CN115035056A - Flexible IC substrate line etching defect detection method and system based on differential geometry - Google Patents

Flexible IC substrate line etching defect detection method and system based on differential geometry Download PDF

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CN115035056A
CN115035056A CN202210604905.4A CN202210604905A CN115035056A CN 115035056 A CN115035056 A CN 115035056A CN 202210604905 A CN202210604905 A CN 202210604905A CN 115035056 A CN115035056 A CN 115035056A
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CN115035056B (en
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胡跃明
王思远
曾勇
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South China University of Technology SCUT
Guangzhou Institute of Modern Industrial Technology
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Abstract

The invention discloses a method and a system for detecting etching defects of a flexible IC substrate circuit based on differential geometry, wherein the method comprises the following steps: acquiring an image of a flexible IC substrate; converting the image into a gray image as a processing object; preprocessing an image; extracting the contour by using a Moore neighborhood boundary tracking algorithm of a Jacob Eliosoff criterion and performing Gaussian smoothing on the contour; and calculating the curvature of the contour by using a two-way difference method based on Euclidean distance weight factors, and judging whether over-etching or under-etching defects exist according to the change of the curvature of the contour and the direction of the change. The invention detects the etching defect through the change of the line contour curvature, converts the etching defect detection problem into the line contour extraction and contour curvature calculation problem, and can be used for solving the detection problems of the over-etching and under-etching defects of the flexible IC substrate line.

Description

Flexible IC substrate circuit etching defect detection method and system based on differential geometry
Technical Field
The invention relates to the technical field of image processing, in particular to a method and a system for detecting etching defects of a flexible IC substrate circuit based on differential geometry.
Background
A Flexible Integrated Circuit Substrate (FICS) is the most lightweight and high integration density of Flexible Printed Circuit (FPC) product lines. Due to the particularity of the manufacturing materials and the manufacturing process, the FICS is soft in texture, easy to bend, high in density of integratable electronic devices and light and thin in material, and is an important component of the third generation semiconductor;
under-etching refers to residual copper between the FICS lines due to other factors such as insufficient process or substrate corrosion, and is an extra component of the standard line with dimension not reaching the short-circuit phenomenon. Over-etching refers to a defect that the width of the circuit does not meet the size requirement due to excessive corrosion of the process or the substrate, and may form weak conduction and cause poor contact. The defect detection of the flexible IC substrate is generally carried out in a targeted manner by combining a digital image processing method with subject knowledge such as mathematics, statistics and the like according to the characteristics of images and the characteristics of defects. Due to the fact that the degree of curvature of the outline of the defective line is different from that of the outline of the normal line caused by the existence of underetching and overetching, the curvature is used for measuring the defect characteristic, and therefore etching defects are detected. There are generally two methods for calculating the curvature of the contour: firstly, carrying out interpolation or fitting on a discrete data point set of a target curve to obtain an approximate parameter equation of the target curve, and then obtaining curvature by using a curvature calculation formula; secondly, the curvature estimation problem is converted into an osculating circle radius estimation by a known discrete data point set. However, because the line profile point sets are dense and distributed in a zigzag manner, the curvature errors calculated by the two methods are large, and the etching defects are difficult to accurately detect.
Disclosure of Invention
In order to overcome the defects and shortcomings in the prior art, the invention provides a method and a system for detecting the etching defects of a flexible IC substrate circuit based on differential geometry.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a method for detecting etching defects of a flexible IC substrate circuit based on differential geometry, which comprises the following steps:
acquiring a color image of the flexible IC substrate;
converting the color image of the flexible IC substrate into a gray scale space to obtain a gray scale image;
carrying out image preprocessing on the gray level image to obtain a binary image;
extracting the outline of the line in the binary image by adopting a Moore neighborhood boundary tracking algorithm of a Jacob Eliosoff criterion to obtain a closed line outline;
segmenting the closed line contour, keeping the coordinates of a contour point set unchanged in the x direction, increasing and decreasing the coordinates in the y direction to serve as the left edge and the right edge of the line contour, taking the rest of the coordinates to serve as the upper edge and the lower edge of the line contour, removing the left edge and the right edge of the line contour, and keeping the upper edge and the lower edge of the line contour;
and performing Gaussian smoothing on the segmented contour, calculating the curvature of the contour by using a two-way difference method based on Euclidean distance weight factors, setting an upper limit of a curvature threshold and a lower limit of the curvature threshold, and judging whether the over-etching defect or the under-etching defect exists according to the change of the curvature of the contour and the change direction.
As a preferred technical solution, the image preprocessing of the grayscale image includes the specific steps of:
denoising the gray level image by adopting a Gaussian filtering method, binarizing the denoised image by using an Otsu threshold value method, and performing closed operation on the binary image to eliminate noise points and fill holes so that the image only contains a connected domain of a line region.
As a preferred technical scheme, the method for extracting the line profile in the binary image by using the molar neighborhood boundary tracking algorithm based on the Jacob Eliosoff criterion comprises the following specific steps:
determining a starting search point for the boundary: scanning each row of pixels from left to right from the upper left corner of the binary image, scanning each column of elements from top to bottom until a white pixel is encountered, taking the white pixel as a boundary initial search point W of the algorithm, and taking a black pixel encountered previously as a mole neighborhood initial search point B;
determining boundary discrimination criteria and search criteria: starting from the initial search point W, searching each pixel in the Moore neighborhood of W in a clockwise direction from B until a next white pixel W1 is met, namely finding a next boundary point, and recording a previously reached black pixel as B1, wherein the next search takes W1 as the boundary initial search point and B1 as the Moore neighborhood initial search point;
determining the termination condition of the search: and repeating the search until the direction of the starting point of the second entry is the same as the direction of the first entry, and stopping the search, wherein all the accessed boundary starting search points are the contour point set of the line in the binary image.
As a preferred technical solution, the gaussian smoothing of the segmented contour is performed in a specific calculation manner including:
Figure BDA0003670944620000031
Figure BDA0003670944620000032
Figure BDA0003670944620000033
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003670944620000034
the value y after Gaussian smoothing of the coordinate position in the y direction of the current k-th contour pixel point i The coordinate position of the ith contour pixel point in the y direction is represented, and m is highSubscript corresponding to the start point of the Gaussian template, n is subscript corresponding to the end point of the Gaussian template, g i Is the value corresponding to the ith point in the Gaussian template, f (x) is the Gaussian function corresponding to the Gaussian template,
Figure BDA0003670944620000035
b is the mean of the gaussian function, and c- σ is the standard deviation of the gaussian function.
As a preferred technical scheme, the contour curvature is calculated by using a two-way difference method based on Euclidean distance weight factors, and the method specifically comprises the following steps:
selecting a plurality of groups of contour pixel points, and estimating the approximate first derivative and second derivative of the contour pixel points according to a difference method;
calculating the Euclidean distance of each group of contour pixel points as a weight influence factor;
calculating accurate first derivative and second derivative of the contour pixel points according to the weight influence factors and the approximate first derivative and second derivative;
obtaining the curvature according to the accurate first derivative and the accurate second derivative by using a curvature calculation formula, wherein the curvature is specifically expressed as follows:
Figure BDA0003670944620000041
wherein, k (x) i ) Denotes the curvature, f' (x) i ) Represents the exact first derivative, f' (x) i ) Indicating the exact second derivative.
As a preferred technical scheme, the selecting of multiple groups of contour pixel points and the estimation of the approximate first derivative and second derivative of the contour pixel points according to a difference method specifically comprise the following steps:
let P 0 (x 0 ，y 0 )，P 1 (x 1 ，y 1 )，P 2 (x 2 ，y 2 )…P n (x n ，y n ) Defining points P for a set of image contour points i (x i ，y i ) The first forward derivative of (d) is:
Figure BDA0003670944620000042
Δx if (l)＝x i -x i-l
Δy if (l)＝y i -y i-l
where l denotes the point P used to calculate the first forward derivative i-l (x i-l ，y i-l ) Distance from current point in X direction, Δ X if (l) Representing point P i (x i ，y i ) First order forward difference, Δ y, in the X direction of the image from a point at a distance l if (l) Represents point P i (x i ，y i ) A first order forward difference in the image Y direction from a point at distance l;
point P i (x i ，y i ) The first backward derivative of (d) is:
Figure BDA0003670944620000051
Δx ib (s)＝x i+s -x i
Δy ib (s)＝y i+s -y i
where s denotes the point P for calculating the first order backward derivative i+s (x i+s ，y i+s ) Distance from current point in X direction, Δ X ib (s) represents a point P i (x i ，y i ) First order backward difference in the X direction of the image from a point at a distance s, Δ y ib (s) represents a point P i (x i ，y i ) First order backward difference in the image Y direction from a point with distance s;
point P i (x i ，y i ) The second forward derivative of (d) is:
Figure BDA0003670944620000052
Δ 2 x if (l)＝(Δx i -Δx i-l )
Δ 2 y if (l)＝(Δy i -Δy i-l )
wherein, Delta 2 x if (l) Representing point P i (x i ，y i ) Second order forward difference, Δ, in the X direction of the image 2 y if (l) Representing point P i (x i ，y i ) Second order forward difference in the image Y direction;
point P i (x i ，y i ) The second backward derivative of (d) is:
Figure BDA0003670944620000053
Δ 2 x ib (s)＝(Δx i+s -Δx i )
Δ 2 y ib (s)＝(Δy i+s -Δy i )
wherein, Delta 2 x ib (s) represents a point P i (x i ，y i ) Second order backward difference, Δ, in the X direction of the image 2 y ib (s) represents a point P i (x i ，y i ) Second order backward difference in the Y direction of the image.
As a preferred technical solution, the calculating of the euclidean distance of each group of contour pixel points as a weight influence factor specifically includes the steps of:
the euclidean distance of the first forward derivative is:
Figure BDA0003670944620000061
the euclidean distance of the first order backward derivative is:
Figure BDA0003670944620000062
the euclidean distance of the second forward derivative is:
Figure BDA0003670944620000063
the euclidean distance of the second backward derivative is:
Figure BDA0003670944620000064
as a preferred technical scheme, the accurate first derivative and second derivative of the contour pixel point are calculated according to the weight influence factor and the approximate first derivative and second derivative, and the specific steps comprise:
the exact first derivative is:
Figure BDA0003670944620000065
the exact second derivative is:
Figure BDA0003670944620000066
as a preferred technical scheme, the method for judging whether the over-etching defect or the under-etching defect exists according to the change of the contour curvature and the change direction comprises the following specific steps:
when the curvature of the contour is between the upper limit of the curvature threshold and the lower limit of the curvature threshold, judging that the line has no etching defect;
when the curvature of the contour has three sudden changes, the first sudden change is a positive curvature, the other sudden change is a negative curvature, and the last sudden change is a positive curvature, the circuit is judged to have an over-etching defect;
when the curvature of the contour has three sudden changes, the first sudden change is a negative curvature, the other sudden change is a positive curvature, and the last sudden change is a negative curvature, the circuit is judged to have an underetched defect;
searching a sudden change position through the extreme point, setting a distance threshold, searching the maximum point and the minimum point of the curvature through a traversal method, judging that two sudden changes exist when the abscissa distance corresponding to two adjacent maximum points or minimum points is greater than the set distance threshold, and otherwise, judging that one sudden change exists.
The invention also provides a system for detecting the etching defect of the flexible IC substrate circuit based on differential geometry, which comprises the following components: the device comprises an image acquisition module, a gray level conversion module, a preprocessing module, a contour extraction module, a contour segmentation module, a Gaussian smoothing module, a contour curvature calculation module, a threshold setting module and a defect judgment module;
the image acquisition module is used for acquiring a color image of the flexible IC substrate;
the gray scale conversion module is used for converting the color image of the flexible IC substrate into a gray scale space to obtain a gray scale image;
the preprocessing module is used for preprocessing the gray level image to obtain a binary image;
the contour extraction module is used for extracting the contour of the line in the binary image by adopting a Moore neighborhood boundary tracking algorithm of a Jacob Eliosoff criterion to obtain a closed line contour;
the contour segmentation module is used for segmenting the closed line contour, contour point set coordinates are kept unchanged in the x direction, the contour point set coordinates are increased and decreased in the y direction and serve as the left edge and the right edge of the line contour, the rest contour point set coordinates are used as the upper edge and the lower edge of the line contour, the left edge and the right edge of the line contour are removed, and the upper edge and the lower edge of the line contour are reserved;
the Gaussian smoothing module is used for performing Gaussian smoothing on the segmented contour;
the contour curvature calculation module is used for calculating the contour curvature by using a two-way difference method based on Euclidean distance weight factors;
the threshold setting module is used for setting an upper curvature threshold and a lower curvature threshold;
the defect judging module is used for judging whether over-etching or under-etching defects exist according to the change of the contour curvature and the change direction.
Compared with the prior art, the invention has the following advantages and beneficial effects:
(1) the invention converts the image from RGB color space to gray space, takes the gray image as a processing object, reduces the operation amount of the image while having obvious contrast and improves the detection speed.
(2) The preprocessing mode adopted by the invention comprises image denoising, image binarization and closed operation, so that the noise influence in the image shot by the industrial camera is reduced, and the image binarization and closed operation provide preconditions for the subsequent extraction of the line profile.
(3) The invention uses the Moore neighborhood boundary tracking algorithm of the Jacob Eliosoff criterion to extract the contour of the line, and can accurately extract a complete and accurate contour.
(4) The invention provides a bidirectional difference method based on Euclidean distance weight factors when calculating the curvature of the contour, and the curvature of the current contour pixel point is calculated by using a plurality of adjacent points, thereby overcoming the defect of inaccurate calculation of the curvature of the contour.
Drawings
FIG. 1 is a schematic flow chart of a method for detecting etching defects of a flexible IC substrate circuit based on differential geometry according to the present invention;
FIG. 2 is a schematic flow chart of the present invention for determining etching defects by calculating curvature based on the Euclidean distance weighting factor using the bidirectional difference method.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
As shown in fig. 1, the present embodiment provides a method for detecting a line etching defect of a flexible IC substrate based on differential geometry, which includes the following steps:
and S1, acquiring a color image of the flexible IC substrate.
In the embodiment, an industrial CCD camera is used for acquiring images of the flexible IC substrate, and a light source is selected as a white light source. The line area is yellow in the image, the background is black, and when the etching defect of the line occurs, only the area of the line area changes, which is often difficult to detect. The circuit is segmented by image segmentation, and then the circuit contour characteristics are detected, so that the circuit etching defects can be accurately and quickly detected.
S2, the image is converted into a gray scale space, and the gray scale image is treated as the object of processing.
In this embodiment, the gray scale space is converted, and the value of the gray scale image is calculated according to the following formula:
Gray(i，j)＝R(i，j)×0.299+G(i，j)×0.587+B(i，j)×0.114
where R (i, j), G (i, j) and G (i, j) represent the values of R, G, B three components at location (i, j), respectively, and Gray (i, j) represents the value at grayscale image location (i, j).
And S3, preprocessing the image.
The image preprocessing of the embodiment is to perform denoising on a gray level image, perform gaussian filtering for image denoising, perform binarization on the denoised image by using an atrazine threshold method, perform closed operation on the image to eliminate noise points and fill holes, so that the image only contains a connected domain of a line region, and the structural element used in the closed operation is a 6 × 6 matrix.
And S4, extracting the contour of the line area.
And extracting the contour of the line in the binary image by using a Moore neighborhood boundary tracking algorithm of a Jacob Eliosoff criterion. The method comprises the following specific steps: first the starting search point of the boundary is determined: scanning each row of pixels from left to right from the upper left corner of the image, scanning each column of elements from top to bottom until a white pixel is encountered, taking the white pixel as a boundary initial search point W of the algorithm, and taking a black pixel encountered previously as a mole neighborhood initial search point B; secondly, determining a boundary judgment criterion and a search criterion: starting from the start search point W, each pixel in the Moore neighborhood of W is searched clockwise from B until the next white pixel W1 is encountered, i.e., the next boundary point is found, and the previously reached black pixel is recorded as B1, and the next search is performed with W1 as the boundary start search point and B1 as the Moore neighborhood start search point. And finally determining the termination condition of the search: repeating the steps until the direction of the algorithm entering the starting point for the second time is the same as the direction of the algorithm entering the starting point for the first time, and stopping searching; all the visited boundary start search points are the contour point set of the line in the binary image.
And S5, calculating the contour curvature to judge whether the defect exists.
And (3) segmenting the closed line contour, wherein the left edge and the right edge of the contour are boundaries of the image but not the boundary of the line, so that the etching defect only exists at the upper edge and the lower edge of the contour, and only the upper edge and the lower edge need to be detected: the contour of the line is a closed contour, the left side and the right side of the closed contour are boundaries of the image, and the coordinates of a contour point set are kept unchanged in the x direction and are increased and decreased only in the y direction. According to the characteristic, the contour point set is traversed, the left end and the right end of the contour are removed, and only the contour sections with possible defects at the upper end and the lower end are reserved.
And performing Gaussian smoothing on the segmented contour, calculating the curvature of the contour by using a two-way difference method based on Euclidean distance weight factors, setting the upper limit and the lower limit of a curvature threshold value according to the experiment of a plurality of pictures, and judging whether over-etching or under-etching defects exist according to the change of the curvature and the change direction.
In this embodiment, the calculation method of gaussian smoothing is as follows:
Figure BDA0003670944620000101
wherein the content of the first and second substances,
Figure BDA0003670944620000102
the value y of the coordinate position of the current k contour pixel point in the y direction after Gaussian smoothing i The coordinate position of the ith contour pixel point in the y direction is represented, m is a subscript corresponding to the starting point of the Gaussian template, n is a subscript corresponding to the end point of the Gaussian template, and g i The value corresponding to the ith point in the Gaussian template is calculated according to the following formula:
Figure BDA0003670944620000103
wherein, f (x) is a Gaussian function corresponding to the Gaussian template, which is defined as the following formula:
Figure BDA0003670944620000111
wherein the content of the first and second substances,
Figure BDA0003670944620000112
and b is the mean value of the gaussian function, and c is the standard deviation of the gaussian function.
As shown in fig. 2, the contour curvature is calculated based on the bidirectional difference method of the euclidean distance weighting factor, and the specific steps include:
and S51, selecting multiple groups of contour pixel points and approximately estimating the first derivative and the second derivative of the contour pixel points according to a difference method.
Let P 0 (x 0 ，y 0 )，P 1 (x 1 ，y 1 )，P 2 (x 2 ，y 2 )…P n (x n ，y n ) Defining points P for a set of image contour points i (x i ，y i ) The first forward derivative of (d) is:
Figure BDA0003670944620000113
where l denotes the point P used to calculate the first forward derivative i-l (x i-l ，y i-l ) Distance from current point in X direction, Δ X if (l) Representing point P i (x i ，y i ) First order forward difference, Δ y, in the X direction of the image from a point at a distance l if (l) Representing point P i (x i ，y i ) First order forward difference in the image Y direction from a point at distance l. Δ x if (l) And Δ y if (l) The calculation formula of (a) is as follows:
Δx if (l)＝x i -x i-l
Δy if (l)＝y i -y i-l
the first order backward derivative is:
Figure BDA0003670944620000114
where s denotes the point P for calculating the first backward derivative i+s (x i+s ，y i+s ) Distance from current point in X direction, Δ X ib (s) represents a point P i (x i ，y i ) First order backward difference in the X direction of the image from a point at a distance s, Δ y ib (s) represents a point P i (x i ，y i ) First order backward difference in the Y direction of the image from a point at distance s. Δ x ib (s) and Δ y ib (s) the specific calculation formula is as follows:
Δx ib (s)＝x i+s -x i
Δy ib (s)＝y i+s -y i
defining its second forward derivative as:
Figure BDA0003670944620000121
wherein, Delta 2 x if (l) Representing point P i (x i ，y i ) Second order forward difference, Δ, in the X direction of the image 2 y if (l) Representing point P i (x i ，y i ) The second-order forward difference in the Y direction of the image is calculated as follows:
Δ 2 x if (l)＝(Δx i -Δx i-l )
Δ 2 y if (l)＝(Δy i -Δy i-l )
define its second backward derivative:
Figure BDA0003670944620000122
wherein, Delta 2 x ib (s) represents a point P i (x i ，y i ) Second order backward difference, Δ, in the X direction of the image 2 y ib (s) represents a point P i (x i ，y i ) The second-order backward difference in the Y direction of the image is specifically calculated as follows:
Δ 2 x ib (s)＝(Δx i+s -Δx i )
Δ 2 y ib (s)＝(Δy i+s -Δy i )
and S52, calculating the Euclidean distance of each group of points as a weight influence factor.
The euclidean distance of the first forward derivative is:
Figure BDA0003670944620000123
the euclidean distance of the first order backward derivative is:
Figure BDA0003670944620000124
the euclidean distance of the second forward derivative is:
Figure BDA0003670944620000125
the euclidean distance of the second backward derivative is:
Figure BDA0003670944620000131
and S53, calculating the accurate first derivative and second derivative of the contour pixel point according to the weight influence factor and the obtained approximate first derivative and second derivative. The definition weight factor decreases as the euclidean distance increases.
Figure BDA0003670944620000132
Where d is the euclidean distance and p is a constant parameter for adjusting the weight.
The exact first derivative is:
Figure BDA0003670944620000133
the exact second derivative is:
Figure BDA0003670944620000134
and S54, obtaining the curvature according to the accurate first-order derivative and the second-order derivative by using a curvature calculation formula.
Figure BDA0003670944620000135
S55, defect decision
Setting appropriate curvature thresholds which are a curvature lower limit m and a curvature upper limit n respectively according to actual experience, wherein if the contour curvature is between the upper limit and the lower limit, the etching defect does not exist; otherwise, an etching defect exists, a maximum value point and a minimum value point of the curvature are searched by a traversal method, the adjacent left and right points of the maximum value point are smaller than the maximum value point, and the adjacent left and right points of the minimum value point are larger than the minimum value point.
The curvature of the profile of a defect-free line fluctuates around 0, and only at the defect the profile curvature changes abruptly. Setting appropriate curvature threshold values which are respectively a curvature lower limit m and a curvature upper limit n according to actual experience, wherein if the contour curvature is between the upper limit and the lower limit, the defect does not exist; otherwise, judging whether the defect exists by the following method. If the line edge has an over-etching defect, the curvature of the contour has three sudden changes, wherein the first sudden change is a positive curvature, which means that another sudden change is generated at the beginning of the defect and immediately short of the line edge to a negative curvature, which means that another sudden change is generated at the highest point of the defect and immediately short of the line edge to a positive curvature, which means the ending point of the defect; if the line edge has an under-etching defect, the contour curvature also has three abrupt changes, and only the negative curvature and the positive curvature occur in the opposite order. The mutation position is searched by the extreme points, and one mutation may generate a plurality of extreme points (minimum points), which will cause misjudgment of the defect if not processed. Therefore, the distance threshold is set to be 10 pixel points, and if the abscissa distance corresponding to two adjacent maximum value points (minimum value points) is greater than the distance threshold, two sudden changes are considered, otherwise, one sudden change is considered.
Example 2
The embodiment provides a flexible IC substrate circuit etching defect detection system based on differential geometry, which includes: the system comprises an image acquisition module, a gray level conversion module, a preprocessing module, a contour extraction module, a contour segmentation module, a Gaussian smoothing module, a contour curvature calculation module, a threshold setting module and a defect judgment module;
in the embodiment, the image acquisition module is used for acquiring a color image of the flexible IC substrate;
in this embodiment, the grayscale conversion module is configured to convert a color image of the flexible IC substrate into a grayscale space to obtain a grayscale image;
in this embodiment, the preprocessing module is configured to perform image preprocessing on the grayscale image to obtain a binary image;
in this embodiment, the contour extraction module is configured to extract a contour of a line in a binary image by using a moore neighborhood boundary tracking algorithm of a Jacob Eliosoff criterion to obtain a closed line contour;
in this embodiment, the contour segmentation module is configured to segment a closed line contour, where coordinates of a contour point set remain unchanged in an x direction, the coordinates are increased or decreased in a y direction and serve as left and right edges of the line contour, and the rest are used as upper and lower edges of the line contour, the left and right edges of the line contour are removed, and the upper and lower edges of the line contour are retained;
in this embodiment, the gaussian smoothing module is configured to perform gaussian smoothing on the segmented contour;
in the embodiment, the contour curvature calculation module is used for calculating the contour curvature by using a two-way difference method based on Euclidean distance weight factors;
in this embodiment, the threshold setting module is configured to set an upper curvature threshold and a lower curvature threshold;
in this embodiment, the defect determining module is configured to determine whether an over-etching defect or an under-etching defect exists according to the change of the curvature of the profile and the direction of the change.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such modifications are intended to be included in the scope of the present invention.

Claims (10)

1. A method for detecting etching defects of a flexible IC substrate circuit based on differential geometry is characterized by comprising the following steps:
acquiring a color image of the flexible IC substrate;
converting the color image of the flexible IC substrate into a gray scale space to obtain a gray scale image;
carrying out image preprocessing on the gray level image to obtain a binary image;
extracting the contour of the line in the binary image by adopting a Moore neighborhood boundary tracking algorithm of a Jacob Eliosoff criterion to obtain a closed line contour;
segmenting the closed line profile, keeping the coordinates of a profile point set unchanged in the x direction, increasing and decreasing the coordinates in the y direction to serve as the left edge and the right edge of the line profile, taking the rest coordinates as the upper edge and the lower edge of the line profile, eliminating the left edge and the right edge of the line profile, and keeping the upper edge and the lower edge of the line profile;
and performing Gaussian smoothing on the segmented contour, calculating the curvature of the contour by using a bidirectional difference method based on Euclidean distance weight factors, setting an upper limit and a lower limit of a curvature threshold, and judging whether over-etching or under-etching defects exist according to the change and the change direction of the curvature of the contour.
2. The method for detecting the etching defect of the flexible IC substrate circuit based on the differential geometry as claimed in claim 1, wherein the step of preprocessing the gray image comprises the following steps:
denoising the gray level image by adopting a Gaussian filtering method, binarizing the denoised image by using an Otsu threshold value method, and performing closed operation on the binary image to eliminate noise points and fill holes so that the image only contains a connected domain of a line region.
3. The method for detecting the etching defect of the flexible IC substrate circuit based on the differential geometry as claimed in claim 1, wherein the Moore neighborhood boundary tracking algorithm adopting the Jacob Eliosoff criterion is used for extracting the contour of the circuit in the binary image, and the method comprises the following specific steps:
determining a starting search point for the boundary: scanning each row of pixels from left to right from the upper left corner of the binary image, scanning each column of elements from top to bottom until a white pixel is encountered, taking the white pixel as a boundary initial search point W of the algorithm, and taking a previously encountered black pixel as a mole neighborhood initial search point B;
determining boundary discrimination criteria and search criteria: starting from the initial search point W, searching each pixel in the Moore neighborhood of W in a clockwise direction from B until a next white pixel W1 is met, namely finding a next boundary point, and recording a previously reached black pixel as B1, wherein the next search takes W1 as the boundary initial search point and B1 as the Moore neighborhood initial search point;
determining the termination condition of the search: and repeating the search until the direction of the starting point of the second entry is the same as the direction of the first entry, and stopping the search, wherein all the accessed boundary starting search points are the contour point set of the line in the binary image.
4. The method as claimed in claim 1, wherein the step of performing Gaussian smoothing on the segmented profile comprises the following specific calculation steps:
Figure FDA0003670944610000021
Figure FDA0003670944610000022
Figure FDA0003670944610000023
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003670944610000024
the value y after Gaussian smoothing of the coordinate position in the y direction of the current k contour pixel point i The coordinate position of the ith contour pixel point in the y direction is represented, m is a subscript corresponding to the starting point of the Gaussian template, n is a subscript corresponding to the end point of the Gaussian template, and g i Is the value corresponding to the ith point in the Gaussian template, f (x) is the Gaussian function corresponding to the Gaussian template,
Figure FDA0003670944610000025
b is the mean of the gaussian function, and c- σ is the standard deviation of the gaussian function.
5. The method for detecting the etching defect of the flexible IC substrate circuit based on the differential geometry as claimed in claim 1, wherein the contour curvature is calculated by using a bidirectional difference method based on Euclidean distance weight factors, and the method comprises the following specific steps:
selecting a plurality of groups of contour pixel points, and estimating the approximate first derivative and second derivative of the contour pixel points according to a difference method;
calculating the Euclidean distance of each group of contour pixel points as a weight influence factor;
calculating accurate first derivative and second derivative of the contour pixel points according to the weight influence factors and the approximate first derivative and second derivative;
obtaining the curvature according to the accurate first derivative and the accurate second derivative by using a curvature calculation formula, wherein the curvature is specifically expressed as follows:
Figure FDA0003670944610000031
wherein, k (x) i ) Denotes the curvature, f' (x) i ) Represents the exact first derivative, f' (x) i ) Indicating the exact second derivative.
6. The method as claimed in claim 5, wherein the steps of selecting multiple groups of contour pixels to estimate the approximate first derivative and second derivative of the contour pixels according to a difference method comprise:
let P 0 (x 0 ，y 0 )，P 1 (x 1 ，y 1 )，P 2 (x 2 ，y 2 )…P n (x n ，y n ) Defining points P for a set of image contour points i (x i ，y i ) The first forward derivative of (d) is:
Figure FDA0003670944610000032
Δx if (l)＝x i -x i-l
Δy if (l)＝y i -y i-l
where l denotes the point P used to calculate the first forward derivative i-l (x i-l ，y i-l ) Distance from current point in X direction, Δ X if (l) Representing point P i (x i ，y i ) First order forward difference, Δ y, in the X direction of the image from a point at a distance l if (l) Representing point P i (x i ，y i ) A first order forward difference in the image Y direction from a point at distance l;
point P i (x i ，y i ) The first backward derivative of (d) is:
Figure FDA0003670944610000033
Δx ib (s)＝x i+s -x i
Δy ib (s)＝y i+s -y i
where s denotes the point P for calculating the first backward derivative i+s (x i+s ，y i+s ) Distance from current point in X direction, Δ X ib (s) represents a point P i (x i ，y i ) First order backward difference in the X direction of the image from a point at a distance s, Δ y ib (s) represents a point P i (x i ，y i ) First order backward difference in the image Y direction from a point with distance s;
point P i (x i ，y i ) Is:
Figure FDA0003670944610000041
Δ 2 x if (l)＝(Δx i -Δx i-l )
Δ 2 y if (l)＝(Δy i -Δy i-l )
wherein, Delta 2 x if (l) Representing point P i (x i ，y i ) Second order forward difference, Δ, in the X direction of the image 2 y if (l) Representing point P i (x i ，y i ) Second order forward difference in the image Y direction;
point P i (x i ，y i ) The second backward derivative of (d) is:
Figure FDA0003670944610000042
Δ 2 x ib (s)＝(Δx i+s -Δx i )
Δ 2 y ib (s)＝(Δy i+s -Δy i )
wherein, Delta 2 x ib (s) represents a point P i (x i ，y i ) Second order backward difference, Δ, in the X direction of the image 2 y ib (s) represents a point P i (x i ，y i ) Second order backward difference in the Y direction of the image.
7. The method according to claim 6, wherein the calculation of Euclidean distance of each group of contour pixels as a weight influence factor comprises the following specific steps:
the euclidean distance of the first forward derivative is:
Figure FDA0003670944610000051
the euclidean distance of the first order backward derivative is:
Figure FDA0003670944610000052
the euclidean distance of the second forward derivative is:
Figure FDA0003670944610000053
the euclidean distance of the second backward derivative is:
Figure FDA0003670944610000054
8. the method for detecting the etching defect of the flexible IC substrate circuit based on the differential geometry as claimed in claim 7, wherein the accurate first derivative and second derivative of the contour pixel point are calculated according to the weight influence factor and the approximate first derivative and second derivative, and the specific steps include:
the exact first derivative is:
Figure FDA0003670944610000055
the exact second derivative is:
Figure FDA0003670944610000056
9. the method for detecting the etching defect of the flexible IC substrate circuit based on the differential geometry as claimed in claim 1, wherein the method for judging whether the over-etching defect or the under-etching defect exists or not according to the change of the contour curvature and the change direction comprises the following specific steps:
when the curvature of the contour is between the upper limit of the curvature threshold and the lower limit of the curvature threshold, judging that the line has no etching defect;
when the curvature of the contour has three sudden changes, the first sudden change is a positive curvature, the other sudden change is a negative curvature, and the last sudden change is a positive curvature, the circuit is judged to have an over-etching defect;
when the curvature of the contour has three sudden changes, the first sudden change is a negative curvature, the other sudden change is a positive curvature, and the last sudden change is a negative curvature, the circuit is judged to have an underetched defect;
searching a sudden change position through the extreme point, setting a distance threshold, searching the maximum point and the minimum point of the curvature through a traversal method, judging that two sudden changes exist when the abscissa distance corresponding to two adjacent maximum points or minimum points is greater than the set distance threshold, and otherwise, judging that one sudden change exists.
10. A flexible IC substrate circuit etching defect detection system based on differential geometry is characterized by comprising: the device comprises an image acquisition module, a gray level conversion module, a preprocessing module, a contour extraction module, a contour segmentation module, a Gaussian smoothing module, a contour curvature calculation module, a threshold setting module and a defect judgment module;
the image acquisition module is used for acquiring a color image of the flexible IC substrate;
the gray scale conversion module is used for converting a color image of the flexible IC substrate into a gray scale space to obtain a gray scale image;
the preprocessing module is used for preprocessing the gray level image to obtain a binary image;
the contour extraction module is used for extracting the contour of the line in the binary image by adopting a Moore neighborhood boundary tracking algorithm of a Jacob Eliosoff criterion to obtain a closed line contour;
the contour segmentation module is used for segmenting the closed line contour, contour point set coordinates are kept unchanged in the x direction, the contour point set coordinates are increased and decreased in the y direction and serve as the left edge and the right edge of the line contour, the rest contour point set coordinates are used as the upper edge and the lower edge of the line contour, the left edge and the right edge of the line contour are removed, and the upper edge and the lower edge of the line contour are reserved;
the Gaussian smoothing module is used for performing Gaussian smoothing on the segmented contour;
the contour curvature calculation module is used for calculating the contour curvature by using a two-way difference method based on Euclidean distance weight factors;
the threshold setting module is used for setting an upper curvature threshold and a lower curvature threshold;
the defect judging module is used for judging whether over-etching or under-etching defects exist according to the change of the contour curvature and the change direction.
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