JP4266920B2 - Classification apparatus and classification method - Google Patents

Description

  The present invention relates to a classification device and a classification method.

  Conventionally, various classification devices are known. For example, in JP-A-8-21803, a defect area to be classified is extracted from an image, and feature quantities such as area, perimeter, ferret diameter, circularity, and barycentric coordinates of the area are obtained in advance. A configuration of an apparatus that classifies defects using a neural network that is trained to output a predetermined defect type with respect to an input pattern of these feature amounts is disclosed.

Japanese Patent Application Laid-Open No. 11-344450 discloses a configuration of a classification device that performs statistical classification based on color information, shape, size, and the like of a defect image, a configuration that supports creation of teaching data (teacher data), and the like. It is shown.
JP-A-8-21803 JP 11-344450 A

  In the above classification device, when classification is performed using feature quantities related to the size (area, ferret diameter, circumference length, etc.) and shape (circularity degree, ferret diameter ratio, etc.) of the classification target area, the target area is extracted. If is not appropriate, the calculated feature value is not appropriate, and an accurate classification result cannot be obtained. The same applies to the teacher data. If the feature quantity is not calculated and created after appropriate extraction, it will cause a decrease in performance in the subsequent classification process.

  For example, when appropriate extraction is performed on the classification target region shown in FIG. 21A (FIG. 21B) and when inappropriate extraction is performed (FIG. 21C). The calculated feature values vary greatly, and even if classification is performed based on a teacher data group including such a difference, accurate classification is not performed.

  On the other hand, it is important to ensure robustness of classification performance against changes in the degree of extraction. When a large amount of teacher data is collected, data with various degrees of extraction exists, and thus robustness against changes in the degree of extraction is easily ensured. However, it is difficult when there is little teacher data. A classification device that shows an effective solution to such a problem has not been proposed.

  The present invention has been made paying attention to such a problem, and the object of the present invention is to provide a classification apparatus and a classification that can ensure the robustness of the classification performance against a change in the degree of extraction using a small number of teacher samples. It is to provide a method.

In order to achieve the above object, a classification device according to a first aspect of the present invention creates a plurality of teacher data by applying a plurality of extraction parameters to an image, and classifies the images based on the plurality of teacher data. a classification apparatus for performing, based on first extraction parameters, a first region extracting means for extracting a first region from the image, operation means for said first region to enter the correct category to be classified First teacher data for creating first teacher data using first feature value calculation means for calculating feature values for the first region, feature values for the first region, and the correct answer category Based on the second extraction parameter, the creation means, the extraction parameter setting means for setting the second extraction parameter different from the first extraction parameter for the region in which the correct answer category is input , 1 A second territory Iki抽 detecting means to extract the second area included in the image as a target of extraction performed by the parameter output, and the second feature quantity calculating means for calculating a feature value for said second region, Based on the feature amount for the second region, it is determined whether the extraction result of the second region is valid, and when it is determined to be valid, the feature amount for the second region, Classification is performed based on a plurality of teacher data including second teacher data creating means for creating second teacher data using the correct answer category , the first teacher data, and the second teacher data. Classification means.

In addition, according to a second aspect of the present invention, in the first aspect, the second teacher data creating unit is configured such that the size of the first and second regions as a feature amount for the second region , Whether or not the second teacher data is to be created is determined based on at least one change amount of the shape.

In addition, according to a third aspect of the present invention, in the first aspect, the second teacher data creating unit is configured such that the size of the first and second regions as a feature amount for the second region , Based on at least one change amount of the shape, it is determined whether or not the extraction result of the second region is valid. When it is determined that the second region is valid, a new second parameter is set by the extraction parameter setting means. Set extraction parameters to perform further region extraction .

The fourth aspect of the present invention, in the first to third any one aspect, further comprising a first display means for identifiably displaying the shape of the extraction of the second region.

According to a fifth aspect of the present invention, in any one of the first to fourth aspects, the classification device performs classification based on a plurality of teacher data positioned in the vicinity of the data of the classification target region in the feature amount space. I do.

According to a sixth aspect of the present invention, in any one of the first to fourth aspects, the classification device includes data representing the teacher data distribution of the same category in the feature amount space, data of the classification target region, Classification based on distance.

  According to the present invention, it is possible to ensure robustness of classification performance against changes in the degree of extraction using a small number of teacher samples.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. Here, a defect classification apparatus for a semiconductor wafer will be described as an embodiment of the present invention. However, the present invention is also applicable to other classification devices that extract classification objects (for example, cells) in an image and perform classification based on the characteristics of the region.

  FIG. 1 is a diagram showing a configuration of a defect classification apparatus according to an embodiment of the present invention, in which a defect area extraction unit 101 that extracts a defect area to be classified from an inspection image and a display unit 102 that displays an extraction result. A feature amount calculation unit 103 that calculates a feature amount with respect to the extracted region, an operation unit 104 that selects an extraction region and inputs a correct category, a teacher data generation unit 105 that generates teacher data, a teacher An area in which a correct category is input by the operation means 104, which includes a teacher data storage means 106 for storing data, a classification means 107 for classifying an extraction area, and an extraction parameter setting means 108 for changing the degree of extraction. In contrast, the extraction parameter setting means 108 creates a plurality of teacher data with different degrees of extraction while changing the degree of extraction. That.

  The configuration related to the acquisition of the inspection image is not particularly limited in the present invention and is not shown. Also, the memory used for holding the acquired image data and each process is not particularly shown.

  The processing of this defect classification apparatus has two processing flows: a teacher data registration process and a classification process. First, with reference to FIG. 2 and FIG. 3, the process flow of the teacher data registration process will be described. First, an image of a defect (teacher sample) for creating teacher data is acquired using an imaging system (not shown) (step S11). Next, the defect area extraction unit 101 extracts a defect area from the acquired inspection image (step S12).

  Here, two methods are shown as the method for extracting the defect area. In the first method, a threshold value that is a non-defective level luminance range is set for the inspection image 133 (FIG. 4A), and a pixel region having a luminance that deviates from this threshold value is extracted as the defect extraction image 140. (B in FIG. 4). Here, the threshold value indicating the non-defective level luminance range may be set in advance, or may be adaptively determined based on the luminance distribution in the image. Moreover, it may be changed according to the position in the image, or may be common in the image (The University of Tokyo Press: Image Analysis Handbook: Mikio Takagi, Yoshihisa Shimoda, Supervision: 502P, binarization, reference).

  In the second method, a non-defective wafer image 134 (or an image of a fixed section that is non-defective) as shown in FIG. 5B is held, and this image and the image shown in FIG. The inspection image 133 (or the corresponding section in the processing target image) is aligned, a luminance difference between overlapping pixels is obtained, and a difference image 135 ((C) in FIG. 5) is created. A defective area is extracted by threshold processing similar to the first method. Note that the region extraction method is changed according to the classification target, and does not limit the contents of the present invention.

  Further, in the case of a semiconductor wafer or the like, the same defect may be divided and extracted at the time of region extraction due to the influence of the ground pattern or dicing line. Therefore, if necessary, processing for connecting regions may be performed using morphological processing ((Reference): Corona: Morphology: Hidefumi Obata).

  6A and 6B show an example of region connection processing using morphological processing (closing processing). The continuous resolution failure 150 and the unevenness 151 shown in FIG. 6A become connection defect regions 150-1 and 151-1 (FIG. 6B) by the region connection processing, respectively.

  After the defective area is extracted, it is displayed using the display means 102 so that the extracted shape of the area can be visually identified quickly (step S13). As a display method, it is conceivable to display the extracted region so as to be superimposed on the inspection image, or to display the outline of the extracted region so as to be superimposed on the inspection image.

  FIG. 8 is a diagram illustrating a state in which the extraction result for the inspection image 160 illustrated in FIG. 7 is displayed by overlapping the extracted region shapes. The extraction results of the defects 1 to 6 existing in the inspection image 160 of FIG. 7 are shown as the shapes of the regions 1 to 6, respectively. FIG. 9 is a diagram illustrating a state in which the extraction result for the inspection image 160 illustrated in FIG. 7 is displayed by superimposing the outlines of the extraction regions. The extraction result is shown by the shape of the outline. It should be noted that the display can be switched to the original inspection image with a simple operation so that the details of the overlapping portion can be easily confirmed. The display scale is also variable so that the details of the shape can be easily confirmed.

  Here, the user operates the operating means 104 to select a region with a good extraction result by, for example, pointing with a pointer, and inputs the correct category of that region (step S14). FIG. 10 shows how a region is selected and a correct category is input. That is, a state in which the region 1 is selected by the pointer 170 and the defect type A is input as the correct category, and a region 5 is selected by the pointer 170 and the defect type B is input as the correct category is shown. ing.

  Next, the feature amount calculation means 103 calculates the feature amount for the region where the correct answer category is input (step S15). Regarding the feature amount, in general, in addition to the size, shape, position, and density information of a single region, the feature amount relates to an arrangement configuration of several regions. The feature amount in the macro inspection is disclosed in Japanese Patent Laid-Open No. 2003-168114 by the present inventor. However, the feature amount calculation method is changed according to the classification target, and the contents of the present invention are It is not limited.

  After the feature amount is calculated, teacher data is created by the teacher data creation means 105 (step S16). The teacher data here is a set of feature amount information calculated by the feature amount calculation unit 103 and correct category information of the target region input using the operation unit 104. FIG. An example of data (plurality) is shown.

  After the teacher data is created in step S16, the extraction parameter setting unit 108 sets the defect area extraction parameter by changing it from the original parameter to the excessive extraction side (step S41).

  Thereafter, according to the set parameters, the defect area extraction unit 101 re-extracts the vicinity of the target area (step S42). The range to be re-extracted includes a method of determining based on the circumscribed rectangular coordinates of the target region and the ferret diameter, and a method of determining in consideration of the exposure section.

  FIG. 12 is a diagram illustrating how the re-extraction range is determined using the ferret diameter. Here, the circumscribed rectangle 201 of the extraction region 200 is obtained, the length in the X direction (Ferret diameter X) and the length in the Y direction (Ferret diameter Y) are each doubled, and the center is the same as the circumscribed rectangle. The rectangle is determined as the re-extraction range 202.

  FIG. 13 is a diagram illustrating how the re-extraction range is determined in consideration of the exposure section. In this case, the range of one section around the exposure section where the extraction area 200 exists in the increasingly square exposure section 210 is determined as the re-extraction range 211.

  After the extraction, the feature amount calculation unit 103 calculates the feature amount of the re-extracted area (step S43). This is the same as the processing in step S15 in FIG. After the feature amount is calculated, the teacher data creation unit 105 determines whether the result of the region re-extraction is appropriate using the feature amount (step S44).

  As a determination method, there is a method of using a feature amount indicating the size or shape of the extraction region. For example, when the shape of the extraction region changes significantly, the values of area, perimeter, and ferret diameter ratio also change accordingly. Therefore, the area of the extraction region, the perimeter, and the amount of change in the ferret diameter ratio when the extraction parameter is changed are confirmed. Alternatively, the re-extraction status may be displayed and the user may be judged.

  If the extraction result is valid (Yes in step S44), teacher data is created using the calculated feature amount (step S45). This is the same as the processing in step S16 described above. After the teacher data is created, the process returns to step S41 again, the extraction parameters are further changed, and the subsequent processing is repeated.

  If the extraction result is no longer valid as a result of repetition (NO in step S44), the extraction parameter is changed from the original parameter to the insufficient extraction side and set (step S46). Subsequent steps S47 to S50 are the same processes as steps S42 to S45, respectively.

  After creating a plurality of teacher data using one teacher sample by the above procedure, the teacher data is stored in the teacher data storage means 106 (step S17), and the teacher data registration process is terminated.

  FIG. 14 is a diagram illustrating changes in the shape of the extraction region when the extraction parameter is changed from the overextraction side to the underextraction side. When the extraction parameter is changed to the excessive extraction side, the region a in the re-extraction range 220 changes as b → c → d. Further, when the extraction parameter is changed to the insufficient extraction side, the region a in the re-extraction range 220 changes as e → f → g. Here, if extraction is appropriate except for d and g, teacher data having five different extraction statuses a, b, c, e, and f are obtained for one defect of the teacher sample.

  The teacher data registration process is completed by repeating the above processing as necessary.

  In this description, the feature amount is calculated after the correct category is input. However, the feature amount is calculated for all the regions first, and the teacher uses the feature amount information only for the region where the correct category is input later. Data may be created. The change amount of the extraction parameter may be set statically in advance, or may be set dynamically based on the change amount of the size and shape of the extraction region caused by the parameter change.

  Next, the flow of the classification process will be described with reference to FIG. Among the processes of the classification process, the image acquisition (step S21), the defect area extraction (step S22), and the feature amount calculation for the defect area (step S23) are performed only when the imaging target is an inspection object. The processing is the same as steps S11, S12, and S15 of the teacher data registration process described above. However, the feature amount is calculated for all the extraction regions.

  After the feature amount of each defective area is calculated in step S23, the classification unit 107 classifies the calculated feature quantity (classification target region data) based on the distance in the feature amount space between the pre-registered teacher data. Is performed (step S24).

  FIG. 16 is a diagram for explaining the principle of classification by the k-nearest neighbor method, which is one of methods for performing classification using teacher data. In FIG. 16, ◯, Δ, and □ indicate positions in the feature amount space of the teacher data of the defect type A, the defect type B, and the defect type C, respectively. On the other hand, P is the position in the feature amount space of the area to be classified.

The k-neighbor method is a method in which the largest defect type among the k (in the example, five (pre-set)) teacher data closest to the target region P is used as the defect type of the target region. In the example, three scratches> In this method, two points in the feature space (N dimension) (xi: teacher data, xj: classification target). ) Is required, but the distance calculation methods include the following.

As a method other than the k-nearest neighbor method, as shown in FIG. 17, the distance from the representative point of the teacher data distribution for each defect type (for example, the center of gravity of the entire distribution or the cluster center of gravity when the distribution is clustered). There is a method to classify based on. In this case, the value μl of the feature value l (el) (1 ≦ l (el) ≦ N) at the representative point is calculated by the following formula, and the above distance calculation is performed to classify the defect type with the closest distance.

  Note that both the k-nearest neighbor method and the representative point distance comparison method increase the calculation load as the number of features (the number of dimensions) increases. It is also possible to calculate the distance after determining the calculation method of the above and performing the feature amount reduction processing based on the calculation method.

  After classifying each defective area, information on the classification result is output (step S25).

  FIG. 18 is a diagram showing a modification of the defect classification apparatus described in FIG. This modification is basically the same as the configuration of FIG. 1, but the user interactively changes the degree of extraction of the defect area for an arbitrary range in the image displayed on the display unit 102 of FIG. It is possible.

  FIG. 19 is a flowchart for explaining the flow of processing of the defect classification apparatus according to this modification. FIG. 19 illustrates only the process corresponding to the process added to the process in FIG. 2 and the processes before and after the process. After the extraction area shape is displayed (step S13), the user checks the degree of extraction and determines whether re-extraction is necessary (step S31).

  If re-extraction is necessary, the operation means 104 is used to set a range that requires re-extraction and extraction parameters (such as a threshold for binarization) (step S32). After the range and parameters are set, the defect area extraction unit 101 re-extracts the defect area based on the set content (step S33). After the re-extraction of the defective area, the process returns to step S13, the extracted area shape is displayed, and the determination in step S31 is performed again.

  The extent of partial range extraction is interactively optimized through the above flow. FIG. 20 shows an example of a screen on which the degree of extraction is adjusted. After defect 4 is selected from defects 1 to 6 existing in inspection image 160, slide bar 170 is slid left and right to extract the defect. It is shown that the degree of is adjusted and optimized.

  As a result of the adjustment, if there is no need for re-extraction, the correct answer category is input to the region where extraction is appropriate (step S14), and thereafter, the teacher data registration process is terminated according to the flow described above.

  As described above, according to an embodiment of the present invention, one teacher sample is extracted with a plurality of extraction parameters within an allowable range, and a feature amount in each extraction situation is calculated to create a plurality of teacher data. It is possible to ensure robustness of classification performance against changes in the degree of extraction using a small number of teacher samples.

It is a figure which shows the structure of the defect classification apparatus which concerns on one Embodiment of this invention. It is a flowchart (the 1) for demonstrating the flow of a process of a teacher data registration process. It is a flowchart (the 2) for demonstrating the flow of a process of a teacher data registration process. It is a figure for demonstrating the 1st method of defect area extraction. It is a figure for demonstrating the 2nd method of defect area extraction. It is a figure for demonstrating an example of the area | region connection process using a morphological process (closing process). It is a figure which shows an example of a process target image. It is a figure which shows a mode that the extraction result with respect to the process target image shown in FIG. 6 was displayed by area | region superimposition. It is a figure which shows a mode that the extraction result with respect to the process target image shown in FIG. 7 was displayed by outline superimposition. It is a figure which shows the mode of selection of an area | region, and the input of a correct category. It is a figure which shows an example of teacher data (plural). It is a figure which shows the mode of the re-extraction range determination using a ferret diameter. It is a figure which shows the mode of the re-extraction range determination in consideration of the exposure division. It is a figure which shows the change of the extraction area | region shape at the time of changing an extraction parameter to the overextraction side and the underextraction side. It is a figure for demonstrating the flow of a process of a classification | category process. It is a figure for demonstrating the principle of the classification | category by the k-nearest neighbor method which is one of the methods of classifying using teacher data. It is a figure for demonstrating the classification method based on the distance with the representative point of the teacher data distribution for every defect type. It is a figure which shows the modification of this defect classification device demonstrated in FIG. It is a flowchart for demonstrating the flow of a process of the defect classification device which concerns on this modification. It is a figure which shows the example of the screen which adjusts the degree of extraction. It is a figure for demonstrating the fault of the conventional classification method.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 101 ... Defect area extraction means, 102 ... Display means, 103 ... Feature-value calculation means, 104 ... Operation means, 105 ... Teacher data creation means, 106 ... Teacher data storage means, 107 ... Classification means, 108 ... Extraction parameter setting means

Claims (6)

A classification device that applies a plurality of extraction parameters to an image, creates a plurality of teacher data, and performs classification based on the plurality of teacher data,
A first region extracting means for extracting a first region from the image based on the first extraction parameter;
An operation means for inputting a correct answer category in which the first region is to be classified;
First feature amount calculating means for calculating a feature amount for the first region;
First teacher data creating means for creating first teacher data using the feature quantity for the first region and the correct answer category;
Extraction parameter setting means for setting a second extraction parameter different from the first extraction parameter for the region where the correct answer category is input ;
Based on the second extraction parameters, a second territory Iki抽 detecting means to extract the second area included in the image as a target of extraction process by the first extraction parameters,
Second feature amount calculating means for calculating a feature amount for the second region;
Based on the feature amount for the second region, it is determined whether the extraction result of the second region is valid, and when it is determined to be valid, the feature amount for the second region, Second teacher data creating means for creating second teacher data using the correct answer category ;
Classification means for performing classification based on a plurality of teacher data including the first teacher data and the second teacher data ;
A classification apparatus comprising: The second teacher data creating unit is configured to determine the second teacher based on at least one change amount of the size and shape of the first and second regions as a feature amount for the second region. The classification apparatus according to claim 1, wherein it is determined whether or not data is created. The second teacher data creating means is configured to determine the second region based on at least one change amount of the size and shape of the first and second regions as a feature amount for the second region. It is determined whether or not the extraction result is valid, and when it is determined to be valid, a new second extraction parameter is set by the extraction parameter setting means to perform further region extraction. Item 2. The classification device according to Item 1. The first, the classification apparatus according to any one of claims 1 to 3, further comprising display means for identifiably displaying the shape of the extraction of the second region.   The classification device according to claim 1, wherein the classification device performs classification based on a plurality of teacher data located in the vicinity of data of a classification target region in a feature amount space.   5. The classification device according to claim 1, wherein the classification device performs classification based on a distance between data representing the teacher data distribution of the same category in the feature amount space and data of the classification target region. Classification device according to.

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