CN108596875A - A kind of semiconductor chip flash rapid detection method based on image segmentation algorithm - Google Patents
A kind of semiconductor chip flash rapid detection method based on image segmentation algorithm Download PDFInfo
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Abstract
The invention discloses a kind of semiconductor chip flash rapid detection method based on image segmentation algorithm, it is intended to by spilling into the fifth wheel on pin and exposed carrier in recognition integrated circuit EMC (Epoxy Molding Compond) encapsulation process, to provide timely feedback information for EMC encapsulation process.First, the collected EMC of industrial camera rear former pictures in package shape are pre-processed, image segmentation, which is carried out, followed by selective search (Selective search) algorithm obtains object candidate area, and chip area to be detected is therefrom filtered out, then the region to filtering out obtains flash region into row threshold division.This method can provide the flash testing result of every chip within the extremely short time, not only optimize the flash detection means in EMC encapsulation process, and provide real-time feedack for EMC encapsulation process, to promote the performance of EMC encapsulation.
Description
Technical Field
The invention belongs to the field of integrated circuit EMC packaging flash detection, and particularly relates to a semiconductor chip flash rapid detection method based on an image segmentation algorithm.
Background
EMC is an important microelectronic packaging material, and a semiconductor chip is coated and protected by a packaging process so as to be prevented from being damaged by the external environment, and meanwhile, the EMC also has a certain heat dissipation effect. The EMC has the characteristics of large-scale production and reasonable reliability, is one of common packaging materials for semiconductor packaging, and occupies more than 95% of packaging markets by virtue of unique advantages. However, in the semiconductor plastic package process, because a gap exists between the lead frame and the plastic package mold, the colloidal plastic package material leaks out from the gap and is solidified on the surface of the lead frame to form a black flash, which is an excess on the integrated circuit pins and the exposed carrier. The flash does not have adverse effect on the performance of the plastic package product, but because the flash covers the terminal pins, if the flash is not processed, an insulation area is formed to affect the subsequent electroplating quality and the product reliability, and the problems of product open circuit, cold welding and the like are caused. Therefore, how to reduce the occurrence of flash and the method of removing flash have been an important problem for researchers and manufacturers.
The reason why EMC package flash is generated is generally: 1) the plastic package material has good fluidity and low viscosity, and the plastic package material/epoxy resin can overflow from a parting surface to cover the terminal pin under the condition of loose die assembly; 2) after the plastic package mold is used for a long time, the surface of the plastic package mold is abraded or the base is not flat, so that flash is caused; 3) the frame precision is not matched with the mould in the semiconductor packaging process, and the edge pressing problem is caused by insufficient meshing. After the flash is formed, the flash itself has no influence on the performance of the plastic package product, but since the flash covers the terminal pins, plating defects may be formed to affect the reliability of the product. At present, manufacturers generally carry out remediation by a method of removing residual flash through a file, sanding and weak acid cleaning after a flash removing process, but the method is easy to scratch a plastic package body and cause the surface of a terminal pin to be rough, so that the appearance quality and the electroplating quality are influenced, and the production progress is also influenced. Therefore, it is important to avoid and reduce the generation of flash. In conclusion, a visual green pollution-free flash detection method is developed, so that the flash condition of a chip can be quickly detected, the requirement of providing reference basis for the EMC packaging process in time is met, and the method has important practical significance.
Thanks to the development of computer technology and image processing technology, a novel visual flash detection technology appears, the detection result of which is a gray image, and the flash detection technology has the characteristics of high detection precision, flexible use, large information amount and the like, and is very suitable for flash detection in the chip packaging production process.
Disclosure of Invention
The invention aims to provide a rapid flash detection strategy aiming at the flash problem in the existing EMC packaging process. The strategy can accurately evaluate the chip flash situation on site, so as to provide feedback information for adjustment and maintenance of in-service equipment, and is an important reference basis for an EMC packaging process.
The purpose of the invention is realized by the following technical scheme: a semiconductor chip flash rapid detection method based on an image segmentation algorithm comprises the following steps:
a semiconductor chip flash rapid detection method based on an image segmentation algorithm is characterized by comprising the following steps:
(1) producing a packaging original sheet by using a semiconductor packaging press (FSAM120-1US) and a QFNQFNB 7X7-48L (T0.75) mould, and acquiring a multi-frame gray image of the packaging original sheet by using an industrial camera;
(2) performing Gaussian blur preprocessing on each frame of image;
(3) based on selective search image segmentation, hundreds of candidate regions are obtained and chip regions to be analyzed are screened out, and the method is realized by the following substeps:
(3.1) over-dividing the single-frame image to obtain n initial regions r1,r2,...,rnForming an initial region set R ═ { R ═ R1,r2,...,rn}; meanwhile, an initialization similarity set S is constructed,
(3.2) calculating the similarity s (r) of every two adjacent areasi,rj) The similarity index adopts color similarity, size similarity, texture similarity, coincidence similarity and four complementary similarities, and the four complementary similarities are respectively combined for the areas from different aspects;
(a) the color similarity is calculated as follows:
dividing each initial region into q intervals according to pixel values to obtain a normalized color histogramWherein,a normalization frequency count representing a kth pixel value interval in the ith initial region;
two adjacent regions ri,rjThe color similarity of (a) is:
merge ri,rjThe new region r obtainedijThe color histogram is:
size(rij)＝size(ri)+size(rj) (19)
wherein size (r)i) Is an initial region riThe size of (d);
(b) the texture similarity calculation method comprises the following steps:
calculating the SIFT (scale-invariant feature transform) feature of each initial region, wherein the value of a scale factor in the patent is 1, and the width of a histogram interval is 10 to obtain the SIFT featureObtained as a textural feature.
Wherein,representing the kth texture feature in the ith initial region;
two adjacent regions ri,rjThe texture similarity of (a) is:
merge ri,rjThe new region r obtainedijThe histogram of the texture is
(C) The calculation method of the size similarity comprises the following steps:
where size (im) represents the size of the entire grayscale image.
(d) The calculation method of the coincidence similarity comprises the following steps:
wherein BBijTo surround only the region riAnd rjThe bounding box of (1).
And finally, integrating the four similarities to obtain the final similarity:
s(ri,rj)＝a1Scolor(ri,rj)+a2Stexture(ri,rj)+a3Ssize(ri,rj)+a4Sfill(ri,rj)(24)
wherein a isiE {0,1}, indicating whether the corresponding similarity is adopted.
(3.3) all the similarities form a similarity set S, and the two adjacent regions S (r) with the highest similarity in the similarity seti,rj) Combine these two regions r max (S)ij＝ri∪rjCalculating the new region rijAnd (3) updating the similarity set with the similarity of adjacent regions: deleting the region r with which the data is mergediAll similarities s (r) of the correlationi,r*) And with the region r being mergedjAll similarities s (r) of the correlationj,r#) And adding the updated similarity set as follows: adding a new region rijSimilarity to adjacent regions;
and (3.4) circulating the step 3.2-3.3, and ending the circulation when the similarity set S is an empty set. Completing the combination of the initial regions to obtain a candidate region set R;
(3.5) screening an upper chip area and a lower chip area to be analyzed from the candidate area set R, so that the size of the area to be analyzed is in the range of one third to one half of the size of the original packaging piece, and the two areas to be analyzed need to be respectively containedAndthe resolution of the 4 key geometric points and the packaging original sheet is a x b.
(4) Performing threshold-based image segmentation on the screened region to be analyzed, wherein the step is realized by the following substeps:
(4.1) calculating a normalized histogram of the region to be analyzed. Let {0, 1.,. L-1} denote L different gray levels in a region image of size M N pixels, NzIndicating the number of pixels with a gray level z. The total number of pixels in the area image is MN ═ n0+n1+n2+...+nL-1，pz＝nzL-1 represents a normalized frequency of z for the gray level in the histogram.
(4.2) for u-0, 1,21(u), the calculation formula is as follows:
(4.3) for u 0,1, 2.
(4.4) calculating the Global Gray mean mGThe calculation formula is as follows:
(4.5) for u-0, 1,2The calculation formula is as follows:
(4.6) obtaining the Otsu threshold u by maximizing the objective function (13)*If the maximum values are not unique, u is obtained by averaging the respective detected maximum values u*；
(4.7) based on the optimal threshold u*The region image is segmented by the following formula to obtain a segmented image:
f (x, y) is the pixel of the pixel point (x, y) in the image of the area to be analyzed. And the segmented pixels are 1 and represent the detected flash part in the production process.
The invention has the beneficial effects that: the invention relates to a rapid flash detection method oriented to an EMC packaging process, which divides an image into hundreds to thousands of candidate areas through a selective search algorithm and screens out a chip area to be analyzed from the candidate areas. And further, the optimal threshold value is utilized to segment the image to be analyzed to obtain a flash image, so that whether the flash condition is serious or not can be judged. According to each frame of image, the method can provide a detection result within 20 seconds, can quickly feed back flash information in an EMC packaging process, and provides a reference basis, so that more flash conditions are avoided.
Drawings
FIG. 1(a) is a flow chart of the semiconductor package of the present invention, FIG. 1(b) is the parameter setting in the actual production operation, and FIG. 1(c) is the final package raw sheet;
FIG. 2 is a schematic diagram of the semiconductor package flash detection system of the present invention, wherein an industrial camera obtains an original gray image and transmits the image to a computer for further analysis;
FIG. 3 is a gray scale image of the original packaging sheet obtained by the present invention;
FIG. 4 is a reduced resolution image obtained by Gaussian blur preprocessing according to the method of the present invention, which retains image integrity information while reducing image resolution;
FIG. 5 is a partial result display obtained after the image is segmented by the selective search of the method of the present invention;
FIG. 6 shows the region to be analyzed selected by the method of the present invention;
FIG. 7 is a final flash image after thresholding by the method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific examples.
(1) EMC (Epoxy molding compound) package: as shown in fig. 1(a), the lead frame is placed in a mold so that each chip is located in an acupuncture point, and the block-shaped EMC is placed in the mold air after the mold is closed. Setting parameters of a packaging press according to the figure 1(b), melting EMC at high temperature, flowing to acupuncture points along a track, gradually covering the chip from the bottom until the chip is completely covered and wrapped, and molding and curing, as shown in figure 1 (c);
(2) image acquisition and preprocessing: the image-based flash detection system is shown in fig. 2, and an industrial camera is used for obtaining a gray scale image of a packaging original sheet shown in fig. 3, wherein the resolution is 3264 × 4896, and the resolution is reduced to 408 × 612 through gaussian blurring processing, so that unnecessary noise is removed while the integrity of image information is maintained, and the image resolution is reduced, thereby accelerating the speed of flash analysis and detection, as shown in fig. 4.
(3.1) over-dividing the single-frame image to obtain n initial regions r1,r2,...,rnForming an initial region set R ═ { R ═ R1,r2,...,rn}; meanwhile, an initialization similarity set S is constructed,
(3.2) calculating the similarity s (r) of every two adjacent areasi,rj) The similarity index adopts color similarity, size similarity, texture similarity, coincidence similarity and four complementary similarities, and the four complementary similarities are respectively combined for the areas from different aspects;
(a) the color similarity is calculated as follows:
dividing each initial region into q intervals according to pixel values to obtain a normalized color histogramWherein,indicating the k-th initial region in the i-th initial regionA normalization frequency count for each pixel value interval;
two adjacent regions ri,rjThe color similarity of (a) is:
merge ri,rjThe new region r obtainedijThe color histogram is:
size(rij)＝size(ri)+size(rj) (33)
wherein size (r)i) Is an initial region riThe size of (d);
(b) the texture similarity calculation method comprises the following steps:
calculating the SIFT (scale-invariant feature transform) feature of each initial region, wherein in the invention, the value of a scale factor in the patent is 1, and the width of a histogram interval is 10 to obtainObtained as a textural feature.
Wherein,representing the kth texture feature in the ith initial region;
two adjacent regions ri,rjThe texture similarity of (a) is:
merge ri,rjThe new region r obtainedijThe histogram of the texture is
(C) The calculation method of the size similarity comprises the following steps:
where size (im) represents the size of the entire grayscale image.
(d) The calculation method of the coincidence similarity comprises the following steps:
wherein BBijTo surround only the region riAnd rjThe bounding box of (1).
And finally, integrating the four similarities to obtain the final similarity:
s(ri,rj)＝a1Scolor(ri,rj)+a2Stexture(ri,rj)+a3Ssize(ri,rj)+a4Sfill(ri,rj)(38)
wherein a isiE {0,1}, indicating whether the corresponding similarity is adopted.
(3.3) all the similarities form a similarity set S, and the two adjacent regions S (r) with the highest similarity in the similarity seti,rj) Combine these two regions r max (S)ij＝ri∪rjCalculating the new region rijAnd (3) updating the similarity set with the similarity of adjacent regions: deleting the region r with which the data is mergediAll similarities of correlations(ri,r*) And with the region r being mergedjAll similarities s (r) of the correlationj,r#) And adding the updated similarity set as follows: adding a new region rijSimilarity to adjacent regions;
and (3.4) circulating the step 3.2-3.3, and ending the circulation when the similarity set S is an empty set. Completing the combination of the initial regions to obtain a candidate region set R;
(3.5) screening an upper chip area and a lower chip area to be analyzed from the candidate area set R, as shown in FIG. 6, so that the size of the area to be analyzed is in the range of one third to one half of the size of the original packaging piece, and the two areas to be analyzed need to be respectively containedAndthe resolution of the 4 key geometric points and the packaging original sheet is a x b.
(3.4) finishing the circulation to obtain a candidate region R; the selective search algorithm step described above is performed on the gray-scale image shown in fig. 4, and 214 candidate regions are obtained, wherein a part of the candidate regions are shown in fig. 5.
(3.5) regional screening: and setting a screening condition, and screening a chip region to be analyzed from the candidate region R. The screening of the grayscale image shown in fig. 4 will result in the region to be analyzed as shown in the right side images of fig. 6.
(4) Performing threshold-based image segmentation on the screened region to be analyzed, wherein the step is realized by the following substeps:
(4) performing threshold-based image segmentation on the screened region to be analyzed, wherein the step is realized by the following substeps:
(4.1) calculating a normalized histogram of the region to be analyzed. Let {0, 1., L-1} represent L different gray levels in a region image of size M x N pixels,nzindicating the number of pixels with a gray level z. The total number of pixels in the area image is MN ═ n0+n1+n2+...+nL-1，pz＝nzthe/MN indicates a normalization frequency of z, which is a gray level in the histogram, and is 0,1, 2.
(4.2) for u-0, 1,21(u), the calculation formula is as follows:
(4.3) for u 0,1, 2.
(4.4) calculating the Global Gray mean mGThe calculation formula is as follows:
(4.5) for u-0, 1,2The calculation formula is as follows:
(4.6) obtaining the Otsu threshold u by maximizing the objective function (13)*If the maximum values are not unique, u is obtained by averaging the respective detected maximum values u*；
(4.7) based on the optimal threshold u*The region image is segmented by the following formula to obtain a segmented image:
f (x, y) is the pixel of the pixel point (x, y) in the image of the area to be analyzed. And the segmented pixels are 1 and represent the detected flash part in the production process. The black part in the dashed line frame in fig. 7 is the flash component in the chip, so that the flash situation can be visually seen, and if the flash situation is serious, the packaging process should be immediately stopped, the mold parameters should be checked, and the generation of more flashes is avoided.
It should be understood that the present invention is not limited to the specific experimental implementation and experimental conditions of the above-mentioned embodiments, and that equivalent modifications or substitutions can be made by those skilled in the art without departing from the spirit of the present invention, and the scope of the present invention is defined by the claims.
Claims (5)
1. A semiconductor chip flash rapid detection method based on an image segmentation algorithm is characterized by comprising the following steps:
(1) acquiring a multi-frame gray image of a packaging original sheet by using an industrial camera;
(2) performing Gaussian blur preprocessing on each frame of image;
(3) based on selective search image segmentation, hundreds of candidate regions are obtained and chip regions to be analyzed are screened out, and the method is realized by the following substeps:
(3.1) on a single frame imageOver-dividing to obtain n initial regions r1,r2,...,rnForming an initial region set R ═ { R ═ R1,r2,...,rn}; meanwhile, an initialization similarity set S is constructed,
(3.2) calculating the similarity s (r) of every two adjacent areasi,rj) The similarity index adopts color similarity, size similarity, texture similarity, coincidence similarity and four complementary similarities, and the four complementary similarities are respectively combined for the areas from different aspects;
(a) the color similarity is calculated as follows:
dividing each initial region into e intervals according to pixel values to obtain a normalized color histogramWherein,the normalization frequency number of the ith pixel value interval in the ith initial area is represented;
two adjacent regions ri,rjThe color similarity of (a) is:
merge ri,rjThe new region r obtainedijThe color histogram is:
size(rij)＝size(ri)+size(rj) (4)
wherein size (r)i) Is an initial region riThe size of (d);
(b) the texture similarity calculation method comprises the following steps:
calculating the SIFT (scale-invariant feature transform) feature of each initial region to obtainAnd obtaining the number of the texture features as v. Wherein,representing the ith texture feature in the ith initial region;
two adjacent regions ri,rjThe texture similarity of (a) is:
merge ri,rjThe new region r obtainedijThe histogram of the texture is
(C) The calculation method of the size similarity comprises the following steps:
where size (im) represents the size of the entire grayscale image.
(d) The calculation method of the coincidence similarity comprises the following steps:
wherein BBijTo surround only the region riAnd rjThe bounding box of (1).
And finally, integrating the four similarities to obtain the final similarity:
s(ri,rj)＝a1Scolor(ri,rj)+a2Stexture(ri,rj)+a3Ssize(ri,rj)+a4Sfill(ri,rj)(9)
wherein a isiE {0,1}, indicating whether the corresponding similarity is adopted.
(3.3) all the similarities form a similarity set S, and the two adjacent regions S (r) with the highest similarity in the similarity seti,rj) Combine these two regions r max (S)ij＝ri∪rjCalculating the new region rijAnd (3) updating the similarity set with the similarity of adjacent regions: deleting the region r with which the data is mergediAll similarities s (r) of the correlationi,r*) And with the region r being mergedjAll similarities s (r) of the correlationj,r#) And adding the updated similarity set as follows: adding a new region rijSimilarity to adjacent regions;
and (3.4) circulating the step 3.2-3.3, and ending the circulation when the similarity set S is an empty set. Completing the combination of the initial regions to obtain a candidate region set R;
(3.5) screening an upper chip area and a lower chip area to be analyzed from the candidate area set R, so that the size of the area to be analyzed is in the range of one third to one half of the size of the original packaging piece, and the two areas to be analyzed need to be respectively containedAndthe resolution of the 4 key geometric points and the packaging original sheet is a x b.
(4) Performing threshold-based image segmentation on the screened region to be analyzed, wherein the step is realized by the following substeps:
(4.1) calculating a normalized histogram of the region to be analyzed. Let {0, 1.,. L-1} denote L different gray levels in a region image of size M N pixels, NzIndicating the number of pixels with a gray level z. The total number of pixels in the area image is MN ═ n0+n1+n2+...+nL-1，pz＝nzthe/MN indicates a normalization frequency of z, which is a gray level in the histogram, and is 0,1, 2.
(4.2) for u-0, 1,21(u), the calculation formula is as follows:
(4.3) for u 0,1, 2.
(4.4) calculating the Global Gray mean mGThe calculation formula is as follows:
(4.5) for u-0, 1,2The calculation formula is as follows:
(4.6) obtaining the Otsu threshold u by maximizing the objective function (13)*If the maximum values are not unique, u is obtained by averaging the respective detected maximum values u*；
(4.7) based on the optimal threshold u*The region image is segmented by the following formula to obtain a segmented image:
f (x, y) is the pixel of the pixel point (x, y) in the image of the area to be analyzed. And the segmented pixels are 1 and represent the detected flash part in the production process.
2. The method according to claim 1, wherein in step 2.1, the larger σ, the wider the band of gaussian blur, the better the smoothness of the image and the higher the loss of detail. By adjusting the sigma parameter, the cancellation of image noise and preservation of integrity can be balanced.
3. The method of claim 1, wherein the packaging raw sheet in step 1 is a packaging raw sheet produced by using a semiconductor packaging press (FSAM120-1US) and a qfnqqnb 7X7-48L (T0.75) die.
4. The method of claim 1, wherein the gaussian blur step of step 2 is as follows:
(2.1) generating a gaussian operand of size (2k +1) × (2k +1) according to equation (1):
wherein k is an integer, wpqIs the weight of the (p, q) coordinate position in the gaussian operand, and σ is the standard deviation of the gaussian distribution.
(2.2) calculating the reciprocal of the sum of Gaussian operand coefficientsAnd multiplying each element in the Gaussian operand by the element to obtain a new Gaussian operand.
And (2.3) reading pixels from the image and performing convolution operation on the pixels and the Gaussian operand of the second step, thereby completing the Gaussian blur preprocessing.
5. The method according to claim 1, wherein the gaussian blur of step 2 removes unnecessary noise while preserving the integrity of the image information, and reduces the resolution of the image to speed up the detection of the flash analysis.
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