Apparatus for detecting defects of object and method for detecting defects of object

Abstract

A method for detecting a defect of an object may acquire image data of a specific region of a target object, determine at least one substance based on the acquired image data, and determine a defect in the determined substance. Accordingly, various substances contained in the target object can be easily recognized through the image data acquired from the target object. In addition, since the substance is identified based on the image acquired from the target object, the structure may be simple and cost may be reduced.

Description

Technical Field

[0001] The embodiment relates to a device for detecting defects of an object and a method for detecting defects of an object. Background Art

[0002] In the case of a manufacturer who produces products based on a target object such as raw materials, he or she often wants to know the types and properties of the substances contained in the raw materials. However, due to reasons such as information security, the raw material supplier does not provide such information, making it difficult for the manufacturer to identify the types and properties of the substances contained in the raw materials.

[0003] When the manufacturer can know the types and properties of the substances contained in the raw materials, he or she can identify how the substances change or the relevant causal relationships during the process of manufacturing the products, and minimize the defects that may occur during product production based on the causal relationships. In addition, when the manufacturer can know the types and properties of the substances contained in the raw materials, information about changing the types of substances or adjusting the properties of substances can be fed back to the raw material supplier, enabling better-quality raw materials to be supplied by the raw material supplier in the future and even enabling better products to be manufactured.

[0004] Therefore, there is a strong demand for a technology that can easily identify the types and properties of substances contained in a target object. Summary of the Invention

[0005] Technical Problem

[0006] The purpose of the embodiment is to solve the above and other problems.

[0007] Another purpose of the embodiment is to provide a device for detecting defects of an object and a method for detecting defects of an object, which can easily identify the substances contained in the target object.

[0008] Another purpose of the embodiment is to provide a device for detecting defects of an object and a method for detecting defects of an object, which can have a simple structure and reduce costs because the substances are identified based on images obtained from the target object.

[0009] Another purpose of the embodiment is to provide a device for detecting defects of an object and a method for detecting defects of an object, which can easily determine the defects of the identified substances.

[0010] The technical problems of the embodiment are not limited to those described in this item, and include those technical problems that can be understood through the description of the present invention.

[0011] Technical Solution

[0012] According to one aspect of the embodiment, to achieve the above or other purposes, a method for detecting defects of an object includes: acquiring image data of a specific area of a target object; determining at least one substance based on the acquired image data; and determining defects in the determined substance.

[0013] The determined substance may include voids, resin, and filler.

[0014] Defects of the determined substance may be determined based on at least one of an area ratio, a size, and a density.

[0015] Determining defects of the determined substance may include determining defects of voids based on the area ratio of voids.

[0016] Determining defects of the determined substance may include determining defects of voids based on the Feret length of voids.

[0017] Determining defects of the determined substance may include determining defects of resin based on the Feret length of resin.

[0018] Determining defects of the determined substance may include determining defects of resin based on the area ratio of the aggregation area of resin as density.

[0019] The aggregation area of resin may be an area less than 0.015 to 1.535% of the AOI size.

[0020] The method for detecting defects of an object may include: when the target object includes a circuit, performing histogram extreme value correction based on the acquired image data.

[0021] According to another aspect of the embodiment, an apparatus for detecting defects of an object includes: an image acquisition unit configured to acquire image data of a specific area of a target object; a substance determination unit configured to determine at least one substance based on the acquired image data; and a defect determination unit configured to determine defects in the determined substance.

[0022] The determined substance may include voids, resin, and filler.

[0023] Defects in the determined substance may be determined based on at least one of an area ratio, a size, and a density.

[0024] The defect determination unit may determine defects of voids based on the area ratio of voids.

[0025] The defect determination unit may determine defects of voids based on the Feret length of voids.

[0026] The defect determination unit may determine defects of resin based on the Feret length of resin.

[0027] The defect determination unit may determine a defect of the resin based on the area ratio of the aggregated region of the determined resin as the density.

[0028] The aggregated region of the resin may be a region less than 0.015 to 1.535% of the AOI size.

[0029] The apparatus for detecting a defect of an object may include: a histogram extreme value correction unit configured to perform histogram extreme value correction based on the acquired image data when the target object includes a circuit.

[0030] Advantageous Effects

[0031] The effects of the apparatus for detecting a defect of an object and the method for detecting a defect of an object according to an embodiment are described below.

[0032] According to at least one embodiment, there are the following advantages: various substances included in the target object can be easily identified from the image data acquired from the target object.

[0033] According to at least one embodiment, there are the following advantages: since substances are identified based on the image acquired from the target object, the structure is simple and the cost can be reduced.

[0034] According to at least one embodiment, it is possible to easily determine whether at least one of the determined (identified) substances is defective. Therefore, not only the type of substances included in the target object can be identified, but also whether the type is defective can be identified, enabling three-dimensional inspection of the target object.

[0035] According to the following detailed description, the additional applicability scope of the embodiment will become apparent. However, since those skilled in the art can clearly understand various changes and modifications within the idea and scope of the embodiment, the detailed description and specific embodiments (such as preferred embodiments) should be understood as being given only as examples. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a cross-sectional view of a circuit board according to an embodiment.

[0037] Figure 2 is a block diagram of an apparatus for detecting a defect of an object according to an embodiment.

[0038] Figure 3 is a flowchart illustrating a method for detecting a defect of an object according to a first embodiment.

[0039] Figure 4 shows the state of substances detected according to brightness.

[0040] Figures 5a to 5f shows the detection of resin.

[0041] Figure 6a and Figure 6b shows the detection of the packing material.

[0042] Figure 7a and Figure 7b shows the detection of the voids.

[0043] Figure 8 shows the detection of the substance according to the color.

[0044] Figure 9a shows the voids distributed in the AOI image.

[0045] Figure 9b is a graph showing the determination of the defect according to the area ratio of the voids.

[0046] Figure 10 shows the determination of the defect using the Feret length of the voids.

[0047] Figure 11 shows the determination of the defect using the Feret length of the resin.

[0048] Figure 12a shows the aggregation region of the resin distributed in the AOI image.

[0049] Figure 12b is a graph showing the determination of the defect according to the area ratio of the aggregation region of the resin.

[0050] Figure 13 is a flowchart illustrating a method for detecting a defect in a target according to a second embodiment.

[0051] Figure 14 shows the histogram extreme value correction. Detailed Description of the Invention

[0052] Hereinafter, embodiments disclosed in the present specification will be described in detail with reference to the accompanying drawings, but the same or similar elements are given the same reference numerals regardless of the reference numerals, and redundant descriptions thereof will be omitted. The suffixes “module” and “unit” of the elements used in the following description may be given or used interchangeably for ease of writing the specification, and they do not have different meanings or functions from each other. In addition, the accompanying drawings are for easy understanding of the embodiments disclosed in the present specification, and the technical idea disclosed in the present specification is not limited to the accompanying drawings. Further, when an element such as a layer, a region, or a substrate is referred to as being “on” another element, this means that it can be directly on the other element or there may be other intermediate elements therebetween.

[0053] The embodiments can provide a method and an apparatus that can easily identify the type of a substance contained in a target object such as a raw material.

[0054] Although the following description presents a circuit board as a representative example of a raw material, embodiments may include raw materials containing at least one substance. In addition, embodiments may include components containing at least one substance other than raw materials.

[0055] The substances described below may have a form, such as resin, filler, epoxy resin, or glass fabric, or may not have a form, such as voids. Substances and forms may be mixed together.

[0056] Figure 1 is a cross-sectional view showing a circuit board according to an embodiment.

[0057] Referring to Figure 1 , a circuit board according to an embodiment may include a plurality of prepregs 110, a resin layer 120 between the prepregs 110, and a plurality of vias 151 and 152.

[0058] The prepreg 110 may be formed by impregnating a fiber layer in the form of a fabric sheet (such as a glass fabric 112 woven with glass yarn) with an epoxy resin 111 and then thermally compressing the fiber layer. However, the embodiments are not limited thereto. That is, the prepreg 110 may also include a fiber layer in the form of a fabric sheet woven with carbon fiber yarns.

[0059] After positioning the resin layer 120 between the prepregs 110, the substrate may be completed by thermal compression pressing using a press. Thereafter, the prepregs 110 are drilled to form vias, and the vias may be filled with a conductive material or plated with a conductive material to form the vias 151 and 152. The conductive material may be any one of the materials selected from Cu, Ag, Sn, Au, Ni, and Pd. The vias may be formed by any one of mechanical, laser, and chemical processing methods.

[0060] The vias 151 and 152 may be electrically connected to circuit patterns 141 to 143 provided on the upper side and / or lower side of the prepregs 110.

[0061] Meanwhile, the unexplained symbol 113 may be a void, which may be a blank space where no substance exists. Additionally, the unexplained symbol 130 is a filler. The filler 130 may include various types of fillers that are different from each other.

[0062] Figure 2 is a block diagram showing a device for detecting defects of an object according to an embodiment.

[0063] In the following description, for ease of explanation, Figure 1 the substrate illustrated in Figure 1Other components other than the substrate described in

[0064] Reference Figure 1 and Figure 2 According to the embodiment, the defect detection device 200 for the target object may include an image acquisition unit 210, a feature extraction unit 220, a substance determination unit 230, and a defect determination unit 240.

[0065] The image acquisition unit 210 may acquire image data of a specific area of the target object.

[0066] The image data may be acquired by a destructive inspection method. For example, the target object may be cut so that a specific cross-section of the target object is exposed, and the image data may be acquired for the cut specific cross-section. For example, the image data may be acquired by using, for example, a microscope, SEM, or TEM, but is not limited thereto.

[0067] Meanwhile, the embodiment may also acquire the image data of the specific area to be photographed by a non-destructive inspection method.

[0068] The feature extraction unit 220 may extract a plurality of features included in the image data. The plurality of features may include, for example, brightness, size, shape, color, sharpness, etc.

[0069] The image data may include various substances that can be distinguished from each other by brightness, boundary, color, sharpness, etc. In other words, these substances can be distinguished from each other by brightness, boundary, color, sharpness, etc. Figure 1 The substrate shown may include various different substances. In other words, the substrate may be formed of a composition of various substances. These substances may be resin, glass fabric, filler, etc. Voids are also included in the substrate and may be classified as a kind of substance.

[0070] The substance determination unit 230 may determine at least one substance based on at least one of the plurality of features extracted by the feature extraction unit 220. In other words, at least one substance may be determined based on at least one of brightness, size, shape, and color.

[0071] Since Figure 1 the substrate shown includes resin, at least one filler, glass fabric, and voids as substances, at least one of resin, filler, glass fabric, and voids may be determined based on at least one of brightness, size, shape, and color.

[0072] For example, the substance determination unit 230 may determine at least one substance based on brightness.

[0073] For example, the substance determination unit 230 may determine at least one substance based on brightness and size.

[0074] For example, the material determination unit 230 may determine at least one material based on brightness, size, and shape.

[0075] For example, the material determination unit 230 may determine at least one material based on brightness, size, shape, and color.

[0076] Meanwhile, the material determination unit 230 may determine voids based on sharpness. As Figure 7a shown, when viewing the image data in color, both the resin and the voids appear black, such that they may not be distinguishable from each other, which may lead to errors in determining the voids and the resin. Therefore, considering sharpness as a feature, the resin and the voids can be distinguished by the magnitude of the sharpness. The sharpness can be increased such that the boundaries of the voids are clearly revealed. In this case, considering that the voids are relatively very small compared to the resin, the sharpness can be increased such that a material with a very small contour compared to the resin can be determined as a void.

[0077] Meanwhile, the defect determination unit 240 may determine whether there is a defect in at least one of the determined materials. Whether each of the at least one material has a defect can be determined based on whether it meets a predetermined condition.

[0078] Meanwhile, the defect detection device 200 of the target object according to an embodiment may include a storage unit (not shown) that stores image data, various information of each material, such as brightness information, size information, shape information, color information, dimension information, etc. The storage unit may store various data or information generated in the embodiment.

[0079] The defect detection unit 200 of the target object according to an embodiment may include a marking unit (not shown) that marks each detected or determined material to distinguish them. The marking may be processed as color, letters, pictures, graphics, highlighting, etc.

[0080] The defect detection unit 200 of the target object according to an embodiment may include a calculation unit (or calculation unit, not shown) that calculates the size or area.

[0081] [First Embodiment]

[0082] Figure 3 is a flowchart illustrating a method for detecting a defect of an object according to the first embodiment.

[0083] As Figure 2 and Figure 3 shown, image data may be acquired by the image acquisition unit 210 (S310), multiple features may be extracted by the feature extraction unit 220 (S320), at least one material may be determined by the material determination unit 230 (S330), and a defect of at least one material determined by the material determination unit 240 may be determined (S340).

[0084] In the following, reference will be made to Figures 4 to 8 a method for determining at least one substance using multiple features will be described.

[0085] Figure 4 The state of the substance is detected according to the brightness.

[0086] As Figure 4 shown, voids, resin, and filler can be determined using brightness as one of the multiple features.

[0087] The image data can include various substances that are distinguishable from each other by size, shape, color, etc. The brightness of these substances can also be different. The number of substances included in the corresponding image data according to the brightness can be shown, as Figure 4 shown. The unit of brightness can be gray-scale, but is not limited thereto.

[0088] The substance corresponding to between gray level 0 and gray level A can be a void, the substance corresponding to between gray level A and gray level B can be resin, and the substance corresponding to gray level B or higher can be filler.

[0089] Therefore, among the substances extracted from the image data, the substances distributed between gray level 0 and gray level A are determined as voids, the substances distributed between gray level A and gray level B are determined as resin, and the substances distributed at gray level B or higher can be determined as filler.

[0090] Unlike Figure 4 shown, other substances can be determined instead of voids, resin, and filler.

[0091] Figures 5a to 5f The detection of resin is shown.

[0092] Figure 5a is the original image data acquired by the image acquisition unit 210 shown by Figure 2 shown, and Figures 5b to 5f shows that the area where resin is detected varies depending on different reference values.

[0093] Figure 5b shows the distribution of resin 311 detected when the reference value exceeds 5 pixels, and only the resin 311 with a total area greater than 5 pixels can be detected. Figure 5c shows the distribution of resin 311 detected when the reference value exceeds 15 pixels, and only the resin 311 with a total area greater than 15 pixels can be detected. Figure 5d shows the distribution of resin 311 detected when the reference value exceeds 25 pixels, and only the resin 311 with a total area greater than 25 pixels can be detected. Figure 5eShows the distribution of the resin 311 detected when the reference value exceeds 35 pixels, and only the resin 311 with a total area greater than 35 pixels can be detected. Figure 5f Shows the distribution of the resin 311 detected when the reference value exceeds 50 pixels, and only the resin 311 with a total area greater than 50 pixels can be detected.

[0094] As Figures 5b to 5f shown, the degree of distribution of the detected resin 311 can vary depending on the size of the reference value. When the reference value is low or high, the detected resin 311 is small or large, and the detection accuracy of the resin 311 can be reduced, such that an optimal reference value setting is required. For example, the optimal reference value can be 25 pixels, but is not limited thereto.

[0095] Meanwhile, at least one of multiple features extracted from the image data can be used to detect the resin 311.

[0096] For example, brightness can be used to detect the resin 311. For example, brightness and size can be used to detect the resin 311. For example, brightness, size, and shape can be used to detect the resin 311. For example, brightness, size, shape, and color can be used to detect the resin 311.

[0097] Figure 6a And Figure 6b shows the detection of the filler.

[0098] Multiple features ( Figure 6a ) can be extracted from the image data, and at least one of the extracted features can be used to detect multiple fillers 312 ( Figure 6b ).

[0099] For example, brightness can be used to detect the filler 312. For example, brightness and size can be used to detect the filler 312. For example, brightness, size, and shape can be used to detect the filler 312. For example, brightness, size, shape, and color can be used to detect the filler 312.

[0100] Figure 7a And Figure 7b shows the detection of the voids.

[0101] By adjusting the sharpness of the image data ( Figure 7a ), the voids 314 ( Figure 7b ) can be detected.

[0102] In Figure 7aIn the image data shown, both the resin and the void 314 appear black, making it difficult to distinguish them from each other. Therefore, the sharpness can be adjusted to distinguish the void 314 from the resin or other components. Even if both the void 314 and the resin appear black, the void 314 can be distinguished from the resin by increasing the sharpness.

[0103] As Figure 7b shown, by adjusting the sharpness, the void 314 can be detected and distinguished from the resin 311.

[0104] Meanwhile, as described above, a substance can be detected based on the color of the substance.

[0105] Figure 8 An example of detecting a substance based on the color of the substance is shown.

[0106] As Figure 8 shown, various substances can be distinguished by the white and black in the image data. Various substances can be detected by the color difference including white and black.

[0107] Figure 8 A prepreg is shown, which may include an epoxy resin 313 and a glass fabric 315.

[0108] For example, the epoxy resin 313 can be indicated in black, and the glass fabric 315 can be indicated in white.

[0109] Substances smaller than the size of the glass fabric 315 can be fillers. Both the glass fabric 315 and the fillers can be represented in white. In this case, when the size of each of the glass fabric 315 and the fillers is known, the substance corresponding to the corresponding size among the substances represented in white can be detected as the glass fabric 315 or the fillers.

[0110] When there are fillers with various sizes, when the size of each of the various fillers is known, each of the fillers can be detected separately.

[0111] Meanwhile, as Figure 3 shown, when at least one substance (S330) is determined, at least one defect of the substance can be determined (S340).

[0112] Hereinafter, a method for determining at least one defect of a substance will be described with reference to Figures 9a to 1 2.

[0113] As shown in FIGS. 5 to Figure 8 shown, at least one substance may include a void 314, a resin 311, and a filler 312.

[0114] As described above, at least one substance, namely the void 314, the resin 311, or the filler 312, can be determined using a plurality of features extracted from the image data.

[0115] Thus, in an embodiment, it is possible to easily identify which substances are included in the target object by analyzing the image data. In addition, it is also possible to easily determine whether the identified substances do not meet a predetermined condition and are thus defective.

[0116] In order to use the target object in a post-processing for product manufacturing, at least one substance included in the target object must meet a predetermined condition. If at least one substance does not meet the predetermined condition, the substance is determined to be defective, and due to the defective substance, the target object itself may also be discarded due to being defective.

[0117] According to an embodiment, even if the type of the substances included in the target object is unknown, it is possible to identify which substances are included in the target object based on the image data obtained from the target object. In addition, it is possible to easily determine whether each identified substance is defective by checking whether each identified substance meets a predetermined condition.

[0118] It is possible to determine the defect of the substance based on at least one of an area ratio, a size, and a density.

[0119] <Method for Determining Defects of Voids Using Area Ratio>

[0120] Figure 9a Shows voids distributed in the AOI image. Figure 9b Is a graph showing defect determination according to the area ratio of voids.

[0121] As Figure 9a As described in, voids 314 may be distributed in an AOI (Automatic Optical Inspection) image 400. The AOI image 400 may be an image acquired by an automatic optical inspection device. The AOI image 400 is an area on which defect detection is to be performed, and may be the image data itself, or one or two or more blocks separated from the image data.

[0122] As Figure 9b As described in, the predetermined condition for determining a defect according to the area ratio of voids 314 may be, for example, 1%, but is not limited thereto. When the area ratio of voids 314 exceeds 1%, it may be determined to be defective. The area ratio of voids 314 may be the ratio of the area occupied by the sum of the areas of each void 314 distributed in the AOI image 400 to the area of the AOI image 400.

[0123] When the predetermined condition is 1%, it means that the defects are very strictly managed, and the predetermined condition can be managed as 10%. In this case, when the area ratio of the void 314 is 10% or more, the void 314 can be determined to be defective. That is, when the area ratio of the void 314 is 10% or more, the possibility of poor conductivity or poor hygroscopicity increases, and the possibility of metal protrusions (or protrusions) being a circuit pattern (or signal pattern) may increase. The predetermined condition can be set differently depending on the magnitude of the dielectric constant. For example, when the first dielectric constant is from 2.0 to 3.5, the predetermined condition can be 5%, but it is not limited thereto. For example, the second dielectric constant can be from 3.5 to 6, and the third dielectric constant can be from 6 to 10.

[0124] <Method for determining voids using dimensional defects>

[0125] Figure 10 Defect determination using the Feret length of voids is shown.

[0126] As Figure 10 illustrated, the void 314 can be included in the AOI image 400. The Feret length (or diameter) of the void 314 can be defined. The tray length can be the measurement of the dimension of the target object along a specified direction. Generally, it can be defined as the distance between two parallel planes that limit the target object perpendicular to that direction. This measurement can be used for particle size analysis, such as microscopy applied to the projection of a three-dimensional (3D) object onto a two-dimensional (2D) plane. In this case, the tray length L1 can be defined as the distance between two parallel tangents.

[0127] For example, the predetermined condition for the defect of the tray length L1 for determining the void 314 can be 1 μm. In this case, when the tray length L1 of the void 314 exceeds 1 μm, the void 314 can be determined to be defective.

[0128] Meanwhile, as Figure 9a and Figure 9b shown, the area ratio of the void 314 satisfies the predetermined condition such that the void 314 can be normal. However, when the Feret length of one of these voids 314 exceeds the predetermined condition of 1 μm as Figure 10 shown, the void 314 can ultimately be determined to be defective.

[0129] <Method for determining resin defects using dimensions>

[0130] Figure 11 Defect determination using the Feret length of the resin is shown.

[0131] As Figure 11As shown, multiple resins 311 can be distributed in the AOI image 400. The maximum Feret length L2 among the multiple resins 311 can be selected.

[0132] For example, the predetermined condition for the defect to determine the Feret length L2 of the resin 311 can be 20% of the long-axis length L_long in the long axis * short axis of the AOI image 400. In this case, when the Feret length L2 of the resin 311 exceeds 20% of the long-axis length L_long of the AOI image 400, the resin 311 can be determined to be defective. L_short can mean the short-axis length.

[0133] <Method for Determining Resin Aggregation Using Density>

[0134] Figure 12a The aggregation region of the resin distributed in the AOI image is shown. Figure 12b It is a graph showing the defect determination according to the area ratio of the aggregation region of the resin.

[0135] As Figure 12a shown, the aggregation region 410 where the resin aggregates can be distributed in the AOI image 400. For example, the aggregation region 410 can be formed by the aggregation of at least two or more resins. Since the resins are not distributed separately from each other but adhere or aggregate together, it is difficult to apply defect determination using the area ratio of voids ( Figure 9a and Figure 9b ). Therefore, it is desirable to determine the defect by considering the density (i.e., the degree of aggregation) of the resin.

[0136] For example, when the resin aggregation region 410 is distributed in the AOI image 400, the defect of the resin can be determined according to whether a predetermined condition is satisfied. Here, the predetermined condition can be 20% of the AOI area. In this case, as Figure 12b shown, when the area ratio of the resin aggregation region 410 exceeds 20%, the resin can be determined to be defective.

[0137] The resin aggregation region 410 can be selected from the regions where there is no filler or glass fabric to improve the accuracy of defect determination.

[0138] Meanwhile, in order to select the resin aggregation region 410, a specific size must be defined so that the resin aggregation region 410 can be selected from the regions smaller than the defined specific size. For example, a specific size can be defined so that the resin aggregation region 410 is selected from the regions occupying 0.015% to 1.535% of the AOI image 400 with a size of 768×666.

[0139] Meanwhile, when there is metal such as a circuit pattern in the target object and histogram analysis is performed on the AOI image 400 obtained for these metals, the metal can be represented by a luminance value corresponding to at least level 255 or higher (i.e., white). In this case, the luminance value of the white region corresponding to the metal has no information value and thus needs to be removed.

[0140] [Second Embodiment]

[0141] Figure 13 is a flowchart illustrating a method for detecting a defect in a target object according to the second embodiment.

[0142] As Figure 2 and Figure 13 shown, image data can be acquired by the image acquisition unit 210 (S310), histogram extreme value correction can be performed on the acquired image data (S350), multiple features can be extracted by the feature extraction unit 220 (S320), and at least one substance can be determined by the substance determination unit 230 (S330).

[0143] S310 can be performed by the image acquisition unit 210 or the feature extraction unit 220. S310 can be performed by a third device or unit different from the image acquisition unit 210 or the feature extraction unit 220.

[0144] When histogram analysis is performed on image data obtained from a target object having a circuit pattern, as Figure 14 shown, there can be multiple quantities for each luminance value greater than or equal to D. The number of pixels corresponding to each luminance value equal to or greater than D reflects the reflectance caused by the metal components of the circuit pattern, and the region having a luminance value equal to or greater than D has no information value and thus needs to be removed. Here, D can be, for example, a luminance value of level 250, but is not limited thereto.

[0145] According to an embodiment, when a circuit is included in the target object by a histogram extreme value correction unit (not shown), histogram extreme value correction can be performed based on the acquired image data. For example, as Figure 14 shown, after removing the image data corresponding to the region having each luminance value equal to or greater than D, the AOI image can be reset based on the remaining data. In this case, the AOI image may not include information about the circuit pattern.

[0146] Meanwhile, the histogram extreme value correction unit can be included in the image acquisition unit 210 or the feature extraction unit 220, but is not limited thereto.

[0147] The above detailed description should not be construed as restrictive in all respects and should be considered illustrative. The scope of the embodiments should be determined by a reasonable interpretation of the appended claims, and all variations within the equivalent scope of the embodiments are included within the scope of the embodiments.

Claims

1. A method for detecting defects of an object, comprising: Obtaining image data of a specific area of a target object; Determining at least one substance based on the obtained image data; And Determining defects in the determined substance.

2. The method for detecting a defect of an object according to claim 1, wherein, The determined substance includes voids, resin, and filler.

3. The method for detecting a defect of an object according to claim 2, wherein, The defects of the determined substance are determined based on at least one of area ratio, size, and density.

4. The method for detecting a defect of an object according to claim 3, wherein, The determining of the defects of the determined substance includes determining the defects of the voids based on the area ratio of the voids.

5. The method for detecting a defect of an object according to claim 3, wherein, The determining of the defects of the determined substance includes determining the defects of the voids based on the Feret length of the voids.

6. The method for detecting a defect of an object according to claim 3, wherein, The determining of the defects of the determined substance includes determining the defects of the resin based on the Feret length of the resin.

7. The method for detecting a defect of an object according to claim 3, wherein, The determining of the defects of the determined substance includes determining the defects of the resin based on the area ratio of the aggregation area of the resin as the density.

8. The method for detecting defects of an object according to claim 7, wherein the aggregation area of the resin is an area less than 0.015 to 1.535% of the AOI size.

9. The method for detecting defects of an object according to claim 1, which includes: When the target object includes a circuit, performing histogram extreme value correction based on the obtained image data.

10. A device for detecting defects of an object, comprising: An image acquisition unit configured to obtain image data of a specific area of a target object; A substance determination unit configured to determine at least one substance based on the obtained image data; And A defect determination unit configured to determine defects in the determined substance.

11. The apparatus for detecting a defect of an object according to claim 10, wherein, The determined substance includes voids, resin, and filler.

12. The device for detecting defects of an object according to claim 11, wherein the defects in the determined substance are determined based on at least one of area ratio, size, and density.

13. The device for detecting defects of an object according to claim 12, wherein the defect determination unit is configured to determine the defects of the voids based on the area ratio of the voids.

14. The device for detecting defects of an object according to claim 12, wherein the defect determination unit is configured to determine the defects of the voids based on the Feret length of the voids.

15. The device for detecting defects of an object according to claim 12, wherein the defect determination unit is configured to determine the defects of the resin based on the Feret length of the resin.

16. The device for detecting defects of an object according to claim 12, wherein the defect determination unit is configured to determine the defects of the resin based on the area ratio of the aggregation area of the determined resin as the density.

17. The device for detecting defects of an object according to claim 16, wherein the aggregation area of the resin is an area less than 0.015 to 1.535% of the AOI size.

18. The device for detecting defects of an object according to claim 10, comprising: Histogram extreme value correction unit, the histogram extreme value correction unit being configured to perform histogram extreme value correction based on the acquired image data when the target object includes a circuit.

Images