# Defect Detection on LED Chips Based on Position Pre-Estimation and Feature Enhancement
###### Abstract  
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 \(\upmu\)m. In summary, the methods proposed in this study meet the requirements of industrial online detection regardless of accuracy, efficiency, or extensibility. LED chip; position pre-estimation; accurate segmentation; defect identification +
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model [10] to extract chip region and proposed a method based on multi-order fractional Discrete Wavelet Packet Decomposition (DWPD) to process high-frequency parts. However, either wavelet transform or DWPD only performs well in regular texture, while the collected images always have complex, irregular textures. In addition, pixel value in texture region may be different when interfered with noise, that is why the calculation work is large, but the effect is poor. The method based on image registration is also used as a hotspot in chip inspection. After procedures such as image registration, subtraction, and segmentation through SURF [11], [PERSON] [12] used contour extraction and pattern recognition to detect defects and successfully applied to integrated circuit chips. [PERSON] [13] proposed a method based on region segmentation search; by using Canny [14] for segmentation and comparing information of the constructed edge, defect edge was well extracted and highlighted. Combining morphological open and close operations with the K-means algorithm could effectively segment chips from images even under Gaussian noise [15], but pixels on the edge were not smooth after segmentation. It is noteworthy that segmentation methods based on image registration perform poorly when a large chip area is missing. In addition, standard chip picture is a crucial factor when using image registration, however, in actual production, different batches of products vary in structure, contour, and color, which increases the difficulty of obtaining standard pictures. Convolutional neural networks (CNNs) [16; 17; 18; 19; 20] are also used in wafer defect detection because of their objective ability to automatically learn image features. [PERSON] [21] designed a six-layer LED-Net to achieve 95% accuracy on line and scratch defects, and used CAM [22] to locate defect location, but owing to the small number of training samples, types of detectable defects could not be expanded. In response to this problem, [PERSON] [23] used affine transformation and generative adversarial networks (GAN) [24] to increase dataset, which enhanced generalization ability of the classifier. A feature algorithm that combines YOLOv3 and GAN was proposed in the same year, and the universality of the network was improved by generating realistic pseudo-defect images through GAN. Based on traditional YOLOv3 [25], [PERSON] [26] improved the backbone of the network and designed YOLOv3-dense model, which not only saved model training time through fewer training pictures, but also improved accuracy.