Contour extraction method, contour extraction system, and contour extraction program

Abstract

Even in cross-sectional images of patterns formed on a substrate, where it is difficult to separate and distinguish the pattern and the substrate using conventional binarization processes, the contour of the pattern shape can be accurately extracted. [Solution] The contour extraction method according to the present disclosure includes a computer acquiring a first image of a cross-section of a pattern formed on a substrate, performing a binarization process on the first image, or a flattened first image, according to a predetermined threshold for discriminating between the substrate and pattern and the background to generate a second image, creating a histogram of the y coordinates of a plurality of binary boundary points in the second image, and extracting the contour of the pattern shape in the region of interest of the cross-section based on the second image and the histogram.

Description

[Technical Field]

[0001] This disclosure relates to a contour extraction method, a contour extraction system, and a contour extraction program. [Background technology]

[0002] Conventionally, as an image processing technique for cross-sectional images of semiconductor resists, for example, Patent Document 1 describes a pattern image measurement method for measuring the sidewall angle of a pattern cross-section from an image (SEM image) of a pattern cross-section obtained by a scanning electron microscope. This method is characterized by including the steps of: extracting contour line coordinate data of the pattern cross-section by image processing of the SEM image; extracting coordinate values ​​of the upper and lower parts of the pattern from the contour line coordinate data and calculating the height of the pattern from both; generating a luminance distribution signal corresponding to the coordinate values ​​of two points in the range for measuring the sidewall angle; removing some signal components from the luminance distribution signal; applying the cross-correlation method to the two signals after removal and calculating the distance between the peaks of the two signals; and calculating the sidewall angle from the height of the measurement range and the distance between the signal peaks. [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2012-68138 [Overview of the project] [Problems that the invention aims to solve]

[0004] However, in cross-sectional images of patterns formed on a substrate, for example, if the substrate itself is made of resin or glass, the pattern and the substrate appear so similarly that it is difficult to separate and distinguish them using conventional binarization processing. As a result, conventional image processing techniques tend not to be able to distinguish between the two. Consequently, it may become impossible to accurately extract the contours of pattern shapes such as vias and dams in patterns formed on the substrate, and thus impossible to accurately measure their shape parameters such as width and height.

[0005] Therefore, this disclosure has been made in view of these circumstances, and aims to accurately extract the outline of the pattern shape, including vias, dams, etc., in a pattern, even when it is difficult to separate and identify the pattern and the substrate in a cross-sectional image of the pattern formed on the substrate using normal binarization processing (for example, when the pattern and the substrate are made of the same or similar material, or when the substrate is made of resin or glass). [Means for solving the problem]

[0006] [1] To achieve the above objective, the contour extraction method according to the present disclosure includes a computer acquiring a first image of a cross-section of a pattern formed on a substrate, performing a binarization process on the first image or a flattened first image according to a predetermined threshold for discriminating between the substrate and pattern and the background to generate a second image, creating a histogram (frequency distribution) for the y coordinates of a plurality of binary boundary points in the second image, and extracting the contour of the pattern shape in the region of interest of the cross-section based on the second image and the histogram. Furthermore, the contour extraction system and contour extraction program according to the present disclosure have substantially equivalent configurations corresponding thereto.

[0007] In this disclosure, "coordinates" refers to two-dimensional (x,y) coordinates, where the x-axis is the axis parallel to the surface of the substrate, and the y-axis is the axis perpendicular to the x-axis, i.e., the axis perpendicular to the surface of the substrate. Therefore, the x-coordinate (x component) in these two-dimensional (x,y) coordinates indicates the position coordinate in the direction parallel to the surface of the substrate, and its y-coordinate (y component) indicates the position coordinate in the direction perpendicular to the surface of the substrate. Furthermore, "region of interest" refers to any region specified by the user in the first image, or set or specified by the contour extraction system described later, and includes, for example, regions containing pattern shapes such as vias and dams.

[0008] In this configuration, the first image is subjected to a binarization process according to a predetermined threshold for discriminating between the substrate and pattern and the background. As a result, in the generated second image, the substrate and pattern are both represented, for example, in white, while the background is represented, for example, in black. Then, a histogram of the y-coordinates at multiple binary boundary points (boundaries between white and black) is created for the second image. The positions of two peaks appearing in the histogram can be identified as the surface levels of the pattern and the substrate included in the region of interest, and the contour points between these surface levels of the pattern and the substrate can be identified as the side walls of the pattern shape (vias, dams, etc.).

[0009] [2] The substrate is not particularly limited, but the method is useful for patterns formed on metal, resin, and glass substrates, and is particularly useful for patterns formed on resin and glass substrates such as bismaleimide triazine resin (BT resin).

[0010] [3] The pattern is not particularly limited, but the method is particularly useful for resin composition patterns formed on a substrate (for example, solder resist, which is a resin composition containing an alkali-soluble resin, a photopolymerization initiator, an epoxy resin, an inorganic filler, etc.).

[0011] [4] Further, in the above configuration, the computer may further include measuring the angle of the side wall based on a first coordinate at a predetermined first height from the surface of the base material and a second coordinate at a predetermined second height from the surface of the base material at a portion corresponding to the side wall in the extracted contour. In this way, based on the straight portion of the side wall in the extracted contour, the inclination angle of the side wall can be suitably measured.

[0012] [5] Further, in the above configuration, the computer may further include dividing the area within the extracted contour into a plurality of sub-areas at predetermined intervals by lines along the direction (x-axis direction) parallel to the surface of the base material, setting a plurality of rectangles corresponding to each of the sub-areas, and measuring the width in the direction (x-axis direction) parallel to the surface of the base material for each of the rectangles. In this way, the width of the pattern shape such as a via can be measured in detail and accurately corresponding to the shape of the side wall.

[0013] [6] Further, the contour extraction system according to the present disclosure is a system for effectively implementing the contour extraction method according to the present disclosure, and includes an acquisition unit that acquires a first image of a cross section of a pattern formed on a base material, and a binarization processing unit that performs binarization processing according to a predetermined threshold for discriminating the base material, the pattern, and the background on the first image or the flattened first image to generate a second image, and a contour extraction unit that creates a histogram for the y coordinates of a plurality of binary boundary points in the second image and extracts the contour of the pattern shape in the region of interest of the cross section based on the second image and the histogram.

[0014] [7] Further, the contour extraction program according to the present disclosure is a program for causing a computer to realize the contour extraction system according to the present disclosure. [Advantages of the Invention]

[0015] According to the contour extraction method, contour extraction system, and contour extraction program according to the present disclosure, by performing binarization processing on the first image, the base material, the pattern, and the background are discriminated, a histogram of the y coordinates at a plurality of binary boundary points is created, and from that histogram, the surface level of the pattern, the surface level of the base material, and the side walls of the pattern shape (such as vias and dams) can be specified. As a result, even when the pattern and the base material are made of the same material or similar materials, or when the base material is made of resin or glass, the contour of the pattern shape in the region of interest of the cross-section can be accurately extracted, and shape parameters such as the width and side wall angle of vias, dams, etc. can be accurately measured. As a result, it is possible to realize contour extraction of a pattern shape with excellent versatility regardless of the type of the base material or the pattern.

Brief Description of the Drawings

[0016] [Figure 1] It is a block diagram showing a schematic configuration of a contour extraction system 100 according to an embodiment of the present disclosure. [Figure 2] It is a flowchart showing an example of a processing procedure in a contour extraction method effectively implemented using a contour extraction system. [Figure 3A] It is a photograph showing an example of the first image acquired by the acquisition unit. [Figure 3B] It is a photograph showing an example of the second image generated by the morphology processing unit. [Figure 3C] It is a graph showing an example of a histogram created based on the y coordinates of the binary boundary points in the second image shown in FIG. 3B. [Figure 3D] It is a photograph showing an example of an image in which the display information of the contour extracted by the contour extraction unit from the second image in FIG. 3B is displayed on the user device. [Figure 3E] It is a photograph showing an example of an image in which a line segment corresponding to a part of the side wall among the frame lines representing the contour shown in FIG. 3D is superimposed and displayed on the first image in FIG. 3A. [Figure 3F] It is a photograph showing an example of an image in which a straight line indicating the inclination of the side wall calculated by the angle measurement unit and a straight line indicating the surface of the base material are superimposed and displayed on the first image in FIG. 3A. [Figure 4A] This is a schematic plan view illustrating an example of the process by which the width measuring unit sets multiple rectangles. [Figure 4B] This photograph shows an example of an image in which the width measuring section divides the area within the contour into multiple sub-regions with lines parallel to the surface of the substrate, and each sub-region is superimposed on the first image in Figure 3A. [Figure 5] This is a block diagram showing an example of a hardware configuration when the contour extraction system 100 is implemented using a computer 500. [Modes for carrying out the invention]

[0017] This embodiment will be described below with reference to the attached drawings. To facilitate understanding of the description, the same reference numerals are used for identical components in each drawing whenever possible, and redundant descriptions are omitted. The following embodiments are illustrative examples for illustrating the present disclosure and are not intended to limit the present disclosure to these embodiments only. Furthermore, the present disclosure can be modified in various ways without departing from its essence. Moreover, those skilled in the art can adopt embodiments in which each of the elements described below is replaced with equivalent components, and such embodiments are also included within the scope of the present disclosure.

[0018] [Example of a contour extraction system configuration] Figure 1 is a block diagram illustrating the schematic configuration of a contour extraction system 100 according to one embodiment of the present disclosure. The contour extraction system 100 is an information processing system for extracting the contour of a pattern shape in a region of interest from a first image, which is a cross-sectional image of a pattern formed on a substrate (for example, a resin composition pattern, specifically a solder resist pattern including vias and dams), and is implemented by a computer and a specific contour extraction program. The contour extraction system 100 is communicated with a user device 200 via a network such as the Internet. The user device 200 is an information processing device used by a user of the contour extraction system 100, and is implemented by, for example, a smartphone, tablet terminal, or personal computer. The user can access the contour extraction system 100 using this user device 200, provide the first image to the contour extraction system 100, and refer to a display of the extracted contour associated with the first image.

[0019] As shown in Figure 1, the contour extraction system 100 comprises a storage unit 110, an acquisition unit 115, a parameter receiving unit 120, a binarization processing unit 125, a morphology processing unit 130, a histogram processing unit 135, a contour extraction unit 140, an angle measurement unit 145, a width measurement unit 150, and a display output unit 155. These are functional units that are realized by using a storage area or by a processor executing a program stored in the storage area.

[0020] The memory unit 110 stores various types of information processed by the contour extraction system 100. More specifically, it can store images such as the first image and the second image, as well as parameter information, display information, and measurement result information, which will be described later.

[0021] The acquisition unit 115 acquires a first image, which is a cross-sectional image of a pattern formed on a substrate, from the user device 200 and stores the first image in the storage unit 110. The first image is not particularly limited and can be, for example, an image taken with a scanning electron microscope (SEM) or an optical microscope. The substrate is also not particularly limited and can be, for example, a metal substrate, a resin substrate such as BT resin, or a glass substrate. Thus, the contour extraction system 100 can handle contour extraction of the pattern shape in a pattern formed on a substrate, regardless of the material of the substrate, and is particularly useful when the pattern and the substrate are made of the same material, or for resin composition patterns formed on resin and glass substrates. In this disclosure, a resin substrate means a substrate that contains resin as one of its main components, and may also be a substrate that contains other components, such as a glass epoxy substrate.

[0022] Of the functional units described above, the parameter receiving unit 120 receives information regarding parameters necessary for executing at least one of the following processes (binarization process, morphology process, and histogram process) specified by the user through the user device 200, and stores it in the storage unit 110.

[0023] The binarization processing unit 125 performs a binarization process on the acquired first image, or on the first image that has been flattened (smoothed) by an appropriate method as needed (for example, when noise reduction is effective), according to a predetermined threshold, to generate a binarized second image and store it in the storage unit 110. Examples of the binarization process here include processing to make pixels with pixel values ​​above a predetermined threshold white and pixels with pixel values ​​below a predetermined threshold black, or processing to specify upper and lower limits of brightness and make pixels within those limits white and pixels outside those limits black. Furthermore, the predetermined threshold in this case only needs to be a threshold that is effective in distinguishing the substrate and pattern from the background, and it is not necessary to distinguish the substrate and pattern from each other through the binarization process.

[0024] The morphology processing unit 130 performs morphological processing on the second image generated by the binarization processing unit 125, including a predetermined number of expansion and contraction processes, as needed, to generate a binarized image and store it in the storage unit 110. Here, the expansion process is, for example, a process to expand the white pixel area in the second image, and the contraction process is, for example, a process to shrink the white pixel area in the second image. These expansion and contraction processes may be performed using a rectangular kernel, an elliptical kernel, a cross-shaped kernel, etc. Examples of morphological processing here include an opening process that performs expansion after contraction and a closing process that performs contraction after expansion. The number and order of the opening and closing processes are not particularly limited. Through such morphological processing, interpolation processing can be performed in the second image, such as filling in parts that may appear black and be recognized as defects, and deleting parts that may appear white and be recognized as noise. When morphological processing is performed, the image after morphological processing is considered the second image.

[0025] The histogram processing unit 135 calculates the y coordinates (y) of multiple binary boundary points (boundaries between white pixels and black pixels) in the generated second image. i A histogram is created for ), and the numerical data and the graphed image are stored in the storage unit 110.

[0026] The contour extraction unit 140 identifies the y coordinates of the two peaks that appear in its histogram as the surface level of the pattern included in the region of interest (highest level in the region of interest) and the surface level of the substrate (lowest level in the region of interest). The contour extraction unit 140 can also identify contour points between these surface levels of the pattern and the surface level of the substrate as side walls such as vias and dams. From the position coordinates of these surface levels and side wall portions in the second image, the contour extraction unit 140 extracts the contour of the pattern shape in the pattern formed on the substrate and stores the extraction result in the storage unit 110.

[0027] The angle measuring unit 145 measures the inclination angle of the side wall in the extracted contour. At that time, the angle measuring unit 145 can measure the angle of the side wall at the pixel in the part of the contour corresponding to the side wall, based on a first coordinate (x1, y1) at a predetermined first height from the surface of the substrate, and a second coordinate (x2, y2) at a predetermined second height from the surface of the substrate. In this case, the first and second heights can be 5% to 20% (e.g., 10%) and 50% to 80% (e.g., 60%) from the surface of the substrate, respectively, when the height and depth from the surface of the pattern to the surface of the substrate are set to 100%.

[0028] The width measuring unit 150 divides the extracted contour area into multiple sub-regions at predetermined intervals using lines parallel to the surface of the substrate (x-axis direction), sets multiple rectangles corresponding to each of these sub-regions, and measures the width (width of vias, dams, etc.) of each rectangle in the direction parallel to the surface of the substrate (x-axis direction). In this case, the number of sub-region divisions is not particularly limited; for example, the sub-regions may be divided into units corresponding to the number of pixels equivalent to several micrometers in actual size.

[0029] The display output unit 155 generates display information for displaying the extracted contours in association with the cross-sectional image, and outputs this display information to, for example, the user device 200. At this time, the display output unit 155 can generate the display information based on the histogram and contour extraction result information stored in the storage unit 110, and can also generate and output the display information including the measurement results from the angle measurement unit 145 and the width measurement unit 150.

[0030] [Example of a contour extraction method processing procedure] Figure 2 is a flowchart showing an example of a processing procedure in a contour extraction method effectively implemented using the contour extraction system 100. In this embodiment, first, in step S21, the acquisition unit 115 acquires a first image, which is a cross-sectional image of a pattern formed on a substrate, from the user device 200 and stores it in the storage unit 110.

[0031] Here, Figure 3A is a photograph showing an example of a first image acquired by the acquisition unit 115. The first image 31 consists of a region 301 showing a part of the substrate, a region 302 showing a part of the resin composition pattern (corresponding to an example of a "pattern" in this disclosure) formed on the substrate, a background region 303 including vias (holes) VH drilled in the resin composition pattern, and a region 304 displaying a caption including a scale SC for the side length.

[0032] Next, in step S22, the parameter receiving unit 120 receives parameter information necessary for performing binarization processing, morphology processing, and histogram processing from the user device 200 and stores it in the storage unit 110.

[0033] Then, in step S23, the binarization processing unit 125 performs a binarization process on the first image 31, or the flattened first image 31 if necessary, according to a predetermined threshold effective for discriminating between the substrate and resin composition pattern and the background, to generate a second image, which is then stored in the storage unit 110. Subsequently, if necessary, in step S24, the morphology processing unit 130 performs a morphology process on the second image, including a predetermined number of expansion processes and a predetermined number of contraction processes, to generate an image (second image), which is then stored in the storage unit 110.

[0034] Here, Figure 3B is a photograph showing an example of a second image generated by the morphology processing unit 130 after binarization processing by the binarization processing unit 125. The second image 32 shows the result of binarization processing and morphology processing, in which the pixels corresponding to the substrate region 301 and the resin composition pattern region 302 in the first image 31 shown in Figure 3A are made white, and the pixels corresponding to the background region 303 including via VH and the pixels corresponding to the region 304 including scale SC are made black. For morphology processing, for example, a 5x5 pixel cross-shaped kernel is used to perform three opening processes, followed by three closing processes (however, the number of opening processes and closing processes are not limited to these).

[0035] Next, in step S25, the histogram processing unit 135 calculates the y-coordinates (y i ) of a plurality of binary boundary points (the boundaries between white pixels and black pixels: within the region surrounded by the closed curve BD in FIG. 3B) in the generated second image, and creates a histogram. For example, for a predetermined y-coordinate (y i ), the values of adjacent pixels are compared along the x-axis, and when the values differ between adjacent pixels, they are detected as binary boundary points, and the number (frequency) of binary boundary points is recorded. The same process is performed for each y-coordinate to create a histogram. As described above, in the present disclosure, the direction parallel to the surface of the substrate is defined as the x-axis direction, the direction perpendicular to the surface of the substrate is defined as the y-axis direction, and as shown in FIG. 3B, the rightward and downward directions in the figure are defined as the positive directions of the x-axis and y-axis, respectively. Here, FIG. 3C is a graph showing an example of a histogram created based on the y-coordinates of the binary boundary points in the second image 32 shown in FIG. 3B, where the horizontal axis represents the y-coordinate and the vertical axis represents the frequency (number of occurrences).

[0036] Furthermore, in step S26, the contour extraction unit 140 first identifies the y-coordinates (y P1 , y P2 : in FIG. 3B, y P1 < y P2 ) of the two peaks P1 and P2 that occur in the histogram shown in FIG. 3C. Among them, the y-coordinate (y P1 ) of peak P1 is identified as the surface level of the resin composition pattern, and the y-coordinate (y P2 ) of peak P2 is identified as the surface level of the substrate. Next, the contour extraction unit 140 can identify the contour points between the surface level of the resin composition pattern and the surface level of the substrate as the side walls of vias, dams, etc. Then, the contour extraction unit 140 extracts the contour of the pattern shape (here, via VH) in the resin composition pattern from the information on such coordinate position levels in the second image 32, stores the extraction result in the storage unit 110, and displays it on the user device 200 by the display output unit 155 (step S29).

[0037] Here, Figure 3D is a photograph showing an example of an image displayed on the user device 200, which displays contour information extracted by the contour extraction unit 140 from the second image 32 in Figure 3B. In this example, regions 301 to 304 are displayed in different colors, and the area corresponding to via VH is filled in with yet another different color, with its contour highlighted using a border line OL. This allows the user to easily grasp the contour of the pattern shape in the region of interest of the first image 31 and the second image 32.

[0038] Next, in step S27, the angle measuring unit 145 measures the inclination angle of the side wall in the contour shown by the frame line OL in Figure 3D, stores the measurement result in the storage unit 110, and displays it on the user device 200 by the display output unit 155 (step S29). Here, Figure 3E is a photograph showing an example of an image in which a line segment corresponding to a part of the side wall in the frame line OL representing the contour shown in Figure 3D is superimposed on the first image 31 in Figure 3A. At this time, the angle measuring unit 145 first measures the first coordinate R1(x) at a predetermined first height from the surface of the substrate as part of the right side wall shown in the figure. R1 ,y R1 ), and the second coordinate R2(x) located at a predetermined second height from the surface of the substrate. R2 ,y R2 Set the first coordinate L1(x L1 ,y L1 ), and the second coordinate L2(x L2 ,y L2 Set the coordinates. In this example, as shown in Figure 3E, the first and second coordinates correspond to the positions of both ends of the portion that can be approximated by a nearly straight line in the cross-section of the side wall. When the height and depth from the surface of the resin composition pattern to the surface of the substrate are set to 100%, the first height is set to a position 10% above the surface of the substrate, and the second height is set to a position 60% above the surface of the substrate (see Figure 3E).

[0039] The angle measuring unit 145 measures the first coordinate R1(x) of the right side wall shown in the figure. R1 ,y R1 ), second coordinate R2(x R2 ,y R2), and the approximate straight line SR of each contour point between the first coordinate R1 and the second coordinate R2 is calculated, and the first coordinate L1(x of the left side wall shown in the figure) is calculated. L1 ,y L1 ), second coordinate L2(x L2 ,y L2 ), and an approximate straight line SL for each contour point between the first coordinate L1 and the second coordinate L2 is calculated. Furthermore, the angle measuring unit 145 measures the inclination angle of the side wall from the inclination of these approximate straight lines SR and SL with respect to the substrate surface, stores the measurement result in the storage unit 110, and displays it on the user device 200 by the display output unit 155 (step S29). Here, Figure 3F is a photograph showing an example of an image in which the approximate straight lines SR and SL indicating the inclination of the side wall calculated by the angle measuring unit 145, and the straight line SP indicating the surface of the substrate are superimposed on the first image 31 of Figure 3A.

[0040] Next, in step S28, the width measuring unit 150 divides the extracted contour (frame line OL in Figure 3D) into multiple sub-regions at predetermined intervals using lines along the x-axis direction, sets multiple rectangles corresponding to each of these sub-regions, measures the width in the x-axis direction (width of vias, dams, etc.) for each of these rectangles, stores the measurement results in the storage unit 110, and displays them on the user device 200 via the display output unit 155 (step S29).

[0041] Here, Figure 4A is a schematic plan view showing an example of the process by which the width measuring unit 150 sets multiple rectangles. The contour line 401 represents, for example, the contour extracted by the contour extraction unit 140 (corresponding to the frame line OL in Figure 3D). Lines 402a, 402b, 402c, 402d, and 402e correspond to lines that divide the area within the contour into sub-regions a, b, c, and d, for example. These sub-regions are areas enclosed by the contour line 401 and the upper and lower two lines from among lines 402a, 402b, 402c, 402d, and 402e. Specifically, sub-region a is the area enclosed by the contour line 401 and lines 402a and 402b, and the same applies to the other sub-regions. Furthermore, if a sub-region is at the top or bottom of the contour, a line passing through the top or bottom of the extracted contour and parallel to the surface of the substrate (a line along the x-axis) will be defined as the top or bottom edge of each rectangle.

[0042] Next, the width measuring unit 150 can set the left and right sides of the rectangles corresponding to each of the sub-regions a, b, c, and d as lines 403a, 404a that circumscribe each sub-region, lines 403b, 404b that circumscribe each sub-region, or lines 403c, 404c that are located between those lines. Note that lines at positions that do not correspond to each other may be set as the left and right sides of the rectangles. That is, if line 403a is set as the left side, line 404b or line 404c that does not correspond to line 403a may be set as the right side. The width measuring unit 150 then measures the distance between opposite sides in each of the multiple rectangles defining the sub-regions a, b, c, and d as the width of the via or dam, and stores the measurement result information related to the measurement result in the storage unit 110.

[0043] Figure 4B shows an example of applying the processing performed by the width measuring unit 150 shown in Figure 4A to the extracted contour shown in Figure 3D. Specifically, Figure 4B is a photograph showing an example of an image in which the width measuring unit 150 divides the area within the contour into multiple sub-regions with lines parallel to the surface of the substrate, and each sub-region is superimposed on the first image 31 in Figure 3A. In this figure, the width measuring unit 150 sets, for example, a rectangle 401a, and measures the distance between the left side 402L and the right side 402R of the rectangle 401a, which are perpendicular to the surface of the substrate, as the width of the rectangle 401a.

[0044] [Example of computer hardware configuration] Figure 5 is a block diagram showing an example of a hardware configuration when the contour extraction system 100 is implemented by a computer 500. The computer 500 is, for example, a server computer, a personal computer (e.g., desktop, laptop, tablet, etc.), a media computer platform (e.g., cable, satellite set-top box, digital video recorder, etc.), a handheld computer device (e.g., PDA, email client, etc.), or another type of computer or communication platform. As shown in Figure 5, the computer 500 includes, for example, a processor 501, memory 502, storage device 503, input I / F (interface) unit 504, data I / F unit 505, communication I / F unit 506, and display device 507.

[0045] The processor 501 is a control unit that controls the processing of various functional units shown in Figure 1, which are realized in the computer 500 by executing programs stored in memory 502. Memory 502 is a storage medium such as RAM (Random Access Memory) and temporarily stores the program code of the program executed by the processor 501 and data required when the program is executed. Storage device 503 is a non-volatile storage medium such as a hard disk drive (HDD) or flash memory and stores the operating system and various programs for realizing the above configurations.

[0046] The input I / F unit 504 is a device for receiving input from the user and consists of, for example, a keyboard, mouse, touch panel, various sensors, wearable devices, etc. This input I / F unit 504 may be connected to the computer 500 via an interface such as USB (Universal Serial Bus). The data I / F unit 505 is a device for inputting data from outside the computer 500 and consists of, for example, a drive device for reading data stored in various storage media. This data I / F unit 505 may be provided outside the computer 500, in which case the data I / F unit 505 is connected to the computer 500 via an interface such as USB, for example. The communication I / F unit 506 is a device for performing data communication with devices outside the computer 500 via a wired or wireless connection over a network such as the Internet. Such a communication I / F unit 506 may be located outside the computer 500, in which case the communication I / F unit 506 is connected to the computer 500 via an interface such as USB.

[0047] The display device 507 is a device for displaying various types of information and is composed of, for example, a liquid crystal display, an organic EL (Electro-Luminescence) display, or a wearable device display. The display device 507 may be provided outside the computer 500, in which case the display device 507 is connected to the computer 500 via, for example, a display cable. Furthermore, if a touch panel is used as the input I / F unit 504, the display device 507 may be configured as an integrated unit with the input I / F unit 504.

[0048] [Effects and Effects of the Embodiment] According to the contour extraction system 100, contour extraction method, and contour extraction program configured as described above, in the second image 32 obtained by performing a binarization process on the first image 31 according to a predetermined threshold for discriminating between the substrate and pattern and the background, the substrate and pattern are both represented as white, for example, while the background is represented as black, for example. Then, a histogram is created for the y-coordinates of a plurality of binary boundary points (boundaries between white pixels and black pixels) in the second image 32.

[0049] And the y coordinates of the two peaks P1 that appear in that histogram (y P1 ) is identified as the surface level of the pattern, and the y coordinate (y P2 The surface level of the pattern can be identified as the surface level of the substrate, and the contour points between the surface level of the pattern and the surface level of the substrate can be identified as the side walls of the pattern shape (vias, dams, etc.). This allows for accurate extraction of the surface of the pattern and the surface of the substrate, eliminating the need to distinguish between the substrate and the pattern during the binarization process. Therefore, even when it is difficult to separate and identify the pattern and the substrate in a normal binarization process, for example, when the pattern and the substrate are made of the same material or when the substrate is made of resin or glass, the contour of the pattern shape in the pattern can be accurately extracted, and as a result, shape parameters such as the width and side wall angle of vias, dams, etc. can be accurately measured.

[0050] Furthermore, the angle measuring unit 145, as part of the right side wall in the pattern shape, measures the first coordinate R1(x) at a predetermined first height from the surface of the substrate. R1 ,y R1 ), and the second coordinate R2(x) located at a predetermined second height from the surface of the substrate. R2 ,y R2The system sets coordinates and measures the inclination angle of the sidewall based on those coordinates, so the inclination angle of the sidewall can be suitably measured based on the straight portion of the sidewall in the extracted contour. Furthermore, the width measuring unit 150 divides the area within the extracted contour into sub-regions a, b, c, and d with lines parallel to the surface of the substrate, and measures the width of each sub-region a, b, c, and d, so the width of pattern shapes such as vias VH can be measured in detail and with high accuracy according to the shape of their sidewalls.

[0051] The embodiments described above with reference to specific examples are provided to facilitate understanding of this disclosure and are not intended to limit its interpretation. In other words, this disclosure is not limited to these specific examples, and modifications made to these examples by those skilled in the art are also included within the technical scope of this disclosure, as long as they retain the features of this disclosure. Furthermore, the elements, arrangements, materials, conditions, shapes, dimensions, scales, etc., of each of the aforementioned specific examples are not limited to those exemplified unless otherwise specified and can be modified as appropriate. Moreover, the elements of each of the aforementioned specific examples can be combined in different ways as appropriate, as long as no technical inconsistencies arise.

[0052] For example, if the pattern shape is a dam instead of a via VH, when binarization is performed, the dam and part of the substrate will be white, and the background outside the dam will be black. In the histogram of the y coordinate of the binary boundary point, the y coordinate of peak P1 will be the same as in the graph shown in Figure 3C. P1 ) is identified as the surface level of the pattern, and the y coordinate of peak P2 is (y P2 ) can be identified as the surface level of the substrate. The width measuring unit 150 may also calculate the width along the y-axis direction of the sub-regions shown in Figure 4B and measure the depth of the via VH by, for example, summing them up, or it may also calculate the average value of the distances between multiple sides. The width measuring unit 150 may also divide the region within the extracted contour into multiple sub-regions at predetermined intervals using lines along the y-axis, thereby measuring the width and depth of the via VH.

[0053] Furthermore, the contour extraction system 100 may be linked with other systems to perform processing by multiple systems in a continuous manner. For example, by linking with a microscope image acquisition system, it may be a system that performs everything from capturing cross-sectional images to contour extraction in a continuous manner. Alternatively, by linking with a pattern simulation system, for example, it may be a system that verifies the simulation accuracy by comparing the contour of the simulated pattern shape with the contour of the actual pattern shape extracted by the contour extraction system 100. In addition, the user device 200 may be an information processing device dedicated to a user, or it may be an information processing device shared among multiple users in an organization to which the user belongs (for example, a company or its various departments). Furthermore, although Figure 1 shows one user device 200 connected to a network, it is of course possible for multiple user devices 200 to be connected.

[0054] Furthermore, the timing at which the parameter receiving unit 120 receives parameter information is not particularly limited, and parameter information may be received before, during, or after the execution of each of the above-described processes. For example, the parameter receiving unit 120 may receive parameter information after at least one of the above-described processes. In this case, the user can refer to display information, such as the contour extraction results displayed on the user device 200, and then re-specify appropriate parameter information. By repeating the process of updating such parameter information multiple times, the user can specify more suitable parameter information and improve the accuracy of contour extraction. [Explanation of Symbols]

[0055] 31...First image, 32...Second image, 100...Contour extraction system, 110...Storage unit, 115...Acquisition unit, 120...Parameter reception unit, 125...Binarization processing unit, 130...Morphology processing unit, 135...Histogram processing unit, 135...Morphology processing unit, 140...Contour extraction unit, 145...Angle measurement unit, 150...Width measurement unit, 155...Display output unit, 200...User device, 301~304...Region, 401...Contour line, 401a...Rectangle, 402a ~402e...line, 402L...left side, 402R...right side, 500...computer, 501...processor, 502...memory, 503...storage device, 504...input I / F section, 505...data I / F section, 506...communication I / F section, 507...display device, a,b,c,d...sub-regions, BD...closed curve, L1,R1...first coordinate, L2,R2...second coordinate, OL...frame line, P1,P2...peak, SC...scale, SL,SR...approximation line, SP...line, VH...via

Claims

1. Computers A first image of the cross-section of the pattern formed on the substrate is obtained. A second image is generated by performing a binarization process on the first image, or a flattened first image, according to a predetermined threshold for discriminating between the substrate and the pattern and the background. A histogram is created for the y-coordinates of the multiple binary boundary points in the second image. Based on the second image and the histogram, the contour of the pattern shape in the region of interest of the cross-section is extracted. A contour extraction method that includes the following.

2. The contour extraction method according to claim 1, wherein the substrate is made of resin or glass.

3. The contour extraction method according to claim 1, wherein the pattern is a resin composition pattern made of a resin composition.

4. The contour extraction method according to claim 1, further comprising the computer measuring the angle of the side wall in a portion of the extracted contour based on a first coordinate at a predetermined first height from the surface of the substrate and a second coordinate at a predetermined second height from the surface of the substrate.

5. Computers The region within the extracted contour is divided into a plurality of sub-regions at predetermined intervals by lines along a direction parallel to the surface of the substrate, A plurality of rectangles corresponding to each of the aforementioned sub-regions are set, For each of the rectangles, measure the width in a direction parallel to the surface of the substrate. The contour extraction method according to claim 1, further comprising the following:

6. An acquisition unit that acquires a first image of the cross-section of a pattern formed on a substrate, A binarization processing unit generates a second image by performing a binarization process on the first image, or a flattened first image, according to a predetermined threshold for discriminating between the substrate and the pattern and the background. A histogram is created for the y-coordinates of the multiple binary boundary points in the second image. A contour extraction unit extracts the contour of the pattern shape in the region of interest of the cross-section based on the second image and the histogram, A contour extraction system equipped with the following features.

7. On the computer, An acquisition unit that acquires a first image of the cross-section of a pattern formed on a substrate, A binarization processing unit generates a second image by performing a binarization process on the first image, or a flattened first image, according to a predetermined threshold for discriminating between the substrate and the pattern and the background. A histogram processing unit that creates a histogram for the y coordinates of multiple binary boundary points in the second image, A contour extraction unit extracts the contour of the pattern shape in the region of interest of the cross-section based on the second image and the histogram, A contour extraction program to achieve this.

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