KR20240044042A - An apparatus for detecting substance in an object and a method for detecting the substance in the object - Google Patents

Abstract

A method for detecting a substance of an object acquires image data for a specific area of the object, extracts a plurality of features included in the obtained image data, and extracts at least one substance based on at least one of the extracted plurality of features. decide.

Description

{An apparatus for detecting substance in an object and a method for detecting the substance in the object}

The embodiment relates to an apparatus for detecting a substance in an object and a method for detecting a substance in the object.

In the case of manufacturers who produce products based on objects such as raw materials, they want to know the types of substances contained in the raw materials or the characteristics of the substances, but the raw material supplier does not provide such information for reasons such as information security. The type or characteristics of the material contained in the raw material are unknown.

If the manufacturer knows the types of substances contained in the raw materials or the characteristics of the substances, the causality of how these substances change or are related in the process of manufacturing the product can be identified, and based on that causality, what may occur during product production can be determined. Defects can be minimized. In addition, if the manufacturer knows the type or characteristics of the material contained in the raw material, information on changing the type of material or adjusting the material characteristics to minimize defects that may occur during product production is provided to the raw material supplier. By providing feedback, you can later receive better quality raw materials from raw material suppliers and manufacture better quality products.

Therefore, there is a strong demand for technology that can easily identify the type or material characteristics of the material contained in the object.

The embodiments aim to solve the above-described problems and other problems.

Another purpose of the embodiment is to provide an apparatus for detecting a substance in an object and a method for detecting a substance in an object that can easily identify substances contained in the object.

Another purpose of the embodiment is to provide an apparatus for detecting a substance in an object and a method for detecting a substance in an object that have a simple structure and can reduce cost by identifying the substance based on an image acquired from the object.

The technical problems of the embodiments are not limited to those described in this item and include those that can be understood through the description of the invention.

According to one aspect of the embodiment to achieve the above or other objects, a method for detecting a substance of an object includes acquiring image data for a specific area 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 plurality of characteristics.

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

Determining the at least one material may include determining the at least one material based on the brightness.

Determining the at least one material may include determining the at least one material based on the brightness and the size.

Determining the at least one material may include determining the at least one material based on the brightness, the size, and the shape.

Determining the at least one material may include determining the at least one material based on the brightness, the size, the shape, and the color.

The plurality of characteristics may include sharpness. The step of determining the at least one material may include determining a void based on the clarity.

The at least one material may be at least one of glass fabric, at least one filler, and resin.

Before extracting the plurality of features, the method may include denoising the acquired image data.

According to another aspect of the embodiment to achieve the above or other objects, an apparatus for detecting a substance of an object includes: an image acquisition unit that acquires image data for a specific area of the object; a feature extraction unit that extracts a plurality of features included in the acquired image data; and a material determination unit that determines at least one material based on at least one of the extracted plurality of features.

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

The material determination unit may determine the at least one material based on the brightness.

The material determination unit may determine the at least one material based on the brightness and the size.

The material determination unit may determine the at least one material based on the brightness, size, and shape.

The material determination unit may determine the at least one material based on the brightness, size, shape, and color.

The plurality of characteristics may include sharpness. The material determination unit may determine the void based on the clarity.

The at least one material may be at least one of glass fabric, at least one filler, and resin.

The apparatus for detecting a substance of the object may include a denoising unit that denoises the acquired image data.

The effects of the device for detecting a substance in an object and the method for detecting a substance in an object according to an embodiment are described as follows.

According to at least one of the embodiments, there is an advantage that various substances contained in the object can be easily identified through image data acquired from the object.

According to at least one of the embodiments, since the material is identified based on the image obtained from the object, there is an advantage in that the structure is simple and the cost can be reduced.

Additional scope of applicability of the embodiments will become apparent from the detailed description that follows. However, since various changes and modifications within the spirit and scope of the embodiments may be clearly understood by those skilled in the art, the detailed description and specific embodiments, such as preferred embodiments, should be understood as being given by way of example only.

1 is a cross-sectional view showing a circuit board according to an embodiment.
Figure 2 is a block diagram showing an apparatus for detecting a substance of an object according to a first embodiment.
Figure 3 is a flowchart explaining a method for detecting a substance in an object according to an embodiment.
Figures 5a and 5b show detection of substances according to size.
Figures 6a to 6f show detection of resin.
Figures 7a and 7b show detection of filler.
Figures 8a and 8b show detection of glass fabric.
9A and 9B show detection of voids.
Figure 10 shows detection of substances according to color.
Figure 11 is a block diagram showing an apparatus for detecting a substance of an object according to a second embodiment.
Figures 12a and 12b show images before and after denoising.

Hereinafter, embodiments disclosed in the present specification will be described in detail with reference to the attached drawings, but identical or similar components will be assigned the same reference numbers regardless of the reference numerals, and duplicate descriptions thereof will be omitted. The suffixes 'module' and 'part' for components used in the following description are given or used interchangeably in consideration of ease of specification preparation, and do not have distinct meanings or roles in themselves. In addition, the attached drawings are intended to facilitate easy understanding of the embodiments disclosed in this specification, and the technical idea disclosed in this specification is not limited by the attached drawings. Additionally, when an element such as a layer, region or substrate is referred to as being 'on' another component, this includes either directly on the other element or there may be other intermediate elements in between. do.

Embodiments may provide a method and device that can easily determine the type of material contained in an object, such as a raw material.

In the following description, a circuit board is representatively presented as a raw material, but embodiments may include raw materials containing at least one or more substances. In addition, the embodiment may include a member containing at least one or more substances in addition to raw materials.

1 is a cross-sectional view showing a circuit board according to an embodiment.

Referring to FIG. 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. .

The prepreg 110 may be formed by impregnating an epoxy resin 111 or the like into a fiber layer in the form of a fabric sheet, such as a glass fabric 112 woven with glass fiber yarn, and then heat-compressing it. However, the embodiment is not limited to this. That is, the prepreg 110 may include a fiber layer in the form of a fabric sheet woven with carbon fiber yarn.

After the resin layer 120 is positioned between the prepregs 110, the substrate can be completed through thermal compression using a press. Thereafter, a via hole is formed by drilling the prepreg 110, and the via hole 151 and 152 can be formed by filling the via hole with a conductive material or plating it with a conductive material. The conductive material may be any one selected from Cu, Ag, Sn, Au, Ni, and Pd. The via hole may be formed by any one of mechanical, laser, and chemical processing.

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

Meanwhile, the unexplained symbol 113 is a void, which may be an empty space where no material exists. Additionally, the unexplained symbol 130 is a filler. The filler 130 may include various different types of fillers.

Figure 2 is a block diagram showing an apparatus for detecting a substance of an object according to a first embodiment.

In the following description, for convenience of explanation, the substrate shown in FIG. 1 is limited to the object, but the embodiment may be equally applied to other members other than the substrate shown in FIG. 1.

Referring to FIGS. 1 and 2 , the apparatus 200 for detecting a substance of an object according to the first embodiment may include an image acquisition unit 210, a feature extraction unit 220, and a substance determination unit 230.

The image acquisition unit 210 may acquire image data for a specific area of the object.

Image data may be acquired through destructive testing methods. 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 cut cross-section. For example, image data may be acquired by, for example, a microscope, SEM or TEM, but there is no limitation thereto.

Meanwhile, in an embodiment, image data for a specific area to be photographed may be acquired through a non-destructive testing method.

The feature extractor 220 may extract a plurality of features included in image data. The plurality of characteristics may include, for example, brightness, size, shape, color, sharpness, etc.

Image data may include various substances that can be distinguished from each other by brightness, border, color, sharpness, etc. In other words, these materials can be distinguished from each other by brightness, border, color, clarity, etc. The substrate shown in FIG. 1 may include a variety of different materials. In other words, the substrate can be formed by composition of various materials. These materials may be resin, glass fabric, filters, etc. Voids are also included in the substrate and can be classified as a type of material.

The material determination unit 230 may determine at least one material based on at least one of the plurality of 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.

Since the substrate shown in FIG. 1 includes resin, at least one filler, glass fabric, and voids, the resin, at least one filler, glass fabric, and voids are determined based on at least one of brightness, size, shape, and color. At least one of these can be determined.

For example, the material determination unit 230 may determine at least one material based on brightness.

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

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

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

Meanwhile, the material determination unit 230 may determine voids based on clarity. As shown in FIG. 9A, when viewed in color in the image data, both the resin and the void appear black, so they may not be distinguished from each other, and an error may occur when determining the void and the resin. Therefore, considering clarity as a characteristic, resin and voids can be distinguished through the size of clarity. By increasing clarity, the boundaries of voids can be clearly revealed. In this case, considering that the void is relatively very small compared to the resin, a material with a contour angle that is much smaller than the resin in size by increasing clarity can be determined to be a void.

Meanwhile, the apparatus 200 for detecting a substance of an object according to the first embodiment has a storage unit (not shown) that stores image data and various information of each substance, that is, brightness information, size information, shape information, color information, size information, etc. Poetry) may be included. The storage unit may store various data or information generated in the embodiment.

The apparatus 200 for detecting a substance of an object according to the first embodiment may include a labeling unit (not shown) that labels each of the detected or determined substances. Labeling can be done with colors, text, pictures, shapes, highlights, etc.

The apparatus 200 for detecting a substance of an object according to the first embodiment may include a calculation unit (or calculation unit, not shown) that calculates the size or area.

Figure 3 is a flowchart explaining a method for detecting a substance in an object according to an embodiment.

2 and 3, image data is acquired by the image acquisition unit 210 (S310), a plurality of features are extracted by the feature extraction unit 220 (S320), and the material determination unit (S320) 230), at least one substance may be determined (S330).

Hereinafter, a method of determining at least one material using a plurality of characteristics will be described with reference to FIGS. 4 to 10.

Figure 4 shows detection of substances according to brightness.

As shown in FIG. 4, voids, resin, and filler can be determined using brightness, which is one of a plurality of characteristics.

Image data may include various substances classified by size, shape, color, etc. The brightness of these materials may also be different. The number of substances included in the image data according to their brightness can be displayed as shown in FIG. 4 . The unit of brightness may be gray-scale, but there is no limitation thereto.

The material corresponding to the 0 gradation and the A gradation may be a void, the material between the A and B gradation may be a resin, and the material corresponding to the B gradation or higher may be a filler.

Therefore, among the substances extracted from image data, those distributed between 0 and A gradations are determined as voids, those distributed between A and B gradations are determined as resin, and those distributed above B gradations are determined as resins. It can be determined by filler.

Unlike shown in FIG. 4, other materials may be used instead of voids, resin, and filler.

Figures 5a and 5b show detection of substances according to size.

As shown in FIGS. 5A and 5B, different substances can be detected depending on their size.

A material equal to or larger than size a may be determined as the glass fabric 315 (Figure 5a), and a material smaller than size b may be determined as the filler 312 (Figure 5b). Size a may be several to dozens of times larger than size b, but is not limited to this.

If the filler 312 is divided into several types, the standard size for size b may be set to size b-1, size b-2, size b-3, etc. Size b-2 may be smaller than size b-1, and size b-2 may be smaller than size b-3.

Figures 6a to 6f show detection of resin.

FIG. 6A is the original image data acquired by the image acquisition unit 210 shown in FIG. 2, and FIGS. 6B to 6F show that the area where resin is detected varies depending on different reference values.

Figure 6b shows the distribution of the resin 311 detected when the reference value of 5 pixels is exceeded, and only the resin 311 larger than the total area of 5 pixels can be detected. Figure 6c shows the distribution of the resin 311 detected when it exceeds the reference value of 15 pixels, and only the resin 311 larger than the total area of 15 pixels can be detected. Figure 6d shows the distribution of the resin 311 detected when the reference value of 25 pixels is exceeded, and only the resin 311 larger than the total area of 25 pixels can be detected. Figure 6e shows the distribution of the resin 311 detected when the reference value of 35 pixels is exceeded, and only the resin 311 larger than the total area of 35 pixels can be detected. Figure 6f shows the distribution of the resin 311 detected when it exceeds the reference value of 50 pixels, and only the resin 311 larger than the total area of 50 pixels can be detected.

As shown in FIGS. 6B to 6F, the degree of distribution of the detected resin 311 may vary depending on the size of the reference value. If the reference value is low or large, the amount of resin 311 detected may be small or large, and the accuracy of detecting the resin 311 may decrease, so setting an optimal reference value is required. For example, the optimal reference value may be 25 pixels, but there is no limitation thereto.

Meanwhile, the resin 311 may be detected using at least one of a plurality of features extracted from image data.

For example, resin 311 can be detected using brightness. For example, resin 311 can be detected using brightness and size. For example, resin 311 can be detected using brightness, size, and shape. For example, resin 311 can be detected using brightness, size, shape, and color.

Figures 7a and 7b show detection of filler.

A plurality of features may be extracted from image data (FIG. 7A), and a plurality of fillers 312 may be detected using at least one of the extracted features (FIG. 7B).

For example, filler 312 may be detected using brightness. For example, filler 312 can be detected using brightness and size. For example, filler 312 can be detected using brightness, size, and shape. For example, filler 312 can be detected using brightness, size, shape, and color.

Figures 8a and 8b show detection of glass fabric.

As shown in FIGS. 1, 8A, and 8B, it can be formed by impregnating an epoxy resin 313, etc. into a fiber layer in the form of a fabric sheet, such as a glass fabric 315 woven with glass fiber threads, and then thermally compressing it. Other resins may be used instead of the epoxy resin 313.

FIG. 8B shows how the epoxy resin 313 is detected, and the distribution position or density of the prepreg 110 can be determined through detection of the epoxy resin 313.

The epoxy resin 313 is included in the propreg (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).

The epoxy resin 313 may be detected using a plurality of characteristics, such as brightness, size, shape, color, etc.

In contrast, since the epoxy resin 313 constitutes the prepreg 110 together with the glass fabric 315 and is located around the glass fabric 315, it may be detected together through detection of the glass fabric 315.

9A and 9B show detection of voids.

By adjusting the sharpness in the image data (FIG. 9A), voids 314 can be detected (FIG. 9B).

In the image data shown in FIG. 9A, both the resin and the void 314 appear black, making it difficult to distinguish them from each other. Accordingly, the clarity can be adjusted to distinguish the void 314 from resin or other members. Even if both the void 314 and the resin appear black, the void 314 can be distinguished from the resin by increasing clarity.

As shown in FIG. 9B, by adjusting the clarity, the voids 314 can be detected separately from the resin.

Meanwhile, as previously described, substances can be detected according to color.

Figure 10 shows detection of substances according to color.

As shown in FIG. 10, various materials can be distinguished by white and black in image data. Various substances can be detected through different colors including white and black.

Figure 10 shows a prepreg, which may include epoxy resin 313 and glass fabric 315.

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

Materials smaller than the size of the glass fabric 315 may be fillers. 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 corresponding to the corresponding size among the materials displayed in white can be detected as the glass fabric 315 or the filler.

When fillers of various sizes exist and the sizes of each of the various fillers are known, each of the fillers can be distinguished and detected.

Figure 11 is a block diagram showing an apparatus for detecting a substance of an object according to a second embodiment.

Referring to FIG. 11, the apparatus 201 for detecting a substance of an object according to the second embodiment includes an image acquisition unit 210, a denoising unit 240, a feature extraction unit 220, and a substance determination unit 230. can do.

Since the image acquisition unit 210, feature extraction unit 220, and material determination unit 230 have been described in the first embodiment (FIG. 2), detailed descriptions are omitted.

The denoise unit 240 can process the shading inside a specific material to be the same while emphasizing the edges of the specific material.

FIG. 12A may be image data on which denoise processing has not been performed, and FIG. 12b may be image data on which denoise processing has been performed.

When denoising processing is not performed (FIG. 12a), it can be seen that the edges of the glass fabric 315 are blurred and the shading inside is not constant. However, when denoising processing is performed (FIG. 12b), it can be seen that the edges of the glass fabric 315 are clearer and the shading inside is the same.

The above detailed description should not be construed as restrictive in any respect and should be considered illustrative. The scope of the embodiments should be determined by reasonable interpretation of the appended claims, and all changes within the equivalent scope of the embodiments are included in the scope of the embodiments.

110: prepreg
120: Resin layer
130: filler
141 to 143: Circuit pattern
151, 152: Via
200, 201: Device for detecting substance of object
210: Image acquisition unit
220: feature extraction unit
230: material crystal unit
240: Denoise department
311: Resin
312: filler
313: Epoxy resin
314: void
315: glass fabric

Claims (18)

Obtaining image data for a specific area of the object;
extracting a plurality of features included in the acquired image data; and
Including, determining at least one substance based on at least one of the extracted plurality of characteristics.
Method for detecting substances in an object. According to paragraph 1,
The plurality of characteristics include brightness, size, shape and color,
Method for detecting substances in an object. According to paragraph 2,
The step of determining the at least one substance is,
Including, determining the at least one substance based on the brightness.
Method for detecting substances in an object. According to paragraph 2,
The step of determining the at least one substance is,
Including, determining the at least one material based on the brightness and the size.
Method for detecting substances in an object. According to paragraph 2,
The step of determining the at least one substance is,
Including, determining the at least one material based on the brightness, the size, and the shape.
Method for detecting substances in an object. According to paragraph 2,
The step of determining the at least one substance is,
Including, determining the at least one material based on the brightness, the size, the shape, and the color.
Method for detecting substances in an object. According to paragraph 2,
The plurality of characteristics include sharpness,
The step of determining the at least one substance is,
Including, determining voids based on the sharpness,
Method for detecting substances in an object. According to paragraph 1,
The at least one material is at least one of glass fabric, at least one filler, and resin,
Method for detecting substances in an object. According to paragraph 1,
Before extracting the plurality of features,
Including, denoising the acquired image data.
Method for detecting substances in an object. An image acquisition unit that acquires image data for a specific area of the object;
a feature extraction unit that extracts a plurality of features included in the acquired image data; and
Including a material determination unit that determines at least one material based on at least one of the extracted plurality of features,
A device for detecting substances in an object. According to clause 10,
The plurality of characteristics include brightness, size, shape and color,
A device for detecting substances in an object. According to clause 11,
The material crystal part,
Determining the at least one substance based on the brightness,
A device for detecting substances in an object. According to clause 11,
The material crystal part,
Determining the at least one substance based on the brightness and the size,
A device for detecting substances in an object. According to clause 11,
The material crystal part,
Determining the at least one substance based on the brightness, the size, and the shape,
A device for detecting substances in an object. According to clause 11,
The material crystal part,
Determining the at least one substance based on the brightness, the size, the shape, and the color,
A device for detecting substances in an object. According to clause 11,
The plurality of characteristics include sharpness,
The material crystal part,
Determining voids based on the clarity,
A device for detecting substances in an object. According to clause 10,
The at least one material is at least one of glass fabric, at least one filler, and resin,
A device for detecting substances in an object. According to clause 10,
A denoising unit that denoises the acquired image data; including,
A device for detecting substances in an object.

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