Apparatus for detecting a substance in a target object and method for detecting a substance in a target object

Abstract

A method for detecting a substance in an object can acquire image data for a specific region of the object, extract a plurality of features included in the acquired image data, and determine at least one substance based on at least one of the extracted features.

Description

[Technical Field]

[0001] The embodiments relate to an apparatus for detecting a substance in an object and a method for detecting a substance in an object. [Background technology]

[0002] Manufacturers who produce products based on objects such as raw materials want to know the types and characteristics of substances contained in the raw materials, but because raw material suppliers do not provide this information for reasons such as information security, they are unable to find out the types and characteristics of substances contained in the raw materials.

[0003] If a manufacturer knows the types of substances contained in raw materials and their properties, they can understand the causality of how these substances change or are involved in the manufacturing process of a product, and minimize defects that may occur during product production based on that causality. Also, if a manufacturer knows the types of substances contained in raw materials and their properties, they can provide feedback to raw material suppliers on changing the types of substances or adjusting the properties of materials to minimize defects that may occur during product production, and in the future they can receive better quality raw materials from raw material suppliers and manufacture better quality products.

[0004] Therefore, there is a strong demand for a technology that can easily grasp the types and characteristics of materials contained in a target object. Summary of the Invention [Problem to be solved by the invention]

[0005] The embodiments are directed to solving the above-mentioned problems and other problems.

[0006] Another object of the embodiments is to provide an apparatus and method for detecting a substance in an object, which can easily identify a substance contained in the object.

[0007] Another object of the present invention is to provide a device and method for detecting a substance in an object, which have a simple structure and can reduce costs by detecting the substance based on an image acquired from the object.

[0008] The technical problems of the embodiments are not limited to those described in this section, but include those that can be understood from the description of the invention. [Means for solving the problem]

[0009] To achieve the above or another object, according to one aspect of an embodiment, a method for detecting a substance in an object includes the steps of acquiring image data for a specific region of the object, extracting a plurality of features included in the acquired image data, and determining at least one substance based on at least one of the extracted features.

[0010] The plurality of characteristics may include brightness, size, shape and color.

[0011] Determining the at least one or more materials may include determining the at least one or more materials based on the brightness.

[0012] Determining the at least one or more substances may include determining the at least one or more substances based on the brightness and the size.

[0013] Determining the at least one or more substances may include determining the at least one or more substances based on the brightness, the size, and the shape.

[0014] Determining the at least one or more substances may include determining the at least one or more substances based on the brightness, the size, the shape, and the color.

[0015] The plurality of characteristics may include a definition. Determining the at least one material may include determining a void based on the definition.

[0016] The at least one or more substances may be at least one or more of a glass fabric, at least one or more fillers, and a resin.

[0017] The method may further include denoising the acquired image data before extracting the plurality of features.

[0018] To achieve the above or another object, according to another aspect of the embodiment, a substance detection device for a target object includes an image acquisition unit that acquires image data for a specific region of the target object, a feature extraction unit that extracts a plurality of features included in the acquired image data, and a substance determination unit that determines at least one substance based on at least one of the extracted features.

[0019] The plurality of characteristics may include brightness, size, shape and color.

[0020] The material determination unit can determine the at least one material based on the brightness.

[0021] The material determination unit can determine the at least one material based on the brightness and the size.

[0022] The material determination unit can determine the at least one material based on the brightness, the size, and the shape.

[0023] The material determination unit can determine the at least one material based on the brightness, the size, the shape, and the color.

[0024] The plurality of characteristics may include a definition, and the material determination unit may determine a void based on the definition.

[0025] The at least one or more substances may be at least one or more of a glass fabric, at least one or more fillers, and a resin.

[0026] The apparatus for detecting a substance in a target object may include a denoising unit for denoising the acquired image data. [Effects of the Invention]

[0027] The effects of the device and method for detecting a substance in a target object according to the embodiment will be described below.

[0028] According to at least one of the embodiments, there is an advantage that various materials contained in the object can be easily identified using image data acquired from the object.

[0029] According to at least one of the embodiments, since a material is identified based on an image acquired from a target object, the structure is simple and the cost can be reduced.

[0030] Further scope of applicability of the embodiments will become apparent from the following detailed description. However, it should be understood that the detailed description and specific embodiments, such as the preferred embodiment, are merely illustrative, as various changes and modifications within the spirit and scope of the embodiments will be apparent to those skilled in the art. [Brief explanation of the drawings]

[0031] [Figure 1] FIG. 1 is a cross-sectional view illustrating a circuit board according to an embodiment.

[0032] [Figure 2] FIG. 2 is a block diagram illustrating the device for detecting a substance in an object according to the first embodiment.

[0033] [Figure 3] FIG. 3 is a flowchart illustrating a method for detecting a substance in a target object according to an embodiment. [Figure 4] FIG. 4 illustrates the detection of materials depending on brightness.

[0034] [Figure 5a] FIG. 5a illustrates the detection of substances according to size. [Figure 5b] FIG. 5b illustrates the detection of substances according to size.

[0035] [Figure 6a] FIG. 6a illustrates the detection of the resin. [Figure 6b] FIG. 6b illustrates the detection of the resin. [Figure 6c] FIG. 6c illustrates the detection of the resin. [Figure 6d] FIG. 6d illustrates the detection of the resin. [Figure 6e] FIG. 6e illustrates the detection of the resin. [Figure 6f] FIG. 6f illustrates the detection of the resin.

[0036] [Figure 7a] FIG. 7a illustrates how fillers are detected. [Figure 7b] FIG. 7b illustrates how fillers are detected.

[0037] [Figure 8a] FIG. 8a illustrates detecting the glass fabric. [Figure 8b] FIG. 8b illustrates detecting the glass fabric.

[0038] [Figure 9a] FIG. 9a illustrates the detection of voids. [Figure 9b] FIG. 9b illustrates how voids are detected.

[0039] [Figure 10] FIG. 10 illustrates detecting substances according to color.

[0040] [Figure 11] FIG. 11 is a block diagram illustrating an apparatus for detecting a substance in an object according to the second embodiment.

[0041] [Figure 12a] FIG. 12a illustrates the image before and after denoising. [Figure 12b] FIG. 12b illustrates the image before and after denoising. DETAILED DESCRIPTION OF THE INVENTION

[0042] Hereinafter, the embodiments disclosed herein will be described in detail with reference to the accompanying drawings. Regardless of the reference numerals, identical or similar components will be designated by the same reference numerals, and redundant description thereof will be omitted. The suffixes "module" and "section" used in the following description are used interchangeably to facilitate the preparation of the specification and do not have any distinguishing meaning or function. The accompanying drawings are provided solely for the purpose of facilitating understanding of the embodiments disclosed herein, and are not intended to limit the technical ideas disclosed herein. Furthermore, when an element such as a layer, region, or substrate is referred to as being "on" another element, this includes whether it is directly on the other element or whether other intermediate elements may exist therebetween.

[0043] The embodiments may provide a method and apparatus that can easily identify the type of substance contained in an object such as a raw material.

[0044] In the following description, a circuit board is typically used as a raw material, but the embodiment may include a raw material containing at least one substance. The embodiment may also include a component containing at least one substance other than the raw material.

[0045] FIG. 1 is a cross-sectional view illustrating a circuit board according to an embodiment.

[0046] Referring to FIG. 1, the circuit board according to the embodiment may include a plurality of prepregs 110, a resin layer 120 between the prepregs 110, and a plurality of vias 151, 152.

[0047] The prepreg 110 may be formed by impregnating a fiber layer in the form of a woven sheet, such as a glass fabric 112 woven with glass fiber yarn, with an epoxy resin 111 and then subjecting the layer to heat compression. However, the embodiment is not limited thereto. That is, the prepreg 110 may also include a fiber layer in the form of a woven sheet woven with carbon fiber yarn.

[0048] After the resin layer 120 is placed between the prepregs 110, the substrate is completed by applying pressure using a thermocompression method using a press. Then, via holes are formed by drilling the prepregs 110, and the via holes are filled with or plated with a conductive material to form vias 151 and 152. The conductive material may be any one selected from Cu, Ag, Sn, Au, Ni, and Pd. The via holes can be formed using any one of mechanical, laser, and chemical processing methods.

[0049] The vias 151 and 152 are electrically connected to the circuit patterns 141 to 143 provided on the upper and / or lower sides of the prepreg 110 .

[0050] Meanwhile, the undescribed reference numeral 113 is a void, which is an empty space where no substance exists. Also, the undescribed reference numeral 130 is a filler. The filler 130 may include various different types of fillers.

[0051] FIG. 2 is a block diagram illustrating the device for detecting a substance in an object according to the first embodiment.

[0052] In the following description, for convenience of explanation, the substrate illustrated in FIG. 1 is limited as the subject matter, but the embodiments can be similarly applied to other members other than the substrate illustrated in FIG. 1.

[0053] 1 and 2, the apparatus 200 for detecting a substance of an object according to the first embodiment may include an image acquiring unit 210, a feature extracting unit 220, and a substance determining unit 230.

[0054] The image acquiring unit 210 can acquire image data for a specific region of the object.

[0055] The image data may be acquired by a destructive testing method. For example, the object may be cut to expose a specific cross section of the object, and image data may be acquired for the specific cross section. For example, the image data may be acquired by a microscope, SEM, TEM, etc., but is not limited thereto.

[0056] Meanwhile, the embodiment may also acquire image data for a specific area to be photographed using a non-destructive inspection method.

[0057] The feature extraction unit 220 can extract a plurality of features included in the image data, such as brightness, size, shape, color, and sharpness.

[0058] Image data includes various materials that can be distinguished from one another based on brightness, boundary, color, definition, etc. That is, these materials can be distinguished from one another based on brightness, boundary, color, definition, etc. The substrate illustrated in FIG. 1 may refer to various different materials. That is, the substrate is formed by a composition of various materials. These materials may be resin, glass fabric, filter, etc. Voids can also be classified as a type of material as they are included in the substrate.

[0059] The material determination unit 230 may determine at least one material based on at least one of the features extracted by the feature extraction unit 220. That is, at least one material may be determined based on at least one of brightness, size, shape, and color.

[0060] Since the substrate illustrated in FIG. 1 includes resin, at least one or more fillers, glass fabric, and voids as materials, at least one or more of the resin, at least one or more fillers, glass fabric, and voids may be determined based on at least one or more of brightness, size, shape, and color.

[0061] For example, the material determining unit 230 may determine at least one material based on brightness.

[0062] For example, the material determining unit 230 may determine at least one material based on brightness and size.

[0063] For example, the material determining unit 230 may determine at least one material based on brightness, size, and shape.

[0064] For example, the material determiner 230 may determine at least one material based on brightness, size, shape, and color.

[0065] Meanwhile, the material determination unit 230 may determine voids based on clarity. As shown in FIG. 9A, when viewing image data in color, both resin and voids appear black, making them indistinguishable from each other and resulting in errors when determining whether they are voids or resin. Therefore, by considering clarity as a characteristic, resin and voids may be distinguished based on the magnitude of clarity. By increasing clarity, the boundaries of voids may be clearly visible. In this case, taking into account that voids are relatively much smaller than resins, clarity may be increased to determine that a material with an outline that is much smaller than resins is a void.

[0066] Meanwhile, the object substance detection device 200 according to the first embodiment may include a storage unit (not shown) that stores image data and various information about each substance, i.e., brightness information, size information, shape information, color information, size information, etc. The storage unit stores various data and information generated in the embodiment.

[0067] The object substance detection device 200 according to the first embodiment may include a labeling unit (not shown) for labeling each of the detected or determined substances. The labeling may be processed using color, text, pictures, shapes, highlights, etc.

[0068] The target substance detection device 200 according to the first embodiment may include a calculation unit (or an arithmetic unit, not shown) that calculates the size and area.

[0069] FIG. 3 is a flowchart illustrating a method for detecting a substance in a target object according to an embodiment.

[0070] As shown in FIGS. 2 and 3, image data may be acquired by the image acquisition unit 210 (S310), a plurality of features may be extracted by the feature extraction unit 220 (S320), and at least one substance may be determined by the substance determination unit 230 (S330).

[0071] Hereinafter, a method for determining at least one substance using a plurality of features will be described with reference to FIGS.

[0072] FIG. 4 illustrates the detection of materials depending on brightness.

[0073] As shown in FIG. 4, brightness is one of several features used to determine voids, resin, and filler.

[0074] Image data may include various materials that are differentiated by size, shape, color, etc. The brightness of these materials may also be different. The number of materials included in the image data according to their brightness may be illustrated as shown in FIG. 4. The unit of brightness may be gray-scale, but is not limited thereto.

[0075] A material between 0 gradation and A gradation is a void, a material between A gradation and B gradation is a resin, and a material above B gradation may be a filler.

[0076] Therefore, among the materials extracted from the video data, materials distributed between 0 gradation and A gradation may be determined as voids, materials distributed between A gradation and B gradation may be determined as resins, and materials distributed above B gradation may be determined as fillers.

[0077] Unlike what is shown in FIG. 4, other materials may be determined instead of the voids, resin, and filler.

[0078] 5a and 5b illustrate the detection of substances according to size.

[0079] As shown in Figures 5a and 5b, different materials are detected according to their size.

[0080] A material having the same size as or larger than the a size may be determined as the glass fabric 315 (FIG. 5a), and a material having a smaller size than the b size may be determined as the filler 312 (FIG. 5b). The a size may be several times to several tens of times larger than the b size, but is not limited thereto.

[0081] If the filler 312 is divided into multiple types, the b size may be set as a reference size such as b-1 size, b-2 size, b-3 size, etc. The b-2 size may be smaller than the b-1 size, and the b-3 size may be smaller than the b-2 size.

[0082] 6a to 6f illustrate how the resin is detected.

[0083] FIG. 6a shows the original image data acquired from the image acquisition unit 210 shown in FIG. 2, and FIGS. 6b to 6f show that the area where resin is detected changes according to different reference values.

[0084] FIG. 6b shows a distribution of resin 311 detected when the reference value of 5 pixels is exceeded, where only resin 311 with an area larger than the entire area of ​​5 pixels may be detected. FIG. 6c shows a distribution of resin 311 detected when the reference value of 15 pixels is exceeded, where only resin 311 with an area larger than the entire area of ​​15 pixels may be detected. FIG. 6d shows a distribution of resin 311 detected when the reference value of 25 pixels is exceeded, where only resin 311 with an area larger than the entire area of ​​25 pixels may be detected. FIG. 6e shows a distribution of resin 311 detected when the reference value of 35 pixels is exceeded, where only resin 311 with an area larger than the entire area of ​​35 pixels may be detected. FIG. 6f shows a distribution of resin 311 detected when the reference value of 50 pixels is exceeded, where only resin 311 with an area larger than the entire area of ​​50 pixels may be detected.

[0085] As shown in Figures 6b to 6f, the distribution of detected resin 311 varies depending on the magnitude of the reference value. If the reference value is low or high, the amount of detected resin 311 will be small or large, which may reduce the accuracy of resin 311 detection, so an optimal reference value setting is required. For example, the optimal reference value may be 25 pixels, but is not limited to this.

[0086] On the other hand, the resin 311 may be detected by using at least one of a plurality of features extracted from the video data.

[0087] For example, the resin 311 may be detected using brightness. For example, the resin 311 may be detected using brightness and size. For example, the resin 311 may be detected using brightness, size, and shape. For example, the resin 311 may be detected using brightness, size, shape, and color.

[0088] 7a and 7b illustrate how fillers are detected.

[0089] A plurality of features may be extracted from the video data (FIG. 7a), and at least one of the extracted features may be used to detect a plurality of fillers 312 (FIG. 7b).

[0090] For example, the filler 312 may be detected using brightness. For example, the filler 312 may be detected using brightness and size. For example, the filler 312 may be detected using brightness, size, and shape. For example, the filler 312 may be detected using brightness, size, shape, and color.

[0091] Figures 8a and 8b illustrate the detection of the glass fabric.

[0092] As shown in Figures 1, 8a and 8b, the glass fabric 315 may be impregnated with epoxy resin 313 or the like and then heat-pressed to form a woven fiber layer such as a glass fabric 315. Other resins may be used instead of the epoxy resin 313.

[0093] FIG. 8b illustrates how the epoxy resin 313 is detected, and the distribution position and density of the prepreg 110 can be ascertained by detecting the epoxy resin 313.

[0094] The epoxy resin 313 may be included in the prepreg (110 in FIG. 1), and the resin 311 shown in FIGS. 6a to 6f may be included in the resin layer (120 in FIG. 1).

[0095] The epoxy 313 may be detected using multiple characteristics, such as brightness, size, shape, color, etc.

[0096] Alternatively, the epoxy resin 313 may be detected together with the glass fabric 315 since it constitutes the prepreg 110 together with the glass fabric 315 and is located around the glass fabric 315 .

[0097] 9a and 9b illustrate how voids are detected.

[0098] By adjusting the sharpness of the image data (FIG. 9a), voids 314 may be detected (FIG. 9b).

[0099] In the image data shown in Figure 9a, both the resin and the voids 314 appear black, making it difficult to distinguish them from each other. Therefore, the clarity is adjusted to distinguish the voids 314 from the resin or other components. Even though both the voids 314 and the resin appear black, increasing the clarity allows the voids 314 to be distinguished from the resin.

[0100] As shown in FIG. 9b, by adjusting the visibility, the voids 314 are detected as being distinguished from the resin.

[0101] On the other hand, as mentioned above, substances may be detected according to color.

[0102] FIG. 10 illustrates detecting substances according to color.

[0103] As shown in Figure 10, various materials are classified into white and black in the image data, and various materials are detected according to the difference in color including white and black.

[0104] FIG. 10 illustrates a prepreg that can include epoxy resin 313 and glass fabric 315 .

[0105] For example, the epoxy resin 313 may be displayed in black and the glass fabric 315 may be displayed in white.

[0106] A material smaller than the size of the glass fabric 315 may be a filler. Both the glass fabric 315 and the filler may be displayed in white. In this case, if the sizes of the glass fabric 315 and the filler are known, the material displayed in white that corresponds to the size can be detected as the glass fabric 315 or the filler.

[0107] When fillers of various sizes are present, each of the fillers can be detected separately by knowing the size of each of the fillers.

[0108] FIG. 11 is a block diagram illustrating an apparatus for detecting a substance in an object according to the second embodiment.

[0109] Referring to FIG. 11, the apparatus 201 for detecting a substance of an object according to the second embodiment may include an image acquiring unit 210, a denoising unit 240, a feature extracting unit 220, and a substance determining unit 230.

[0110] The image acquiring unit 210, the feature extracting unit 220, and the material determining unit 230 have been described in the first embodiment (FIG. 2), so detailed description thereof will be omitted.

[0111] The denoising unit 240 can enhance the edges of the specific material and process the shading within the specific material to be uniform.

[0112] FIG. 12a shows video data that has not been subjected to denoising processing, and FIG. 12b shows video data that has been subjected to denoising processing.

[0113] Without denoising (Fig. 12a), it can be seen that the edges of the glass fabric 315 are blurred and the shading within it is not consistent. However, with denoising (Fig. 12b), it can be seen that the edges of the glass fabric 315 are sharper and the shading within it is consistent.

[0114] The above detailed description should not be construed as limiting in all respects, but should be considered as illustrative. The scope of the embodiments should be determined by a reasonable analysis of the appended claims, and all modifications within the equivalent range of the embodiments are included in the scope of the embodiments.

Claims

1. acquiring image data for a specific region of the object; extracting a plurality of features contained in the acquired image data; determining at least one substance based on at least one of the extracted features.

2. The method for detecting a substance in an object according to claim 1 , wherein the plurality of features include brightness, size, shape, and color.

3. The step of determining the at least one substance comprises: The method of claim 2 , further comprising determining the at least one material based on the brightness.

4. The step of determining the at least one substance comprises: The method of claim 2 , further comprising determining the at least one material based on the brightness and the size.

5. The step of determining the at least one substance comprises: The method of claim 2 , further comprising determining the at least one material based on the brightness, the size, and the shape.

6. The step of determining the at least one substance comprises: The method of claim 2 , further comprising determining the at least one substance based on the brightness, the size, the shape, and the color.

7. the plurality of characteristics includes clarity; The step of determining the at least one substance comprises: The method of claim 2 , further comprising determining voids based on the visibility.

8. The method for detecting a substance in an object according to claim 1 , wherein the at least one substance is at least one of glass fabric, at least one filler, and resin.

9. The method of claim 1 , further comprising the step of denoising the acquired image data before extracting the plurality of features.

10. an image acquisition unit for acquiring image data for a specific region of the object; a feature extraction unit for extracting a plurality of features included in the acquired image data; and a substance determination unit that determines at least one substance based on at least one of the extracted features.

11. The apparatus for detecting a substance in an object according to claim 10 , wherein the plurality of characteristics include brightness, size, shape, and color.

12. The device for detecting a substance of an object according to claim 11 , wherein the substance determination unit determines the at least one substance based on the brightness.

13. The device for detecting a substance of an object according to claim 11 , wherein the substance determination unit determines the at least one substance based on the brightness and the size.

14. The device for detecting a substance of an object according to claim 11 , wherein the substance determination unit determines the at least one substance based on the brightness, the size, and the shape.

15. The device for detecting a substance of an object according to claim 11 , wherein the substance determination unit determines the at least one substance based on the brightness, the size, the shape, and the color.

16. the plurality of characteristics includes clarity; The device for detecting a material of an object according to claim 11 , wherein the material determination unit determines a void based on the degree of definition.

17. The device for detecting a substance of an object according to claim 10 , wherein the at least one or more substances are at least one or more of a glass fabric, at least one or more fillers, and a resin.

18. The apparatus for detecting a substance in an object according to claim 10, further comprising a denoising unit for denoising the acquired image data.

Images