CN115035122A - Artificial intelligence-based integrated circuit wafer surface defect detection method - Google Patents

Artificial intelligence-based integrated circuit wafer surface defect detection method Download PDF

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CN115035122A
CN115035122A CN202210965350.6A CN202210965350A CN115035122A CN 115035122 A CN115035122 A CN 115035122A CN 202210965350 A CN202210965350 A CN 202210965350A CN 115035122 A CN115035122 A CN 115035122A
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刘卫卫
金琼洁
欧阳一冉
邹阳春
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Ningbo Xinxin Microelectronics Technology Co ltd
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Abstract

The invention discloses an integrated circuit wafer surface defect detection method based on artificial intelligence, and relates to the field of defect detection. The method comprises the following steps: acquiring a surface image of a wafer to be detected; coding the gradient variation of the pixel points to obtain a gradient variation chain code of the surface image of the wafer to be detected and the surface image of the standard wafer; calculating gradient similarity to divide the area of the surface image of the wafer to be detected; calculating the pixel characteristic value of each pixel point; calculating the suspected defect degree of each area according to the frequency of each characteristic value; dividing the characteristic histogram according to the suspected defect degree; and obtaining the defect area of the wafer to be detected by performing edge detection after obtaining the enhanced image of the surface of the wafer to be detected. According to the method, the gradient change of the pixel points of the surface image of the wafer to be detected is calculated, the region of the image to be detected is divided by the gradient change and the gray difference, the contrast of the defect is improved by self-adaptive enhancement of different defect degrees, and the accuracy of defect detection can be effectively improved.

Description

Artificial intelligence-based integrated circuit wafer surface defect detection method
Technical Field
The application relates to the field of defect detection, in particular to an integrated circuit wafer surface defect detection method based on artificial intelligence.
Background
The wafer fabrication process requires many steps such as doping, etching, photolithography, dicing, etc. before the final packaging process is performed to form an integrated circuit. During these steps, it is important to detect faulty wafers before packaging is completed, since wafers are inevitably damaged and later integrated circuit performance is affected.
There are many surface defects that affect the acceptability of a wafer product, and among the defect types of wafers, unpatterned wafers and patterned wafers are the most common two types of wafers. Wafer surface redundancy, crystal defects, mechanical damage (scratch pattern) are more common defects. The redundancy is a common defect type on the surface of the wafer, and mainly comprises nanometer-sized micro particles, micron-sized dust and residues of related processes. With the smaller and smaller semiconductor size, the finer and finer wafer is manufactured, and the problems of difficulty in distinguishing fine defects from non-defects, similarity of defect shapes and background patterns, low recognition accuracy and the like exist in the aspect of defect detection.
In the prior art, a chip to be detected and a standard chip between virtual layers are compared, and when the chip to be detected and the standard wafer have obvious difference, the chip to be detected and the standard wafer can be detected. In the wafer defect detection, the position of the wafer can be found under the conditions of being blocked or unfilled corner and uneven illumination, and the gray value-based template matching algorithm cannot process the types of interference; therefore, the method has large limitation, is not suitable for detecting objects with non-fixed shapes, and cannot detect the fine defects on the surface of the wafer.
Disclosure of Invention
In order to solve the technical problems, the invention provides an artificial intelligence-based integrated circuit wafer surface defect detection method, which comprises the following steps:
acquiring a surface image of a wafer to be detected;
by using
Figure DEST_PATH_IMAGE001
The operator respectively calculates the gradient variation of each pixel point in the surface image of the wafer to be detected and the standard surface image of the wafer, and codes according to the gradient variation to respectively obtain the gradient variation chain code of the surface image of the wafer to be detected and the gradient variation chain code of the surface image of the standard wafer;
calculating the gradient similarity of the surface image of the wafer to be detected and the surface image of the standard wafer according to the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer;
dividing the area of the surface image of the wafer to be detected according to the gradient similarity and the defect threshold, and taking the gradient similarity corresponding to each area as the abnormal degree of the area;
calculating the pixel characteristic value of each pixel point according to the gray value of each pixel point in each pixel point and the area where the pixel point is located, and calculating the area characteristic value of each area according to the pixel characteristic value of each pixel point in each area;
calculating the suspected defect degree of each area by using the frequency of the pixel characteristic value of each pixel point in each area;
constructing a characteristic histogram by using the frequency of the characteristic value of each region, and dividing the characteristic histogram by using the suspected defect degree of each region to obtain different types of histogram regions;
carrying out self-adaptive enhancement on the surface image of the wafer to be detected by utilizing the obtained histogram areas of different types to obtain a self-adaptively enhanced surface enhanced image of the wafer to be detected;
and carrying out edge detection on the enhanced image of the surface of the wafer to be detected to obtain a defect area of the wafer to be detected.
The method for respectively obtaining the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer by coding according to the gradient change comprises the following steps:
setting code elements in eight neighborhood directions;
if the gradient amplitude of the gradient variable quantity of the pixel point in a certain direction of the eight neighborhood directions changes, taking the gradient in the direction as the change direction of the gradient of the pixel point, and coding according to a code element in the gradient change direction;
if the gradient amplitude variation of the gradient variation of the pixel point in the eight neighborhood directions is the same, taking 0 as the code element of the pixel point;
and coding the surface image of the wafer to be detected and the surface image of the standard wafer according to the method to obtain the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer.
The process of calculating the gradient similarity between the surface image of the wafer to be detected and the surface image of the standard wafer according to the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer is as follows:
matching the gradient change chain code of the surface image of the wafer to be detected with the gradient change chain code of the surface image of the standard wafer, calculating the difference degree between the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer according to the matching result, and taking the obtained difference degree as the gradient similarity of the surface image of the wafer to be detected and the surface image of the standard wafer, wherein the calculation formula of the gradient similarity of the surface image of the wafer to be detected and the surface image of the standard wafer is as follows:
Figure DEST_PATH_IMAGE003
in the formula:
Figure 64846DEST_PATH_IMAGE004
representing the gradient similarity between the detected wafer surface image and the standard wafer surface image,
Figure DEST_PATH_IMAGE005
the code length difference between the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer,
Figure 919670DEST_PATH_IMAGE006
the minimum code length difference between the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer is shown,
Figure DEST_PATH_IMAGE007
and the maximum code length difference between the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer is represented.
The calculation method of the code length difference of the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer comprises the following steps:
the calculation formula of the code length difference of the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer is as follows:
Figure DEST_PATH_IMAGE009
in the formula:
Figure 542150DEST_PATH_IMAGE010
the code element length of the shortest abnormal chain code in the gradient change chain codes of the surface image of the wafer to be detected and the gradient change chain codes of the surface image of the standard wafer is shown,
Figure DEST_PATH_IMAGE011
the second of the gradient chain codes representing the image of the surface of the standard wafer
Figure 875042DEST_PATH_IMAGE012
The length of a segment of a code is,
Figure DEST_PATH_IMAGE013
the second step of the gradient change chain code representing the surface image of the wafer to be detected
Figure 892677DEST_PATH_IMAGE014
The length of a code segment;
the code segment dividing method comprises the following steps: and respectively carrying out code segment division on the gradient change chain codes of the surface image of the wafer to be detected and the gradient change chain codes of the surface image of the standard wafer by utilizing the matching results of the gradient change chain codes of the surface image of the wafer to be detected and the gradient change chain codes of the surface image of the standard wafer, and dividing the gradient change chain codes into abnormal chain codes and normal chain codes by taking the abnormal chain codes in the matching results as boundaries.
The method for dividing the area of the wafer surface image to be detected according to the gradient similarity and the defect threshold comprises the following steps:
dividing the surface image of the wafer to be detected into three types of areas according to the gradient similarity: defect-free regions, lightly defective regions, and highly defective regions;
and setting a low defect threshold and a high defect threshold, taking an image area corresponding to the gradient similarity greater than or equal to the low defect threshold as a non-defective area, taking an image area corresponding to the gradient similarity between the low defect threshold and the high defect threshold as a light defective area, and taking an image area corresponding to the gradient similarity less than or equal to the high defect threshold as a heavy defective area.
The method for acquiring the low defect threshold and the high defect threshold comprises the following steps:
dividing an initial region according to a gradient similarity histogram of a wafer surface image to be detected and the gradient similarity histogram, establishing a gray level histogram of an initial light defect region, and calculating the defect area according to the frequency of each gray level value and the number of pixel points in the divided initial light defect region
Figure DEST_PATH_IMAGE015
And normal area
Figure 885778DEST_PATH_IMAGE016
Area of defect
Figure 683970DEST_PATH_IMAGE015
The calculation formula of (a) is as follows:
Figure 289395DEST_PATH_IMAGE018
wherein,
Figure 489432DEST_PATH_IMAGE014
the number of pixels of the wafer surface image to be detected,
Figure DEST_PATH_IMAGE019
is a gray scale of
Figure 420479DEST_PATH_IMAGE020
The frequency of occurrence of the pixels of (a),
Figure DEST_PATH_IMAGE021
the gray level corresponding to the first trough in the gray level histogram;
the area of the normal area is the area of the initial mild defect area minus the defect area, and then the defect threshold is low
Figure 112491DEST_PATH_IMAGE022
Comprises the following steps:
Figure 754563DEST_PATH_IMAGE024
the high defect threshold is obtained by iterating the above process with the initial heavily defective region.
The process of calculating the region characteristic value of each region according to the pixel characteristic value of each pixel point in each region is as follows:
calculating the gray values of each pixel point and all pixel points in the area of the pixel pointThe pixel characteristic value of the pixel point is calculated by the following method: for region
Figure DEST_PATH_IMAGE025
Maximum value and area in gray value difference absolute values of intermediate gray value mean value and each pixel point
Figure 215631DEST_PATH_IMAGE025
The average value of the intermediate gray values is differed with the minimum value in the absolute value of the gray value difference of each pixel point, and the obtained difference value is used as the pixel characteristic value of the pixel point;
calculating the area characteristic value of the area according to the pixel characteristic value of each pixel point and the pixel characteristic values of other pixel points in the area where the pixel point is located, wherein the calculation formula is as follows:
Figure DEST_PATH_IMAGE027
wherein,
Figure 583159DEST_PATH_IMAGE028
is a pixel point
Figure DEST_PATH_IMAGE029
The region characteristic value of the region in which the region is located,
Figure 293626DEST_PATH_IMAGE030
is a pixel point
Figure 723077DEST_PATH_IMAGE029
The value of the characteristic of the pixel of (a),
Figure DEST_PATH_IMAGE031
is a pixel point
Figure 225602DEST_PATH_IMAGE029
In the region of
Figure 498452DEST_PATH_IMAGE025
The mean value of the gray values of all the pixel points in the image,
Figure 24111DEST_PATH_IMAGE032
is a pixel point
Figure 273565DEST_PATH_IMAGE029
The gray value of (a).
The method for carrying out self-adaptive enhancement on the surface image of the wafer to be detected by utilizing the obtained histogram regions of different types comprises the following steps:
dividing the feature histogram according to the trough positions in the feature histogram to obtain two parts, and performing image enhancement according to the division result, wherein the method comprises the following steps:
Figure DEST_PATH_IMAGE033
wherein:
Figure 709225DEST_PATH_IMAGE034
which represents the image after the enhancement, is,
Figure DEST_PATH_IMAGE035
indicating area
Figure 418555DEST_PATH_IMAGE025
To middle
Figure 775718DEST_PATH_IMAGE020
The characteristic value of each pixel point is calculated,
Figure 189382DEST_PATH_IMAGE014
is a region
Figure 446926DEST_PATH_IMAGE025
The number of the middle pixel points is increased,
Figure 920632DEST_PATH_IMAGE020
region(s)
Figure 765092DEST_PATH_IMAGE025
The serial number of the middle pixel point is,
Figure 716867DEST_PATH_IMAGE036
representing pixel points
Figure 595962DEST_PATH_IMAGE029
The degree of suspected defects in the area of the defect,
Figure DEST_PATH_IMAGE037
、
Figure 506149DEST_PATH_IMAGE038
respectively the abscissa of the feature histogram boundary,
Figure 837904DEST_PATH_IMAGE028
is a pixel point
Figure 593370DEST_PATH_IMAGE029
In the area of
Figure 559927DEST_PATH_IMAGE025
The area feature value of (1).
The calculation formula for calculating the suspected defect degree of each area by using the occurrence frequency of the pixel characteristic value of each pixel point in each area is as follows:
Figure 375436DEST_PATH_IMAGE040
in the formula:
Figure 194488DEST_PATH_IMAGE036
representing pixel points
Figure 753645DEST_PATH_IMAGE029
In the area of
Figure 341752DEST_PATH_IMAGE025
The degree of suspected defect of (a) is,
Figure DEST_PATH_IMAGE041
is a region
Figure 469108DEST_PATH_IMAGE025
To middle
Figure 900090DEST_PATH_IMAGE042
The frequency of the seed pixel characteristic value is,
Figure DEST_PATH_IMAGE043
is a region
Figure 43364DEST_PATH_IMAGE025
The types of different pixel characteristic values.
Compared with the prior art, the embodiment of the invention has the beneficial effects that: the gradient change of the pixel points of the surface image of the wafer to be detected and the surface image of the standard wafer is calculated, the gradient change chain codes of the surface image of the wafer to be detected and the surface image of the standard wafer are obtained according to the gradient change of the pixel points of the surface image of the wafer to be detected and the surface image of the standard wafer, the gradient similarity of the wafer to be detected and the standard wafer is obtained according to the gradient change chain codes of the surface image of the wafer to be detected and the surface image of the standard wafer, and the gradient similarity reflects the difference between the surface image of the wafer to be detected and the surface image of the standard wafer; the abnormal degree of the surface defect is obtained according to the gradient similarity, the self-adaptive image enhancement is carried out according to the abnormal degree, the defect detection is carried out according to the enhanced image, the defect on the surface of the wafer can be more obvious, and the accuracy of the wafer defect detection is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flowchart of a system for an artificial intelligence based method for detecting surface defects of an integrated circuit wafer according to an embodiment of the present invention;
FIG. 2 is a flowchart of the method steps provided by the method for detecting surface defects of an IC wafer based on artificial intelligence in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram of an image acquisition environment provided by an artificial intelligence-based method for detecting surface defects of an integrated circuit wafer according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a wafer defect provided by an artificial intelligence based integrated circuit wafer surface defect detection method according to an embodiment of the present invention;
FIG. 5 is a code element diagram provided by an artificial intelligence based integrated circuit wafer surface defect detection method according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of the classification of characteristic values provided by the method for detecting surface defects of an integrated circuit wafer based on artificial intelligence according to the embodiment of the present invention.
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. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
The terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to implicitly indicate the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature; in the description of the present embodiment, the meaning of "a plurality" is two or more unless otherwise specified.
Example 1
The embodiment of the invention provides an integrated circuit wafer surface defect detection method based on artificial intelligence, as shown in fig. 1 and fig. 2, comprising:
s101, acquiring a surface image of a wafer to be detected
In this embodiment, the gradient change of the pixel points of the image on the surface of the wafer is analyzed to further determine whether the defect exists and the position of the defect, so that the image on the surface of the wafer to be detected needs to be obtained first for subsequent analysis of the wafer to be detected.
S102, obtaining the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer
And coding the gradient variation according to the gradient variation of the pixel points of the acquired surface image of the wafer to be detected to obtain a gradient variation chain code, and reflecting the size of the defect area on the surface of the wafer to be detected by comparing the gradient variation chain code with the gradient variation chain code of the standard surface image of the wafer.
S103, calculating the gradient similarity between the surface image of the wafer to be detected and the surface image of the standard wafer
The code segment of the abnormal pixel, namely the abnormal chain code, is obtained by comparing the gradient change chain code of the surface image of the wafer to be detected with the gradient change chain code of the surface image of the standard wafer, the gradient similarity of the surface image of the wafer to be detected and the surface image of the standard wafer is further calculated according to the abnormal chain code, the difference between the gray levels is expressed by the gradient similarity, and the defect area of the surface of the wafer to be detected can be more obviously represented.
S104, carrying out area division on the surface image of the wafer to be detected
The larger the gradient similarity is, the smaller the defect area exists or the defect does not exist; the smaller the gradient similarity is, the larger the defect area exists is, the area division is carried out on the surface image of the wafer to be detected according to the gradient similarity, the image enhancement of different degrees is carried out on the divided areas according to the self characteristics of each area, the characteristics of the defect area are more obvious, and the final defect detection efficiency and accuracy can be improved.
S105, calculating the suspected defect degree of each area
The region division is carried out according to the gradient similarity, light defects in the heavy defect region are not divided, and when the contrast of the defect region is not greatly different from that of a normal background region, the statistics of the histogram can be influenced, and brightness errors can occur after the enhancement. Therefore, the traditional gray stretching enhancement cannot well enhance the image, the embodiment performs local adaptive image enhancement according to the gray change in the image, calculates the pixel characteristic value of each pixel according to the gray value of each pixel, and calculates the suspected defect degree of the region according to the pixel characteristic value of the pixel in the region.
S106, obtaining the self-adaptively enhanced to-be-detected wafer surface enhanced image
Because only the pixel points of the gradient-changed area in the wafer surface image to be detected can be described according to the gradient change, the initial area of the suspected defect is obtained, but whether the defect is true or not cannot be distinguished. Therefore, the surface image of the wafer to be detected needs to be enhanced, so that the defect area is more obvious, and the defect on the surface of the wafer can be better detected.
S107, acquiring the defect area of the wafer to be detected
Because the obtained enhanced image of the surface of the wafer to be detected can obviously reflect the suspected defect area of the surface of the wafer to be detected, the more accurate defect area of the surface of the wafer to be detected can be obtained by utilizing edge detection according to the adaptively enhanced image of the surface of the wafer to be detected.
Example 2
The embodiment of the invention provides an integrated circuit wafer surface defect detection method based on artificial intelligence, which comprises the following specific contents as shown in figures 1 and 2:
s201, acquiring surface image of wafer to be detected
Since the wafer is a very tiny integrated circuit device, an industrial high-resolution camera is required to obtain a clear image of the wafer surface when the image is collected, and the illumination of the light source needs to be uniform, and cannot be too strong or too weak, otherwise, the gray value of the wafer surface is changed, so that some defects are covered by the light, and the detection is inaccurate. A schematic of the environment in which the image is acquired is shown in fig. 3.
1. The collected wafer image is preprocessed, and because the wafer components are tiny, noise is formed when the image is collected, the accuracy of subsequent wafer surface defects is influenced, and therefore denoising processing is carried out. In this embodiment, the noise reduction of the image is performed by using a mean filtering method, and a 3 × 3 filtering window is used.
2. During defect detection, only defect detection is needed to be performed on the region of the wafer surface, after denoising processing is performed, semantic segmentation is performed on the denoised wafer image, background pixels are removed, and only the wafer surface image to be detected including the wafer is obtained, wherein the wafer defect schematic diagram is shown in fig. 4.
The relevant content of the DNN network is as follows:
the data set used is an overlook acquired wafer RGB image data set to be detected.
The pixels needing to be segmented are divided into two types, namely the labeling process of the corresponding labels of the training set is as follows: and in the semantic label of the single channel, the label of the pixel at the corresponding position belonging to the background class is 0, and the label of the pixel belonging to the wafer area is 1.
The task of the network is to classify, and all the used loss functions are cross entropy loss functions.
The 0-1 mask image obtained by semantic segmentation is multiplied by the original image, and the obtained image only contains the image of the wafer area, so that the interference of the background is removed.
And reasoning according to the acquired RGB image through the trained DNN, obtaining the image of the wafer area in the image, and carrying out gray processing on the image of the wafer area to obtain the corresponding wafer surface image to be detected.
S202, obtaining the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer
1. Calculating the gradient variation of each pixel point in the surface image of the wafer to be detected and the standard wafer surface image
The uniformity of pixel points in the surface image of the wafer to be detected is represented according to the gradient direction and the amplitude value in the surface image of the wafer to be detected, then the surface image of the wafer to be detected is subjected to gradient change coding, and the abnormal code segment of the surface image of the wafer to be detected is obtained by matching with the gradient change chain code of the surface image of the standard wafer.
In order to obtain gradient change chain codes, the horizontal gradient and the vertical gradient of each pixel point need to be calculated first, and a kernel with the size of 1 can be used
Figure 876191DEST_PATH_IMAGE044
Operators to obtain the same result. About
Figure 908869DEST_PATH_IMAGE044
The details of calculating the gradient are well known and well-established techniques and will not be further described herein.
Then recalculate
Figure DEST_PATH_IMAGE045
And
Figure 834272DEST_PATH_IMAGE046
the resultant gradient of the directional gradient, including magnitude and direction:
Figure 610598DEST_PATH_IMAGE048
Figure 937412DEST_PATH_IMAGE050
in the formula,
Figure DEST_PATH_IMAGE051
which is representative of the magnitude of the gradient,
Figure 937729DEST_PATH_IMAGE052
which represents the direction of the gradient and,
Figure DEST_PATH_IMAGE053
and indicating the serial numbers of the pixel points of the wafer surface image to be detected. The gradient direction will take the absolute value, so the resulting angular range is
Figure 484248DEST_PATH_IMAGE054
. At each pixel point, the gradient has a magnitude and a direction.
Figure 766063DEST_PATH_IMAGE045
The directional gradient map will accentuate the vertical edge features,
Figure 183269DEST_PATH_IMAGE046
the directional gradient map emphasizes horizontal edge features. This allows useful features (contours) to be preserved, with irrelevant unimportant information removed.
Figure DEST_PATH_IMAGE055
Representing the gradient amplitude and the change vector of the gradient direction of the pixel point on the surface image of the wafer to be detected, representing by the vector, and obtaining the gradient change quantity of the pixel point on the surface image of the wafer to be detected according to the gradient amplitude and the gradient direction:
Figure 354487DEST_PATH_IMAGE056
，
Figure DEST_PATH_IMAGE057
and expressing the gradient variable quantity to obtain the gradient variable quantity of all pixel points on the surface image of the wafer to be detected. The gradient variation of the standard wafer surface image is obtained by the method
Figure 558941DEST_PATH_IMAGE058
Modulus value of
Figure DEST_PATH_IMAGE059
Gradient variation of all pixels on the surface image of the standard wafer is performed by performing gradient variation of the standard wafer image and gradient variation of the to-be-detected wafer image according to the gradient variationThe coding of (2).
2. Acquiring gradient change chain codes of surface images of wafers to be detected and gradient change chain codes of surface images of standard wafers
And obtaining the gradient variation of the standard wafer surface image and the gradient variation of the wafer surface image to be detected according to the steps, then coding the obtained gradient variations, and obtaining the gradient similarity of the two images according to the difference degree between the chain code of the gradient variation of the standard wafer surface image and the chain code of the gradient variation of the wafer surface image to be detected.
The encoding according to the gradient change amount is as follows:
setting the code elements in the eight neighborhood directions, wherein the code element diagram is shown in FIG. 5;
if the gradient amplitude of the gradient variable quantity of the pixel point in a certain direction of the eight neighborhood directions changes, taking the gradient in the direction as the change direction of the gradient of the pixel point, and encoding according to a code element in the gradient change direction;
if the gradient amplitude variation of the gradient variation of the pixel point in the eight neighborhood directions is the same, taking 0 as the code element of the pixel point;
and coding the surface image of the wafer to be detected and the surface image of the standard wafer according to the method to obtain the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer.
Encoding gradient change values of pixel points in the wafer surface image to be detected and the standard wafer surface image, and encoding the images from left to right and from top to bottom, for example:
the gradient change chain code of the surface image of the wafer to be detected is { [1]0, 0, 0, 1, 3, D, 2, C, D, D, 4, 3, A, B, 1, 4, 0, 0}, wherein [1] of the chain code represents an identification code, [1] represents the wafer to be detected, [0] represents a standard wafer, and {0} represents that the gradient changes of the pixel point in all directions are the same.
The gradient change chain code of the standard wafer surface image is obtained by encoding the standard wafer surface image through the method.
S203, calculating the gradient similarity between the surface image of the wafer to be detected and the surface image of the standard wafer
Comparing the gradient change chain code of the surface image of the wafer to be detected with the gradient change chain code of the surface image of the standard wafer, and when the gradient change chain code of the surface image of the wafer to be detected is completely matched with the gradient change chain code of the surface image of the standard wafer, indicating that no defect exists on the surface of the wafer to be detected; when the gradient change chain code of the surface image of the wafer to be detected is different from the gradient change chain code of the surface image of the standard wafer, calculating the difference degree between the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer, and taking the obtained difference degree as the gradient similarity of the surface image of the wafer to be detected and the surface image of the standard wafer.
The calculation formula of the gradient similarity between the surface image of the wafer to be detected and the surface image of the standard wafer is as follows:
Figure 942649DEST_PATH_IMAGE060
in the formula:
Figure DEST_PATH_IMAGE061
representing the gradient similarity between the detected wafer surface image and the standard wafer surface image,
Figure 745520DEST_PATH_IMAGE062
the code length difference between the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer,
Figure DEST_PATH_IMAGE063
the minimum code length difference between the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer is represented,
Figure 586175DEST_PATH_IMAGE064
gradient change chain code for representing surface image of wafer to be detected and gradient change chain code for standard wafer surface imageThe maximum code length difference.
The calculation formula of the code length difference of the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer is as follows:
Figure 576128DEST_PATH_IMAGE009
in the formula:
Figure DEST_PATH_IMAGE065
the code element length of the shortest abnormal chain code in the gradient change chain codes of the surface image of the wafer to be detected and the gradient change chain codes of the surface image of the standard wafer is shown,
Figure 278373DEST_PATH_IMAGE066
the second of the gradient chain codes representing the image of the surface of the standard wafer
Figure DEST_PATH_IMAGE067
The length of each of the code segments is,
Figure 466909DEST_PATH_IMAGE068
the second of the gradient change chain codes representing the surface image of the wafer to be detected
Figure DEST_PATH_IMAGE069
The length of each code segment;
the code segment dividing method comprises the following steps: and respectively carrying out code segment division on the gradient change chain codes of the surface image of the wafer to be detected and the gradient change chain codes of the surface image of the standard wafer by utilizing the matching results of the gradient change chain codes of the surface image of the wafer to be detected and the gradient change chain codes of the surface image of the standard wafer, and dividing the gradient change chain codes into abnormal chain codes and normal chain codes by taking the abnormal chain codes in the matching results as boundaries.
S204, carrying out region division on the surface image of the wafer to be detected
The gradient similarity between the standard wafer and the wafer to be detected is obtained through the steps and is used for representing the change of the gray value of the pixel point on the surface of the wafer, and the larger the gradient similarity is, the smaller the defect area exists or the defect does not exist; the smaller the gradient similarity is, the larger the defect area exists, the size of the defect area is divided according to the gradient similarity, and three conditions exist, namely a non-defect area, a light defect area and a heavy defect area.
Setting a low defect threshold
Figure 478465DEST_PATH_IMAGE070
And high defect threshold
Figure DEST_PATH_IMAGE071
The image area corresponding to the gradient similarity greater than or equal to the low defect threshold is set as a non-defective area, the image area corresponding to the gradient similarity between the low defect threshold and the high defect threshold is set as a light-defective area, and the image area corresponding to the gradient similarity less than or equal to the high defect threshold is set as a heavy-defective area. The gradient similarity of each region is used as the degree of abnormality of the region.
Low defect threshold
Figure 486872DEST_PATH_IMAGE070
And high defect threshold
Figure 681225DEST_PATH_IMAGE071
The acquisition method comprises the following steps:
dividing an initial region according to a gradient similarity histogram of a wafer surface image to be detected and the gradient similarity histogram, establishing a gray level histogram of the initial light defect region, and calculating the defect area according to the frequency of each gray level value and the number of pixel points in the divided initial light defect region
Figure 52163DEST_PATH_IMAGE072
And normal area
Figure DEST_PATH_IMAGE073
Area of defect
Figure 470506DEST_PATH_IMAGE072
The calculation formula of (c) is as follows:
Figure 668007DEST_PATH_IMAGE074
wherein,
Figure 56263DEST_PATH_IMAGE069
the number of pixels of the wafer surface image to be detected,
Figure DEST_PATH_IMAGE075
is a gray scale of
Figure 688233DEST_PATH_IMAGE076
The frequency of occurrence of the pixels of (a),
Figure DEST_PATH_IMAGE077
the gray level corresponding to the first trough in the gray level histogram;
the area of the normal area is the area of the initial mild defect area minus the defect area, and then the defect threshold is low
Figure 277477DEST_PATH_IMAGE070
Comprises the following steps:
Figure 588373DEST_PATH_IMAGE024
and the high defect threshold is obtained by iterating the above process by using the initial severe defect area.
S205, calculating the suspected defect degree of each area
The region division is carried out according to the gradient similarity, light defects in the heavy defect region are not divided, and when the contrast of the defect region is not greatly different from that of a normal background region, the statistics of the histogram can be influenced, and brightness errors can occur after the enhancement. Therefore, the traditional gray stretching enhancement cannot well enhance the image, the embodiment performs local adaptive image enhancement according to the gray change in the image, calculates the pixel characteristic value of each pixel according to the gray value of each pixel, calculates the suspected defect degree of the area according to the pixel characteristic value of the pixel in the area, and re-assigns the pixel in the area according to the suspected defect degree to obtain the characteristic value of the pixel with more obvious difference.
1. Calculating the characteristic value of each pixel point in each region
Calculating the pixel characteristic value of each pixel point according to the gray values of each pixel point and all the pixel points in the area of the pixel point, wherein the calculation method comprises the following steps: to the region
Figure 390106DEST_PATH_IMAGE078
Maximum value and area in gray value difference absolute values of intermediate gray value mean value and each pixel point
Figure 470058DEST_PATH_IMAGE078
And (3) making a difference between the average value of the intermediate gray values and the minimum value in the absolute value of the gray value difference of each pixel point, and taking the obtained difference as the pixel characteristic value of the pixel point, wherein the calculation formula is as follows:
Figure 728739DEST_PATH_IMAGE080
in the formula:
Figure DEST_PATH_IMAGE081
is a pixel point
Figure 667876DEST_PATH_IMAGE082
The value of the characteristic of the pixel of (c),
Figure DEST_PATH_IMAGE083
is a pixel point
Figure 804459DEST_PATH_IMAGE082
In the region of
Figure 738917DEST_PATH_IMAGE078
The mean value of the gray values of all the pixel points in the image,
Figure 404385DEST_PATH_IMAGE084
is a pixel point
Figure 689873DEST_PATH_IMAGE082
The gray value of (a).
2. Calculating a region feature value of each region
Calculating the characteristic value of each pixel point according to the pixel characteristic value of each pixel point and the pixel characteristic values of other pixel points in the area of the pixel point, wherein the calculation formula is as follows:
Figure 331944DEST_PATH_IMAGE086
wherein,
Figure DEST_PATH_IMAGE087
is a pixel point
Figure 58592DEST_PATH_IMAGE082
The region characteristic value of the region in which the region is located,
Figure 894961DEST_PATH_IMAGE081
is a pixel point
Figure 402165DEST_PATH_IMAGE082
The value of the characteristic of the pixel of (a),
Figure 208447DEST_PATH_IMAGE083
is a pixel point
Figure 258443DEST_PATH_IMAGE082
In the region of
Figure 124768DEST_PATH_IMAGE078
The mean value of the gray values of all the pixel points,
Figure 758749DEST_PATH_IMAGE084
is a pixel point
Figure 368722DEST_PATH_IMAGE082
The gray value of (a).
3. Calculating the suspected defect degree of each area
Figure 273224DEST_PATH_IMAGE040
In the formula:
Figure 310450DEST_PATH_IMAGE088
representing pixel points
Figure 933193DEST_PATH_IMAGE082
In the region of
Figure 487802DEST_PATH_IMAGE078
The degree of suspected defect of (a) is,
Figure 371444DEST_PATH_IMAGE041
is a region
Figure 219052DEST_PATH_IMAGE078
To middle
Figure DEST_PATH_IMAGE089
The frequency of the seed characteristic value is determined,
Figure 453725DEST_PATH_IMAGE090
is a region
Figure 546446DEST_PATH_IMAGE078
Of different eigenvalues.
S206, obtaining the self-adaptive enhanced wafer surface enhanced image to be detected
Because only the pixel points of the gradient-changed area in the wafer surface image to be detected can be described according to the gradient change, the initial area of the suspected defect is obtained, but whether the defect is true or not cannot be distinguished. Therefore, the surface image of the wafer to be detected needs to be enhanced, so that the defect area is more obvious, and the defect on the surface of the wafer can be better detected.
To obtain a more finely enhanced imageDividing the characteristic values according to the obtained characteristic values, counting a characteristic value quantity histogram, and dividing the characteristic value histogram according to the grade of the characteristic values, wherein the quantity of peak values contained in the histogram is uncertain because the quantity of the defect areas is uncertain:
Figure DEST_PATH_IMAGE091
representing the boundaries of the partition, typically at the bottom of the histogram.
Figure 691119DEST_PATH_IMAGE092
The number of the limits is indicated and,
Figure 70148DEST_PATH_IMAGE092
the boundaries can divide the image into
Figure DEST_PATH_IMAGE093
And (4) section. In this embodiment, the histogram is divided into two parts according to different feature value levels, and a schematic diagram of feature value level division is shown in fig. 6.
The enhancement process is as follows:
Figure 933062DEST_PATH_IMAGE033
wherein:
Figure 62429DEST_PATH_IMAGE094
the enhanced image is represented by a representation of the image,
Figure DEST_PATH_IMAGE095
indicating area
Figure 796030DEST_PATH_IMAGE078
To middle
Figure 486906DEST_PATH_IMAGE076
The characteristic value of each pixel point is calculated,
Figure 430591DEST_PATH_IMAGE069
is a region
Figure 98070DEST_PATH_IMAGE078
The number of the middle pixel points is increased,
Figure 810811DEST_PATH_IMAGE076
region(s)
Figure 407009DEST_PATH_IMAGE078
The serial number of the middle pixel point is,
Figure 883996DEST_PATH_IMAGE088
representing pixel points
Figure 627871DEST_PATH_IMAGE082
The extent of suspected defects in the area of the defect,
Figure 319752DEST_PATH_IMAGE096
、
Figure DEST_PATH_IMAGE097
respectively the abscissa of the feature histogram limit,
Figure 382124DEST_PATH_IMAGE087
is a pixel point
Figure 300402DEST_PATH_IMAGE082
In the area of
Figure 811148DEST_PATH_IMAGE078
The area feature value of (1).
S207, acquiring a defect area of the wafer to be detected
The obtained enhanced image of the surface of the wafer to be detected contains defect areas with different degrees and different sizes, the suspected defect area of the enhanced image of the surface of the wafer to be detected is very obvious, and the enhanced image of the surface of the wafer to be detected is utilized
Figure 13328DEST_PATH_IMAGE098
The operator carries out edge detection to obtain the defect area of the surface of the wafer to be detected,and finishing the defect detection of the wafer to be detected.
And recycling or secondarily processing the wafer to be detected with the defects according to the defect area obtained by the defect detection and the defect position defect type of the defect area, so that the wafer to be delivered can reach the delivery quality standard.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention.

Claims (9)

1. An integrated circuit wafer surface defect detection method based on artificial intelligence is characterized by comprising the following steps:
acquiring a surface image of a wafer to be detected;
by using
Figure DEST_PATH_IMAGE002
The operator respectively calculates the gradient variation of each pixel point in the surface image of the wafer to be detected and the standard wafer surface image, and the gradient variation chain code of the surface image of the wafer to be detected and the gradient variation chain code of the standard wafer surface image are respectively obtained by coding according to the gradient variation;
calculating the gradient similarity of the surface image of the wafer to be detected and the surface image of the standard wafer according to the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer;
dividing the area of the surface image of the wafer to be detected according to the gradient similarity and the defect threshold, and taking the gradient similarity corresponding to each area as the abnormal degree of the area;
calculating the pixel characteristic value of each pixel point according to the gray value of each pixel point in each pixel point and the area where the pixel point is located, and calculating the area characteristic value of each area according to the pixel characteristic value of each pixel point in each area;
calculating the suspected defect degree of each area by using the frequency of the pixel characteristic value of each pixel point in each area;
constructing a characteristic histogram by using the frequency of the characteristic value of each region, and dividing the characteristic histogram by using the suspected defect degree of each region to obtain different types of histogram regions;
carrying out self-adaptive enhancement on the surface image of the wafer to be detected by utilizing the obtained histogram areas of different types to obtain a self-adaptively enhanced surface enhanced image of the wafer to be detected;
and carrying out edge detection on the enhanced image of the surface of the wafer to be detected to obtain a defect area of the wafer to be detected.
2. The method for detecting the surface defects of the integrated circuit wafer based on the artificial intelligence as claimed in claim 1, wherein the method for respectively obtaining the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer by encoding according to the gradient change amount comprises the following steps:
setting code elements in eight neighborhood directions;
if the gradient amplitude of the gradient variable quantity of the pixel point in a certain direction of the eight neighborhood directions changes, taking the gradient in the direction as the change direction of the gradient of the pixel point, and encoding according to a code element in the gradient change direction;
if the gradient amplitude variation of the gradient variation of the pixel point in the eight neighborhood directions is the same, taking 0 as the code element of the pixel point;
and coding the surface image of the wafer to be detected and the surface image of the standard wafer according to the method to obtain the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer.
3. The method for detecting surface defects of an integrated circuit wafer based on artificial intelligence as claimed in claim 1, wherein the process of calculating the gradient similarity between the surface image of the wafer to be detected and the surface image of the standard wafer according to the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer is as follows:
matching the gradient change chain code of the surface image of the wafer to be detected with the gradient change chain code of the surface image of the standard wafer, calculating the difference degree between the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer according to the matching result, and taking the obtained difference degree as the gradient similarity of the surface image of the wafer to be detected and the surface image of the standard wafer, wherein the calculation formula of the gradient similarity of the surface image of the wafer to be detected and the surface image of the standard wafer is as follows:
Figure DEST_PATH_IMAGE004
in the formula:
Figure DEST_PATH_IMAGE006
representing the gradient similarity between the detected wafer surface image and the standard wafer surface image,
Figure DEST_PATH_IMAGE008
the code length difference between the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer,
Figure DEST_PATH_IMAGE010
the minimum code length difference between the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer is shown,
Figure DEST_PATH_IMAGE012
and the maximum code length difference between the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer is represented.
4. The method as claimed in claim 3, wherein the calculating method of the code length difference between the gradient change chain code of the image on the surface of the wafer to be detected and the gradient change chain code of the image on the surface of the standard wafer comprises:
the calculation formula of the code length difference of the gradient change chain code of the surface image of the wafer to be detected and the gradient change chain code of the surface image of the standard wafer is as follows:
Figure DEST_PATH_IMAGE014
in the formula:
Figure DEST_PATH_IMAGE016
the code element length of the shortest abnormal chain code in the gradient change chain codes of the surface image of the wafer to be detected and the gradient change chain codes of the surface image of the standard wafer is shown,
Figure DEST_PATH_IMAGE018
the second of the gradient chain codes representing the image of the surface of the standard wafer
Figure DEST_PATH_IMAGE020
The length of a segment of a code is,
Figure DEST_PATH_IMAGE022
the second of the gradient change chain codes representing the surface image of the wafer to be detected
Figure DEST_PATH_IMAGE024
The length of a code segment;
the code segment dividing method comprises the following steps: and respectively carrying out code segment division on the gradient change chain codes of the surface image of the wafer to be detected and the gradient change chain codes of the surface image of the standard wafer by utilizing the matching results of the gradient change chain codes of the surface image of the wafer to be detected and the gradient change chain codes of the surface image of the standard wafer, and dividing the gradient change chain codes into abnormal chain codes and normal chain codes by taking the abnormal chain codes in the matching results as boundaries.
5. The method as claimed in claim 1, wherein the method for dividing the regions of the wafer surface image to be detected according to the gradient similarity and the defect threshold comprises:
dividing the surface image of the wafer to be detected into three types of regions according to the gradient similarity: a defect-free region, a lightly defective region, and a highly defective region;
and setting a low defect threshold and a high defect threshold, taking an image area corresponding to the gradient similarity greater than or equal to the low defect threshold as a non-defective area, taking an image area corresponding to the gradient similarity between the low defect threshold and the high defect threshold as a light defective area, and taking an image area corresponding to the gradient similarity less than or equal to the high defect threshold as a heavy defective area.
6. The method of claim 5, wherein the obtaining of the low defect threshold and the high defect threshold comprises:
dividing an initial region according to a gradient similarity histogram of a wafer surface image to be detected and the gradient similarity histogram, establishing a gray level histogram of the initial light defect region, and calculating the defect area according to the frequency of each gray level value and the number of pixel points in the divided initial light defect region
Figure DEST_PATH_IMAGE026
And normal area
Figure DEST_PATH_IMAGE028
Area of defect
Figure 83676DEST_PATH_IMAGE026
The calculation formula of (a) is as follows:
Figure DEST_PATH_IMAGE030
wherein,
Figure 109138DEST_PATH_IMAGE024
the number of pixels of the wafer surface image to be detected,
Figure DEST_PATH_IMAGE032
is a gray scale of
Figure DEST_PATH_IMAGE034
The frequency of occurrence of the pixels of (a),
Figure DEST_PATH_IMAGE036
the gray scale corresponding to the first wave trough in the gray scale histogram;
the area of the normal area is the area of the initial mild defect area minus the defect area, and then the defect threshold is low
Figure DEST_PATH_IMAGE038
Comprises the following steps:
Figure DEST_PATH_IMAGE040
the high defect threshold is obtained by iterating the above process with the initial heavily defective region.
7. The method as claimed in claim 1, wherein the calculating of the region feature value of each region according to the pixel feature value of each pixel in each region comprises:
calculating the pixel characteristic value of each pixel point according to the gray values of each pixel point and all the pixel points in the area of the pixel point, wherein the calculation method comprises the following steps: to the region
Figure DEST_PATH_IMAGE042
Maximum value and area in gray value difference absolute values of intermediate gray value mean value and each pixel point
Figure 872564DEST_PATH_IMAGE042
Mean value of medium grey values andthe minimum value in the gray value difference absolute values of all the pixel points is used as a difference value, and the obtained difference value is used as a pixel characteristic value of the pixel point;
calculating the area characteristic value of the area according to the pixel characteristic value of each pixel point and the pixel characteristic values of other pixel points in the area where the pixel point is located, wherein the calculation formula is as follows:
Figure DEST_PATH_IMAGE044
wherein,
Figure DEST_PATH_IMAGE046
is a pixel point
Figure DEST_PATH_IMAGE048
The region characteristic value of the region in which the region is located,
Figure DEST_PATH_IMAGE050
is a pixel point
Figure 782620DEST_PATH_IMAGE048
The value of the characteristic of the pixel of (a),
Figure DEST_PATH_IMAGE052
is a pixel point
Figure 800254DEST_PATH_IMAGE048
In the region of
Figure 294821DEST_PATH_IMAGE042
The mean value of the gray values of all the pixel points in the image,
Figure DEST_PATH_IMAGE054
is a pixel point
Figure 998072DEST_PATH_IMAGE048
The gray value of (a).
8. The method as claimed in claim 1, wherein the method for adaptively enhancing the surface image of the wafer to be detected by using the histogram regions of different types comprises:
dividing the feature histogram according to the trough positions in the feature histogram to obtain two parts, and performing image enhancement according to the division result, wherein the method comprises the following steps:
Figure DEST_PATH_IMAGE056
wherein:
Figure DEST_PATH_IMAGE058
the enhanced image is represented by a representation of the image,
Figure DEST_PATH_IMAGE060
indicating area
Figure 10022DEST_PATH_IMAGE042
To middle
Figure 646277DEST_PATH_IMAGE034
The characteristic value of each pixel point is calculated,
Figure 374062DEST_PATH_IMAGE024
is a region
Figure 800495DEST_PATH_IMAGE042
The number of the middle pixel points is larger,
Figure 6348DEST_PATH_IMAGE034
region(s)
Figure 998575DEST_PATH_IMAGE042
The serial number of the middle pixel point is,
Figure DEST_PATH_IMAGE062
representing pixel points
Figure 631682DEST_PATH_IMAGE048
The extent of suspected defects in the area of the defect,
Figure DEST_PATH_IMAGE064
、
Figure DEST_PATH_IMAGE066
respectively the abscissa of the feature histogram limit,
Figure 778367DEST_PATH_IMAGE046
is a pixel point
Figure 725595DEST_PATH_IMAGE048
In the region of
Figure 837907DEST_PATH_IMAGE042
The area feature value of (1).
9. The method as claimed in claim 1, wherein the calculation formula for calculating the suspected defect level of each region using the frequency of occurrence of the pixel feature value of each pixel in each region is as follows:
Figure DEST_PATH_IMAGE068
in the formula:
Figure DEST_PATH_IMAGE070
representing pixel points
Figure DEST_PATH_IMAGE072
In the area of
Figure DEST_PATH_IMAGE074
The degree of suspected defect of (a) is,
Figure DEST_PATH_IMAGE076
is a region
Figure 687920DEST_PATH_IMAGE074
To middle
Figure DEST_PATH_IMAGE078
The frequency of the seed pixel characteristic value is,
Figure DEST_PATH_IMAGE080
is a region
Figure 790743DEST_PATH_IMAGE074
The types of different pixel characteristic values.
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CN116912242A (en) * 2023-09-12 2023-10-20 青岛天仁微纳科技有限责任公司 Method for positioning defects of nanoimprint wafer
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CN116958125B (en) * 2023-09-18 2023-12-26 惠州市鑫晖源科技有限公司 Electronic contest host power supply element defect visual detection method based on image processing
CN116958125A (en) * 2023-09-18 2023-10-27 惠州市鑫晖源科技有限公司 Electronic contest host power supply element defect visual detection method based on image processing
CN117114420B (en) * 2023-10-17 2024-01-05 南京启泰控股集团有限公司 Image recognition-based industrial and trade safety accident risk management and control system and method
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CN117252878A (en) * 2023-11-17 2023-12-19 青岛天仁微纳科技有限责任公司 Image defect detection method of nano-imprint mold
CN117252878B (en) * 2023-11-17 2024-02-02 青岛天仁微纳科技有限责任公司 Image defect detection method of nano-imprint mold
CN117784405A (en) * 2023-12-27 2024-03-29 苏州矽行半导体技术有限公司 Wafer bright field pupil modulation method based on diffraction microlens array
CN117784405B (en) * 2023-12-27 2024-08-02 苏州矽行半导体技术有限公司 Wafer bright field pupil modulation method based on diffraction microlens array
CN117808809A (en) * 2024-03-01 2024-04-02 深圳市志合云创科技有限公司 Visual inspection method and system for wafer surface defects
CN117808809B (en) * 2024-03-01 2024-05-14 深圳市志合云创科技有限公司 Visual inspection method and system for wafer surface defects

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