Automated optical inspection system for surface mount device light emitting diodes
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Surface-mount device light emitting diode (SMD-LED) is characterized by small size, wide viewing angle and light weight. It becomes the main package type of LED gradually. The traditional visual inspection is likely to cause misrecognition due to personal subjectivity and different defect recognition standards. Therefore, this study develops an automatic SMD-LED defect detection system, which is characterized by non-contact inspection, defect recognition standardization and upgrading product quality. It detects the common and important defects in LED package components, including missing component, no chip, wire shift and foreign material. In this study the gray scale characteristic of histogram is used as the rapid sieving analysis indicator of missing component defect, and then the component and solder joint are positioned by using fast normalized cross-correlation, and the maximum correlation coefficient value is used as judgment indicator of no chip defect. In order to overcome the difficult identification as the weld line is subject to light rays, the improved Michelson-like contrast (MLC) enhancement is proposed, and the segmentation threshold is selected by entropy information to segment the weld line successfully. Furthermore, in order to overcome the effect of the tolerance of component size and internal electrode and unfixed weld line position resulted from lead frame process on foreign material detection result, the multiscale adaptive Fourier analysis (MAFA) is proposed in the concept of texture anomaly detection for foreign material defect detection. The result proves that the proposed method can segment the defect effectively and correctly compared with the phase-only transform (PHOT) and multiscale phase-only transform (MPHOT), and it can be used in other fields of texture anomaly detection. The overall recognition rate of this system is 98.25%, contributing to the large market demand and high component quality of LED industry.
KeywordsLED package component Defect inspection Texture inspection Adaptive Fourier analysis
The research was supported by the Ministry of Science and Technology of the Republic of China under the Grant No. MOST 104-2221-E-011-156.
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