Defect inspection device, defect inspection method, and defect inspection program

Abstract

To improve inspection accuracy in defect areas.SOLUTION: A defect inspection device 10 includes an acquisition section 20A, an evaluation target image generation section 20C, a defect estimation image generation section 20D, and an iteration control section 20I. The acquisition section 20A acquires an inspection image obtained by imaging an inspection target and a reference image of the inspection target in design. The evaluation target image generation section 20C generates an evaluation target image according to the inspection image and the reference image. The defect estimation image generation section 20D generates a defect estimation image obtained by defining a defect estimation value for each pixel on the basis of a feature of a defect candidate area corresponding to an image area of consecutive pixels each of which has a pixel value equal to or greater than a first threshold value and which are included in the evaluation target image. The iteration control section 20I controls the defect estimation image generation section 20D to iterate defect estimation image generation processing using the defect estimation image as the evaluation target image.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] An embodiment of the present invention relates to a defect inspection apparatus, a defect inspection method, and a defect inspection program. [Background technology]

[0002] Systems are known that use inspection images of inspection objects, such as semiconductor mask patterns and printed circuit boards, to determine defective areas in the object. For example, a technology has been disclosed that compares the inspection image with a reference image of the object at the time of design and determines defective areas based on changes in edge shapes contained in the image. Another technology has been disclosed that generates multidimensional feature images by applying multiple filter processes to the inspection image and detects defects from the multidimensional feature images.

[0003] However, with determination techniques based on changes in edge shape, the shapes of patterns and defects to be determined are limited, making it difficult to determine defect areas included in inspection images that include patterns and defects of shapes other than those to be determined. Furthermore, with techniques that use multidimensional feature images, there are cases where false defects that are not actual defects are detected as defects due to the influence of noise included in the inspection image, detection conditions, and the like. In other words, with conventional techniques, it has been difficult to inspect defect areas with high accuracy. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 6358351 Summary of the Invention [Problem to be solved by the invention]

[0005] The present invention has been made in view of the above, and has as its object to provide a defect inspection device, a defect inspection method, and a defect inspection program that can improve the inspection accuracy of defective areas. [Means for solving the problem]

[0006] A defect inspection apparatus according to an embodiment includes an acquisition unit, an evaluation target image generation unit, a defect estimation image generation unit, and a repeat control unit. The acquisition unit acquires an inspection image obtained by photographing an inspection target and a reference image of the inspection target at the time of design. The evaluation target image generation unit generates an evaluation target image corresponding to the inspection image and the reference image. The defect estimation image generation unit generates a defect estimation image in which a defect estimation value is defined for each pixel based on a feature amount of a defect candidate area corresponding to an image area in which pixels having a pixel value equal to or greater than a first threshold are consecutive, which is included in the evaluation target image. The repeat control unit controls the defect estimation image generation unit to repeat a defect estimation image generation process using the defect estimation image as the evaluation target image. The defect estimation image generation unit includes an identification unit that identifies the defect candidate area corresponding to the image area included in the image to be evaluated where pixels having pixel values ​​equal to or greater than the first threshold are consecutive; a feature calculation unit that calculates a feature amount of the identified defect candidate area in the image to be evaluated; a corrected feature calculation unit that calculates a corrected feature amount by correcting the feature amount using a second threshold; and a defect determination unit that generates the defect estimation image using the image to be evaluated and the corrected feature amount, wherein the feature amount of the defect candidate area is represented by a group of feature values ​​of pixels that constitute the defect candidate area in the image to be evaluated, and the corrected feature calculation unit calculates the feature amount after correcting, to 0, the feature values ​​of the pixels that constitute the defect candidate area in the image to be evaluated that are less than the second threshold. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 is a block diagram of a defect inspection device. [Figure 2A] Schematic diagram of a reference image. [Figure 2B] Schematic diagram of an inspection image. [Figure 3A] FIG. [Figure 3B] Schematic diagram of a reference image. [Figure 4] FIG. [Figure 5A] FIG. 10 is an explanatory diagram of identifying a defect candidate region. [Figure 5B] FIG. 10 is an explanatory diagram of identifying a defect candidate region. [Figure 5C] FIG. 10 is an explanatory diagram of identifying a defect candidate region. [Figure 6A] Schematic diagram of a defect estimation image. [Figure 6B] Schematic diagram of a defect estimation image. [Figure 7] 1 is a flowchart of the flow of information processing. [Figure 8] Hardware configuration diagram. DETAILED DESCRIPTION OF THE INVENTION

[0008] The defect inspection device, defect inspection method, and defect inspection program of the present embodiment will be described in detail below with reference to the accompanying drawings.

[0009] FIG. 1 is a block diagram showing an example of the configuration of a defect inspection apparatus 10 according to this embodiment.

[0010] The defect inspection apparatus 10 is an information processing apparatus for inspecting a defect area contained in an inspection image by using an inspection image obtained by photographing an inspection object. The inspection object and the inspection image will be described in detail later.

[0011] The defect inspection apparatus 10 includes an imaging unit 12, a storage unit 14, a communication unit 16, a UI (user interface) unit 18, and a control unit 20. The imaging unit 12, the storage unit 14, the communication unit 16, the UI unit 18, and the control unit 20 are communicatively connected via a bus 19 or the like.

[0012] The photographing unit 12 acquires photographed image data by photographing. In the following description, the photographed image data will be referred to as a photographed image. The storage unit 14 stores various types of information.

[0013] The communication unit 16 is a communication interface for communicating with an external information processing device of the defect inspection apparatus 10. For example, the communication unit 16 communicates with an external information processing device or electronic device via a wired network such as Ethernet (registered trademark), a wireless network such as Wi-Fi (Wireless Fidelity) or Bluetooth (registered trademark), or the like.

[0014] The UI section 18 includes an output section 18A and an input section 18B.

[0015] The output unit 18A outputs various types of information. The output unit 18A is, for example, a display unit, a speaker, a projection device, etc. The input unit 18B accepts operation instructions from a user. The input unit 18B is, for example, a pointing device such as a mouse or a touchpad, a keyboard, etc. The UI unit 18 may be a touch panel in which the output unit 18A and the input unit 18B are integrally configured.

[0016] The control unit 20 executes information processing in the defect inspection apparatus 10. The control unit 20 includes an acquisition unit 20A, a pattern area identification unit 20B, an evaluation target image generation unit 20C, a defect estimation image generation unit 20D, a repetition control unit 20I, and an output control unit 20J. The defect estimation image generation unit 20D includes an identification unit 20E, a feature calculation unit 20F, a corrected feature calculation unit 20G, and a defect determination unit 20H.

[0017] The acquisition unit 20A, pattern area identification unit 20B, evaluation target image generation unit 20C, defect estimation image generation unit 20D, identification unit 20E, feature amount calculation unit 20F, corrected feature amount calculation unit 20G, defect determination unit 20H, repetitive control unit 20I, and output control unit 20J are realized, for example, by one or more processors. For example, each of the above units may be realized by having a processor such as a CPU (Central Processing Unit) execute a program, i.e., by software. Each of the above units may be realized by a processor such as a dedicated IC, i.e., by hardware. Each of the above units may be realized by a combination of software and hardware. When multiple processors are used, each processor may realize one of the units, or two or more of the units.

[0018] At least one of the above-described units included in the control unit 20 may be mounted on an external information processing device communicatively connected to the defect inspection apparatus 10 via a network or the like. At least one of the various pieces of information stored in the storage unit 14 may be stored in an external storage device communicatively connected to the defect inspection apparatus 10 via a network or the like. At least one of the imaging unit 12, the storage unit 14, and the UI unit 18 may be mounted on an external information processing device communicatively connected to the defect inspection apparatus 10. In this case, a system including the externally mounted components and the defect inspection apparatus 10 may be configured as a defect inspection apparatus system.

[0019] The acquisition unit 20A acquires an inspection image obtained by photographing the inspection object and a reference image of the inspection object at the time of design.

[0020] An inspection object is an object that is the subject of defect inspection. For example, an inspection object is an object manufactured by a manufacturing system or the like according to specifications expressed by design data. Specific examples of inspection objects include, but are not limited to, patterned printed circuit boards, semiconductor mask patterns, metal plates, steel strips, etc.

[0021] In this embodiment, the inspection object is a semiconductor mask pattern, and is manufactured by a manufacturing system or the like according to specifications expressed by design data.

[0022] 2A is a schematic diagram of an example of a reference image 30A. The reference image 30A is an example of the reference image 30.

[0023] The reference image 30 is an image of the inspection target at the time of design. In other words, the reference image 30 is an image of an ideal inspection target that does not contain defects or noise. As described above, in this embodiment, the inspection target is manufactured by a manufacturing system or the like according to specifications represented by design data. For example, an external information processing device generates virtual inspection target data of a two-dimensional or three-dimensional virtual inspection target virtually manufactured according to the design data, and generates an image of the virtual inspection target represented by the virtual inspection target data virtually photographed from a predetermined direction as the reference image 30. Note that the control unit 20 of the defect inspection device 10 may generate the reference image 30 in advance using the design data. In this embodiment, an example will be described in which the previously generated reference image 30 is stored in the storage unit 14.

[0024] 2B is a schematic diagram of an example of an inspection image 40A. The inspection image 40A is an example of the inspection image 40.

[0025] The inspection image 40 is a photographed image of the inspection object. The photographing direction of the inspection object and the above-mentioned predetermined direction, which is the photographing direction of the virtual inspection object used to generate the reference image 30, are the same. For example, the acquisition unit 20A acquires the inspection image 40 from the photographing unit 12. Alternatively, the acquisition unit 20A may acquire the inspection image 40 by reading the inspection image 40 from the memory unit 14. Alternatively, the acquisition unit 20A may acquire the inspection image 40 from an external information processing device via the communication unit 16.

[0026] If the inspection object is manufactured to faithfully reproduce the design data, and if the inspection object does not contain defects such as dust or other particles, damage, misalignment, or deformation, and if the captured image does not contain noise, there is a high possibility that the inspection image 40 will match the reference image 30. However, in reality, the inspection image 40 may contain defects or noise.

[0027] Therefore, the defect inspection apparatus 10 of this embodiment inspects defect areas representing defects contained in the inspection image 40 with high accuracy.

[0028] Returning to Figure 1, we continue the explanation.

[0029] The pattern region specifying unit 20B specifies a pattern region included in the reference image 30.

[0030] Fig. 3A is an explanatory diagram of an example of specifying a pattern region, and is a schematic diagram of an example of a reference image 30A.

[0031] For example, the pattern region identifying unit 20B identifies, as a pattern region, a region in the reference image 30A where pixels having pixel values ​​equal to or greater than a third threshold are consecutive. The third threshold may be determined in advance. For example, the third threshold may be determined in advance according to the specifications represented by the design data of the inspection object, the inspection specifications of the inspection object, etc. The third threshold may also be changeable as appropriate by a user's operation instruction via the UI unit 18, etc.

[0032] Specifically, the pattern area identification unit 20B reads the pixel values ​​of each pixel included in the reference image 30A. Then, the pattern area identification unit 20B identifies, among the pixels included in the reference image 30A, an area where consecutive pixels with pixel values ​​equal to or greater than a third threshold are located as a pattern area P. An area where consecutive pixels are located means an area where pixels are arranged adjacent to each other on the image. Note that the pattern area P may be an area consisting of one or a group of multiple pixels, and is not limited to an area consisting of multiple pixels.

[0033] 3A shows an example of a state in which the pattern area specifying unit 20B has specified pattern area P1 and pattern area P2 as pattern areas P. The pattern area specifying unit 20B performs labeling by assigning a label L to each of the specified pattern areas P. FIG. 3A shows an example in which a label L1 has been assigned to pattern area P1 and a label L2 has been assigned to pattern area P2.

[0034] Labeling identifies the position of each pattern region P in the reference image 30A. The position in the reference image 30A is represented by, for example, the pixel position of each pixel included in the pattern region P to which the label L is assigned, the position coordinates of each pixel, the centroid coordinates of the pattern region P, etc.

[0035] For example, the pattern area specifying unit 20B associates the label L with the pixel position or position coordinates of each pixel included in the pattern area P to which the label L is assigned, for each specified pattern area P, and stores them in the storage unit 14. The label L of the pattern area P may be treated as information including the ID (identification information) of the label L and the pixel position or position coordinates of each pixel included in the pattern area P. Furthermore, as described above, the label L may be information further including the centroid coordinates of the pattern area P.

[0036] 3A shows an example in which the shape of the pattern region P included in the reference image 30 is rectangular. However, the shape of the pattern region P included in the reference image 30 is not limited to a rectangle.

[0037] 3B is a schematic diagram of an example of reference image 30B. Reference image 30B is an example of reference image 30. As shown in FIG. 3B, reference image 30B may include a pattern area P having a shape other than a rectangle, such as a circle. Even when a pattern area P having a shape other than a rectangle is included, pattern area identification unit 20B may identify pattern area P by performing similar processing.

[0038] 3B shows an example of a state in which the pattern area specifying unit 20B has specified pattern areas P1, P2, and P3 as pattern areas P. The pattern area specifying unit 20B performs labeling by assigning a label L to each of the specified pattern areas P. FIG. 3B shows an example in which a label L1 has been assigned to pattern area P1, a label L2 to pattern area P2, and a label L3 to pattern area P3.

[0039] Returning to Figure 1, we continue the explanation.

[0040] The evaluation object image generating unit 20C generates an evaluation object image corresponding to the reference image 30 and the inspection image 40.

[0041] 4 is an explanatory diagram of an example of generation of an evaluation target image 50 A. The evaluation target image 50 A is an example of the evaluation target image 50.

[0042] The evaluation target image 50 is an image used as a target for defect evaluation of the inspection image 40. The evaluation target image generation unit 20C generates one evaluation target image 50 from one reference image 30 and one inspection image 40. In detail, the evaluation target image generation unit 20C generates, as the evaluation target image 50, a difference image between the reference image 30 and the inspection image 40, or a composite image of the reference image 30 and the inspection image 40.

[0043] A difference image is an image in which the difference between the pixel values ​​of each pixel in the same pixel position between the pixel values ​​of each pixel constituting reference image 30 and the pixel values ​​of each pixel constituting inspection image 40 is defined for each pixel. Alternatively, the difference image may be an image in which the value of each pixel is defined after applying a predetermined weighting value, saturation processing, etc. to the pixel value difference.

[0044] A composite image is an image in which a composite value is defined for each pixel, which is the composite value of the pixel values ​​of each pixel in the reference image 30 and the pixel values ​​of each pixel in the inspection image 40 at the same pixel position. The composite value may be an addition value, a multiplication value, or a value obtained by applying weighting or saturation processing to the addition value or multiplication value.

[0045] In this embodiment, an example will be described in which the evaluation target image generating unit 20C generates a differential image between the reference image 30 and the inspection image 40 as the evaluation target image 50. In Fig. 4, a differential image between the reference image 30A and the inspection image 40A is shown as the evaluation target image 50A.

[0046] Specifically, the evaluation target image generating unit 20C calculates the difference between the pixel values ​​of each pixel constituting the reference image 30A and the pixel values ​​of each pixel constituting the inspection image 40A at the same pixel position. Then, the evaluation target image generating unit 20C sets a setting value H representing "image dynamic range / 2" as the pixel value of a pixel whose pixel value difference is 0. The image dynamic range is the dynamic range of the reference image 30A or the inspection image 40A. Note that the following description will be given assuming that the dynamic ranges of the reference image 30A and the inspection image 40A are the same.

[0047] The evaluation target image generating unit 20C then performs saturation processing on each of the pixel value differences calculated for each pixel, so that the values ​​fall within the range of 0 to the maximum pixel value -1, with the above-mentioned set value H used when the pixel value difference is 0 as the reference.The evaluation target image generating unit 20C then sets the value obtained after performing saturation processing on the pixel value difference as the pixel value of the pixel at that pixel position.By performing these processes, the evaluation target image generating unit 20C generates the evaluation target image 50.

[0048] Returning to Figure 1, we continue the explanation.

[0049] The defect estimation image generation unit 20D generates a defect estimation image in which a defect estimation value is specified for each pixel based on the feature amount of a defect candidate area corresponding to an image area in which pixels with pixel values ​​equal to or greater than a first threshold value are consecutive, which is included in the evaluation target image 50.

[0050] The defect estimation image generating unit 20D includes an identifying unit 20E, a feature amount calculating unit 20F, a corrected feature amount calculating unit 20G, and a defect determining unit 20H.

[0051] The identifying unit 20E identifies a defect candidate area corresponding to an image area in which pixels having pixel values ​​equal to or greater than a first threshold value are consecutive, which is included in the evaluation target image 50.

[0052] Fig. 5A is an explanatory diagram of an example of the identification unit 20E identifying a defect candidate region D. Fig. 5A shows an evaluation target image 50A as an example.

[0053] The identification unit 20E reads the pixel values ​​of each pixel included in the evaluation target image 50A. Then, the identification unit 20E identifies an image region Q where, among the pixels included in the evaluation target image 50A, pixels having pixel values ​​equal to or greater than a first threshold value are consecutive. Note that the image region Q may be a region consisting of one or a group of pixels, and is not limited to a region consisting of a plurality of pixels.

[0054] The first threshold may be determined in advance. For example, the first threshold may be determined in advance depending on the type of inspection object, the defect inspection accuracy required for the inspection object, the method for generating the evaluation object image 50A, etc. Furthermore, the first threshold may be changeable as appropriate by a user's operation instruction on the UI unit 18, etc.

[0055] FIG. 5A shows an example of a scene in which the specifying unit 20E specifies image regions Q1 to Q5 as image regions Q included in the evaluation target image 50A.

[0056] For example, the identifying unit 20E identifies the identified image area Q as a defect candidate area D. In detail, Fig. 5A shows an example of a scene in which image areas Q1 to Q5 are identified as defect candidate areas D1 to D5, respectively.

[0057] The identifying unit 20E then performs labeling by assigning a label L to each of the identified defect candidate areas D. Fig. 5A shows an example of a scene in which labels LA to LE are assigned to defect candidate areas D1 to D5, respectively.

[0058] The labeling performed by the identifying unit 20E identifies the position of the defect candidate area D in the evaluation target image 50A, the number of pixels that make up the defect candidate area D, the pixel values ​​of the pixels included in the defect candidate area D, the maximum pixel value among the pixel values ​​of the pixels included in the defect candidate area D, etc. The position of the defect candidate area D in the evaluation target image 50A is represented by, for example, the pixel position of each pixel included in the defect candidate area D to which the label L has been assigned, the position coordinates of each pixel, the coordinates of the center of gravity of the defect candidate area D, etc.

[0059] For example, for each identified defect candidate area D, the identifying unit 20E associates a label L with the position of the defect candidate area D in the evaluation target image 50A, the number of pixels that make up the defect candidate area D, the pixel values ​​of the pixels included in the defect candidate area D, and the maximum pixel value among the pixel values ​​of the pixels included in the defect candidate area D, and stores these in the storage unit 14. Note that the label L of a defect candidate area D may be treated as information including the ID (identification information) of the label L, the position of the defect candidate area D in the evaluation target image 50A, the number of pixels that make up the defect candidate area D, the pixel values ​​of the pixels included in the defect candidate area D, and the maximum pixel value among the pixel values ​​of the pixels included in the defect candidate area D.

[0060] The identifying unit 20E preferably identifies, as one defect candidate area D, a plurality of image areas Q that overlap the same pattern area P among the image areas Q in the evaluation target image 50.

[0061] 5B and 5C are explanatory diagrams of an example of identifying a defect candidate area D. An evaluation target image 50B is shown as an example in FIGS. 5A and 5B. The evaluation target image 50B is an example of the evaluation target image 50.

[0062] For example, assume that the specifying unit 20E specifies image regions Q1 to Q4 as image regions Q where pixels having pixel values ​​equal to or greater than the first threshold are consecutive among the pixels included in the evaluation target image 50B.

[0063] The identifying unit 20E virtually arranges the pattern area P on the evaluation target image 50B by arranging the pattern area P identified by the pattern area identifying unit 20B at the same pixel position indicated by the pattern area P in the evaluation target image 50B. Fig. 5B shows an example of a scene in which a pattern area P1 assigned with a label L1 and a pattern area P2 assigned with a label L2 are virtually arranged.

[0064] The identifying unit 20E then identifies, as one defect candidate area D, a plurality of image areas Q that overlap the same pattern area P from among the image areas Q1 to Q4 in the evaluation target image 50B.

[0065] In detail, the identification unit 20E identifies multiple image areas Q that overlap the same pattern area P. In the example shown in FIG. 5B, the identification unit 20E identifies image areas Q1, Q2, and Q3 as image areas Q that overlap pattern area P1 to which label L1 has been assigned.

[0066] Overlapping with a pattern area P means that at least a portion of the image area Q overlaps with the pattern area P. Note that there are cases where one image area Q is arranged so as to overlap with multiple different pattern areas P. In this case, among the multiple overlapping pattern areas P, the pattern area P with the largest overlapping area with the image area Q can be identified as the pattern area P with which the image area Q overlaps.

[0067] Then, the identifying unit 20E identifies, among the multiple image regions Q (image regions Q1 to Q3) that overlap the same pattern region P1 in the evaluation target image 50B, an image region Q whose distance between the coordinates of its center of gravity is equal to or less than a predetermined value as one defect candidate region D. This predetermined value may be determined in advance. Furthermore, this predetermined value may be changeable as appropriate by a user's operation instruction via the UI unit 18, for example.

[0068] Furthermore, the identifying unit 20E may identify, as one defect candidate area D, the maximum feature amount area, which is the image area Q with the largest feature amount among multiple image areas Q (image areas Q1 to Q3) that overlap the same pattern area P1 in the evaluation target image 50B, and another image area Q whose centroid coordinates are a distance from the maximum feature amount area to the centroid coordinates of the maximum feature amount area that is equal to or less than a predetermined value. This predetermined value may be determined in advance. Furthermore, this predetermined value may be changeable as appropriate by a user's operation instruction via the UI unit 18, for example.

[0069] The feature amount of the image region Q may be calculated in the same manner as the feature amount calculation unit 20F described later.

[0070] For example, assume that, of image regions Q1 to Q3 that overlap pattern region P1, image region Q2 has the largest feature amount. In this case, the identification unit 20E calculates the distance between the centroid coordinate of image region Q2 and the centroid coordinates of image region Q1 and image region Q3, which are other image regions Q that overlap pattern region P1. For example, assume that the distance between the centroid coordinate of image region Q2 and the centroid coordinate of image region Q1 is equal to or less than a predetermined value. Also assume that the distance between the centroid coordinate of image region Q2 and the centroid coordinate of image region Q3 exceeds a predetermined value.

[0071] 5C, the identification unit 20E identifies image area Q2 and image area Q1 as one defect candidate area D1. Then, the identification unit 20E assigns, for example, label LA as a label L to the defect candidate area D1 consisting of image area Q2 and image area Q1. Then, the identification unit 20E identifies each of the other image areas Q (image area Q3, image area Q4) as a separate defect candidate area D (defect candidate area D2, defect candidate area D3) and assigns labels L (label LB, label LC).

[0072] In this way, the identifying unit 20E may identify, as one defect candidate area D, a plurality of image areas Q that overlap the same pattern area P among the image areas Q in the evaluation target image 50.

[0073] Returning to Figure 1, we continue the explanation.

[0074] The feature amount calculation unit 20F calculates the feature amount of the identified defect candidate area D in the evaluation target image 50.

[0075] The feature amount of defect candidate area D is represented by a group of feature values ​​of the pixels that make up defect candidate area D in evaluation target image 50. Specifically, the group of feature values ​​represents, for example, the distribution of feature values, the maximum value of feature values, the number of feature values ​​(i.e., the number of pixels that make up defect candidate area D or the area represented by the group of pixels), the maximum value of the difference in pixel values ​​between reference image 30 and inspection image 40 used to derive the feature values, etc.

[0076] The feature amount calculation unit 20F calculates the feature amount of the defect candidate area D by performing filtering on the defect candidate area D included in the evaluation target image 50 using an image processing filter.

[0077] The image processing filter may be a filter that can classify noise and patterns contained in the evaluation target image 50. The pattern contained in the evaluation target image 50 is a region that corresponds to the pattern region P in the evaluation target image 50.

[0078] The feature amount calculation unit 20F uses, as an image processing filter, an image processing filter such as a Gaussian filter or a DoG (Difference of Gaussian). The feature amount calculation unit 20F may also use a combination of multiple types of filters, such as a Gaussian filter and a DoG, as an image processing filter. The feature amount calculation unit 20F may also use a filter in frequency space, such as a wavelet transform, as an image processing filter.

[0079] The feature amount calculation unit 20F performs filtering using an image processing filter on the defect candidate area D included in the evaluation target image 50, thereby obtaining feature values ​​for each pixel included in the defect candidate area D. Then, the feature amount calculation unit 20F calculates, for each defect candidate area D included in the evaluation target image 50, a feature amount represented by a group of feature values ​​for each pixel included in the defect candidate area D.

[0080] The corrected feature amount calculation unit 20G calculates a corrected feature amount by correcting the feature amount using the second threshold value.

[0081] As described above, the feature amount is represented by a group of feature values ​​of pixels that make up the defect candidate area D in the evaluation target image 50.

[0082] For each defect candidate area D in the evaluation target image 50, the corrected feature amount calculation unit 20G corrects the feature values ​​of the pixels constituting the defect candidate area D that are not equal to the second threshold value to 0. Then, the corrected feature amount calculation unit 20G calculates the feature amount represented by the group of the corrected feature values ​​as the corrected feature amount.

[0083] For example, the corrected feature value calculation unit 20G corrects the feature values ​​of each of the pixels that make up the defect candidate area D using the above-mentioned process using the second threshold, and calculates the sum of the corrected feature values ​​of each of the pixels that make up the defect candidate area D as the corrected feature value of the defect candidate area D.

[0084] At this time, the corrected feature value calculation unit 20G may smooth the corrected feature values ​​of each pixel included in the defect candidate area D, for example, by processing such as a Gaussian filter, in order to reduce noise, and then calculate the sum of the smoothed feature values ​​as the corrected feature value of the defect candidate area D.

[0085] The corrected feature amount calculation unit 20G may update the second threshold value each time the defect estimated image generation process executed by the evaluation object image generation unit 20C is repeated.

[0086] The defect estimation image generation process is a series of processes performed by identification unit 20E, feature amount calculation unit 20F, corrected feature amount calculation unit 20G, and defect determination unit 20H, all of which are included in defect estimation image generation unit 20D. In detail, the defect estimation image generation process is a series of processes that sequentially execute a process of identifying a defect candidate area D by identification unit 20E, a process of calculating a feature amount by feature amount calculation unit 20F, a process of calculating a corrected feature amount by corrected feature amount calculation unit 20G, and a process of generating a defect estimation image by defect determination unit 20H, which will be described later.

[0087] Although details will be described later, in this embodiment, the defect estimation image generating unit 20D repeats the defect estimation image generating process, which is the series of processes described above, under the control of a repetition control unit 20I, which will be described later.

[0088] The corrected feature amount calculation unit 20G sets, for example, a predetermined initial value as the second threshold value during the first defect estimation image generation process for one evaluation target image 50. The initial value of the second threshold value is, for example, "0", but is not limited to this value.

[0089] Then, the corrected feature quantity calculation unit 20G updates the predetermined initial value of the second threshold value each time the defect estimation image generation process is repeated. Specifically, the corrected feature quantity calculation unit 20G updates the second threshold value each time the defect estimation image generation process is repeated so that the second threshold value is proportional to at least one of the following: the variation in the corrected feature quantities of one or more defect candidate areas D included in the evaluation target image 50 used as the current processing target; the maximum value of the corrected feature quantities of one or more defect candidate areas D; and the number of times the defect estimation image generation process is repeated.

[0090] The second threshold value updated by the corrected feature amount calculation unit 20G is used as the second threshold value in the next defect estimation image generation process.

[0091] Next, the defect determination unit 20H will be described. The defect determination unit 20H uses the evaluation target image 50 and the corrected feature amounts of the defect candidate area D to generate a defect estimation image.

[0092] 6A is a schematic diagram showing an example of a defect estimation image 60. The defect estimation image 60 is an image in which a defect estimation value is defined as a pixel value for each pixel. In other words, the defect estimation image 60 is an image in which the pixel values ​​of the evaluation target image 50 are replaced with defect estimation values.

[0093] For example, the defect determination unit 20H generates a defect estimation image 60 that specifies, for each pixel included in a defect candidate area D in the evaluation target image 50, a defect estimation value corresponding to the multiplication value of the pixel value of the pixel in the defect candidate area D in the evaluation target image 50 and the corrected feature amount of the defect candidate area D to which the pixel belongs.

[0094] Specifically, the defect determination unit 20H calculates the defect estimation value of the pixel at the position of coordinates (x, y) that constitutes the defect estimation image 60 using the following equation (1).

[0095] E(x,y)=α×P(x,y)×W(labelF(x,y))...Equation (1)

[0096] In equation (1), E(x, y) represents the defect estimation value of the pixel at the coordinate (x, y). α represents an adjustment coefficient. P(x, y) represents the pixel value at the coordinate (x, y) of the evaluation target image 50. labelF(x, y) represents the corrected feature amount of the defect candidate area D to which the pixel at the coordinate (x, y) belongs. W is a function that calculates a weighting coefficient from the corrected feature amount of the defect candidate area D.

[0097] Of the pixels constituting the evaluation target image 50, the defect estimation value of a pixel at a pixel position that does not belong to any of the defect candidate areas D is defined as a value calculated with labelF(x, y)=0.

[0098] The values ​​of α and W may be adjusted so as to satisfy the relationship E(x, y)≦P(x, y).

[0099] The defect determining section 20H may further determine the defect area based on the generated defect estimated image 60.

[0100] Specifically, the defect determination unit 20H determines, as a defective area, pixels whose defect estimation value E(x, y) of each pixel included in the generated defect estimation image 60 is equal to or greater than a fourth threshold. The fourth threshold may be determined in advance. The fourth threshold may be changeable as appropriate by a user's operation instruction via the UI unit 18, for example.

[0101] For example, assume that the defect estimation values ​​of the pixels included in each of defect candidate areas D1 to D5 in defect estimation image 60A shown in Fig. 6A are equal to or greater than the fourth threshold value. In this case, defect determination unit 20H determines each of defect candidate areas D1 to D5 as a defect area.

[0102] Furthermore, the defect determination unit 20H may determine, as a defective area, pixels whose defect estimation value E(x, y) is equal to or greater than a fourth threshold value and the surrounding pixels of the pixels included in the generated defect estimation image 60. The surrounding pixels of the pixel refer to pixels adjacent to the pixel whose defect estimation value E(x, y) is equal to or greater than the fourth threshold value, as well as N pixels in a direction away from the pixel whose defect estimation value E(x, y) is equal to or greater than the fourth threshold value and the pixels adjacent to the pixel. N is an integer equal to or greater than 1 and may be determined in advance. Furthermore, N may be changeable as needed by a user operating the UI unit 18, for example.

[0103] Furthermore, when the defect estimation value E(x, y) of each pixel included in the generated defect estimation image 60 is equal to or greater than a fifth threshold and less than a fourth threshold, and when M or more pixels having defect estimation values ​​E(x, y) within the range are arranged consecutively, the defect determination unit 20H may determine an area consisting of these pixels as a defective area. The fifth threshold may be a value less than the fourth threshold. The values ​​of the fifth threshold and M may be determined in advance. Furthermore, the fifth threshold and M may be changeable as appropriate by a user's operation instruction via the UI unit 18, for example.

[0104] Returning to Figure 1, we continue the explanation.

[0105] The repetitive control unit 20I controls the defect estimation image generating unit 20D to repeat the defect estimation image generating process using the defect estimation image 60 generated by the defect determination unit 20H as the evaluation target image 50. In other words, the repetitive control unit 20I controls the defect estimation image generating unit 20D to repeat the defect estimation image generating process for one inspection image 40 using the newly generated defect estimation image 60 as the evaluation target image 50.

[0106] In detail, the repeat control unit 20I controls the defect estimation image generation unit 20D to use the defect estimation image 60 generated by the defect judgment unit 20H as the evaluation target image 50 to be used in the next defect estimation image generation process, and to repeat the defect estimation image generation process, which is the series of processes described above, until it is determined that a predetermined termination condition is met.

[0107] The termination condition may be determined in advance, and specifically, the termination condition indicates that at least one of the following conditions is satisfied: the defect estimation image generation process is repeatedly executed a predetermined number of times or more; the number of defect candidate areas D included in the defect estimation image 60 is a predetermined number or less; and the number of times that the defect estimation image 60 generated in the previous execution of the defect estimation image generation process and the defect estimation image 60 generated in the current execution of the defect estimation image generation process match is a predetermined number or more.

[0108] These predetermined times and predetermined numbers may be set in advance. Furthermore, these predetermined times and predetermined numbers may be changeable as appropriate by a user's operation instruction via the UI unit 18. Furthermore, the repetition control unit 20I may adjust which one or more of the above conditions to use as the termination condition depending on the conditions specific to the pattern represented by the pattern region P included in the reference image 30 and the performance of the defect inspection apparatus 10.

[0109] The fewer the number of times the defect estimation image generation process is repeatedly executed, the more likely it is that noise or false defects will be included in the defect estimation image 60. Furthermore, the greater the number of defect candidate areas D included in the defect estimation image 60, the more likely it is that noise or false defects will be included in the defect estimation image 60. Furthermore, the more times the defect estimation image 60 generated in the previous execution of the defect estimation image generation process matches with the defect estimation image 60 generated in the current execution of the defect estimation image generation process, the more likely it is that a more accurate defect estimation image 60 has already been generated.

[0110] Therefore, by controlling the repeat control unit 20I to repeat the defect estimation image generation process until it is determined that the above processing conditions are satisfied, it becomes possible to separate defect areas from pseudo defect areas and noise with high accuracy. Furthermore, by adjusting which one or more of the above conditions are used as the termination condition depending on the conditions specific to the pattern and the performance of the defect inspection device 10, it becomes possible to efficiently inspect defect areas.

[0111] By controlling the defect estimation image generation unit 20D so that the repetitive control unit 20I repeats the defect estimation image generation process, the defect estimation image 60 generated at a stage where the termination condition is satisfied becomes a defect estimation image 60 from which pixels with low defect estimation values ​​and noise have been removed, compared to the defect estimation image 60 generated at a stage where the termination condition of the defect estimation image generation process is not satisfied.

[0112] 6A is a schematic diagram showing an example of a defect estimation image 60A generated at a stage where the termination condition of the estimated image generation process is not satisfied. FIG. 6B is a schematic diagram showing an example of a defect estimation image 60B generated at a stage where the termination condition of the estimated image generation process is satisfied. The defect estimation image 60B is an example of the defect estimation image 60.

[0113] 6A, the defect estimation image 60A generated at a stage where the termination condition is not satisfied includes, for example, defect candidate area D4 and defect candidate area D5, which are areas where the defect estimation value becomes low due to noise or repeated execution of the defect estimation image generation process. On the other hand, as shown in FIG. 6B, the defect estimation image 60B generated at a stage where the termination condition is satisfied does not include, for example, defect candidate area D4 and defect candidate area D5, which are areas where the defect estimation value becomes low due to noise or repeated execution of the defect estimation image generation process, that were included in the defect estimation image 60A generated at a stage where the termination condition is not satisfied.

[0114] Therefore, by controlling the defect estimation image generation unit 20D so that the repetitive control unit 20I repeats the defect estimation image generation process, the defect estimation image generation unit 20D can generate a defect estimation image 60 that can inspect the defect area with higher accuracy.

[0115] Note that even in an area where the defect estimation value decreases as the defect estimation image generation process is repeated, there may be cases where the area should actually be detected as a defect. For example, there may be cases where multiple image areas Q described above exist on the same pattern area P. At least some of these multiple image areas Q are likely to be areas that should be detected as defects, even in areas where the defect estimation value decreases as the defect estimation image generation process is repeated.

[0116] Therefore, as described above, the identification unit 20E of the defect estimation image generation unit 20D of this embodiment identifies multiple image areas Q in the evaluation target image 50 that overlap with the same pattern area P as a single defect candidate area D.

[0117] In detail, as described above, the identification unit 20E identifies, as one defect candidate area D, the maximum feature area, which is the image area Q with the largest feature amount, among multiple image areas Q that overlap the same pattern area P in the evaluation target image 50, and another image area Q whose distance between the center of gravity coordinates of the maximum feature area and the center of gravity coordinates of the maximum feature area is less than a predetermined value.

[0118] Therefore, the defect estimation image generation unit 20D can make adjustments in advance before the defect estimation image 60 generation process so that the feature value or corrected feature value becomes large for areas that are likely to be detected as defects, even if the defect estimation value becomes low due to repeated defect estimation image generation processes.

[0119] Therefore, the defect estimation image generation unit 20D of this embodiment can generate a defect estimation image 60 that can inspect with high accuracy areas that are likely to be detected as defects, even in areas where the defect estimation value becomes low due to repeated defect estimation image generation processing.

[0120] Returning to Figure 1, we continue the explanation.

[0121] When the repetition control unit 20I determines that the termination condition is satisfied, the output control unit 20J outputs the defect estimation image 60 generated at the end of the repetition of the defect estimation image generation process by the defect estimation image generation unit 20D to the UI unit 18. The output control unit 20J may also output at least one of the inspection image 40 and the reference image 30 used in the defect estimation image generation process of the defect estimation image 60, together with the defect estimation image 60, to the UI unit 18. The output control unit 20J may also transmit the defect estimation image 60 to an external information processing device via the communication unit 16. The output control unit 20J may also store the defect estimation image 60 in the storage unit 14.

[0122] Additionally, the output control unit 20J may output the determination result of the defective area represented by the defect estimation image 60 together with or instead of the defect estimation image 60. The determination result of the defective area by the defect determination unit 20H may be used as the determination result. The determination result is represented, for example, by the positions of pixels constituting the defective area in the defect estimation image 60. The positions of each pixel included in the defect estimation image 60 correspond to the positions of pixels in the reference image 30, the inspection image 40, and the evaluation target image 50. Therefore, by outputting the determination result of the defective area represented by the defect estimation image 60, the output control unit 20J can output information indicating which pixel positions in the inspection image 40 are defective.

[0123] Next, an example of the flow of information processing executed by the defect inspection apparatus 10 of this embodiment will be described.

[0124] FIG. 7 is a flowchart showing an example of the flow of information processing executed by the defect inspection apparatus 10 of this embodiment.

[0125] The acquiring unit 20A acquires the reference image 30 and the inspection image 40 (step S100). The pattern region identifying unit 20B identifies the pattern region P included in the reference image 30 acquired in step S100 (step S102).

[0126] The evaluation target image generating unit 20C generates an evaluation target image 50 from the reference image 30 and the inspection image 40 acquired in step S100 (step S104).

[0127] The identifying unit 20E identifies the defect candidate region D included in the evaluation target image 50 generated in step S104 (step S106).

[0128] The feature amount calculation unit 20F calculates the feature amount of the defect candidate area D identified in step S106 in the evaluation target image 50 generated in step S104 (step S108).

[0129] The corrected feature amount calculation unit 20G corrects the feature amount calculated in step S108 using the second threshold value, and calculates a corrected feature amount (step S110).

[0130] The corrected feature amount calculation unit 20G updates the second threshold value used in calculating the corrected feature amount in step S110 (step S112).

[0131] The defect determination unit 20H generates a defect estimation image 60 using the evaluation target image 50 generated in step S104 and the corrected feature amount of the defect candidate area D calculated in step S110 (step S114).

[0132] The repeat control unit 20I determines whether the termination condition is satisfied (step S116). If it is determined that the termination condition is not satisfied (step S116: No), the process proceeds to step S118.

[0133] In step S118, the repeat control unit 20I sets the defect estimated image 60 generated in step S114 as the evaluation target image 50 (step S118). The repeat control unit 20I controls each of the identification unit 20E, feature amount calculation unit 20F, corrected feature amount calculation unit 20G, and defect determination unit 20H so that processing is performed using the defect estimated image 60 generated in step S114 as the evaluation target image 50, instead of the evaluation target image 50 generated in step S104. Then, the process returns to step S106.

[0134] Therefore, in the first defect estimation image generation process (the processes of steps S106 to S114) for one evaluation target image 50, the defect estimation image generation unit 20D uses the evaluation target image 50 generated in step S104. On the other hand, when the defect estimation image generation process is repeated for the second time or later, the defect estimation image generation unit 20D executes the defect estimation image generation process using the defect estimation image 60 generated by the previous defect estimation image generation process as the evaluation target image 50.

[0135] If the repetition control unit 20I determines that the termination condition is satisfied (step S116: Yes), the process proceeds to step S120. In step S120, the output control unit 20J outputs the defect estimation image 60 generated at the end of the repetition of the defect estimation image generation process by the defect estimation image generation unit 20D to the UI unit 18 (step S120). Then, this routine ends.

[0136] As described above, the defect inspection apparatus 10 of this embodiment includes an acquisition unit 20A, an evaluation target image generation unit 20C, a defect estimation image generation unit 20D, and a repetition control unit 20I. The acquisition unit 20A acquires an inspection image 40 obtained by photographing an inspection target and a reference image 30 of the inspection target at the time of design. The evaluation target image generation unit 20C generates an evaluation target image 50 corresponding to the inspection image 40 and the reference image 30. The defect estimation image generation unit 20D generates a defect estimation image 60 in which a defect estimation value is defined for each pixel based on the feature amount of a defect candidate area D corresponding to an image area Q in the evaluation target image 50 that has consecutive pixels with pixel values ​​equal to or greater than a first threshold. The repetition control unit 20I controls the defect estimation image generation unit 20D to repeat the defect estimation image generation process using the defect estimation image 60 as the evaluation target image 50.

[0137] Here, as a conventional technique, a technique for comparing an inspection image with a reference image at the time of designing the inspection object and determining a defect area based on changes in the edge shape contained in the image has been disclosed. Also, a technique for generating a multidimensional feature image by applying multiple filter processes to the inspection image and detecting defects from the multidimensional feature image has been disclosed.

[0138] However, with determination techniques based on changes in edge shape, the shapes of patterns and defects to be determined are limited, making it difficult to determine defect areas included in inspection images that include patterns and defects of shapes other than those to be determined. Furthermore, with techniques that use multidimensional feature images, there are cases where false defects that are not actual defects are detected as defects due to the influence of noise included in the inspection image, detection conditions, and the like. In other words, with conventional techniques, it has been difficult to inspect defect areas with high accuracy.

[0139] On the other hand, the defect inspection device 10 of this embodiment repeatedly executes a defect estimation image generation process to generate a defect estimation image 60 based on the features of the defect candidate area D contained in the evaluation target image 50 generated according to the inspection image 40 and the reference image 30, using the generated defect estimation image 60 as the evaluation target image 50.

[0140] Therefore, by repeatedly executing the defect estimation image generation process, the defect inspection apparatus 10 of this embodiment can generate a defect estimation image 60 that does not include a defect candidate area D whose defect estimation value becomes low as the defect estimation image generation process is repeated. That is, by repeatedly executing the defect estimation image generation process, the defect inspection apparatus 10 can generate a defect estimation image 60 from which pixels with low defect estimation values ​​and noise have been removed. Furthermore, the defect inspection apparatus 10 can prevent pseudo defects, which are not defects, from being included in the defect estimation image 60. That is, by controlling the defect estimation image generation process to be repeated, the defect inspection apparatus 10 of this embodiment can separate defect areas from pseudo defect areas and noise with high accuracy.

[0141] Therefore, the defect inspection apparatus 10 of this embodiment can generate a defect estimation image 60 that allows for highly accurate inspection of a defect area. In other words, the defect inspection apparatus 10 can inspect a defect area with high accuracy by using a defect estimation image 60 that allows for highly accurate inspection of a defect area.

[0142] Therefore, the defect inspection apparatus 10 of this embodiment can improve the inspection accuracy of defective areas.

[0143] Furthermore, the defect inspection apparatus 10 of this embodiment generates a defect estimation image 60 based on the feature quantities of the defect candidate area D included in the evaluation target image 50 generated based on the inspection image 40 and the reference image 30. Therefore, in addition to the above-mentioned effects, the defect inspection apparatus 10 of this embodiment can inspect defect areas with high accuracy regardless of the patterns and defect shapes included in the inspection image 40.

[0144] Furthermore, the defect inspection apparatus 10 of this embodiment generates the defect estimation image 60 without using a multidimensional feature image, thereby reducing the computational load. Furthermore, the defect inspection apparatus 10 of this embodiment can inspect defect areas with high accuracy and efficiency.

[0145] Furthermore, the defect inspection apparatus 10 of this embodiment does not directly apply filtering to the inspection image 40, but instead generates the defect estimation image 60 using the reference image 30 and the evaluation target image 50 that corresponds to the inspection image 40. In this way, the defect inspection apparatus 10 of this embodiment does not directly apply filtering to the inspection image 40, and therefore can provide the defect estimation image 60 that retains the shape of the defect.

[0146] Furthermore, the corrected feature quantity calculation unit 20G of the defect inspection apparatus 10 of this embodiment updates the second threshold value each time the defect estimation image generation process is repeated. Specifically, the corrected feature quantity calculation unit 20G updates the second threshold value each time the defect estimation image generation process is repeated so that the second threshold value is proportional to at least one of the following: the variation in the corrected feature quantities of one or more defect candidate areas D included in the evaluation target image 50 used as the current processing target; the maximum value of the corrected feature quantities of one or more defect candidate areas D; and the number of times the defect estimation image generation process is repeated.

[0147] Therefore, the corrected feature calculation unit 20G can calculate a corrected feature according to the state of noise contained in the previously generated evaluation target image 50. Therefore, the defect inspection apparatus 10 of this embodiment can generate an evaluation target image 50 with further reduced noise by repeatedly executing the defect estimation image generation process.

[0148] Next, an example of the hardware configuration of the defect inspection device 10 of the above embodiment will be described.

[0149] FIG. 8 is a diagram showing an example of the hardware configuration of the defect inspection device 10 according to the above embodiment.

[0150] The defect inspection device 10 of the above embodiment has a hardware configuration that utilizes a conventional computer, in which a CPU (Central Processing Unit) 81, a ROM (Read Only Memory) 82, a RAM (Random Access Memory) 83, a communication I / F 84, etc. are interconnected by a bus 85.

[0151] The CPU 81 is a computing device that controls the defect inspection apparatus 10 of the above embodiment. The ROM 82 stores programs and the like that realize various processes by the CPU 81. Although a CPU is used in the description here, a GPU (Graphics Processing Unit) may also be used as the computing device that controls the defect inspection apparatus 10. The RAM 83 stores data necessary for various processes by the CPU 81. The communication I / F 84 is an interface that is connected to the UI unit 18, etc., and is used to send and receive data.

[0152] In the defect inspection device 10 of the above embodiment, the CPU 81 reads out a program from the ROM 82 onto the RAM 83 and executes it, thereby realizing each of the above functions on the computer.

[0153] The programs for executing the above processes executed by the defect inspection apparatus 10 of the embodiment may be stored in an HDD (hard disk drive). Also, the programs for executing the above processes executed by the defect inspection apparatus 10 of the embodiment may be provided by being pre-installed in the ROM 82.

[0154] The program for executing the above-described processes executed by the defect inspection apparatus 10 of the above embodiment may be stored in an installable or executable file format on a computer-readable storage medium such as a CD-ROM, CD-R, memory card, DVD (Digital Versatile Disk), or flexible disk (FD) and provided as a computer program product. The program for executing the above-described processes executed by the defect inspection apparatus 10 of the above embodiment may be stored on a computer connected to a network such as the Internet and provided by downloading it via the network. The program for executing the above-described processes executed by the defect inspection apparatus 10 of the above embodiment may be provided or distributed via a network such as the Internet.

[0155] Although the embodiment of the present invention has been described above, this embodiment is presented as an example and is not intended to limit the scope of the invention. This novel embodiment can be embodied in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. This embodiment and its modifications are included within the scope and spirit of the invention, and are also included in the invention and its equivalents as defined in the claims. [Explanation of symbols]

[0156] 10 Defect inspection equipment 20A Acquisition Department 20B Pattern area specifying unit 20C Evaluation target image generation unit 20D Defect estimation image generation unit 20E Specific part 20F Feature calculation unit 20G Corrected feature calculation unit 20H Defect Judgment Section 20I Repeat control section 30 Reference Images 40 Inspection images 50 images to be evaluated 60 Defect Estimation Images

Claims

1. an acquisition unit that acquires an inspection image obtained by photographing an inspection object and a reference image at the time of design of the inspection object; an evaluation object image generating unit that generates an evaluation object image according to the inspection image and the reference image; a defect estimation image generating unit that generates a defect estimation image in which a defect estimation value is defined for each pixel based on a feature amount of a defect candidate area corresponding to an image area in which pixels having pixel values ​​equal to or greater than a first threshold value are consecutive, which is included in the evaluation target image; a repeat control unit that controls the defect estimation image generation unit to repeat a defect estimation image generation process using the defect estimation image as the evaluation target image; Equipped with The defect estimation image generation unit an identifying unit that identifies the defect candidate area according to the image area in which pixels having pixel values ​​equal to or greater than the first threshold value are consecutive, which is included in the evaluation target image; a feature amount calculation unit that calculates a feature amount of the defect candidate area identified in the evaluation target image; a corrected feature amount calculation unit that calculates a corrected feature amount by correcting the feature amount using a second threshold; a defect determination unit that generates the defect estimation image using the evaluation target image and the corrected feature amount; and the feature amount of the defect candidate area is represented by a group of feature values ​​of pixels constituting the defect candidate area in the evaluation target image, The corrected feature amount calculation unit calculating, as the corrected feature amount, the feature amount obtained by correcting the feature value of the pixels constituting the defect candidate area in the evaluation target image that is less than the second threshold to 0; Defect inspection equipment.

2. The repeat control unit the defect estimation image generated by the defect determination unit is set as the evaluation target image to be used in the next defect estimation image generation process, and the defect estimation image generation unit is controlled to repeat the defect estimation image generation process, which is a series of processes that sequentially execute a process of identifying the defect candidate area, a process of calculating the feature amount, a process of calculating the corrected feature amount, and a process of generating the defect estimation image, until it is determined that a predetermined termination condition is satisfied. The defect inspection device according to claim 1 .

3. The termination condition is: the defect estimation image generation process is repeatedly executed a predetermined number of times or more; the number of the defect candidate areas included in the defect estimation image is equal to or less than a predetermined number; and, the number of times that the defect estimation image generated in the previous execution of the defect estimation image generation process and the defect estimation image generated in the current execution of the defect estimation image generation process match is equal to or greater than a predetermined number; At least one of the following conditions is satisfied: The defect inspection device according to claim 2 .

4. a pattern area specifying unit that specifies a pattern area in which pixels having a pixel value equal to or greater than a third threshold are consecutive and that is included in the reference image; The identification unit Among the image areas in the evaluation target image, a plurality of the image areas that overlap the same pattern area are identified as one of the defect candidate areas. The defect inspection device according to claim 1 .

5. The identification unit Among the plurality of image regions in the image to be evaluated that overlap the same pattern region, a maximum feature amount region, which is the image region having the largest feature amount, and an image region whose distance between the center of gravity of the maximum feature amount region and the center of gravity of the maximum feature amount region is equal to or less than a predetermined value is identified as one of the defect candidate regions. The defect inspection device according to claim 4.

6. The corrected feature amount calculation unit updating the predetermined initial value of the second threshold value each time the defect estimation image generation process is repeated; The defect inspection device according to claim 2 .

7. The corrected feature amount calculation unit updating the second threshold value for each repetition of the defect estimation image generation process so that the second threshold value is proportional to at least one of the variation in the corrected feature values ​​of the one or more defect candidate areas included in the image to be evaluated, the maximum value of the corrected feature values ​​of the one or more defect candidate areas, and the number of repetitions of the defect estimation image generation process; The defect inspection device according to claim 6.

8. The defect determination unit generating the defect estimation image, in which the defect estimation value is defined according to the product of the pixel value and the corrected feature amount of the defect candidate area to which the pixel belongs, for each pixel included in the defect candidate area in the evaluation target image; The defect inspection device according to claim 1 .

9. The evaluation target image generation unit generating a difference image between the inspection image and the reference image as the evaluation target image; The defect inspection device according to claim 1 .

10. an acquisition step of acquiring an inspection image obtained by photographing an inspection object and a reference image at the time of designing the inspection object; an evaluation object image generating step of generating an evaluation object image according to the inspection image and the reference image; a defect estimation image generating step of generating a defect estimation image in which a defect estimation value is defined for each pixel based on a feature amount of a defect candidate area corresponding to an image area in which pixels having pixel values ​​equal to or greater than a first threshold value are consecutive, which is included in the evaluation target image; a repeat control step of controlling the defect estimation image generation process to be repeated using the defect estimation image as the evaluation target image; Including, The defect estimation image generating step includes: a specifying step of specifying the defect candidate area corresponding to the image area in which pixels having pixel values ​​equal to or greater than the first threshold value are consecutive, which is included in the evaluation target image; a feature amount calculation step of calculating a feature amount of the defect candidate area identified in the evaluation target image; a corrected feature amount calculation step of calculating a corrected feature amount by correcting the feature amount using a second threshold value; a defect determination step of generating the defect estimation image using the evaluation target image and the corrected feature amount; and the feature amount of the defect candidate area is represented by a group of feature values ​​of pixels constituting the defect candidate area in the evaluation target image, The corrected feature value calculation step includes: calculating, as the corrected feature amount, the feature amount obtained by correcting the feature value of the pixels constituting the defect candidate area in the evaluation target image that is less than the second threshold to 0; Defect inspection methods.

11. A defect inspection program to be executed by a computer, comprising: an acquisition step of acquiring an inspection image obtained by photographing an inspection object and a reference image at the time of designing the inspection object; an evaluation object image generating step of generating an evaluation object image according to the inspection image and the reference image; a defect estimation image generating step of generating a defect estimation image in which a defect estimation value is defined for each pixel based on a feature amount of a defect candidate area corresponding to an image area in which pixels having pixel values ​​equal to or greater than a first threshold value are consecutive, which is included in the evaluation target image; a repeat control step of controlling the defect estimation image generation process to be repeated using the defect estimation image as the evaluation target image; Including, The defect estimation image generating step includes: a specifying step of specifying the defect candidate area corresponding to the image area in which pixels having pixel values ​​equal to or greater than the first threshold value are consecutive, which is included in the evaluation target image; a feature amount calculation step of calculating a feature amount of the defect candidate area identified in the evaluation target image; a corrected feature amount calculation step of calculating a corrected feature amount by correcting the feature amount using a second threshold value; a defect determination step of generating the defect estimation image using the evaluation target image and the corrected feature amount; and the feature amount of the defect candidate area is represented by a group of feature values ​​of pixels constituting the defect candidate area in the evaluation target image, The corrected feature value calculation step includes: calculating, as the corrected feature amount, the feature amount obtained by correcting the feature value of the pixels constituting the defect candidate area in the evaluation target image that is less than the second threshold to 0; Defect inspection program.

Images