Dimension measurement system, estimation system, and dimension measurement method

Abstract

Provided is a dimension measurement system or the like which uses a method in which a human inputs and sets some information while making it possible to reduce the manual operation time or burden for the human. This dimension measurement system involves: displaying, for a user, a screen which includes an image and a graphical user interface (GUI), designating a reference line (interface reference line) for identifying part of a region of a structure to be subjected to dimension measurement, on the basis of a manual user operation, on an image on the screen, and designating a reference point (region reference point) within said region, said point being for identifying the region of the structure to be subjected to the dimension measurement (step 3); detecting an outline or an edge of the region of the structure to be subjected to a dimension measurement in the image by using the reference line and the reference point (step 4); and measuring a dimension of the structure to be subjected to a dimension measurement in the image by using the detected outline or edge information (step 5).

Description

[Technical Field]

[0001] The present disclosure relates to a technique for measuring dimensions of a device structure or the like from an image of a sample such as a semiconductor device. [Background technology]

[0002] There are techniques for measuring (sometimes referred to as length measurement) the dimensions of device structures and the like from images of samples such as semiconductor devices, as well as for observing, analyzing, evaluating, and inspecting samples, using charged particle beam devices such as scanning electron microscopes (SEM) and transmission electron microscopes (TEM) to perform these processes (sometimes collectively referred to as dimension measurement).A computer system built into or externally connected to the charged particle beam device processes the images captured by the charged particle beam device and measures the dimensions of target structures and patterns (e.g., trenches) in device structures and the like.

[0003] For example, the computer system detects the contour of a structure from a cross-sectional image of a semiconductor device (for example, a cross-section obtained by cleavage), and measures the dimensions (for example, trench width and trench depth) based on the contour.

[0004] An example of prior art is Japanese Patent No. 4865147 (Patent Document 1). Patent Document 1 describes, as a "pattern measurement method and measurement system using a display microscope image," "a step of setting an edge reference line on the side of a pattern in a microscope image," "a step of setting a plurality of edge points along the edge reference line," "a step of integrating a luminance profile near a search line that intersects with the edge reference line at one edge point and calculating the edge position for one edge point," "a step of calculating an approximation line for the side from the edge positions calculated for each of the plurality of edge points," and "a step of measuring the shape of the pattern using the approximation line" (Claim 1). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Patent No. 4865147 (corresponding U.S. Patent No. 7054506) Summary of the Invention [Problem to be solved by the invention]

[0006] As a technology for a dimension measurement system that measures the dimensions of an object based on the detection of the contour of the object structure from an image (e.g., a cross-sectional image) captured using the charged particle beam device, there is a first technology in which a computer system automatically performs contour detection and dimension measurement by image processing such as template matching, etc. Also, there is a second technology in which a person manually inputs and sets information required for contour detection and dimension measurement, and the computer system performs contour detection and dimension measurement based on that information.

[0007] In the first technique, it may be difficult to appropriately detect contours using image processing such as template matching, or the calculation load may be too high.

[0008] The second technique requires a person to set the information necessary for contour detection and dimension measurement. For example, in the case of Patent Document 1, the user sets information such as "edge reference lines" and "edge points." Using the information set by a person can facilitate image processing by a computer system. However, the second technique requires a lot of manual operation by a person, which can reduce work efficiency.

[0009] An object of the present disclosure is to provide a technology for a dimension measurement system that measures the dimensions of a target structure from an image captured using the charged particle beam device, which can reduce the amount of manual operation required by a person and improve work efficiency while allowing a person to input and set some of the information. Another object is to provide a technology that can reduce the influence of errors due to manual operation by a person. [Means for solving the problem]

[0010] A representative embodiment of the present disclosure has the following configuration: A dimension measurement system according to the embodiment is a dimension measurement system for measuring dimensions of a structure in an image, comprising a processor and a memory resource for storing data processed by the processor, wherein the processor causes a user to display a screen including the image and a graphical user interface (GUI), specifies a reference line on the image on the screen based on a manual operation by the user for identifying a portion of an area of ​​the structure to be measured, acquires position information of the reference line, specifies a reference point on the image on the screen based on a manual operation by the user for being included in the area to identify the area of ​​the structure to be measured, acquires position information of the reference point, detects a contour or edge of the area of ​​the structure to be measured in the image using the reference line and the reference point, and measures the dimensions of the structure to be measured in the image using information about the detected contour or edge.

[0011] An estimation system according to an embodiment is an estimation system for estimating a structure in an image, and includes a processor and memory resources for storing data processed by the processor. The processor displays to a user a screen including the image and a graphical user interface (GUI), specifies, on the image on the screen, a reference line for identifying a portion of the area of ​​the structure to be measured based on manual operation by the user, acquires positional information of the reference line, specifies, based on manual operation by the user, a reference point to be included within the area to identify the area of ​​the structure to be measured, acquires positional information of the reference point, accumulates data including the image, the reference line, and the reference point as learning data, uses the learning data to train a machine learning learning model for estimating the reference line and the reference point from an input image, inputs the input image to be estimated into the learning model, and outputs information about the estimated reference line and the estimated reference point as estimation results by the learning model. [Effects of the Invention]

[0012] According to a representative embodiment of the present disclosure, with regard to a technology such as a dimension measurement system that measures the dimensions of an object from an image captured using the charged particle beam device, a method is adopted in which a person inputs and sets some of the information, but the effort and burden of manual operation by a person can be reduced, and work efficiency can be improved. In addition, the influence of errors due to manual operation by a person can be reduced. Problems, configurations, effects, etc. other than those described above are described in the description for carrying out the invention. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a diagram showing an example of the configuration of a system including a dimension measurement system according to a first embodiment. [Figure 2] FIG. 2 is a diagram showing an example of the configuration of the dimension measurement system according to the first embodiment as a computer system. [Figure 3] FIG. 2 is a diagram showing a configuration example of an SEM in the first embodiment. [Figure 4] FIG. 2 is a diagram showing the structure, dimensions, etc. of a semiconductor device in the first embodiment. [Figure 5] FIG. 3 is a diagram showing a processing flow of the dimension measurement method according to the first embodiment. [Figure 6] FIG. 2 is a diagram showing an example of a GUI screen in the first embodiment. [Figure 7] FIG. 3 is a diagram showing an example of definition information in the first embodiment. [Figure 8] 3A to 3C are diagrams showing examples of cross-sectional images and an interface selection GUI in the first embodiment. [Figure 9] 3A and 3B are diagrams showing examples of cross-sectional images and a region selection GUI in the first embodiment. [Figure 10] FIG. 3 is a diagram showing an example of an algorithm for detecting contours in a cross-sectional image in the first embodiment. [Figure 11] FIG. 3 is a diagram showing an example of an algorithm for detecting contours in a cross-sectional image in the first embodiment. [Figure 12] FIG. 3 is a diagram showing a line profile graph in the first embodiment. [Figure 13]FIG. 10 is a diagram showing an example of applying the second rule to edge candidate extraction in the first embodiment. [Figure 14] FIG. 3 is a diagram showing an example of a combination of edge candidates in the first embodiment. [Figure 15] FIG. 3 is a diagram showing an example of detected contours (edge ​​points and contour lines) in the first embodiment. [Figure 16] FIG. 3 is a diagram showing an example of contour detection related to a mask in the first embodiment. [Figure 17] FIG. 3 is a diagram showing an example of pillar contour detection in the first embodiment. [Figure 18] FIG. 2 is a diagram showing an example of measurement target dimensions in the first embodiment. [Figure 19] FIG. 2 is a diagram showing an example of measuring the area of ​​a structure in the first embodiment. [Figure 20] FIG. 4 is a diagram showing an example of a GUI screen that displays a dimension measurement result in the first embodiment. [Figure 21] 4A to 4C are diagrams showing examples of cross-sectional images etc. when dust is attached in the first embodiment. [Figure 22] 3A and 3B are diagrams showing examples of an image of an etching processing target pattern and an image after actual processing in the first embodiment. [Figure 23] FIG. 10 is a diagram showing an example in which a plurality of area reference points or the like are designated collectively within a cross-sectional image in the first embodiment. [Figure 24] FIG. 4 is a diagram showing a first example of setting two interface reference lines in a cross-sectional image in the first embodiment. [Figure 25] FIG. 10 is a diagram showing a second example of setting two interface reference lines in a cross-sectional image in the first embodiment. [Figure 26] FIG. 10 is a diagram showing an example of a GUI screen for an interface reference line adjustment function, as a first modification of the first embodiment. [Figure 27] 10A and 10B are diagrams showing cross-sectional images etc. in a case where a reference line is set to a point other than an interface, as a second modification of the first embodiment. [Figure 28] FIG. 10 is a diagram showing another method for setting a line profile as a third modification of the first embodiment. [Figure 29] FIG. 13 is a diagram showing a method for setting a dimension measurement target line using area reference points, as a fourth modification of the first embodiment. [Figure 30] FIG. 13 is a diagram showing a method for setting on / off of a dimension measurement target using an area reference point, as a fifth modification of the first embodiment. [Figure 31] 10 is a diagram showing a method for setting a line profile in the horizontal direction in the dimension measurement system according to the second embodiment. [Figure 32] 10 is a diagram showing a method for setting a line profile in the radial direction in the dimension measurement system 2 of the third embodiment. FIG. [Figure 33] FIG. 10 is a diagram showing an example of the configuration of the estimation system according to the fourth embodiment as a computer system. [Figure 34] FIG. 10 is a diagram showing the input / output configuration of a learning model in the fourth embodiment. [Figure 35] FIG. 10 is a diagram showing the configuration of a learning model in the fourth embodiment. [Figure 36] FIG. 13 is a diagram showing a learning process flow in the fourth embodiment. [Figure 37] FIG. 13 is a diagram showing an example of a GUI screen of an estimation function in the fourth embodiment. [Figure 38] FIG. 10 is a diagram showing the input / output configuration of a learning model in a modified example of the fourth embodiment. [Figure 39] FIG. 10 is a diagram showing the input / output configuration of a learning model in a modified example of the fourth embodiment. [Figure 40] FIG. 13 is a diagram showing an example of a GUI screen in a modification of the fourth embodiment. [Figure 41] FIG. 13 is a diagram showing an example of the configuration of a dimension measurement system according to a fifth embodiment as a computer system. [Figure 42] FIG. 13 is a diagram showing a dimension measurement processing flow in the fifth embodiment. [Figure 43] FIG. 10 is a diagram showing an example of setting points and lines to an image to support contour detection in a first comparative example to the embodiment. [Figure 44] 10A and 10B are diagrams showing an example of setting points and lines to an image to support dimension measurement in a second comparative example to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0014] Hereinafter, embodiments of the present disclosure will be described in detail 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 representation of components may not represent their actual positions, sizes, shapes, ranges, etc., in order to facilitate understanding of the invention.

[0015] For the sake of explanation, when describing processing by a program, the program, function, processing unit, etc. may be described as the main body, but the main hardware body for these is a processor, or a controller, device, computer, system, etc. that is configured with the processor, etc. A computer executes processing according to a program read into memory using resources such as memory and communication interfaces as appropriate through the processor. This realizes predetermined functions, processing units, etc. A processor is configured, for example, with semiconductor devices such as a CPU / MPU or GPU. Processing is not limited to software program processing, but can also be implemented using dedicated circuits. Dedicated circuits such as FPGAs, ASICs, and CPLDs can be used.

[0016] The program may be pre-installed as data on the target computer, or may be distributed as data from a program source to the target computer. The program source may be a program distribution server on a communication 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, but not limited to, tables and lists. Expressions such as identification information, identifiers, IDs, names, and numbers are interchangeable.

[0017] [Assignments, etc.] We will provide additional information on the challenges. In recent years, device structures have been miniaturized to improve semiconductor device performance, and device structures, such as Fin-FET (Field Effect Transistor) and 3D-NAND, have become more three-dimensional and complex. The dimensions of these cutting-edge devices are on the order of nanometers (nm), and the film structures between structures now require processing precision on the order of sub-nm.

[0018] Semiconductor devices are generally manufactured by combining and repeating processes such as lithography, etching, film formation, and planarization. For example, in the etching process, as the difficulty of processing increases, efforts are being made to optimize etching conditions by combining post-processing dimensional data with machine learning, and technology is needed to measure dimensions accurately at high speed and in large quantities.

[0019] Charged particle beam instruments such as SEM and TEM are often used as a method for precise dimensional measurement because they use charged particle beams with wavelengths shorter than that of light, allowing for high-resolution observation of sub-nanometer order microstructures that cannot be observed with optical microscopes.

[0020] When comparing SEM and TEM as measurement methods, observation with a TEM requires pre-processing using a focused ion beam (FIB). Therefore, when cross-sectional observation and dimensional measurement need to be performed in a shorter time, a method of cleaving a sample after etching and observing the cross-section with an SEM may be selected.

[0021] However, due to damage and contamination caused by cleavage, the cross section of a cleaved sample may be partially destroyed or contaminated, resulting in the observed structure being different from the structure formed during the original etching process. When measuring the dimensions of a structure with such cleavage artifacts, there is a risk of unintended feedback when adjusting the etching conditions based on the measurement results. Therefore, it is necessary to measure only structures that have been properly cleaved, i.e., structures that have been cleaved without any destruction or contamination.

[0022] In addition, in dimensional measurements during the etching process, the processing depth of the layer to be etched (for example, trench depth) is often measured using the interface between the mask layer and the layer to be etched (for example, a Si layer) as a reference, so it is also important to detect the position of such an interface (in other words, the material interface, the reference interface).

[0023] However, it is difficult to determine the normality of a structure such as the cross section of the cleaved sample or to accurately detect the interface position using conventional image processing methods such as template matching. Therefore, the normality of a structure whose dimensions are to be measured has traditionally been left to the subjective judgment of the operator who is measuring it.

[0024] Even if the correct dimension measurement target structure and the accurate position of the reference interface are determined, there are still issues with the accuracy of the measurement process after determination. When observing the cross section of a cleaved sample using an SEM, a white shadow-like area (hereinafter referred to as a "white band") appears in the captured image at the boundary between the device structure (e.g., a pillar) and the vacuum region (i.e., the open area), i.e., the area near the contour of the device structure. This white band is a high-brightness pixel area on the image, and is caused by, for example, secondary electrons from a structure located in the depth direction of the captured image. Therefore, it is difficult to automatically detect the contour of the device structure from the image using image processing such as binarization. In other words, there are issues with the efficiency and accuracy of detecting the contour or edge of the device structure.

[0025] For this reason, conventional dimensional measurement of cross-sectional SEM images is often performed manually by an operator, which is time-consuming. Additionally, there are issues with differences in the precision and accuracy of contour detection and dimensional measurement due to errors in manual operation and judgment from operator to operator.

[0026] For example, Patent Document 1 describes an example in which the cross-sectional structure of a magnetic head device is observed with an SEM to measure dimensions such as the height and sidewall angle of the structure. Patent Document 1 describes that for a single magnetic head structure, an edge reference line and multiple edge points are specified on an SEM image by user input, thereby calculating approximate straight lines for each side of the structure and using them to measure dimensions and calculate the angle of the structure's sidewall.

[0027] In the case of a method in which the user inputs and sets some of the information required for contour detection and dimension measurement as described above (the second technique mentioned above), the number of input operations by the user increases, resulting in a lot of work. When multiple target structures are included in a cross-sectional SEM image, the number of input operations required by the user is equal to the product of the number of target structures and the number of measurement dimension types (e.g., width, height, etc.). When performing dimensional measurements on multiple samples or complex structures, this increases the burden on the user and leads to reduced work efficiency.

[0028] In particular, when measuring dimensions after etching, it is necessary to measure various dimensions of the remaining mask pattern, various dimensions of the trench portion dug by etching, and various dimensions of the fin or pillar-type structure directly below the mask pattern. In such cases, there is a need for automation of the contour detection and dimensional measurement of the target structure pattern from the cross-sectional image, reduction of the burden of manual input by the user, and improvement of work efficiency.

[0029] [First and second technologies] In conventional semiconductor device manufacturing processes, structures such as masks, pillars, and trenches (sometimes referred to as structures, patterns, etc.) are formed on wafers by processes such as deposition and etching. In the manufacturing process, it is necessary to confirm and inspect the appropriateness of the three-dimensional structures obtained as a result of such processing. In this case, as mentioned above, a cross section of the structure is created using a method such as cleavage or FIB processing, and an image of the cross section is captured using a method such as an SEM. A computer system and an operator then perform processing and operations such as length measurement and observation on the cross-sectional image to confirm and inspect whether the shape of the structure is appropriate.

[0030] For this purpose, it is necessary to determine and detect the contours or edges of structures from cross-sectional images and measure their dimensions. To streamline this type of processing and work, automation and efficiency using computer systems is required. However, as mentioned above, it is difficult to achieve a fully automated process for detecting the contours of structures from cross-sectional images and measuring their dimensions.

[0031] Conventionally, when a computer system is used to fully automatically detect the contour of a target structure from a cross-sectional SEM image and measure its dimensions, as in the first technique mentioned above, the contour detection process is difficult, making it difficult to efficiently and accurately detect the contour. Therefore, the second technique mentioned above is a method in which a user manually inputs and sets some information for a cross-sectional SEM image to support the computer system's contour detection and dimension measurement processes. For example, reference lines and points are specified and set for the cross-sectional image. The computer system performs contour detection and dimension measurement processes using the set information for the lines and points for the cross-sectional image. This type of method is a combination of manual operation and automatic processing (a general term including methods, devices, etc.), and for purposes of explanation, is sometimes referred to as a semi-automatic method.

[0032] [Comparative Example 1] FIG. 43 is an explanatory diagram of a technology of Comparative Example 1 in comparison with the embodiment. The technology of Comparative Example 1 corresponds to the technology of Patent Document 1. The technology of Comparative Example 1 involves a user manually setting points, lines, and the like that serve as support for contour detection on a cross-sectional image. (A) of FIG. 43 is a schematic diagram of an example cross-sectional image, showing regions of pillars 4301 and trenches 4302 that are roughly trapezoidal in shape on a vacuum background (shown in white in the schematic but black in luminance). (B) of FIG. 43 shows an example in which a user manually inputs and sets points and lines to support contour detection on the image of (A). In this example, the user sets edge points ep1 to ep6 on the sides of the trapezoid of pillar 4301. In other words, the user sets edge lines EL1 to EL3 (corresponding to the "edge reference lines" in Patent Document 1) on the sides of the trapezoid of pillar 4301. For example, a straight line connecting edge point ep1, which is the starting point, and edge point ep2, which is the end point, becomes edge line EL1.

[0033] The computer system determines the edges that form the contour of the pillar 4301 using the set edge points ep1 to ep6 or edge lines EL1 to EL3. For example, the computer system detects the edge of the pillar 4301 by referencing a brightness profile in the vertical direction on the edge line EL1 and searching for brightness on the brightness profile. The computer system determines an approximate line for the contour by connecting the detected edges. Based on information about the detected contour, the computer system measures the dimensions of the structure pattern, such as the width and height of the pillar 4301.

[0034] In Comparative Example 1, the number of manual operations and inputs required by the user is such that at least two edge points must be input and set for each side of a structure in an image, for example, a trapezoidal pattern. Therefore, the more structures such as pillars 4301 and the number of types of dimensions to be measured increase, the more time and effort the user must spend on manual operations. Furthermore, the more complex the pattern shape becomes, the more time and effort is required.

[0035] Comparative Example 1 can be considered as a technique in which a user inputs and sets lines, points, etc. for support by tracing the contours or edges of a structure pattern in an image. Such a technique requires a lot of manual operation. Furthermore, because it is a manual operation, the user is not always able to accurately set lines, points, etc. for support for the contours or edges of a structure pattern in an image, and errors occur for each user.

[0036] Comparative Example 2 FIG. 44 shows Comparative Example 2 as another example of conventional technology. The technology of Comparative Example 2 allows a user to manually set lines, points, and the like on a cross-sectional image to support dimension measurement. (A) of FIG. 44 shows a schematic diagram of an example cross-sectional image, which has, for example, four pillars 4401 and four trenches 4402. In Comparative Example 2, a user manually inputs and sets the location where the user wishes to measure the dimension, i.e., the position that defines the dimension of the measurement target, for a structural pattern in the cross-sectional image, for example, a pillar 4401 or a trench 4402 that has a roughly trapezoidal shape.

[0037] For example, (B) of FIG. 44 shows a case where trench depth is measured. When measuring trench depth, the user determines and specifies two end points (points a1 and a2) that define the trench depth (corresponding dimension measurement target line 4403) as the dimension measurement target points for each trench 4402. For example, the user performs an operation such as clicking a mouse for each point. Point a1 indicates the top of trench 4402, and point a2 indicates the bottom of trench 4402. One end of dimension measurement target line 4403 is point a1, and the other end is point a2. The computer system then measures the portion of dimension measurement target line 4403 defined by the two specified points (points a1 and a2) as the trench depth. In other words, the two points are the starting point and the ending point of the dimension.

[0038] Similarly, in (C) of Figure 44, when it is desired to measure the pillar width, the user determines and specifies two end points (points b1 and b2) that define the pillar width (corresponding dimension measurement target line 4404) as the dimension measurement target points for each pillar 4401. Point b1 is a point on the left sidewall of pillar 4401, and point b2 is a point on the right sidewall of pillar 4401. One end of dimension measurement target line 4404 is point b1, and the other end is point b2. Thereafter, the computer system measures the portion of dimension measurement target line 4404 defined by the specified two points (points b1 and b2) as the pillar width.

[0039] Thus, in Comparative Example 2, the user must input at least two points for each target structure pattern, and for each dimension measurement target location and dimension type. Therefore, in Comparative Example 2, similar to Comparative Example 1, the greater the number of target structures and dimension types, the greater the amount of manual operation required by the user. Furthermore, errors may occur due to manual human operation (such as mouse operation) or subjective judgment. The two points specified by the user for the dimension measurement target location and dimension (for example, trench depth) may deviate from the actual situation, and individual differences may also occur. Such errors affect the accuracy of the dimension measurement.

[0040] [Solution, etc.] In consideration of the above-mentioned problems, the dimension measurement system and the like according to the embodiment have the following means for solving the problems.

[0041] The dimension measurement system of the embodiment has a processor and memory resources, and performs processing related to contour detection and dimension measurement on an image (in other words, a cross-sectional image, a target image, a captured image, etc.) captured by a charged particle beam device such as an electron microscope so as to capture the cross-sectional structure of a semiconductor device as a sample. The processor performs processing according to the dimension measurement method of the embodiment on data and information such as images stored in the memory resources.

[0042] The dimension measurement system of the embodiment is a system that employs a semi-automatic method that combines a method in which a user or other person manually inputs, specifies, and sets predetermined information, such as reference lines and reference points, for supporting contour detection and dimension measurement on cross-sectional images, with an automatic method in which a computer automatically processes the cross-sectional images for which the predetermined information has been set. The embodiment incorporates innovations in this semi-automatic method. Note that a system or method that manually specifies and selects material interfaces or structure regions to be measured and automatically performs contour detection and dimension measurement may also be referred to as a "semi-automatic method."

[0043] The dimension measurement method of the embodiment includes the steps of: designating, based on manual operation by a person such as a user, a location such as a material interface that is difficult to automatically detect or recognize, as a reference line (in other words, an interface reference line, a support line, etc.) on a cross-sectional image; and designating, based on manual operation by a user, an area of ​​a structure pattern of a dimension measurement target that is difficult to automatically detect or recognize, as a reference point (in other words, an area reference point, a support point, etc.) on the cross-sectional image. These steps are steps related to manual operation by a person. The dimension measurement method of the embodiment further includes the steps of automatically detecting the contour of the structure pattern of the dimension measurement target for the image in which the reference line and reference point are set, and automatically measuring the dimension of the measurement target based on information about the detected contour. These steps are steps related to automatic computer processing.

[0044] The dimension measurement system and method of the embodiment are characterized by a manual setting method in which a user manually operates a GUI screen to specify an interface reference line, which is a reference line for indicating the material interface of the target structure, for a cross-sectional image, and also specifies an area reference point, which is a reference point for indicating the area of ​​the structure to be measured. These manual operation steps are realized as a new user interface that differs from conventional examples. This reduces the amount of input, etc., reducing the user's effort and burden, and also reducing errors due to manual user operation.

[0045] The automatic method of the dimension measurement system and method of the embodiment is characterized in that, for a cross-sectional image in which the predetermined information has been set as described above, a computer system processes the cross-sectional image using a predetermined algorithm to identify the area of ​​the target structure pattern based on the reference lines and reference points, and detects the edges that make up the contour of the target structure pattern.

[0046] The computer system then measures the specified type of dimension in the region of the structure pattern to be measured based on the reference lines and reference points and the information on the detected contours. The type of dimension to be measured (for example, trench width, trench depth, etc.) can be set or specified by the user on the GUI screen.

[0047] The computer system displays at least one of the detected contour information and the dimension measurement results to the user on a GUI screen.

[0048] The algorithm for the contour detection process is new and different from the prior art (see, for example, Figure 10, which will be described later), and can detect the contour of a structure more efficiently and accurately.Then, the computer system can measure the dimensions of the object more efficiently and accurately based on the information on the detected contour.

[0049] <First Embodiment> The dimension measurement system and method according to the first embodiment will be described with reference to FIG.

[0050] [Semiconductor dimension measurement system] FIG. 1 shows an example of the configuration of a semiconductor dimension measurement system, which is a system including the dimension measurement system of the first embodiment. The entire system of FIG. 1 is a semiconductor dimension measurement system, and is configured to include a scanning electron microscope (SEM) 2 and a computer system 1, which is the dimension measurement system 1 of the first embodiment. The example of the system configuration of FIG. 1 also has a database (DB) 3, semiconductor device manufacturing equipment 4, a manufacturing execution system (MES) 5, a client terminal (operation PC) 6, and the like, on a communication network 9. These components are interconnected via, for example, a LAN as the communication network 9, and can communicate with each other. The computer system 1, which is the dimension measurement system 1 of the first embodiment, is configured as a server on the communication network 9, and is connected via communication with devices such as the scanning electron microscope (SEM) 2 and the operation PC 6.

[0051] The dimension measurement system 1 of the first embodiment is configured by a computer system 1 described below (FIG. 2). This computer system 1 realizes the functions of the dimension measurement system 1 based on program processing by a processor. The dimension measurement system 1 has at least a contour detection function F1 and a dimension measurement function F2 as its functions. The contour detection function F1 is a function that detects the contour of a structure (in other words, a pattern) from an image 7 that is a cross-sectional image of the target sample. The dimension measurement function F2 is a function that measures the dimensions of the target based on information about the contour detected by the contour detection function F1 and outputs a dimension measurement result 8.

[0052] The SEM 2 is an example of a cross-sectional image capturing device. The SEM 2 has a function of capturing a cross-section of a semiconductor device (e.g., a wafer) that is a target sample, and obtaining the cross-sectional image as an image 7. As shown in FIG. 3, which will be described later, the SEM 2 is configured to include a main body 301 and a controller 302 connected to the main body 301.

[0053] The SEM2 or other components may have a function of creating a cross section of a target sample. For example, the SEM2 may be an apparatus having a function of creating a cross section of a target semiconductor device. For example, the SEM2 may be an FIB-SEM apparatus equipped with a focused ion beam mechanism. The FIB-SEM apparatus creates a cross section of a part of a sample by FIB processing. A device having a function of creating a cross section may exist separately from the SEM2. For example, a cleaving apparatus that creates a cross section by cleaving the sample may be provided on the communication network 9.

[0054] The SEM 2 and the computer system 1 may communicate with external devices as needed to input and output necessary data and information. For example, the SEM 2 may transmit data of an image 7 obtained by photographing to the computer system 1 or the operating PC 6. Alternatively, the SEM 2 may store the data of the image 7 in the DB 3. In this case, the computer system 1 obtains the image 7 from the DB 3. The DB 3 may be, for example, a DB server, and may store the data of the image 7 and various information such as design data.

[0055] The semiconductor device manufacturing equipment 4 is equipment for manufacturing semiconductor devices, which are target samples, and includes equipment for each manufacturing process, such as an etching equipment. The MES 5 is a system for executing and managing the manufacturing of semiconductor devices using the semiconductor device manufacturing equipment 4, and has design data and manufacturing process information for the sample. The computer system 1 may refer to the design data, manufacturing process information, etc. from the MES 5, the semiconductor device manufacturing equipment 4, etc.

[0056] The client terminal 6 is an operating PC used by the user U1, and is an information processing terminal device that has the function of accessing the dimension measurement system 1 (particularly the server function) via the communication network 9. The user U1, such as an operator, may operate the dimension measurement system 1 directly, or may use the dimension measurement system 1 by accessing it from the client terminal 6. The client terminal 6 can be a general PC or the like, and may have an input device for input operations and an output device for display etc. built-in or externally connected. The user U1 may access and use the SEM 2 etc. from the client terminal 6.

[0057] In the first embodiment, the server, which is the dimension measurement system 1, is configured as a client-server system with the client terminal 6. The dimension measurement system 1 is responsible for the main processing, and the client terminal 6 is responsible for the GUI function. The computer system 1 and the client terminal 6 may be integrated into a system. In other words, the computer system 1 may have input / output and GUI functions.

[0058] The dimension measurement system 1 of the first embodiment is not limited to the configuration example shown in FIG. 1. For example, the dimension measurement system 1 may be integrated into the controller 302 (FIG. 3) of the SEM 2. The dimension measurement system 1 may be integrated into another component of FIG. 1. The computer system 1 is not limited to a single computer, and may be realized by a cloud computing system or the like.

[0059] Not limited to the example of FIG. 1, an external device that is another component may have a cross-section observation device that observes the cross-section of a sample to perform analysis, inspection, etc. The cross-section observation device may be, for example, a TEM device. Alternatively, the SEM2 may be the cross-section observation device. The SEM2 may be a STEM (scanning transmission electron microscope), another type of microscope, or an imaging device.

[0060] A program distribution server (not shown) or the like may be provided in the communication network 9. The program distribution server distributes data such as a program (software 110 in FIG. 2) for realizing the dimension measurement system 1 of the first embodiment to the computer system 1. Furthermore, the data such as the image 7 and the program may be input and output in a form stored in a storage medium such as a memory card, rather than being input and output via communication.

[0061] [Computer System] FIG. 2 shows an example of the configuration of the dimension measurement system 1 according to the first embodiment as a computer system 1. The computer system 1 includes predetermined hardware and software that work together to realize functions. The computer system 1 in FIG. 2 includes a processor 101, memory resources 102, a communication interface 103, an input / output interface device 104, and the like. These components are connected to a bus and can communicate with each other. The memory resources 102 include a ROM 105, a RAM 106, a storage 107, and the like.

[0062] The processor 101 is configured by a semiconductor device such as a CPU, MPU, or GPU. The processor 101 may also include a ROM, a RAM, various peripheral functions, and the like. The processor 101 executes processing in accordance with software 110, including a control program stored in the memory resource 102, such as a dimension measurement program that is read from the ROM 106 or storage 107 to the RAM 105 and deployed therein. This realizes predetermined functions such as the contour detection function F1 and the dimension measurement function F2 in FIG. 1. The software 110 controls all the functions and operations described in the first embodiment. When the computer system 1 is started up, an execution module based on the processor 101 and the software 110 operates.

[0063] The storage 107 stores various types of data and information, such as an image 7, shooting information 111, definition information 112, and dimension measurement results 8. The image 7 is image data acquired from the SEM 2. The dimension measurement results 8 are data such as dimension measurement results obtained by the dimension measurement function F2. The shooting information 111 is shooting information when the image 7 is captured by the SEM 2, and includes, for example, shooting conditions and optical setting information for the SEM 2, attribute information for the image 7, and the like. The shooting information 111 includes at least information such as pixel size, which is the size in real space per pixel in the image 7, or a magnification at which the pixel size can be calculated.

[0064] The definition information 112, which will be described later (FIG. 7, etc.), is definition information and setting information required for the functions of the dimension measurement system 1, and includes system setting information, user setting information, etc. In addition, the memory resource 102 stores data and the like that are generated in the processing process as appropriate. Note that the dimension measurement system 1 may also use external memory resources (for example, DB3 or external storage device) as appropriate.

[0065] In the computer system 1, for example, data such as an image 7 acquired in advance from the SEM 2, DB 3, or operation PC 6 in Fig. 1 is stored in the storage 107. The image 7 is managed in association with shooting information 111, definition information 112, dimension measurement results 8, etc.

[0066] The communication interface 103 is a device that has a communication interface for the SEM 2, the communication network 9, etc. The input / output interface 104 is a device that has an input / output interface, and can externally connect to input devices and output devices. Examples of input devices include a keyboard, a mouse, and a microphone. Examples of output devices include a display, a printer, and a speaker. Note that the input devices and output devices may be built into the computer system 1.

[0067] Based on input of instructions and the like by user U1 on GUI screen 60, computer system 1 uses the input image 7 of the dimension measurement target, shooting information 111, and definition information 112 to detect contours related to the structure pattern in the target image 7, measure the specified type of dimension, and generate and store the dimension measurement result 8. Then, computer system 1 transmits the dimension measurement result 8 to operation PC 6. Operation PC 6 displays the image 7 and dimension measurement result 8 together with the GUI on GUI screen 60. User U1 can check the dimension measurement result 8, etc. on GUI screen 60.

[0068] A user U1, such as an operator, uses the computer system 1 by operating an input device and displaying an output device on a client terminal 6, which is an operation PC. The operation PC 6 includes a processor, memory, a communication interface, a monitor display, a mouse, a keyboard, etc., all of which are not shown. A GUI screen 60 is displayed on the screen of the monitor display.

[0069] Client-server communication is performed between the computer system 1 and the client terminal 6, and the following communications and operations are performed, for example: User U1 operates the client terminal 6 to access the computer system 1. The client terminal 6 accesses the server function of the computer system 1 via the communication network 9 and sends a request. In response to the request, the server function of the computer system 1 generates screen data including a GUI (graphical user interface) and sends it to the client terminal 6. The screen data may be a web page or the like. Alternatively, the target data and information may be directly transmitted and received instead of screen data. Based on the received screen data, the client terminal 6 displays a GUI screen 60, such as a web page, on its display screen. User U1 views the GUI screen 60 to check information about the functions of the dimension measurement system 1 and inputs settings and instructions as necessary.

[0070] The client terminal 6 sends requests and information based on user input information to the computer system 1. The computer system 1 executes processing related to the function based on the received requests and information and saves the results. The computer system 1 sends screen data including, for example, an image 7 and dimension measurement results 8 to the client terminal 6. The client terminal 6 displays a GUI screen 60 including the image 7 and dimension measurement results 8 based on the received data. The user U1 can view and confirm the image 7 and dimension measurement results 8 on the GUI screen 60.

[0071] 1 shows one user U1 and one client terminal 6, but the system may have multiple users and multiple client terminals 6. The computer system 1 and processor 101 are not limited to being physically one, and multiple processors may exist.

[0072] [SEM] 3 shows an example of the configuration of an SEM 2. The SEM 2 is broadly divided into an SEM main body 301 and a controller 302 connected to the main body 301. The main body 301 further comprises an electron optical column (hereinafter referred to as the "column") and a sample chamber provided below the column. The controller 302 is a system that controls imaging by the main body 301. The controller 302 includes an overall control unit 320, a signal processing unit 321, a storage unit 322, a communication interface 323, etc., and is externally connected to an input device 324 and an output device 325.

[0073] The column of the main body 301 includes, as its components, an electron gun 311, an accelerating electrode 312, a focusing lens 313, a deflection lens 314, an objective lens 315, and a detector 317. The electron gun 311 emits an electron beam b1, which is a charged particle beam. The accelerating electrode 312 accelerates the electron beam b1 emitted from the electron gun 311. The focusing lens 313 focuses the electron beam b1. The deflection lens 314 deflects the trajectory of the electron beam b1. The objective lens 315 controls the height at which the electron beam b1 is focused. The sample chamber is a room in which samples such as wafers and coupons (broken pieces of wafers) are stored, and includes a stage 316 for moving the observation position on the sample to the irradiation point of the electron beam.

[0074] The stage 316 is a sample stage on which a semiconductor device, which is the target sample 30, is placed. The stage 316 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, and can move the captured field of view in the horizontal and vertical directions relative to the front-facing image, or in a rotational direction within the field of view. This allows the field of view for imaging to be set.

[0075] The detector 317 detects, as an electric signal, particles b2 such as secondary electrons and backscattered electrons generated from the sample 30 irradiated with the electron beam b1, and outputs a detection signal that is an electric signal.

[0076] The overall control unit 320 controls the operations of the controller 302 and the main body 301. The overall control unit 320 issues instructions such as drive control to each unit. Each unit such as the overall control unit 320 can be implemented by a computer or a dedicated circuit. The signal processing unit 321 receives a detection signal from the detector 317, performs processing such as analog / digital conversion to generate an image signal, and stores the image data in the memory unit 322. The memory unit 322 can be implemented by a non-volatile storage device or the like. The overall control unit 320 also stores image-related information and the like in the memory unit 322 in association with the image data.

[0077] The communication interface 323 is a device equipped with a communication interface for the communication network 9 and the computer system 1. For example, in response to a request from the computer system 1, the overall control unit 320 transmits data such as images and imaging information stored in the storage unit 322 to the computer system 1 via the communication interface 323. The computer system 1 stores data such as images 7 received from the controller 302 of the SEM 2 in the memory resource 102.

[0078] [Semiconductor Devices] 4 shows an example of a structure pattern of a semiconductor device, which is a target sample in the first embodiment. FIG. 4A shows a perspective view of the shape of a design pattern of a three-dimensional structure, for example, a cross section in the XZ plane. This cross section is formed by, for example, cleavage or FIB processing. This structure has a mask 401, pillars 402, and trenches 403. The mask 401 and pillars 402 are structures with trapezoidal cross sections, and the trench 403 is a structure with an inverted trapezoidal cross section, with the pillars 402 formed below the portion where the mask 401 is present and the trench 403 formed below the portion where the mask 401 is not present.

[0079] 4B is a cross-sectional view corresponding to the cross-section in the XZ plane of (A), showing an example of a dimension to be measured. The semiconductor device has a mask layer 411 made of a first material on a layer to be etched 412 made of a second material in the Z direction. A mask 401 is formed on the mask layer 411, and pillars 402 and trenches 403 are formed in the layer to be etched 412. The material interface 413 between the mask 401 and pillars 402 in the Z direction is indicated by a dashed line.

[0080] Dimension A1 is the trench width at the upper surface (in other words, the top) 414 of the trench 403. The position of the upper surface 414 of the trench 403 corresponds to the position of the material interface 413 between the mask 401 and the pillar 402. Dimension A2 is the trench width at the bottom surface (in other words, the bottom) 415 of the trench 403. Dimension A3 is the trench width at, for example, an intermediate position (e.g., the 50% position) in the height-depth direction (Z direction) of the trench 403. The position of dimension A3 is not limited to being defined as a percentage (%), and may be defined by the distance from the top 414, the distance from the bottom 415, or the like. Dimensions A1 to A3 are lengths in the X direction.

[0081] Dimension A4 is the trench depth, which is the length in the Z direction from the top 414 to the bottom 415 of trench 403. Dimension A5 is the trench sidewall angle, which is, for example, the angle that sidewall 416 (the slope portion of the trapezoid) makes with respect to bottom 415 of trench 403. The trench sidewall angle may be defined as the angle at a given position in the Z direction.

[0082] Dimension B1 is the pillar width at the top surface (i.e., the top) of pillar 402. The position of the top of pillar 402 corresponds to the position of material interface 413. Dimension B2 is the pillar width at the bottom surface (i.e., the bottom) of pillar 402. The position of the bottom of pillar 402 corresponds to the position of the bottom of trench 403. Dimension B3 is the pillar width in the height direction (Z direction) of pillar 402, for example, at an intermediate position (e.g., 50% position). The position of dimension B3 is not limited to being defined as a percentage (%), but may also be defined by the distance from the top, the distance from the bottom, or the like. Dimensions B1 to B3 are lengths in the X direction.

[0083] Dimension B4 is the pillar height, which is the length in the Z direction from the top to the bottom of the pillar 402. Dimension B5 is the pillar sidewall angle, e.g., the angle that the sidewall makes with respect to the bottom surface of the pillar 402. The pillar sidewall angle may be defined as the angle at a given position in the Z direction.

[0084] The structures and dimensions of the measurement targets are not limited to the example in Fig. 4. Other examples of structures include holes, fins, etc. Other examples of dimensions include the area of ​​trench 403, etc.

[0085] [Dimension measurement method] A dimension measurement method according to the first embodiment will now be described. FIG. 5 shows a flow diagram of a dimension measurement process executed by the computer system 1, which is the dimension measurement system 1 according to the first embodiment. This flow includes steps S1 to S6. The dimension measurement system 1 executes the dimension measurement method according to the first embodiment according to the flow shown in FIG. 5. In summary, the user U1 selects and displays the target image 70 on the GUI screen 60 (FIG. 6) (step S1), and manually specifies and sets the interface reference line 11 (FIG. 8) and the area reference point 12 (FIG. 9) (step S3). Based on the set information, the computer system 1 identifies the area of ​​the structure to be measured and detects its contour (step S4), measures the specified type of dimension based on the information of the detected contour (step S5), and displays the dimension measurement result 8 on the GUI screen 60 (FIG. 20) (step S6).

[0086] In step S1, the computer system 1 specifies an image 7 whose dimensions are to be measured. In this example, the user U1 operates the mouse or the like of the operation PC 6 to move the cursor 609 on the GUI screen 60 (FIG. 6), and operates the GUI such as menus and buttons to select the image file of the target image 7.

[0087] In step S2, the computer system 1 specifies the definition information 112 (FIG. 2) to be applied to this dimension measurement. If the definition information 112 to be applied has already been specified and set, it is sufficient to simply refer to that setting. In this example, the user U1 selects a file for the definition information 112 on the GUI screen 60. The definition information 112 may be prepared in advance by the business operator as part of the software 110 (FIG. 2), or may be set and saved by the user U1 on the GUI screen 60. As will be described later (FIG. 7), the definition information 112 defines the structure pattern of the dimension measurement target, the dimensions of the measurement target (in other words, the type of dimension), details of the measurement of the dimensions, etc., to be applied to the target image 7.

[0088] In step S3, the computer system 1 specifies an interface reference line 11 and an area reference point 12 for the target image 70 on the GUI screen 60 (FIG. 6, FIG. 9, etc.) based on manual operation by the user U1. Multiple area reference points 12 can be specified at once as needed. Information (also referred to as reference information) such as the position coordinates of the interface reference line 11 and area reference point 12 set for the image 7 is appropriately saved in the memory resource 102 (FIG. 2). For example, the reference information is saved as part of the dimension measurement result 6.

[0089] In step S4, the computer system 1 detects the contour of the region of the structure pattern of the dimension measurement target in the target image 70 based on the interface reference line 11 and region reference point 12 specified in step S3 and the applied definition information 112. In the first embodiment, a distinction is made between contours and edges, and edges that make up the contour are particularly detected. Edges are represented by data structures such as edge points or contour lines. Information about the detected contours (information about the edge points and contour lines; also referred to as contour information) is also stored appropriately in the memory resource 102 (FIG. 2). For example, the contour information is stored as part of the dimension measurement result 8.

[0090] In step S5, the computer system 1 measures the specified type of dimension of the target structure using the information on the interface reference line 11 and area reference point 12 specified in step S3 and the contour information detected in step S4. The computer system 1 stores the data of the dimension measurement result 8 generated as a result in the memory resource 102.

[0091] In step S6, the computer system 1 outputs the dimension measurement result 8 to the user U1. The computer system 1 transmits the dimension measurement result 8 to the operation PC 6, thereby causing the image 7 and the dimension measurement result 8 to be displayed on the GUI screen 60.

[0092] Below, details and specific examples of each step will be explained using drawings.

[0093] [Step S1: Target image] Initially, a GUI screen 60 such as the example in Fig. 6 is displayed on the display of the operation PC 6. The upper part of Fig. 6 shows the initial display state of the GUI screen 60, and the lower part shows the state in which a target image 70 is displayed in an image field 601. The GUI screen 60 includes an image field 601, a toolbar 602, tabs 603, etc. In the initial display state, a target image has not yet been specified, so no image is displayed in the image field 601. Tabs 603 such as "Semi-auto" and "Auto" are also not activated.

[0094] The user U1 clicks on the "Semi-auto" tab 604 and selects the image file of the image to be measured using the "File" button 605 on the toolbar 602. The target image is the image 7 that the user wants to display in the image field 601. Operations such as clicking and selecting on the GUI screen 60 can be performed using input devices such as the mouse and keyboard of the operating PC 6 and a GUI that corresponds to those operations.

[0095] The "Semi-auto" tab 604 is used to select the "semi-automatic method" as the mode. This "semi-automatic method" mode corresponds to the dimension measurement method of the first embodiment, and is a method in which the interface reference line 11 and the area reference point 12 are manually specified as described above. The "Auto" and "train" tabs are modes used in the fourth embodiment, which will be described later.

[0096] When user U1 wishes to measure the dimensions of the target image, he or she presses the "Measure" button 606. In response to this operation, the computer system 1 transitions the screen to the state shown in the lower part of FIG. 6. On the screen in the lower state, the specified target image 70 is displayed in the image field 601. On this screen, buttons such as the "Mask" button 611, the "Pillar" button 612, and the "Trench" button 613 are also activated. The lower state corresponds to the interface reference line setting mode, which will be described later. The "Mask" button 611 and the like are buttons provided for each type of structure.

[0097] The user U1 clicks one of the "Mask" button 611, "Pillar" button 612, and "Trench" button 613 depending on the structure pattern of the dimension measurement target in the target image 70. These buttons are used to select, for example, the mask 401, pillar 402, trench 403, etc. shown in FIG. 4 as the type of structure pattern for which the interface reference line 11 and area reference point 12 are to be specified. By specifying the type of target structure pattern in this way, the target and content of processing by the computer system 1 can be limited, making the process easier. Note that if there are other types of structure patterns, other buttons are similarly provided.

[0098] 1, if the user U1 wants to measure a trench in the target image 70, in other words, if the user U1 wants to set an interface reference line 11 or an area reference line 12 for the trench, the user U1 first presses the "Trench" button 613. This allows the computer system 1 to determine the type of structure pattern to be processed.

[0099] [Step S2: Definition information] In step S2, a file of definition information 112 to be applied to the target image 70 is selected. The file of definition information 112 is a file that stores, for each target structure pattern, definition information, setting information, etc., required for dimensional measurement of the target structure pattern.

[0100] An example of a file of definition information 112 is shown in table format at the top of FIG. 7. This file has the following column items in the table shown: row number (#), "pattern type," "measurement name," "measurement type," and "measurement definition information." The "pattern type" item indicates the type of structure pattern (such as the trench mentioned above) that is the target of dimension measurement. The "measurement name" item indicates the name of the measurement to be performed. This measurement name represents the target dimension as shown in FIG. 4 mentioned above, and can be given any name by user U1. The "measurement type" item indicates the type of measurement to be performed. For example, the value "CD from interface" means "CD (Critical Dimension) at a position a specified distance from the interface."

[0101] The "measurement definition information" item indicates the definition information of measurement for each "measurement type." For example, a value of "0 nm from reference line" means that the measurement is performed at a "position of 0 nm from the interface reference line." This corresponds to information that defines the vertical positions of dimensions A1 to A3 in FIG. 4. The predetermined distance corresponds to, for example, distance d in FIG. 18(C) described later.

[0102] These definition information 112 necessary for the measurement may be set and stored in a file in advance. For example, the file of the definition information 112 can be set on the GUI screen 60, the file to be applied can be selected, and the same information can be reused for each measurement. Alternatively, the definition information 112 may be input in step S2 each time a measurement is performed. In dimensional measurement using an image of a semiconductor device, it is often the case that dimensions of a fixed location on a sample of the same structure are repeatedly measured. When performing such measurements, if setting information related to the dimensional measurement, such as the measurement position and measurement type information, is set once as definition information 112, it becomes unnecessary to set the setting information each time a measurement is performed from the next time onward.

[0103] The lower part of FIG. 7 shows an example of the configuration of a GUI screen for selecting definition information 112. After specifying a trench for the target image 70 using, for example, the "Trench" button 613, a definition information selection GUI 701 such as a list box is displayed on the GUI screen, and the user U1 can select and apply one or more files of definition information 112 to be applied—in this example, the definition information in a specified row of the table shown at the top. This function is realized by cooperation between the processor 101 and the software 110. Alternatively, a program for selecting the definition information 112 may be stored in a storage device provided in the client terminal 6, and the operation shown in the lower part of FIG. 7 may be performed by a processor provided in the client terminal 6. Furthermore, if a program for editing the definition information 112 is stored in the client terminal 6, the definition information 112 can be pre-set on the client terminal 6 side.

[0104] When there are multiple candidate definition information items associated with a specified target structure pattern (e.g., a trench), the user U1 can select one or more of the definition information items to be applied by operating the GUI screen 60. For example, in the example of definition information 112 in FIG. 7, there are three types of candidate definition information items for trench 403, as shown in rows numbered #3 to #5. The user U1 selects one or more of the candidate definition information items to be applied from these candidates. In this way, it is possible to measure multiple types of dimensions simultaneously, which improves the efficiency of processing and work.

[0105] [Specifying and setting target dimensions using definition information] In the first embodiment, as described above, the definition information 112 (in other words, dimension measurement setting information, dimension setting information) can be used by the user U1 to specify and set the measurement target dimensions (including the dimension type and measurement position as shown in FIG. 4) for each target structure pattern in the target image. In summary, the definition information 112 is information that determines what types of dimensions are to be measured and how for the interface reference line 11, area reference point 12, and detected contour information.

[0106] The target dimensions and definition information 112 may be specified and set on the GUI screen 60 as follows. In a modified example, the computer system 1 displays, on the GUI screen 60, an image relating to dimensions, such as that shown in FIG. 4B, as a GUI image based on design pattern information, and also displays a table of definition information 112, such as that shown in FIG. 7. The computer system 1 displays, on the GUI screen 60, an image (e.g., an arrow image) of a dimension measurement target line representing a candidate dimension (e.g., dimension A1) for each structure pattern, such as trench 403. The computer system 1 accepts an operation of a cursor or the like by user U1 on the GUI screen 60 to select a target dimension, and sets detailed definition information for each selected target dimension in the table of definition information 112. For example, the vertical position defining dimension A3 can be set using a ratio, distance, or the like.

[0107] [Step S3: (1) Interface Reference Line] In step S3, on the GUI screen 60, the user U1 manually operates a mouse or the like on the target image 70 to first specify and set an interface reference line 11 for a structure pattern to be subjected to dimension measurement, for example, a trench or the like specified by the "Trench" button 613 or the like in FIG. 6. This interface reference line 11 is specified in accordance with the material interface 413 as shown in FIG. 4. The bottom of the mask 401, the top of the pillar 402, and the top of the trench 403 are located in the same vertical position as the material interface 413.

[0108] In the interface reference line setting mode, the user U1 operates the interface selection GUI 61 displayed in the image field 601 to specify the interface reference line 11 at a desired position aligned with the material interface 413 in the target image 70. The computer system 1 sets the interface reference line 11 at the specified position in the vertical direction (Z direction) in the target image 70.

[0109] The interface selection method and interface reference line setting method in embodiment 1 will be described with reference to Figure 8 etc. Figure 8(A) shows an example of the target image 70 in the image field 601 corresponding to Figure 6, and Figure 8(B) shows a state in which the interface reference line 11 is displayed as the interface selection GUI 61 on the target image 70 in (A).

[0110] The example of target image 70 in (A) includes a mask 401, pillars 402, and trenches 403. Note that, depending on the image, only some of these may be included. Note that in the drawings, the color brightness of the image is represented as a black and white schematic diagram. In this example, the vacuum region, which is the background region including trenches 403, has the lowest brightness and is darkest, and is illustrated with a black pattern. Compared to trenches 403, the regions of pillars 402 and masks 401 have higher brightness and are brighter in this order. In addition, a white band 406 appears near the boundary between the background region including trenches 403 and the regions of masks 401 and pillars 402. This region 406 has the highest brightness and is brightest, and is illustrated with a white pattern.

[0111] In the example of (B), in the initial state, an interface reference line 11 is displayed as an interface selection GUI 61 at an arbitrary position within the target image 70, for example, at a midpoint in the Z direction. The interface selection GUI 61 is displayed as a dotted straight line extending in the X direction, and corresponds to the interface reference line 11. The user U1 operates a cursor 609 in the image field 601 based on the operation of an input device such as a mouse to operate the interface reference line 11 of the interface selection GUI 61. In this way, the user U1 specifies the interface reference line 11 at a position in the vertical direction (Z direction) aligned with the material interface 413 ( FIG. 4 ), in this example, the interface between the mask 401 and the pillar 402.

[0112] The interface selection GUI 61 allows the position of the interface reference line 11, indicated by a dotted straight line, in the Z direction (vertical direction) to be changed by, for example, clicking and dragging the cursor 609. For example, the user U1 positions the cursor 609 on the interface reference line 11, clicks, and drags it to move the cursor 609 up and down, thereby changing the position of the interface reference line 11 up and down. The user U1 can roughly determine the position of the material interface 413 from the difference in brightness between different materials (e.g., the mask 401 and the pillar 402) in the image 70, and therefore places the interface reference line 11 in accordance with the position of the material interface 413. The user U1 determines the position of the interface reference line 11 by a predetermined operation (e.g., releasing the click button).

[0113] In this example, the interface reference line 11 is a straight line extending in the X direction as shown in the figure, and specifying the interface reference line 11 can be achieved by, at a minimum, simply specifying the height position of one point within the target image 70, which reduces the amount of input required.

[0114] The computer system 1 sets the interface reference line 11 at the position on the target image 70 specified by the above operation. The setting information for the interface reference line 11 includes, for example, position coordinate information in the vertical direction (Z direction) as shown in the figure. The computer system 1 displays the set interface reference line 11 in a predetermined representation. For example, the interface reference line 11 in the interface selection GUI 61 may change from a dotted line of a predetermined color to a solid line of a predetermined color. The interface selection GUI 61 is not limited to the above example. For example, up and down buttons or a vertical slide bar may be used, or a form for inputting vertical position coordinates may be used.

[0115] In this example, since the material interface 413 extends in the horizontal direction (X direction) in the cross-sectional image, the interface reference line 11 is set to extend in the horizontal direction (X direction) in accordance with the X direction. However, the present invention is not limited to this, and the interface reference line 11 may extend in a direction other than the horizontal direction in accordance with the actual situation of the direction of the material interface in the cross-sectional image. As a modified example, the extension direction of the interface reference line 11 may be specified and set by the user U1 on the GUI screen 60.

[0116] After setting the interface reference line 11, the user U1 operates the "Next" button 610 in the GUI screen 60 (FIG. 6). In response to this operation, the computer system 1 transitions the GUI screen 60 from the interface reference line setting mode shown in FIG. 6 to the area reference point setting mode shown in FIG. 9.

[0117] [About the function of the interface reference line] In the first embodiment, the interface reference line 11 is a line for supporting and serving as a reference for contour detection and dimension measurement processing. In particular, the interface reference line 11 is a line that allows the user U1 to determine and specify the material interface 413. In particular, the interface reference line 11 is a line that can also be used as a reference for detecting and determining the region of a structure to be measured and the dimensions of the measurement target.

[0118] In the first embodiment, as will be described later, when measuring dimensions, the measurement target dimensions can be determined based on the detected contour lines and definition information 112, with the specified interface reference line 11 as the reference. For example, in FIG. 4, the trench width, which is dimension A1, and the trench depth, which is dimension A4, are defined based on the detected contour lines and in accordance with the position in the Z direction of the specified interface reference line 11. For example, the position of the top edge, which is one end of the trench depth, is defined by the interface reference line 11. Furthermore, the dimension A3 is defined as a position at a predetermined distance or rate below the position of the specified interface reference line 11 as the reference.

[0119] [Step S3: (2) Area Reference Point] In step S3, the user U1 manually specifies and sets area reference points 12 that roughly represent the area of ​​the structure pattern to be subjected to dimension measurement for the target image 70 in the image field 601 on the GUI screen 60. At this time, one or more area reference points 12 are specified for the structure pattern of the type specified by the above-mentioned button using the area selection GUI 62 as a predetermined GUI.

[0120] A method for selecting the area reference point 12 of the area of ​​the structure to be measured will be described with reference to Figure 9. Figure 9 shows the state of the area reference point setting mode on the GUI screen 60. The lower part of Figure 9 shows an enlarged view of the target image 70 in the image field 601. In this mode, in addition to the set interface reference line 11 described above, the area reference point 12 and the like are displayed as an area selection GUI 62 on the target image 70 in the image field 601. In the initial state, the area reference point 12 is not displayed. The user U1 operates the area selection GUI 62 by operating a cursor 609 based on a mouse or the like, and specifies the area reference point 12 within the area of ​​a trench or the like to be measured.

[0121] In a modified example, the area reference point 12 and the perpendicular line 13 may be displayed at any position within the target image 70 from the initial state.

[0122] In this example, region reference points 901, 902, and 903 are displayed at positions designated by the user U1 as region reference points 12 in the region selection GUI 62. In this example, region reference points 901 and 902 are designated within the regions of the two masks 401 after clicking the "Mask" button 611, and region reference point 903 is designated within the region of the trench 403 after clicking the "Trench" button 613. The computer system 1 displays the region reference points 12 at the designated positions in a predetermined manner (for example, a circle, a predetermined color, etc.). As shown in the figure, the regions of the masks 401 and the trench 403 are partially delimited by interface reference lines 11.

[0123] Furthermore, upon designation of the region reference point 12, the computer system 1 draws a perpendicular line 13, which is a straight line perpendicular to the interface reference line 11 in the vertical direction (Z direction), from the position of the designated region reference point 12, and displays it in a predetermined manner (for example, a dotted line). In this example, a perpendicular line 904 is drawn upward from the region reference point 903, and perpendicular lines 905 are drawn downward from the region reference points 901 and 902, respectively. In this example, the display of the perpendicular line 13 is included as part of the region selection GUI 62, but the display of the perpendicular line 13 may be omitted.

[0124] Not limited to the above example, in other examples of the area selection GUI 60, the user U1 may move and place the initially displayed area reference point 12 to a desired position by clicking, dragging, or the like with the cursor 609. Not limited to the above example, the area reference point 12 can also be specified for the pillar 402 in a similar manner. Also, if necessary, it is possible to return to the interface reference line setting mode and redo the setting of the interface reference line 11. In a modified example, the interface reference line 11 and the area reference point 12 may be set simultaneously in parallel within the target image 70 in the image field 601.

[0125] In the example of FIG. 9, a point at an arbitrary position included within the area of ​​a certain trench 403 (the inverted trapezoidal area enclosed by the interface reference line 11 as shown) is specified as the area reference point 903. This area reference point 903 indicates the area of ​​that trench 403. A perpendicular line 13 (904) is drawn vertically upward from the area reference point 903, i.e., in the direction of the interface reference line 11. The perpendicular line 13 is automatically determined once the area reference point 11 is specified, so there is no need for the user U1 to perform the operation of drawing the perpendicular line 13. As described above, the area selection GUI 62 requires only the operation of specifying one point at a rough position by clicking, etc. for each structure pattern, and there is no need to specify a precise position, so the operation is minimal.

[0126] Furthermore, multiple area reference points 12 and associated perpendicular lines 13 can be specified collectively within the target image 70 as necessary. In this example, when two masks 401 and one trench 403 are to be subjected to dimension measurement, three area reference points 12 are specified. The computer system 1 determines the type of structure associated with the specified area reference point 12 in accordance with the specification of the type of structure using the "Mask" button 611 or the like. The computer system 1 sets the specified area reference point 12 for each structure pattern in the target image 70.

[0127] When the user U1 has finished specifying and setting the interface reference line 11 and the area reference point 12, he or she presses the "Finish" button 614 on the GUI screen 60. In response to this operation, the computer system 1 ends the area reference point setting mode, thereby completing step S3. Thereafter, based on the setting information, the computer system 1 automatically performs contour detection and dimension measurement (steps S4 and S5).

[0128] [About the function of area reference points] In the first embodiment, the area reference point 12 is a point for supporting the contour detection and dimension measurement processes. In particular, the area reference point 12 is a point for the user U1 to roughly specify the area of ​​the structure pattern to be measured.

[0129] For example, the structure to be measured is a trench 403, and the dimension (or, in other words, the type of dimension) to be measured is the trench width (for example, dimension A3 in FIG. 4, or dimension 1803 in FIG. 18C described below). In this case, the area reference point 12 merely roughly indicates the area of ​​the target trench 403. By processing using an algorithm described below based on this area reference point 12 and the interface reference line 11, the area of ​​the trench 403 is identified and its outline is detected. The area reference point 12 does not specify the height position at which the trench width is measured, nor does it specify the two points at both ends of the dimension measurement target line that defines the trench width. Therefore, the effort required for manipulating the area reference point 12 is small.

[0130] Furthermore, in the first embodiment, one of the functions of the area reference point 12 is not only to indicate the area of ​​the structure whose dimensions are to be measured, but also to have the following function. That is, as shown in Fig. 10 (to be described later), the position of the area reference point 12 in the Z direction separates an upper rectangular area from which a line profile 20 is extracted in the X direction, and a lower semicircular area from which a line profile 20 is extracted in the radial direction. The method of generating these line profiles 20 changes depending on the position of the area reference point 12.

[0131] In a modified example (described later), the area reference point 12 can also be used as a reference for determining the dimensions of the object to be measured by the computer system 1.

[0132] [Material interfaces and dimensional measurement areas] A material interface of a structure pattern in a cross-sectional image (e.g., material interface 413 in FIG. 4) can serve as a reference for dimensional measurement. For example, there are cases where it is desired to measure a dimension (e.g., trench depth) starting from a material interface. Therefore, it is effective to grasp the material interface as accurately as possible. However, as described above, conventional techniques such as image processing have difficulty in determining and detecting a material interface from a cross-sectional image, and this is inefficient. Therefore, in the first embodiment, a semi-automatic method is adopted, in which a user U1 specifies and sets an interface reference line 11 indicating the material interface in the cross-sectional image by subjective judgment and manual operation. This allows the computer system 1 to perform dimensional measurement, etc., based on the interface reference line 11, enabling efficient processing.

[0133] Furthermore, in conventional technologies, it is difficult for a computer to automatically determine and detect the area of ​​a structure (e.g., a trench) whose dimensions are to be measured from a cross-sectional image. In general conventional technologies (e.g., Comparative Example 2), the user manually specifies the area whose dimensions are to be measured. However, such conventional technologies require a lot of effort from the user. Therefore, in the first embodiment, a semi-automatic method is adopted, in which the user U1 manually specifies and sets the area reference point 12 that roughly indicates the area whose dimensions are to be measured in the cross-sectional image, and then the computer system 1 automatically identifies the area of ​​the target structure and measures its dimensions.

[0134] [Step S4: Contour detection part 1] In step S4, a contour detection process is performed on the structure pattern whose dimensions are to be measured, using the position coordinate information of the selected / specified interface reference line 11 and the position coordinate information of the area reference point 12. Below, the algorithm of the process when contour detection is performed on the trench 403 will be described with reference to FIG.

[0135] 10 is a schematic explanatory diagram of contour detection using an interface reference line 11 and an area reference point 12 in a target image 70. This example shows contour detection using an area reference point 903 and a perpendicular line 904 related to a trench 403. FIG. 11 is an explanatory diagram corresponding to FIG. 10, in which the image content is simplified by making the background area white for easier viewing, and supplementary information about a line profile 20, etc. is shown.

[0136] [Line Profile] The computer system 1 acquires a line profile 20 in the X direction (horizontal direction in the image), which is perpendicular to a perpendicular line 13 (904) extending upward in the Z direction (vertical direction in the image) from the area reference point 12 (903), starting from the perpendicular line 13. The line profile 20 is data configured from the luminance values ​​of pixels on that line in the pixel data of the image 7. In the example of FIG. 10, 15 line profiles 20 numbered #1 to #15 are acquired as the multiple line profiles 20.

[0137] The multiple line profiles 20 are obtained, for example, as follows. In FIGS. 10 and 11 , the computer system 1 draws a perpendicular line 13 from the region reference point 12 to the upper interface reference line 11, and extracts an intersection 14 between the perpendicular line 13 and the interface reference line 11. Starting from the intersection 14, the computer system 1 first extracts line profiles 20 (#1, #15) on the interface reference line 11 in the left and right directions in the X direction. In this example, the line segment extending from the intersection 14 to the left to the end of the range 23 is the line profile 20 numbered #1, and the line segment extending from the intersection 14 to the right to the end of the range 23 is the line profile 20 numbered #15. Note that these left and right line profiles 20 are set as two lines, not a single line.

[0138] Next, the computer system 1 sets a plurality of points (division points 15) on the perpendicular line 13 connecting the intersection point 14 on the interface reference line 11 and the area reference point 12, for example, at a predetermined interval or number. These division points 15 define positions for extracting a plurality of X-direction line profiles 20. The computer system 1 similarly extracts line profiles 20 extending in the X direction within a range 23 on the left and right sides of the X direction, starting from each division point 15. In this example, three division points 15 ( FIG. 11 ) are added, and line profiles 20 numbered #2 to #4 are obtained on the left side, and line profiles 20 numbered #12 to #14 are obtained on the right side. Through the above process, first, eight line profiles 20 numbered #1 to #4 and #12 to #15 are obtained in the range above the area reference point 12.

[0139] The range 23 is a range for extracting the line profile 20. The range 23 is a rectangular area extending from the area reference point 12 in the Z direction upward to the left and right of the perpendicular line 13, and is a semicircular area extending from the area reference point 12 to a predetermined radius downward from the area reference point 12 in the Z direction.

[0140] In the algorithm for the contour detection process in the dimension measurement method of the first embodiment, as shown in the figure, a line profile 20 extending in the X direction is set in the rectangular region above the region reference point 12, and a line profile 20 extending in the radial direction at each position in the radial direction is set in the semicircular region below the region reference point 12. The first embodiment is a method for detecting the contour (particularly the edges that make up the contour) of the target structural pattern using these two types of line profiles 20 (in other words, brightness profile information).

[0141] Next, the computer system 1 acquires multiple line profiles 20 extending in radial directions within a semicircular range 23 below the region reference point 12 (903). First, the computer system 1 acquires the line profile 20 numbered #5 extending to the left in the X direction from the position of the region reference point 12, and the line profile 20 numbered #11 extending to the right in the X direction. The computer system 1 also sets multiple radial directions by dividing the semicircular region from the line profile 20 numbered #5 to the line profile 20 numbered #11, with the region reference point 12 as the center point, at predetermined angular intervals or numbers. The computer system 1 then acquires line profiles 20 extending in each radial direction (in other words, the direction of the rotation angle). In this example, five line profiles 20 numbered #6 to #10 are acquired.

[0142] It should be noted that the two line profiles 20 numbered #5 and #11 extend in the X direction and can be said to belong to both the upper and lower ranges, so it can also be said that a total of 10 line profiles 20 are acquired in the upper range, including these two.

[0143] 11, when setting the multiple line profiles 20, identification numbers (IDs) are assigned in an order starting from the left side and wrapping around to the right side as shown by the dashed arrows, but this is not limitative. Also, the processing order of the multiple line profiles 20 does not have to be limited to the order of the numbers.

[0144] The range 23 (e.g., length from the area reference point 12) and quantity (e.g., division interval and number) for acquiring the line profile 20 may be set as fixed setting values ​​in advance in the implementation of the software 110, or may be set by the user as setting values ​​according to the structure pattern in the definition information 112.

[0145] The method of using line profiles 20 in two directions in the first embodiment corresponds to the characteristics of the shape of the target structure pattern. For example, a structure pattern such as a trench 403 has a roughly concave or convex or trapezoidal shape. For such a structure pattern shape, it is appropriate to extract line profiles 20 in the X direction or the radial direction. In the example of FIG. 10, when detecting the contour of a region of a trench 403 having a concave and inverted trapezoidal shape, the line profiles 20 are set in the radial direction (i.e., the radial direction) centered on the region reference point 12 in the range below the region reference point 12. The radial direction line profiles 20 (set for numbers #6 to #10 in FIG. 11) pass near the sidewalls and bottom of the trench 403 as shown in the figure, making it possible to extract edge candidate points 21 near the sidewalls and bottom of the trench 403 (edge ​​candidate points present on each of the line profiles #6 to #10).

[0146] In embodiment 1, it is possible to automatically select how to set two types of line profile 20 depending on the specification of the target structure pattern or the positional relationship, such as whether the area reference point 12 is above or below the interface reference line 11 in the Z direction.

[0147] [Extract edge candidates] Next, the computer system 1 extracts, from each of the acquired line profiles 20, edge candidate points 21 that are edge candidates related to the contour of the region of the structure whose dimensions are to be measured. The computer system 1 extracts edge candidate points 21 from one line profile 20 based on a predetermined rule. The edge candidate points 21 are points that are candidates to become edges that form the contour of the target structure pattern. There are cases where no edge candidate points 21 can be extracted from each line profile 20, and cases where two or more edge candidate points 21 can be extracted.

[0148] Here, a distinction is made between contours and edges. In FIG. 10, edge candidate points 21 extracted on the line profile 20 are indicated by white and black squares. In this example, when two edge candidate points 21 are extracted, for the sake of distinction, the first point is designated as edge candidate point 21a indicated by a white square, and the second point is designated as edge candidate point 21b indicated by a black square. The contour of a trench 402 or the like may be vague, as in the case of a white band 406. Here, an edge (end point) refers to, for example, the pixel (or the position or coordinate of that pixel) that is most likely to be the contour of a structure among the pixels that make up the white band 406, or a line formed by a set of pixels at the most likely positions. Note that the coordinates between pixels can also be defined as an edge.

[0149] The computer system 1 determines the brightness change on each line profile 20 and extracts edge candidate points 21 according to a predetermined rule. In this processing for each line profile 20, it is sufficient to determine the brightness change in a single direction, for example, the X direction. This makes it easier to detect contours or edges compared to the prior art example, which determines the brightness change in each direction within a two-dimensional image.

[0150] The predetermined rule is a provision regarding the direction and amount of brightness change for determining the edge candidate point 21. For example, the predetermined rule may be that the point is one where brightness increases from low to high, that the amount of brightness change is equal to or greater than a threshold, and that the median value of the difference in brightness change is used. The details of the predetermined rule are not limited.

[0151] [Extraction of edge candidate points on line profile] 12 shows a graph plotting the line profile 20 numbered #1 in FIG. 10, with the horizontal axis representing the position coordinate in the horizontal direction (X direction) within the target image 70 and the vertical axis representing the luminance value of a pixel on the target image 70. In this example, the left side of the horizontal axis represents the side closer to the perpendicular line 13 (positive X direction), and the right side represents the side farther from the perpendicular line 13 (negative X direction). The position of the origin of the graph (coordinate x0) corresponds to the position of the intersection 14, and the position of the right end of the graph (coordinate x5) corresponds to the position where the end of the range 23 is reached.

[0152] The lower part of FIG. 12 shows a graph in which the positive and negative X directions of the upper graph are reversed, and this graph coincides with the directions in FIG. 10 and the like.

[0153] On the line profile 20 numbered #1, moving left from the position of intersection 14 (coordinate x0), the area changes from the trench 403 area corresponding to the background to the pillar 402 or mask 401 area, resulting in an increase in brightness. At this time, the area passes through a white band 406, resulting in a significant increase in brightness at that point. In this example, the brightness increases from v1 to v2. The area from coordinate x1 to coordinate x2 corresponds to the white band 406. After that, near coordinate x2, the area enters the pillar 402 or mask 401 area, resulting in a decrease in brightness. In this example, the brightness decreases from v2 to v4. The area from coordinate x2 to coordinate x3 corresponds to the pillar 402 or mask 401 area (particularly the material interface 413). Near coordinate x3, the area again enters the white band 406, resulting in a significant increase in brightness, in this example, from v4 to v2. The area from coordinate x3 to coordinate x4 corresponds to the white band 406. Around coordinate x4, the area leaves the white band 406 and enters the background area again, so the brightness value decreases significantly, decreasing from brightness value v2 to brightness value v1 in this example.

[0154] In the first embodiment, the following rules are applied as an example of the process for extracting edge candidate points 21 on the line profile 20. In a graph such as that shown in FIG. 12, the position of the edge candidate point 21 to be extracted is defined as a position between the minimum and maximum brightness values. In this example, near coordinate x1, the edge candidate point 21 is defined as a position between brightness value v1, which is the minimum value, and brightness value v2, which is the maximum value. For example, the coordinate x1 of the position corresponding to brightness value v3, which is intermediate between the minimum and maximum values, is the edge candidate point 21 (first edge candidate point 21a) indicated by a white square point. The position from which the edge candidate point 21 is extracted is defined as, for example, the intermediate value between the minimum and maximum values, but is not limited to this.

[0155] The definition of which position of the brightness change portion is extracted as the edge candidate point 21 is not limited to the example of using the intermediate value. For example, a threshold value Th [%] relating to the rate of brightness value is set as a setting. Here, the brightness value is set as B, and the maximum value in the brightness change around the edge candidate is set as B. max , the minimum value is B min The brightness value to be used as an edge candidate is B th For example, in the following equation 1, th Calculate B th The position where

[0156] Formula 1: B th =(B max -B min )*Th / 100+B min

[0157] In Equation 1, the position of the brightness value corresponding to the ratio of the threshold value Th to the difference between the maximum value and the minimum value is set to the position of the edge candidate point 21. When the threshold value Th is set to 50%, this corresponds to the example of the intermediate value in FIG.

[0158] The above-described rule for determining the edge candidate points 21 is merely one example. As another example of a rule for determining the edge candidate points 21, a method of determining the edge candidate points 21 at positions shifted by a certain amount from the positions of the maximum or minimum values ​​as the edge candidate points 21 can also be applied.

[0159] In addition, in the first embodiment, one of the rules for extracting edge candidate points 21 is that, in contour detection of the trench 403 region in FIG. 10 and the like, edge candidate points 21 are extracted from locations where the side closer to the perpendicular line 13 is dark and the side farther from the perpendicular line 13 is bright. In other words, the rule is to determine edge candidate points 21 from locations where the brightness increases along the line profile 20. For example, in the graph of FIG. 12, locations where the brightness changes significantly include near coordinates x1 and x2. Near coordinate x1, the brightness value v1 in the positive X direction near the perpendicular line 13 is small, while the brightness value v2 in the negative X direction farther from the perpendicular line 13 is large, resulting in an increase in brightness from low to high. Near coordinate x2, the brightness value v2 in the positive X direction near the perpendicular line 13 is large, while the brightness value v4 in the negative X direction farther from the perpendicular line 13 is large, resulting in a decrease in brightness from high to low.

[0160] Therefore, according to this rule, of the two locations with large brightness changes, an edge candidate point 21 (21a) is extracted near the coordinate x1, for example, where the brightness change increases from low to high. An edge candidate point 21 is not extracted near the coordinate x2, for example, where the brightness change decreases from high to low. Based on this rule, in this example, an edge candidate point 21a (white square point) is extracted at the position of the coordinate x1, and an edge candidate point 21b (black square point) is extracted at the position of the coordinate x3.

[0161] For example, near coordinate x1, the computer system 1 determines the amount of change in luminance at a point where luminance value v1, which is a minimum value, increases to luminance value v2, which is a maximum value, calculates luminance value v3, which is an intermediate value, and extracts edge candidate point 21a at coordinate x1 corresponding to luminance value v3. The computer system 1 determines and extracts edge candidate point 21 for each line profile 20 using the same rules.

[0162] In a modified example, with respect to the rules for the locations of brightness changes for extracting the edge candidate point 21, if a first rule is a rule for extracting the edge candidate point 21 at a location where brightness increases and a second rule is a rule for extracting the edge candidate point 21 at a location where brightness decreases, the second rule may be applied instead of the first rule. When the first rule is applied, the edge candidate point 21 is extracted on the side of the white band 406 closer to the perpendicular line 13 (for example, near the coordinate x1), as described above. However, when the second rule is applied, the edge candidate point 21 (for example, the position of brightness value v5) is extracted on the opposite side of the white band 406, i.e., the side farther from the perpendicular line 13 (for example, near the coordinate x2). For example, near the coordinate x2, the brightness value decreases from the maximum value v2 to the minimum value v4, and the coordinate x2 of the position corresponding to, for example, the intermediate brightness value v5 between them is extracted as the edge candidate point 21.

[0163] 13 shows, in a modified example, edge candidate points 21 extracted for trench 403 when the rule applied is changed from the first rule to the second rule for a target image 70 similar to that of FIG. 10. These edge candidate points 21 are arranged at positions that are expanded outward overall (positions closer to pillar 402) compared to the edge candidate points 21 in the first rule of FIG. 11.

[0164] Different rules may be applied depending on the type of structure pattern. For example, when the target is trench 403, a first rule for extracting points where brightness increases may be applied, and when the target is mask 401 or pillar 402, a second rule for extracting points where brightness decreases may be applied. Different edge candidate points 21 are extracted depending on the rule applied. For example, the contour line of trench 403 and the contour line of pillar 402 can be detected as separate contour lines with different position coordinates, or these contour lines can be detected as contour lines with similar position coordinates.

[0165] The application of various rules and methods, including the rules for extracting the edge candidate points 21, may be preset as a fixed setting in the software 110, or the computer system 1 may allow the user U1 to set the rules on the GUI screen 60. A rule may be selected and applied according to the structure pattern based on the definition information 112.

[0166] Furthermore, in the example of FIG. 10 etc., in addition to the first edge candidate point 21a (white square point), a second edge candidate point 21b (black square point) is also extracted on the line profile 20 numbered #1, for example. This viewpoint will be explained below. In the example of the above rule, the luminance change on the line profile 20 in the direction from the perpendicular line 13 side to the end of the range 23 is examined, and a location where the amount of luminance change is sufficiently large, for example, a position corresponding to the intermediate value at a location where there is a significant luminance change near the white band 406, is extracted as the edge candidate point 21. Depending on such rules and methods, the second edge candidate point 21b (black square point) may also be extracted, as shown in the figure.

[0167] In this example, the first edge candidate point 21a is an edge that constitutes the contour of the target trench 403, but the second edge candidate point 21b does not correspond to an edge that constitutes the contour of the actual trench 403. However, in other examples, the second edge candidate point 21b may also be an edge that constitutes the contour of the target structure pattern.

[0168] The computer system 1 in the first embodiment extracts edge candidate points 21 on the line profile 20 in the same manner as described above for all of the multiple line profiles 20, thereby obtaining multiple edge candidate points 21. For example, as shown in FIG. 10 , multiple (e.g., 15) edge candidate points 21a indicated by white square points and multiple (e.g., 13) edge candidate points 21b indicated by black square points are obtained. The computer system 1 in the first embodiment makes a comprehensive judgment from these multiple edge candidate points 21 and detects edges that form the contour of the target structure pattern.

[0169] The line profiles 20 numbered #1 to #5 and #11 to #15 correspond to the transition from the trench 403 to the pillar 402, and edge candidate points 21a and 21b corresponding to the sidewalls of the trench 403 and the pillar 402 are obtained. Note that the line profiles 20 numbered #1 and #15 are on the interface reference line 11. Therefore, for example, the brightness value v4 in FIG. 12 is a brightness value near the material interface, and although it is a constant value in the example of FIG. 12, it may actually be a fluctuating value.

[0170] For the line profiles 20 numbered #6 to #10 in the radial direction, edge candidate points 21 can be similarly extracted in the direction of each line profile 20. For example, for the line profile 20 numbered #6, only one edge candidate point 21 (white square point) is extracted. For the line profile 20 numbered #7, edge candidate point 21a near the bottom surface of trench 403 and edge candidate point 21b corresponding to the boundary with the underlying material layer are extracted.

[0171] It should be noted that the same processing as that for the trench 403 can be applied to extracting edge candidate points 21 from the mask 401 or pillar 402.

[0172] [Step S4: Contour detection part 2] Next, an embodiment of a method for determining edges constituting the contour of a structure pattern of a dimension measurement target, for example, a trench 403, based on the plurality of edge candidate points 21 of the plurality of line profiles 20 will be described.

[0173] The computer system 1 selects arbitrary edge candidate points 21 for each line profile 20 and creates combinations of multiple edge candidate points 21. In doing so, the computer system 1 calculates an evaluation value for the likelihood of the combination of edge candidate points 21 being an edge of the contour. The computer system 1 determines the combination of edge candidate points 21 with the highest evaluation value as the edge of the contour (in other words, an edge point). The computer system 1 determines which edge point to select based on factors such as the positional relationship between the multiple edge candidate points 21 in the combination. An algorithm for such a determination is also implemented in the software 110.

[0174] FIG. 14 shows examples of combinations of edge candidate points 21. Combination #1 is a correct example. Small white circles indicate one edge point 22 selected from the edge candidate points 21 for each line profile 20. Connecting the selected edge points 22 forms a contour line that constitutes the contour of the sidewall of the trench 403. Combination #2 is an incorrect example, in which the second edge candidate point 21b is selected in the line profile 20 numbered #1. Combination #3 is an incorrect example, in which the second edge candidate point 21b is selected in the line profile 20 numbered #2. Combination #4 is an incorrect example, in which the second edge candidate point 21b is selected in the line profiles 20 numbered #1 and #2.

[0175] There are various cases where one line profile 20 has no edge candidate point 21, only one edge candidate point 21, or three or more edge candidate points 21. In these cases, combinations can be created in the same way.

[0176] [Evaluation of edgeiness] Here, there are at least two evaluation criteria for evaluating edge-likeness. The first evaluation criteria is the amount of change in brightness value. Because FIG. 10 and other figures are shown as schematic diagrams, only clear edges are visible, but in actual cross-sectional images taken with an electron microscope, the edges may be unclear. For example, due to the aforementioned collapse and contamination, minute brightness changes may occur near the contours of structures in cross-sectional images caused by dust adhesion or noise during imaging. It would be incorrect to determine an edge based on such brightness changes as edge candidate points 21.

[0177] In the first embodiment, in order to reduce the influence of these factors, it is defined that the larger the amount of change in brightness during the evaluation, the higher the edge-likeness, i.e., the larger the evaluation value. A specific calculation of the evaluation value is performed by calculating the difference between the maximum and minimum brightness values ​​in the brightness change area around the edge candidate point 21 (ΔB=(B max -B min )) Alternatively, it may be defined as a gradient value of brightness. As an example of evaluating the amount of change in brightness, in the example of FIG. 12, the difference ΔB near the coordinate x1 can be calculated.

[0178] The second evaluation viewpoint is the distance between the edge candidate points 21. Because the contour of a structure should be continuously connected, it is defined that the closer the distance between the edge candidate points 21, the higher the edge-likeness. If the distance between the edge candidate points 21 is far, there is a high possibility that the edge of something other than the target object, such as dust attached to the cross section of the structure, has been detected. For example, in the examples of FIGS. 10 and 14, the edge candidate point 21a indicated by the white square point in the line profile 20 numbered #1 is close to the edge candidate point 21a indicated by the white square point in the line profile 20 numbered #2, while the edge candidate point 21b indicated by the black square point in the line profile 20 numbered #2 is far from it. These correspond to combinations #1 and #3 in FIG. 14.

[0179] Therefore, it is considered that the edge-likeness of the contour is higher when the edge candidate point 21a of the line profile 20 numbered #2 is connected to the edge candidate point 21a of the line profile 20 numbered #1 (combination #1, etc.). In other words, the computer system 1 calculates the combination so that the evaluation value increases as the distance between the edge candidate points 21 decreases.

[0180] When calculating the evaluation value quantitatively, it can be said that the greater the amount of change in luminance of an edge, the higher the edge-likeness, as in the first viewpoint described above. However, based on that viewpoint alone, the evaluation value of edge candidates in locations other than the currently focused structure may also be high. Therefore, in the first embodiment, in addition to the first viewpoint, the second viewpoint described above is also applied to calculate the evaluation value of edge-likeness, in order to include the continuity of the contour in the evaluation. Based on the evaluation results, the computer system 1 selects a plurality of edge points 22 that are considered to have the highest edge-likeness. For example, combination #1 is selected.

[0181] To specifically calculate the evaluation value for the distance between edge candidate points 21, any edge candidate points 21 are selected from each line profile 20 to create a combination as shown in FIG. 14, and an evaluation value E of the contour line made up of these edge candidate points 21 is calculated. The evaluation value E is an evaluation value of the continuity of the contour, and can be defined, for example, by the following equation 2.

[0182] Equation 2: E=Σ^(N-1)_(n=1){ΔB n / D n,n+1}

[0183] ΔB in Equation 2 n is the difference between the maximum and minimum brightness values ​​around the edge candidate point 21 selected in the n-th line profile 20. n,n+1is the distance between the edge candidate point 21 selected in the n-th line profile 20 and the edge candidate point 21 selected in the (n+1)-th line profile 20. The computer system 1 selects and determines, from among all the combinations of edge candidate points 21, the combination with the largest evaluation value E as the edge point 22. The computer system 1 may create a contour formed by those edge points 22, for example, a contour line connecting the edge points 22 in order, and use that contour line as the contour detection result.

[0184] However, the calculation using the method for determining the edge with the maximum evaluation value E may be NP-hard. For example, if there are 100 line profiles 20 and each line profile 20 has 10 edge candidate points 21, there are 10 to the power of 100 combinations, and it is difficult to calculate the evaluation value E for all combinations. Therefore, an approximation method may be used to find the combination of edge candidate points 21 with approximately the maximum evaluation value E. For example, if an arbitrary edge candidate point 21 of the n-th line profile 20 is determined as P n Let P be an arbitrary edge candidate point 21 of the (n+1)th line profile 20 adjacent to the nth one. n+1 Let's say. P n and P n+1 Let D be the distance between n+1 The difference between the maximum and minimum brightness values ​​around the edge candidate point 21 is ΔB n In this case, the temporary evaluation value is E temp and is defined by the following equation 3.

[0185] Formula 3: E temp =ΔB / D

[0186] Then, the computer system 1 selects one edge candidate point 21 from the edge candidate points 21 of the first line profile 20 and calculates an evaluation value E tempThe edge candidate points 21 of the second line profile 20 that maximizes the evaluation value E are determined. The edge candidate points 21 of the third and subsequent line profiles 20 are determined in a similar manner. The computer system 1 calculates the evaluation value E from the obtained combination of edge candidate points 21. Thereafter, the computer system 1 selects another edge candidate point 21 of the first line profile 20, performs the same process, and calculates the evaluation value E from the obtained combination of edge candidates. The computer system 1 determines the combination of edge candidate points 21 with the highest evaluation value E among the combinations of edge candidate points 21 obtained from the edge candidate points 21 selected in the first line profile 20 as the combination of edge candidate points 21 that approximately maximizes the evaluation value E. This approximation method significantly reduces calculation time and enables automatic contour detection within a finite time. The method for determining edge points 22 is not limited to the above method.

[0187] [Detect Edge Points and Contours] In the example of trench 403 specified by region reference point 12 in FIG. 10, edge points 22 corresponding to 15 edge candidate points 21 (21a) indicated by white square points are obtained as the contour of trench 403. FIG. 15 shows an example of the contour detection result. As shown in FIG. 15, computer system 1 may form a contour line 24 of trench 403 by connecting these edge points 22 with lines. This contour line 24 corresponds to part of the contour detection result.

[0188] 15, a contour line 24 is formed by connecting detected edge points 22 (shown as white circles) in the target image 70 with straight lines. In the example of Fig. 15, the contour line 24 represents the contours of the sidewalls and bottom of the trench 403 region. The top surface of the trench 403 region is open and is separated by the interface reference line 11.

[0189] Without being limited to this, the computer system 1 may, for example, create an approximation curve as a smoothing process based on the plurality of edge points 22, and use this as the contour line 22. Also, the contour line 24 may be omitted, and the contour may be expressed only by the plurality of edge points 22. If the number of edge points 22 is sufficiently large, the contour can still be expressed.

[0190] 15 , the computer system 1 may display edge points 22 or contour lines 24, which are the contour detection results, in the GUI screen 60, for example, on the target image in the image field 601. The computer system 1 may also display dimensional values ​​of the dimension measurement results 8, which will be described later, together with the contour lines 24, etc., on the GUI screen 60.

[0191] [Contour detection for masks and pillars] While the above example shows the case of trench 403, it is similarly possible for pillar 402 and the like. FIG. 16 similarly shows the case of contour detection (particularly edge candidate point extraction) for mask 401, and FIG. 17 shows the case of contour detection (particularly edge candidate point extraction) for pillar 402. Note that the examples of FIGS. 16 and 17 show the case where the first rule for extraction at brightness-increased portions, described above, is applied as the rule for extracting edge candidate points 21. In this case, for example, the contour edge of trench 403 described above and the contour edge of pillar 402 will result in different results.

[0192] In the example of FIG. 16 , a region reference point 12 is specified within a region of a certain mask 401. The computer system 1 draws a perpendicular line 13 downward from the region reference point 12 to the interface reference line 11 and extracts an intersection point 14. The computer system 1 extracts multiple line profiles 20 in the X direction in the region from the region reference point 12 to the intersection point 14. The computer system 1 extracts multiple line profiles 20 in the radial direction in the region above the region reference point 12. The computer system 1 extracts edge candidate points 21 from each line profile 20 based on the first rule described above, etc. The computer system 1 determines edge points 22 for the mask 401 based on a combination of the multiple edge candidate points 21.

[0193] As shown in the figure, in the case of a mask 401, a radial line profile 20 is applied to the range above the area reference point 12. As a result, the radial line profile 20 passes through the contour of the mask 401, which has a convex, trapezoidal shape, and edge candidate points 21 of the contour of the mask 401 can be extracted.

[0194] In the example of FIG. 17 , a region reference point 12 is specified within the region of a certain pillar 402. The computer system 1 draws a perpendicular line 13 upward from the region reference point 12 to the interface reference line 11 and extracts an intersection point 14. The computer system 1 extracts multiple line profiles 20 in the X direction in the region from the region reference point 12 to the intersection point 14. The computer system 1 extracts multiple line profiles 20 in the radial direction in the region below the region reference point 12. The computer system 1 extracts edge candidate points 21 from each line profile 20 based on the first rule described above, etc. The computer system 1 determines an edge point 22 for the pillar 402 based on a combination of the multiple edge candidate points 21.

[0195] [Step S5: Dimension measurement] In step S5, the computer system 1 performs dimension measurement processing on the contour information detected in step S4 above, referring to the definition information 112 selected in step S2, obtains dimension measurement results 8, and displays them on the GUI screen 60.

[0196] 18 is a schematic explanatory diagram showing an example of measuring the dimensions of a trench 403 based on the contour information of the trench 403. As a premise, contour information in a two-dimensional target image 70 includes information on the edge points 22 and contour lines 24 as described above.

[0197] FIG. 18A shows the case where trench depth (dimension A4 in FIG. 4) is measured. The computer system 1 sets a dimension measurement target line 1801 for measuring trench depth dimension A4. First, trench depth dimension measurement target line 1801 can be defined as a line drawn perpendicular to the edge of the contour that is furthest away in the vertical direction from interface reference line 11 specified in step S1 (edge ​​point p8 in this example). The computer system 1 measures dimension A4 using the set dimension measurement target line 1801.

[0198] 18B shows a case where the trench width (dimension A1 in FIG. 4) at the position of interface reference line 11 corresponding to material interface 413 is measured as the trench width. Computer system 1 sets dimension measurement target line 1802 for dimension A1. Computer system 1 takes edge points p1 and p15, which are two intersections between interface reference line 11 specified in step S3 and the outline of trench 403, and can define dimension measurement target line 1802 as a line drawn between these edge points.

[0199] FIG. 18C illustrates the case where the trench width is measured at a predetermined distance d, 30 nm below the interface reference line 11 corresponding to the material interface 413. Here, d = 30 nm is the distance specified and set by the user U1 in the definition information 112. As with the dimension A3 in FIG. 4, this can also be specified as a percentage (%) rather than a distance. The computer system 1 sets a dimension measurement target line 1803 at a predetermined distance. Based on information such as the pixel size of the target image, the computer system 1 calculates how many pixels away the distance 30 nm corresponds to from the interface reference line 11 on the target image. The computer system 1 extracts two intersections (end points 1811 and 1812 in this example) between a horizontal line (a straight line extending in the X direction) at the calculated position and the contour (contour line 24) of the trench 403. The computer system 1 can define the dimension measurement target line 1803 as a line drawn between these two intersections.

[0200] The dimensions of the pillars 304 and the mask 303 can be measured in a similar manner. Other types of dimensions can also be measured in a similar manner. For example, the computer system 1 may measure an angle calculated from a partial region of the contour of a structure (for example, the angle of a sidewall).

[0201] FIG. 18D shows a case where a sidewall angle 1804 of a trench 403 is measured. In this example, based on the definition information 112, an angle 1804 formed by the left sidewall is measured with respect to a position specified a distance (e.g., 30 nm) below the interface reference line 11. The computer system 1 calculates an approximate straight line relating to the sidewall from edge points (edge ​​points p1 to p3 in this example) in a partial region of the contour of the trench 403, in this example, the left sidewall, in a region 1820 extending 30 nm below the interface reference line 11. Alternatively, the computer system 1 may refer to a portion of the contour line 24 already calculated from the edge point 22. The computer system 1 then calculates the angle 1804 formed by the approximate straight line or contour line 24 with respect to the horizontal line at the specified position. In this way, the sidewall angle at the specified position can be measured.

[0202] As another example of a different type of dimension, the computer system 1 may measure the area of ​​a region surrounded by the contour (contour line 24) of the structure and the material interface 413 (interface reference line 11), for example, a trench area. The computer system 1 can measure the area of ​​the region of the target structure based on the interface reference line 11, the region reference point 12, the definition information 112, the detected contour line 24, etc.

[0203] FIG. 19 shows an example of measuring the area of ​​a trench 403. On the GUI screen 60, the user U1 specifies area as the type of measurement target dimension based on the definition information 112, and specifies the interface reference line 11 and the region reference point 12 for the target image 70. The computer system 1 detects the edge points 22 and the contour line 24 as described above based on the specified interface reference line 11 and region reference point 12. The computer system 1 extracts a closed region including the region reference point 12, which is composed of the interface reference line 11 and the contour line 24 defined by the multiple edge points 22. This closed region corresponds to the region of the trench 403. The computer system 1 counts the number of pixels included in the extracted region of the trench 403 and calculates the area based on the pixel information, etc. This results in an area measurement result.

[0204] [Step S6: Output dimension measurement results] The computer system 1 stores the data of the dimension measurement result 8, which is the result of step S5, in the memory resource 102 and displays it to the user U1 on the GUI screen 60. The dimension measurement result 8 may include position coordinate information of the interface reference line 11 and position coordinate information of the area reference point 12 specified in step S3.

[0205] FIG. 20 shows an example of a GUI screen 60 displaying dimension measurement results 8. In the GUI screen 60 of FIG. 20, the measurement target dimension (e.g., trench width dimension A4) of the dimension measurement target structure (e.g., trench 403) is displayed superimposed on the target image 70 in the image field 601 described above. For example, a dimension measurement target line (indicated by a white arrow) for the target dimension is displayed, and the target dimension (e.g., "A4" / "Depth") and dimension measurement value (e.g., "100 nm") are displayed with leader lines added to the dimension measurement target line as necessary. In addition, in a result field 2001 below, information such as the dimension measurement value for each target dimension is displayed in a table. As another example, information such as the position coordinates of two points defining the dimension measurement target line may also be displayed.

[0206] [Effects of the First Embodiment] According to the first embodiment, a user U1 manually specifies and sets the interface reference line 11 and the region reference point 12 for the cross-sectional image, which are difficult to automatically detect, and the rough position of the region of the structure to be measured. The computer system 1 then automatically detects the contour and measures the dimensions based on the interface reference line 11 and the region reference point 12. This semi-automatic method reduces the time required for processing and performing dimension measurement on the cross-sectional image, thereby reducing the effort required by the user U1. It also reduces the impact of errors due to manual operation by humans. In the prior art examples (Comparative Examples 1 and 2), the contour of the structure or the target dimension location is specified by hand, resulting in many errors, which affect the accuracy of the dimension measurement. In contrast, according to the first embodiment, after the contour is detected by the semi-automatic method, automatic dimension measurement is performed, resulting in fewer errors and higher accuracy of the dimension measurement.

[0207] In the first embodiment, the user U1 simply specifies an interface reference line 11 for the material interface in the cross-sectional image and roughly specifies a region reference point 12 for the region of the structure to be measured, based on subjective judgment and manual operation. The computer system 1 detects edges constituting the contour of the structure pattern in the cross-sectional image by processing a predetermined algorithm based on the specified interface reference line 11 and region reference point 12. The computer system 1 measures the specified type of dimension based on the information on the detected edges. Thus, according to the first embodiment, the main manual operation by the user U1 is limited to specifying the interface reference line 11 and region reference point 12, which reduces the amount of input required and the effort required by the user U1. Furthermore, because the computer system 1 accurately detects the contour and measures the dimensions based on the interface reference line 11 and region reference point 12, human error is less likely to be reflected in the dimension measurement results.

[0208] The semi-automatic method in the first embodiment has at least the following first and second advantages over the automatic dimension measurement method of the prior art.

[0209] [Regarding the first advantage] The first advantage is that it is possible to measure dimensions while excluding structural patterns that are not suitable for dimension measurement. Examples of "structural patterns that are not suitable for dimension measurement" include structures that are collapsed or have dust attached, as mentioned above.

[0210] As an example of conventional technology, there is a method of recognizing the cross-sectional structure of a semiconductor based on pattern matching / template matching and detecting the contours / edges of the recognized cross-sectional structure. However, with this method, the dimensions of all objects, including dust, etc., recognized and detected within the target image based on pattern matching / template matching are measured. Areas caused by distortion, dust, noise during photography, etc. may also be recognized and detected as contours / edges based on pattern matching / template matching and may end up being included in the dimension measurement targets. Areas with such dust or other contaminants are inappropriate and should be excluded from the dimension measurement targets.

[0211] FIG. 21 shows an example of the results of contour detection and dimension measurement in a target image 2100 that includes a structure pattern with dust attached. Assume that dust due to contamination has adhered to the cross section of the target sample. This type of dust adhesion can occur in an SEM used to observe cleaved substrates. In the example of FIG. 21, a dust portion 2110 corresponding to the dust is present on the right sidewall of a trench 2102 in the target image 2100.

[0212] 21A, the contour edges (edge ​​points 22 and the contour lines connecting them) of three trenches 2101-2103 are detected, and the trench widths are measured at a position, for example, 30 nm below interface reference line 11 corresponding to the material interface. Dimension measurement target lines 2111-2113 for each trench 2101-2103 are indicated by arrows.

[0213] At this time, in this example, contour detection and dimension measurement are also performed on dust portion 2110. Dust portion 2110 attached to the right sidewall of trench 2102 protrudes into the area of ​​trench 2102 in a convex shape, and a contour including edge point 2105 is detected at the convex portion. A dimension measurement target line 2112 for trench 2102 is set between the contour of the left sidewall and the contour of dust portion 2110. Therefore, the measurement value for dimension measurement target line 2112 will be shorter than the measurement value for a trench structure (e.g., trench 2101) without dust.

[0214] In contrast, according to the first embodiment, even in the case of a target image 2100 that includes a dust part 2110 as shown in (A), it is possible to take measures to exclude the dust part 2110. (B) at the bottom of FIG. 21 shows the measures taken in the first embodiment. In the manual operation step described above, the user U1 visually checks the contents of the target image 2110, and if the user U1 determines that the trench 2102 containing the dust part 2110 is inappropriate for length measurement, the user U1 does not specify the area reference point 12 for the trench 2102. As a result, for example, length measurements are performed for trenches 2101 and 2103 for which area reference point 12 has been specified, but length measurement is not performed for trench 2102. In this way, it is possible to exclude parts that will result in inappropriate length measurements.

[0215] Another modification may be as follows. As in (A) of FIG. 21, area reference points 12 (not shown) are specified for three trenches, and dimension measurement results are obtained. Then, user U1 checks those dimension measurement results on GUI screen 60. If user U1 wants to exclude the dimension measurement results for trench 403 that includes dust portion 2110, he or she changes area reference point 12 displayed in the target image from on to off by clicking or the like. Following this operation, computer system 1 rejects the dimension measurement results for that trench 403, and, as in (B), also erases the display of the dimension measurement results.

[0216] [Regarding the second advantage] The second advantage is that it is possible to measure the dimensions of a structure pattern that is different from the design pattern or template pattern. An example of a "structure pattern that is different from the design pattern or template pattern" is a structure pattern such as that shown in FIG.

[0217] FIG. 22A shows an image 2201 of the cross-sectional structure of the target shape of etching, for example, and FIG. 22B shows an image 2202 of the processed shape after actual etching. (A) corresponds to the design pattern, etc. As can be seen by comparing (A) and (B) of FIG. 22, the structure of trench 403B shown in (B) has a shallower trench depth and is different from the structure of trench 403A in (A). In other words, trench 403B has a larger trench depth difference than trench 403A.

[0218] In such a case, it is difficult to match a template image using image 2201 in Fig. 22(A) with image 2202 in Fig. 22(B) using the template matching method of the conventional art example. In this case, the computer system cannot match, and so determines that there is no object for dimension measurement.

[0219] It is also difficult to prepare a template image equivalent to image 2202 in Figure 22(B) in advance. Generally, when observing the cross section of a semiconductor processed under certain processing conditions to confirm its structure, the target structure can be determined, but the structure that results from the actual processing cannot be known until it is confirmed with an electron microscope. Of course, there are many cases where an electron microscope image under optimal etching conditions is not readily available.

[0220] In contrast, according to embodiment 1, as shown in (C) of Figure 22, even without a template image, the dimensions of trench 403B (e.g., trench depth 2203) can be measured simply by specifying interface reference line 11 and area reference line 12 on image 2202.

[0221] [Multiple target structures] According to the first embodiment, in step S3, the user U1 can also set the interface reference line 11 and the area reference point 12 collectively for a plurality of structure patterns in the cross-sectional image 70 on the GUI screen 60.

[0222] FIG. 23 shows an example of setting multiple area reference points 12 and the like for multiple structure patterns in a target image 70. In the example of FIG. 23, the target image 70 includes four masks 401, four pillars 402, and three trenches 403. User U1 performs an operation to specify one interface reference line 11 at the material interface between the masks 401 and the pillars 402. User U1 also performs an operation to specify area reference points 12 for each structure area. For example, user U1 operates a mouse or the like to specify multiple area reference points 12 by sequentially clicking the position of each area with a cursor 609. In this example, the area reference point 12 for the mask 401 is displayed as a triangle, the area reference point 12 for the pillars 402 is displayed as a square, and the area reference point 12 for the trench 403 is displayed as a circle.

[0223] As described above, according to the first embodiment, when measuring the lengths of multiple structures, less manual work is required than in the prior art. When multiple material interfaces are at the same height, it is sufficient to specify one interface reference line 11. For the regions of multiple structures, any position within each region can be roughly specified, and the position of the region reference point 12 does not need to be precise.

[0224] [Two or more interface reference lines] In the first embodiment, a case where one interface reference line 11 is set for one material interface in a cross-sectional image has been described, but the present invention is not limited to this. It is also possible to set two or more interface reference lines 11 for two or more material interfaces in a cross-sectional image.

[0225] FIG. 24 shows a case where two interface reference lines 11 (11A, 11B) are set in a cross-sectional image 2400. In this example, the cross-sectional image 2400 has three-layer structures on a base layer: a first-layer structure 2401, a second-layer structure 2402, and a third-layer structure 2403. Each layer is made of a different material and has a different brightness. As material interfaces, there is a first interface between the first-layer structure 2401 and the second-layer structure 2402, and a second interface between the second-layer structure 2402 and the third-layer structure 2403. On the GUI screen 60, a user U1 sets interface reference lines 11 for each of the two material interfaces for this cross-sectional image 2400. An interface reference line 11A is specified for the first interface, and an interface reference line 11B is specified for the second interface.

[0226] In addition, user U1 can specify region reference points 12 for regions separated by interface reference lines 11 corresponding to each material interface. In this example, region reference points 12A, 12B, and 12C are specified for a first-layer structure 2401, a second-layer structure 2402, and a third-layer structure 2403. Contour detection and dimension measurement are possible for each region specified by the region reference points 12. For example, in the case of region reference point 12B, as shown in the lower part of FIG. 24, perpendicular lines 13 are drawn from region reference point 12B to the upper and lower interface reference lines 11A and 11B, respectively. In this example, computer system 1 applies an X-direction line profile 20 to the region including region reference point 12B. Computer system 1 extracts edge candidate points 21 on each of the multiple X-direction line profiles 20 within the region. Based on this information, computer system 1 can detect the contour of second-layer structure 2402 including region reference point 12B and measure specified dimensions (e.g., width).

[0227] 25 shows an example of setting two interface reference lines 11 (11a, 11b) at different heights when there are two material interfaces at different heights in a cross-sectional image 2500. In this example, the cross-sectional image 2500 includes a structure 2501 at a first height, a structure 2502 at a second height, a structure 2503 at a third height, and a structure 2504 at a fourth height. The material interfaces include a first material interface between the structures 2501 and 2502, and a second material interface between the structures 2503 and 2504. The heights of the first material interface and the second material interface are different.

[0228] Using the interface selection GUI 61 on the GUI screen 60, the user U1 specifies the interface reference lines 11 according to the height positions of the respective material interfaces. An interface reference line 11a is specified for the first material interface, and an interface reference line 11b is specified for the second material interface. In this example, the interface selection GUI 61 is a GUI that allows specification of each location as a line segment within the cross-sectional image 2500. For example, the user U1 operates a cursor 609 with a mouse or the like to specify a start point and an end point for each interface reference line 11. This allows the interface reference lines 11a and 11b to be drawn at desired positions. The computer system 1 can then detect the contours and measure the dimensions of the structures 2501 to 2504 based on the respective interface reference lines 11 and area reference points 12.

[0229] In this way, when the height positions of material interfaces at a plurality of locations in a cross-sectional image are different, it is possible to specify interface reference lines 11 at different height positions.

[0230] In the first embodiment, the case where the interface reference line 11 is set for the material interface between different materials has been described. However, this is not limiting. As another modification, a reference line for supporting contour detection or the like may be set at a location other than the material interface. For example, in FIGS. 4 and 10, an X-direction reference line may be set to indicate the bottom of the pillar 402. Furthermore, when it is desired to divide an area of ​​an arbitrary structure in an image into multiple sections, reference lines may be set to indicate those sections. The computer system 1 can use these reference lines as boundaries or edges for predetermined processing such as contour detection.

[0231] [Contour detection system] In the dimension measurement system and method of the first embodiment, the functional portion up to step S4 in FIG. 5 can be regarded as a contour detection system and method, and may be implemented as such a contour detection system and method. In this contour detection system and method, computer system 1 stores and outputs information about the contour or edge of the structure pattern detected from the target image in step S4. For example, computer system 1 displays an image including edge points 22 and contour lines 24, as shown in FIG. 15, to user U1 on GUI screen 60. Any system (e.g., observation device, inspection device, etc.) can use the information about the contour or edge generated and output by this contour detection system.

[0232] [Variations] The first embodiment can be modified in various ways as follows.

[0233] In the first embodiment, the interface reference line 11 is a straight line extending in the horizontal direction (X direction) according to the direction of the material interface in the cross-sectional image, but is not limited to this. In a modified example, the interface reference line 11 can be a line extending in the vertical direction (Z direction), for example, according to the content of the cross-sectional image of the target sample.

[0234] [Modification 1: Interface reference line adjustment function] The dimension measurement system and method of variant 1 of embodiment 1 may have an adjustment function to adjust the positional deviation between the material interface of the structure pattern and the interface reference line 11 specified by user U1 if there is such a deviation.

[0235] 26 is an explanatory diagram of the adjustment function (in other words, the interface reference line adjustment function) in Modification 1. In FIG. 26, an example of a GUI screen 60 has an adjustment field 2601 for the interface reference line 11 in addition to an image field 601.

[0236] The position of the interface reference line 11 specified and set on the GUI screen 60 in Figure 6 can serve as a reference for defining the region of the structure to be measured and the dimensions of the object to be measured. This interface reference line 11 can be set by manual selection by the user U1 as shown in Figure 8, or by estimation by AI, as described below. However, when the boundaries between the regions that make up the cross-sectional image are unclear, the interface reference line set by a human eye or estimated by AI may not necessarily coincide with the actual material interface. If this deviation is greater than a certain level, it can have a significant impact on the accuracy of the dimension measurement.

[0237] Therefore, the computer system 1 in Modification 1 adjusts the positional deviation of the interface reference line 11 using vertical luminance information of the pixels of the target image. In Modification 1, when an image file is selected in step S1 of FIG. 2, a GUI screen 60 such as that shown in FIG. 26 is displayed. The computer system 1 displays an average luminance graph 2604 (in other words, an average profile of y-coordinate luminance values) in the adjustment field 2601 to the right of the target image 70 in the image field 601. This average luminance graph 2604 is a graph of the average luminance values ​​for each y-coordinate (vertical coordinate within the image) of the pixel data constituting the target image 70. In this example, this graph is displayed with the y-coordinate of the target image 70 as the vertical axis within the screen and the average luminance value at each y-coordinate as the horizontal axis within the screen.

[0238] 2, it is assumed that the user U1 manually sets the interface reference line 11c on the target image 70. This interface reference line 11c has a certain degree of deviation from the actual interface between the mask and the pillar, for example, the interface reference line 11c enters the area on the pillar side.

[0239] In this state, if the user U1 wishes to make an adjustment, he or she presses the "Correct" button 2605. The computer system 1 then detects a position (position 2606) corresponding to the interface on the average profile (average graph 2604) of brightness values ​​of the y coordinate. This detection is possible based on a determination of brightness changes, as shown in the figure. The processor then corrects the interface reference line 11 so that it matches the position 2606. In this example, the corrected interface reference line 11d is displayed as having been moved vertically upward from the interface reference line 11c before correction.

[0240] As an algorithm for detecting the interface position using the average graph 2604, a method of calculating the first derivative of the profile can be applied, since the average brightness values ​​of the profile differ between the mask 401 and the pillar 402. Alternatively, a method of calculating the local minimum of the profile can be applied. One method for setting the search range for the interface is to set a range limited by an appropriate number of pixels in the vertical direction from the interface reference line 11c set by the user U1.

[0241] The adjustment function in Modification 1 may be included in the software 110 in Fig. 2, or may be prepared as separate adjustment software and stored in the storage 107, etc. According to Modification 1, it is possible to reduce the influence on the dimension measurement accuracy when there is a large deviation in the interface reference line 11.

[0242] As another variant, in the interface selection GUI 61 (Figure 6) for setting the interface reference line 11, when the interface reference line 11 is displayed as the initial state, the above adjustment function may be used from the beginning for the target image to automatically determine and display the interface reference line 11.

[0243] [Variation 2: Reference lines other than the interface] Modification 2 has a function of specifying and setting a location other than an interface in a structure in a cross-sectional image as a reference line.

[0244] 27A shows an example of setting a reference line 11C for a certain cross-sectional image 2701 in Modification Example 2, and (B) shows an example of setting a reference line 11D for a certain cross-sectional image 2701. In (A), reference line 11C is set below material interface 413 in the vertical direction (Z direction), within the region of pillar 402. In (B), reference line 11D is set above material interface 413 in the vertical direction (Z direction), within the region of mask 401.

[0245] For example, if the location where the user U1 wishes to measure the dimensions is not based on the material interface 413, it is not necessary to align the position of the reference line (the aforementioned interface reference line 11) with the material interface 413.

[0246] 27A, as a specific example, dimension measurement is performed at a position a predetermined distance above the bottom (bottom 2705) of trench 403, for example, 20 nm. A dimension measurement target line 2710 at that position is shown. In this case, reference line 11C is specified at a position below material interface 413. Furthermore, although not shown, a region reference point 12 is specified below reference line 11C. Based on reference line 11C and region reference point 12, computer system 1 detects the outline of the region corresponding to trench 403, and measures the dimension at the position of dimension measurement target line 2710 based on definition information 112.

[0247] 27(B), when dimension measurement is performed at the same position as in (A), reference line 11D is specified at a position above material interface 413. Similarly, in (B), the dimension can be measured at the position of dimension measurement target line 2710. In both (A) and (B), the measurement position is the same, and the dimension value at dimension measurement target line 2710 is the same. In either case, there is no problem because the dimension at the position of dimension measurement target line 2710 is not a dimension defined based on the positions of material interface 413 and reference lines 11C and 11D.

[0248] 27, the bottom (bottom 2705) of trench 403 can be determined based on the position coordinate information of the edge of the contour detected by the above-described method. For example, of the edge points 22 described above (FIG. 15), the edge point 22 positioned lowest in the Z direction may be determined as the bottom 2705 of trench 403. Alternatively, the average value of multiple edge points 22 positioned close to each other in the Z direction may be determined as the bottom 2705. A dimension measurement target line 2710 can be set at a position, for example, 20 nm above the bottom 2705 in accordance with the definition information 112.

[0249] As in Modification 2, the reference line specified in step S3 may be largely deviated from material interface 413, and may also be intentionally specified at a location other than material interface 413. Reference lines 11C and 11D in Modification 2 do not have the function of indicating material interface 413, but rather have the function of roughly indicating a portion of the region of target trench 403 (for example, the top).

[0250] 27C shows an example of setting measurement target dimensions such as those shown in the examples of Fig. 27A and 27B in the definition information 112. Such dimensions can also be set and selected on the GUI screen 60 described above.

[0251] [Variation 3: Area Reference Point] The function of the area reference point 12 may be modified as follows.

[0252] Fig. 28 is an explanatory diagram of Modification 3. In Fig. 28, when specifying a region reference point 12 for trench 403 in a target image 70 similar to that in Fig. 10, region reference point 12 is specified at a position higher in the vertical direction than in the example in Fig. 10. This region reference point 12 roughly indicates the region of trench 403.

[0253] In Modification 3, instead of setting the X-direction line profile 20 and the radial direction line profile 20 with the position of the area reference point 12 as the boundary as in FIG. 10 , these two types of line profiles 20 are set using another predetermined method. In FIG. 28 , the computer system 1 not only draws a perpendicular line 13 from the area reference point 12 toward the interface reference line 11, but also draws a vertical line 2801 (shown as a dashed line) in the opposite negative Z direction. For example, as a pre-defined setting, the computer system 1 sets a reference point 16 (shown as a diamond dot) on the perpendicular line 13 and the vertical line 2801 at a predetermined distance below the intersection 14 of the interface reference line 11 in the Z direction. Then, the computer system 1 determines a rectangular area 2810 from the intersection 14 to the reference point 16 as a first area from which the X-direction line profile 20 is extracted, and a semicircular area 2820 further below the reference point 16 as a second area from which the radial direction line profile 20 is extracted.

[0254] The distance from the intersection 14 to the reference point 16 may be set by the software 110 or the definition information 112. In addition, the intervals and number of line profiles 20 to be extracted in the first region and the second region may also be set by the software 110 or the definition information 112.

[0255] In the third modification, two types of line profiles 20 are generated regardless of the Z-direction position at which the area reference point 12 is specified. In the example of Fig. 23, the reference point 16 is set below the area reference point 12, but this is not limited to this, and the same holds true even if the reference point 16 is set above the area reference point 12.

[0256] [Variation 4: Area Reference Point] In the fourth modification, the area reference point 12 has the function of not only roughly indicating the area of ​​the structure to be measured, but also of specifying the position that defines the dimensions of the object to be measured.

[0257] 29 shows examples of the target image 70, interface reference line 11, area reference point 12, dimension measurement target line 2901, etc. in Modification Example 4. The specified area reference point 12 not only roughly indicates the area of ​​trench 403, but also specifies the vertical position for measuring the trench width (dimension A3 in FIG. 4) as shown by the dimension measurement target line 2901. In other words, in this example, the vertical position for measuring the trench width (dimension A3) as shown by the dimension measurement target line 2901 is specified not by the distance or rate in the definition information 112 described above, but by the position of the area reference point 12 manually operated by user U1.

[0258] The computer system 1 detects the contour of the trench 403 based on the specified interface reference line 11 and area reference point 12, sets a dimension measurement target line 2901 for measuring the trench width (dimension A3) at the position of the specified area reference point 12, and measures the dimension A3.

[0259] In another example, the region reference point 12 can also be used to specify the position in the X direction relative to the trench depth (dimension A4 in FIG. 4) as the measurement target dimension.

[0260] In this variant example 4, the position of the area reference point 12 is specified in detail, which increases the burden on the user slightly, but compared to the method of specifying two points for the measurement target dimension as in comparison example 2, only one point needs to be specified.

[0261] [Variation 5] 21, in step S3 of Fig. 5, the user U1 looks at the cross-sectional image on the GUI screen 60 to check for inappropriate areas such as dust, and excludes such areas from the target by not specifying the area reference point 12. Thereafter, the computer system 1 automatically executes contour detection and dimension measurement, and outputs the results.

[0262] As a fifth modification, a confirmation step by the user U1 may be added between the contour detection process of step S4 in Fig. 5 and the dimension measurement process of step S5. This fifth modification has the following processing and operations. In the fifth modification, after the contour detection process of step S4 in Fig. 5, a step is added in which the user U1 confirms the contour detection result on the GUI screen 60. In this additional step, the computer system 1 displays the contour detection result of step S4 superimposed on the cross-sectional image on the GUI screen 60.

[0263] FIG. 30 shows an example of displaying contour detection results for a target image 3000 in an additional step in Modification 5. In FIG. 30, for example, when area reference points 12 (12c, 12d, 12e) are specified for three trenches 3001 to 3003, the contours (edge ​​points 22, etc.) of those three trenches are detected and displayed as contour detection results. User U1 checks and determines whether to use these contour detection results as targets for dimensional measurement as is, and if so, presses a dimension measurement execution button (not shown) on GUI screen 60. This causes computer system 1 to execute dimension measurement processing based on the contour detection results and display the dimension measurement results.

[0264] Alternatively, if user U1 wishes to exclude a portion of the contour detection result from the dimension measurement, he or she can specify that portion. For example, by re-specifying the displayed region reference point 12 with the cursor 609 or the like, the region reference point 12 can be changed from an ON state to an OFF state. For example, the trench 3002 contains a dust portion 3010, which is reflected in the contour detection result. To exclude the trench 3002 from the dimension measurement target, user U1 clicks or otherwise operates the region reference point 12d to turn it OFF. For example, the display of the region reference point 12d is changed from an ON state to an OFF state. Then, user U1 presses the dimension measurement execution button. As a result, the computer system 1 excludes the trench 3002 including the OFF region reference point 12d from the dimension measurement target, executes the dimension measurement process, and displays the dimension measurement results.

[0265] The dimension measurement results are displayed, for example, as shown in (B) at the bottom of Figure 30. For trenches 3001 and 3003, the trench width, for example, is displayed as the dimension measurement result. In this way, in Modification 5, user U1 can determine the dimension measurement target after viewing the contour detection results.

[0266] <Embodiment 2> A dimension measurement system and the like according to the second embodiment will be described with reference to FIG. 31 and the like. The basic configuration of the second embodiment and the like is the same as that of the first embodiment, and the following mainly describes the components of the second embodiment and the like that are different from the first embodiment. In the first embodiment, as shown in FIG. 10 and the like, a method for detecting the edge of the contour of a structure was described using two types of line profiles 20, namely, a line profile 20 extending in the X direction and a line profile 20 in the radial direction. However, this is not limiting, and there are cases where edge detection using the line profile 20 in the radial direction is not necessary. The second embodiment describes a method for detecting the edge of the contour using only the line profile 20 extending in the X direction (lateral direction) without using the line profile 20 in the radial direction.

[0267] [Contour detection using only horizontal line profiles] Fig. 31 shows an example of detecting edges constituting the outline of a pillar 402 using only a line profile 20 extending in the horizontal direction in the second embodiment. (A) of Fig. 31 shows an example of an interface reference line 11, an area reference point 12, a dimension measurement target line 3101, etc. in a certain target image 3100. (B) of Fig. 31 shows an example of setting a line profile 20 in the X direction based on (A).

[0268] As shown in (A), in the structure of a pillar 402, the pillar width (dimension B3 in FIG. 4) is to be measured at a position a predetermined distance, for example 30 nm, below the interface reference line 11 corresponding to the material interface in the Z direction. In this case, a dimension measurement target line 3101 for the pillar width is indicated. User U1 specifies the interface reference line 11 in accordance with the material interface, and specifies an area reference point 12 within the area of ​​the pillar 402 to be measured.

[0269] In this example, the user U1 specifies the area reference point 12 at an arbitrary position in the Z direction below the dimension measurement target line 3101. However, here, the position of the area reference point 12 in the Z direction is assumed to be lower than the Z direction position at which the pillar width, which is the measurement target dimension, is measured (condition A).

[0270] In this case, the computer system 1 draws a perpendicular line 13 from the specified area reference point 12 to the interface reference line 11 and extracts an intersection 14, as shown in (B). The computer system 1 extracts a plurality of line profiles 20 extending in the X direction in the area 3110 between the area reference point 12 and the intersection 14. The line profiles 20 are straight lines perpendicular to the perpendicular line 13 and the direction of a predetermined distance (30 nm). The method for generating the line profiles 20 in the X direction is the same as in the first embodiment. In this example, ten line profiles 20 numbered #1 to #10 are extracted.

[0271] The computer system 1 uses the acquired multiple X-direction line profiles 20 to detect edges that make up the contour of the pillar 402 in the same manner as in the first embodiment. In this example, multiple edge candidate points 21 indicated by white squares and edge candidate points 21 indicated by black squares are detected in areas where brightness decreases on the line profile 20. Edge points 22 are determined for the multiple edge candidate points 21 based on the above-mentioned evaluation. For example, the multiple edge candidate points 21 indicated by white squares are determined as edge points 22 that make up the contour line of the pillar 402.

[0272] Based on the detected edge information and definition information 112, the computer system 1 can measure the pillar width (dimension B3) of the dimension measurement target line 3101 at a position on the pillar 402 that is a predetermined distance (30 nm) below the interface reference line 11 corresponding to the material interface. Thus, in this example, edge detection using the radial line profile 20 is not necessary.

[0273] Condition A may be satisfied as follows: The computer system 1 displays, in the target image 3100, a guideline image corresponding to a dimension measurement target line 3101 at a position where the pillar width, which is the measurement target dimension, is to be measured, based on the definition information 112 and the interface reference line 11, and accepts designation of an area reference point 12 at a position below the dimension measurement target line 3101. By looking at the guideline image of the dimension measurement target line 3101, the user U1 can designate the area reference point 12 at a position below it.

[0274] Alternatively, the following may be done to accommodate cases where user U1 specifies area reference point 12 above the position where the pillar width is measured. The computer system 1 not only draws a perpendicular line 13 upward from the specified area reference point 12, but also draws a vertical line downward from area reference point 12. The computer system 1 sets a range on the perpendicular line 13 and the perpendicular line for extracting the X-direction line profile 20. For example, the computer system 1 may set a range for extracting the X-direction line profile 20 from intersection point 14 to a position a set distance below, or from area reference point 12 to a position a set distance below. This satisfies condition A.

[0275] As in the above example, in the second embodiment, contour detection and dimension measurement can be realized by omitting the radial line profile 20 and performing edge detection using only the X-direction line profile 20 depending on the area reference point 12, the structure to be measured, and its dimensions.

[0276] The second embodiment may be implemented with only a function for edge detection using only the horizontal line profile 20. Alternatively, the second embodiment may be implemented with both a function for edge detection in the X direction and a function for edge detection in the radial direction (described in the third embodiment below), and the computer system 1 or the user U1 may select which function to use. For example, the computer system 1 may receive the specification of the definition information 112 and the measurement target structure (e.g., a pillar) on the GUI screen 60, as well as the specification of the interface reference line 11 and the area reference point 12, and determine which function to use based on the positional relationship between them, and automatically apply the function.

[0277] <Third Embodiment> The third embodiment shows a method for detecting the edge of a contour using only the line profile 20 in the radial direction, without using the line profile 20 in the horizontal direction.

[0278] [Contour detection using only radial line profiles] 32 shows an example of detecting edges constituting the contour of trench 403 using only radial line profile 20 in embodiment 3. Fig. 32 shows an example of interface reference line 11, area reference point 12, dimension measurement target line 3201, radial line profile 20, etc. in a certain target image 3200.

[0279] For example, when detecting the contour and measuring the dimensions of a shallow trench structure (a trench 403 with a relatively small trench depth) as shown in FIG. 32 , the region reference point 12 may be specified and placed on the interface reference line 11 corresponding to the material interface. In this case, as shown in the figure, the aforementioned perpendicular line 13 does not exist. Therefore, in the third embodiment, the computer system 1 sets only radial line profiles 20 in a semicircular range 3210 below the region reference point 12. In this example, seven line profiles 20, numbered #1 to #7, are set. The computer system 1 detects the contour edges of the trench 403 on the radial line profiles 20. In this example, edge candidate points 21 are detected on each line profile 20. The aforementioned edge points 22 are determined based on the edge candidate points 21. The contour line of the trench 403 can be formed based on the edge points 22.

[0280] In addition, in Figure 32, the computer system 1 may consider the area reference point 12 to be specified on the interface reference line 11 and apply edge detection in the radial direction, even if the vertical positions of the interface reference line 11 and the area reference point 12 specified by the user U1 do not match, if the difference between their positions is small within a specified range.

[0281] In this example, an example of the measurement target dimension is the trench depth (dimension A4 in FIG. 4), and the corresponding dimension measurement target line 3201 (indicated by a white arrow) is shown. Based on the information on such detected edges and the definition information 112, the computer system 1 can measure the trench depth for the dimension measurement target line 3201 set between the interface reference line 11 corresponding to the top of the trench 403 and the bottom of the trench 403 based on the contour line. Thus, in this example, edge detection using the X-direction line profile 20 is not necessary.

[0282] As with embodiment 2, embodiment 3 may be implemented in a form that only implements the function of edge detection in the radial direction, or in a form that has both functions and allows the user to select the function to use, or in a form in which the computer system 1 automatically determines the function to use.

[0283] <Fourth Embodiment> The estimation system and method of the fourth embodiment will be described with reference to Fig. 33 etc. In the fourth embodiment and the fifth embodiment described later, machine learning by AI is added to the dimension measurement system and method of the first embodiment etc. In the fourth and fifth embodiments, machine learning by AI is applied to the step of specifying and setting the interface reference line 11 and the area reference point 12 by manual operation by the user U1 in step S3 of Fig. 5.

[0284] In particular, the estimation system and method of embodiment 4 are independent systems and methods that only perform machine learning and estimation of interface reference line 11 and area reference point 12 in cross-sectional images. The estimation system and method of embodiment 4 additionally includes a step in which computer system 1 performs machine learning on interface reference line 11 and area reference point 12 for cross-sectional images, estimates interface reference line 11 and area reference point 12 from an input target image based on a learned model, and presents the results to user U1. Embodiment 5, described below, corresponds to a form in which the functional parts of embodiment 4 are added to the dimension measurement system and method of embodiment 1.

[0285] This makes it easier to identify material interfaces and structure pattern regions from cross-sectional images in embodiment 4. In embodiment 5, the effort required for the user U1 to manually input, specify, and set the interface reference line 11 and the region reference point 12 can be further reduced, thereby further improving the efficiency of the entire process.

[0286] In other words, the system in the fourth embodiment is an interface reference line and area reference point estimation system, a material interface estimation system, a dimension measurement target structure area estimation system, a measurement target dimension estimation system, and the like.

[0287] [Target of machine learning] In the fourth embodiment, the following three forms are possible for the target of machine learning. In the fourth embodiment, the following case 3 (FIG. 34) will be particularly described, but the present invention is not limited to this, and the following cases 1 and 2 are also possible, as shown in a modified example (FIG. 38). 1. A form in which machine learning and estimation are performed only on the interface reference line 11. 2. A form in which machine learning and estimation are performed only on area reference points 12. 3. A form in which machine learning and estimation are performed on both the interface reference line 11 and the area reference point 12.

[0288] In the fourth embodiment, the computer system 1 constructs a DB including a machine learning model using the cross-sectional image and information including the position coordinate information of the interface reference line 11 and the position coordinate information of the area reference point 12, which are included in the dimension measurement results in the first embodiment. By utilizing image recognition technology using machine learning or the like, the computer system 1 realizes estimation of the material interface (the corresponding interface reference 11) and the area of ​​the structure to be measured (the corresponding area reference point 12), which are difficult to detect using normal image recognition.

[0289] The dimension measurement result 8 (FIG. 2) in the first embodiment includes information on the interface reference line 11 and the area reference point 12 set in step S3. Alternatively, the information on the interface reference line 11 and the area reference point 12 may be stored in a form associated with the information on the dimension values, etc. in the dimension measurement result 8. In the fourth embodiment, this information is used.

[0290] [Computer System] Fig. 33 shows the configuration of a computer system 1D, which is an estimation system according to embodiment 4. The configuration of Fig. 33 is roughly the same as that of Fig. 2, but differs as follows: The computer system 1D has a function of estimating interface reference lines 11 and area reference points 12 in cross-sectional images using AI machine learning.

[0291] Software 110D including a learning and estimation program is stored in memory resources 102 of computer system 1D, for example RAM 106. This software 110D is data that realizes processing functions including machine learning and estimation of the AI ​​in embodiment 4. Computer system 1D stores DB 140 for AI machine learning and the like in storage 107. DB 140 is data handled by software 110D and includes learning data 141 related to machine learning and the like and learning model data 142.

[0292] The learning data 141 is a dataset for training an AI, in other words, a dataset necessary for machine learning, a dataset for training a model, and includes data such as the aforementioned image 7. The learning model data 142 is a dataset for a machine learning model, is data for a model generated based on the learning data 141, and includes data on learning parameters optimized by learning. The contents of the learning data are as shown in FIG. 34, etc.

[0293] [Machine Learning and Inference] FIG. 34 shows the configuration of the input / output relationship for the machine learning learning model 200 in the fourth embodiment. In the learning phase, the computer system 1D uses learning data 141 to construct and train the learning model 200. The learning model 200 is a model in which learning parameters included in learning model data 142 are set. The learning data 141 is data including pixel data of the cross-sectional image, coordinates of the interface reference line 11, coordinates of the area reference point 12 (if there are multiple points, they are designated as points #1, #2, ...), etc. The learning data 141 can be data in which the user U1 has specified the interface reference line 11 and the area reference point 12 in the target image, which has been used and saved in the system of the first embodiment in the past.

[0294] In the estimation phase, the computer system 1D inputs input data 201, including cross-sectional images and the like that are actually the subject of dimension measurement, to the latest trained learning model 200. The computer system 1D performs estimation processing on the input data 201 using the learning model 200, and outputs the estimation results as output data 202. The output data 202 is data of feature amounts estimated from the input target image, and includes the coordinates of the estimated interface reference line 11 (referred to as estimated interface reference line 11X), the coordinates of the estimated area reference point 12 (referred to as estimated area reference point 12X), and the like.

[0295] The computer system 1D or any other system can arbitrarily use the estimation result 3302 ( FIG. 33 ), which is the output data 202. For example, the computer system 1D may display the estimated interface reference line 11X and estimated area reference point 12X, which are the estimation result 3302, on the target image on the GUI screen 60, and present them to the user U1 as suggested or recommended information. The user U1 may check the estimated interface reference line 11X and estimated area reference point 12X on the GUI screen 60 and use them as they are, or may modify them before use.

[0296] The processor 101 of the computer system 1D in FIG. 33 executes all processing in accordance with the software 110D loaded from the storage 107 to the RAM 106. This realizes the processing functions related to learning and estimation as shown in FIG. 34. Image data 3301 is image data newly input to the AI. For example, image 7 is acquired from the SEM 2 to the operation PC 6, and the image 7 is set as image data 3301. The operation PC 6 transmits the image data 3301 to the computer system 1D. The computer system 1D stores the image data 3301 as part of the training data 141. This is not a limitation, and for example, the image data 3301 of image 7 acquired by the computer system 1D from the SEM 2 may be used as training data. Image data stored in another external storage device, DB server, or the like external to the computer system 1D may also be used as training data.

[0297] The estimation result 3302 is data of the estimation result by the AI ​​learning model 200. The computer system 1D generates, stores, and outputs the estimation result 3302 as output data 202 of the learning model 200 for a target image of the input data 201 (for example, an image specified by the operation PC 6). The computer system 1D transmits the estimation result 3302 to the operation PC 6. The operation PC 6 displays, on the GUI screen 60, an estimated interface reference line 11X and an estimated area reference point 12X as the estimation result 3302 on a cross-sectional image corresponding to the input target image (FIG. 37 described below).

[0298] [Accumulation of learning data] The computer system 1D accumulates learning data 141. To this end, related information such as image data and shooting information 111, coordinates of interface reference lines 11 included in past dimension measurement results 8, and coordinates of area reference points 12 are accumulated in the learning data 141 of the DB 140 from the SEM 2 and the operation PC 6. In the following, it is assumed that the learning data 141 has already been accumulated in the DB 140 of the storage 107.

[0299] [Generating learning models] The computer system 1D constructs and generates a learning model 200 based on the learning data 141 in the DB 140. FIG. 35 shows an example of the configuration of the machine learning learning model 200. In this example, a CNN (convolutional neural network), which is an example of a neural network, is used as the learning model 200. This CNN propagates pixel information 3501 of an image input to the input layer through the intermediate layer and the output layer in order, and performs calculations. As a result, this CNN outputs estimated interface coordinates (coordinates of the estimated interface reference line 11X) and one or more structure area coordinates (coordinates of the estimated area reference point 12X) from the output layer. In the intermediate layer, multiple convolutional layers and the like are repeated. The interface coordinates are estimated values ​​of position coordinates of the interface reference line 11 representing the material interface 413. The structure area coordinates are estimated values ​​of position coordinates of the area reference point 12 representing the area of ​​the structure pattern that is the target of dimension measurement, etc.

[0300] In the learning phase of Figure 34, the parameters of the intermediate layer (included in the learning model parameters 142) are adjusted so that the error between the interface coordinates and structure area coordinates output by the learning model 200, which is a CNN, and the correct interface coordinates and structure area coordinates is minimized.

[0301] The specific layer structure in CNN varies depending on the learning model employed, and details are not limited. In the fourth embodiment (FIG. 35), the case of CNN is shown as the learning model 200 to be applied, but the present invention is not limited to this and can be applied to other models. Any machine learning model that can output interface coordinates and structure region coordinates in response to input of image information such as pixel data can be applied.

[0302] [Learning Phase Flow] 36 shows a learning process flow for the learning phase in the fourth embodiment, and includes steps S41 to S45. In step S41, user U1 operates the GUI on GUI screen 60 on the display of operation PC 6 to load data of the target image and the dimension measurement results 8 of the target image into computer system 1D. In step S42, computer system 1D registers and saves data including the coordinates of interface reference line 11 and the coordinates of one or more area reference points 12 contained in the data of the target image and dimension measurement results 8 loaded in step S41 in DB 140 as learning data 141.

[0303] To train the AI ​​learning model 200, sufficient training data is required to achieve accuracy. Therefore, the computer system 1D does not train the learning model 200 until the amount of registered and accumulated training data reaches or exceeds a predetermined certain amount. That is, in step S43, the computer system 1D determines whether the amount of training data accumulated as training data 141 reaches or exceeds a predetermined certain amount. If the amount has not yet reached or exceeds the predetermined certain amount (N), the computer system 1D returns to step S41 and repeats the process; if the amount has reached or exceeds the predetermined certain amount (Y), the computer system 1D proceeds to step S44.

[0304] In step S44, the computer system 1D acquires the pixel data of the target image, the interface reference line coordinates, and one or more area reference point coordinates in the learning data 141 stored in the storage 107 as training data for the learning model 200 (learning data 141 in Figure 34).

[0305] Then, in step S45, the computer system 1D uses the teacher data acquired in step S44 (learning data 141 in FIG. 34) to learn and train the learning model 200, and generates and updates the learning model. As described above, in learning, pixel data of the target image of the teacher data is input as input data 201 to the learning model 200 (CNN in FIG. 35), and learning is performed so that the interface coordinates and structure area coordinates in the output data 202 output as the estimation result become closer to the coordinates of the interface reference point 11 and the coordinates of the area reference point 12 of the correct solution in the teacher data.

[0306] Examples of GUIs on the GUI screen 60 related to the learning phase include the following: In the GUI screen 60 of FIG. 6 described above, a "train" tab is provided as one of the tabs 603. To perform model learning, the user U1 clicks on the "train" tab, for example. The computer system 1D switches the content / mode of the GUI screen 60 to a model learning GUI screen in response to this operation. On the model learning GUI screen, the user U1 operates the "File" button and a mouse or other device to select a target image file to be used for learning, and then operates the "Enter" button to load the data of the selected file. In addition to the pixel data of the selected target image, the computer system 1D also loads various data associated with the image, such as the interface reference line 11 and the area reference point 12 in the dimension measurement result 8.

[0307] [GUI screen: Estimation phase] FIG. 37 shows an example of the display of the GUI screen 60 in the estimation phase. FIG. 37 is an example of a screen for the estimation function. This screen has an estimation type field 3701, in which interface coordinates (in other words, interface reference line coordinates) and structure area coordinates (in other words, area reference point coordinates) can be selected as the estimation target by turning them on or off. In the estimation target image field 3702, the input estimation target image is selected and displayed. In the estimation result image field 3703, the output estimation result information is displayed superimposed on the estimation target image. In this example, the estimation result image field 3703 displays an estimated interface reference line 11X and an estimated area reference point 12X related to the mask and trench. The type of structure to be estimated may also be selectable on this screen.

[0308] [Effects of the fourth embodiment] According to the estimation system and method of the fourth embodiment, machine learning can be used to automate the manual setting steps in the semi-automatic method of the first embodiment, thereby reducing the amount of manual operation required by the user U1.

[0309] Of course, it is also possible to add various functions and steps of the first to third embodiments to the system and method of the fourth embodiment.

[0310] [Variation (1)] The following modifications of the fourth embodiment are possible. In the fourth embodiment, as shown in Fig. 34, learning by the learning model 200 involves learning of both the interface reference line 11 and the area reference point 12. In contrast, in the modification, the learning is divided into two parts: learning of the interface reference line 11 and learning of the area reference point 12.

[0311] FIG. 38 shows a learning model etc. in a modified example. (A) shows the first learning model 200A, and (B) shows the second learning model 200B. In this modified example, for example, the coordinates of the interface reference line 11 representing the material interface are learned using the first learning model 200A, and the coordinates of the area reference point 12 representing the area of ​​the structure pattern to be subjected to dimensional measurement etc. are learned using the second learning model 200B. The structure of the learning models 200A and 200B can be, for example, a CNN, as in FIG. 35.

[0312] In the learning phase, the first learning model 200A (A) is learned using learning data 141A including the coordinates of the interface reference line 11. The second learning model 200B (B) is learned using learning data 141B including the coordinates of one or more area reference points 12.

[0313] In the estimation phase, a target image is input as input data 201A to a first learning model 200A (A), and data including the coordinates of an estimated interface reference line 11X is output as output data 202A. A target image is input as input data 201B to a second learning model 200B (B), and data including the coordinates of an estimated area reference point 12X is output as output data 202B.

[0314] As a variant, the system and method may be (A) a system and method that only performs learning and estimation using the first learning model 200A, or (B) a system and method that only performs learning and estimation using the second learning model 200B.

[0315] [Variation (2)] As another modification of the fourth embodiment, the following is possible. In this modification, a function of estimating base point coordinates for dimension measurement is provided. In this modification, the estimation target is not the coordinates of the area reference point 12 of the structure pattern area, but the position of the measurement target dimension in the structure pattern area. In particular, the estimation target is the start point coordinates and end point coordinates of the measurement target dimension, in other words, the coordinates of two points that define the dimension measurement target line.

[0316] 39 shows a learning model 200C etc. in this modified example. Learning data 141C in the learning phase includes data on the coordinates of the start and end points of the measurement object dimensions of the target image. Output data 202C output as an estimation result from learning model 200C in the estimation phase includes data on the coordinates of the start and end points of the measurement object dimensions.

[0317] 40 shows an example of displaying estimation results on the GUI screen 60 in this modified example. In this example, "coordinates of start and end points of dimension" is selected in the estimation type field 4001. Furthermore, in the dimension type field 4004, for example, trench width (middle CD) is selected as the dimension type of the estimation target. In the estimation target image field 4002, the estimation target image is displayed. In the estimation result image field 4003, the coordinates of the start point 4011 and end point 4012 of the measurement target dimension as the estimation result are plotted on the estimation target image, and are displayed together with the dimension measurement target line 4010.

[0318] <Fifth Embodiment> A dimension measurement system according to the fifth embodiment will be described with reference to Fig. 41 etc. The fifth embodiment is a combination of the first and fourth embodiments, and is equipped with both the semi-automatic contour detection and dimension measurement functions described above and an estimation function based on machine learning.

[0319] In the learning phase, the system and method of the fifth embodiment use the functions of the system of the first embodiment to allow the user U1 to specify the interface reference line 11 and the area reference point 12 for the target image, perform contour detection and dimension measurement in a semi-automatic manner, and accumulate data including the interface reference line 11 and the area reference point 12 at that time as learning data 141. The system and method of the fifth embodiment uses the learning data 141 to train the learning model 200.

[0320] In the estimation phase, the system and method of the fifth embodiment use the functions of the system of the fourth embodiment to output, as estimation results, an estimated interface reference line 11X representing the material interface and one or more estimated area reference points 12X representing the area of ​​the structure to be measured. The system and method of the fifth embodiment then perform contour detection and dimensional measurement of the target image based on the estimation results. The system and method of the fifth embodiment may also display the estimated interface reference line 11X and the estimated area reference points 12X to the user U1 on the GUI screen 60, allowing the user U1 to check and correct them.

[0321] [Dimension measurement system] FIG. 41 shows an example of the configuration of a computer system 1E, which is a dimension measurement system according to a fifth embodiment. This computer system 1E is a system in which the computer system 1 in FIG. 2 and the computer system 1D in FIG. 33 are combined into one. This computer system 1E automatically accumulates dimension measurement results obtained by a semi-automatic method as learning data 141, and uses a learning and estimation function to learn and estimate the interface reference line 11 and the area reference point 12. This computer system 1E displays estimation results 4102 (estimated interface reference line 11X and estimated area reference point 12X) obtained by the learning model on a GUI screen 60, performs automatic dimension measurement using the estimation results 4102, and displays the dimension measurement results 4102(8) on the GUI screen 60.

[0322] In the memory resource 102 of the computer system 1E, for example, the RAM 106, software 110 including the dimension measurement program of the first embodiment and software 110D including the learning and estimation program of the fourth embodiment are stored. The processor 101 realizes each function by performing processing in accordance with the software 110 and the software 110D. The software 110 also realizes a function of automatically performing dimension measurement using the estimation result 4102 by the software 110D. Furthermore, in the fifth embodiment, the user U1 can select whether to use manual setting in the semi-automatic method of the first embodiment or automatic estimation using the estimation function of the fourth embodiment, depending on the mode setting.

[0323] In addition to the data and information similar to those in the first embodiment, the storage 107 also stores the estimation results 4102 obtained by the software 110D. Another storage 107D includes the DB 140 similar to that in the fourth embodiment. The storage 107 and the storage 107D may be integrated into one.

[0324] The target image 7 and the associated shooting information 111 and definition information 112 can be input to the computer system 1E from the SEM 2 or the operation PC 6, as in the fourth embodiment, and are saved in the storage 107 or the storage 107D. The computer system 1E transmits the estimation result 4102 and the dimension measurement result 4101(8) to the operation PC 6, and the operation PC 6 displays the target image 7, the estimation result 4102, the dimension measurement result 4101(8), etc. on the GUI screen 60.

[0325] [Automatic dimension measurement] 42 shows a processing flow of automatic dimension measurement in the fifth embodiment, and includes steps S51 to S59. In step S51, the computer system 1E inputs the image 7 of the dimension measurement target, the shooting information 111, and the definition information 112 based on the input operation of the user U1 from the operation PC 6, performs dimension measurement using the dimension measurement function in the semi-automatic method in the first embodiment, and obtains the dimension measurement result 8. The image 7 and the dimension measurement result 8 are displayed on the GUI screen 60 of the operation PC 6.

[0326] In step S52, the computer system 1E automatically generates learning data 141 for learning from the dimension measurement results 8 in step S51 (data including the interface reference line 11 and the area reference point 12) and registers it in the DB 140. In step S53, the computer system 1E uses the selected learning data 141 to construct and train a learning model 200 (similar to, for example, FIG. 34 ).

[0327] In step S54, the computer system 1E checks whether the accuracy of estimation by the learning model is sufficient. If it is sufficient (Y), the process proceeds to step S57; if it is insufficient (N), the process proceeds to step S55. In step S55, the computer system 1E checks whether the learning model is overfitted, and if it is overfitted (Y), the process proceeds to step S56; if it is not overfitted (N), the process returns to step S52. In step S56, the computer system 1E reselects learning data 141 to be used for learning, and returns to step S53.

[0328] In step S57, the computer system 1E registers a learning model with sufficient accuracy as a trained model. In step S58, the computer system 1E inputs the image 7 of the object to be measured based on the operation of the user U1 on the operation PC 6. The user U1 also selects the fully automatic dimension measurement mode on the GUI screen 60. In step S59, the computer system 1E performs dimension measurement on the object image, which is the input image, in the fully automatic dimension measurement mode. Details of step S59 may be, for example, as follows.

[0329] In a first processing example, the computer system 1E automatically performs contour detection and dimension measurement without presenting the estimation result 4102 to the user U1. In this case, the computer system 1E inputs pixel data of the input image into a learning model of the estimation function and obtains the estimated interface reference line 11X and the estimated area reference point 12X as the estimation result 4102. The computer system 1E detects the contour of the target structure from the target image using the position coordinate information of the estimated interface reference line 11X and the estimated area reference point 12X, measures specified dimensions based on the detected contour, etc., and obtains the dimension measurement result 4101. The computer system 1E transmits the dimension measurement result 4101(8) to the operation PC 6 and displays the dimension measurement result 4101(8) on the GUI screen 60.

[0330] In a second processing example, the computer system 1E presents the estimation result 4102 to the user U1, and after the user U1 confirms the estimation result 4102, it performs contour detection and dimension measurement. In this case, the computer system 1E inputs pixel data of the input image into a learning model of the estimation function and obtains the estimated interface reference line 11X and the estimated area reference point 12X as the estimation result 4102. The computer system 1E transmits the estimation result 4102, including the estimated interface reference line 11X and the estimated area reference point 12X, to the operation PC 6. The operation PC 6 displays the estimated interface reference line 11X and the estimated area reference point 12X of the estimation result 4102 on the target image on the GUI screen 60. The user U1 confirms the estimation result 4102 on the GUI screen 60, and applies it as is by operating a predetermined button or the like if the user wants to apply it as is; if the user wants to modify it, manually modify it and then apply it; and if the user wants to reject (not apply) it, reject it by operating a predetermined button or the like. When the user U1 confirms and applies the estimation result 4102, the computer system 1E detects a contour from the target image using the applied estimated interface reference line 11X and estimated area reference point 12X, measures the specified dimensions based on the detected contour, etc., and obtains the dimension measurement result 4101. The computer system 1E transmits the dimension measurement result 4101(8) to the operation PC 6 and displays the dimension measurement result 4101(8) on the GUI screen 60.

[0331] [Effects of the fifth embodiment] The dimension measurement system and method of the fifth embodiment have the following advantages. By performing length measurement using the semi-automatic method of the first embodiment, user U1 can accumulate learning data without being particularly conscious of it. According to the fifth embodiment, the accumulated learning data can be used to train the learning model of the fourth embodiment, thereby improving the accuracy of estimation of material interfaces, structure regions, etc. Then, using the estimation results from the learning model, the interface reference line 11 and region reference point 12 can be set more easily and accurately, making length measurement work more efficient.

[0332] Although the embodiments of the present disclosure have been specifically described above, they are not limited to the above-described embodiments and can be modified in various ways without departing from the spirit of the present disclosure. Except for essential components, components can be added, deleted, or replaced in each embodiment. Unless otherwise specified, each component can be singular or plural. A combination of each embodiment and its variations is also possible. [Explanation of symbols]

[0333] 1...Dimension measurement system (computer system), 2...SEM, 6...Client terminal device (operation PC), 7...Image, 8...Dimension measurement results, 11...Interface reference line, 12...Area reference point, 13...Perpendicular line, 14...Intersection point, 20...Line profile, 21...Edge candidate point, 60...GUI screen, 70...Target image, 101...Processor, 102...Memory resources, 111...Shooting information, 112...Definition information, 401...Mask, 402...Pillar, 403...Trench, U1...User.

Claims

1. A dimension measurement system for measuring dimensions of a structure displayed as an image including a plurality of regions with different brightness, a computer system including a processor and a memory resource for storing data to be processed by the processor; a display on which a graphical user interface (GUI) is displayed, which is manually operated by a user; The processor: determining a reference line for identifying a portion of a boundary between the plurality of regions and position information of the reference line, and a reference point set within any one of the plurality of regions and position information of the reference point, based on a manual operation by the user performed on the GUI; Detecting a contour or edge of the structure in the image using the reference line and the reference point; measuring the dimensions of the structure using the detected contour or edge information; Dimensional measurement system.

2. 2. The dimension measurement system according to claim 1, The processor: On a perpendicular line set between the reference point and the reference line, a plurality of line profiles are set extending in a direction parallel to the reference line; extracting candidates for the contour or edge from the line profile based on a change in luminance of pixels that make up the line profile; detecting the contour or edge based on the extracted candidates; Dimensional measurement system.

3. 2. The dimension measurement system according to claim 1, The processor: A plurality of line profiles are set on positions extending in a radial direction from the reference point; extracting candidates for the contour or edge from the line profile based on a change in luminance of pixels that make up the line profile; detecting the contour or edge based on the extracted candidates; Dimensional measurement system.

4. 2. The dimension measurement system according to claim 1, The processor: setting a plurality of first line profiles extending in a direction parallel to the reference line on a perpendicular line set between the reference point and the reference line; setting a plurality of second line profiles at positions extending in a radial direction from the reference point; extracting candidates for the contour or edge on the first and second line profiles based on luminance changes of pixels constituting the first and second line profiles; Detecting the contour or edge based on the extracted candidates. Dimensional measurement system.

5. 2. The dimension measurement system according to claim 1, The processor: determining the dimensions of the structure using information on the detected contour or edge and definition information that defines the dimensions of the measurement object; Dimensional measurement system.

6. 2. The dimension measurement system according to claim 1, the reference line is designated relative to a material interface within the image; Dimensional measurement system.

7. 2. The dimension measurement system according to claim 1, The computer system includes: Provide an adjustment function for adjusting the reference line set on the GUI to a probable reference line position on the structure. Dimensional measurement system.

8. 2. The dimension measurement system according to claim 1, The image is a cross-sectional image of a semiconductor device in which at least one of a mask, a pillar, or a trench is formed. Dimensional measurement system.

9. The dimension measurement system according to any one of claims 2 to 4, the processor creates combinations of the candidates for the contour or edge in a plurality of the line profiles; detecting the contour or edge by selecting the contour or edge based on an evaluation value based on a distance between the candidates or an amount of change in brightness; Dimensional measurement system.

10. 2. The dimension measurement system according to claim 1, the processor causes the specified reference line and the specified reference point to be superimposed on the image displayed on the GUI; Dimensional measurement system.

11. 2. The dimension measurement system according to claim 1, the processor causes the detected contour or edge to be superimposed on the image displayed on the GUI; Dimensional measurement system.

12. 2. The dimension measurement system according to claim 1, the processor causes the measured dimensions to be superimposed on the image displayed on the GUI; Dimensional measurement system.

13. 2. The dimension measurement system according to claim 1, the computer system includes a storage device in which learning data including the image, the reference line, and the reference point is stored; a learning model is implemented in the memory resource, which inputs the image and outputs the reference line and the reference point for the image; the processor detects a contour or an edge of the structure included in a new input image using information on the reference lines and the reference points for the new input image estimated by the learning model; measuring the dimensions of the structure using the detected contour or edge information; Dimensional measurement system.

14. 14. The dimension measurement system according to claim 13, The processor trains the learning model using the learning data stored in the storage. Dimensional measurement system.

15. 14. The dimension measurement system according to claim 13, the processor stores the image, and the position information of the reference line and the position information of the reference point calculated for the image based on the user's manual operation, in the storage as the learning data; Dimensional measurement system.

16. A dimension measurement method for measuring dimensions of a structure displayed as an image including a plurality of regions with different brightness, using a dimension measurement system including a processor, a memory resource for storing data processed by the processor, and a display on which a graphical user interface (GUI) is displayed, the method comprising: setting a reference line that identifies a part of a boundary between the plurality of regions and a reference point that is designated within one of the plurality of regions based on a manual operation by a user of the dimension measurement system performed on the image of the structure displayed on the GUI; Acquire position information of the reference line and the reference point; detecting a contour or an edge of the structure using the reference line and the reference point, and measuring the dimensions of the structure using information on the detected contour or edge; Dimension measurement method.

17. 17. The dimension measurement method according to claim 16, On a perpendicular line set between the reference point and the reference line, a plurality of line profiles are set extending in a direction parallel to the reference line; extracting candidates for the contour or edge on the line profile based on a change in luminance of pixels constituting each line profile; detecting the contour or edge based on the extracted candidates; Dimension measurement method.

18. 17. The dimension measurement method according to claim 16, A plurality of line profiles are set on positions extending in a radial direction from the reference point; extracting candidates for the contour or edge on the line profile based on a change in luminance of pixels constituting each line profile; detecting the contour or edge based on the extracted candidates; Dimension measurement method.

19. A non-volatile recording medium storing a program for causing a computer equipped with a display that displays a graphical user interface (GUI) to execute the following steps in order to measure the dimensions of a structure displayed as an image including multiple regions of different brightness. setting a reference line that identifies a portion of a boundary between the plurality of regions and a reference point that is designated within any one of the plurality of regions based on a manual operation by a user of the dimension measurement system performed on the image of the structure displayed on the GUI; acquiring position information of the reference line and the reference point; detecting a contour or edge of the structure using the reference line and the reference points; A step of measuring dimensions of the structure using information about the detected contour or edge.

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