CN108596875B - Image segmentation algorithm-based semiconductor chip flash rapid detection method - Google Patents
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Abstract
本发明公开了一种基于图像分割算法的半导体芯片溢料快速检测方法,旨在通过识别集成电路EMC(Epoxy Molding Compond)封装过程中溢出到引脚和外露载体上的多余物,从而为EMC封装过程提供及时的反馈信息。首先,将工业相机采集到的EMC封装成形后原片图像进行预处理,接着利用选择性搜索(Selective search)算法进行图像分割得到目标候选区域,并从中筛选出待检测芯片区域,再对筛选出的区域进行阈值分割得到溢料区域。该方法能够在极短的时间内给出每片芯片的溢料检测结果,不仅优化了EMC封装过程中的溢料检测手段,并且为EMC封装过程提供了实时的反馈的信息,从而提升EMC封装的性能。
The invention discloses a rapid detection method for semiconductor chip overflow based on an image segmentation algorithm, which aims to identify the excess material that overflows onto pins and exposed carriers during the packaging process of integrated circuit EMC (Epoxy Molding Comppond), so as to provide EMC packaging. The process provides timely feedback. First, preprocess the original image of the EMC package collected by the industrial camera, and then use the Selective search algorithm to segment the image to obtain the target candidate area, and filter out the area of the chip to be detected. The area of is subjected to threshold segmentation to obtain the overflow area. The method can give the flash detection result of each chip in a very short time, which not only optimizes the flash detection method in the EMC packaging process, but also provides real-time feedback information for the EMC packaging process, thereby improving the EMC packaging process. performance.
Description
技术领域technical field
本发明属于集成电路EMC封装溢料检测领域,特别是涉及一种基于图像分割算法的半导体芯片溢料快速检测方法。The invention belongs to the field of integrated circuit EMC package overflow detection, and in particular relates to a semiconductor chip overflow rapid detection method based on an image segmentation algorithm.
背景技术Background technique
EMC是一种重要的微电子封装材料,通过封装工艺将半导体芯片包覆形成保护,以免受到外部环境的破坏,同时EMC也起到一定的散热效果。EMC具备可规模化生产和合理的可靠性特点,是半导体封装常见的封装材料之一,以其独特的优势占据了95%以上的封装市场。但是,在半导体塑封工艺中,由于引线框架和塑封膜具之间存在缝隙,胶状塑封材料会从缝隙中渗漏出来,固化在引线体表面,形成黑色溢料,是集成电路引脚和外露载体上的多余物。溢料本身不会对塑封产品的性能产生不良影响,但是由于溢出的塑封聊覆盖在引线脚上,如不处理则会形成绝缘区域而影响后续电镀质量、产品可靠性,造成产品断路、虚焊等问题。因此,如何减少溢料的发生和去除溢料方法一直以来都是困扰着研究人员及生产商的重要问题。EMC is an important microelectronic packaging material. The semiconductor chip is covered by the packaging process to form protection to avoid damage from the external environment. At the same time, EMC also has a certain heat dissipation effect. EMC has the characteristics of large-scale production and reasonable reliability. It is one of the common packaging materials for semiconductor packaging. It occupies more than 95% of the packaging market with its unique advantages. However, in the semiconductor plastic packaging process, due to the gap between the lead frame and the plastic packaging film, the glue-like plastic packaging material will leak out from the gap, solidify on the surface of the lead body, and form black overflow, which is the integrated circuit pin and exposed excess on the carrier. The overflow itself will not adversely affect the performance of the plastic-encapsulated product, but because the overflowing plastic-encapsulated chatter covers the lead pins, if it is not treated, an insulating area will be formed, which will affect the subsequent plating quality and product reliability, resulting in product disconnection and virtual welding. And other issues. Therefore, how to reduce the occurrence of flash and how to remove the flash has always been an important problem for researchers and manufacturers.
EMC封装溢料产生的原因通常为:1)塑封料流动性好、黏度低,合模不紧的情况下塑封料/环氧树脂可能会从分型面溢出而覆盖在引线脚上;2)塑封模具长期使用后表面出现磨损或基座不平整的情况从而造成溢料;3)半导体封装工艺过程中框架精度与模具不匹配,啮合不足造成压边问题。溢料形成后其本身对塑封产品的性能没有影响,但是由于溢出的塑封料覆盖在引线脚上,可能形成镀层缺陷而影响产品的可靠性。目前生产商一般都是经去溢料工艺后再用锉刀锉、砂纸打磨、弱酸清洗去除残留溢料的办法进行补救,但是这种方法容易擦伤塑封体和造成引线脚表面粗糙,影响外观质量和电镀质量,同时也影响生产进度。因此如何避免和减少溢料的产生显得尤为重要。综上,研发可视化的绿色无污染的溢料检测方法,使得能够快速检测芯片溢料情况,以满足及时为EMC封装过程提供参考依据的需求,具有重要的现实意义。The reasons for EMC packaging overflow are usually: 1) The plastic sealing compound has good fluidity and low viscosity, and the plastic sealing compound/epoxy resin may overflow from the parting surface and cover the lead pins if the mold is not tightly closed; 2) After long-term use of the plastic packaging mold, the surface is worn or the base is not flat, which causes flashing; 3) During the semiconductor packaging process, the frame accuracy does not match the mold, and the lack of meshing causes the problem of edge blanking. After the overflow is formed, it has no effect on the performance of the plastic encapsulated product, but because the overflowed plastic encapsulates the lead pins, it may form plating defects and affect the reliability of the product. At present, manufacturers generally use a file, sandpaper grinding, and weak acid cleaning to remove residual flash after the flash removal process. However, this method is easy to scratch the plastic body and cause the surface of the lead pins to be rough, which affects the appearance quality. and electroplating quality, but also affect the production progress. Therefore, how to avoid and reduce the generation of flash is particularly important. In conclusion, it is of great practical significance to develop a visual green and pollution-free overflow detection method, which enables rapid detection of chip overflow to meet the needs of timely provision of reference for the EMC packaging process.
得益于计算机技术和图像处理技术的发展,出现了一种新型可视化溢料检测技术,其检测结果为灰度图像,具有检测精度高、使用灵活、信息量大等特点,非常适合芯片封装生产过程的溢料检测。Thanks to the development of computer technology and image processing technology, a new type of visual flash detection technology has emerged. The detection result is a grayscale image. It has the characteristics of high detection accuracy, flexible use and large amount of information, which is very suitable for chip packaging production. Process overflow detection.
发明内容SUMMARY OF THE INVENTION
本发明的目的在于针对现有EMC封装过程中存在的溢料问题,提供一种快速的溢料检测策略。该策略可在现场对芯片溢料情况进行准确的评估,进而为在役设备调整和维护提供反馈信息,是EMC封装过程的重要参考依据。The purpose of the present invention is to provide a fast flash detection strategy for the flash problem existing in the existing EMC packaging process. This strategy can accurately assess the chip overflow situation on site, and then provide feedback information for the adjustment and maintenance of in-service equipment, which is an important reference for the EMC packaging process.
本发明的目的是通过以下技术方案实现的:一种基于图像分割算法的半导体芯片溢料快速检测方法,该方法包括以下步骤:The object of the present invention is achieved through the following technical solutions: a method for fast detection of semiconductor chip flashing based on an image segmentation algorithm, the method comprises the following steps:
一种基于图像分割算法的半导体芯片溢料快速检测方法,其特征在于,该方法包括以下步骤:A method for rapid detection of semiconductor chip flashing based on an image segmentation algorithm, characterized in that the method comprises the following steps:
(1)利用半导体封装压机(FSAM120-1US)和QFNQFNWB7X7-48L(T0.75)模具生产出封装原片,并利用工业相机获取封装原片的多帧灰度图像;(1) Using a semiconductor packaging press (FSAM120-1US) and a QFNQFNWB7X7-48L (T0.75) mold to produce a packaged original, and use an industrial camera to obtain multi-frame grayscale images of the packaged original;
(2)对每帧图像进行高斯模糊预处理;(2) Gaussian blur preprocessing is performed on each frame of image;
(3)基于选择性搜索的图像分割,得到数百个候选区域并从中筛选出待分析芯片区域,该步骤由以下子步骤来实现:(3) Image segmentation based on selective search, to obtain hundreds of candidate regions and screen out the chip region to be analyzed, this step is realized by the following sub-steps:
(3.1)对单帧图像进行过分割,分割得到n个初始区域r1,r2,...,rn,构成初始区域集合R={r1,r2,...,rn};同时,构建一个初始化相似度集合S, (3.1) Over-segment a single frame image, and obtain n initial regions r 1 , r 2 ,..., rn , and form the initial region set R={r 1 , r 2 ,..., rn } ; At the same time, construct an initialization similarity set S,
(3.2)计算每两个相邻区域的相似度s(ri,rj),相似度指标采取颜色相似度,尺寸相似度,纹理相似度,吻合相似度,四种互补的相似度,分别从不同方面对区域进行合并;(3.2) Calculate the similarity s(r i , r j ) of each two adjacent regions, the similarity index adopts color similarity, size similarity, texture similarity, fit similarity, and four kinds of complementary similarity, respectively. Consolidate regions from different aspects;
(a)颜色相似度的计算方法如下:(a) The calculation method of color similarity is as follows:
对每个初始区域按照像素值划分为q个区间,得到归一化颜色直方图其中,表示第i个初始区域中第k个像素值区间的归一化频数;Divide each initial area into q intervals according to the pixel value to obtain a normalized color histogram in, Indicates the normalized frequency of the kth pixel value interval in the ith initial region;
两个相邻区域ri,rj的颜色相似度为:The color similarity of two adjacent regions r i , r j is:
合并ri,rj得到的新区域rij,其颜色直方图为:The new region r ij obtained by merging r i , r j , its color histogram is:
size(rij)=size(ri)+size(rj) (19)size(r ij )=size(r i )+size(r j ) (19)
其中size(ri)为初始区域ri的尺寸;where size( ri ) is the size of the initial region ri ;
(b)纹理相似度的计算方法为:(b) The calculation method of texture similarity is:
对每个初始区域,计算其SIFT(scale-invariant feature transform)特征,其中尺度因子在本专利中取值为1,直方图区间宽度为10,得到得到作为纹理特征。For each initial region, calculate its SIFT (scale-invariant feature transform) feature, where the scale factor is 1 in this patent, and the histogram interval width is 10, obtaining obtained as texture features.
其中,表示第i个初始区域中第k个纹理特征;in, Represents the kth texture feature in the ith initial region;
两个相邻区域ri,rj的纹理相似度为:The texture similarity of two adjacent regions r i , r j is:
合并ri,rj得到的新区域rij,其纹理直方图为The new region r ij obtained by merging r i , r j has a texture histogram of
(C)尺寸相似度的计算方法为:(C) The calculation method of size similarity is:
其中size(im)表示整个灰度图像的尺寸。where size(im) represents the size of the entire grayscale image.
(d)吻合相似度的计算方法为:(d) The calculation method of coincidence similarity is:
其中BBij为仅包围区域ri和rj的边界盒。where BB ij is a bounding box that only encloses regions ri and r j .
最终将四个相似度综合起来得到最终的相似度:Finally, the four similarities are combined to obtain the final similarity:
s(ri,rj)=a1Scolor(ri,rj)+a2Stexture(ri,rj)+a3Ssize(ri,rj)+a4Sfill(ri,rj)(24)s(r i ,r j )=a 1 Scolor(r i ,r j )+a 2 Stexture(r i ,r j )+a 3 Ssize(r i ,r j )+a 4 Sfill(r i ,r j )(24)
其中ai∈{0,1},表明对应相似度是否被采用。where a i ∈ {0,1}, indicating whether the corresponding similarity is adopted.
(3.3)所有的相似度构成相似度集合S,相似度集合中相似度最大的两个相邻区域s(ri,rj)=max(S),将这两个区域合并rij=ri∪rj,计算该新的区域rij与相邻区域的相似度,更新相似度集合:删除其中与被合并的区域ri相关的所有相似度s(ri,r*),,以及与被合并的区域rj相关的所有相似度s(rj,r#),,加入更新后的相似度集合为:加入新的区域rij与相邻区域的相似度;(3.3) All the similarities constitute the similarity set S, the two adjacent regions with the largest similarity in the similarity set s(r i , r j )=max(S), merge these two regions r ij =r i ∪r j , calculate the similarity between the new region r ij and adjacent regions, update the similarity set: delete all the similarities s(r i , r * ), and All the similarities s(r j , r # ), which are related to the merged region r j , are added to the updated similarity set as: adding the similarity between the new region r ij and the adjacent region;
(3.4)循环步骤3.2-3.3,当相似度集合S为空集时,循环结束。完成初始区域的合并,得到候选区域集合R;(3.4) Loop steps 3.2-3.3, when the similarity set S is an empty set, the loop ends. Complete the merging of the initial regions to obtain the candidate region set R;
(3.5)从候选区域集合R中筛选出上下两个待分析芯片区域,使得待分析区域尺寸在封装原片尺寸的三分之一到二分之一范围内,同时两个待分析区域需要分别包含和这4个关键几何点,封装原片的分辨率为a*b。(3.5) Screen out the upper and lower chip regions to be analyzed from the candidate region set R, so that the size of the region to be analyzed is in the range of one-third to one-half of the size of the original package, and the two regions to be analyzed need to be separately Include and For these four key geometric points, the resolution of the original package is a*b.
(4)对筛选出的待分析区域进行基于阈值的图像分割,该步骤通过以下子步骤来实现:(4) image segmentation based on the threshold is performed on the screened area to be analyzed, and this step is realized by the following sub-steps:
(4.1)计算待分析区域的归一化直方图。令{0,1,...,L-1}表示大小为M*N像素的区域图像中的L个不同的灰度级,nz表示灰度级为z的像素个数。区域图像中的像素总数为MN=n0+n1+n2+...+nL-1,pz=nz/MN,z=0,1,2,...,L-1表示该直方图中的灰度级为z的归一化频数。(4.1) Calculate the normalized histogram of the area to be analyzed. Let {0,1,...,L-1} denote L different gray levels in an area image of size M*N pixels, and n z denote the number of pixels with gray level z. The total number of pixels in the area image is MN=n 0 +n 1 +n 2 +...+n L-1 , p z =n z /MN,z=0,1,2,...,L-1 Represents the normalized frequency of the gray level z in this histogram.
(4.2)对于u=0,1,2,...,L-1,计算累计和P1(u),计算公式如下:(4.2) For u=0,1,2,...,L-1, calculate the cumulative sum P 1 (u), the calculation formula is as follows:
(4.3)对于u=0,1,2,...,L-1,计算累计均值m(u),计算公式如下:(4.3) For u=0,1,2,...,L-1, calculate the cumulative mean value m(u), the calculation formula is as follows:
(4.4)计算全局灰度均值mG,计算公式如下:(4.4) Calculate the global grayscale mean value m G , the calculation formula is as follows:
(4.5)对于u=0,1,2,...,L-1,计算类间方差计算公式如下:(4.5) For u=0,1,2,...,L-1, calculate the inter-class variance Calculated as follows:
(4.6)通过使得目标函数(13)取得最大值,得到Otsu阈值u*,如果最大值不唯一,用相应检测到的各个最大值u的平均得到u*;(4.6) Otsu threshold value u * is obtained by making the objective function (13) obtain the maximum value, if the maximum value is not unique, u * is obtained by the average of the respective detected maximum values u;
(4.7)基于最佳阈值u*利用如下公式对区域图像进行分割,得到分割后图像:(4.7) Segment the regional image based on the optimal threshold u * using the following formula to obtain the segmented image:
f(x,y)为待分析区域图像中像素点(x,y)的像素。分割后像素点为1的代表检测出来的生产过程中的溢料部分。f(x,y) is the pixel of the pixel point (x,y) in the image of the area to be analyzed. After segmentation, the pixel point of 1 represents the detected flashing part in the production process.
本发明的有益效果在于:本发明是面向EMC封装过程的快速溢料检测方法,通过选择性搜索算法将图像分割成几百到几千个候选区域,并从候选区域中筛选出待分析芯片区域。进一步利用最优阈值对待分析图像进行分割得到溢料图像,从而可以衡量溢料情况严重与否。针对每帧图像,本发明都可以在20秒的时间内给出检测结果,能够快速的为EMC封装过程反馈溢料信息,提供参考依据,进而避免更多溢料情况的产生。The beneficial effects of the present invention are as follows: the present invention is a rapid flash detection method oriented to the EMC packaging process, and the image is divided into hundreds to thousands of candidate regions through a selective search algorithm, and the chip region to be analyzed is screened from the candidate regions. . Further, the optimal threshold is used to segment the image to be analyzed to obtain the overflow image, so that the seriousness of the overflow can be measured. For each frame of image, the present invention can provide the detection result within 20 seconds, can quickly feed back the flashing information for the EMC packaging process, and provide a reference basis, thereby avoiding the occurrence of more flashing situations.
附图说明Description of drawings
图1(a)是本发明半导体封装流程图,图1(b)是实际生产操作中的参数设定,图1(c)是最终得到的封装原片;Fig. 1(a) is a flow chart of the semiconductor packaging of the present invention, Fig. 1(b) is the parameter setting in the actual production operation, and Fig. 1(c) is the final package original sheet;
图2是本发明半导体封装溢料检测系统,由工业相机获取原始灰度图像,并传入电脑进行下一步分析;Fig. 2 is the semiconductor package overflow detection system of the present invention, the original grayscale image is obtained by the industrial camera, and transmitted to the computer for the next step analysis;
图3是本发明得到的封装原片灰度图像;Fig. 3 is the encapsulated original sheet grayscale image obtained by the present invention;
图4是本发明方法利用高斯模糊预处理得到的降分辨率图像,在降低了图像分辨率的同时保留了图像完整信息;4 is a reduced resolution image obtained by the method of the present invention utilizing Gaussian blurring preprocessing, which retains the complete information of the image while reducing the image resolution;
图5是本发明方法选择性搜索对图像进行分割后得到的部分结果展示;5 is a partial result display obtained after the method of the present invention is selectively searched to segment the image;
图6是本发明方法筛选出的待分析区域;Fig. 6 is the to-be-analyzed area screened out by the method of the present invention;
图7是本发明方法得到的阈值分割后的最终溢料图像。FIG. 7 is the final flash image obtained by the method of the present invention after threshold segmentation.
具体实施方式Detailed ways
下面结合附图及具体实例,对本发明作进一步详细说明。The present invention will be described in further detail below in conjunction with the accompanying drawings and specific examples.
(1)EMC(Epoxy molding compound)封装:如图1(a)所示,将引线框架置于磨具中,使得每个芯片位于穴位中,模具合模后将块状EMC放入模具空中。按图1(b)所示设定封装压机参数,高温下,EMC开始融化,顺着轨道流向穴位,从底部开始逐渐覆盖芯片,直至完全覆盖包裹完毕,成型固化,如图1(c)所示;(1) EMC (Epoxy molding compound) package: As shown in Figure 1(a), place the lead frame in the mold so that each chip is located in the cavity, and place the bulk EMC in the mold air after the mold is closed. Set the parameters of the packaging press as shown in Figure 1(b). At high temperature, the EMC begins to melt, flows to the acupoints along the track, and gradually covers the chip from the bottom until it is completely covered and wrapped, forming and solidifying, as shown in Figure 1(c) shown;
(2)图像获取与预处理:基于图像的溢料检测系统如图2所示,利用工业相机获取如图3所示的封装原片的灰度图像,其分辨率为3264*4896,通过高斯模糊处理,将其分辨率降为408*612,保留了图像信息完整性的同时,去除了不必要的噪声,降低了图像分辨率从而加快了溢料分析检测的速度,如图4所示。(2) Image acquisition and preprocessing: The image-based flash detection system is shown in Figure 2. The industrial camera is used to obtain the grayscale image of the packaged original film as shown in Figure 3, and its resolution is 3264*4896. The blurring process reduces its resolution to 408*612, which retains the integrity of the image information, removes unnecessary noise, reduces the image resolution, and speeds up the flash analysis and detection, as shown in Figure 4.
(3.1)对单帧图像进行过分割,分割得到n个初始区域r1,r2,...,rn,构成初始区域集合R={r1,r2,...,rn};同时,构建一个初始化相似度集合S, (3.1) Over-segment a single frame image, and obtain n initial regions r 1 , r 2 ,..., rn , and form the initial region set R={r 1 , r 2 ,..., rn } ; At the same time, construct an initialization similarity set S,
(3.2)计算每两个相邻区域的相似度s(ri,rj),相似度指标采取颜色相似度,尺寸相似度,纹理相似度,吻合相似度,四种互补的相似度,分别从不同方面对区域进行合并;(3.2) Calculate the similarity s(r i , r j ) of each two adjacent regions, the similarity index adopts color similarity, size similarity, texture similarity, fit similarity, and four kinds of complementary similarity, respectively. Consolidate regions from different aspects;
(a)颜色相似度的计算方法如下:(a) The calculation method of color similarity is as follows:
对每个初始区域按照像素值划分为q个区间,得到归一化颜色直方图其中,表示第i个初始区域中第k个像素值区间的归一化频数;Divide each initial area into q intervals according to the pixel value to obtain a normalized color histogram in, Indicates the normalized frequency of the kth pixel value interval in the ith initial region;
两个相邻区域ri,rj的颜色相似度为:The color similarity of two adjacent regions r i , r j is:
合并ri,rj得到的新区域rij,其颜色直方图为:The new region r ij obtained by merging r i , r j , its color histogram is:
size(rij)=size(ri)+size(rj) (33)size(r ij )=size(r i )+size(r j ) (33)
其中size(ri)为初始区域ri的尺寸;where size( ri ) is the size of the initial region ri ;
(b)纹理相似度的计算方法为:(b) The calculation method of texture similarity is:
对每个初始区域,计算其SIFT(scale-invariant feature transform)特征,在本发明中,尺度因子在本专利中取值为1,直方图区间宽度为10,得到得到作为纹理特征。For each initial area, calculate its SIFT (scale-invariant feature transform) feature, in the present invention, the scale factor is 1 in this patent, and the histogram interval width is 10, obtaining obtained as texture features.
其中,表示第i个初始区域中第k个纹理特征;in, Represents the kth texture feature in the ith initial region;
两个相邻区域ri,rj的纹理相似度为:The texture similarity of two adjacent regions r i , r j is:
合并ri,rj得到的新区域rij,其纹理直方图为The new region r ij obtained by merging r i , r j has a texture histogram of
(C)尺寸相似度的计算方法为:(C) The calculation method of size similarity is:
其中size(im)表示整个灰度图像的尺寸。where size(im) represents the size of the entire grayscale image.
(d)吻合相似度的计算方法为:(d) The calculation method of coincidence similarity is:
其中BBij为仅包围区域ri和rj的边界盒。where BB ij is a bounding box that only encloses regions ri and r j .
最终将四个相似度综合起来得到最终的相似度:Finally, the four similarities are combined to obtain the final similarity:
s(ri,rj)=a1Scolor(ri,rj)+a2Stexture(ri,rj)+a3Ssize(ri,rj)+a4Sfill(ri,rj)(38)s(r i ,r j )=a 1 Scolor(r i ,r j )+a 2 Stexture(r i ,r j )+a 3 Ssize(r i ,r j )+a 4 Sfill(r i ,r j )(38)
其中ai∈{0,1},表明对应相似度是否被采用。where a i ∈ {0,1}, indicating whether the corresponding similarity is adopted.
(3.3)所有的相似度构成相似度集合S,相似度集合中相似度最大的两个相邻区域s(ri,rj)=max(S),将这两个区域合并rij=ri∪rj,计算该新的区域rij与相邻区域的相似度,更新相似度集合:删除其中与被合并的区域ri相关的所有相似度s(ri,r*),,以及与被合并的区域rj相关的所有相似度s(rj,r#),,加入更新后的相似度集合为:加入新的区域rij与相邻区域的相似度;(3.3) All the similarities constitute the similarity set S, the two adjacent regions with the largest similarity in the similarity set s(r i , r j )=max(S), merge these two regions r ij =r i ∪r j , calculate the similarity between the new region r ij and adjacent regions, update the similarity set: delete all the similarities s(r i , r * ), and All the similarities s(r j , r # ), which are related to the merged region r j , are added to the updated similarity set as: adding the similarity between the new region r ij and the adjacent region;
(3.4)循环步骤3.2-3.3,当相似度集合S为空集时,循环结束。完成初始区域的合并,得到候选区域集合R;(3.4) Loop steps 3.2-3.3, when the similarity set S is an empty set, the loop ends. Complete the merging of the initial regions to obtain the candidate region set R;
(3.5)从候选区域集合R中筛选出上下两个待分析芯片区域,如图6所示,使得待分析区域尺寸在封装原片尺寸的三分之一到二分之一范围内,同时两个待分析区域需要分别包含和这4个关键几何点,封装原片的分辨率为a*b。(3.5) Screen out the upper and lower chip regions to be analyzed from the candidate region set R, as shown in Figure 6, so that the size of the region to be analyzed is within the range of one-third to one-half of the size of the original package, while the two Each area to be analyzed needs to contain and For these four key geometric points, the resolution of the original package is a*b.
(3.4)循环结束,得到候选区域R;对图4所示灰度图像进行上述选择性搜索算法步骤,将得到214个候选区域,其中部分候选区域如图5所示。(3.4) At the end of the cycle, a candidate region R is obtained; the above-mentioned selective search algorithm steps are performed on the grayscale image shown in Figure 4, and 214 candidate regions will be obtained, some of which are shown in Figure 5.
(3.5)区域筛选:设置筛选条件,从候选区域R中筛选出待分析芯片区域。对图4所示灰度图像进行筛选,将得到如图6右侧两边图像所示的待分析区域。(3.5) Region screening: set the screening conditions, and select the chip region to be analyzed from the candidate region R. Screening the grayscale image shown in Fig. 4 will obtain the area to be analyzed as shown in the images on the right side of Fig. 6 .
(4)对筛选出的待分析区域进行基于阈值的图像分割,该步骤通过以下子步骤来实现:(4) image segmentation based on the threshold is performed on the screened area to be analyzed, and this step is realized by the following sub-steps:
(4)对筛选出的待分析区域进行基于阈值的图像分割,该步骤通过以下子步骤来实现:(4) image segmentation based on the threshold is performed on the screened area to be analyzed, and this step is realized by the following sub-steps:
(4.1)计算待分析区域的归一化直方图。令{0,1,...,L-1}表示大小为M*N像素的区域图像中的L个不同的灰度级,nz表示灰度级为z的像素个数。区域图像中的像素总数为MN=n0+n1+n2+...+nL-1,pz=nz/MN表示该直方图中的灰度级为z的归一化频数,z=0,1,2,...,L-1。(4.1) Calculate the normalized histogram of the area to be analyzed. Let {0,1,...,L-1} denote L different gray levels in an area image of size M*N pixels, and n z denote the number of pixels with gray level z. The total number of pixels in the area image is MN=n 0 +n 1 +n 2 +...+n L-1 , p z =n z /MN represents the normalized frequency of the gray level z in the histogram , z=0,1,2,...,L-1.
(4.2)对于u=0,1,2,...,L-1,计算累计和P1(u),计算公式如下:(4.2) For u=0,1,2,...,L-1, calculate the cumulative sum P 1 (u), the calculation formula is as follows:
(4.3)对于u=0,1,2,...,L-1,计算累计均值m(u),计算公式如下:(4.3) For u=0,1,2,...,L-1, calculate the cumulative mean value m(u), the calculation formula is as follows:
(4.4)计算全局灰度均值mG,计算公式如下:(4.4) Calculate the global grayscale mean value m G , the calculation formula is as follows:
(4.5)对于u=0,1,2,...,L-1,计算类间方差计算公式如下:(4.5) For u=0,1,2,...,L-1, calculate the inter-class variance Calculated as follows:
(4.6)通过使得目标函数(13)取得最大值,得到Otsu阈值u*,如果最大值不唯一,用相应检测到的各个最大值u的平均得到u*;(4.6) Otsu threshold value u * is obtained by making the objective function (13) obtain the maximum value, if the maximum value is not unique, u * is obtained by the average of the respective detected maximum values u;
(4.7)基于最佳阈值u*利用如下公式对区域图像进行分割,得到分割后图像:(4.7) Segment the regional image based on the optimal threshold u * using the following formula to obtain the segmented image:
f(x,y)为待分析区域图像中像素点(x,y)的像素。分割后像素点为1的代表检测出来的生产过程中的溢料部分。图7中虚线框内黑色部分即为芯片中的溢料成分,可以直观的看出溢料情况,如果溢料情况严重,应该立即停止封装过程,检查模具参数,避免更多溢料的产生。f(x,y) is the pixel of the pixel point (x,y) in the image of the area to be analyzed. After segmentation, the pixel point of 1 represents the detected flashing part in the production process. The black part in the dashed box in Figure 7 is the flash component in the chip, and the flashing situation can be seen intuitively. If the flashing is serious, the packaging process should be stopped immediately, and the mold parameters should be checked to avoid more flashing.
应该理解,本发明作为一种检测算法,并不局限于上述具体实施例具体实验设施与实验条件,凡是熟悉本领域的技术人员在不违背本发明精神的前提下还可做出等同变形或替换,这些等同的变型或替换均包含在本申请权利要求所限定的范围内。It should be understood that the present invention, as a detection algorithm, is not limited to the specific experimental facilities and experimental conditions of the above-mentioned specific embodiments, and those skilled in the art can also make equivalent deformations or substitutions without departing from the spirit of the present invention. , these equivalent modifications or substitutions are all included within the scope defined by the claims of the present application.
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