CN112557402A - Dislocation detection system - Google Patents
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- HBMJWWWQQXIZIP-UHFFFAOYSA-N silicon carbide Chemical compound [Si+]#[C-] HBMJWWWQQXIZIP-UHFFFAOYSA-N 0.000 claims description 31
- 229910010271 silicon carbide Inorganic materials 0.000 claims description 31
- 239000013078 crystal Substances 0.000 claims description 24
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- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
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- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
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Abstract
The application discloses dislocation detecting system, dislocation detecting system adopts and focuses the module and surveys light according to predetermineeing the way to the sample outgoing that awaits measuring, and receives the sample that awaits measuring reflects survey light, and according to receiving survey light forms and surveys the image, then it is right based on image processing technique to utilize data processing module the image that awaits measuring carries out automatic identification, in order to obtain including the dislocation information that awaits measuring includes to the realization carries out automated inspection's purpose to the dislocation information of the sample that awaits measuring, is favorable to improving the detection efficiency to the dislocation information of the sample that awaits measuring, and compare in the method that the manual detection dislocation can be comparatively easy increase to the collection point quantity of the sample that awaits measuring, be favorable to improving the estimation precision of dislocation density.
Description
Technical Field
The present application relates to the field of semiconductor technology, and more particularly, to a dislocation detection system.
Background
The method comprises the steps of carrying out preferential etching on a silicon carbide single crystal wafer (or called a silicon carbide single crystal wafer) by adopting molten potassium hydroxide, amplifying dislocation defects in the wafer, selecting a plurality of specific regions to carry out photographing observation by using an optical microscope, manually counting the total number of dislocations in each observation region and the number of different dislocation types, and dividing the total number of dislocations by the area of the observation region to obtain the average total dislocation density of the silicon carbide single crystal wafer and the average density of different dislocations. The method is a conventional dislocation density detection method for the silicon carbide single crystal wafer at present, but the method is low in efficiency because the method mainly depends on manual detection.
Disclosure of Invention
In order to solve the technical problem, the application provides a dislocation detection system to realize the purpose of automatically detecting the dislocation information of a sample to be detected, and the dislocation detection system is favorable for improving the detection efficiency of the dislocation information of the sample to be detected.
In order to achieve the technical purpose, the embodiment of the application provides the following technical scheme:
a dislocation detection system comprising: the focusing module and the data processing module; wherein,
the focusing module is used for emitting detection light to a sample to be detected according to a preset path, and the sample to be detected is a corrosion piece comprising a plurality of dislocations; the detection light is used for receiving the detection light reflected by the sample to be detected, and an image to be detected is formed according to the received detection light;
the data processing module is used for automatically identifying the image to be detected so as to obtain dislocation information included in the image to be detected, wherein the dislocation information at least comprises the number of screw dislocations, the number of edge dislocations, the number of base plane dislocations, the position of each screw dislocation, the position of each edge dislocation and the position of each base plane dislocation.
Optionally, the focusing module includes: a laser focusing assembly and an optical microscope assembly; wherein,
the laser focusing assembly is used for automatically focusing the sample to be detected and emitting detection light to the sample to be detected according to a preset path;
the optical microscope unit is used for receiving the detection light reflected by the sample to be detected and forming the image to be detected according to the received detection light.
Optionally, the etched wafer is a silicon carbide single wafer which is etched and has a surface with a c-direction deflection angle value range of 2-8 degrees.
Optionally, the etch wafer comprises a non-standard silicon carbide single wafer or standard silicon carbide single wafers having dimensions of 2 inches, 3 inches, 4 inches, 6 inches, and 8 inches.
Optionally, the preset path includes: a cross path, a matrix pattern path, and a partial rectangular path.
Optionally, the cross path includes: a plurality of collection points distributed along two perpendicular diameters of the erosion sheet;
the cross path includes a plurality of collection points distributed along a plurality of diameters of the corrosion coupon, the plurality of diameters forming a cross shape;
the matrix mode path comprises a plurality of acquisition points which are arranged in a vertical and horizontal matrix mode;
the localized rectangular path includes a plurality of collection points located within a localized rectangular region of the erosion sheet.
Optionally, the matrix pattern path includes a mesh matrix path and a continuous matrix path;
preset intervals are included among the acquisition points in the grid matrix path;
the acquisition points in the continuous matrix path are in contact with each other.
Optionally, the data processing module is further configured to store the dislocation information.
Optionally, the data processing module is further configured to store the dislocation picture corresponding to the acquisition point, and to display the dislocation picture corresponding to the acquisition point when receiving the display instruction corresponding to the acquisition point.
Optionally, the data processing module is further configured to generate a dislocation distribution map according to the dislocation information, where different types of dislocations in the dislocation distribution map are represented by different colors.
According to the technical scheme, the dislocation detection system comprises a focusing module, a detection light source, a data processing module, a detection light source, a data processing module and a control circuit, wherein the focusing module is used for emitting the detection light to a sample to be detected according to a preset path, the detection light is received by the sample to be detected and reflected by the sample to be detected, the detection light is received to form a detection image, the detection image is automatically recognized by the data processing module based on an image processing technology, the dislocation information included in the image to be detected is obtained, the purpose of automatically detecting the dislocation information of the sample to be detected is achieved, the detection efficiency of the dislocation information of the sample to be detected is improved, and the number of.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a schematic diagram of a dislocation detection system according to one embodiment of the present application;
FIG. 2 is a schematic diagram of a dislocation detection system according to another embodiment of the present application;
FIG. 3 is a diagram showing dislocation distribution of 4 inches etched wafer and statistical results of mean dislocation density (unit: piece/cm) measured by a 22X 22 matrix pattern path2)；
FIG. 4 is a diagram showing dislocation distribution of 4 inches etched wafer and statistical results of mean dislocation density (unit: piece/cm) measured by a 25-point cross path2)；
FIG. 5 is a graph of a dislocation profile of 4 inches etched wafer and a statistical mean dislocation density (units: one/cm) measured using a 22X 22 matrix pattern of paths2)；
FIG. 6 is a dislocation distribution graph and a statistical result of the average dislocation density (unit: number/cm) of 6 inches of etched wafer detected by a 98 × 98 matrix pattern path2)；
FIG. 7 is a dislocation distribution graph and a statistical result of the average dislocation density (unit: one/cm) of 4-inch etched wafer detected by a local rectangular path2)；
FIG. 8 is a diagram showing dislocation distribution and mean dislocation density statistics (unit: piece/cm) of 4 inch etched wafer detected in 33X 33 matrix mode2)；
FIG. 9 is a diagram showing dislocation distribution and mean dislocation density statistics (unit: piece/cm) of 4 inch etched wafer detected by 9-point Mi-Chi path2)；
FIG. 10 shows 4 "etched wafer dislocation using a 9-point cross pathLayout and average dislocation density statistics (unit: pieces/cm)2)。
Detailed Description
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 only a part of the embodiments of the present application, and not all of the embodiments. 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 application.
An embodiment of the present application provides a dislocation detection system, as shown in fig. 1, including: the focusing module and the data processing module; wherein,
the focusing module is used for emitting detection light to a sample to be detected according to a preset path, and the sample to be detected is a corrosion piece comprising a plurality of dislocations; the detection light is used for receiving the detection light reflected by the sample to be detected, and an image to be detected is formed according to the received detection light;
the data processing module is used for automatically identifying the image to be detected so as to obtain dislocation information included in the image to be detected, wherein the dislocation information at least comprises the number of screw dislocations, the number of edge dislocations, the number of base plane dislocations, the position of each screw dislocation, the position of each edge dislocation and the position of each base plane dislocation.
In fig. 1, in addition to the focusing module and the data processing module, a sample stage and a sample to be measured are shown.
In this embodiment, in order to enable the focusing module to obtain a clear image to be measured, the focusing module may further perform automatic focusing according to the dislocation distribution of the surface of the sample to be measured, specifically, the detection light emitted by the focusing module irradiates the surface of the etching sheet in a slightly inclined manner, the detection light is reflected by the etching sheet and then is projected to a fixed position on the surface of a photosensitive device (e.g., a CCD) of the focusing module, if the height of the surface of the etching sheet changes, the position of a projected point on the surface of the photosensitive device changes accordingly, and the projected point on the photosensitive device returns to the fixed position by adjusting the height of the sample stage, so as to keep the distance from the current collection point on the surface of the etching sheet to the focusing module unchanged.
The collecting point refers to a position where the detection light is currently irradiated, and at the position of the collecting point, the detection light can be kept static for a period of time, so that the photosensitive device of the focusing module has enough time to obtain an image at the position.
The automatic identification function of the data processing module for the image to be detected may specifically include: the method comprises the functions of extracting the boundary of dislocation in an image, learning the morphology and the morphology of the dislocation in an artificial intelligence manner and analyzing the size distribution diagram of the dislocation. Specifically, after the data processing module extracts the boundary of the dislocation included in the etch stop, the edge morphology of the dislocation can be obtained, generally, the edge morphology of the dislocation is a screw dislocation (TSD) when the edge morphology of the dislocation is hexagonal, the edge morphology of the dislocation is a threading dislocation (TED) when the edge morphology of the dislocation is circular, and the edge morphology of the dislocation is a Base Plane Dislocation (BPD) when the edge morphology of the dislocation is triangular, so that after the edge morphology of the boundary is obtained after the boundary of the dislocation is extracted, the dislocations with different edge morphologies can be classified by using a pre-trained classifier to realize automatic identification of the dislocation, and finally, parameters such as the number and the position of various dislocations can be counted by using a unit with a counting function. The pre-trained classifier may include an artificial intelligence module such as a pre-trained neural network.
On the basis of the above embodiments, in an embodiment of the present application, as shown in fig. 2, the focusing module includes: a laser focusing assembly and an optical microscope assembly; wherein,
the laser focusing assembly is used for automatically focusing the sample to be detected and emitting detection light to the sample to be detected according to a preset path;
the optical microscope unit is used for receiving the detection light reflected by the sample to be detected and forming the image to be detected according to the received detection light.
As mentioned above, the laser focusing assembly has the functions of auto-focusing and detecting the emergence of light, and the detecting light may be laser. The detection light is projected to the surface of a photosensitive device of the optical microscope unit after being amplified by the optical microscope unit, so that the image to be detected is formed.
For the corrosion piece, optionally, the corrosion piece is a silicon carbide single crystal piece which is corroded and has a surface with a c-direction deflection angle value range of 2-8 degrees. In this embodiment, the silicon carbide single crystal wafer having a surface with a c-direction deflection angle in a range of 2 ° to 8 ° is more favorable for the presentation of the basal plane dislocations of the etched wafer, because the basal plane dislocations lie flat in the c-plane, and if the etched wafer is a pure flat silicon carbide single crystal wafer, the basal plane dislocations are not or are not easily presented.
The etch wafers include non-standard silicon carbide single wafers or standard silicon carbide single wafers having dimensions of 2 inches, 3 inches, 4 inches, 6 inches, and 8 inches.
For the etch pits of different types of dislocation, the length of the side of the etch pit of the screw dislocation is 40-150 microns, the length of the side of the etch pit of the edge dislocation is 20-100 microns, and the length of the side of the etch pit of the basal plane dislocation is 10-60 microns. Specifically, optionally, the length of the side of the etch pit of the screw dislocation is 80 micrometers, the length of the etch pit of the edge dislocation is 47 micrometers, and the length of the side of the etch pit of the basal plane dislocation is 39 micrometers.
According to the dislocation detection system provided by the embodiment of the application, according to actual use inspection, the misjudgment rate of the screw dislocation detection can be less than 10%, the missing judgment rate is less than 10%, and the total error is less than 5%; the misjudgment rate of the edge dislocation can be less than 10%, the missing judgment rate can be less than 10%, and the total error can be less than 5%; the misjudgment rate of the dislocation of the base plane can be less than 20%, the missing judgment rate can be less than 20%, and the total error can be less than 10%.
On the basis of the above embodiment, in another embodiment of the present application, the preset path includes: a cross path, a matrix pattern path, and a partial rectangular path.
Wherein the cross path comprises: and a plurality of collecting points distributed along two vertical diameters of the corrosion piece, specifically, the number of the radial collecting points and the edge removing amount can be set by taking the circle center of the corrosion piece as the center, and the removing variable means that the collecting points are not distributed in a set removing variable range.
The cross path includes a plurality of collection points distributed along a plurality of diameters of the corrosion coupon, the plurality of diameters forming a cross shape; specifically, the path in the shape of a Chinese character 'mi' refers to the arrangement of the number of collection points and the de-variation of the mirror image by taking the center of a circle of the corrosion piece as the center.
The matrix mode path comprises a plurality of acquisition points which are arranged in a vertical and horizontal matrix mode; specifically, the rectangular pattern path includes longitudinal and transverse matrix pattern collection of erosion pieces, and setting of radial collection point number and de-variation, in the pattern, sampling points are mostly distributed in an M × N matrix mode, and M and N may be the same or different.
The localized rectangular path includes a plurality of collection points located within a localized rectangular region of the erosion sheet. The local rectangular path refers to the collection of a specified rectangular area of the corrosion coupon.
The matrix pattern path comprises a grid matrix path and a continuous matrix path;
the acquisition points in the grid matrix path include a preset interval therebetween, that is, in the grid matrix path, adjacent acquisition points are discontinuous.
The acquisition points in the continuous matrix path are in contact with each other, i.e. in the continuous matrix path, adjacent acquisition points are consecutive.
On the basis of the above embodiment, in a further embodiment of the present application, the data processing module is further configured to store the dislocation information.
Optionally, the data processing module is further configured to store the dislocation picture corresponding to the acquisition point, and to display the dislocation picture corresponding to the acquisition point when receiving the display instruction corresponding to the acquisition point.
The data processing module is further used for generating a dislocation distribution map according to the dislocation information, and different types of dislocations in the dislocation distribution map are represented in different colors.
The effect of the dislocation detection device is described below with reference to a specific embodiment, after a 4-inch silicon carbide single crystal wafer is immersed in a molten potassium hydroxide solution at 550 ℃ for 10 minutes, the silicon carbide single crystal wafer is taken out, cooled, washed clean with clear water, and observed under a general optical microscope of an etched wafer after being dried, it is found that the size of a screw dislocation is about 80 micrometers, the size of a blade dislocation is about 50 micrometers, and the size of a basal plane dislocation is about 40 micrometers, the silicon carbide single crystal wafer is placed on a sample stage of the dislocation detection device provided in the embodiment of the present application, a main positioning edge of the sample corresponds to a calibration position, the size of the etched wafer is 4 inches, a 22 × 22 matrix mode path is selected, and a variance is 1mm (that is, no collection point is arranged within 1mm from the edge). Then, the detection was performed to obtain the detection result shown in fig. 3. FIG. 3 is a graph showing a distribution of 4-inch etched dislocation patterns and a statistical result of the average dislocation density (unit: piece/cm) measured by a 22X 22 (number of collection points, the same applies below) matrix pattern path2)。
A4-inch silicon carbide single crystal wafer is immersed in a 500-DEG C molten potassium hydroxide solution for 15 minutes, then taken out, washed clean by clear water after being cooled, observed under a common optical microscope of an air-dried corrosion wafer, and found that the size of screw dislocation is about 60 micrometers, the size of edge dislocation is about 45 micrometers, and the size of basal plane dislocation is about 35 micrometers, the silicon carbide single crystal wafer is placed on a sample platform of the dislocation detection device provided by the embodiment of the application, the main positioning edge of the sample corresponds to the calibration position, the size of the corrosion wafer is selected to be 4 inches, a 25-point cross path is selected, and the removal amount is 1mm (namely, no collection point is arranged within 1mm from the edge). Then, the detection was performed to obtain the detection result shown in fig. 4. FIG. 4 is a graph of a dislocation distribution of 4 inches etched pieces and a statistical result of the mean dislocation density (unit: pieces/cm) measured using a 25-point (pick point) cross path2)。
Immersing a 4-inch silicon carbide single crystal wafer in a 550 deg.C molten hydrogen hydroxideAfter 15 minutes in the potassium solution, taking out the silicon carbide single crystal wafer, waiting for cooling, then washing the silicon carbide single crystal wafer with clean water, observing the silicon carbide single crystal wafer under a common optical microscope after airing, finding that the size of screw dislocation is about 100 micrometers, the size of edge dislocation is about 70 micrometers, and the size of basal plane dislocation is about 48 micrometers, placing the silicon carbide single crystal wafer on a sample table of the dislocation detection device provided by the embodiment of the application, wherein the main positioning edge of the sample corresponds to a calibration position, the size of the corrosion wafer is 4 inches, a 22 multiplied by 22 matrix mode path is selected, and the variance is 1mm (namely, no collection point is arranged within 1mm from the edge). Then, the detection was performed to obtain the detection result shown in fig. 5. FIG. 5 is a graph of a dislocation profile of 4 inches etched wafer and a statistical mean dislocation density (units: one/cm) measured using a 22X 22 matrix pattern of paths2)。
A6-inch silicon carbide single crystal wafer is immersed in a molten potassium hydroxide solution at 550 ℃ for 10 minutes, then taken out, washed clean by clear water after being cooled, and observed under a common optical microscope of an air-dried corrosion wafer, and the silicon carbide single crystal wafer is placed on a sample table of the dislocation detection device provided by the embodiment of the application, wherein the size of the screw dislocation is about 80 micrometers, the size of the edge dislocation is about 50 micrometers, and the size of the basal plane dislocation is about 40 micrometers, the main positioning edge of the sample corresponds to the calibration position, the size of the corrosion wafer is selected to be 6 inches, a 98 x 98 matrix mode path is selected, and the variance is 1mm (namely, no collection point is arranged within 1mm from the edge). Then, the detection was performed, and the detection result shown in fig. 6 was obtained. FIG. 6 is a dislocation distribution graph and a statistical result of the average dislocation density (unit: number/cm) of 6 inches of etched wafer detected by a 98 × 98 matrix pattern path2)。
Immersing a 4-inch silicon carbide single crystal wafer into a molten potassium hydroxide solution at 550 ℃ for 10 minutes, taking out the silicon carbide single crystal wafer, waiting for cooling, washing the silicon carbide single crystal wafer with clear water, observing the dried corrosion wafer under a common optical microscope, finding that the size of screw dislocation is about 80 micrometers, the size of edge dislocation is about 50 micrometers, and the size of basal plane dislocation is about 40 micrometers, and putting the silicon carbide single crystal wafer on a sample platform of the dislocation detection device provided by the embodiment of the applicationThe main positioning edge of the sample corresponds to the calibration position, the size of the corrosion piece is selected to be 4 inches, a local rectangular path is selected, and the position and the size of the rectangle are selected. Then, the detection was performed to obtain the detection result shown in fig. 7. Referring to FIG. 7, FIG. 7 is a dislocation distribution graph and a statistical result (unit: number/cm) of the average dislocation density of 4-inch etched wafer detected in a partial rectangular path2)。
Through similar tests, referring to FIG. 8, FIG. 8 is a graph showing dislocation distribution of 4 inches etched wafer and statistical results of average dislocation density (unit: piece/cm) measured in 33X 33 matrix mode2)。
Referring to FIG. 9, FIG. 9 is a graph showing dislocation distribution and mean dislocation density statistics (unit: piece/cm) of 4 inches etched wafer detected by 9-point Mi-Chi path2)。
Referring to FIG. 10, FIG. 10 is a graph showing dislocation distribution of 4 inches etched wafer and statistical results (unit: piece/cm) of average dislocation density measured by a 9-point cross path2)。
To sum up, this application embodiment provides a dislocation detecting system, dislocation detecting system adopts and focuses the module and surveys light according to predetermineeing the way to the sample outgoing that awaits measuring, and receives the sample reflection that awaits measuring survey light, and according to receiving survey light forms and surveys the image, then it is right based on image processing technique to utilize data processing module the image that awaits measuring carries out automatic identification, in order to obtain including the dislocation information that awaits measuring includes to the realization carries out automated inspection's purpose to the dislocation information of the sample that awaits measuring, is favorable to improving the detection efficiency to the dislocation information of the sample that awaits measuring, and compare in the method that the manual detection dislocation can be comparatively easy increase to the collection point quantity of the sample that awaits measuring, be favorable to improving the estimation precision of dislocation density.
Features described in the embodiments in the present specification may be replaced with or combined with each other, each embodiment is described with a focus on differences from other embodiments, and the same and similar portions among the embodiments may be referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (9)
1. A dislocation detection system, comprising: the focusing module and the data processing module; wherein,
the focusing module is used for emitting detection light to a sample to be detected according to a preset path, and the sample to be detected is a corrosion piece comprising a plurality of dislocations; the detection light is used for receiving the detection light reflected by the sample to be detected, and an image to be detected is formed according to the received detection light;
the data processing module is used for automatically identifying the image to be detected so as to obtain dislocation information included in the image to be detected, wherein the dislocation information at least comprises the number of screw dislocations, the number of edge dislocations, the number of base plane dislocations, the position of each screw dislocation, the position of each edge dislocation and the position of each base plane dislocation;
the corrosion piece is a silicon carbide single crystal piece which is corroded and has a surface with a c-direction deflection angle value range of 2-8 degrees.
2. Dislocation detection system according to claim 1, characterized in that said focusing module comprises: a laser focusing assembly and an optical microscope assembly; wherein,
the laser focusing assembly is used for automatically focusing the sample to be detected and emitting detection light to the sample to be detected according to a preset path;
the optical microscope unit is used for receiving the detection light reflected by the sample to be detected and forming the image to be detected according to the received detection light.
3. Dislocation detection system according to claim 1, characterized in that the etched wafer comprises non-standard silicon carbide single wafers or standard silicon carbide single wafers with dimensions of 2 ", 3", 4 ", 6" and 8 ".
4. Dislocation detection system according to claim 3, characterized in that said preset path comprises: a cross path, a matrix pattern path, and a partial rectangular path.
5. Dislocation detection system according to claim 4, characterized in that said cross path comprises: a plurality of collection points distributed along two perpendicular diameters of the erosion sheet;
the cross path includes a plurality of collection points distributed along a plurality of diameters of the corrosion coupon, the plurality of diameters forming a cross shape;
the matrix mode path comprises a plurality of acquisition points which are arranged in a vertical and horizontal matrix mode;
the localized rectangular path includes a plurality of collection points located within a localized rectangular region of the erosion sheet.
6. Dislocation detection system according to claim 5, characterized in that said matrix pattern paths comprise a grid matrix path and a continuous matrix path;
preset intervals are included among the acquisition points in the grid matrix path;
the acquisition points in the continuous matrix path are in contact with each other.
7. Dislocation detection system according to claim 5, characterized in that said data processing module is also adapted to store said dislocation information.
8. The dislocation detection system according to claim 7, wherein the data processing module is further configured to store dislocation pictures corresponding to the collection points, and to display dislocation pictures corresponding to the collection points upon receiving display instructions corresponding to the collection points.
9. The dislocation detection system of claim 7, wherein the data processing module is further configured to generate a dislocation profile from the dislocation information, wherein different types of dislocations in the dislocation profile are represented in different colors.
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| CN202011411979.3A CN112557402B (en) | 2020-12-04 | 2020-12-04 | Dislocation detection system |
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| CN112557402A true CN112557402A (en) | 2021-03-26 |
| CN112557402B CN112557402B (en) | 2023-03-14 |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111829988A (en) * | 2020-06-29 | 2020-10-27 | 哈尔滨工程大学 | Method for improving light spot scanning measurement resolution of batten bonding surface |
| CN113298068A (en) * | 2021-05-26 | 2021-08-24 | 厦门彼格科技有限公司 | Wafer dislocation image acquisition device and acquisition method thereof |
| CN115424099A (en) * | 2022-11-07 | 2022-12-02 | 浙江大学杭州国际科创中心 | Model training method, recognition method and device for recognizing silicon carbide dislocation |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102009009355A1 (en) * | 2009-02-18 | 2010-09-09 | Texas Instruments Deutschland Gmbh | Semiconductor wafer's defect inspecting apparatus, has data processing unit comparing set of images with other set of images of surface of wafer previously acquired by sensors, and automatically detecting defect based on image comparison |
| CN109540004A (en) * | 2018-11-29 | 2019-03-29 | 中国科学院微电子研究所 | Optical detection system and detection method thereof |
| CN110031478A (en) * | 2019-04-18 | 2019-07-19 | 中国科学院光电技术研究所 | A kind of super-resolution defect detecting device and detection method based on hyperoscillating lens |
| CN110494894A (en) * | 2017-04-07 | 2019-11-22 | 科磊股份有限公司 | Defects detection based on profile |
| CN110501347A (en) * | 2019-07-03 | 2019-11-26 | 深圳市一纳科技有限公司 | A kind of rapid automatized Systems for optical inspection and method |
| CN111208144A (en) * | 2020-03-05 | 2020-05-29 | 上海御微半导体技术有限公司 | Defect detection system and defect detection method |
| CN111238910A (en) * | 2020-01-15 | 2020-06-05 | 山东天岳先进材料科技有限公司 | Dislocation identification method of silicon carbide crystal |
| US20200365685A1 (en) * | 2019-05-17 | 2020-11-19 | Cree, Inc. | Nondestructive characterization for crystalline wafers |
-
2020
- 2020-12-04 CN CN202011411979.3A patent/CN112557402B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102009009355A1 (en) * | 2009-02-18 | 2010-09-09 | Texas Instruments Deutschland Gmbh | Semiconductor wafer's defect inspecting apparatus, has data processing unit comparing set of images with other set of images of surface of wafer previously acquired by sensors, and automatically detecting defect based on image comparison |
| CN110494894A (en) * | 2017-04-07 | 2019-11-22 | 科磊股份有限公司 | Defects detection based on profile |
| CN109540004A (en) * | 2018-11-29 | 2019-03-29 | 中国科学院微电子研究所 | Optical detection system and detection method thereof |
| CN110031478A (en) * | 2019-04-18 | 2019-07-19 | 中国科学院光电技术研究所 | A kind of super-resolution defect detecting device and detection method based on hyperoscillating lens |
| US20200365685A1 (en) * | 2019-05-17 | 2020-11-19 | Cree, Inc. | Nondestructive characterization for crystalline wafers |
| CN110501347A (en) * | 2019-07-03 | 2019-11-26 | 深圳市一纳科技有限公司 | A kind of rapid automatized Systems for optical inspection and method |
| CN111238910A (en) * | 2020-01-15 | 2020-06-05 | 山东天岳先进材料科技有限公司 | Dislocation identification method of silicon carbide crystal |
| CN111208144A (en) * | 2020-03-05 | 2020-05-29 | 上海御微半导体技术有限公司 | Defect detection system and defect detection method |
Non-Patent Citations (1)
| Title |
|---|
| 郭钰 等: ""4H-SiC外延层中堆垛层错与衬底缺陷的关联性研究"", 《无机材料学报》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111829988A (en) * | 2020-06-29 | 2020-10-27 | 哈尔滨工程大学 | Method for improving light spot scanning measurement resolution of batten bonding surface |
| CN111829988B (en) * | 2020-06-29 | 2022-12-09 | 哈尔滨工程大学 | Method for improving light spot scanning measurement resolution of batten bonding surface |
| CN113298068A (en) * | 2021-05-26 | 2021-08-24 | 厦门彼格科技有限公司 | Wafer dislocation image acquisition device and acquisition method thereof |
| CN113298068B (en) * | 2021-05-26 | 2024-02-02 | 厦门彼格科技有限公司 | Wafer dislocation image acquisition device and acquisition method thereof |
| CN115424099A (en) * | 2022-11-07 | 2022-12-02 | 浙江大学杭州国际科创中心 | Model training method, recognition method and device for recognizing silicon carbide dislocation |
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