CN115147422A - Method, device and equipment for generating crystal grains at center of wafer and storage medium - Google Patents
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- 239000013078 crystal Substances 0.000 title claims abstract description 121
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- 238000001514 detection method Methods 0.000 claims abstract description 30
- 238000012549 training Methods 0.000 claims abstract description 28
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- 238000004422 calculation algorithm Methods 0.000 claims description 8
- 230000004927 fusion Effects 0.000 claims description 7
- 235000020985 whole grains Nutrition 0.000 claims description 2
- 230000007547 defect Effects 0.000 abstract description 7
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Abstract
The invention discloses a method, a device, equipment and a storage medium for generating crystal grains at the center of a wafer, and relates to the technical field of optical defect detection. The method for generating the wafer center crystal grains comprises the following steps: splicing the plurality of crystal grain detection frame images into a crystal grain general image comprising a plurality of complete crystal grains; searching all complete crystal grains in the crystal grain general diagram and cutting to generate a data set for training the central crystal grains; and training the data set by adopting an image fusion method to generate the central crystal grain. The invention can automatically generate the central crystal grain with higher precision without depending on manual participation.
Description
Technical Field
The invention relates to the technical field of optical defect detection, in particular to a method, a device and equipment for generating a crystal grain at the center of a wafer and a storage medium.
Background
Wafer (Wafer) defect detection is an essential process flow in the field of semiconductor production. The wafer defect detection process requires that the difference between each grain (Die) to be detected and the central grain (Golden Die) can be output according to each frame of image to be detected, so as to judge whether the difference position is a defect.
However, how to select a Golden Die is a difficult task. The difficulty is that distortion, bright and dark fields and the like are generated due to unstable optical conditions during shooting. Meanwhile, due to the fact that the Die is too large, in most cases, one frame of image cannot completely contain the whole Die, multiple frames of images are used for respectively shooting different parts of the same Die, then useful information of the same Die is obtained from the multiple frames, and it is more difficult to generate a Golden Die image.
The traditional Golden Die generation method is a fully manual or semi-automatic method. The full-manual mode is to manually screen a plurality of Die meeting the requirements and then perform manual alignment, and the alignment mode has some problems: a) Skilled workers are required to select samples; b) The samples are too large to be selected; c) The selection of images contained in multiple frames by the same Die is difficult; d) The manual alignment has the problem of unstable results of multiple operations.
The semi-automatic generation mode is based on the full-manual generation mode, and is added with a function of automatically selecting the same Die frame image, and although the function can reduce the misjudgment of manual operation, the improvement of the alignment precision is not large, and the problem of unstable result still exists.
Disclosure of Invention
In view of the defects in the prior art, the first aspect of the present invention provides a method for generating a central crystal grain of a wafer, which can automatically generate a central crystal grain with high precision without depending on manual participation.
In order to achieve the above purposes, the technical scheme adopted by the invention is as follows:
a method for forming a central grain of a wafer comprises the following steps:
splicing the crystal grain detection frame images into a crystal grain general image comprising a plurality of complete crystal grains;
searching all complete crystal grains in the crystal grain general diagram and cutting to generate a data set for training the central crystal grains;
and training the data set by adopting an image fusion method to generate the central crystal grain.
In some embodiments, the multiple grain detection frame images are spliced into a grain general image including multiple complete grains by an image splicing algorithm of feature point matching.
In some embodiments, all the complete dies in the die summary map are searched and cut based on the template matching method.
In some embodiments, the searching and cutting all the complete dies in the die summary map based on the template matching method includes:
selecting a candidate complete crystal grain;
and using the alternative complete crystal grains as a template, searching a part similar to the alternative complete crystal grains in the crystal grain general diagram, and searching and cutting all the complete crystal grains in the crystal grain general diagram.
In some embodiments, the training the dataset with the method of image fusion includes:
acquiring gray information of a plurality of cut complete crystal grain images in a data set;
and training the data set in a manner of calculating the gray value of the fusion image by endowing a weight to the gray value of each complete crystal grain image at the same pixel point.
In some embodiments, before the splicing the plurality of die inspection frame maps into the die general map including the plurality of complete dies, the method further includes:
and correcting the brightness of each crystal grain detection frame image according to the brightness model parameters of the pre-measured fitting camera.
In some embodiments, after performing brightness correction on each of the die inspection frame maps and before the splicing the plurality of die inspection frame maps into a die overview map including a plurality of complete dies, the method further includes:
and carrying out distortion correction on the crystal grain detection frame image with uniform brightness according to the calibrated camera distortion parameters.
A second aspect of the present invention provides a device for generating a central crystal grain of a wafer, which is capable of automatically generating a central crystal grain with high accuracy without human intervention.
In order to achieve the purpose, the invention adopts the technical scheme that:
a wafer center die generation apparatus, comprising:
the splicing module is used for splicing a plurality of frame images related to the crystal grains into a crystal grain general image comprising a plurality of complete crystal grains;
the generation module is used for searching all complete crystal grains in the crystal grain general diagram and cutting the whole crystal grains to generate a data set used for training the central crystal grains;
and the training module trains the data set by adopting an image fusion method to generate the central crystal grain.
A third aspect of the present invention provides an apparatus capable of automatically generating a central grain with high accuracy without depending on human involvement.
In order to achieve the above purposes, the technical scheme adopted by the invention is as follows:
an apparatus comprising a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program when executed by the processor implements the steps of a wafer center die generation method as described above.
A fourth aspect of the present invention provides a computer-readable storage medium capable of automatically generating a highly accurate center grain without relying on human intervention.
In order to achieve the purpose, the invention adopts the technical scheme that:
a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a wafer center die generation method as described above.
Compared with the prior art, the invention has the advantages that:
the method for generating the crystal grains at the center of the wafer comprises the steps of splicing a plurality of crystal grain detection frame images into a crystal grain general image comprising a plurality of complete crystal grains; searching all complete crystal grains in the crystal grain general diagram and cutting to generate a data set for training the center crystal grains; and finally, training the data set by adopting an image fusion method to generate the central crystal grain. Therefore, the problem that the crystal grains can be obtained through a plurality of frame images in the prior art is solved, and on the other hand, the crystal grain general image is spliced firstly and Golden Die training is carried out, so that the problem of large alignment error can be eliminated, and the precision of the Golden Die image is improved. The whole algorithm is automatically executed in the whole process, manual participation is not needed, the process is simple, and the use threshold is low. The method solves the problems that Golden Die generated by the traditional method needs manual participation, wastes time and labor and is low in generation precision.
Drawings
FIG. 1 is a flow chart of a method for generating a wafer center die in accordance with an embodiment of the present invention;
FIG. 2 is a general diagram of a die including a plurality of complete dies in an embodiment of the invention;
FIG. 3 is a diagram of a center die in an embodiment of the invention.
Detailed Description
For the purpose of making the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all embodiments of the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making creative efforts shall fall within the protection scope of the present application.
Referring to fig. 1, an embodiment of the present invention discloses a method for generating a central grain of a wafer, including the following steps:
s1, splicing a plurality of crystal grain detection frame images into a crystal grain general image comprising a plurality of complete crystal grains.
Aiming at the problems that in the prior art, due to the fact that the Die is too large, the whole Die cannot be completely included in one frame of image under most conditions, multi-frame images respectively shoot different parts of the same Die, and useful information of the same Die is obtained from the multi-frame images, in the embodiment, a plurality of crystal grain detection frame images are spliced, and a crystal grain general image including complete crystal grains can be obtained after splicing, so that the situation that the information is obtained by respectively shooting different parts of the same Die through the multi-frame images is avoided.
In the specific implementation, a plurality of crystal grain detection frame images are spliced into a crystal grain general image comprising a plurality of complete crystal grains mainly through an image splicing algorithm matched with feature points. Image stitching is a technology for stitching a plurality of small-sized images in the same scene into a large-sized image, and an image stitching algorithm based on feature point matching is a technology known to those skilled in the art, and is not described herein in detail.
S2, searching all complete crystal grains in the crystal grain general diagram and cutting to generate a data set for training the central crystal grains.
The embodiment of the invention adopts a mode based on template matching, searches all complete grains in the grain general diagram and cuts the grains.
Template matching is the most basic pattern recognition method, and is the most basic and most common matching method in image processing. The template is a known small image, the template matching is to search for an object in a large image, the object to be found in the image is known, the size, the direction and the image elements of the object and the template are the same, and the object can be found in the image through a certain algorithm to determine the coordinate position of the object.
In this embodiment, a candidate complete die is selected first, and then the candidate complete die is used as a template to find a portion similar to the candidate complete die in a die overview, so as to search and cut all the complete dies in the die overview.
Specifically, after step S1, a general map of the spliced die is obtained, as shown in fig. 2. The purpose of step S2 is to divide all the complete dies in the die overview (the pattern shown in fig. 3 is a complete die), and there are 10 complete dies in the die overview in fig. 2, the method is: dividing a complete grain is called as an alternative complete grain, then the alternative complete grain is used as a template, and a part similar to the alternative complete grain is searched in a grain general diagram, so that the remaining 9 complete grains can be divided. Specifically, one of 10 complete dies is randomly selected, four vertex coordinates are recorded, and the complete die is cut. Then, a portion having similar characteristics to the candidate complete grains is searched in fig. 2, and then the remaining 9 complete grains are found.
And S3, training the data set by adopting an image fusion method to generate the central crystal grain.
It can be understood that, after the processing of step S2, a plurality of complete dies can be obtained, and these complete dies are mainly used as a data set for training the central Die Golden Die.
In the embodiment of the invention, a nearly perfect (without any defect information) Die can be trained in a plurality of Die images as a Golden Die mainly by a pixel weighted average method.
Specifically, gray information of a plurality of cut complete crystal grain images in a data set is obtained; and then, a weight is given to the gray value of each complete crystal grain image at the same pixel point, so that the data set is trained in a mode of calculating the gray value of the fusion image.
That is, a weight is given to each image gray value at the same pixel point according to the gray information of the plurality of images, and the gray of the fusion image is the weighted sum of all the image gray values.
In particular implementations, for example, the images to be fused are recorded as
All the length and width areM×NSharing images to be fusedGSheet, then fused imageFCan be expressed as
An image weighting factor.
In addition, it should be noted that, before the step of splicing the plurality of die inspection frame maps into the die overview map including the plurality of complete dies, the embodiment of the present invention further includes: and correcting the brightness of each crystal grain detection frame image according to the brightness model parameters of the pre-measured fitting camera.
Further, after performing brightness correction on each of the die inspection frame images and before splicing the plurality of die inspection frame images into a die overall image including a plurality of complete dies, the method further includes: and carrying out distortion correction on the crystal grain detection frame image with uniform brightness according to the calibrated camera distortion parameters.
The flat field correction mainly corrects the brightness of the crystal grain detection frame image according to the pre-fitted camera brightness parameters, namely, the detection image is automatically corrected through upper computer software. Of course, the flat field correction may also be performed through a brightness parameter manually input by a user, where the brightness parameter includes a brightness value and a brightness adjustment area, which is not limited in the present invention. The distortion correction comprises the step of correcting the distortion of the crystal grain detection frame image according to the camera distortion parameters calibrated in advance. Therefore, the interference of the grain detection image on the detection process due to the problems of brightness, distortion and the like can be avoided, and the frame image with uniform brightness and no obvious distortion can be obtained.
In summary, in the method for generating a central die of a wafer according to the present invention, a plurality of die inspection frame images are spliced into a die overview including a plurality of complete dies; searching all complete crystal grains in the crystal grain general diagram and cutting to generate a data set for training the center crystal grains; and finally, training the data set by adopting an image fusion method to generate the central crystal grain. Therefore, the problem that the crystal grains can be obtained through a plurality of frame images in the prior art is solved, on the other hand, the crystal grain general image is spliced firstly, golden Die training is carried out, the problem of large alignment error can be eliminated, and the precision of the Golden Die image is improved. The whole algorithm is automatically executed in the whole process, manual participation is not needed, the process is simple, and the use threshold is low. The method solves the problems that Golden Die generated by the traditional method needs manual participation, wastes time and labor and is low in generation precision.
The embodiment of the invention also discloses a device for generating the crystal grains at the center of the wafer, which comprises a splicing module, a generating module and a training module.
The splicing module is used for splicing a plurality of frame images related to the crystal grains into a crystal grain general image comprising a plurality of complete crystal grains. The generation module is used for searching all complete crystal grains in the crystal grain general diagram and cutting the complete crystal grains so as to generate a data set for training the central crystal grains; and the training module trains the data set by adopting an image fusion method to generate the central crystal grain.
In some embodiments, the stitching module stitches the plurality of grain detection frame images into a grain general image including a plurality of complete grains by using an image stitching algorithm of feature point matching.
In some embodiments, the generation module searches all complete dies in the die summary map and performs cutting based on a template matching manner.
Further, the generating module searches all complete grains in the grain general diagram and cuts the whole grains based on a template matching mode, and the method comprises the following steps:
selecting a candidate complete crystal grain;
and using the alternative complete crystal grains as a template, searching a part similar to the alternative complete crystal grains in the crystal grain general diagram, and searching and cutting all the complete crystal grains in the crystal grain general diagram.
In some embodiments, the training module trains the dataset using a method of image fusion, including:
acquiring gray information of a plurality of cut complete crystal grain images in a data set;
and training the data set in a manner of calculating the gray value of the fusion image by endowing a weight to the gray value of each complete crystal grain image at the same pixel point.
In some embodiments, the wafer center die generation apparatus further includes a brightness correction module, and before the plurality of die detection frame images are spliced into the die overview including the plurality of complete dies, the brightness correction module is configured to perform brightness correction on each die detection frame image according to a brightness model parameter of a pre-measurement fitting camera.
In some embodiments, the wafer center die generation apparatus further includes a distortion correction module, and after performing brightness correction on each die detection frame image and before splicing the plurality of die detection frame images into a die overall image including a plurality of complete dies, the distortion correction module is configured to perform distortion correction on the die detection frame images with uniform brightness according to a calibrated camera distortion parameter.
An embodiment of the present invention further provides an apparatus, which includes a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of the wafer center die generation method are implemented.
It should be understood that the Processor may be a Central Processing Unit (CPU), and the Processor may be other general purpose processors, digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, etc. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the wafer center die generation method are implemented.
It will be understood by those of ordinary skill in the art that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, or suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable storage media, which may include computer readable storage media (or non-transitory media) and communication media (or transitory media).
The term computer readable storage medium includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those skilled in the art. Computer-readable storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as is well known to those skilled in the art.
For example, the computer readable storage medium may be an internal storage unit of the electronic device of the foregoing embodiment, such as a hard disk or a memory of the electronic device. The computer readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk provided on the electronic device, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like.
The above description is only a specific example of the embodiments of the present invention, but the scope of the embodiments of the present invention is not limited thereto, and those skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the embodiments of the present invention, and these modifications or substitutions should be covered by the scope of the embodiments of the present invention. Therefore, the protection scope of the embodiments of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method for forming a central grain of a wafer, the method comprising the steps of:
splicing the plurality of crystal grain detection frame images into a crystal grain general image comprising a plurality of complete crystal grains;
searching all complete crystal grains in the crystal grain general diagram and cutting to generate a data set for training the central crystal grains;
and training the data set by adopting an image fusion method to generate the central crystal grain.
2. The method as claimed in claim 1, wherein: and splicing the plurality of crystal grain detection frame images into a crystal grain general image comprising a plurality of complete crystal grains by using a feature point matching image splicing algorithm.
3. The method as claimed in claim 1, wherein: and searching all complete grains in the grain general diagram and cutting the whole grains based on a template matching mode.
4. The method as claimed in claim 3, wherein the searching and dicing all the complete dies in the die overview based on the template matching comprises:
selecting a candidate complete crystal grain;
and using the alternative complete crystal grains as a template, searching a part similar to the alternative complete crystal grains in the crystal grain general diagram, and searching and cutting all the complete crystal grains in the crystal grain general diagram.
5. The method as claimed in claim 1, wherein the training the data set by image fusion comprises:
acquiring gray information of a plurality of cut complete crystal grain images in a data set;
and training the data set by assigning a weight to the gray value of each complete grain image at the same pixel point in a manner of calculating the gray value of the fused image.
6. The method according to claim 1, wherein before the step of stitching the plurality of die-inspection frame maps into a die overview map comprising a plurality of complete dies, the method further comprises:
and correcting the brightness of each crystal grain detection frame image according to the brightness model parameters of the pre-measured fitting camera.
7. The method as claimed in claim 6, wherein after performing brightness correction on each of the die-inspection frame images and before splicing the plurality of die-inspection frame images into a die overall image including a plurality of complete dies, the method further comprises:
and carrying out distortion correction on the crystal grain detection frame image with uniform brightness according to the calibrated camera distortion parameters.
8. A wafer center die generation apparatus, comprising:
the splicing module is used for splicing a plurality of frame images related to the crystal grains into a crystal grain general image comprising a plurality of complete crystal grains;
the generating module is used for searching all complete crystal grains in the crystal grain general diagram and cutting the complete crystal grains so as to generate a data set used for training the central crystal grains;
and the training module trains the data set by adopting an image fusion method to generate the central crystal grain.
9. An apparatus comprising a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program, when executed by the processor, performs the steps of a wafer center grain generation method as recited in any one of claims 1 to 7.
10. A computer-readable storage medium, having a computer program stored thereon, wherein the computer program, when executed by a processor, performs the steps of a method as claimed in any one of claims 1 to 7.
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Application publication date: 20221004 |