Research On Detection Algorithms And Key Technologies For Surface Defects Of Ultra-Thin High-Density Flexible Integrated Circuit Package Substrates

Ultra-thin and high-density flexible integrated circuit packaging substrates(FICS)are developing in the direction of higher integration density,lighter and thinner,more flexible and twisted based on flexible circuit boards.As the IC industry enters the 7-14nanometer process,ultra-thin and high-density FICS has also entered the process below2m,and the quality control performance requirements for materials and manufacturing processes are also constantly improving.In the FICS surface defect detection process,only relying on the traditional detection methods of substrates can no longer meet the accuracy requirements of industrial production.In FICS image preprocessing,there are problems such as removing noise while removing texture information of the original image.For the FICS substrate microscopic imaging image obtained under a high-power microscope,the texture structure and defects are magnified at the same time,and various types of defects show diversity.Among them,the pixel value of the oxidation area shows a discrete distribution,and there are problems such as inaccurate detection of oxidation defects.For ultra-thin and high-density FICS line inspection without reference template,the inspection results are prone to cause problems such as line discontinuity and inaccurate positioning of line defects.How to design a high-precision,efficient,and robust detection algorithm at the same time has become a major problem.This paper mainly proposes the following five mathematical models for the problems that occur in the appearance of ultra-thin and high-density FICS visual inspection:1.In the pre-processing of ultra-thin and high-density FICS images,two smooth models that combine level set curvature features and gradient features are proposed.In order to effectively remove noise while retaining more original image details.First,the two models proposed in this paper combine the level set curvature feature of the image with the gradient threshold,and use richer second-order differential information as a detection factor to remove noise in the image.Secondly,theoretical analysis shows that the de-noised image obtained by the two proposed models can retain more detailed texture information and edge information of the original image.In addition,experimental analysis shows that compared with other models,the proposed model has the highest structural similarity and peak signal-to-noise ratio,and has a relatively high edge retention index and the lowest mean square error.In particular,the de-noised image through model 1has the highest structural similarity and peak signal-to-noise ratio,and the lowest mean square error.The de-noised image through model 2 has a relatively high edge preservation index.The method proposed in this paper can effectively remove the noise of ultra-thin and high-density FICS images,and retain the original details and edge information of the image.2.In the preprocessing of ultra-thin and high-density FICS images,an image de-noising model that combines gradient and adaptive curvature features is proposed.The model proposed in this paper can adaptively adjust the weight of the level set curvature and gradient features of the image.Through this method,richer image first-order and second-order differential information can be used as detection factors for image de-noising processing.Theoretical analysis shows that:the diffusion power of the model in the flat area of the image is greater than that of the P-M model;at the corners and peaks of the image,the model in this paper can suppress the reduction of gray value and retain more details and edge information of the image.The experimental analysis shows that the model in this paper is compared with the P-M model,C model,and G-C model to remove Gaussian noise and salt and pepper noise.Experimental results show that the model in this paper has the highest structural similarity,peak signal-to-noise ratio and edge retention index.The method proposed in this paper can effectively remove the noise of ultra-thin,high-density flexible FICS images,while retaining more original details and edge information of the image,which has practical engineering significance.3.Aiming at solving the problem of inaccurate detection of surface oxidation defect distribution in ultra-thin and high-density fisc under high magnification,a regional vari-ational level set image segmentation model is proposed.In the process of ultra-thin and high-density FICS image defect detection,first of all,for the images inside the evolution curve,this paper uses the weighted sum of the mean value of the cluster center points obtained after K-means clustering and the filtered image as the fitting value inside the evolution curve.At the same time,for the image filtering inside the evolution curve,an anisotropic diffusion model that combines the curvature and gradient features of the level set is proposed for filtering.Secondly,the model in this paper uses the maximum absolute mean difference to adaptively adjust the weight ratio between the inside and outside of the evolution curve.Finally,the algorithm is applied to the detection of ultra-thin and high-density FICS surface oxidation defects.The experimental comparison results show that the application of the regional variational level set segmentation method proposed in this paper makes the extracted oxidation defects have higher segmentation accuracy.4.Aiming at solving the problem of discontinuous lines and inaccurate defect loca-tion in the ultra-thin and high-density FICS line inspection without reference template,a detection algorithm for extracting FICS image line features is proposed.In the process of ultra-thin and high-density FICS image defect detection,firstly,the detection algo-rithm steps proposed in this paper perform K-Means classification of color FICS images.Secondly,perform median filtering,morphological filling,and closing operations to ob-tain the binary image to be segmented.In the last step,an image segmentation model with convexity-preserving and indirect regularity level set is proposed,which is used in extracting ultra-thin and high-density FICS image line features.The proposed energy functional model consists of data item,connection item,and regular item.The data item guides the contour to move to the target boundary;the connection item is the connection item between the level set function and the auxiliary function,so that the level set func-tion cannot deviate from the auxiliary function in the evolution process;the regular item is the regular item of the auxiliary function,which prevents the level set function from oscillating during the evolution process,and the excessive smoothness of the level set function is weakened to avoid boundary leakage.The model proposed in this paper is a strictly convex function in a given L~2(?)space,and there is a globally unique minimum.Experimental simulation proves that the application of the algorithm in this paper in extracting FICS line features can achieve precise extraction of line features and smooth line boundaries,which is an important pavement for high-precision measurement of line width and line spacing,and also is an important pavement for high-precision location of defects.