Computer System and Analysis Method

Abstract

Provided is technology with which it is possible to easily and efficiently assess / detect an abnormality through comparison of similar cell structures within an observation image. This computer system analyzes an observation image of a sample by using a charged electron beam device. The computer system carries out: an extraction process (step S3) for extracting, from an observation image and as a similar image, one or more second cell regions that are similar to a reference image, the reference image being a first cell region that is designated by a user or that is automatically set; an assessment process (step S6) for comparing, between the reference image and the extracted similar image, a plurality of regions of interest (ROIs) included in the reference image on the basis of a rule in which the relationship between the plurality of ROIs is stipulated, thereby assessing whether an abnormality is present; and an output process (step S7) for outputting, to the user as assessment results, the position of each cell region and whether an abnormality is present therein.

Description

Technical Field

[0001] The present invention relates to technologies such as charged particle beam devices, and particularly relates to the analysis processing of observation images.

Background Art

[0002] Charged particle beam devices such as scanning electron microscopes (SEM) can obtain images such as VC (Voltage Contrast) images as observation images based on scanning of a beam, light, etc. on a sample. A computer system connected to the charged particle beam device performs analysis processing for detecting, for example, abnormalities or defects (hereinafter collectively referred to as abnormalities) based on the observation image.

[0003] For defect analysis of semiconductor devices such as logic devices as samples, for example, a VC observation method is used. In a VC observation image obtained by an SEM, the difference in VC appears as brightness. VC is caused by a change in the emission efficiency of secondary electrons due to a potential difference generated on the charged surface of the sample. In this method, abnormalities are determined by analyzing the brightness in the observation image.

[0004] As a prior art example, Japanese Patent Application Laid-Open No. 2010-25836 (Patent Document 1) can be cited. Patent Document 1 describes that an appearance inspection device capable of intuitively and quantitatively evaluating the variation in the complex structure of a semiconductor device is provided. Patent Document 1 describes that brightness and shape are compared by comparison with a template image.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] In the prior art, when analyzing defects such as logic devices, for the VC observation images obtained by a charged particle beam device such as an SEM, the presence or absence of abnormalities is determined by visually observing the images by a person and comparing the brightness between cell structures. The cell structure includes a plug region or the like, which is an element constituting a device such as a transistor. However, in such a prior art, in an observation image in which a large number of identical or similar cell structures are arranged, for example, it is difficult to compare the brightness values between plug regions having the same positional relationship, and even if it is possible, it is very time-consuming and laborious. Further, in such an observation image, there may be a case where a plurality of plug regions in a certain cell structure are distributed as images in an inverted relationship such as upside-down or left-right inversion. In that case, it is necessary to discriminate those images as well, but it is very difficult and time-consuming by visual observation.

[0007] An object of the present disclosure is to provide a technique that can automatically and easily and efficiently realize the determination and detection of abnormalities by comparing the same or similar cell structures in an observation image to a certain extent or more without relying on visual observation, regarding a technique such as a computer system that analyzes an observation image obtained by a charged particle beam device.

Means for Solving the Problems

[0008] Typical embodiments of the present disclosure have the following configurations. An embodiment is a computer system that analyzes an observation image of a sample by a charged particle beam device. The observation image includes a plurality of cell regions, and each cell region may include a plurality of regions that are elements constituting the cell region. The computer system extracts, as a reference image, a first cell region specified by a user or automatically set from the observation image, and extracts one or more other second cell regions similar to the reference image as similar images. An extraction process, and a determination process of determining the presence or absence of an abnormality in the cell region of the similar image by comparing the plurality of regions of interest based on a rule that defines the relationship of the plurality of regions of interest included in the reference image and the extracted similar image. And an output process of outputting to the user the position and the presence or absence of an abnormality of each cell region as a determination result.

Advantages of the Invention

[0009] According to typical embodiments of the present disclosure, regarding technologies such as a computer system that analyzes an observation image obtained by a charged particle beam device, etc., the determination and detection of an abnormality by comparing the same or similar cell structures in the observation image can be performed to a certain extent automatically and easily and efficiently without relying on visual observation. Other problems, configurations, effects, etc. than those described above are shown in the embodiments for carrying out the invention.

Brief Description of the Drawings

[0010]

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Modes for Carrying Out the Invention

[0011] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In the drawings, the same components are generally denoted by the same reference numerals, and repeated descriptions are omitted. In the drawings, the representation of each component may not represent the actual position, size, shape, and range, etc. for the purpose of facilitating the understanding of the invention.

[0012] For the purpose of explanation, when explaining the processing by a program, the program, function, processing unit, etc. may be described mainly, but the main body of these as hardware is a processor, or a controller, device, computer, system, etc. composed of such a processor. The computer executes processing according to the program read onto the memory while appropriately using resources such as a memory and a communication interface by the processor. Thereby, a predetermined function, processing unit, etc. are realized. The processor is composed of a semiconductor device such as a CPU or a GPU, for example. The processor is composed of a device or circuit capable of performing a predetermined operation. The processing is not limited to software program processing and can also be implemented by a dedicated circuit. For the dedicated circuit, an FPGA, an ASIC, a CPLD, etc. are applicable.

[0013] The program may be installed in advance as data in the target computer, or may be distributed and installed as data from the 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 (for example, a memory card). The program may be composed of a plurality of modules. The computer system may be composed of a plurality of devices. The computer system may be composed of a client-server system, a cloud computing system, etc. Also, various data and information are represented and implemented in a structure such as a table or a list, for example, but are not limited thereto. Also, the expressions of identification information, identifier, ID, name, number, etc. are mutually replaceable.

[0014] <Embodiment 1> Using FIGS. 1 to 21, the computer system of Embodiment 1 will be described. The computer system of Embodiment 1 acquires and inputs an observation image, which is a VC image obtained by imaging a semiconductor device, which is a sample (object to be observed), with a charged particle beam device, and has a function (hereinafter referred to as an analysis function) of analyzing the observation image. In this analysis function (the corresponding software), a reference image (in other words, a reference region) for comparison in the observation image is set by a user's operation input or automatic processing. The software extracts, as a similar image (in other words, a similar region), a region (such as a cell structure) that is the same as or similar to the reference image within the observation image. The software compares the reference region with the extracted similar region and determines an abnormality based on the set determination rule. The determination rule is a rule that defines the relationship (for example, positional relationship, size relationship, luminance relationship, etc.) between a plurality of plug regions included in the cell region as a plurality of regions of interest (ROI: Region On Interest). The user can set a reference cell region including a plurality of ROIs and a determination rule on the screen. The software outputs, as a determination result, a determination result (in other words, an abnormality detection result) including each cell region and the presence or absence of an abnormality to the user.

[0015] [Charged Particle Beam Device] FIG. 1 shows the configuration of a charged particle beam device 2, which is a system configured to include the computer system 1 of Embodiment 1. In Embodiment 1, the charged particle beam device 2 is an SEM, but is not limited thereto. The main body 3 of the charged particle beam device 2 functions as an imaging device that captures an observation image. The computer system 1 is connected to the main body 3 of the charged particle beam device 2 via communication means such as a signal line. The computer system 1 corresponds to a controller that controls the charged particle beam device 2. The computer system 1 may be an externally connected computer system with respect to the main body 3 of the charged particle beam device 2, or may be a computer system built into the main body 3 of the charged particle beam device 2. The computer system 1 may be configured by, for example, a PC or a server.

[0016] The computer system 1 has external input / output devices connected thereto via an input / output interface 205, such as a display device 206 like a liquid crystal display, and an operation input device 207 like a keyboard and a mouse. The input / output devices may be built into the computer system 1. The user is a person such as an operator or a worker who operates and uses the computer system 1. The user operates the operation input device 207 to input instructions and information while viewing the screen of the display device 206. The user operates the charged particle beam device 2 through the computer system 1.

[0017] The main body 3 is a part including a lens barrel (in other words, a housing) that constitutes an SEM. In the configuration example of FIG. 1, the main body 3 includes an electron gun 101, a condenser lens 102, a deflection coil 103, an objective lens 104, a detector 105, a stage 106, a vacuum pump 107, a sample chamber 110, and the like. The sample chamber 110 has a stage 106 that is a sample stage, and a sample 5 is placed and held on the stage 106. The stage 106 is movable at least in the horizontal directions (X, Y directions) based on drive control from a controller, and thereby the imaging field of view can be changed. The inside of the sample chamber 110 is evacuated by the vacuum pump 107. The sample chamber 110 may be provided with sensors for measuring states such as the degree of vacuum, temperature, vibration, and electromagnetic waves.

[0018] The charged particle beam b1 generated in the vertical direction (Z direction) based on the electron gun 101 is controlled to be irradiated in the X, Y directions through the actions of the condenser lens 102, the deflection coil 103, the objective lens 104, and the like. The condenser lens 102 and the objective lens 104 condense the charged particle beam b1. The deflection coil 103 deflects the charged particle beam b1 in the X, Y directions. Thereby, the charged particle beam b1 is irradiated while being scanned in the X, Y directions on the surface of the sample 5. Due to the irradiation of the charged particle beam b1, secondary electrons b2 and the like are generated from the surface of the sample 5. A potential difference occurs on the charged surface of the sample 5, and the potential difference changes the efficiency of the emission of the secondary electrons b2. This potential difference appears as a difference in luminance in the observation image (VC image) of the SEM.

[0019] Secondary electrons b2 and the like generated from the surface of the sample 5 are detected by the detector 105. The detector 105 is, for example, a device in which imaging elements are arranged, and it converts secondary electrons b2 and the like into electrical signals for detection. The detector 105 outputs the detected electrical signal as an image signal 150 through an amplification circuit, an analog / digital conversion circuit, and the like. The image signal 150 corresponds to the observed image. The image signal 150 is input into the computer system 1 through communication means such as signal lines and the communication interface 204, and is stored, for example, as data related to the observed image in the storage device 203. The processor 201 acquires and refers to the data of the observed image, performs processing on the memory 202, and stores the processing result data in the storage device 203.

[0020] The processor 201 of the computer system 1 performs drive control and the like on each part of the main body 3 and acquires the observed image. Further, the processor 201 reads out the program stored in the storage device 203 into the memory 203, and realizes a predetermined function (such as an analysis function) by executing program processing based on the program.

[0021] The computer system 1 includes a processor 201, a memory 202, a storage device 203, a communication interface 204, an input / output interface 205, etc., and they are interconnected via a bus. Various programs, data, and information are stored in the storage device 203.

[0022] The processor 201 generates a screen that serves as a graphical user interface (GUI) related to the analysis function and displays it on the display screen of the display device 206. The processor 201 receives inputs from the user through the operation input device 207 and the GUI on the screen. The processor 201 may output voice serving as a user interface through a speaker (not shown) and the like.

[0023] Based on the image signal 150 transferred from the main body 3, the processor 201 creates an observation image, which is a two-dimensional image, in the storage device 203 or the memory 202. The observation image may be stored in association with management information and related information such as date and time, target sample information, position coordinate information of the sample surface corresponding to the field of view, imaging conditions, and sensor values. As will be described later, the processor 201 displays the observation image on a screen with a GUI. Note that in the first embodiment, the observation image is an image used to detect abnormal portions on the surface by observing the surface of the sample 5. Depending on the purpose, the observation image may also be called an inspection image or the like.

[0024] Not limited to the configuration of the computer system 1 in FIG. 1, for example, an external storage, a server, etc. may be communicatively connected, and necessary data, etc. may be stored in the external device. The computer system 1 may be configured as a client-server system or a cloud computing system. For example, a user accesses the server of the computer system 1 from a client PC and obtains data such as a screen (for example, a web page).

[0025] [Sample] In the first embodiment, the sample 5, which is the object for obtaining the observation image, is, for example, a semiconductor device corresponding to a logic device as a product. During or after the manufacture of the product, the semiconductor device is used as the sample 5, and the observation image is obtained. The user, who is an operator, observes the observation image and uses the analysis function of the computer system 1 to confirm the presence or absence of abnormalities on the surface of the semiconductor device. According to the analysis function, with a minimum of manual work by the user, the determination results such as the presence or absence of abnormalities are generated and output almost automatically (in other words, semi-automatically). Therefore, the user only needs to perform the work of confirming the determination results, and with a minimum of visual observation, the labor and time of the work can be significantly reduced.

[0026] In Embodiment 1, on the surface of the semiconductor device which is the sample 5, as will be described later (Fig. 3 etc.), a plurality of identical or similar structures (in other words, patterns) are arranged. In the analysis function and for the sake of explanation, each structure or pattern is described as a cell or a cell region. As a specific example, this cell corresponds to a device (circuit element) such as a transistor. Also, a plurality of plugs (contact plugs) may be included in one cell region. The analysis function can set each plug as an ROI (region of interest) for image processing. As a specific example, this plug is an element constituting a device such as a transistor, and is, for example, a region corresponding to a source, a drain, a gate, etc. This plug region is formed by the semiconductor's stacked structure, and due to differences in materials etc., a difference in luminance appears when viewed as an image.

[0027] [Processing Flow] Fig. 2 shows the flow of main processing by the computer system 1 (particularly the analysis function) of Embodiment 1, and has steps S1 to S7. In step S1, the processor 201 of the computer system 1 inputs and acquires an observation image from the main body 3. The processor 201 may acquire the observation image data already stored in the storage device 203. The processor 201 performs the following analysis processing on the observation image specified by the user.

[0028] In step S2, the processor 201 sets a reference image (in other words, a reference cell region) in the observation image (also described as the overall image). The setting of the reference image is possible on the setting screen (Fig. 10) described later. In the setting of the reference cell region, if there is already set reference image setting information, it may be selected, read out, and applied.

[0029] In step S3, the processor 201 extracts a similar image (in other words, a similar cell region) from the observation image based on the reference image. This extraction processing can be specified and executed as one of the commands in the software (Fig. 11 described later).

[0030] In step S4, the processor 201 sets a plurality of ROIs (Regions of Interest) for a plurality of plugs in the reference image by using the reference image and the plurality of extracted similar cell regions. The setting of the plurality of ROIs can be performed on a setting screen described later (FIG. 12 described later). In the setting of the ROI, if there is already set ROI setting information, it may be selected, read, and applied.

[0031] In Embodiment 1, specifically, the processor 201 uses the plurality of similar cell regions extracted by the extraction process to calculate, as a statistical process, for example, the average luminance value for each plug (plugs at the same arrangement position within the cell), and sets the average luminance value for each ROI of the plurality of ROIs in the reference cell region. Also, in the setting of the ROI and the like, the processor 201 extracts, as a plug region, a region that is equal to or higher than a predetermined luminance threshold with respect to the luminance of the darkest background region on the device surface. The range of the luminance value is, for example, 0 (black) to 255 (white).

[0032] In step S5, the processor 201 sets a determination rule (which may sometimes be simply referred to as a rule) based on the user's operation and confirmation on a setting screen (FIG. 14 and the like) described later. The determination rule is a rule applied during the determination process. One or more determination rules are set, and one or more can be applied. The determination rule to be applied may be selected and referred to if there is already set determination rule setting information.

[0033] In step S6, the processor 201 determines an abnormality in the cell region (particularly the plug that is the ROI) by comparing the reference image (reference cell region) with the similar image (similar cell region) based on the determination rule to be applied. The abnormality determination includes determination of the presence or absence of an abnormality and the location of the abnormality. In Embodiment 1, it is simply a binary determination of the presence or absence of an abnormality, but it is not limited to this, and it may be a multi-value determination regarding the degree or possibility of an abnormality or defect. For example, it is also possible to divide the degree of abnormality into a plurality by using a plurality of thresholds.

[0034] In step S7, the processor 201 outputs the determination result information to the user by, for example, displaying it on a screen having a GUI. Various data and information created by the processing of the above-described analysis function are stored in the storage device 203.

[0035] The processing flow of the first embodiment is a flow in which the computer system 1 automatically generates and presents a suitable one as a plurality of ROIs (in other words, a reference cell region including a plurality of ROIs) of the reference cell region, and performs the extraction process of step S3 before step S4. A suitable reference cell region generated using the extracted similar cells is presented, and the user can confirm it and determine it as the reference cell region.

[0036] [Observation Image] FIG. 3 shows an example of an observation image 301 of a certain sample 5 (referred to as sample A) in the first embodiment. In sample A, the same or similar cell structures are arranged on the surface (X-Y plane). The cell consists of an array of one or more plugs. The X direction (X axis) is the horizontal direction in the image, and the Y direction (Y axis) is the vertical direction in the image. The observation image 301 is a rectangular image having a predetermined size (the number of pixels in the X direction and the number of pixels in the Y direction). Each pixel has position coordinate information on the surface of the sample 5. The cell 302 is an example of a certain cell region. This cell 302 includes, for example, three plugs 311 (plug regions) arranged in a predetermined positional relationship, and the three plugs 311 have a predetermined luminance relationship. In this example, the luminance of each plug 311 is different. In the observation image 301, cells (a plurality of plugs corresponding thereto) that are the same as or similar to the cell 302 (a plurality of plugs corresponding thereto) are arranged. The cells 303 and 304 are examples of cells having a structure different from that of the cell 302 and include two plugs. The background region of the observation image 301 has the lowest luminance and is close to black, and the luminance of each plug is higher than that of the background region. The user can set a desired cell, for example, the cell 302, as a reference image (reference cell region). Then, a plurality of plugs in the cell 302 can be set as ROIs described later.

[0037] FIG. 4 shows an example of an observation image 401 of another sample 5 (referred to as sample B) in Embodiment 1. Similar to sample A, sample B has the same or similar cell structures arranged on the surface (X-Y plane), and further includes various inverted arrangements (patterns) with respect to a certain cell structure. Cell 402 is an example of a certain cell region. This cell 402 includes, for example, four plugs 411 (plug regions) arranged in a predetermined positional relationship, and the four plugs 411 have a predetermined luminance relationship. In this example, the luminance of each plug 411 is different. In the observation image 401, cells (corresponding plural plugs) identical or similar to cell 402 (corresponding plural plugs) are arranged side by side. Cells 403 and 404 are examples of cells having a structure different from that of cell 402, and the number, position, shape, or luminance of the plugs are different.

[0038] Also, cell 405 is an example of a cell having the same arrangement of a plurality of plugs as cell 402, cell 406 is a left-right inversion of cell 402, cell 407 is an up-down inversion of cell 402, and cell 408 is an example of a cell having an arrangement of a plurality of plugs obliquely inverted with respect to cell 402. The user can set a desired cell, for example, cell 402, as a reference image. Then, a plurality of plugs within that cell 402 can be set as ROIs. Note that plugs 421 and 422 show examples where the luminance deviates from the luminance of other plugs, and such plugs (cells including them) are determined to be abnormal in the abnormality determination described later.

[0039] Note that the data of the observation image is not only data of luminance values for each pixel but also data having position coordinate information obtained by SEM. For example, the observation image has position coordinate information for each pixel. For example, the observation image has position coordinate information of the upper left point and the lower right point. By having position coordinate information in the observation image, the map display described later becomes possible.

[0040] [Reference Image (Reference Cell Region)] Figure 5 shows an example of the setting of a reference cell region (reference image) and a plurality of ROIs (regions of interest) corresponding to the observation image 301 of sample A in Figure 3. Such a reference image 501 is set as a reference for searching and extracting similar cell regions and as a reference for abnormality determination. This reference image (reference cell region) 501 set corresponding to cell 302 in Figure 3 includes three plug regions: plug 511, plug 512, and plug 513. The plug regions have, for example, an elliptical shape. Here, for clarity, each plug region is illustrated with a plug number (#) described later. The three plug regions are arranged in a predetermined positional relationship as shown in the figure. For example, with respect to the center of gravity or the center of the cell region, plug 511 is arranged in the upper left, plug 512 is arranged in the upper right, and plug 513 is arranged below. The reference cell region 501 is displayed in a predetermined representation (e.g., a yellow dashed frame) on the screen described later.

[0041] Each plug region has a luminance, and the luminance may be different between plug regions. The three plugs in this example have a luminance relationship where the luminance decreases from high (white) to low (black) in the order of plug 513 with ROI number (#)=3, plug 511 with #=1, and plug 512 with #=2. At the bottom of Figure 5, the luminance relationship of the three plugs in the reference cell region 501 is shown. In the drawing, the plug regions and luminance are illustrated schematically by a dot pattern. The background region is the darkest and has a predetermined luminance value, but here it is illustrated in white and excluded from consideration. Plug 513 with ROI number (#)=3 has a first luminance value, plug 511 with #=1 has a second luminance value, plug 512 with #=2 has a third luminance value, and the luminance increases in the direction from the third luminance value to the first luminance value. When there is a luminance distribution within one plug region, the average luminance value within the plug region can be calculated and used as the luminance value of the plug. Also, the luminance may be different between the edge and the center of the plug. In this case, only the luminance of the edge portion can be used as the average luminance value. The user can also select whether to adopt the average luminance value of the entire plug or the average luminance value of the edge portion of the plug during rule setting.

[0042] FIG. 6 shows an example of setting a reference cell region (reference image) and a plurality of ROIs corresponding to the observation image 401 of sample B in FIG. 4. Such a reference image 601 is set as a reference for searching and extracting similar cell regions and as a reference for abnormality determination. This reference image (reference cell region) 601 set corresponding to cell 402 in FIG. 4 includes four plug regions indicated by ROI numbers (#) = 1 to 4. The four plug regions are arranged in a predetermined positional relationship as shown in the figure. For example, with respect to the center of gravity or the center of the cell region, a plug with # = 1 is arranged near the center of gravity or the center, a plug with # = 2 is arranged at the lower left, a plug with # = 3 is arranged at the upper right, and a plug with # = 4 is arranged at the upper left.

[0043] At the bottom of FIG. 6, the luminance relationship of the four plugs in the reference cell region 601 is shown. The plug with ROI number (#) = 1 has a first luminance value, the plug with # = 2 has a second luminance value, the plug with # = 3 has a third luminance value, the plug with # = 4 has a fourth luminance value, and the luminance increases in the direction from the fourth luminance value to the first luminance value.

[0044] Also, as shown in FIG. 4 described above, for a certain cell region, there are cell regions with arrangement patterns inverted left and right, up and down, diagonally, etc. For example, let the reference cell region 601 be pattern A with the same arrangement (no inversion). For the cell in pattern A (reference cell region 601), a cell region 602 with an arrangement inverted left and right about the Y-axis is defined as pattern B. For the cell in pattern A, a cell region 603 with an arrangement inverted up and down about the X-axis is defined as pattern C. For the cell in pattern A, a cell region 604 with a diagonally inverted arrangement, in other words, an arrangement inverted left and right and up and down, is defined as pattern D. Cell regions with these various arrangement patterns are included in the observation image 401 in FIG. 4. Depending on the sample and region, some arrangement patterns may be included only partially.

[0045] Cell regions of various arrangement patterns including the reference cell region 601 are displayed in a predetermined representation according to the arrangement pattern on a screen (Fig. 18) described later. For example, the reference cell region 601 of pattern A is displayed with a yellow dashed frame, the cell region 602 of pattern B is displayed with an orange solid frame, the cell region 603 of pattern C is displayed with a green solid frame, and the cell region 604 of pattern D is displayed with a light blue solid frame. Also, the representation may be distinguished according to whether a certain cell region is a reference image (similar image) or not, or the representation may be distinguished according to whether a certain cell region is abnormal or not. Note that regions such as the reference cell region are usually set as rectangular regions for efficient image processing.

[0046] [Extraction process] Fig. 7 shows an example of a similar cell region extracted by the similar image extraction process (step S3 in Fig. 2) corresponding to the case of the observation image 301 of sample A in Fig. 3. When one cell region near the upper left is set as the reference image 701, a similar cell region 702 similar to the reference image 701 is extracted from the observation image 301. Each similar cell region 702 is displayed surrounded by a rectangular solid frame. Also, when the reference image is set in advance, the region displayed as the reference image 701 in Fig. 7 is also extracted as the similar image 702. Here, the background region is shown in white.

[0047] FIG. 8 shows an example of similar cell regions extracted by the similar image extraction process (step S3 in FIG. 2) corresponding to the case of the observation image 401 of sample B in FIG. 4. When one cell region near the upper left is set as the reference image 801, similar cell regions (811, 812, 813, 814) similar to the reference image 801 are extracted from the observation image 401. Each similar cell region is shown surrounded by a solid rectangle frame. Here, the background region is shown in white. Also, cell regions with various arrangement patterns as in FIG. 6 are extracted, and are displayed separately, for example, by changing the color for each pattern, or by adding characters or marks for identifying the patterns. For example, the similar cell region 811 is a cell of pattern A with the same arrangement and no inversion. The similar cell region 812 is a cell of pattern B with left - right inversion. The similar cell region 813 is a cell of pattern C with up - down inversion. The similar cell region 814 is a cell of pattern D with diagonal inversion. Note that the plugs 421 and 422 are examples of abnormal plugs. The plug 421 has a lower luminance compared to the luminance of the central plug in the reference cell region 801. The plug 422 has a higher luminance compared to the luminance of the upper - right plug in the reference cell region 801.

[0048] [GUI Screen] Next, an example of a screen having a GUI related to the analysis function of the computer system 1 will be described. Each screen may be provided, for example, as a web page.

[0049] [Image Input (Step S1)] FIG. 9 shows an example of a screen for image input (step S1 in FIG. 3). In the screen of FIG. 9, buttons corresponding to each step of the flow related to the work are provided in the upper column 901, and the progress state of the steps of the flow is represented by the display state of the buttons and the like. In this example, as the flow buttons, an image input (“Open Image”) button 911, a reference cell setting (“Set Reference Cell”) button 912, a region of interest setting (“Set ROI”) button 913, a data (“Data”) button 914, a determination rule setting (“Set rule”) button 915, a determination result (“Result”) button 916, and a multiple determination (“Multiply Result”) button 917 are provided. The buttons are connected by arrows.

[0050] First, when the image input button 911 is pressed by the user's operation, the image input button 911 becomes prominently displayed, and a GUI for selecting and opening an observation image file for image input is displayed in the lower column 902. With this GUI, the user selects the observation image file to be analyzed and presses the Open button. Then, the screen transitions to the next step.

[0051] [Reference Image Setting (Step S2)] Figure 10 shows an example screen of reference image setting (step S2 in FIG. 2). When the reference image setting button 912 is pressed, a GUI for setting the reference image (reference cell region) is displayed in the lower column. In this column, the target observation image 1001 is displayed. The observation image 1001 in this example corresponds to the observation image 301 of sample A in FIG. 3. The user checks by looking at the observation image 1001 and sets the desired cell structure as the reference image 1002 by operation. For example, the user can set it as the reference image 1002 by surrounding a desired area with a rectangle by operating a mouse or the like within the observation image 1001, or by specifying the start and end points of the rectangle. When the user proceeds to the next step, the user presses the Next button, and when the user wants to redo the setting, the user presses the Clear button to redo it. Also, when the user proceeds to the extraction process, the user presses the Extraction (Split) button 1003. If a reference image has been prepared in advance, it can be set as the reference image to be applied by calling the image from the memory 202.

[0052] [Extraction of Similar Cell Regions (Step S3)] Figure 11 shows an example screen of the extraction process (step S3 in FIG. 2) of similar cell regions (similar images). When the extraction button 1003 is pressed, the extraction result (corresponding to FIG. 7) of the similar image on the observation image 1001 is displayed in the lower column. For example, each similar cell region 1101 with respect to the reference cell region 1002 is displayed with a solid rectangle frame.

[0053] [Setting of Region of Interest (Step S4)] Figure 12 shows an example screen of the setting of the region of interest (ROI) (step S4 in FIG. 2). When the region of interest setting button 913 is pressed, a GUI for setting a plurality of ROIs (plugs) within the reference cell region is displayed in the lower columns 1201 and 1202. In the left column 1201, the reference cell region 1203 during the setting operation and the ROI 1204 therein are displayed. In the right column 1202, information about the plurality of extracted ROIs within the reference cell region is arranged and displayed, for example, in a table format. This table 1205 has items such as ROI number, "Apply", etc., and may also have items such as luminance values.

[0054] In Embodiment 1, the processor 201 automatically generates a suitable reference cell region (including the setting of the luminance values of a plurality of ROIs) using the plurality of similar cell regions extracted by the extraction process in step S3 of FIG. 2, and displays the generated reference cell region in column 1201. The user checks the generated reference cell region 1203 and ROI 1204 in column 1201, and if the content is acceptable, formally sets the reference cell region 1203 and ROI 1204. That is, by proceeding to the next step with the Next button in column 1202, the reference cell region 1203 and ROI 1204 in column 1201 are set as setting information.

[0055] In this example, first, the three plugs (# = 1 to 3) that have been extracted are displayed within the reference cell region 1203. The user can also switch the application / non - application as an ROI by clicking or surrounding the desired plug region in the left - hand column 1201. Also, in the right - hand column 1202, by operating the check mark in the "Use" item of the row with the desired ROI number (#), the application / non - application as an ROI can be switched. In this example, the three plug regions of # = 1 to 3 are surrounded and displayed by a broken - line frame of a circle or an ellipse (for example, displayed in red), and all are set to be applied as ROIs. In addition, the user can add other plugs as ROIs to the reference cell image 1203 by means of the Add button and manual operations, and can delete unnecessary plug partial images from the reference cell image 1203 by means of the Remove button (for example, filled with the same luminance as the background region). When the user manually sets an ROI, for example, in column 1201, an elliptical frame with a desired shape and size can be specified by means of a mouse operation or the like, and the elliptical frame can be set as an ROI.

[0056] [Data Confirmation and Saving] FIG. 13 shows an example of a data confirmation / saving screen. When the data button 914 is pressed, various types of data (including setting information) created so far (in steps S1 to S4) are displayed on the screen. If the user checks the data on the screen and it is acceptable in terms of its content, the user can save it by pressing the Save button. The setting information and information regarding the luminance are held inside the processor 201 and can also be displayed as needed. The processor 201 associates those data / information and saves them in the storage device 203. The setting information is the setting information of the reference cell area and a plurality of ROIs therein. The user can save each data by naming it. On the screen, the file names of each data etc. may be displayed as a list. The screen example of FIG. 13 is an example of checking and saving the setting information regarding the reference image (reference cell area) and a plurality of ROIs in a certain observation image as data examples. In column 1301, the set reference cell area and the extracted similar cell area are displayed on the observation image. An area number may be displayed for each area. Table 1302 shows the luminance values of each ROI of a plurality of ROIs constituting the reference image (reference cell area) identified by the area number (#). By pressing the Save button, a file (for example, in csv format) of the setting information as shown in Table 1302 can be saved. Not limited to this, as other data, information such as the relative position coordinate information of each ROI of the reference image and the diameter of the size of each ROI can also be similarly checked and saved. Further, Table 1302 can be said to be data that defines the positional relationship between the regions of interest regarding the arrangement pattern of a plurality of regions of interest of the reference image (reference cell area).

[0057] [Setting of the determination rule (step S5)] FIG. 14 shows an example of a screen for setting a determination rule (step S5 in FIG. 2). When the determination rule setting button 915 is pressed, a GUI for setting the determination rule is displayed in the lower columns 1401 and 1402. In column 1401, the set reference cell region 1403 associated with the determination rule and a plurality of ROIs 1404 therein are displayed. In column 1402, first, a template (conditional expression template) 1405 for setting the rule is displayed. The template 1405 is composed of, for example, five items (buttons) 1406. The items (buttons) 1406 include an ROI number (#) item and a symbol item. The five items 1406 of the template 1405 are arranged, for example, in order from the left as ROI number, symbol, ROI number, symbol, ROI number. The user can select and set the ROI number and symbol with the desired item 1406. The ROI number (#) item can also be changed to a luminance value item (in other words, a threshold value item) by the user's operation. The ROI number (#) item is an item for setting the ROI number, the luminance value item is an item for setting the luminance value, and the symbol item is an item for setting a symbol (inequality symbol, minus symbol, etc. For example: <, >, ≤, ≥, -).

[0058] By the user operating the items 1406 of the template 1405, a conditional expression can be configured. For example, in the upper row, as the conditional expression 1407, #2 < #1 < #3 is set. This conditional expression defines the magnitude relationship of the luminance values between the ROIs of each ROI number in the reference cell region 1403. Specifically, this conditional expression 1407 defines that the ROI with ROI number (#) 1 has a luminance value greater than the ROI with # = 2 and less than the ROI with # = 3. This conditional expression corresponds to the determination rule. Similarly, a conditional expression can be set for each row, and further, by setting the AND / OR logic between the rows, a conditional expression formed by combining a plurality of conditional expressions with the AND / OR logic can be configured as the determination rule.

[0059] One determination rule can be set on one page in this column 1402. The page can be switched by operating the page button 1408. On other pages, similarly, another determination rule can be set. Also, the determination rule can be added by the Add button. The determination rule can be deleted by the Remove button. The Next button allows proceeding to the next step.

[0060] The determination rule of the conditional expression 1407 in FIG. 14 is an example of the first type of determination rule. The first type of determination rule is a rule that defines the luminance relationship between ROIs. This determination rule determines that there is an abnormality when the conditional expression 1407 is not satisfied. It is also possible to set only such one determination rule and end the determination rule setting step. Hereinafter, the case of setting the second and subsequent determination rules will be described.

[0061] FIG. 15 shows an example screen when setting the second type of determination rule as another type. In this example, in column 1402 of the same screen, the case of setting the second type of determination rule as the second determination rule on the second page (the numerical value of the page button 1408 is 2) is shown. Based on the operation of the template, in the upper row, #3 - #1 < 50 is set as the conditional expression 1501. This conditional expression defines the relationship between the luminance value of the target ROI (the second ROI) and the threshold value (in other words, the luminance threshold) of the difference (in other words, the deviation) of the luminance value of the reference ROI (the first ROI) within the reference cell area 1403. Specifically, this conditional expression 1501 designates the ROI with ROI number (#) 1 as the reference ROI, the ROI with # = 3 as the target ROI, and stipulates that the luminance difference between the target ROI with # = 3 and the reference ROI with # = 1 is smaller than the threshold value of 50. This conditional expression 1501 corresponds to the second second-type determination rule. This determination rule determines that there is an abnormality when the conditional expression 1501 is not satisfied.

[0062] The determination rule of the conditional expression 1501 in this example is that the luminance deviation between ROIs is smaller than the threshold value. However, it is not limited to this, and a rule that the luminance difference between ROIs is larger than the threshold value can also be set.

[0063] FIG. 16 shows an example of a screen when setting a third type of determination rule as another type. In this example, in column 1402 of the same screen, the case of setting the third type of determination rule as the third determination rule on the third page (the numerical value of the page button 1408 is 3) is shown. Based on the operation of the template, in the upper row, as the conditional expression 1601, 100 < #1 < 150 is set. This conditional expression defines the relationship between the luminance value of the target ROI and the range of the threshold value (luminance threshold) within the reference cell area 1403. Specifically, this conditional expression 1601 stipulates that for the ROI with ROI number (#) 1, the luminance of this ROI is within the range greater than the lower threshold value of 100 and smaller than the upper threshold value of 150. This conditional expression 1601 corresponds to the third type of the third determination rule. This determination rule determines that there is an abnormality when the conditional expression 1601 is not satisfied.

[0064] Note that the determination rules for each type are rules for determining that there is no abnormality when the conditional expression is satisfied and that there is an abnormality when the conditional expression is not satisfied. However, it is not limited to this, and conversely, a form of setting rules for determining that there is an abnormality when the conditional expression is satisfied and that there is no abnormality when the conditional expression is not satisfied is also possible. After the three determination rules in the above example are set and the Next button is pressed, the process proceeds to the next step (abnormality determination). When multiple determination rules are set on the screen, automatically, those multiple determination rules are applied to the abnormality determination. The abnormality determination process is performed for each determination rule, and the determination result is generated for each determination rule.

[0065] As described above, on the determination rule setting screen, the user can check and set various determination rules. In this example, a case where various determination rules can be set based on the same GUI is shown. However, this is not the only case, and for each type of determination rule, it may be possible to allow user settings using a different GUI. In this example, a case where three types of abnormal determinations are made using three types of determination rules is shown. However, the three determination rules in the above example can be set not only simultaneously, but also by setting only the second type or the third type as the determination rule, and as will be described later, it is also possible to perform an abnormal determination by randomly combining two of them.

[0066] It can be said that in the above-described setting examples of the determination rules in FIGS. 14 to 16, the luminance relationships (conditional expressions 1407, 1501, 1601) between the plurality of regions of interest included in the cell region of the reference image are set.

[0067] [Abnormal determination and determination result output (Steps S6, S7)] FIG. 17 shows an example of a screen for abnormal determination (Step S6 in FIG. 2) and output of a determination result (Step S7 in FIG. 2). The example in FIG. 17 is an example of a determination result for an observation image of Sample A. When the determination result button 916 is pressed, an abnormal determination is executed based on the set determination rule, and the determination result is displayed in the lower column 1701. In this example, using the three determination rules set in the previous step, an abnormal determination is executed for each determination rule, and a determination result for each determination rule is generated. In the screen example of FIG. 17, on the first page of column 1701 (the numerical value of the page button 1702 is 1), the determination result using the first determination rule (FIG. 14) is displayed. Also, on this screen, it is possible to display the determination result obtained by combining these three determination rules.

[0068] In column 1701, on the target observation image 1703, a similar cell region 1704 is displayed, for example, with a solid yellow frame. Further, a reference cell region may be separately displayed on the observation image 1703. Although not shown, the content of the reference cell region may be displayed in another column. Further, when a cell region with an abnormality is detected as a determination result, on the observation image 1703, the cell region 1705 with the abnormality is separately displayed, for example, with a solid red frame (shown as a white frame in the drawing). Thereby, the user can confirm which cells in the observation image have an abnormality. Note that the display of the presence or absence of an abnormality is not limited to the cell region unit, and may be displayed in the plug region unit. When the page button 1702 is operated, the page transitions to another page, and other determination results can be similarly confirmed.

[0069] The user can also select a desired cell region with or without an abnormality and press the Image button to enlarge and display the cell region to check the details. Also, in that case, the reference cell image and the selected cell image may be displayed in parallel within the screen to enable comparison and confirmation. Further, when the user presses the Rule button, the content of the above-described determination rule corresponding to the determination process of that page can be displayed and confirmed. Further, when the user presses the All save button, all reference images, similar images, set rules, determination results, etc. can be saved as data. Also, the user can save only the selected determination result as a result of checking the determination result on the screen. When the save operation is performed, the processor 201 associates the determination result data with each data saved on the above-described data screen and saves it in the storage device 203.

[0070] The screen example in FIG. 18 is also an example of the determination result for the observation image of sample B. Note that for sample A and sample B, the processing is in another similar flow. In column 1701, the determination result is displayed on the target observation image 1801. Abnormality determination is executed based on the set determination rules, and the determination result for each determination rule is generated. Note that in FIG. 18, the brightness of the background area is brightened for easy viewing. Note that the brightness of the background area may be changed to a desired brightness by the user's operation.

[0071] In this example, on the first page of column 1701, the determination result using the first determination rule is displayed. This determination result also corresponds to the extraction result in FIG. 8. As an example of the display of the determination result, based on the reference cell area and the similar cell areas of various arrangement patterns, for the cell areas without abnormality, a broken line frame is used and the character indicating no abnormality (OK) is added for display, and for the cell areas with abnormality, a solid line frame is used and the character indicating abnormality (NG) is added for display. Also, it is displayed in color-coding for each arrangement pattern of the cell areas. As described above (FIG. 6 and FIG. 8), for example, pattern A is displayed in yellow, pattern B in orange, pattern C in green, and pattern D in light blue.

[0072] It is not limited to such identification display. For example, the reference cell area and other similar cell areas may be displayed separately. The cell areas with abnormality may be displayed with a red frame, the cell areas without abnormality with a predetermined color frame, and the distinction of the arrangement pattern may be only by characters. It may be possible to switch the on / off of the identification display of the arrangement pattern by a button not shown. Also, by a button not shown, the user may select only a specific arrangement pattern and display only the cell areas of the specific arrangement pattern (for example, pattern A).

[0073] In this example, as the determination result, cells 1811 and 1812 are determined to be abnormal, and some of the plugs (plugs 421 and 422 in FIGS. 4 and 8) within those cells are determined to be abnormal. Thus, with this analysis function, the arrangement patterns of multiple plugs in a cell can be automatically judged, extracted, and displayed as similar, including the relationship of inversion. The user can easily perform the discrimination of the inversion pattern and the abnormality determination by visual observation, which was difficult in the past.

[0074] It is also possible to set a determination rule by combining multiple types of determination rules and apply it to the abnormality determination. For example, on a certain page in the aforementioned determination rule setting screen, a first conditional expression corresponding to the first type of determination rule is set in the first row, and through the logic of AND or OR, a second conditional expression corresponding to the second type of determination rule is set in the second row. In this way, a determination rule based on a conditional expression configured by combining the first type of determination rule and the second type of determination rule can be set.

[0075] [Multiple Determinations] Figure 19 shows an example of a multiple determination screen when a plurality of determination buttons 917 are pressed. The user can finish the operation up to the confirmation of the determination result in FIG. 18. Further, on the screen of FIG. 19, multiple determinations, that is, batch anomaly determination using the same determination rule for a plurality of observation images can also be performed. On this screen, a GUI area 1902 for selecting a group of observation image files to be processed in batch is displayed within column 1901. The user inputs a group of observation image files to be processed in batch into this GUI area 1902. Information such as the file names of the group of observation image files is arranged and displayed within area 1902. With the Select button, an observation image file can be selected. With the Folder button, a folder of the group of observation image files can be selected. When the Execute button is pressed, the processor 201 applies the determination rule set in the previous step to the group of observation image files within area 1902, executes anomaly determination, generates a determination result for each observation image file, and saves the determination result data. The determination result for each observation image file can be confirmed on a similar screen by returning to the previous determination result step according to the operation of the determination result button 916.

[0076] [Map Display] Figure 20 shows an example of a map display screen that is displayed when the Map button is pressed on the determination result screen or the multiple determination result screen. The map in this example is generated by integrating the multiple determination results of a plurality of observation images (in other words, device areas) for the semiconductor device that is sample 5 into one. Within column 2001, a map 2002 is displayed together with information on the object (device) that is sample 5. Map 2002 is a plane having position coordinates on the X-axis and Y-axis corresponding to the surface of the sample, and a coordinate origin (Origin of coordinate) is set in advance. The coordinate origin of this device is, in other words, the reference coordinate of the device. The coordinates referred to here indicate relative coordinates on the device centered on the origin coordinates of the device or absolute coordinates of the SEM stage.

[0077] On the plane of the map 2002, the cell regions with abnormalities detected as abnormal determination results are displayed so as to be distinguishable in modes such as frame lines, colors, characters, or marks. In particular, when including an inversion arrangement pattern like sample B, various arrangement patterns are distinguished and displayed. Also, the cell regions with abnormalities are displayed with a predetermined mark (in this example, a red cross mark) at the locations of the ROIs (plugs) with abnormalities therein. Further, the cell regions with abnormalities also display the position coordinate information (X, Y) in the coordinate system of the map 2002 (i.e., the device surface). This position coordinate information is the relative position from the device coordinate origin or the absolute position of the SEM stage. In this example, this position coordinate information is specifically the position coordinate information of the ROIs (plugs) with abnormalities within the cell region, but it is not limited to this, and it can also be the position coordinate information such as the center point of the cell region having the ROIs (plugs) with abnormalities.

[0078] By viewing such a map display, the user can easily confirm the locations with abnormalities and their distributions throughout the device. It is also possible to specify a desired location in the map to display details. Further, by operating the page buttons, the map display for each determination rule can be switched. Note that the map can also be enlarged, reduced, scrolled, or paged.

[0079] [Method for Automatically Extracting and Setting a Reference Cell Region] In Embodiment 1, as described above (Steps S2 to S4), based on the extraction of similar cell regions for the cell region specified by the user (Step S3), a plurality of ROIs (especially luminance, etc.) of a suitable reference cell region can be generated, presented, and set. The average luminance, etc. can be calculated by statistical processing from the plurality of extracted similar cell regions and set as the luminance of the plurality of ROIs of the reference cell region. The user can confirm the automatically generated and presented reference cell region and, if necessary, the user can also change and set it. A detailed processing example of such a method will be described below.

[0080] FIG. 21 shows a part of a screen example when setting a reference cell region as an explanatory diagram of a detailed processing example of Embodiment 1. For example, in column 2101, information for the user to check and set the reference cell region is displayed. In column 2102, the initial reference cell region on the observation image and the extraction results of similar cells are displayed.

[0081] First, the user designates an initial reference cell region within the observation image on the screen. Note that this designation of the initial reference cell region is provisional, and the reference cell region will be determined later. This designation may be, for example, a designation that encloses a region from the observation image, or a designation of the position coordinates of the region (for example, the upper left point and the lower right point of a rectangle).

[0082] In column 2101, the user can set a plurality of ROIs within the initial reference cell region 2103. For example, the plug region may be surrounded by an ellipse or a rectangle. This example shows the case of surrounding with a broken line frame of an ellipse. The position coordinates of the upper left point and the lower right point of the figure surrounding the plug may be designated, or the position coordinates of the center of gravity or the center point of the figure surrounding the plug may be designated.

[0083] Also, the designation of this ROI is not limited to the user's manual operation, and the processor 201 may perform automatic processing to assist. For example, the processor 201 calculates the luminance distribution using image processing techniques such as binarization from within the initial reference cell region designated by the user, estimates and extracts the plug region, and presents to the user whether to use the extracted plug region as the ROI.

[0084] The processor 201 identifies the positional relationship of a plurality of specified ROIs included within the initial reference cell region, and sets the initial reference cell region including the plurality of ROIs as the reference cell region. Alternatively, when making this identification, the user may set the positional relationship between the ROIs.

[0085] Also, the processor 201 uses a threshold value of the luminance value to distinguish a plurality of ROIs from the background area (the darkest area of the device), and sets an area where the luminance value is equal to or greater than the threshold value as an ROI. For example, in the initial reference cell area 2104 of column 2101, three plugs surrounded by a red dashed frame are specified. The three plugs are separated from the background area and identified, and are set as three ROIs included in the initial reference cell area.

[0086] The initial reference cell area 2104 of column 2101 shows an example of specifying the positional relationship of a plurality of ROIs. In this example, the position coordinates of the center of the ellipse of the plug area are specified as the position coordinates of each ROI. In the table on the right, the position coordinates are displayed for each ROI number (#). These ROI position coordinates may be absolute position coordinates or relative position coordinates with a certain ROI as a reference ROI. As another example of processing, the distance between the ROI position coordinates (illustrated by a line segment) may be set. As another example of processing, the size of each ROI, for example, the diameter (illustrated by an arrow) may be set.

[0087] The processor 201 uses the thus set initial reference cell area and the plurality of ROIs (initial ROIs) included therein, and extracts, as described above, an area similar to the initial reference cell area including the plurality of ROIs from the observation image which is the entire image, as a similar image, based on a preset condition (described as an extraction condition).

[0088] In this extraction process, the following may be applied as extraction conditions. For example, the relative position coordinates of each ROI within the initial reference cell area and the size (for example, the diameter) of each ROI are specified in advance. The processor 201 sequentially determines and specifies an area similar to this reference cell area (a plurality of ROIs) in the entire image, and extracts it as a similar image. In this determination, an extraction condition for determining the relationship of the plurality of ROIs included in the reference cell area is used.

[0089] Examples of such extraction conditions include whether the distance corresponding to the relative position coordinates of the ROI or the distance corresponding to the difference between the absolute position coordinates of the ROI is within a threshold range.

[0090] In this extraction condition, for example, if the range is narrowed, a strict determination of identity can be made, and if the range is widened, a loose determination can be made.

[0091] Note that this extraction condition is different from the determination rule in the abnormality determination. In the determination using this extraction condition, it is necessary to form a population of the inspection target (sample 5). Even if there is an abnormality in the semiconductor device itself of the inspection target, it needs to be included in the population. Also, parts of the semiconductor device of the inspection target that are not the inspection target should not be included in the population. That is, in a strict determination, the inspection area including the abnormal part must not be missed, and in a loose determination, parts that are not the inspection target can also not be included.

[0092] In Embodiment 1, in order to first determine the formation of the population, before setting the determination rule for the subsequent abnormality determination, an extraction process (step S3) for setting the reference cell area and setting a plurality of ROIs included in the reference cell area is performed, confirmed on the screen, and the reference cell area including the plurality of ROIs is determined. Thereby, the certainty of specifying the inspection target (target area) can be enhanced, and the subsequent abnormality determination can be performed with high accuracy.

[0093] The extraction conditions are designed and set in advance in the software of this computer system 1. Alternatively, using the user setting function of this software, the extraction conditions may be displayed on the screen, and the user may be able to perform user settings such as selecting and changing the algorithm and parameter values. A plurality of templates may be set and prepared in advance so that the extraction conditions and setting information (reference cell area including a plurality of ROIs, determination rules, etc.) to be applied can be easily selected for each customer or each target device.

[0094] In addition, when there is little need to first identify the inspection target, as a modification, after setting the determination rules for subsequent abnormality determination, the extraction process for setting the reference cell region and a plurality of ROIs within the reference cell region may be automatically performed.

[0095] [Effects, etc.] As described above, according to Embodiment 1, in the computer system 1 that analyzes the VC image (observation image) obtained by the charged particle beam device (SEM), the determination and detection of abnormalities by comparing the luminance between the same or similar structures in the observation image can be realized automatically to a certain extent without relying on visual observation, easily and efficiently. The analysis function of the computer system 1 of Embodiment 1 extracts similar cell regions and determines the abnormalities in the cell regions based on the relationships of a plurality of plugs (ROIs) included in the cell regions in the observation image. As the relationships, the positional relationship and the luminance relationship between the ROIs are determined. According to Embodiment 1, even in the VC image of a sample (semiconductor device) having a complex structure, the abnormal locations and the like can be specified and detected easily and semi-automatically, that is, mainly automatically except for some operations such as setting and instruction.

[0096] According to Embodiment 1, various determinations are possible according to the setting of the determination rules that define the relationships between the ROIs within the cell. According to Embodiment 1, if the relative relationships between the ROIs within the cell are defined, they can be regarded as similar and extracted regardless of the arrangement patterns such as upside-down. And for the similar patterns, abnormality determination according to the determination rules is possible. As a modification, it is also possible to distinguish the inverted arrangement patterns and perform abnormality determination for each arrangement pattern. For example, it is also possible to perform abnormality determination only for the aforementioned Pattern A.

[0097] [Modification 1: Processing flow] Figure 22 shows the main processing flow of the computer system 1 (particularly the analysis function) of the first modification example (referred to as Modification Example 1), which includes steps S21 to S27. The flow of the modification example in Figure 22 is different from the flow in Figure 2 in that the setting process is first performed collectively, and then the extraction process, determination process, and output process are performed. That is, it is the same as moving step S3 in Figure 2 behind step S5 and performing the process.

[0098] In step S21, the processor 201 of the computer system 1 inputs and acquires an observation image from the main body 3. In step S22, the processor 201 sets a reference image (reference cell region) in the observation image. In step S23, the processor 201 sets a plurality of ROIs (regions of interest) in the reference image. In the modification example, specifically, the user manually sets a plurality of ROIs in the reference image. In step S24, the processor 201 sets a determination rule based on the user's operations and confirmations on the setting screen.

[0099] In step S25, the processor 201 extracts a similar image (similar cell region) from the observation image based on the reference image. In step S26, the processor 201 determines the abnormality of the cell region (particularly the plug that is an ROI) by comparing between the reference image (reference cell region) and the similar image (similar cell region) based on the determination rule to be applied. In step S27, the processor 201 outputs the determination result to the user, such as by displaying it on the screen of the GUI. Even in the modification example with such a flow, the same or similar effects as those in the first embodiment can be obtained.

[0100] According to step S3 of the extraction process in Figure 2 and step S25 of the extraction process in Figure 22 described above, the extraction process (S3, S25) can be said to be a process of calculating the positional relationship between the regions of interest for the arrangement pattern of the plurality of regions of interest in the reference image and extracting as similar including the same arrangement pattern and various types of inverted arrangement patterns.

[0101] [Modification Example 2: ROI Size] In another modification (hereinafter, Modification 2), the sizes of a plurality of ROIs in a reference image are judged. As one of the judgment rules, a relationship regarding the size of the ROIs can be set. The processor 201 performs an abnormality judgment using the judgment rule.

[0102] Fig. 23 shows an example of setting a rule related to ROI size in an example of a judgment rule setting screen in this modified example. On the screen, the user can set a rule for comparing the size of each ROI in the reference cell area (the size relationship of each ROI or between ROIs) as a conditional expression. For example, it is possible to set a rule for judging an ROI whose ROI size is outside (or within) a certain range indicated by a threshold value as abnormal. In this modified example, for example, as shown in template 2303 in the third row, the ROI size can also be selected and set in the template items.

[0103] In the example of setting the judgment rule in Fig. 23, the conditional formula 2301 in the first line sets the relationship based on a comparison of the diameters of the sizes of each ROI (#1, #2, #3) in the reference cell area. For example, it specifies that the diameter (major axis) of the elliptical area of ​​the ROI with ROI number (#) = 1 is larger than the diameter of the ROI with # = 2 and smaller than the diameter of the ROI with # = 3. In this example, the ROI area is an ellipse, and the major axis of the ellipse is used as the size (similar to Fig. 21), but this is not limiting, and the minor axis, the area of ​​the ellipse, etc. may also be used.

[0104] Conditional formula 2302 on the second line sets a range based on a threshold (size threshold) for the size of the ROI with #=1. In this example, the conditional formula 2301 and conditional formula 2302 are ANDed. For example, it specifies that the diameter (long axis) of the ROI with #=1 is greater than 100 (lower threshold) and less than 150 (upper threshold). Each threshold is the diameter (long axis). By narrowing the threshold range, it is possible to make a strict judgment as to whether the two are the same or different. By widening the threshold range, it is possible to make a looser judgment.

[0105] As another example, when the diameter is reduced and the upper threshold value of the inequality is eliminated (for example, 10 < #1), for the ROI with # = 1, it is possible to make a determination while allowing various sizes. However, as the size of the ROI with # = 1 increases, it affects the size comparison between the ROI with # = 3 and the ROI with # = 1 in relation to the conditional expression 2301 in the first line (AND condition). Thus, in this modification example, the relationship regarding the sizes of multiple ROIs can be flexibly set as a determination rule. Also, a determination rule combining the above-mentioned conditions such as luminance with such size conditions can be set.

[0106] It can be said that the setting of the determination rule in FIG. 23 described above sets the relationship of shape or size (conditional expression 2301, conditional expression 2302) between the regions of interest for a plurality of regions of interest included in the cell region of the reference image.

[0107] It can be said that the setting of the determination rules in FIGS. 14 to 16 and FIG. 23 described above sets the relationships (luminance, shape, size) between the regions of interest for a plurality of regions of interest included in the cell region of the reference image.

[0108] As described above, the embodiments of the present disclosure have been specifically described, but the present disclosure is not limited to the above-described embodiments, and various modifications can be made without departing from the gist. Regarding the above-described embodiments, except for the essential components, addition, deletion, replacement, etc. of the components are possible. A form combining each embodiment and modification example is also possible.

[0109] In addition, in the above example, the sample 5 to be observed was described as the observation of a semiconductor device, but it is not limited to this. For example, by applying it to the observation of the composition of a material or the observation of a biological tissue, from among similar structures in the observation image, a plurality of regions of interest are grouped as a reference cell region, and based on the rule defining the relationship of the plurality of regions of interest, by comparing the relevance of the plurality of regions of interest, in an image of a sample having a complex structure, it is possible to easily and semi-automatically, in other words, automatically perform the main processing except for some operations such as setting and instruction, and identify and detect abnormal portions.

Description of Reference Numerals

[0110] 1... computer system, 2... charged particle beam device, 3... main body, 5... sample, 150... image signal (observation image), 201... processor, 202... memory.

Claims

1. A computer system for analyzing an observation image of a sample by a charged particle beam device, wherein the observation image includes a plurality of cell regions, and each cell region may include a plurality of regions that are elements constituting the cell region, the computer system, an extraction process of extracting, as a reference image, a first cell region specified by a user or automatically set from the observation image, and extracting, as a similar image, one or more other second cell regions similar to the reference image; a determination process of determining the presence or absence of an abnormality in the cell region of the similar image by comparing the plurality of regions of interest based on a rule that defines the relationship of the plurality of regions of interest included in the reference image with respect to the reference image and the extracted similar image; an output process of outputting to the user the position of each cell region and the presence or absence of an abnormality as a determination result; performs, in the rule, for the plurality of regions of interest included in the cell region of the reference image, the relationship of the luminance between the regions of interest is set, performs a setting process of setting the plurality of regions of interest of the reference image based on a user's operation input or an automatic process, the extraction process is a process of calculating the positional relationship between the regions of interest for the arrangement pattern of the plurality of regions of interest of the reference image, and extracting as similar including the same arrangement pattern and various types of inverted arrangement patterns, the determination process is a process of determining the presence or absence of an abnormality by calculating the luminance relationship between the regions of interest within the cell region for the similar image having various types of arrangement patterns, the output process is a process of outputting the determination result in a manner that can be identified for each type of extracted arrangement pattern, the plurality of regions that are elements constituting the cell region are an arrangement pattern of a plurality of spaced plugs, with respect to the arrangement pattern serving as a reference for the first cell region, it may have a left-right inverted arrangement pattern, an up-down inverted arrangement pattern, or a diagonal inverted arrangement pattern as various types of inverted arrangement patterns, the luminance relationship between the regions of interest within the cell region is the luminance relationship between the plugs of the plurality of plugs, a computer system.

2. In the computer system according to Claim 1, in the rule, for the plurality of regions of interest included in the cell region of the reference image, the relationship of the shape or size between the regions of interest is set, The determination process is a process of determining the presence or absence of an abnormality by calculating the relationship of shape or size in addition to the luminance relationship between the regions of interest within the cell region for the similar images having various arrangement patterns. Computer system.

3. In the computer system according to claim 1, the setting process includes, as the rule, a process of setting a rule that defines the magnitude relationship of luminance between the regions of interest for a plurality of regions of interest within the first cell region of the reference image. Computer system.

4. In the computer system according to claim 1, the setting process includes, as the rule, a process of setting a rule that defines that there is an abnormality when the difference in luminance of the second region of interest with respect to the designated first region of interest is greater than or equal to a threshold value or less than or equal to the threshold value for a plurality of regions of interest within the first cell region of the reference image. Computer system.

5. In the computer system according to claim 1, the setting process includes, as the rule, a process of setting a rule that defines that there is an abnormality when the luminance value of the designated region of interest is outside the designated luminance threshold range or within the luminance threshold range for a plurality of regions of interest within the first cell region of the reference image. Computer system.

6. An analysis method in a computer system for analyzing an observation image of a sample by a charged particle beam device, wherein the observation image includes a plurality of cell regions, and each cell region may include a plurality of regions that are elements constituting the cell region, as steps executed by the computer system, an extraction process step of extracting, as a reference image, a first cell region designated by a user or automatically set from the observation image, and one or more other second cell regions similar to the reference image as similar images; a determination process step of determining the presence or absence of an abnormality in the cell region of the similar image by comparing the plurality of regions of interest based on a rule that defines the relationship of the plurality of regions of interest included in the reference image in the reference image and the extracted similar image; an output process step of outputting to the user the position of each cell region and the presence or absence of an abnormality as a determination result; and having in the rule, the relationship of luminance between the regions of interest is set for the plurality of regions of interest included in the cell region of the reference image. A setting processing step of setting the plurality of regions of interest in the reference image based on a user's operation input or automatic processing; The extraction processing step is a processing step of extracting as similar including the same arrangement pattern and various types of inverted arrangement patterns by calculating the positional relationship between the regions of interest for the arrangement pattern of the plurality of regions of interest in the reference image; The determination processing step is a processing step of determining the presence or absence of an abnormality by calculating the luminance relationship between the regions of interest in the cell region for the similar images having various types of arrangement patterns; The output processing step is a processing step of outputting the determination result in a manner distinguishable for each of the extracted various types of arrangement patterns; The plurality of regions which are elements constituting the cell region are an arrangement pattern of a plurality of spaced plugs; For the arrangement pattern serving as a reference for the first cell region, there may be a left-right inverted arrangement pattern, an up-down inverted arrangement pattern, or a diagonal inverted arrangement pattern as various types of inverted arrangement patterns; The luminance relationship between the regions of interest in the cell region is the luminance relationship between the plugs of the plurality of plugs; Analysis method.

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