Abstract:
A mask inspection system includes irradiation means for irradiating a sample with an electron beam, electron detection means for detecting a quantity of electrons generated from the sample, image processing means, storage means, and control means for calculating the number of divided images, which foam an entire combined image, on the basis of a size of a specified observed area of the sample, determining divided areas in such a way that divided images adjacent to each other overlap with each other, acquiring the divided images of the respective divided areas, and storing the divided images in the storage means, the divided images forming an entire combined image. The control means extracts two divided images adjacent to each other in a predetermined sequence starting from a specified one of the divided images. For each of the two divided images adjacent to each other, the control means then detects an image of a same pattern formation area included in an overlap area, and determines the detected image to be a combination reference image. The control means then combines the two divided images adjacent to each other on the basis of the combination reference image to thereby form an entire SEM image of the observed area.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application is based upon and claims the benefits of priority of the prior Japanese Patent Application No. 2010-091555, filed on Apr. 12, 2010, the entire contents of which are incorporated herein by reference. 
       BACKGROUND OF THE INVENTION 
       [0002]    1. Field of the Invention 
         [0003]    The embodiment discussed herein is related to a mask inspection apparatus having a function to generate a wide field of view and high definition image and also to an image generation method. 
         [0004]    2. Description of the Related Art 
         [0005]    In a lithography process of semiconductor manufacturing processes, a pattern formed on a photomask is exposed to light to be transferred onto a wafer by use of an exposure device. If the pattern formed on the photomask has a defect or distortion, such a defect or distortion causes a failure to transfer the pattern at a desired position, a failure to form the pattern into an accurate shape or the like, i.e., causes a reduction in accuracy of the exposure. In order to prevent such a reduction in accuracy of the exposure, an inspection is conducted to find a positional error and a defect in a photomask. 
         [0006]    As a method of inspecting photomasks, there is an inspection method using an SEM image of a mask captured with a scanning electron microscope. In the scanning electron microscope, a sample is irradiated with incident electrons while the surface of the sample in an electron beam scanning region is scanned by the incident electrons, and secondary electrons emitted from the sample are acquired through a scintillator. Thereafter, the quantity of the acquired electrons is converted into luminance to acquire SEM image data. Then, the SEM image data is displayed on a display. 
         [0007]    For example, an inspection using a line width of a pattern formed on a mask is conducted by the following procedures. A predetermined region of a pattern formed on a photomask is displayed on a display. Then, an electron beam is aimed at and applied onto a measurement point set within the display region. Thereafter, a luminance distribution waveform is acquired on the basis of secondary electrons reflected from the measurement point. Thereafter, pattern edge positions are found by conducting analysis on the luminance distribution waveform to thereby define them as the line width. Whether or not the line width thus found falls within a tolerance range is determined to judge whether the quality of the photomask is good or not. 
         [0008]    In addition, there is a mask inspection method in which a mask and a mask model are compared with a result of a transfer simulation onto a wafer. In this mask inspection method, a simulation to find how a pattern is transferred onto a wafer is performed on the basis of an inspection image obtainable from transmitted light and reflected light using a mask. Then, the result of the simulation is compared with a result of a simulation performed to find how the pattern is transferred onto the wafer with a correct mask. It results in inspecting whether or not a defect exists in the pattern on the mask, and so on. This transfer simulation requires a field of view of approximately 10 micron, and the mask model and an SEM image are compared to inspect whether or not a defect exists in the pattern formed on the mask, and so on. The pattern of the entire photomask is reflected to this mask model. Thus, the SEM image for comparison with the mask model is required to be a wide field of view, as well. 
         [0009]    In the mask inspection apparatus using the aforementioned scanning electron microscope or the like, however, highly accurate measurement is required. For this reason, an SEM image is acquired by using a limited, narrow field of view with a high magnification in general. Moreover, in a case of a normal length measurement SEM, scanning with a wide field of view causes aberrations such as astigmatism, field curvature and distortion, and therefore requires dynamic adjustment of such aberrations in linkage with the scanning. Therefore, this inspection not only causes a significant load for correction, but also results in a situation where the aberrations are not sufficiently corrected. 
         [0010]    In this respect, Japanese Laid-open Patent Publication No. 2000-294183 (hereinafter, referred to as Patent Document 1) describes a technique to take a wide field patched photograph of a sample with an SEM while automatically driving a sample stage at the time of capturing divided SEM images of the sample. 
         [0011]    As described above, in order to acquire a wide field SEM image, SEM images are captured in a divided manner and the captured SEM images are patched together. 
         [0012]    However, with the technique described in Patent Document 1, when moving the sample stage to a divided area, there is no guarantee of being able to move it to the correct position. Thus, even when patched images are captured, there is no guarantee that the images are combined into a single wide field image. 
         [0013]    In addition, when the SEM images acquired in a divided manner are to be patched together, the operator detects target images for joining two areas together, and then combines the two areas in such a way that the two target images are connected to each other. As described, generation of a high definition SEM image takes some effort. 
       SUMMARY OF THE INVENTION 
       [0014]    The present invention has an object to provide a mask inspection apparatus and an image generation method by which an SEM image with wide field of view and high definition can be easily created at a high speed. 
         [0015]    The above problem is solved by a mask inspection apparatus including irradiation means for irradiating a sample with an electron beam, electron detection means for detecting a quantity of electrons generated from the sample having a pattern formed thereon by the irradiation with the electron beam, image processing means for generating image data of the pattern on the basis of the quantity of the electrons, storage means for storing therein the image data, and control means for calculating the number of divided images forming an entire combined image on the basis of a size of a specified observed area of the sample, determining divided areas in such a way that the divided images adjacent to each other overlap with each other, acquiring the divided images of the respective divided areas, and storing the divided images in the storage means. And in the mask inspection apparatus, the control means extracts the divided images adjacent to each other in a predetermined sequence starting from a specified one of the divided images of the respective divided areas stored in the storage unit, detecting, for each two of the divided images adjacent to each other, an image of a common pattern formation area included in an overlap area between the divided images, determining the detected image to be a combination reference image, and combining the two of the divided images adjacent to each other on the basis of the combination reference image to thereby form an entire SEM image of the observed area. 
         [0016]    In the mask inspection apparatus according to this aspect, from the overlap area of the two divided images adjacent to each other, the control means may detect an image of an area having image information equivalent to image information of an area specified in the specified one of the divided images, the control means may measure coordinate data of a periphery of the pattern formation area in each of the adjacent divided images before combining the divided images, the control means may correct the coordinate data of the periphery of the pattern formation area included in each of the two divided images adjacent to each other on the basis of coordinate data of the combination reference image when combining the two divided images adjacent to each other, and, when a plurality of pattern formation areas exist in each of the divided areas, and two divided images adjacent to each other are defined as a divided image A and a divided image B to be combined with the divided image A, the control means may set an image of the pattern formation area as the combination reference image, the pattern formation area lying over a frame of the divided image A on a side adjacent to the divided image B. 
         [0017]    A different form of the present invention provides an image generation method implemented in the mask inspection apparatus according to the above-described form. The image generation method according to that different form includes the steps of calculating the number of divided images, which foams an entire combined image, on the basis of a size of a specified observed area of the sample, and determining divided areas in such a way that divided images adjacent to each other overlap with each other, acquiring the divided images of the respective divided areas, extracting one specified divided image from the divided images of the respective divided areas, extracting two divided images adjacent to each other in a predetermined sequence starting from the extracted divided image, for each of the extracted two divided images adjacent to each other, determining a combination reference image by detecting an image of a same pattern formation area included in an overlap area between the adjacent divided images, combining the two divided images adjacent to each other on the basis of the combination reference image, and generating an entire SEM image. 
         [0018]    In the image generation method according to this form, in the step of determining the combination reference image, an image of an area having image information equivalent to image information of an area specified in the specified one of the divided images may be detected from the overlap area of the two divided images adjacent to each other and then be set to be the combination reference image, a step of measuring coordinate data of a periphery of a pattern formation area in each of the adjacent divided images before the step of combining the divided images may be further included, and the step of combining the divided images may include a step of, when a plurality of pattern formation areas exist in each of the divided areas, and the two divided images adjacent to each other are defined as a divided image A and a divided image B to be combined with the divided image A, setting an image of the pattern formation area as the combination reference image, the pattern formation area lying over a frame of the divided image A on a side adjacent to the divided image B. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0019]      FIG. 1  is a configuration diagram of a scanning electron microscope used in an embodiment of the present invention. 
           [0020]      FIGS. 2A to 2D  are diagrams for describing an electron image and profiles acquired by a signal processor. 
           [0021]      FIGS. 3A to 3C  are diagrams for describing a concept of a method of acquiring a SEM image with wide field of view and high accuracy. 
           [0022]      FIGS. 4A and 4B  are diagrams for describing division for acquiring a SEM image with wide field of view and high accuracy. 
           [0023]      FIGS. 5A to 5D  are diagrams for describing combination of divided images with high accuracy into a SEM image with wide field of view and high accuracy. 
           [0024]      FIG. 6  is a flowchart illustrating an example of image acquisition processing to acquire a SEM image with wide field of view and high accuracy. 
           [0025]      FIGS. 7A to 7C  are diagrams (part  1 ) for describing processing to combine divided images. 
           [0026]      FIGS. 8A to 8C  are diagrams (part  2 ) for describing the processing to combine divided images. 
           [0027]      FIG. 9  is a diagram for describing processing to combine divided images when no pattern exists in an overlap area. 
           [0028]      FIG. 10  is a diagram for describing a method of measuring edges of a pattern. 
           [0029]      FIG. 11  is a flowchart illustrating an example of processing to detect edge positions of a periphery of a pattern. 
           [0030]      FIGS. 12A to 12D  are diagrams for describing a method of detecting edge positions of a periphery of a pattern. 
           [0031]      FIG. 13  is a flowchart illustrating an example of image acquisition processing to acquire a SEM image with wide field of view and high accuracy when a plurality of patterns exist in a divided image. 
           [0032]      FIGS. 14A to 14D  are diagrams for describing processing to acquire a SEM image with wide field of view and high accuracy when a plurality of patterns exist in a divided image. 
       
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENT 
       [0033]    An embodiment of the present invention is described below with reference to the drawings. 
         [0034]    First, a configuration of a scanning electron microscope used as a mask inspection apparatus is described. Next, measurement of a pattern size using an SEM image in general is described. Then, acquisition of SEM image with wide field of view and high accuracy is described. 
         [0035]    (Configuration of Scanning Electron Microscope) 
         [0036]      FIG. 1  is a configuration diagram of a scanning electron microscope according to this embodiment. 
         [0037]    The scanning electron microscope  100  mainly includes an electron scanning unit  10 , a signal processor  30 , a display unit  40 , a storage unit  55 , and a controller  20  configured to control each of the electron scanning unit  10 , the signal processor  30 , the display unit  40  and the storage unit  55 . The controller  20  has a profile creation unit  21 , a differential profile creation unit  22  and an edge detector  23 . 
         [0038]    The electron scanning unit  10  has an electron gun  1 , a condenser lens  2 , a deflection coil  3 , an objective lens  4 , a movable stage  5  and a sample holder  6 . 
         [0039]    A sample  7  on the movable stage  5  is irradiated with charged particles  9  emitted from the electron gun  1  through the condenser lens  2 , the deflection coil  3  and the objective lens  4 . 
         [0040]    The sample  7  is irradiated with the charged particles  9  (primary electron beam) while being scanned in two dimensions. As a result, secondary electrons are emitted from that irradiated portion and detected by an electron detector  8  configured of a scintillator or the like. The quantity of the secondary electrons thus detected is converted into a digital quantity by an AD converter of the signal processor  30  and then stored in the storage unit  55  as image data. The image data is converted into luminance signals and then displayed on the display unit  40 . The image data is arranged on a two dimensional array so as to be arranged at the same position as the scanning position of the primary electron beam on the sample  7 . In this manner, a two-dimensional digital image is obtained. Each pixel of the two-dimensional digital image expresses luminance data with 8-bit information. 
         [0041]    In addition, the signal processor  30  functions as an image processor to process the image data and performs processing to combine SEM images acquired in divided areas as will be described later. 
         [0042]    The controller  20  controls the electron-deflection amount of the deflection coil  3  and the image-scanning amount of the display unit  40 . In addition, the controller  20  stores therein a program relating to execution of edge detection of a pattern and combination processing for a SEM image with wide field of view. 
         [0043]    The profile creation unit  21  creates line profiles showing luminance signals of SEM image data in a specified region. Each line profile shows a luminance signal corresponding to the quantity of the secondary electrons. 
         [0044]    The differential profile creation unit  22  subjects the line profile to primary differential processing to create a primary differential profile. 
         [0045]    The edge detector  23  detects edges of a pattern from the line profile and the primary differential profile. 
         [0046]    (Measurement of Pattern Size Using SEM Image in General) 
         [0047]    Next, a description is given of measurement of a pattern size using an SEM image. The measurement is carried out using the scanning electron microscope  100  illustrated in  FIG. 1 , and includes the edge detection of a pattern in a sample illustrated in  FIG. 2A . 
         [0048]    The target is the sample  7  in which a wiring pattern  51  is formed on a photomask substrate  50  as illustrated in  FIG. 2A . A part of the sample  7  includes a planer surface as illustrated in  FIG. 2A . Here, the portion surrounded by a broken line  52  shows an observed area of the scanning electron microscope  100 . 
         [0049]      FIG. 2B  illustrates an example of an SEM image displayed. The SEM image displayed is acquired by scanning the sample with the electron beam, detecting the quantity of emitted secondary electrons or the like by the electron detector  8 , converting the detected quantity of the electrons into luminance signals, and displaying the SEM image while synchronizing electron beam scanning and CRT scanning of the display (display unit  40 ). 
         [0050]    A length measurement area is specified on the SEM image illustrated in  FIG. 2B , and a corresponding SEM image is extracted therefrom. The length measurement area is set to be an area having a width H equal to  400  pixels and a length L, for example. The operator selects this area by an upper line marker LM 1 , a lower line marker LM 2 , a left line marker LM 3  and a right line marker LM 4 . 
         [0051]    The extracted SEM image pixel data is divided into areas with respect to the direction H of the length measurement area, and a line profile corresponding to luminance distribution is found for each of the divided areas. Note that, when the line profile is to be found, it is possible to reduce noise components by performing smoothing processing in the length L direction with a three-pixel width, for example. 
         [0052]      FIG. 2C  is a diagram illustrating a line profile corresponding to the quantity of the secondary electrons emitted from the sample, which can be acquired upon irradiation with an electron beam along the I-I line of  FIG. 2A . As illustrated in  FIG. 2C , the line profile (contrast profile) drastically changes at the edge portions of the pattern. In order to find the position where the profile drastically changes, the line profile is differentiated to find the maximum peak and the minimum peak of the amount of differential signal. 
         [0053]    Furthermore, as illustrated in  FIG. 2D , differential waveforms C 1  and C 2  are found by pixel interpolation based on a plurality of differential signals Dx around peaks. Then, the peak positions of a first peak P 1  and a second peak P 2  are calculated with 1/100 resolution. A line width W 1  of the line pattern is found as a distance between the first peak P 1  and the second peak P 2 . 
         [0054]    The aforementioned processing is performed for each of the divided areas. Then, the average value of the widths of the pattern calculated for the respective areas is defined as a length measurement value. In this manner, a more accurate width W 1  of the line pattern can be found. 
         [0055]    (Acquisition of an SEM Image with Wide Field of View and High Accuracy) 
         [0056]      FIGS. 3A to 3C  are diagrams for describing a concept of a method of acquiring an SEM image with wide field of view and high accuracy.  FIG. 3A  illustrates an example in which a desired area  31  is specified from a pattern formed on a sample. In order to acquire an SEM image of the entire specified area  31 , there are a case of capturing the entire SEM image at once, and the other case of first dividing the specified area into several areas, capturing SEM images of the divided areas, and then combining them to acquire an SEM image of the entire area. 
         [0057]    In a case where an SEM image of the entire specified area is acquired at once, the SEM image can be acquired in a short period of time. However, if the specified area is a wide area, aberrations increase as the area becomes distant from the optical axis. Accordingly, the accuracy of the acquired SEM image is degraded. 
         [0058]    When a scanning electron microscope is used as a mask inspection apparatus, it is capable of checking whether or not a pattern formed as a mask has a defect such as discontinuity by use of the acquired SEM image. However, in case of conducting a highly accurate inspection such as an inspection based on comparison with a pattern model by a mask simulation, highly accurate acquisition of an SEM image is required. For this reason, in this embodiment, in order to allow highly accurate acquisition of an SEM image with wide field of view, a specified area is divided into areas where SEM images with high accuracy can be acquired, and then, the divided SEM images of the respective divided areas are combined to acquire an SEM image with wide field of view. 
         [0059]    In  FIG. 3A , the specified area  31  is divided into  16  areas ( 31   a,    31   b  and so forth) in order that an SEM image with high accuracy can be acquired in each of the divided areas. Actually, as illustrated in  FIG. 3B , the specified area  31  is divided in such a way that divided areas adjacent to each other have an overlap area where the adjacent divided areas overlap with each other. Then, the SEM images with high accuracy, which are acquired in the respective divided areas, are subjected to alignment processing using coordinate data of the divided areas and edge information of a pattern existing in the overlap areas. Thus, the divided SEM images are combined to acquire an SEM image with wide field of view and high accuracy as illustrated in  FIG. 3C . 
         [0060]      FIGS. 4A and 4B  are diagrams illustrating an SEM image of a sample in which a pattern is formed.  FIG. 4A  is a wide field SEM image with a low magnification.  FIG. 4B  is a diagram illustrating a division method. 
         [0061]      FIG. 4A  illustrates an image on which the entire desired area  41  is photographed in a wide field range  43  with a magnification of 10K. In the desired area  41 , there are indicated an image  42   a  of a pattern formation area and images  42   b  of pattern formation areas existing around the image  42   a  while being apart from the image  42   a.  The desired area  41  is an area of 10×10 μm, for example. 
         [0062]    The number of divided areas is calculated for the desired area  41  in  FIG. 4A , and SEM images with high accuracy are acquired for the respective divided areas. The number of divided areas is determined in accordance with a predetermined size that allows each image to be captured highly accurately. For example, in  FIG. 4B , the size that allows each image to be captured highly accurately is set to 2.5×2.5 μm, and the desired area is divided into 16 areas. For the divided areas, the images are captured with a magnification of 50K from a divided area  44   a  to a divided area  44   p  in a predetermined sequence (sequence indicated by the arrow of  FIG. 4B ). In this image capturing, the images are captured with an image capturing range  45  being set larger than each of the divided areas so that adjacent divided areas can overlap with each other. 
         [0063]      FIGS. 5A to 5D  are diagrams illustrating an overview of the processing to acquire an SEM image with wide field of view and high accuracy.  FIG. 5A  is a wide field SEM image acquired with a low magnification. The images  42   a  and  42   b  of pattern formation areas are illustrated in the desired area  41 . When a point in the pattern  42   a  of the SEM image is specified, a divided SEM image acquired with a high magnification and corresponding to this point is extracted. 
         [0064]      FIG. 5B  is a diagram illustrating the images of the divided areas  44   a  to  44   p  individually. By detecting where the same coordinate data as the coordinate data of the specified point of  FIG. 5A  is included, the corresponding specified point is found. It is supposed that the specified point is included in a pattern image  56  of the divided area  44   g  of  FIG. 5B . In the pattern image  56  of the divided area  44   g,  the specified point is determined to be a pattern formation area (non-transparent area) on the basis of a relationship corresponding to the wide field SEM image. 
         [0065]    The combination processing is performed on this divided image and divided images adjacent to this divided image. The combination processing is performed in accordance with a predetermined sequence. For example, the combination processing to combine the divided area  44   g  and the divided area  44   f  adjacent on the right is performed first. Next, the combination processing to combine the divided area  44   f  and the divided area  44   k  positioned below the divided area  44   f  is performed. The combination processing is repeated in this manner for each two adjacent divided areas in such a way that the divided area  44   g  including the initial specified point is surrounded, thus combining all the divided areas. 
         [0066]    When a pattern image  57  of the divided area  44   f  is determined to be in the same pattern formation area as that of the pattern image  56  of the divided area  44   g,  both of the divided areas are combined on the basis of coordinate information of the pattern images  56  and  57 . This processing is performed on the divided images in a predetermined sequence. The coordinates of the periphery of each of the pattern formation areas are corrected as illustrated in  FIG. 5D  to combine the divided images into an SEM image with wide field of view and high accuracy. 
         [0067]    Here, detection of the coordinates of the periphery of the pattern enables to distinguish the pattern formation area including the specified point from the outside portion of the area, and thereby to detect a pattern formation area of the same type (non-transparent area or transparent area) as the pattern formation area including the specified point in the overlap area between the divided areas. 
         [0068]    Next, the processing to acquire an SEM image with wide field of view and high accuracy is described with reference to  FIGS. 6 to 9 .  FIG. 6  is a flowchart illustrating an example of the image acquisition processing to acquire an SEM image with wide field of view and high accuracy.  FIGS. 7A to 9  are diagrams for describing the processing to combine the divided areas.  FIGS. 7A to 7C  and  FIGS. 8A to 8C  illustrate a portion including the divided areas  44   g,    44   f,    44   k  and  44   j  of  FIG. 5B . The divided areas  44   g,    44   f,    44   k  and  44   j  correspond to divided areas DA 1 , DA 2 , DA 3  and DA 4 , respectively, and overlap areas existing between adjacent divided areas are shown as well.  FIG. 9  further illustrates divided areas DA 5  and DA 6  corresponding to the divided areas  44   h  and  44   i  of  FIG. 5B , respectively. 
         [0069]    Here, the following assumptions are made in the image acquisition processing of  FIG. 6 . The center X and Y coordinate values and the field of view for the target wide field SEM image are already specified. Moreover, the required number of divided areas is already calculated on the basis of the size of the wide field area, and the size of the area which enables to acquire a highly accurate SEM image. In addition, the SEM image with a low magnification in accordance with the field of view and the divided SEM images with a high magnification in the respective divided areas are already acquired. Furthermore, the sequence to combine the divided images is previously defined. 
         [0070]    First, the initial setting is made in step S 11 . In this initial setting, the number of divided images HM-SEM is set to D, and the counter of the divided areas for the sequence to combine the divided images HM-SEM is set to C, and then, C is set to 1. 
         [0071]    Next, in step S 12 , the coordinate values of a specified point of the SEM image with a low magnification are acquired. 
         [0072]    In step S 13 , the type of the specified position is set. This type specifies whether the area including the specified position belongs to the non-transparent area in which the pattern is formed and which is displayed in black as an SEM image, or to the transparent area in which no pattern is formed and which is displayed in a lighter color than that of the non-transparent area as an SEM image. 
         [0073]    In step S 14 , a divided image HM-SEM(C) including the specified position is extracted. Since the divided images are already acquired and stored in the storage unit  55 , the corresponding divided image is extracted from the storage unit  55 . 
         [0074]    In step S 15 , the coordinates of the periphery of a pattern including the specified position of the divided image HM-SEM(C) (referred to as a base pattern) are calculated. The coordinates of the periphery of the image  42   a  of the pattern formation area existing in each of the divided areas DA 1  to DA 4  are calculated. The extraction of the coordinates (edges) of the periphery of the pattern will be described later in detail. 
         [0075]    In step S 16 , an SEM image of a pattern of the same type as the base pattern in the overlap area between the divided image HM-SEM(C) and a divided image HM-SEM(C+1) adjacent thereto is detected. 
         [0076]    In step S 17 , whether this SEM image exists or not is determined. If such an SEM image exists, the processing moves to step S 18 , and if not, the processing moves to step S 20 . 
         [0077]    In step S 18 , the coordinates of the periphery of the pattern in the divided image HM-SEM(C+1) are calculated. 
         [0078]    In  FIG. 7B , the divided area DA 1  and the divided area DA 2  adjacent thereto are the targets for the combination processing. An overlap area  73  exists between a pattern area  71  in the divided area DA 1  and a pattern area  72  in the divided area DA 2 . Thus, the coordinates of the periphery of the pattern  72  in the divided area DA 2  are calculated. 
         [0079]    In step S 19 , the coordinate values of the periphery of the pattern are corrected. Since the pattern area  71  in the divided area DA 1  and the pattern area  72  in the divided area DA 2  are determined to be in the same pattern area, the coordinate data pieces are corrected in such a way that the coordinate data pieces for the overlapping sides of the overlap area  73  coincide with each other. As a result, the coordinates of the periphery of the combined pattern are updated as illustrated with a periphery  74  of  FIG. 7C . 
         [0080]      FIG. 8A  is a diagram for describing the combination processing to combine the divided area DA 2  and the divided area DA 3 .  FIG. 8A  illustrates a case where an overlap area  82  exists between the pattern area  72  in the divided area DA 2  and a pattern area  81  in the divided area DA 3 , and the pattern area  72  and the pattern area  81  are determined to be in the same pattern area. In this case, as in the case of  FIG. 7C , the coordinate data pieces are corrected in such a way that the coordinate data pieces for the overlapping sides of the pattern area  82  coincide with each other. As a result, the coordinates of the periphery of the combined pattern are updated as illustrated with a periphery  83  of  FIG. 8B . 
         [0081]      FIG. 8C  illustrates a situation where the combination processing for the divided area DA 1  to the divided area DA 4  is completed, and the coordinates of a periphery  84  of the pattern  42   a  are updated. 
         [0082]      FIG. 9  illustrates a situation where a pattern of the same type as the base pattern does not exist in an overlap area. Although a pattern area  91  exists in the divided area DA 4  and a pattern area  92  exists in the divided area DA 6 , no pattern area exists in an overlap area  93 . In this case, the two adjacent divided areas are combined on the basis of the coordinate data of the divided areas. 
         [0083]    In step S 20 , whether or not the aforementioned processing is performed for all of the divided images is determined. If the processing is not yet performed for all of the divided images, the counter C is incremented by 1 in step S 21 , and then, the aforementioned processing is performed for the next adjacent divided image. If the processing is performed for all of the divided images, the image acquisition processing for an SEM image with wide field of view and high accuracy is terminated. 
         [0084]    With the image combination processing described above, an SEM image of a mask in a specified region is outputted in a highly accurate manner even when the SEM image is a wide field image. 
         [0085]    Hereinafter, a description is given of the calculation of the coordinates of the periphery of a pattern, which is performed in steps S 15  and S 18 . Here, edge detection processing for the periphery of a pattern (calculation of the coordinates of a periphery) is described with reference to  FIG. 11  and  FIGS. 12A to 12D  while a pattern having a shape illustrated in  FIG. 10  is used as an example.  FIG. 11  is a flowchart illustrating an example of the edge detection processing for the periphery of a pattern. In addition,  FIGS. 12A to 12D  are diagrams for describing the edge detection for the periphery of a pattern. Here, it is supposed that a start position ES to detect edge positions of the periphery of a pattern is determined in advance. 
         [0086]    First, the initial setting is made in step S 31  of  FIG. 11 . In the initial setting, a predetermined interval for detecting edges of the periphery of a pattern is specified (hereinafter, referred to as a specified step). For example, the specified step is set to a distance corresponding to a predetermined number of pixels. In addition, a counter k indicating the position of a detection edge of the periphery of the pattern is set to 0. 
         [0087]    In steps S 32  to S 34 , an edge position located apart from the start position ES by a predetermined specified step d is detected. 
         [0088]    In step S 32 , a temporary edge is detected at a position apart from the start position ES by a distance (specified step d×2). Specifically, as illustrated in  FIG. 12A , a line profile is created using a line HL as the reference line for creating the line profile, and an edge E 11  is detected. Here, the line HL orthogonally crosses a straight line VL at a position apart from the start position ES by a distance (specified step d×2), and the straight line VL extends downward (−Y direction) in  FIG. 12A . The detected edge E 11  is termed as a temporary detection edge E 11 . Note that, in exchange for detecting the edge in −Y direction from the start position ES in  FIG. 12A , the edge may be detected in X direction from the start position ES depending on the shape of the pattern. 
         [0089]    In step S 33 , the temporary detection edge E 11  detected in step S 32  is redetected. The start position ES and the temporary detection edge position E 11  are connected each other with a straight line, followed by finding a position apart from the start position ES by the distance (specified step d×2) on the straight line. A line orthogonally crossing the straight line at the position is set as the reference line for creating a profile. Then, a line profile on the reference line is found, and the temporary detection edge position is redetected. By the redetection of this temporary detection edge position, the distance from the start position ES is made closer to the distance (specified step d×2). 
         [0090]    Next, in step S 34 , a first edge position is detected. The start position ES and the redetected temporary detection edge position E 12  are connected each other with a straight line IL 1 . Then, a line profile is found on a line orthogonally crossing the straight line IL 1  at an intermediate position MP 1 , and an edge EP k  (x k , y k ) is detected. In  FIG. 12B , an edge EP 1  is detected as the first edge. Detection of the edge EP k  (x k , y k ) as described above enables to detect the edge on the line nearly perpendicular to the periphery of the pattern. Thus, the edge position can be accurately detected. 
         [0091]    In step S 35 , the edge EP k  (x k , y k ) is set to the starting point for the next edge detection. In  FIG. 12C , the edge EP 1  is set to the starting point. 
         [0092]    From step S 36  to step S 38 , an edge position EP k+1  (x k+1 , y k+1 ) apart from the starting edge position EP k  (x k , y k ) by a specified step is detected. 
         [0093]    In step S 36 , the starting point EP 1  and the redetected temporary detection edge E 12  are connected each other with a straight line IL 2 , followed by finding a position apart from the starting point EP 1  by the distance (specifying step d×2) on the straight line IL 2 . A line orthogonally crossing a straight line IL 2  at the position is set as the reference line for creating the profile. Then, a line profile is created on the basis of the reference line and an edge is detected. Here, the edge detected here is termed as a temporary detection edge E 21 . 
         [0094]    In step S 37 , in the same manner as step S 34 , the starting point EP 1  and the temporary detection edge E 21  are connected each other with a straight line, followed by finding a position apart from the starting point EP 1  by the distance (specifying step d×2) on the straight line. A line orthogonally crossing a straight line IL 2  at the position is set as the reference line for creating the profile. Then, the line profile on the reference line is found, and the temporary detection edge position EP 22  is redetected. 
         [0095]    Next, in step S 38 , the starting point EP 1  and the redetected temporary detection edge EP 22  are connected each other with a straight line IL 3 . Then, a line profile is found on a line orthogonally crossing the straight line IL 3  at an intermediate position MP 2 , and the edge EP k+1  is detected. In  FIG. 12D , the edge EP 2  is detected as the second edge. 
         [0096]    In step S 39 , it is determined whether or not all of the edges on the periphery of the pattern are detected. If it is determined that all of the edges are detected, the processing is terminated. If it is determined that all of the edges are not yet detected, the processing moves to step S 40 . 
         [0097]    In step S 40 , k=k+1 is set to move to step S 35 , and the next edge position is detected. 
         [0098]    By the aforementioned processing, the edge positions of the periphery of the pattern are detected in the order of the EP 0 , EP 1 , . . . as illustrated in  FIG. 10 . When the edges on the periphery of the pattern are detected in the manner described above, the detected edge position and a temporary edge position apart from the detected edge position by a predetermined interval are connected with a straight line, followed by finding a line orthogonally crossing the straight line at the intermediate position, and then the next edge position is detected from the line profile on the line. It enables to detect the edge on the line nearly perpendicular to the periphery of the pattern, thereby to detect the accurate edge positions. 
         [0099]    Next, description is perform for the processing to acquire an SEM image with wide field of view and high accuracy in a case where a plurality of pattern formation areas exist in a divided area with reference to  FIG. 13  and  FIGS. 14A to 14D . 
         [0100]    Here, the following assumptions are made in the image acquisition processing of  FIG. 13 . The center coordinate values and the field of view of the target wide field SEM image are already specified. Moreover, the required number of divided areas is already calculated on the basis of the size of the wide field area, and the size of the area which enables highly accurate acquisition of an SEM image. In addition, the SEM image with a low magnification in accordance with the field of view and the divided SEM images with a high magnification in the respective divided areas are already acquired and stored in the storage unit  55 . Furthermore, the sequence to combine the divided images is previously defined. 
         [0101]    First, the initial setting is made in step S 51  of  FIG. 13 . In this initial setting, the number of divided images HM-SEM is set to D 1 , and the counter for the sequence to combine the divided images HM-SEM is set to C 1 , and then, C 1  is set to 1. 
         [0102]    In step S 52 , the edges of patterns are detected. This edge detection is performed for the patterns as target existing in all of the divided areas. The edges of the patterns are detected for each of the divided areas from an acquired SEM image. In this edge detection, edge detection to distinguish between an area where a pattern is formed and an area where no pattern is formed is performed by binarizing the SEM image and then detecting a pixel having a discontinuous value, for example. Then, the precise edge detection as described in  FIG. 10  to  FIG. 12D  is performed on the detected pattern formation areas. 
         [0103]    In step S 53 , one HM-SEM (C 1 ) is extracted from the storage unit  55 .  FIG. 14A  illustrates a divided area DA 15  extracted from among divided areas DA 11  to DA 19  obtained by dividing an area into nine pieces. In the divided area DA 15 , pattern formation areas PR 1  to PR 5  are illustrated. 
         [0104]    In step S 54 , a pattern lying over an image frame is extracted. In  FIG. 14A , it is supposed that the sequence of the combination processing is defined as DA 15 →DA 14 →DA 11 →DA 12 →DA 13 →DA 16 →DA 19 →DA 18 →DA 17 . In this case, a pattern PR 2  is detected as a pattern lying over the image frame of the divided area DA 15  on the side adjacent to the divided area DA 14  which is to be combined with the divided area DA 15 . 
         [0105]    In step S 55 , whether or not a pattern exists in HM-SEM (C 1 +1) is determined. In the case of  FIG. 14A , whether or not a pattern corresponding to the pattern PR 2  exists in the divided area DA 14  is detected. If such a pattern exists, the processing moves to step S 57 , and if no such pattern exists, the processing moves to step S 56 . 
         [0106]    In step S 56 , whether or not another pattern lying over the image frame is determined. If a pattern lying over the image frame exists, the processing moves to step S 60  and this pattern is extracted. Then, the processing continues to the processing of step S 55 . If a pattern lying over the image frame does not exist, the processing moves to step S 57 . 
         [0107]    In step S 57 , the combination processing for the divided images is performed. This combination processing is performed in the same manner as the processing from steps S 16  to S 19  of  FIG. 6 . 
         [0108]      FIG. 14B  illustrates a result of performing the combination processing to combine the divided area DA 15  and the divided area DA 14 . The pattern PR 2  in the divided area DA 15  and a pattern PR 7  in the divided area DA 14  are determined to belong to the same pattern, and thus, the divided area DA 15  and the divided area DA 14  are combined. In this combination processing, the coordinates of the peripheries of the pattern PR 2  and the pattern PR 7  are updated. In addition, the type of the pattern is determined to be different between the inside and outside of the pattern PR 7 . Then, the type of the pattern in the outside of the pattern PR 7  is determined to be different from the type of a pattern PR 6  and a pattern PR 8 . As a result, the pattern PR 6 , the pattern PR 7  and the pattern PR 8  are all determined to be the same type. 
         [0109]      FIG. 14C  illustrates a state where the entire combined image is acquired by performing the same processing as the processing described above, and the processing to detect all the patterns in each of the divided areas and to adjust the coordinate values. 
         [0110]    In step S 58 , whether or not the aforementioned processing is performed for all of the divided areas is determined. If the processing is not yet performed for all of the divided areas, the counter C 1  is incremented by 1 in step S 59 , and the aforementioned processing is performed for the next adjacent divided area. If the processing is performed for all of the divided areas, the image acquisition processing is terminated. 
         [0111]    Note that  FIG. 14D  illustrates the handing of a case where no pattern exists in the overlap area of the adjacent divided areas. If no pattern exists in the overlap area, a divided area is further set so as to include a pattern, and the image thereof is acquired. The divided areas are connected together on the basis of this image, and the coordinate information of the original isolated pattern is also updated. 
         [0112]    As described above, in this embodiment, the specified observed area of the sample is automatically divided into divided areas in such a way that adjacent divided areas overlap with each other, and then, SEM images with high accuracy are acquired in the respective divided areas. When the divided areas are to be combined, the image of the divided area corresponding to the one specified point of a pattern in the wide field SEM image is extracted. Then, adjacent divided areas are automatically combined in a predetermined sequence to acquire a wide field SEM image of the specified area. 
         [0113]    In this manner, the specified wide area is divided into the narrow areas, and then, the SEM images thereof are acquired. Thus, SEM images with high accuracy are acquired. In addition, the coordinate positions are corrected by using the coordinate information of the divided areas and edge information of the pattern between the divided areas. Thus, the SEM images can be combined in a highly accurate manner. Moreover, by detecting a pattern involved with combination of divided areas due to the specifying of one point of a pattern in the wide field SEM image alone, and then automatically combining the divided areas, it is possible to acquire an SEM image with wide field of view and high accuracy at a high speed. 
         [0114]    Note that it is also possible to conduct a defect inspection of a mask pattern in the following manner. Specifically, general data stream (GDS) data is generated from the aforementioned wide field SEM image data. Then, the GDS data is fed back to a mask design simulator and thereafter compared with design data.