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- W4385237903 abstract "Abstract In the process of chip production and transportation, the surface of the package body is prone to various defects, and these defects will have a certain impact on the quality of the chip. Therefore, in order to ensure the quality of the chip products and meet the needs of relevant industries, it is necessary to use certain technical equipment and algorithms to achieve efficient acquisition and processing of the chip image and timely and accurately identify the defects on the chip surface. In this study, a series of pre-processing including gray-scale, geometric correction, region segmentation and enhancement, threshold segmentation and noise reduction are carried out on the collected chip images. Secondly, an effective method of scratch determination and character segmentation is proposed to realize the feature extraction of scratch and character on the surface of the chip package. Then recognition of the position and size of the scratch is realized by using the centroid coordinates, boundary and roundness of the connected domain of the scratch binary image. Finally, convolutional neural network is used to establish the character data set, adjust the network frame and parameters, and train a model with a recognition rate of 90.63%. The research content and results of this paper have certain reference significance for ensuring chip quality." @default.
- W4385237903 created "2023-07-26" @default.
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- W4385237903 date "2023-07-25" @default.
- W4385237903 modified "2023-10-16" @default.
- W4385237903 title "Research on preprocessing and recognition of surface defects of chip package" @default.
- W4385237903 doi "https://doi.org/10.21203/rs.3.rs-3169059/v1" @default.
- W4385237903 hasPublicationYear "2023" @default.
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