KR20150094118A - Operating method of review device - Google Patents

Operating method of review device Download PDF

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Publication number
KR20150094118A
KR20150094118A KR1020140015005A KR20140015005A KR20150094118A KR 20150094118 A KR20150094118 A KR 20150094118A KR 1020140015005 A KR1020140015005 A KR 1020140015005A KR 20140015005 A KR20140015005 A KR 20140015005A KR 20150094118 A KR20150094118 A KR 20150094118A
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South Korea
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defect
magnification image
image
high magnification
detected
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KR1020140015005A
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Korean (ko)
Inventor
정용덕
박미라
박성홍
안병설
양유신
이상길
전충삼
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삼성전자주식회사
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Priority to KR1020140015005A priority Critical patent/KR20150094118A/en
Publication of KR20150094118A publication Critical patent/KR20150094118A/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • 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
    • G01N21/88—Investigating the presence of flaws or contamination
    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • 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
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
    • G01N21/9501—Semiconductor wafers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • 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
    • G01N21/88—Investigating the presence of flaws or contamination
    • 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
    • G01N2021/8854—Grading and classifying of flaws

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  • Chemical & Material Sciences (AREA)
  • Biochemistry (AREA)
  • Pathology (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Immunology (AREA)
  • Analytical Chemistry (AREA)
  • General Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Signal Processing (AREA)
  • Testing Or Measuring Of Semiconductors Or The Like (AREA)

Abstract

The purpose of the present invention is to provide an operating method of a review device capable of classifying the type of defect which is detected based on magnetic image comparison without a reference image. The operating method of the review device according to an embodiment of the present invention comprises: a step of detecting a defect of a semiconductor wafer; a step of obtaining a high magnification image of an area including the defect detected among areas of the semiconductor wafer; a step of detecting the position of the defect by performing a magnetic image comparison operation based on the obtained high magnification image; a step of extracting a characteristic of the defect based on the detected position of the defect; and a step of classifying the type of the detected defect based on the extracted characteristic.

Description

{OPERATING METHOD OF REVIEW DEVICE}

The present invention relates to semiconductor processing, and more particularly, to a method of operation of an inspection apparatus.

 The semiconductor manufacturing process is performed on a nanoscale. At this time, a defect occurs on the semiconductor wafer due to a problem of a by-product or process equipment occurring in the semiconductor manufacturing process. A test apparatus included in the semiconductor inspection system is used to detect the above-described defects. The inspection apparatus scans the front surface of the semiconductor wafer on which the pattern is formed to detect defects existing on the semiconductor wafer. Thereafter, the review apparatus included in the semiconductor inspection system acquires the image and reference image of the region where the defect exists and compares the obtained image and reference image to detect the position of the detected defect. The review device extracts the feature of the detected defect based on the sensed position. The user analyzes the extracted features and classifies the types of detected defects.

As described above, the conventional review apparatus must acquire a plurality of images in order to detect the position of the detected defects. Further, there is a problem that intervention of the operator is required to classify the type of the detected defect.

It is an object of the present invention to provide an operation method of a review apparatus for classifying types of defects detected based on magnetic image comparison without a reference image.

An operation method of a review apparatus for classifying defects detected in a semiconductor wafer according to an embodiment of the present invention includes: obtaining a high magnification image of a region of the semiconductor wafer including the detected defects; Performing a magnetic image comparison operation based on the acquired high magnification image to detect a position of the defect; Extracting a feature of the defect based on the position of the detected defect; And classifying the type of the detected defect based on the extracted feature.

In an embodiment, the FOV (Field Of View) of the high magnification image is 1 to 3 um.

In an embodiment, the semiconductor wafer includes a plurality of dies, and obtaining a high magnification image of an area of the semiconductor wafer where the detected defects are located includes detecting the defects of the plurality of dies And obtaining a high magnification image of a region of the die.

As an embodiment, the step of performing a magnetic image comparison operation based on the acquired high magnification image to detect the position of the defect may include performing two-dimensional FFT on the acquired high magnification image; Obtaining a filtered high magnification image by removing horizontal and vertical patterns of the 2-dimensional fast Fourier transformed high magnification image; Binarizing the filtered high magnification image; Removing noise of the binarized high magnification image; Overlaying the noise-removed high-magnification image and the acquired high-magnification image; And sensing the position of the detected defect based on the overlaid high magnification image.

In an embodiment, the magnetic image comparison operation is performed based on at least one of a Fractal encoding, a reference generation technique using self-similarity, and a morphological operator-based pattern recognition technique.

According to an embodiment of the present invention, the step of classifying the type of the detected defect based on the extracted feature may include comparing the extracted feature and the type reference value, and classifying the type of the detected defect based on the comparison result .

In an embodiment, the method of operation of the review apparatus further comprises updating the type reference value based on a feature of the defect classified as the type.

In an embodiment, the method of operation of the review apparatus further comprises classifying the type of a plurality of defects detected on the semiconductor wafer based on the updated type reference value.

In an embodiment, the review device includes an electron microscope and acquires the high magnification image through the electron microscope.

According to the present invention, it is possible to classify the types of defects detected based on magnetic image comparison without reference images. In addition, the consistency of the defect types is improved by updating the type reference values used for the type classification. Thus, a method of operation of an inspection apparatus with improved performance and reliability is provided.

Figure 1 is an exemplary illustration of a wafer inspection system.
2 is a flowchart showing the operation of the inspection apparatus shown in FIG.
FIG. 3 is a flowchart showing the review operation and classification operation shown in FIG. 2 in detail.
FIGS. 4 and 5 are illustrations showing errors of a low magnification image and a low magnification reference image.
6 is a flowchart showing the operation of the inspection apparatus according to the embodiment of the present invention.
7 is a detailed view showing the operation of step S130 shown in FIG.
FIG. 8 is a flowchart showing the operation of the inspection apparatus according to another embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings, so that those skilled in the art can easily carry out the technical idea of the present invention. .

1 is a view showing a semiconductor inspection system. Referring to FIG. 1, a semiconductor inspection system 100 includes an inspection apparatus 110, a review apparatus 120, and a semiconductor wafer 101. The inspection apparatus 110 can detect a defect (DEF, defect) occurring on the surface of the semiconductor wafer 101. For example, the inspection apparatus 110 can inspect the entire surface of the semiconductor wafer 101 using optical equipment. The inspection apparatus 110 can detect the defects DEF included in the semiconductor wafer 101 based on the inspection result.

The review apparatus 120 can identify the type of the detected defect (DEF), and classify the detected defect by type. For example, the review device 120 may include a scanning electron microscope (SEM) for acquiring an image of the semiconductor wafer 101. The review apparatus 120 can acquire an image of an area (e.g., one die) in which a detected defect is located through an electron microscope. Illustratively, the defect DEF indicates that a normal pattern is not formed on the semiconductor wafer 101 due to factors such as dust admitted from the outside, abnormality of the process equipment, by-products generated during the process, and the like.

The semiconductor wafer 101 includes a plurality of dies 102 (Die). Each of the plurality of dice 102 is comprised of a semiconductor circuit such as a memory device, an integrated circuit, and the like. Some of the plurality of dies 102 may include defects (DEF, Defect). Defects (DEFs) can be caused by a variety of factors, such as particles from outside, process equipment failures, and so on.

The inspection apparatus 110 scans the entire surface of the semiconductor wafer 101 to detect the defect DEF to determine the presence or absence of a defect DEF of the semiconductor wafer 101. [ The operation of this inspection apparatus 110 is called a checking operation.

When a defect DEF is detected in the inspection operation, the review apparatus 120 can acquire an image of the detected defect DEF. The review apparatus 120 can sense the position of the detected defect DEF based on the acquired image. The operation of this inspection apparatus 110 is called a review operation.

The review apparatus 120 can extract the features of the detected DEF based on the acquired image. The reviewing device 120 may classify the types of defects (DEFs) detected based on the extracted features. The operation of this review device 110 is referred to as a sorting operation. The operation of the above-described inspection apparatuses and review apparatuses 110 and 120 will be described in more detail with reference to the following drawings.

The review device 120 can classify the type of the defect DEF to eliminate the cause of the defect DEF or recover the detected defect DEF.

FIG. 2 is a flowchart showing the operation of the inspection apparatus and the review apparatus shown in FIG. 1. FIG. Referring to FIGS. 1 and 2, in step S10, the inspection apparatus 110 can detect defects (DEF) contained in the semiconductor wafer 101. FIG. For example, the semiconductor wafer 101 may contain defects (DEF) caused by various factors in the process. The inspection apparatus 110 can scan over the semiconductor wafer 101 using a high-resolution optical device (not shown), and detect defects DEF based on the scanned result. Illustratively, the inspection apparatus 110 may detect the number and location (e.g., (x, y) coordinates of the detected defects DEF). Illustratively, the operation of step S10 is referred to as a test operation.

In step S20, the review apparatus 110 can acquire the image of the detected defect. For example, the inspection apparatus 110 can acquire an image of a region where the detected defect is located using an electron microscope (SEM). The inspection apparatus 110 can sense the precise position (e.g., coordinates of (x, y)) of the detected defect DEF based on the acquired image. Illustratively, the operation in step S20 is referred to as a review operation.

In step S30, the review apparatus 120 can classify the type of the detected defect based on the acquired image. For example, defects contained on the semiconductor wafer 101 may be caused by various factors (e.g., foreign particles, normal pattern formation, etc.). The review apparatus 120 can extract the feature of the defect DEF based on the acquired image. The review device 120 may compare the extracted feature and the preset type reference value, and classify the type of the detected defect according to the comparison result.

FIG. 3 is a flowchart illustrating the review operation and classification operation of the review apparatus shown in FIG. 2 in detail. For the sake of brevity, review apparatus 120 is assumed to detect one defect (DEF) and classify the type of one defect (DEF) detected. However, the scope of the present invention is not limited thereto, and the review apparatus 120 may detect a plurality of defects and classify each type of the detected plurality of defects.

Illustratively, steps S21 to S24 shown in FIG. 3 indicate a review operation of the review apparatus 120, and steps S31 to S33 indicate a review operation of the review apparatus 120. FIG.

Referring to FIGS. 1 to 3, in step S21, the review apparatus 120 can obtain a low-magnification image of a region where a defect is detected. For example, the review apparatus 120 can use an optical apparatus to obtain a low magnification image of a region where a defect is detected (e.g., a die region including a defect DEF).

In step S22, the review apparatus 120 can obtain a low magnification reference image of an area adjacent to the area where the defect is detected. For example, the review apparatus 120 can acquire a low magnification reference image of an area adjacent to a region where a defect is detected (e.g., a die area including a defect DEF) have.

In step S23, the review apparatus 120 can compare the acquired low magnification image and the low magnification reference image. For example, the semiconductor wafer 101 may comprise a plurality of dies having a repeating pattern. That is, when the acquired low magnification image and the low magnification reference image are compared, the low magnification image and the low magnification reference image will have different patterns at the position where the defect (DEF) exists. The review apparatus 120 may compare the low magnification image and the low magnification reference image to redetect the defect DEF. The review apparatus 120 can detect the position of the defect DEF by re-detecting the defect DEF.

For example, the re-detection process of the defect DEF is performed to detect the correct position of the defect DEF by correcting the coordinate error occurring inside the review device 120 or the coordinate error of the stage moving the semiconductor wafer 101 do. Illustratively, the field of view (FOV) of the low magnification image may be 10 um.

In step S24, the review apparatus 120 can acquire a high magnification image of the area where the defect is detected based on the comparison result. For example, the review apparatus 120 may detect the location of the defect DEF by re-detecting the defect. The reviewing device 120 can obtain a high magnification image of the existing region of the defect DEF based on the position of the detected defect DEF. At this time, the acquired high-magnification image is an image in a narrower area than the low-magnification image obtained in step S22. Illustratively, the FOV of the high magnification image may be 3 um.

In step S31, the review apparatus 120 can compare the low magnification image and the low magnification reference image. For example, the review device 120 may compare the low magnification image and the low magnification reference image to redetect the defect. The review apparatus 120 can detect the position of the defect by re-detecting the defect.

In step S32, the review apparatus 120 can extract the feature of the defect DEF based on the comparison result. For example, the review apparatus 120 may extract the feature of the defect in the high magnification image based on the position of the defect DEF detected in step S25. By way of example, the characteristics of a defect in a high magnification image indicate indicators such as width, height, area, brightness, and the like.

In step S33, the review apparatus 120 classifies the types of defects (DEFs) based on the extracted features. For example, the review device 120 may classify the extracted features according to a predetermined reference value.

FIGS. 4 and 5 are illustrations showing errors of a low magnification image and a low magnification reference image. 3 to 5, the review apparatus 120 obtains a low magnification image and a low magnification reference image to classify the type of defect (DEF), compares the obtained low magnification image and a low magnification reference image, Lt; / RTI >

The review device 120 can move the semiconductor wafer 101 to obtain a low magnification reference image. At this time, the low-magnification image and the low magnification reference image may not have the same pattern as shown in Fig. 3 due to the coordinate error in which the semiconductor wafer 101 moves. In this case, the review apparatus 120 will not be able to redetect the DEF even if the low magnification image and the low magnification reference image are compared.

Also, the low-magnification image and the low-magnification reference image obtained as shown in FIG. 4 may have different brightness and gradation. In this case, the review apparatus 120 will not be able to redetect the DEF.

As described above, in the process of acquiring the low magnification image and the low magnification reference image to detect the deficiency (DEF), the review apparatus 120 performs accurate defect (DEF) redetection due to factors such as coordinate error, image brightness difference, Can be difficult. Also, since the review apparatus 120 requires at least three image acquisitions, such as a low magnification image, a low magnification reference image, and a high magnification image, in order to detect one defect (DEF), the defect type classification time increases.

The review apparatus 120 according to the embodiment of the present invention can perform defect detection and type classification without using a reference image. The operation method of the review apparatus 120 according to the embodiment of the present invention will be described in detail with reference to the following drawings.

6 is a flowchart showing the operation of the review apparatus according to the embodiment of the present invention. Illustratively, the step S110 shown in FIG. 6 corresponds to the review operation of the review apparatus 120, and the operation S120 through S140 correspond to the sort operation of the review apparatus 120. FIG.

Referring to FIGS. 1 and 6, in step S110, the review apparatus 120 can obtain a high magnification image of a region where a defect (DEF) is detected. For example, the review apparatus 120 can acquire a high magnification image of an area in which a defect is detected (for example, a part of a die including a defect). Illustratively, the review operation of the review apparatus 120 shown in Fig. 6 is omitted from the operation of acquiring the low magnification image and the low magnification reference image, unlike the review operation of the review apparatus 120 shown in Fig. That is, the review apparatus 120 according to the embodiment of the present invention can acquire a high magnification image after the inspection operation without acquiring the low magnification image and the low magnification reference image.

In step S120, the review apparatus 120 may perform a magnetic image comparison based on the acquired high magnification image. For example, the review apparatus 120 may perform a magnetic image comparison to detect the location of the defect DEF. Illustratively, the magnetic image comparison method can be implemented with various algorithms such as fractal encoding, reference generation using self-similarity, and morphological operator-based pattern recognition. Illustratively, since the semiconductor wafer 101 includes a plurality of dies in which the same pattern is repeatedly arranged, a magnetic image comparison algorithm can be applied to detect the position of the defect DEF.

In step S130, the review apparatus 120 can extract the feature of the defect based on the magnetic image comparison result. For example, the review apparatus 120 may perform a magnetic image comparison to detect the location of the defect DEF. The reviewing device 120 can extract the features of the defect from the high magnification image acquired based on the detected position. By way of example, the nature of the defect may refer to indicators such as width, height, area, brightness, etc. in a high magnification image.

In step S140, the review apparatus 120 can classify the defect type based on the extracted features. For example, the defects included in the semiconductor wafer 101 may be caused by a plurality of factors such as dust, pattern defect, etc. introduced from the outside. The review device 120 can classify the type of the detected defect by determining whether the extracted features correspond to a predetermined type reference value.

7 is a detailed view showing the operation of step S130 shown in FIG. Illustratively, the review apparatus 120 may perform a magnetic-image comparison operation based on the process shown in FIG. However, the scope of the present invention is not limited thereto, and the magnetic image comparison operation can be implemented by various algorithms.

Referring to FIGS. 1, 6, and 7, the review apparatus 120 can perform a magnetic image comparison operation based on the acquired high magnification image. First, the review apparatus 120 can acquire a high magnification image in a region where a defect (DEF) is located. (1) Illustratively, the FOV of a high magnification image may have a range of 1 to 3 um.

Next, the review apparatus 120 can perform a two-dimensional fast Fourier transform (2D FFT) on the acquired high magnification image to recognize a pattern repeatedly appearing in the acquired high magnification image. ) Illustratively, the semiconductor wafer 101 can recognize the horizontal and vertical patterns repeatedly formed through the 2D FFT because the same pattern is repeatedly arranged.

Next, the review apparatus 120 can remove the components of the horizontal and vertical patterns in the high magnification image in which the 2D FFT has been performed. ((3), (4)) The review apparatus 120 extracts a high magnification image Fourier filtering to obtain a filtered high magnification image. (5) An exemplary filtered high magnification image may include only defects (DEF) by eliminating repeated horizontal and vertical patterns.

Next, the review apparatus 120 can binarize the filtered high-magnification image. (6) Next, the review apparatus 120 can remove the noise included in the binarized high-magnification image using a close operator. (7) Illustratively, noise refers to components other than the defect (DEF) included in the high magnification image.

Finally, the review apparatus 120 can overlay the high magnification image and the noise-canceled high magnification image. (8) The review apparatus 120 detects the exact position of the defect DEF based on the overlaid high magnification image .

Illustratively, the review device 120 can extract the feature of the defect DEF from the overlaid high magnification image. For example, the review apparatus 120 can extract a feature of defects (DEF) such as the length, width, area, curvature, brightness, etc. of defects (DEF) from the overlaid high magnification image. The review device 120 may classify the type of defect (DEF) based on the extracted features.

According to the embodiment of the present invention described above, the review apparatus 120 can acquire a high magnification image and perform a magnetic image comparison operation based on the acquired high magnification image. The review apparatus 120 can extract the exact position and characteristics of the defect DEF as a result of the magnetic image comparison operation. That is, since the low magnification image and the low magnification reference image are not required, the review operation of the review apparatus 120 is simplified. In addition, the review apparatus 120 can automatically classify the defect type based on the feature of the defect (DEF) extracted without the operator's intervention. Thus, a review device with improved performance is provided.

8 is a flowchart showing the operation of the review apparatus according to another embodiment of the present invention. Illustratively, the reference value update operation of the review apparatus 120 is described with reference to FIG. Referring to FIGS. 1 and 8, in step S310, the review apparatus 120 can select some of defects classified as defect types as sample defects. For example, a plurality of semiconductor wafers may include a plurality of defects. Review device 120 may perform an inspection and review operation on a plurality of semiconductor wafers to classify the defect types of a plurality of defects. The review device 120 may select some of the classified defects as sample defects.

In step S320, the review apparatus 120 may update the defect type reference value based on the selected sample defects. For example, review device 120 will repeatedly perform inspection and review operations on a plurality of semiconductor wafers. At this time, the integrity of the defect type reference value may be lowered due to an external factor such as a new defect type occurring or a change of the review device 120. [ Review device 120 may update the defect type reference value based on the characteristics of the selected sample defects to improve the integrity of the defect type due to the external factors described above.

Illustratively, the defect type classification algorithm of the review device 120 may be a training based algorithm such as Neural Net, SVM (Support Vector Machine), and the like. A training-based algorithm refers to an algorithm that compares a pre-determined training set with an extracted feature, and classifies the types based on the comparison results. In this case, the review apparatus 120 may update the defect type reference value (i.e., the training set) based on the selected sample defects.

In step S330, the review apparatus 120 may classify the defect type of the detected defect DEF based on the updated defect type reference value.

By way of example, the review apparatus 120 can repeatedly perform the defect inspection operation, the review operation, and the defect type update operation described above. In addition, the review apparatus 120 can perform the above-described inspection operation and review operation each time a specific pattern is formed in the process of forming the semiconductor wafer 101. [ Or review device 120 may perform the above-described inspection and review operations after the process of the semiconductor wafer 101 is completed.

According to the embodiment of the present invention described above, the review apparatus acquires a high magnification image of a region in which a defect is located. The review apparatus performs a magnetic image comparison operation based on the obtained high magnification image to detect the exact position of the defect. The review apparatus can extract the feature of the defect from the position of the detected defect, and classify the defect type of the detected defect based on the extracted feature. In addition, the review device may select sample defects from a plurality of defects and update the defect type reference value based on the characteristics of the selected sample defects. The review device may classify the defect type of the detected defects based on the updated defect type reference value. Thus, the speed of review and review operations of the review apparatus is improved, and the reliability of the defect type classification is improved.

Although the preferred embodiments of the present invention have been disclosed for illustrative purposes, those skilled in the art will appreciate that various modifications, additions and substitutions are possible, without departing from the scope of the present invention. Therefore, the scope of the present invention should not be limited to the above-described embodiments, but should be determined by the claims equivalent to the claims of the present invention as well as the following claims.

100: semiconductor inspection system
101: Semiconductor wafer
110: Inspection device
120: Review device
DEF: Defective

Claims (9)

A method of operating a review apparatus for classifying defects detected on a semiconductor wafer,
Obtaining a high magnification image of a region of the semiconductor wafer including the detected defect;
Performing a magnetic image comparison operation based on the acquired high magnification image to detect a position of the defect;
Extracting a feature of the defect based on the position of the detected defect; And
And classifying the detected type of defect based on the extracted feature. The method according to claim 1,
Wherein the field of view (FOV) of the high magnification image is from 1 to 3 um. The method according to claim 1,
Wherein the semiconductor wafer includes a plurality of dies,
Wherein obtaining a high magnification image of a region of the semiconductor wafer where the detected defect is located comprises:
Obtaining a high magnification image of a portion of the die comprising the detected defect of the plurality of dies. The method according to claim 1,
Performing a magnetic image comparison operation based on the acquired high magnification image to detect a position of the defect,
Performing two-dimensional fast Fourier transform on the acquired high magnification image;
Obtaining a filtered high magnification image by removing horizontal and vertical patterns of the 2-dimensional fast Fourier transformed high magnification image;
Binarizing the filtered high magnification image;
Removing noise of the binarized high magnification image;
Overlaying the noise-removed high-magnification image and the acquired high-magnification image; And
Detecting a position of the detected defect based on the overlaid high magnification image. The method according to claim 1,
Wherein the magnetic image comparison operation is performed based on at least one of an algorithm of Fractal encoding, a reference generation technique using self-similarity, and a morphological operator-based pattern recognition technique. The method according to claim 1,
The step of classifying the type of the detected defect based on the extracted feature
Comparing the extracted feature and type reference value, and classifying the detected type of defect based on the comparison result. The method according to claim 6,
The method of operation of the review device
Further comprising updating the type reference value based on a feature of the type of defect classified. 8. The method of claim 7,
The method of operation of the review device
Further comprising classifying a type of a plurality of defects detected on the semiconductor wafer based on the updated type reference value. The method according to claim 1,
Wherein the review apparatus includes an electron microscope and acquires the high magnification image through the electron microscope.
KR1020140015005A 2014-02-10 2014-02-10 Operating method of review device KR20150094118A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110346394A (en) * 2019-07-22 2019-10-18 德淮半导体有限公司 Defect inspection method and defect detecting system

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110346394A (en) * 2019-07-22 2019-10-18 德淮半导体有限公司 Defect inspection method and defect detecting system

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