WO2026018408A1 - Image processing system, image processing method, and display device - Google Patents

Abstract

The present invention provides a technology for acquiring parameter values according to which a contour line close to a contour line that a user anticipates and expects is extracted.　A controller 102 comprises: an image display unit 1021 that displays an observation image; a contour extraction unit 1022 that extracts first contour information for dividing the structure of a sample from the observation image on the basis of a preset first parameter value; a first contour information display unit 1023 that displays the first contour information extracted by using the first parameter value on the displayed observation image; a second contour information accepting unit 1024 that accepts, on the observation image, input of second contour information indicating the contour of the structure included in the observation image; and a parameter value acquisition unit 1025 that acquires a second parameter value, different from the first parameter value, for extracting third contour information for which the difference from the second contour information in terms of display is a minimum.

Description

Image processing system, image processing method, and display device

This disclosure relates to technologies such as image processing systems, image processing methods, and display devices.

Many of the observation images captured using charged particle beam devices are used for shape and structural analysis of samples. In order to perform these numerical processes, image processing to extract the contours of structures is almost essential. For this reason, a method for accurately extracting contours is important.

In order to accommodate a wide variety of images, algorithms for extracting contours within observed images can control their operation using the numerical values of variables called parameters. For example, one typical parameter is the threshold. Contour extraction algorithms often include a process for selecting pixels that will become part of the contour from among pixels in the observed image that show large changes in brightness. The threshold is used in the process of selecting pixels. Even when extracting contours from the same image, changing the threshold value will change the extracted contour. Conversely, to accurately extract contours within an image, the parameters included in the algorithm, including the threshold, must be set to optimal values.

If the optimization of the contour extraction algorithm or the parameter values associated with this algorithm is performed automatically by computer calculation, improved accuracy and efficiency can be expected compared to manual optimization. To achieve this, in many cases, numerical data that serves as the basis for calculating the optimal parameter values is accepted as input separate from the observed image, and an algorithm that extracts the desired contour line is selected or parameter values are calculated. Patent Documents 1 and 2 can be cited as examples of prior art.

Japanese Patent Application Laid-Open No. 2008-116207 JP 2012-21832 A

In research and development and quality assurance settings across a variety of industrial fields, transmission electron microscopes (TEM), scanning transmission electron microscopes (STEM), scanning electron microscopes (SEM), and other instruments are often used to capture numerous observation images, and shape analysis, such as length and angle measurements, is performed on all of these images. These shape analyses require the extraction of the contours of structures within the observation images. For this reason, users must configure the contour extraction algorithm to suit the observation images, optimizing the parameter values in particular, before conducting shape analysis.

However, for a given observation image, there are few cases where there is a clear relationship between the parameter values and the extracted contours, and no systematic method for adjusting parameter values exists. As a result, users must search for the optimal parameter values for the contour extraction algorithm by trial and error for each observation image. As mentioned above, when there are a large number of observation images, the amount of work increases in proportion to the number of observation images, and this increased workload represents a non-negligible cost for manufacturers. Furthermore, for semiconductor device manufacturers, for example, as the structures of semiconductor devices become finer and more complex, the shapes of the contours to be extracted also become more complex, making it more difficult to adjust to the optimal parameter values.

In order to solve this problem, technologies related to the automatic optimization of contour extraction algorithms have been proposed, such as those in Patent Documents 1 and 2. It should be noted here that the method described in Patent Document 1 is only applicable when the manufacturing process of the sample is known. Therefore, it is difficult to apply it to naturally occurring samples, such as biological samples, which do not have a manufacturing process in the first place. Furthermore, the method described in Patent Document 2 automatically determines parameter values that minimize the influence of noise in the observed image on contour extraction, or the roughness of the contour, but does not guarantee the extraction of contours that the user envisions, including rough contours. There is a need for a method that can automatically obtain algorithm parameter values that can extract contours that meet the user's expectations and assumptions.

The purpose of this disclosure is to provide technology that can obtain parameter values that extract contours that are closer to the contours that the user envisions and expects.

A representative embodiment of the present disclosure has the following configuration. An image processing system in one embodiment processes an observation image of a sample obtained by a charged particle beam device. The image processing system includes an image display unit that displays the observation image, a contour extraction unit that extracts first contour information related to the structural boundaries of the sample from the observation image based on a first set value of a preset parameter, a first contour information display unit that displays the first contour information extracted by the contour extraction unit on the observation image, a second contour information receiving unit that receives input of second contour information related to boundaries visible in the observation image, and a parameter value acquisition unit that acquires a second set value different from the first set value so as to minimize the difference between the first contour information and the second contour information in the observation image.

An image processing method executed by an image processing system that processes an observation image of a sample obtained by a charged particle beam device according to one embodiment, wherein the image processing system displays the observation image, extracts first contour information relating to the structural boundaries of the sample from the observation image based on a first setting value of a preset parameter, displays the extracted first contour information on the observation image, accepts input of second contour information relating to boundaries visible in the observation image, and obtains a second setting value different from the first setting value so as to minimize the difference between the first contour information and the second contour information in the observation image.

In one embodiment, the display device is used in an image processing system that processes observation images of a sample obtained by a charged particle beam device. The display device displays first contour information relating to the structural boundaries of the sample extracted from the observation image based on a first setting value of a preset parameter on the observation image, accepts input of second contour information relating to boundaries visible in the observation image, acquires a second setting value different from the first setting value so as to minimize the difference between the first contour information and the second contour information in the observation image, and displays third contour information extracted based on the acquired second setting value on the observation image.

A representative embodiment of the present disclosure provides technology for acquiring parameter values that extract a contour line that is closer to the contour line that the user envisions and expects. Issues, configurations, effects, etc. other than those described above are described in the detailed description of the invention.

1 is a diagram illustrating an example of the configuration of a charged particle beam apparatus according to a first embodiment. FIG. 2 illustrates an example of functional blocks of a controller according to the first embodiment. 10 is a flowchart showing a flow of processing of a functional block according to the first embodiment; FIG. 2 is a diagram illustrating an example of a cross-sectional image of a sample according to the first embodiment. FIG. 2 illustrates an example of a user interface according to the first embodiment. FIG. 2 illustrates an example of a reference line according to the first embodiment; 3A and 3B are diagrams illustrating an example of a line profile and a contour line according to the first embodiment; 10A and 10B are diagrams for explaining an example of acquiring a horizontal line profile according to the first embodiment; FIG. 10 illustrates an example of a line profile in the horizontal direction according to the first embodiment. FIG. 10 is a diagram illustrating an example of a line profile in the radial direction according to the first embodiment. FIG. 10 is a diagram illustrating an example of an initial contour calculated using initial setting values of two parameters according to the first embodiment. FIG. 10 illustrates an example of a range in which movement of an outline point is restricted according to the first embodiment; FIG. 10 is a diagram illustrating an example of a correct contour line according to the first embodiment; FIG. 10 is a diagram illustrating an example of a corrected contour line corrected based on a corrected contour line according to the first embodiment. FIG. 10 illustrates an example of a display during execution of an optimization algorithm according to the first embodiment. FIG. 10 is a diagram illustrating an example of a cross-sectional image of a sample according to the second embodiment. 10A and 10B are diagrams illustrating an example of contour points detected by a contour point detection algorithm according to a second embodiment; FIG. 10 is a diagram illustrating an example of display of corrected contour points according to the second embodiment. FIG. 10 illustrates an example of a display on a display unit during execution of an optimization algorithm according to a second embodiment. FIG. 10 illustrates an example of a top-view image according to the second embodiment. FIG. 11 is a diagram showing an example of initial contour points and correct contour points extracted from a top-view image according to the second embodiment. FIG. 11 is a diagram illustrating an example of a corrected contour point that is closest to a correct contour point according to the second embodiment. FIG. 10 is a diagram showing an example of a particle sample image obtained when observing and analyzing a particulate sample according to the third embodiment. FIG. 13 is a diagram showing an example of a particle sample image after separation processing according to the third embodiment. FIG. 13 is a diagram showing an example of a particle sample image after noise removal processing according to the third embodiment. FIG. 13 is a diagram showing an example of a particle sample image after extraction processing of a "certain background region" according to the third embodiment. FIG. 13 is a diagram showing an example of a particle sample image after extraction processing of a “reliable particle region” according to the third embodiment. FIG. 11 is a diagram showing an example of a particle sample image after image processing in which parameter value setting is not required according to the third embodiment. FIG. 13 is a diagram showing an example of a particle sample image according to the third embodiment. FIG. 13 is a diagram showing an example of a particle sample image according to the third embodiment. FIG. 13 illustrates an example of a display on a display unit during execution of an optimization algorithm according to the third embodiment.

Embodiments of the present disclosure will be described in detail below with reference to the drawings. In the drawings, identical parts are generally designated by the same reference numerals, and repeated explanations will be omitted. In the drawings, the depiction of components may not represent their actual position, size, shape, range, etc., in order to facilitate understanding of the invention.

For the purpose of explanation, when describing processing by a program, the program, function, processing unit, etc. may be described as the main focus, but the main hardware component of these is the processor, or a controller, device, computer system, system, etc. that is made up of that processor, etc. A computer system uses the processor to execute processing in accordance with a program read into memory, appropriately using resources such as memory and communication interfaces. This realizes specified functions, processing units, etc. A processor is made up of semiconductor devices such as a CPU/MPU or GPU, for example. Processing is not limited to software program processing, and can also be implemented using dedicated circuits. Dedicated circuits such as FPGAs, ASICs, and CPLDs can be used.

The program may be pre-installed as data on the target computer system, or may be distributed as data to the target computer system from a program source. The program source may be a program distribution server on a communications network, or a non-transitory computer-readable storage medium such as a memory card or disk. The program may be composed of multiple modules. The computer system may be composed of multiple devices. The computer system may be composed of a client-server system, a cloud computing system, an IoT system, etc. Various data and information may be composed of structures such as tables and lists, for example, but are not limited to these. Expressions such as identification information, identifiers, IDs, names, and numbers are interchangeable.

Below, as an example of the technology of this embodiment, we will describe an application case of this technology in extracting contour lines from a cross-sectional image of a sample that is a semiconductor device (first embodiment). Then, as an applied example, we will show that this technology can also be applied to contour points, which are minute elements of a contour line (second embodiment). Finally, we will show an example of the application of this technology when a contour line forms the boundary line of an area within an observed image (third embodiment).

(First embodiment)
A well-known method of observing samples, such as semiconductor devices, using a charged particle beam device is so-called top-view observation and measurement, in which the wafer surface is viewed from above using a scanning electron microscope, such as a critical dimension scanning electron microscope (CD-SEM). However, semiconductor devices are three-dimensional structures, and so observation of the wafer surface from the side, i.e., cross-sectional observation and measurement, also plays an important role in the device development and quality assurance processes. For this reason, the embodiments mainly focus on image processing techniques for observing and measuring the cross sections of semiconductor devices.

<Configuration of scanning electron microscope>
FIG. 1 is a diagram showing an example of the configuration of a charged particle beam device (hereinafter referred to as "SEM") 100. The SEM 100 roughly comprises an SEM main body 101 and a controller 102 connected to the main body 101. The main body 101 further comprises an electron optical column (hereinafter referred to as "column") and a sample chamber provided below the column. The controller 102 is a system that controls imaging by the main body 101, etc. The controller 102 comprises an overall control unit 120, a signal processing unit 121, a storage unit 122, a communication interface 123, etc. An input device 124 and an output device 125 are externally connected to the controller 102.

The column of the main body 101 comprises, as its components, an electron gun 111, a focusing lens 112, a deflection lens 113, and an objective lens 114. The electron gun 111 emits an electron beam b1, which is a beam of charged particles. The focusing lens 112 focuses the electron beam b1. The deflection lens 113 deflects the trajectory of the electron beam b1. The objective lens 114 controls the focusing height of the electron beam b1. The sample chamber is a room where samples such as wafers and coupons (broken pieces of wafers) are stored. The sample chamber comprises a stage 115 and a detector 116. A sample 130 is placed on the stage 115.

The stage 115 is a sample stage on which the target sample 130, a semiconductor device, is placed. The stage 115 has the ability to move not only in the X and Y directions but also in the Z direction, and to rotate about the XY, YX, or Z axes. As a result, the stage 115 can move the captured field of view in the horizontal and vertical directions relative to the front-on image, or in a rotational direction within the field of view. This allows the field of view for imaging to be set.

The detector 116 detects particles b2, such as secondary electrons and backscattered electrons, generated from the sample 130 irradiated with the electron beam b1 as an electrical signal. The detector 116 outputs a detection signal, which is an electrical signal.

The overall control unit 120 controls the operation of the controller 102 and the main body 101. The overall control unit 120 issues instructions such as drive control to each unit. Each unit, including the overall control unit 120, can be implemented as a computer or dedicated circuit. The signal processing unit 121 receives the detection signal from the detector 116, performs processing such as analog/digital conversion to generate an image signal, and stores it as image data in the memory unit 122. The memory unit 122 can be implemented as a non-volatile storage device, etc. The overall control unit 120 also associates and stores shooting information related to the image in the memory unit 122. The communication interface 123 is a device that has a communication interface for a communication network (not shown) or an external computer (not shown).

The overall control unit 120 outputs data such as images and shooting information stored in the storage unit 122 to the output device 125 in response to a request from the input device 124, for example. The overall control unit 120 may receive requests from an external computer via the communication interface 123, and in response to the received request, transmit data such as images and shooting information stored in the storage unit 122 to the external computer.

<Function block>
FIG. 2 is a diagram showing an example of functional blocks of the controller 102. As shown in FIG. 2, the controller 102 includes an image display unit 1021, a contour extraction unit 1022, a first contour information display unit 1023, a second contour information receiving unit 1024, a parameter value acquisition unit 1025, and a third contour information display unit 1026. The overall control unit 120 reads a program stored in the storage unit 122, for example, and executes the read program, thereby causing the controller 102 to realize the functions of the image display unit 1021, the contour extraction unit 1022, the first contour information display unit 1023, the second contour information receiving unit 1024, the parameter value acquisition unit 1025, and the third contour information display unit 1026. By realizing these functions, the controller 102 operates as an image processing system that performs image processing of contours in an observation image of the sample 130 obtained by the SEM 100. In this embodiment, the controller 102 will be described as being connected to the SEM 10, but the present invention is not limited to this. For example, the controller 102 may be configured independent of the SEM. In this case, the controller 102 may have an image reading function for reading an image acquired by an external SEM or the like, and may be configured to implement the functions shown in FIG. 2 for the image read by this image reading function.

The image display unit 1021 displays an observation image of the sample captured by the charged particle beam device. For example, the image display unit 1021 displays a sample cross-section image G10 (described below) of the sample 130 captured by the SEM 100.

The contour extraction unit 1022 extracts first contour information relating to the structural boundary of the sample from the observation image based on first setting values of preset parameters. The contour extraction unit 1022 includes setting values for multiple parameters. In this embodiment, the contour extraction unit 1022 extracts first contour information from the observation image based on setting values for multiple parameters. For example, as described below, the contour extraction unit 1022 extracts an initial contour line 20 (first contour information) relating to the structural boundary of the sample 130 from the sample cross-sectional image G10 based on preset parameters, such as initial setting values (first setting values) of smooth and threshold. There are multiple types of parameters, such as smooth and threshold.

The first contour information display unit 1023 displays the first contour information extracted by the contour extraction unit 1022 on the observation image. For example, as described below, the first contour information display unit 1023 displays the initial contour line 20 extracted using parameter values, such as the initial settings for smooth and threshold, on the sample cross-section image G10 displayed on the image display unit 1021.

The second contour information receiving unit 1024 receives input of second contour information relating to the boundary visible in the observation image. For example, the second contour line receiving unit 1024 receives input of the correct contour line 21 relating to the boundary of the pillar 11 visible in the sample cross-sectional image G10 (described below) displayed on the image display unit 1021, as described below.

The parameter value acquisition unit 1025 acquires a second setting value that is different from the first setting value so as to minimize the difference between the first contour information and the second contour information in the observed image. For example, as described below, the parameter value acquisition unit 1025 acquires a parameter setting value (second setting value) that is different from the initial parameter setting value so as to minimize the difference between the initial contour line 20 and the correct contour line 21.

The third contour information display unit 1026 displays on the observed image the third contour information extracted by the contour extraction unit based on the second setting value acquired by the parameter value acquisition unit 1025. For example, as described below, the third contour information display unit 1026 displays the corrected contour line 22 extracted by the contour extraction unit 1022 based on the parameter value, smooth, and threshold setting values acquired by the parameter value acquisition unit 1025.

<Image processing flow>
FIG. 3 is a flowchart showing the flow of processing by the functional blocks.
As shown in FIG. 3, the image processing of this embodiment is as follows:
The steps are executed in the following order: Step ST110: Image display processing by image display unit 1021; Step ST120: Contour extraction processing by contour extraction unit 1022; Step ST130: First contour information display processing by first contour information display unit 1023; Step ST140: Second contour information reception processing by second contour information reception unit 1024; Step ST150: Parameter value acquisition processing by parameter value acquisition unit 1025; Step ST160: Third contour information display processing by third contour information display unit 1026.

The processing executed by the controller 102, which is an image processing system, will be described in more detail below.
The image display unit 1021 operates the SEM main body 101 to observe the cross section of the sample (semiconductor device) 130 and acquire a sample cross-sectional image with sufficient image quality for the purpose of analysis. Fig. 4 is a diagram showing an example of the sample cross-sectional image G10. In this embodiment, the sample cross-sectional image (observation image) G10 is acquired by the SEM 100, but it may also be acquired by a TEM or STEM.

As shown in Figure 4, the sample cross-sectional image G10 includes a protrusion structure (hereinafter referred to as a "pillar") 11 and a valley structure (hereinafter referred to as a "trench") 12 between two pillars. Note that in the sample cross-sectional image G10, a white area is provided as the background for the areas where symbols and lead lines are added, but the white area behind the symbols and lead lines is not an image. In the following, with regard to symbols and lead lines in image displays, a white area will be provided and the symbols and lead lines will be added.

No specific image file format is assumed for the image file format of the sample cross-section image G10. However, the harmonic components contained in the image data are important in the contour extraction process of the contour extraction unit 1022. For this reason, it is desirable for the image file format to be an uncompressed image file format such as Tagged Image File Format (TIFF format) or Microsoft Windows Bitmap Image (BMP format).

Next, the image display unit 1021 loads the sample cross-sectional image G10 shown in Figure 4 into the shape analysis software. To this end, the shape analysis software has a user interface (UI) for loading the image format output by the SEM 100. Figure 5 is a diagram showing an example of the user interface. In this embodiment, a graphical user interface (GUI: display device) 1251 is used as the user interface.

As shown in FIG. 5, GUI 1251 has buttons for import image button 1251A, detect button 1251B, edit button 1251C, and setting adjustment button 1251D. Import image button 1251A is a button that instructs the shape analysis software to load the sample cross-sectional image G10 and display the observed image. Detect button 1251B is a button that instructs the software to extract structural units, in other words, the contour lines of structural boundaries, from the sample cross-sectional image G10. Edit button 1251C is a button that instructs, for example, the second contour information receiving unit 1024 to accept editing of the contour lines. Setting adjustment button 1251D is a button that instructs, for example, the parameter value acquisition unit 1025 to adjust the setting values of parameter values. GUI 1251 also has a display unit 1251E. The display unit 1251E displays, for example, a sample cross-sectional image G10 loaded into shape analysis software under the control of the image display unit 1021.

In this embodiment, the analysis of the sample cross-sectional image G10 is performed using the pillars 11 and trenches 12 shown in Figure 4 as structural units (hereinafter referred to as "unit structures"). Below, we will explain the contour extraction process using an algorithm specialized for extracting contours around these unit structures. The contour extraction process is performed by the contour extraction unit 1022.

The contour extraction unit 1022 sets a reference line in the horizontal direction of the image for the sample cross-sectional image G10 shown in Figure 4. Figure 6 is a diagram showing an example of the reference line 13. The contour extraction unit 1022 extracts the contour lines of the unit structures in the upper portion 14 above the reference line 13 for the pillars 11, and in the lower portion 15 below the reference line 13 for the trenches 12.

Next, as shown in FIG. 6, the contour extraction unit 1022 sets reference points 16 near the tip of the pillar 11 or trench 12 for which contour extraction is desired. Any number of reference points 16 can be used. By setting the reference lines 13 and reference points 16, the contour extraction unit 1022 can appropriately set line profiles 17 and 18, which will be described later, and can appropriately extract the contours of structures such as pillars 11 and trenches 12 from the sample cross-sectional image G10.

The process of extracting the contour around the pillar 11 will be explained below.
After setting the reference line 13 and the reference point 16, the contour extraction unit 1022 acquires a line profile of pixel values at a certain width along the horizontal direction of the image if the line is below the reference point 16, and along the radial direction around the reference point 16 if the line is above the reference point 16.

Figure 7 shows an example of a line profile and a contour line. Figure 7 shows a line profile 17 in the horizontal direction of the image and a line profile 18 in the radial direction. For example, the contour extraction unit 1022 acquires horizontal line profiles 17 in one-pixel increments below the reference point 16, and acquires radial line profiles 18 in one-degree increments above the reference point 16. In addition, a contour line is formed by multiple contour points 19.

Here, we will explain the process by which the contour extraction unit 1022 acquires a line profile 17 on a horizontal line below the reference point 16. Figure 8 is a diagram illustrating an example of acquiring a horizontal line profile 17.

The (x, y) coordinate system in Figure 8 corresponds to the pixel coordinates of the sample cross-sectional image G10. The pixel value at pixel coordinates (x, y) is represented as I(x, y). A case will be described in which a line profile 17 is acquired on a horizontal line segment y = _y0 ( _xi < x < _{xt )} . In this case, in order to remove noise, the contour extraction unit 1022 takes the average of pixel values in a range of width _2ys pixels in the direction perpendicular to the horizontal line segment, and sets the average value as the profile value. In other words, if the profile value _Ip (x) at point (x, _y0 ) on the horizontal line segment y = _y0 ( _xi < x < _xt ) is
The number of pixels _ys, which determines the range of this average calculation, must be set as a parameter. Hereinafter, the number of pixels _ys will be referred to as "smooth." Smooth is a parameter for removing noise from an image. By setting the value of smooth, the contour extraction unit 1022 can remove desired noise from, for example, the sample cross-sectional image G10.

9 is a diagram showing an example of a horizontal line profile 17. In this line profile 17, the contour extraction unit 1022 acquires the maximum value IP ^MAX and minimum value IP ^MIN of the profile value, and when the minimum value is 0% and the maximum value is 100%, detects a point having a profile value equivalent to T% (0<T<100) as an edge point. In other words, when the edge point is x _e , _IP (x _e ) satisfies the following formula:
As with smoothing, the edge point _xe also needs to be set as a parameter. The threshold value of the parameter for the edge point _xe is hereinafter referred to as the "threshold." The threshold is a parameter that determines the threshold for distinguishing between image regions. By setting the threshold value, the contour extraction unit 1022 can distinguish, for example, the pillar 11 region from the region other than the pillar 11 in the sample cross-sectional image G10. The contour extraction unit 1022 can obtain the edge point _xe of the line profile 17 below the reference line 13 by repeating the above process for all horizontal line segments y = _y0 below the reference point 16.

The contour extraction unit 1022 detects the edge point _xe above the reference point 16 in the same manner as the process below the reference point 16. Fig. 10 is a diagram showing an example of a line profile 18 in the radial direction.

10 , in the case of an area above the reference point 16, the contour extraction unit 1022 performs noise removal by averaging pixel values in a range of 2θ _s degrees, with the radial line segment (centered on the reference point 16) from which the line profile 18 is obtained as the center. In this case, the smoothness may be θ _s itself or a constant multiple of θ _s .

Through the above process, the contour extraction unit 1022 can obtain contour points 19 of each line profile 17, 18 above and below the reference point 16. The first contour information display unit 1023 can display the group of contour points 19 obtained in this way, as shown in Figure 7, which has already been described. In Figure 7, multiple contour points 19 are displayed so as to roughly follow the shape of the pillar 11.

The two parameters (smooth and threshold) used in the contour extraction process are adjusted to optimal values by applying the technology of this embodiment. The shape analysis software has default settings for smooth and threshold. Therefore, the contour extraction unit 1022 first executes the contour extraction process using the default settings for the two parameters, and extracts an initial contour line (first contour information) formed by multiple contour points 19.

Next, the first contour information display unit 1023 displays the extracted initial contour on the sample cross-section image G10 displayed on the display unit 1251E of the GUI 1251 shown in FIG. 5. FIG. 11 is a diagram showing an example of an initial contour 20 calculated using the initial setting values of two parameters. The display in FIG. 11 is displayed, for example, when the Detect button 1251B is pressed.

As shown in FIG. 11 , if the initial contour line 20 displayed on the display unit 1251E differs from the shape of the pillar 11 visible to the user, in other words, if it differs from the contour line the user envisions or expects, the user modifies the initial contour line 20 by operating the input device 124. The input device 124 is, for example, a mouse. The user uses the mouse pointer as a pen tip to push out the initial contour line 20 displayed on the display unit 1251E, thereby shaping the displayed initial contour line 20 into the desired contour line. In this case, since the initial contour line 20 deviates from the shape of the pillar 11, the user uses the mouse to align the initial contour line 20 with the shape of the pillar 11, thereby creating a correct contour line 21 (second contour information) to be displayed in FIG. 13 , which will be described later.

The shaping of the initial contour line 20 is achieved, for example, by moving each of the contour points 19 that make up the initial contour line 20. The movement of each contour point 19 is restricted to a certain range. Figure 12 is a diagram showing an example of the range in which the movement of a contour point is restricted. The display in Figure 12 is displayed, for example, by pressing edit button 1251C and selecting a contour point 19. In Figure 12, the double-headed arrows AW1 and AW2 indicate the range in which the movement of the contour point 19 is restricted. The double-headed arrow AW1 indicates the range in which the contour point 19 obtained from the horizontal line profile 17 can be moved. The double-headed arrow AW2 indicates the range in which the contour point 19 obtained from the radial line profile 18 can be moved.

The double-headed arrow AW1 extends horizontally from the reference point 16, with the contour point 191 at its center. Contour point 191 is one of multiple contour points 19 located below the reference point 16. One end of the double-headed arrow AW1 extends a distance L1 to a line extending vertically from the reference point 16. The other end of the double-headed arrow AW1 extends a distance L1 from the reference point 16 on the opposite side to the one end. Contour point 191, below the reference point 16 and to the left of the reference point 16 as viewed from the reference point 16, cannot move up or down, and cannot be moved horizontally from the position of contour point 191 shown in Figure 12 to the right of the reference point 16.

The double-headed arrow AW2 extends in the radial direction from the reference point 16, centered on the contour point 192. Contour point 192 is one of multiple contour points 19 located above the reference point 16. One end of the double-headed arrow AW2 extends a distance L2 to the reference point 16. The other end of the double-headed arrow AW2 extends a distance L2 from the reference point 16 on the opposite side to the one end. Contour point 192 above the reference point 16 can only move on the straight line connecting the position shown in Figure 12 and contour point 192; contour point 192 cannot be moved below the reference point 16 from the position of contour point 192 shown in Figure 12.

The double arrows AW1, AW2 and portions NG1, NG2 allow the user to visually confirm the range in which contour points 191, 192 can be moved. Furthermore, by limiting the range in which contour points 191, 192 can be moved, it is possible to prevent the user from inputting the wrong contour points 191, 192. Here, inputting the wrong contour points 191, 192 means, for example, moving contour points 191, 192 to positions that are clearly unrelated to the contour of a structure such as pillar 11. Because it is possible to prevent inputting such wrong movement positions, the contour extraction unit 1022 can properly execute the contour extraction process.

The user uses the mouse to input a correct contour line within the limited range illustrated by the double arrows AW1 and AW2 in Figure 12. The user modifies the initial contour line 20, for example, to fit the shape of the pillar 11. The correct contour line is stored, for example, as information indicating the positions of multiple contour points 19 after each contour point 19 has been moved. Figure 13 is a diagram showing an example of a correct contour line 21. In Figure 13, the correct contour line 21 is displayed, fitting the shape of the pillar 11. The second contour information receiving unit 1024 stores the multiple contour points 19 that form the received correct contour line 21, for example, in a specified memory.

Next, when the user presses the setting adjustment button 1251D, the parameter value acquisition unit 1025 executes a parameter optimization algorithm. More specifically, the parameter value acquisition unit 1025 changes the smooth and threshold values from the initial settings, repeats the process of calculating the loss, and ultimately acquires the smooth value and threshold setting values that minimize the loss. Here, the smooth value and threshold value that minimize the loss are the values that, when the contour extraction unit 1022 extracts a contour, extracts a contour whose display difference is closest to the correct contour 21.

After the parameter values are acquired by the parameter value acquisition unit 1025 in this way, the contour extraction unit 1022 extracts the contour again, and the third contour information display unit 1026 displays the corrected contour. Figure 14 is a diagram showing an example of a corrected contour 22 (third contour information) that has been corrected based on the corrected contour 21. Figure 14 displays the corrected contour 22 that follows the shape of the pillar 11.

The procedure for setting a series of parameter values in the processing of the parameter value acquisition unit 1025 differs depending on the optimization algorithm used. The optimization algorithm may be, for example, a gradient method represented by the steepest descent method, a method using random numbers such as simulated annealing, or a statistical method such as Bayesian optimization. Here, the initial contour 20 calculated by the algorithm in the processing of the contour extraction unit 1022 described above is a point group made up of contour points 19 extracted on a specific line profile 17, as shown in FIG. 7. Therefore, the loss is the sum of squares of the distances between corresponding contour points on two contours:
and the maximum distance between corresponding contour points:
Here, x _i (t) and y _i (t) are the image coordinates of the i-th contour point at a certain time t during execution of the optimization algorithm, and x _i ^R and y _i ^R are the image coordinates of the i-th contour point of the correct contour 21.

FIG. 15 is a diagram showing an example of the display of the display unit 1251E while an optimization algorithm is running. In FIG. 15, in addition to the sample cross-section image G10, a loss calculation display G15 that displays the loss calculation is displayed. The loss calculation display G15 displays the calculation results of the loss for the parameters. The loss calculation display G15 displays a graph showing the calculation results of the loss result. The graph showing the calculation results of the loss result does not have to be displayed. Also, depending on the optimization algorithm, if not all losses are calculated, a smooth curve such as the solid line in FIG. 15 is not drawn, and the loss values calculated at each time t may be dotted as shown by A' in FIG. 15, etc.

15 , the parameter value acquisition unit 1025 calculates the loss from the initial contour 20 (consisting of A and the like shown in the figure) and the correct contour 21 at a certain time t during execution of the optimization algorithm, using _L2 shown in the above-mentioned equation (3) and _L1M shown in equation (4). The indicator A is one of the contour points 19 forming the initial contour 20. The loss is displayed, for example, as a square indicator A' on a graph showing the loss results in the loss calculation indicator G15.

The parameter value acquisition unit 1025 updates the parameter values according to the optimization algorithm, and similarly calculates the loss for the contour line (consisting of B, etc., as shown) detected one step later at time t+1. Point B is one of the contour points 19 that form the correct contour line 21. The loss is displayed, for example, as point B' in the loss calculation display G15.

After updating the parameter values, the parameter value acquisition unit 1025 repeats the process of calculating the loss. By repeating the process of calculating the loss a sufficient number of times, the parameter value acquisition unit 1025 can obtain the parameter value that minimizes the loss. For example, in the loss calculation display G25, the parameter value that minimizes the loss is displayed as point O'. The contour extracted by the contour extraction unit 1022 using the parameter value that minimizes the loss becomes the corrected contour 22 that is closer to the correct contour 21.

Even in cases other than those described above, automatic optimization of parameter values is possible in a similar procedure in contour extraction on a sample cross-sectional image G10 obtained from a charged particle beam device such as SEM 100. However, for contour extraction algorithms other than the algorithm used in the contour extraction process described above, there may be cases where contour points cannot be matched between two given contours. In such cases, it is not possible to calculate the losses L ₂ shown in equation (3) and L _M shown in equation (4). Therefore, when contour points 19 cannot be matched, the parameter value acquisition unit 1025 creates binary arrays for the initial contour 20 and the correct contour 21, in which the pixel values of the pixels constituting the contour in the sample cross-sectional image G10 are set to 1 and the other pixel values are set to 0, and calculates the loss L _R by subtracting the product of the elements of the created arrays.
where (x, y) are image coordinates in the sample cross-sectional image G10, W and H are the width and height (both in number of pixels) of the image, respectively, I _B (x, y; t) is an element value corresponding to the (x, y) image coordinates in the binary array created from the initial contour 20 at a certain time t during execution of the optimization algorithm, and I _B ^R (x, y) is an element value corresponding to the (x, y) image coordinates in the binary array created from the correct contour 21.

According to this embodiment, the controller 102 can acquire parameter setting values that minimize the difference from the correct contour line 21 assumed and expected by the user; in other words, that extract a corrected contour line 22 that is closest to the correct contour line 21. This technology for acquiring parameter setting values can be applied to any type of observation image and any shape of contour line captured by a charged particle beam device such as SEM 100. The controller 102 can also display the corrected contour line 22 extracted using the acquired parameter values on the display unit 1251E. This allows the user to visually see how close the corrected contour line 22 is to the correct contour line 21 assumed and expected.

Second Embodiment
In the contour extraction process for the sample cross-sectional image G10 of the sample 130 described in the first embodiment, there are cases where it is not necessary to extract all contours. In the second embodiment, processing by the image processing system for such cases will be described.

<Example of application to point-to-point measurement of sample cross-sectional images>
16 is a diagram showing an example of a sample cross-sectional image (observation image) G20. As shown in FIG. 16, when measuring the distance AW3 from a contour point 193 of one pillar 11 to a contour point 194 of another pillar 11, and when the positions of the two contour points to be measured are approximately determined, the contour extraction unit 1022 does not need to extract all contour points. Instead, it is sufficient for the contour extraction unit 1022 to extract one contour point 193, 194 for each pillar 11 within a specified small region in the sample cross-sectional image G20.

In this case, the simplest algorithm for the contour extraction unit 1022 to extract contour points 19 is to apply smoothing filter processing to the pixels of the small region. Here, smoothing filter processing is a filter processing for blurring and smoothing an image. By applying smoothing filter processing to the small region, the contour extraction unit 1022 can smooth the image of the small region. After this filter processing, the contour extraction unit 1022 detects the pixel with the highest pixel value as the contour point 193 (or contour point 194). The image I _S (x, y) obtained by applying smoothing filter processing to the image I(x, y) is expressed as follows:
That is, I _S (x, y) is an image obtained by averaging the pixel values of a pixel region of area K × K centered on each pixel of the original image I _S (x, y). K used here is a positive odd number, and is a parameter called kernel size. The parameter value acquisition unit 1025 can adjust this parameter value using the same optimization algorithm as in the first embodiment.

Figure 17 is a diagram showing an example of contour points detected by the contour point detection algorithm. Figure 17 shows initial contour points 231, 232 (first contour information) for each pillar 11. The user can modify the positions of the initial contour points 231, 232 extracted by the contour point detection algorithm by mouse operation as described in the first embodiment. In this case, the range in which the initial contour points 231, 232 can be moved may be limited.

The initial contour points 231, 232, whose positions have been corrected by the user, are designated as correct contour points 241, 242 (second contour information). The parameter value acquisition unit 1025 minimizes the sum of squares of the distances between the initial contour points 231, 232 and the correct contour points 241, 242 for all small regions as a loss, and can obtain the optimal parameter values, i.e., kernel size values, for detecting correct contour points that minimize the correct contour points 241, 242. Based on the parameter setting values obtained in this way, the contour extraction unit 1022 extracts the contour, and the third contour information display unit 1026 displays the correct contour points that are the extraction results on the display unit 1251E.

Figure 18 shows an example of the display of corrected contour points 251, 252 (third contour information). In Figure 18, corrected contour points 251, 252 are displayed near correct contour points 241, 241.

FIG. 19 is a diagram showing an example of the display of the display unit 1251E while the optimization algorithm is running. In FIG. 19, in addition to the sample cross-section image G20, a loss calculation display G25 that displays the loss calculation is displayed. The loss calculation display G25 displays the calculation results of the loss for the parameters. The loss calculation display G25 displays a graph showing the calculation results of the loss result. The parameter value that minimizes the loss is displayed as point O' on the graph. The processing order of this optimization algorithm is the same as that explained using FIG. 15 in the first embodiment. The graph showing the calculation results of the loss result does not have to be displayed, and may be dotted, as explained using FIG. 15.

As explained above, according to this embodiment, when the controller 102 measures the distance from a contour point 193 of one pillar 11 to a contour point 194 of another pillar 11, and when the positions of the two contour points to be measured are nearly determined, it can obtain parameter values with the least loss without extracting all contour points. This reduces the image processing load on the controller 102.

<Image processing using top-view images>
The extraction process for corrected contour points 251 and 252 described above is effective not only for the sample cross-sectional image G20, but also for a top-view image of the sample 130. FIG. 20 is a diagram showing an example of a top-view image (observation image) G30 of the sample 130. The sample 130 is a semiconductor device. FIG. 21 is a diagram showing an example of initial contour points 261 and 262 (first contour information) and correct contour points 271 and 272 (second contour information) extracted from the top-view image G30. FIG. 22 is a diagram showing an example of corrected contour points 281 and 282 (third contour information) that minimize the display difference from the correct contour points.

In the top-view image G30 shown in FIG. 20, which is provided by processing by the image display unit 1021, the contour extraction unit 1022 specifies the positions of two points to be measured, detects the pixel with the highest brightness after smoothing filter processing, and extracts initial contour points 261 and 262. The first contour information display unit 1023 displays the initial contour points 261 and 262 in the top-view image G30, as shown in FIG. 21.

The user operates a mouse or the like to correct the positions of initial contour points 261, 262 and input correct contour points 271, 272. As a result, second contour information receiving unit 1024 receives the positions of correct contour points 271, 272. Next, parameter value acquisition unit 1025 executes an optimization algorithm to obtain parameter setting values that minimize the difference from correct contour points 271, 271. Based on the setting values thus obtained, contour extraction unit 1022 extracts corrected contour points 281, 282. As shown in FIG. 22, third contour information display unit 1026 displays corrected contour points 281, 282 in top-view image G30. This allows the user to visually recognize corrected contour points 281, 282 that are closest to correct contour points 271, 272.

(Third embodiment)
In the third embodiment, image processing by the controller 102 when expected values of the geometric feature amounts of the contour lines are given in advance will be described.

<Particle sample image>
23 is a diagram showing an example of a particle sample image (observation image) G40 obtained when observing and analyzing a particulate sample using SEM 100. A region extraction algorithm can be used to extract the boundaries, i.e., contours, of individual particles in particle sample image G40 of such sample 130. As shown in FIG. 24, in particle sample image G40, multiple circular particles are displayed in pixel area PA1.

<Particle region extraction process>
In this embodiment, prior to the outline extraction process described above, the outline extraction unit 1022 performs particle region extraction processing on the particle sample image to be analyzed. Here, we consider an image in which the particle region is brighter than the background region. In many cases, the particle region extraction processing for a given image involves the following image processing:
Step ST1: Rough separation of particle regions (white) and background regions (black) by binarization. Step ST2: Noise removal by opening/closing. Step ST3: Extraction of "certain background regions" by expansion. Step ST4: Extraction of "certain particle regions" by distance transformation and binarization.

In this embodiment, the contour extraction unit 1022 performs image processing of particle images using steps ST1 to ST4. Furthermore, step ST4 is followed by extraction processing of "areas where it is unclear whether they are particles or background," labeling processing of each area, and watershed processing; however, these are image processes that do not require parameter value setting, and therefore will not be discussed in detail in this embodiment.

Figure 24 is a diagram showing an example of a particle sample image G40 after the separation process of step ST1. Figure 25 is a diagram showing an example of a particle sample image G40 after the noise removal process of step ST2. Figure 26 is a diagram showing an example of a particle sample image G40 after the extraction process of step ST3. Figure 27 is a diagram showing an example of a particle sample image G40 after the extraction process of step ST4.

In the processes of steps ST1 to ST4 described above, the parameter setting values that the contour extraction unit 1022 should set when executing image processing of the particle sample image G40 are as follows:
In the separation process of step ST1, it is the threshold of the binarization process (parameter P1). In the noise removal process of step ST2, it is the kernel size and number of repetitions of the opening process/closing process (parameter P2). In the extraction process of step ST3, it is the kernel size and number of repetitions of the expansion process (parameter P3). In the extraction process of step ST4, it is the threshold of the distance transformation/binarization process (parameter P4). The relationship between each image process and the setting value will be explained below.

<Binarization processing>
The image I _B (x, y) obtained by binarizing the image I(x, y) using a threshold I _T is expressed as follows:
where I _MAX is the maximum pixel value in the bit depth of the original image I(x, y); for example, if the bit depth is 8, then I _MAX = 2 ⁸ - 1 = 255. Through the binarization process, pixel areas that appear to be particle regions become white (I _B (x, y) = I _MAX ), and pixel areas that appear to be background regions become black (I _B (x, y) = 0), resulting in the image shown in FIG. 24. As shown in FIG. 24, the particle sample image G40 contains a pixel area PA2 that appears to be a particle region, as well as multiple black spot noises N1 and white spot noises N2. The black spot noise N1 is black spot-shaped noise in the white region. The white spot noise N2 is black spot-shaped noise in the black region.

<Expansion/Contraction Processing>
The particle sample image G40 obtained by the binarization process shown in Fig. 24 contains sesame-like noise N. The opening process/closing process is performed to remove this noise N. The opening process/closing process is a combination of expansion/contraction processes, so it will be explained first.

Both the expansion process and the contraction process are processes performed on a binarized image. First, the expansion process is performed on the binarized image I _B (x, y) with a kernel size K _D and one repetition to obtain an image I _{(D, 1) (x, y)} .
In other words, for each pixel of the original image _IB (x,y), if there is at least one pixel with pixel value _IMAX (white) within a pixel region of area _KD x _KD centered on the pixel, then the expansion process sets I(x,y) = _IMAX . Note that _KD is a positive odd number. With this process, as shown in Figure 24, black dot noise N1 (black) in the white region is replaced with white and removed. Hereinafter, for simplicity of notation,
That is, for example, an image I _(D,n)(x,y) obtained by performing expansion processing on a binary image _IB (x,y) with a kernel size _KD and repeated n times is expressed as follows:
where o represents the composition of the process and has the following meaning:

The erosion process is the inverse operation of the expansion process. The image I _{(E,1)(x,y) obtained by performing the erosion process on the binary image IB(x,y) with} the kernel size K _E and one repetition is given by
In other words, for each pixel of the original image _IB (x,y), if there is even one pixel with a pixel value of 0 (black) within a pixel region of area K _E × K _E centered on the pixel, then I(x,y) is set to 0 (this is the contraction process). Note that K _E is a positive odd number, just like K _D. With this process, as shown in FIG. 24, the black dot noise N2 (white) in the black region is replaced with black and removed.

<Opening/closing process>
Dilation processing can be used to remove black spot noise N1 from the white area, but performing dilation processing also expands the white area itself. Therefore, by performing erosion processing after dilation processing, the contour extraction unit 1022 can remove black spot noise N1 from the white area without changing the white area. This process of performing dilation followed by erosion is called opening processing. In other words, the image I(O,1)(x,y) obtained by performing opening processing on the binary image (I _{B (x,y)} with a kernel size K _O and one repetition is
It is calculated by: where _{K O} is a positive odd number.

The closing process is the reverse process of the opening process. In the closing process, the contour extraction unit 1022 can remove white spot noise from black areas without changing the black areas. That is, the image I( _C ,1)(x,y) obtained by performing the closing process on the binarized image IB _(x,y) with a kernel size _KC and one repetition is expressed as follows:
where _KC is a positive odd number. The contour extraction unit 1022 may repeat both the opening process and the closing process.

In analyzing particle images, in order to find reliable background regions, noise is removed by performing an opening process (closing process) on the particle sample image shown in Figure 24 with a kernel size K _O (K _C ) and a repetition count N _O (N _C ), and then a dilation process is performed with a kernel size K _D and a repetition count N _D. As a result, larger regions including areas that appear to be particle regions become white, and the remaining black regions can be determined to be "reliable background regions."

<Distance transformation>
A reliable particle region is considered to be a pixel region located near the center of the "likely particle region (white)" PA3 shown in Fig. 25, that is, a pixel region that is sufficiently far from the "reliable background region" (black). In order to identify such a region, an image transformation process called distance transformation is performed.

Distance transformation is a process performed on a binarized image. The distance map D(x, y; I _B ) obtained by performing distance transformation on a binarized image I _B (x, y) is expressed as follows:
That is, for each pixel in the original image _IB (x,y), the distance from the pixel to the nearest black pixel ( _IB (x',y')=0) is defined as D(x,y; _IB ). When the distance map is visualized, the white area PA4 that appears to be a particle area is displayed with stepped brightness as shown in FIG. 26, with the brighter the area, the farther it is from the "certain background area" (black). The brighter the area, the more likely it is to be a "certain particle area".

The distance map image shown in FIG. 26 is binarized using a threshold specified by the user, to obtain the binarized image shown in FIG. 27. The threshold binarization process here is similar to that of the previously described equation (7), but the specific value of the parameter threshold _IT is different, and the user specifies and sets an initial value suitable for the image of the particle region. This specification is performed, for example, using the input device 124. The white particle region PA5 in the particle sample image G40 shown in FIG. 27 is a region that is determined to be sufficiently far from the "certain background region," and is therefore considered to be the "certain particle region."

The above is an explanation of the image processing involved in particle analysis. After these image processing steps that require the setting of parameter values, the contour extraction unit 1022 performs image processing that does not require the setting of parameter values, thereby obtaining particle regions and their contour lines as shown in Figure 28. Figure 28 is a diagram showing an example of a particle sample image G40 after image processing that does not require the setting of parameter values. The particle sample image G40 shown in Figure 28 displays multiple particle regions PA6.

When performing the image processing of steps ST1 to ST4 described above, if the actual particle dimensions (average diameter, area, etc.) are known in advance, the user can input these values into the software as geometric features. If the average particle diameter is input as a geometric feature, the contour extraction unit 1022 uses an optimization algorithm for each parameter shown in parameters P1 to P4 to determine the initial values of parameters P1 to P4 so as to minimize the difference between the diameter value input by the user and the average diameter of the particle region determined by the processing of steps ST1 to ST4 and the subsequent processing.

Using the initial values of parameters P1 to P4 determined in this way, or the initial settings of parameters P1 to P4 held as constants by the software, the contour extraction unit 1022 performs steps ST1 to ST4 and the subsequent image processing. Next, the first contour information display unit 1023 displays the particle area and initial contour line obtained based on the results of the image processing on the display unit 1251E of the GUI 1251.

Next, the user modifies the initial contour displayed on the display unit 1251E using the mouse operation described in the first embodiment to input the correct contour. When the second contour information receiving unit 1024 receives the input correct contour, the second contour information receiving unit 1024 stores the correct contour in the memory of the overall control unit 120, for example.

Figure 29 is a diagram showing an example of a particle sample image G40. Figure 29 shows a top view image of one particle. In Figure 29, multiple initial contour points 29 and multiple correct contour points 30 are displayed for one particle. The multiple initial contour points 29 are displayed by the first contour information display unit 1023. The multiple correct contour points 30 are contour points that the user assumes or expects and inputs by operating a mouse or the like. The positions of the multiple correct contour points 30 are each accepted by the second contour information acceptance unit 1024.

By using an optimization algorithm that operates on the values of the parameters P1 to P4, the parameter value acquisition unit 1025 can obtain the values of the parameters P1 to P4 so as to minimize the difference between the initial contour point 29 and the correct contour point 30.

Figure 30 is a diagram showing an example of a particle sample image G40. In Figure 30, multiple corrected contour points 31 are displayed on the particle sample image G40 for one particle. The corrected contour points 31 are contour points that minimize the difference between the initial contour points 29 and the correct contour points 30.

Figure 31 is a diagram showing an example of the display on the display unit 1251E while the optimization algorithm is running. In Figure 31, in addition to the particle sample image G40, a loss calculation display G45 that displays the loss calculation is displayed. The loss calculation display G45 displays the calculation results of the loss for the parameters. The loss calculation display G45 displays a graph showing the calculation results of the loss result. The parameter value that minimizes the loss is displayed as point O' on the graph. The processing order of this optimization algorithm is the same as that explained using Figure 15 in the first embodiment. The graph showing the calculation results of the loss result does not have to be displayed, and may be dotted, as explained using Figure 15.

The process of setting the initial values of the parameters P1 to P4 described above can also be applied to the contour extraction process of the contour extraction unit 1022 for the sample cross-sectional image G10 as shown in the first embodiment. For example, if the expected value of the periodic width (referred to as the pitch) of the horizontally arranged pillars 11 shown in Figure 4 described above is known in advance, the contour extraction unit 1022 can obtain the pitch calculated from the initial contour extracted using that expected value. The contour extraction unit 1022 can then obtain initial settings for the smooth and threshold parameters used in the contour extraction process so that the displayed difference between the pitch of the contour points extracted using the initial settings and the pitch obtained from the expected values is minimized. This allows the contour extraction unit 1022 to extract the initial contour so that it more accurately follows the structure of the pillars 11. After the first contour information display unit 1023 displays the initial contour, the second contour information receiving unit 1024 receives the correct contour, and the parameter value acquisition unit 1025 optimizes the parameter values P1 to P4, etc., are the same as the processing described in the first embodiment.

The above provides a specific description of the embodiments of the present disclosure, but the present disclosure is not limited to the above embodiments and various modifications are possible without departing from the spirit of the present disclosure. Except for essential components, each embodiment allows for the addition, deletion, or substitution of components. Unless otherwise specified, each component may be singular or plural. Combinations of the embodiments and variations are also possible. Some or all of the aforementioned configurations, functions, processing units, etc. may be realized by hardware, such as an integrated circuit design, or by software in which a processor interprets and executes a program. Data and information such as programs, tables, and files that realize each function may be stored in a storage device such as memory, hard disk, or SSD, or on a storage medium such as an IC card, SD card, or DVD.

11...protrusion structure (pillar), 12...valley structure (trench), 13...reference line, 16...reference point, 17, 18...line profile, 19...contour point, 20...initial contour line, 21...correct contour line, 22...corrected contour line, 29, 231, 232, 261, 262...initial contour points, 30, 241, 242, 271, 272...correct contour points, 31, 251, 252, 281, 282...corrected contour points, 100...charged particle beam device (SEM), 101...main body, 102...controller, 111...electron gun, 112...focusing lens, 113...deflection lens, 114...objective lens, 114...and objective lens, 115...stage, 116...detector, 130... Sample, 1021...Image display, 1022...Contour extraction, 1023...First contour information display, 1024...Second contour line reception, 1024...Second contour information reception, 1025...Parameter value acquisition, 1026...Third contour information display, 1251...Graphical user interface, 1251A...Import image button, 1251B...Detect button, 1251C...Edit button, 1251D...Settings adjustment button, 1251D...User settings adjustment button, AW1, AW2...Double arrows, G10, G20...Sample cross-section image, G15, G25, G45...Loss calculation display, G30...Top view image, G40...Particle sample image

Claims (11)

An image processing system for processing an observation image of a sample obtained by a charged particle beam device, comprising:
an image display unit that displays the observed image;
a contour extraction unit that extracts first contour information related to a structural boundary of the sample from the observation image based on a first set value of a preset parameter;
a first contour information display unit that displays the first contour information extracted by the contour extraction unit on the observation image;
a second contour information receiving unit that receives input of second contour information relating to a boundary visible in the observation image;
a parameter value acquisition unit that acquires a second setting value that is different from the first setting value so as to minimize a difference between the first contour information and the second contour information in the observation image,
Image processing system. 2. The image processing system according to claim 1,
a third contour information display unit that displays, on the observation image, third contour information extracted by the contour extraction unit based on the second setting value acquired by the parameter value acquisition unit.
Image processing system. 2. The image processing system according to claim 1,
There are multiple types of parameters,
one of the plurality of parameters is a parameter for removing noise from an image;
Image processing system. 2. The image processing system according to claim 1,
There are multiple types of parameters,
one of the plurality of parameters is a parameter that defines a threshold for distinguishing regions of the image;
Image processing system. 2. The image processing system according to claim 1,
the contour extraction unit receives input of a reference line and a reference point for defining the structural boundary based on the observation image displayed on the image display unit, and extracts the first contour information from the observation image based on the reference line and the reference point and the first setting value;
Image processing system. 2. The image processing system according to claim 1,
the first contour information is moved on the observed image displayed on the image display unit,
the first contour information is moved to shape the second contour information;
a range within which the first contour information can be moved on the observation image is displayed on the observation image;
Image processing system. 2. The image processing system according to claim 1,
There are multiple types of parameters,
one of the plurality of parameters is a kernel size of a smoothing filter process that smoothes an image of a selected region of the observed image;
Image processing system. 2. The image processing system according to claim 1,
There are multiple types of parameters,
one of the plurality of parameters is a kernel size of an opening process that removes black dot noise from a white region of the observed image without changing the white region of the observed image;
Image processing system. 2. The image processing system according to claim 1,
There are multiple types of parameters,
one of the plurality of parameters is a kernel size of a closing process that removes white dot noise from the black region of the observed image without changing the black region of the observed image;
Image processing system. 1. An image processing method executed by an image processing system that processes an observation image of a sample observed by a charged particle beam device, comprising:
the image processing system,
Displaying the observed image;
extracting first contour information relating to a structural boundary of the sample from the observed image based on a first set value of a preset parameter;
Displaying the extracted first contour information on the observed image;
accepting input of second contour information relating to a boundary visible in the observation image;
acquiring a second setting value different from the first setting value so as to minimize a difference between the first contour information and the second contour information in the observed image;
Image processing methods. A display device used in an image processing system that processes an observation image of a sample using a charged particle beam device,
The display device includes:
displaying, on the observed image, first contour information relating to a structural boundary of the sample extracted from the observed image based on a first set value of a preset parameter;
accepting input of second contour information relating to a boundary visible in the observation image;
a second setting value different from the first setting value is acquired so as to minimize a difference between the first contour information and the second contour information in the observation image, and third contour information extracted based on the acquired second setting value is displayed on the observation image.
Display device.