Analysis device, analysis method, and program

Abstract

To accurately count defects of a semiconductor device.SOLUTION: An analysis device comprises: an image acquisition unit that acquires images photographing a semiconductor device; a defect extraction unit that extracts a plurality of defect parts from the image; and a defect determination unit that compares a distance between two defect parts included in the plurality of defect parts with a predetermined determination threshold, and determines whether the two defect parts is a single defect, in which the defect determination unit is configured to correct at least one of the distance between the two defect parts and the determination threshold on the basis of a characteristic of the two defect parts.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an analysis device, an analysis method, and a program. [Background technology]

[0002] 2. Description of the Related Art In an inspection apparatus for semiconductor devices such as semiconductor wafers, techniques for detecting defects present in the semiconductor devices are known (see, for example, Patent Documents 1 to 4). Patent Document 1 Patent No. 2986868 Patent Document 2: JP 2005-83843 A Patent Document 3: JP 2008-268232 A Patent Document 4: JP 2009-180627 A Summary of the Invention [Problem to be solved by the invention]

[0003] In the inspection of semiconductor devices, it is preferable to be able to count the number of defects with high accuracy. [Means for solving the problem]

[0004] In a first aspect of the present invention, there is provided an analysis device. The analysis device may include an image acquisition unit that acquires an image of a semiconductor device. The analysis device may include a defect extraction unit that extracts a plurality of defect parts from the image. The analysis device may include a defect determination unit that compares a distance between two defect parts included in the plurality of defect parts with a predetermined determination threshold to determine whether the two defect parts are one defect or not. The defect determination unit may correct at least one of the distance between the two defect parts and the determination threshold based on characteristics of the two defect parts.

[0005] The image acquisition section may acquire a first image of the semiconductor device and a second image including the defective portion in the first image and having a higher resolution than the first image. The defect determination section may determine characteristics of each of the defective portions based on the second image.

[0006] The defect determination section may calculate the distance between the two defective portions based on the distance between the two second images.

[0007] The defect determination section may classify each defective portion into a plurality of predetermined types based on the second image, and extract the distance between two defective portions classified into the same type.

[0008] The defect determining section may correct at least one of the distance between the two defective portions and the determination threshold value based on whether or not the defective portion reaches an edge of the second image.

[0009] The defect determination section may correct at least one of the distance and the determination threshold value based on whether or not a defect portion of a different type exists between two defect portions classified as the same type.

[0010] The defect determination section may correct at least one of the distance and the determination threshold value based on the size of the defective portion in the image.

[0011] The defect determination section may correct at least one of the distance and the determination threshold value based on the shape of the defective portion in the image.

[0012] The defect determination section may correct at least one of the distance and the determination threshold value based on the direction connecting the two defective portions and the direction identified from the shape of the defective portion.

[0013] In a second aspect of the present invention, there is provided an analysis method. The analysis method may include an image acquisition step of acquiring an image of a semiconductor device. The analysis method may include a defect extraction step of extracting a plurality of defect parts from the image. The analysis method may include a defect determination step of comparing a distance between two defect parts included in the plurality of defect parts with a predetermined determination threshold to determine whether the two defect parts are one defect or not. The analysis method may include a correction step of correcting at least one of the distance between the two defect parts and the determination threshold based on characteristics of the two defect parts.

[0014] In a third aspect of the present invention, there is provided a program for causing a computer to execute the analysis method according to the second aspect.

[0015] The above summary of the invention does not list all of the features of the present invention. Also, subcombinations of these features may also be inventions. [Brief description of the drawings]

[0016] [Figure 1] FIG. 1 is a diagram showing an example of the configuration of an inspection system 300 according to an embodiment of the present invention. [Diagram 2] 2 is a diagram showing an example of a first image 201 captured by the imaging device 20 of the surface of the semiconductor device 10. FIG. [Diagram 3] An example is shown in which the second image 202 is used to determine the distance and / or characteristics of each defect portion 210. [Figure 4] FIG. 2 is a diagram showing another example of a plurality of second images 202. [Diagram 5] FIG. 2 is a diagram showing another example of a plurality of second images 202. [Figure 6] FIG. 2 is a diagram showing another example of a plurality of second images 202. [Figure 7] 10 is a flowchart outlining an example of the operation of analysis device 100. [Figure 8] FIG. 13 is a diagram showing an example of a defect analysis result. [Figure 9] FIG. 11 is a diagram showing another example of a defect analysis result. [Figure 10] FIG. 13 is a diagram illustrating an example of correction of a determination threshold value. [Figure 11] FIG. 13 is a diagram illustrating another example of correction of the determination threshold value. [Figure 12] FIG. 13 is a diagram illustrating another example of correction of the determination threshold value. [Figure 13] FIG. 13 is a diagram illustrating another example of correction of the determination threshold value. [Figure 14] FIG. 13 is a diagram illustrating another example of correction of the determination threshold value. [Figure 15]22 illustrates an example computer 2200 in which aspects of the present invention may be embodied, in whole or in part. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] The present invention will be described below through embodiments of the invention, but the following embodiments do not limit the scope of the invention according to the claims. Furthermore, not all of the combinations of features described in the embodiments are necessarily essential to the solution of the invention.

[0018] 1 is a diagram showing an example of the configuration of an inspection system 300 according to an embodiment of the present invention. The inspection system 300 detects defects present in the semiconductor device 10 based on an image of the semiconductor device 10. The semiconductor device 10 may be a semiconductor wafer or a semiconductor chip. The semiconductor wafer or the semiconductor chip is formed of a semiconductor material such as silicon or a compound semiconductor.

[0019] The inspection system 300 includes an imaging device 20 and an analysis device 100. The imaging device 20 images the surface of the semiconductor device 10. For example, the imaging device 20 images the surface of a semiconductor wafer on which multiple semiconductor chips are formed. The imaging device 20 may capture the entire surface of the semiconductor device 10 in a single image, or may capture multiple images by changing the imaging position on the surface of the semiconductor device 10.

[0020] The analysis device 100 extracts defects present in the semiconductor device 10 based on the image captured by the imaging device 20. The analysis device 100 of this example includes an image acquisition section 110, a defect extraction section 120, a defect determination section 130, and a result output section 140. The analysis device 100 of this example is a computer that processes information. The analysis device 100 may be provided with a program for causing the computer to operate as the image acquisition section 110, the defect extraction section 120, the defect determination section 130, and the result output section 140.

[0021] The image acquiring unit 110 acquires an image of the surface of the semiconductor device 10 captured by the imaging device 20. The image acquiring unit 110 may communicate with the imaging device 20 via a wired or wireless manner. The image acquiring unit 110 may acquire the image from the imaging device 20 via a network such as the Internet or a local network. Furthermore, the analysis device 100 and the imaging device 20 may be arranged in the same housing.

[0022] The defect extraction unit 120 extracts a plurality of defect parts from the image acquired by the image acquisition unit 110. The defect extraction unit 120 may extract the defect parts by extracting a portion in the image where the pixel brightness or color tone is different from other regions. The defect extraction unit 120 may extract the defect parts based on a preset rule, or may extract the defect parts using an extraction module trained by machine learning. A known method such as those described in any of Patent Documents 1-4 can be used as a method for extracting the defect parts from the image. The defect extraction unit 120 may perform a predetermined process on the image before extracting the defect parts from the image. For example, the defect extraction unit 120 may perform at least one of a process of normalizing the brightness of each pixel of the image and a process of enhancing the contrast in the image.

[0023] The defect determination section 130 detects defects to be treated as one defect among the multiple defect parts extracted by the defect extraction section 120. The defect determination section 130 compares the distance between any two defect parts with a predetermined determination threshold. If the distance is smaller than the determination threshold, the defect determination section 130 determines that the two defect parts are one defect. Based on the distance between one of the two defect parts determined to be one defect and the other defect part, the defect determination section 130 may determine whether the other defect part is also included in one defect. In other words, the defect determination section 130 determines two or more defect parts that are relatively close to each other in an image as one defect.

[0024] The defect determination unit 130 may correct the determination threshold based on the characteristics of the two defective parts to be determined. The characteristics of the defective parts may include at least one of the type, size, and shape of the defective parts. The type of the defective parts may be a classification indicating the cause of the formation of the defective parts, such as scattering of materials used in the manufacturing process, bleeding of materials, chipping, adhesion of other foreign matter, and the like. The type of the defective parts may be determined based on an image including the defective parts according to a predetermined rule, or the type of the defective parts may be determined using a determination module trained by machine learning. The type of the defective parts may be determined by a known method such as those described in any of Patent Documents 1-4. The distribution of the size of one defect differs for each type of defective part. Therefore, even if two defective parts are separated by the same distance in an image, the possibility that the two defective parts are one defect may differ depending on the type of the defective parts. Therefore, by correcting the determination threshold according to the type of the defective parts, it is possible to accurately determine whether the two defective parts to be determined are one defect or not. The defect determination section 130 may store in advance correction data for correcting the determination threshold for each type of defect portion, or may store the determination threshold itself for each type of defect portion.

[0025] The size of the defective portion may be the area of ​​the defective portion in the image, or the number of pixels contained in the defective portion. The defect determining unit 130 may increase the determination threshold as the defective portion becomes larger. The larger the defective portion that appears in the image, the higher the possibility that the defect including the defective portion spreads over a wide area. Therefore, by increasing the determination threshold according to the size of the defective portion, it is possible to accurately determine whether the two defective portions to be determined are one defect or not.

[0026] The defect determination unit 130 may correct the judgment threshold based on a parameter that changes depending on the shape of the defective part. The parameter may be a direction determined by the shape, such as the longitudinal direction or lateral direction of the defective part, or may be a parameter related to the width of the defective part, such as the maximum width, minimum width, or the ratio of the maximum width to the minimum width of the defective part, or may be another parameter. The longitudinal direction is the direction in which the defective part shows the maximum width, and the lateral direction is the direction in which the defective part shows the minimum width. For example, the defect determination unit 130 sets a judgment threshold for two defective parts arranged along the longitudinal direction of one of the defective parts to be larger than a judgment threshold for two defective parts arranged along the lateral direction of one of the defective parts. That is, when a second defective part exists in the direction in which the first defective part extends, the defect determination unit 130 sets the judgment threshold for the first defective part and the second defective part to be relatively large. This makes it possible to accurately determine whether the two defective parts to be determined are one defect or not.

[0027] The defect determination section 130 may correct the distance between two defective parts to be determined instead of correcting the judgment threshold. For example, the defect determination section 130 may correct the distance to be smaller instead of correcting the judgment threshold to be larger, and may correct the distance to be larger instead of correcting the judgment threshold to be smaller. By correcting the distance instead of correcting the judgment threshold, the same effect as when the judgment threshold is corrected can be obtained. The defect determination section may also correct both the judgment threshold and the distance. In this case, the same effect as when the judgment threshold is corrected can be obtained. In this specification, an embodiment in which the judgment threshold is corrected will be described, but in each embodiment, the distance between the defective parts may be corrected instead of the judgment threshold, or both the judgment threshold and the distance may be corrected.

[0028] The defect determination section 130 may determine whether or not all defective parts included in the image are one defect together with other defective parts. In other words, the defect determination section 130 may determine whether or not all combinations of two defective parts selected from a plurality of defective parts included in the image are one defect. The defect determination section 130 may also determine whether or not some combinations of defective parts that are relatively close to each other are one defect.

[0029] The result output section 140 outputs information according to the determination result in the defect determination section 130. For example, the result output section 140 outputs information including the number of defects included in the semiconductor device 10. The result output section 140 may display, print, or transmit the information.

[0030] 2 is a diagram showing an example of a first image 201 captured by the imaging device 20 of the surface of the semiconductor device 10. A plurality of regions that will become semiconductor chips 12 when cut out are formed on the semiconductor device 10. The first image 201 may be an image of the entire surface of the semiconductor device 10 captured in one go, or may be an image obtained by scanning the surface of the semiconductor device 10 and combining a plurality of partial images captured of portions of the entire surface of the semiconductor device 10.

[0031] As shown in Fig. 2, first image 201 includes images of a plurality of defect portions 210. As described in Fig. 1, analysis device 100 determines whether each defect portion 210 is one defect with other defect portions 210. The distance between each defect portion 210 and other defect portions 210 may be determined from first image 201, or may be determined from the other images. The characteristics of each defect portion 210 may be determined from first image 201, or may be determined from the other images.

[0032] FIG. 3 shows an example in which at least one of the distance and the characteristic of each defect portion 210 is determined by using the second image 202. The second image 202 includes the defect portion 210. The image acquisition section 110 may cause the imaging device 20 to capture an image of the vicinity of each defect portion 210 included in the first image 201 to acquire the second image 202. The defect extraction section 120 may extract coordinates of each defect portion 210 in the first image 201 and notify the image acquisition section 110. The image acquisition section 110 may control a position at which the imaging device 20 captures the second image 202 based on the coordinates. The imaging device 20 does not capture an area of ​​the semiconductor device 10 where no defect portion 210 exists as the second image 202. This allows the second image 202 to be acquired efficiently.

[0033] The second image 202 has a higher resolution than the first image 201. The resolution may include at least one of spatial resolution, luminance resolution, and color resolution. That is, the second image 202 includes more information about the defective portion 210 than the first image 201. For example, the second image 202 includes detailed information about the shape, brightness, and color of the defective portion 210.

[0034] The defect determination section 130 of this example determines the characteristics of each defective portion 210 based on the second image 202. Since the second image 202 contains a large amount of information about the defective portion 210, the characteristics of the defective portion 210 can be determined with high accuracy by using the second image 202. The defect determination section 130 determines the characteristics of the defective portion 210 based on the shape, size, brightness, and the like of the defective portion 210. As described above, the defect determination section 130 may classify the characteristics of the defective portion 210 based on predetermined rules, or may classify the characteristics using a machine learning discrimination module.

[0035] The defect determination section 130 may determine the distance between two defective parts 210 included in each second image 202 based on the distance between the two second images 202. The distance between the second images 202 corresponds to the distance between the center positions of the second images 202 on the surface of the semiconductor device 10. The distance between the second images 202 may also be the distance between two second images 202 in the first image 201. The second image 202 is an image of an area centered on a predetermined coordinate position in the first image 201. The distance between the center coordinates of the second images 202 may be the distance between the second images 202. The sizes of the second images 202 may be the same or different. The imaging device 20 may adjust the size of each second image 202 so that each second image 202 does not include multiple defective parts 210. The multiple defective parts 210 refer to multiple defective parts 210 separated in the first image 201.

[0036] 3 illustrates a second image 202-1 including a defective portion 210-1 and a second image 202-2 including a defective portion 210-2. The defect determination section 130 detects a distance D1 between the two second images 202. The defect determination section 130 may calculate the distance between the central coordinates of the two second images 202. Note that it is preferable that the defect determination section 130 classifies each of the defective portions 210 into a plurality of predetermined types, extracts the distance D1 between the defective portions 210 classified into the same type, and determines whether or not they are a single defect. This makes it possible to prevent the defective portions 210 of different types from being erroneously determined to be a single defect.

[0037] The defect determination section 130 compares the distance D1 with a determination threshold according to the characteristics of the defective portion 210. The determination threshold may be a value according to the characteristics of at least one of the two defective portions 210. The defect determination section 130 may use a determination threshold according to the characteristics of the two defective portions 210, such as the sum of the sizes of the two defective portions 210. The defect determination section 130 may also use a determination threshold according to the characteristics of one of the defective portions 210, such as the size of the larger defective portion 210.

[0038] If the distance D1 is smaller than the judgment threshold, the defect judgment section 130 judges that the two defective portions 210 are one defect. If the distance D1 is equal to or greater than the judgment threshold, the defect judgment section 130 judges that the two defective portions 210 are separate defects. In the example of Fig. 3, since the distance D1 is greater than the judgment threshold, the defective portions 210-1 and 210-2 are judged to be separate defects. The defect judgment section 130 may count the number of defects after the judgment process.

[0039] When adjusting the judgment threshold according to the size of the defective portion 210, the judgment threshold may be adjusted according to the sizes of a plurality of defective portions 210 classified into the same type. For example, the judgment threshold for the defective portions 210 of the same type may be adjusted based on the average value of the sizes of all the defective portions 210 of the same type included in the semiconductor device 10.

[0040] Fig. 4 is a diagram showing another example of the plurality of second images 202. Four second images 202-1, 202-2, 202-3, and 202-4 are shown in Fig. 4. Each of the second images 202 includes a defective portion 210-1, 210-2, 210-3, and 210-4.

[0041] In this example, the distance D1 between the second image 202-1 and the second image 202-2 is smaller than the judgment threshold. In this case, the defect judgment unit 130 judges that the defective portion 210-1 of the second image 202-1 and the defective portion 210-2 of the second image 202-2 are one defect. Note that the distance D between the second image 202-1 and the second image 202-3, and between the second image 202-2 and the second image 202-4 is also smaller than the judgment threshold. The defect judgment unit 130 judges that the defective portions 210-1, 210-2, 210-3, and 210-4 are one defect. By such processing, it is possible to prevent a large defect that does not fit within the range of one second image 202 from being counted as a plurality of defects. Therefore, the number of defects can be counted with high accuracy.

[0042] When adjusting the judgment threshold value according to the size of the defective portion 210, if the defective portion 210 reaches an end of the second image 202 as shown in Fig. 4, the size of the second image 202 may be used as the size of the defective portion 210. For example, the square root of the sum of the squares of the width and height of the second image 202 may be used as the size of the defective portion 210.

[0043] Fig. 5 is a diagram showing another example of the plurality of second images 202. Two second images 202-1 and 202-2 are shown in Fig. 5. Each of the second images 202 includes a defective portion 210-1 or 210-2.

[0044] The imaging device 20 may capture a plurality of second images 202 such that some areas overlap as shown in Fig. 5. Even in such a case, the defect determination section 130 may determine whether or not the two defective portions 210 are one defect 211 based on the distance D1 between the second images 202.

[0045] Fig. 6 is a diagram showing another example of the plurality of second images 202. Two second images 202-1 and 202-2 are shown in Fig. 6. Each of the second images 202 includes a defective portion 210-1 or 210-2.

[0046] 6, second images 202-1 and 202-2 including defective portions 210-1 and 210-2 constituting one defect 211 may be separated from each other. Even in such a case, it is possible to determine whether the two defective portions 210 are one defect 211 or not based on the distance D1 between the second images 202.

[0047] 7 is a flowchart for explaining an outline of an example of the operation of the analysis device 100. The analysis device 100 may perform the operations explained in each embodiment. First, in a first image acquisition step S302, the image acquisition unit 110 acquires a first image 201 obtained by capturing an image of the surface of the semiconductor device 10.

[0048] Next, in defect extraction step S304, the defect extraction unit 120 extracts the coordinates of the defect portions 210 in the first image 201. In S304, the coordinates of all the defect portions 210 in the first image 201 may be extracted.

[0049] Next, in a second image acquisition step S306, the image acquisition unit 110 acquires a second image 202 including the defect coordinates extracted in S304. In S306, the second image 202 is acquired for all the defect portions 210. It is preferable that one second image 202 includes only one defect portion 210. The imaging device 20 may acquire the second image 202 centered on the designated defect coordinates. When the second image 202 includes a plurality of defect portions 210 separated on the image, the analysis device 100 extracts the defect portion 210 including the center position of the second image 202, and does not have to treat the other defect portions 210 as the defect portion 210 in the second image 202. The other defect portion 210 is disposed at the center position of the other second image 202, and is treated as the defect portion 210 in the other second image 202.

[0050] Next, in selection step S308, the defect determination section 130 selects two defective portions 210 from the multiple defective portions 210. Before selection step S308, the defect determination section 130 may determine the type of each defective portion 210. In this case, in selection step S308, two defective portions 210 classified as the same type are selected.

[0051] Next, in a characteristic discrimination step S310, the defect judgment section 130 discriminates the characteristics of the two selected defective portions 210. Next, in a correction step S312, the judgment thresholds for the two defective portions 210 are corrected based on the characteristics of the two defective portions 210. As described above, the characteristics of the defective portions 210 include the type, shape, size, etc. of the defective portions 210.

[0052] Next, in defect determination step S314, defect determination section 130 compares distance D1 between two defective portions 210 with the corrected determination threshold to determine whether or not the two defective portions 210 are one defect. Note that correcting the determination threshold according to the characteristics includes not only a process of correcting a reference determination threshold with a correction coefficient according to the characteristics, but also a process of selecting a determination threshold according to the characteristics. In other words, correcting the determination threshold according to the characteristics only requires that the determination threshold be a value according to the characteristics.

[0053] Next, in an end determination step S316, the defect determination section 130 determines whether or not all the defective portions 210 to be determined have been processed. If all the processes have not been completed, a new defective portion 210 is selected in S308, and the processes are repeated. In S316, it may be determined whether or not combinations with all the defective portions 210 have been processed for each defective portion 210, or it may be determined whether or not combinations with some of the defective portions 210 within a predetermined range have been processed for each defective portion 210. For example, the defect determination section 130 selects other second images 202 for one second image 202 in order starting from the image having the closest distance D1. Then, other second images 202 may be selected in order and the processes from S310 to S314 may be repeated until the distance D1 exceeds the determination threshold.

[0054] In S316, when the processing for all defective portions 210 is completed, the defect determination section 130 generates an analysis result for the defect. Next, in a defect output step S318, the result output section 140 outputs the analysis result for the defect. In the above description of the operation example, the characteristic determination step S310 is performed only after the selection step S308, but a characteristic determination step S307 for determining the type of defect may be performed before the selection step S308. In this case, defects of the same type are selected in the selection step S308.

[0055] 8 is a diagram showing an example of a defect analysis result. The defect determination section 130 of this example outputs an analysis result indicating the position of a defect on the surface of the semiconductor device 10. In the analysis result, as described with reference to FIGS. 1 to 7, two defect portions 210 whose distance D1 is smaller than the determination threshold are displayed as one defect. The defect determination section 130 may indicate the position of the defect on the surface of the semiconductor device 10 by distinguishing between types of defects (e.g., defect A, defect B).

[0056] 9 is a diagram showing another example of the defect analysis result. The defect determination section 130 of this example outputs the result of counting the number of defects contained in the semiconductor device 10 for each predetermined unit such as a lot of the semiconductor device 10. In the analysis result, as described with reference to FIGS. 1 to 7, two defective portions 210 whose distance D1 is smaller than the determination threshold are counted as one defect. The defect determination section 130 may indicate the number of defects by differentiating between different types of defects.

[0057] 10 is a diagram showing an example of correction of the judgment threshold. The defect judgment section 130 of this example corrects the judgment threshold based on the sizes of the two defective parts 210-1 and 210-2 to be judged. The defect judgment section 130 may use the radii T1 and T2 of the circumscribing circle of the defective part 210 as the size of the defective part 210. The defect judgment section 130 may increase the judgment threshold as the sum T1+T2 of the sizes of the two defective parts 210 is larger. The defect judgment section 130 may use T1+T2 as the judgment threshold, may use 2T1+2T2 as the judgment threshold, or may use a value obtained by multiplying T1+T2 by another coefficient as the judgment threshold.

[0058] 11 is a diagram showing another example of correction of the judgment threshold. The defect judgment section 130 corrects the judgment threshold based on whether or not the defective portion 210 reaches the end of the second image 202. The defect judgment section 130 of this example corrects the judgment threshold based on the size of the defective portion 210. If the defective portion 210 does not reach the end of the second image 202, the defect judgment section 130 may take the radius of the circumscribing circle of the defective portion 210 as the size of the defective portion 210, as shown in FIG.

[0059] When the defective portion 210 reaches the end of the second image 202, the defect determination section 130 may determine the size of the defective portion 210 based on the size of the second image 202. For example, the defect determination section 130 may set the radii T3 and T4 of the circumscribing circle of the second image 202 as the size of the defective portion 210. By correcting the determination threshold with the size of the defective portion 210 determined by such processing, the determination threshold can be corrected based on whether the defective portion 210 reaches the end of the second image 202. Note that the size of the second image 202 may be calculated by using the square root of the sum of the squares of the width and height of the second image.

[0060] 12 is a diagram showing another example of correction of the judgment threshold. In this example, the defect judgment section 130 corrects the judgment threshold based on the size of the defective portion 210 in the first image 201. The size of each defective portion 210 may be the radius of a circumscribing circle, may be the square root of the sum of the squares of the width and height, or may be the number of pixels in the first image 201. In addition, the defect judgment section 130 may compare the distance D1 of the defective portion 210 in the first image 201 with the judgment threshold. The distance D1 may be the distance between the center coordinates of the defective portions 210.

[0061] Also in this example, it is preferable that the imaging device 20 captures the second image 202 including each of the defective portions 210. By capturing the second image 202, it is possible to accurately classify the type of each of the defective portions 210. It is preferable that the defect determination section 130 determines whether or not defective portions 210 of the same type are one defect.

[0062] 13 is a diagram showing another example of correction of the judgment threshold. In this example, the defect judgment section 130 corrects the judgment threshold based on the shape of the defective portion 210. It is preferable that the shape of the defective portion 210 is determined based on the second image 202 having a higher resolution.

[0063] The defect determination section 130 may correct the determination threshold value based on a direction identified from the shape of the defective portion 210. The direction identified from the shape is, for example, the longitudinal direction or lateral direction of the defective portion 210, or a direction having a predetermined angle with respect to these directions. In the example of Fig. 13, the longitudinal direction of the defective portion 210-1 is indicated by an arrow. In this example, the longitudinal direction is used as the direction identified from the shape, but other directions can be used instead of the longitudinal direction.

[0064] The defect determination section 130 may correct the determination threshold value based on the direction connecting the two defective portions 210 and the longitudinal direction of the defective portions 210. The direction connecting the two defective portions 210 may be the direction of a straight line connecting the centers of the defective portions 210, or may be the direction of a straight line connecting the central coordinates of the second image 202 including the defective portion 210. In Fig. 13, the direction parallel to the inter-image direction connecting the central coordinates of the second image 202 is indicated by an arrow.

[0065] The defect determination section 130 may increase the determination threshold value as the inter-image direction is closer to the longitudinal direction of the defective portion 210. Two directions being close to each other means that the angle between the two directions is small. This makes it possible to accurately extract two defective portions 210 that are likely to be one defect.

[0066] In the example of FIG. 13, the difference between the inter-image direction and the longitudinal direction is small between defective portion 210-1 and defective portion 210-2. Therefore, defect determination section 130 increases the determination threshold value to make it easier to determine that defective portion 210-1 and defective portion 210-2 are one defect. In contrast, the difference between the inter-image direction and the longitudinal direction is large between defective portion 210-1 and defective portion 210-3. Therefore, defect determination section 130 decreases the determination threshold value to make it easier to determine that defective portion 210-1 and defective portion 210-3 are different defects.

[0067] The defect determination section 130 may use the longitudinal direction of one of the two defective portions 210. When the angle formed by the longitudinal directions of the two defective portions 210 is smaller than a predetermined reference angle, the defect determination section 130 may correct the determination threshold value according to the longitudinal direction of the defective portion 210.

[0068] 14 is a diagram showing another example of correction of the judgment threshold. In this example, the defect judgment section 130 corrects the judgment threshold based on whether or not a defective portion 210-3 of another type exists between two defective portions 210-1 and 210-2 classified as the same type. When a defective portion 210-3 of another type exists between the defective portions 210-1 and 210-2, the defect judgment section 130 may increase the judgment threshold for the defective portions 210-1 and 210-2 compared to when the defective portion 210-3 does not exist.

[0069] For example, when a foreign matter is attached on a linear defect, the linear defect may be divided into a plurality of defect portions 210 by the foreign matter. It is preferable that the divided defect portions 210 can be counted as one defect. According to this example, it becomes easier to count the divided defect portions 210 as one defect. When a different type of defect portion 210-3 and a same type of defect portion 210-2 are arranged in order in a region obtained by extending the defect portion 210-1 in the longitudinal direction, the defect determination section 130 may increase the determination threshold as described in FIG. 14.

[0070] 15 illustrates an example of a computer 2200 in which aspects of the present invention may be embodied in whole or in part. Programs installed on the computer 2200 may cause the computer 2200 to function as or perform operations associated with an apparatus or one or more sections of the apparatus according to an embodiment of the present invention and / or to perform a method or steps of a method according to an embodiment of the present invention. Such programs may be executed by the CPU 2212 to cause the computer 2200 to perform specific operations associated with some or all of the blocks of the flowcharts and block diagrams described herein.

[0071] The computer 2200 according to this embodiment includes a CPU 2212, a RAM 2214, a graphic controller 2216, and a display device 2218, which are connected to each other by a host controller 2210. The computer 2200 also includes input / output units such as a communication interface 2222, a hard disk drive 2224, a DVD-ROM drive 2226, and an IC card drive, which are connected to the host controller 2210 via an input / output controller 2220. The computer also includes legacy input / output units such as a ROM 2230 and a keyboard 2242, which are connected to the input / output controller 2220 via an input / output chip 2240.

[0072] The CPU 2212 operates according to a program stored in the ROM 2230 and the RAM 2214, thereby controlling each unit. The graphic controller 2216 retrieves image data generated by the CPU 2212 into a frame buffer or the like provided in the RAM 2214 or into the graphic controller 2216 itself, and causes the image data to be displayed on the display device 2218.

[0073] The communication interface 2222 communicates with other electronic devices via a network. The hard disk drive 2224 stores programs and data used by the CPU 2212 in the computer 2200. The DVD-ROM drive 2226 reads programs or data from the DVD-ROM 2201 and provides the programs or data to the hard disk drive 2224 via the RAM 2214. The IC card drive reads programs and data from an IC card and / or writes programs and data to an IC card.

[0074] The ROM 2230 stores therein a boot program or the like executed by the computer 2200 upon activation, and / or a program that depends on the hardware of the computer 2200. The input / output chip 2240 may also connect various input / output units to the input / output controller 2220 via a parallel port, a serial port, a keyboard port, a mouse port, and the like.

[0075] The programs are provided by a computer-readable medium such as a DVD-ROM 2201 or an IC card. The programs are read from the computer-readable medium, installed in the hard disk drive 2224, the RAM 2214, or the ROM 2230, which are also examples of computer-readable media, and executed by the CPU 2212. Information processing described in these programs is read by the computer 2200, and brings about cooperation between the programs and the various types of hardware resources described above. An apparatus or method may be constructed by realizing information manipulation or processing according to the use of the computer 2200.

[0076] For example, when communication is performed between the computer 2200 and an external device, the CPU 2212 may execute a communication program loaded in the RAM 2214 and instruct the communication interface 2222 to perform communication processing based on the processing described in the communication program. Under the control of the CPU 2212, the communication interface 2222 reads transmission data stored in a transmission buffer processing area provided in the RAM 2214, the hard disk drive 2224, the DVD-ROM 2201, or a recording medium such as an IC card, and transmits the read transmission data to the network, or writes reception data received from the network to a reception buffer processing area or the like provided on the recording medium.

[0077] Furthermore, the CPU 2212 may cause all or a necessary portion of a file or database stored in an external recording medium such as the hard disk drive 2224, the DVD-ROM drive 2226 (DVD-ROM 2201), an IC card, etc. to be read into the RAM 2214, and perform various types of processing on the data on the RAM 2214. The CPU 2212 then writes back the processed data to the external recording medium.

[0078] Various types of information, such as various types of programs, data, tables, and databases, may be stored in the recording medium and undergo information processing. The CPU 2212 may perform various types of processing on the data read from the RAM 2214, including various types of operations, information processing, conditional judgment, conditional branching, unconditional branching, information search / replacement, etc., as described throughout this disclosure and specified by the instruction sequence of the program, and write back the results to the RAM 2214. The CPU 2212 may also search for information in a file, database, etc. in the recording medium. For example, when a plurality of entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored in the recording medium, the CPU 2212 may search for an entry that matches a condition, in which an attribute value of the first attribute is specified, from among the plurality of entries, read the attribute value of the second attribute stored in the entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

[0079] The above-described programs or software modules may be stored in a computer-readable medium on the computer 2200 or in the vicinity of the computer 2200. Also, a recording medium such as a hard disk or a RAM provided in a server system connected to a dedicated communication network or the Internet can be used as a computer-readable medium, thereby providing the programs to the computer 2200 via the network.

[0080] Although the present invention has been described above using the embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It is clear to those skilled in the art that various modifications and improvements can be made to the above embodiments. It is clear from the description of the claims that such modifications and improvements can also be included in the technical scope of the present invention.

[0081] It should be noted that the order of execution of each process, such as operations, procedures, steps, and stages, in the devices, systems, programs, and methods shown in the claims, specifications, and drawings is not specifically stated as "before," "prior to," etc., and may be realized in any order unless the output of a previous process is used in a later process. Even if the operational flow in the claims, specifications, and drawings is explained using "first," "next," etc. for convenience, it does not mean that it is essential to perform the process in this order. [Explanation of symbols]

[0082] REFERENCE SIGNS LIST 10 semiconductor device, 12 semiconductor chip, 20 imaging device, 100 analysis device, 110 image acquisition section, 120 defect extraction section, 130 defect determination section, 140 result output section, 201 first image, 202 second image, 210 defective portion, 211 defect, 300 inspection system

Claims

1. An image acquisition unit for acquiring an image of a semiconductor device; a defect extraction unit that extracts a plurality of defect portions from the image; a defect determination unit that compares a distance between two defect parts included in the plurality of defect parts with a predetermined determination threshold value to determine whether the two defect parts are a single defect or not; Equipped with The defect determination unit corrects at least one of the distance between the two defective portions and the determination threshold value based on a direction connecting the two defective portions and a direction specified from a shape of the defective portion. Analysis device.

2. An image acquisition unit that acquires a first image and a second image that captures a range narrower than the range of the first image, the second image being an image of a semiconductor device; a defect extraction unit that extracts a plurality of defect portions from the image; a defect determination unit that compares a distance between two defect parts included in the plurality of defect parts with a predetermined determination threshold value to determine whether the two defect parts are part of the same defect; Equipped with the defect determination unit corrects at least one of the distance between the two defective portions and the determination threshold value based on characteristics of the two defective portions; the second image includes the defective portion in the first image and has a higher resolution than the first image; The defect determination unit determines characteristics of each of the defective portions based on the second image. Analysis device.

3. The image acquisition unit acquires two of the second images, one of the second images includes one of the two defect portions; the other second image includes the other of the two defect portions; The defect determination unit calculates the distance between the two defective portions based on the distance between the two second images. The analysis device according to claim 2 .

4. The defect determination unit classifies each of the defect portions into a plurality of predetermined types based on the second image, and extracts a distance between the two defect portions classified into the same type. The analysis device according to claim 2 or 3.

5. The defect extraction unit extracts coordinates of the defective portion from the first image, The image acquisition unit acquires the second image based on coordinates of the defect portion, The defect determination unit corrects at least one of the distance between the two defective portions and the determination threshold value based on whether the defective portion reaches an end of the second image. The analysis device according to claim 2 .

6. The defect determination unit corrects at least one of the distance and the determination threshold value based on whether or not a defect portion of another type exists between the two defect portions classified as the same type. The analysis device according to claim 4.

7. The defect determination unit corrects at least one of the distance and the determination threshold value based on a size of the defect portion in the image. The analysis device according to any one of claims 1 to 6.

8. An image acquisition step of acquiring an image of the semiconductor device; A defect extraction step of extracting a plurality of defect portions from the image; a defect determination step of comparing a distance between two defect parts included in the plurality of defect parts with a predetermined determination threshold value to determine whether the two defect parts are a single defect; a correction step of correcting at least one of the distance between the two defective portions and the determination threshold value based on a direction connecting the two defective portions and a direction specified from the shape of the defective portions; The analysis method includes:

9. An image acquisition step of acquiring images of a semiconductor device, the image acquisition step including acquiring a first image and a second image capturing a range narrower than the range of the first image; A defect extraction step of extracting a plurality of defect portions from the image; a defect determination step of comparing a distance between two defect portions included in the plurality of defect portions with a predetermined determination threshold value to determine whether the two defect portions are part of the same defect; a correction step of correcting at least one of the distance between the two defect portions and the judgment threshold value based on characteristics of the two defect portions; determining a characteristic of each of the defects based on the second image; Equipped with The second image includes the defective portion in the first image and has a higher resolution than the first image. Analysis method.

10. A program for causing a computer to execute the analysis method according to claim 8 or 9.

Images