2 µm. In summary, the methods proposed in this study meet the requirements of industrial online detection regardless of accuracy, efficiency, or extensibility.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12031265