Abstract:
Cases in which defects are analyzed in a manufacturing process stage in which a pattern is not formed or in a manufacturing process in which a pattern formed on a lower layer does not appear in the captured image are increasing. However, in these cases, there is a problem of not being able to synthesize a favorable reference image and failing to detect a defect when a periodic pattern cannot be recognized in the pattern. In the present invention, a defect occupation rate, which is the percentage of an image being inspected occupied by a defect region, is found, it is determined whether the defect occupation rate is higher or lower than a threshold, and, in accordance with the determination results, it is determined whether to create, as the reference image, an image comprising pixels having the average luminance value of the luminance values of a plurality of pixels contained in the image being inspected. In particular, when the defect occupation rate is low, an image comprising pixels having the average luminance value of the luminance values of a plurality of pixels contained in the image being inspected is used as the reference image.

Description:
TECHNICAL FIELD 
       [0001]    The present invention relates to a defect observation method and a defect observation device in a semiconductor manufacturing process. 
       BACKGROUND ART 
       [0002]    In a semiconductor manufacturing process, it is important to discover a defect occurring in the manufacturing process early and take measures, to secure a high yield. 
         [0003]    A scanning electron microscope (SEM)-type defect observation device (also called a defect review device) is a device to observe a defect occurred in the semiconductor manufacturing process, particularly. The SEM-type defect observation device is generally a device to observe an image of the defect coordinates detected by an upper defect inspection device with definition higher than definition in the upper defect inspection device. Specifically, the SEM-type defect observation device moves a sample stage to the defect coordinates output by the upper defect inspection device, executes imaging with a low magnification where a defect to be observed enters a view, detects the defect coordinates from an imaged low-magnification image, moves the sample stage such that the defect is positioned at the center of the view or moves the center of the imaging, and acquires a high-magnification image for observation with the high magnification suitable for the defect observation. 
         [0004]    As such, the defect coordinates are detected from the low-magnification image, because error is included in the defect coordinates output by the upper defect inspection device, in a range of a device specification, and processing for correcting the error is necessary when the high-definition defect image is acquired by the SEM-type defect observation device. 
         [0005]    Automatic defect review or automatic defect redetection (ADR) automates a process for acquiring the high-definition defect image. In the ADR, an acquisition condition of the low-magnification image to detect the defect or an acquisition condition of the high-magnification image to observe the defect should be optimized while a balance of the defect detection rate and the throughput of the ADR is considered, according to coordinate precision of defect detection in the upper defect inspection device, a characteristic of a sample, or a kind of the defect to be observed. 
         [0006]    Patent Literature 1 describes “acquiring an image including a defect with a first magnification using a scanning electron microscope, generating a reference image from the acquired image including the defect with the first magnification, comparing the acquired image including the defect with the first magnification and the reference image generated from the image including the defect with the first magnification to detect the defect, and imaging the detected defect with a second magnification larger than the first magnification. According to this, “because a process for imaging the reference image with the low magnification can be omitted, the defect can be reviewed more efficiently” is described. 
       CITATION LIST 
     Patent Literature 
       [0000]    
       
         Patent Literature 1: JP-A-2007-40910 (US 2007/0031026 A) 
       
     
       SUMMARY OF INVENTION 
     Technical Problem 
       [0008]    Recently, with miniaturization of a design pattern and complexity of a manufacturing process, defects affecting the yield diversify and manufacturing processes to be observation targets increase. Particularly, a case in which a minute detect not brought into question in the past also becomes an observation target and in a manufacturing process of a step in which a pattern is not formed or a manufacturing process in which a pattern formed on a lower layer does not appear in an imaging image, erroneous detection of a false defect is accepted to some extent, a supersensitive defect inspection is executed, defect candidates detected including the false defect are observed by a defect observation device, and a real defect is analyzed increases. 
         [0009]    However, for a combination of a reference image in the above case, when a periodic pattern cannot be recognized in a defect image, a superior reference image cannot be combined and defect detection is not successfully executed, in the method that combines a reference image not including the defect, using periodicity of the pattern manufactured from the defect image including the defect, as in Patent Literature 1. 
         [0010]    For this reason, there is a need to develop a method of generating a reference image to be suitable for the case in which a pattern does not exist in a view of an image and a defect observation system capable of stably realizing high-precision defect detection using the generated reference image. 
       Solution to Problem 
       [0011]    In order to resolve the above problems, the present invention is characterized in that a defect occupation rate to be a ratio of a defect area in an inspected image is calculated, the magnitude of the defect occupation rate and a threshold value is determined, and it is determined whether an image configured by pixels having an average brightness value of a plurality of pixels included in the inspected image is generated as the reference image, according to a result of the determination. 
       Advantageous Effects of Invention 
       [0012]    According to the present invention, a method of generating a reference image to be suitable for the case in which a pattern does not exist in a view of an image and a defect observation system capable of stably realizing high-precision defect detection using the generated reference image can be provided. 
         [0013]    Other objects, configurations, and effects of the present invention will become apparent from the following description of embodiments of the present invention. 
     
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         [0014]      FIG. 1  is an example of a schematic configuration diagram of an SEM-type defect observation system. 
           [0015]      FIG. 2  is an example of a configuration diagram of an operation/analysis unit of the SEM-type defect observation system. 
           [0016]      FIG. 3  is an example of a functional block diagram in the operation/analysis unit to execute defect detection. 
           [0017]      FIG. 4  is an example of an ADR flowchart to acquire a representative reference image. 
           [0018]      FIG. 5  is an example of a schematic diagram illustrating a defect detection method using a representative reference image. 
           [0019]      FIG. 6  is an example of an ADR flowchart according to Embodiment 1. 
           [0020]      FIG. 7  is an example of a schematic diagram illustrating the defect detection method according to Embodiment 1. 
           [0021]      FIG. 8  is an example of a schematic diagram illustrating a problem of the defect detection method according to Embodiment 1. 
           [0022]      FIG. 9  is an example of an ADR flowchart according to Embodiment 2. 
           [0023]      FIG. 10  is an example of a schematic diagram illustrating a defect detection method according to Embodiment 2. 
           [0024]      FIG. 11  is an example of an ADR flowchart according to Embodiment 3. 
           [0025]      FIG. 12  is an example of a schematic diagram illustrating a defect detection method according to Embodiment 3. 
           [0026]      FIG. 13  is an example of an ADR flowchart according to Embodiment 4. 
           [0027]      FIG. 14  is an example of a schematic diagram illustrating a defect detection method according to Embodiment 4. 
           [0028]      FIG. 15  is an example of an ADR flowchart according to Embodiment 5. 
           [0029]      FIG. 16  is an example of a schematic diagram illustrating a defect detection method according to Embodiment 5. 
           [0030]      FIG. 17  is an example of a schematic diagram illustrating a method of calculating a defect occupation rate described in Embodiment 6. 
           [0031]      FIG. 18  is an example of a schematic diagram illustrating a method of calculating a defect occupation rate described in Embodiment 7. 
           [0032]      FIG. 19  is an example of a schematic diagram illustrating a method of determining a defect occupation rate described in Embodiment 8. 
           [0033]      FIG. 20  is an example of a GUI capable of selecting a defect detection method described in Embodiment 9. 
       
    
    
     DESCRIPTION OF EMBODIMENTS  
       [0034]    Configuration examples of a defect detection method, a defect observation device, and a defect observation system to be stable in which both the defect detection precision and the throughput are realized even when a pattern does not exist in a view of an image will be described. The defect observation system to be described below is an example of the present invention and the present invention is not limited to embodiments to be described below. In the present invention, the defect observation device is a device to image an image of a sample using a charged particle beam and includes devices to detect a defect by comparing a plurality of images widely. In addition, the defect observation system is a system in which the defect observation device is connected to other device by a network and includes systems including the defect observation device widely. 
         [0035]    As one configuration example of the defect observation system including the defect observation device, an example of the case in which a defect image is acquired by ADR in an SEM-type defect observation device is described. However, a system configuration is not limited thereto and a part or all of devices configuring the defect observation system may be configured by different devices. For example, ADR processing according to this embodiment may be executed by an ADR processing device or an image management device connected to the SEM-type defect observation device by the network or a review management device and may be executed by a program executing desired operation processing by a central processing unit (CPU) mounted in a general-purpose computer in the system. In addition, an existing device can be upgraded by a storage medium on which the program is recorded. 
         [0036]    In the present specification, the “defect” is not limited to a foreign material and includes observation target materials such as a material defect or a structure defect of a sample widely. 
         [0037]    In addition, in the present specification, the “sample” may be a process of a step after a pattern is manufactured as well as a wafer of a process of a step before the pattern is manufactured. The embodiments to be described below are effective particularly when the pattern is not imaged or does not exist in a view of an observation image, regardless of whether the pattern is actually manufactured. 
         [0038]    In addition, in the present specification, a “defect image” is an image for which a defect is inspected (inspected image) and includes defect candidate images or false defect images as well as a real defect image. A “reference image” is a so-called standard image used for comparison with the defect image to extract the defect and is an image of a normal area, that is, an area estimated as an area not having a defect. 
         [0039]    Hereinafter, the embodiments of the present invention will be described in detail with reference to the drawings. 
       Embodiment 1 
       [0040]    The SEM-type defect observation device is a device that acquires a high-definition SEM image of the defect coordinates under a condition suitable for observation or analysis, using the defect coordinates detected by a defect inspection device such as an optical-type or SEM-type inspection device as input information. As the information input to the SEM-type observation device, in addition to the defect coordinates detected by the defect inspection device, coordinate information of an observation point extracted by simulation based on design layout data can be used. 
         [0041]      FIG. 1  is a schematic diagram illustrating an entire configuration of an SEM-type observation system in this embodiment. An SEM-type defect observation device  118  of  FIG. 1  includes an electronic optical system configured by optical elements such as an electron gun  101 , a lens  102 , a scanning deflector  103 , an objective lens  104 , a sample  105 , and a secondary particle detector  109 , a stage  106  to move a sample stand holding a sample to be observed in an XY plane, an electronic optical system control unit  110  to control the various optical elements included in the electronic optical system, an A/D conversion unit  111  to quantize an output signal of the secondary particle detector  109 , a stage control unit  112  to control the stage  106 , an entire control/analysis unit  113 , an image processing unit  114 , an operation unit  115  including a display, a keyboard, and a mouse, a storage device  116  to store an acquired image, and an optical microscope  117 . The electronic optical system, the electronic optical system control unit  110 , the A/D conversion unit  111 , the stage  106 , and the stage control unit  112  configure a scanning electronic microscope to be an imaging unit of an SEM image. 
         [0042]    A primary electron beam  107  emitted from the electron gun  101  is converged by the lens  102 , is deflected by the scanning deflector  102 , is converged by the objective lens  104 , and is emitted to the sample  105 . From the sample  105  to which the primary electron beam  107  has been emitted, a secondary particle  108  such as a secondary electron or a reflection electron is generated according to a shape or a material of the sample. The generated secondary particle  108  is detected by the secondary particle detector  109  and is converted into a digital signal by the A/D conversion unit  111 . An output signal of the secondary particle detector converted into the digital signal is also called an image signal. The output signal of the A/D conversion unit  111  is output to the image processing unit  114  and forms an SEM image. Of course, in addition to the components, other lens, electrode, and detector may be included in the device, partial components may be different from the components, and a configuration of a charged particle optical system is not limited thereto. 
         [0043]    The image processing unit  114  executes various image analysis processing such as ADR processing to execute image processing such as defect detection and automatic defect classification (ADC) processing to automatically classify defects for each kind, using the generated SEM image. In the SEM-type observation device according to this embodiment, images of an observation target can be acquired with a plurality of different magnifications. For example, the observation target can be observed by changing a scanning range of the scanning deflector  103  and changing the magnification. 
         [0044]    Control of the optical elements in the electronic optical system such as the lens  102 , the scanning deflector  103 , and the objective lens  104  is executed by the electronic optical system control unit  110 . Positional control of the sample is executed by the stage  106  controlled by the stage control unit  112 . The entire control/analysis unit  113  is a control unit that wholly controls the entire SEM-type observation device and interprets input information from the operation unit  115  including the display, the keyboard, and the mouse and the storage device  116 , controls the electronic optical system control unit  110 , the stage control unit  112 , and the image processing unit  114 , and outputs a processing result to a display unit included in the operation unit  115  or the storage device  116  according to necessity. 
         [0045]    The processing executed by the image processing unit  114  may be configured as hardware by a dedicated circuit board and may be realized by software executed by a computer connected to the defect observation device. When the processing is configured by the hardware, the processing can be realized by integrating a plurality of operation units executing the processing into a wiring substrate, a semiconductor chip, or a package. When the processing is configured by the software, the processing can be realized by mounting a high-speed CPU to the image processing unit  114  and executing a desired operation process by a program. 
         [0046]      FIG. 1  illustrates an example of the case in which the SEM-type defect observation device  118 , a recipe management device  120 , and a defect information database  121  are connected by a local area network (LAN)  119  as an example the defect observation system. An image acquired by the SEM-type defect observation device  118  is stored in the defect information database  121 . In addition, information regarding the defect, for example, an imaging condition of the defect image or the detected defect coordinates is stored in the defect information database  121 . The recipe management device  120  acquires defect information necessary for recipe preparation from the defect information database  121 , executes operation processing including image processing, and prepares a recipe in which a condition and a sequence to execute the ADR or ADC processing are recorded. A parameter used for the operation processing or the prepared recipe may be stored in a storage device embedded in the recipe management device and may be stored in the defect information database. As such, the “defect information” includes information regarding the defect, such as the coordinates of the defect detected by the inspection device, an image imaged by the inspection device, a result analyzed by an analysis function of the inspection device, the coordinates of the defect detected again by the defect observation device, an image imaged by the defect observation device, and a result analyzed by an analysis function of the defect observation device. 
         [0047]      FIG. 2  is an example of a detailed diagram of the entire control unit and analysis unit  113 , the operation unit  115 , and the storage device  116  of  FIG. 1 . Here, an operation/analysis unit  201  is obtained by integrating the entire control/analysis unit  113  and the operation unit  115  of  FIG. 1 . The operation/analysis unit  201  includes a plurality of functional blocks realized by executing a predetermined program by a CPU embedded in the entire control/analysis unit  113 , according to an operation instruction from the operation unit  115 . As such, a configuration is not limited to a configuration in which the entire control/analysis unit illustrated in  FIG. 1  is embedded in the SEM-type observation device and the operation/analysis unit  201  illustrated in  FIG. 2  may be configured independently form the SEM-type observation device illustrated in  FIG. 1  and the components of  FIGS. 1 and 2  may be coupled by network connection. 
         [0048]    When the components of  FIG. 2  are embedded in the defect observation system of  FIG. 1 , a defect data storage unit  202 , an image data storage unit  203 , an analysis parameter storage unit  204 , and an analysis result data storage unit  205  may be integrated into the storage device  116  of  FIG. 1 . 
         [0049]    Defect information such as the defect coordinates is stored in the defect data storage unit  201 . A defect image imaged by the SEM-type observation device is stored in the image data storage unit  202 . Conditions such as an ADR condition and an ADC condition executed when an image is acquired or analyzed are stored in the analysis parameter storage unit  204  and the analysis parameter storage unit  204  can reproduce the plurality of conditions. A processing result is stored in the analysis result data storage unit  205 . 
         [0050]    As another embodiment, a function of the operation/analysis unit  201  can be realized by the recipe management device  120  in the SEM-type defect observation system illustrated in  FIG. 1 . In addition, the defect data storage unit  202 , the image data storage unit  203 , the analysis parameter storage unit  204 , and the analysis result data storage unit  205  can be realized by the defect information database  121  in the SEM-type defect observation system illustrated in  FIG. 1 . 
         [0051]      FIG. 3  is an example of a block diagram of a function to execute defect detection processing to be described below. In the functional block diagram of  FIG. 3 , all of functions corresponding to all of the following embodiments are illustrated to simplify the description, but only a part thereof may be mounted to the device. The details will be described in the individual embodiments. 
         [0052]    A reference image generation processing unit  306  generates a low-magnification reference image from a low-magnification defect image, using methods described in the following embodiments. As described after Embodiment 2, when the low-magnification reference image is not combined and a newly imaged low-magnification reference image is used, an acquired low-magnification reference image  302  is output as an image for a comparison target as it is. A defect detection processing unit  308  uses a low-magnification defect image  301  and a low-magnification reference image  307  as input information to detect a difference area of these images as a defect area. Detected defect coordinates  309  are output as defect coordinate information to acquire a high-magnification image. 
         [0053]    A defect occupation rate determination processing unit  305  determines a defect occupation rate using the low-magnification defect image  301  imaged by the SEM-type observation device as input information. The details will be described after Embodiment 2. In this case, the reference image generation processing unit  306  determines a reference image generation method, on the basis of a result determined by the defect occupation rate determination processing unit  305 . The low-magnification reference image  307  is generated using the low-magnification defect image  301 , the low-magnification reference image  302 , an acquired low-magnification defect image  303 , or an acquired low-magnification reference image  304  as input information, on the basis of the determined generation method. Here, “acquired” means that a defect image other than the defect image  301  corresponding to an inspected target is imaged in the past. A method using the acquired low-magnification defect image or the acquired low-magnification reference image will be described in detail in Embodiments 3 to 5. 
         [0054]    As such, a method of generating a low-magnification reference image used for defect detection is optimized on the basis of a defect occupation rate of a low-magnification defect image to be an analysis target, so that precision of the defect detection can be improved. In addition, because an operator does not need to select the acquisition or generation method of the low-magnification reference image by automating processing for optimizing the method of generating the low-magnification reference image, on the basis of the defect occupation rate, the workload can be reduced. 
         [0055]      FIG. 4  is an example of a flowchart of ADR to acquire a representative low-magnification reference image in the first of an ADR sequence. A low-magnification reference image corresponding to a low-magnification defect image does not need to be acquired every time for a sample when a pattern does not exist in a view of an image, that is, in a manufacturing process of a step in which the pattern is not formed or a state in which a pattern formed in a lower layer does not appear in an imaging image by application of a film material. Therefore, for high-speed processing, one low-magnification reference image is acquired in the first of the ADR sequence and the acquired low-magnification reference image and each low-magnification defect image are compared and defect detection is performed. As such, an acquired representative image is called the “representative low-magnification reference image” to be used as a comparison standard of each low-magnification defect image. 
         [0056]    First, the representative low-magnification reference image is acquired in the first of the ADR sequence ( 401 ). Because it is important not to include the pattern to be manufactured in the defect or the sample in the representative low-magnification reference image, an image of the center coordinates of the sample in which the possibility of the pattern not existing is high or the coordinates designated by the recipe by the user is acquired. When the defect is included in the representative low-magnification reference image, a difference other than the defect to be observed is detected at the time of detecting a difference with the low-magnification defect image. For this reason, the defect detection is not successfully executed. 
         [0057]    Next, the sample stage is moved such that the coordinates of the observation target enter a view and the low-magnification defect image is acquired ( 402 ). The acquired low-magnification defect image and the representative low-magnification reference image acquired in the first of the sequence are compared and a difference is detected as a defect area ( 403 ). At the coordinates of the detected defect area, a high-magnification defect image is acquired with a magnification suitable for observation ( 404 ). Here, the magnification suitable for the observation is generally a magnification higher than the magnification of the low-magnification image. Processing of  402  to  404  is executed on the entire samples and the entire inspection target points of the observation target ( 405 ). As such, the low-magnification reference image is not acquired for each observation target and the representative low-magnification reference image is acquired in the first of the ADR sequence and is repetitively used. As a result, the throughput can be improved. 
         [0058]      FIG. 5  is an example of a schematic diagram illustrating defect detection processing using the representative low-magnification reference image. A problem of the method described in  FIG. 4  will be described using the schematic diagram. In a method using the representative low-magnification reference image, a defect image  501  and a representative low-magnification reference image  502  are compared and a difference image  503  is acquired. The difference image  503  is acquired by comparing the low-magnification defect image  501  and the representative low-magnification reference image  502  in a unit of a pixel and it is determined that a significant difference exists when a difference of gradation values is equal to or more than a threshold value in the difference image  503 . In the difference image  503 , pixels in which it is determined that the significant difference exists are shown by white and pixels in which it is determined that the significant difference does not exist are shown by black. Hereinafter, in the present specification, a white portion is described as an area determined as a defect portion and a black portion is described as an area determined as a normal portion. 
         [0059]    In the case of  FIG. 5 , a defect  504  having a curved line shape exists on the low-magnification defect image  501  and a defect area  505  is detected on the difference image  503 . However, the defect area  505  is detected in a broken line shape by an influence of noises existing in the representative low-magnification reference image  502 . In addition to the defect area  505 , a difference, which may be a cause of erroneous detection of the defect, is extracted from the difference image  503  by an influence of noises existing in the low-magnification defect image  501  and the representative low-magnification reference image  502 . 
         [0060]    Technology for reducing noises at the time of imaging or technology for reducing an influence of noises by image processing is developed. However, this processing is insufficient and defect detection processing that is suitable for when a pattern does not exist in a view of an image and has a low operation cost and robustness against a noise is demanded. Particularly, in the SEM-type observation device, because a charged state is changed by a material of a sample or a difference of a structure and visibility of an acquired image is changed, defect detection processing corresponding to the change in the visibility of the representative low-magnification reference image and the low-magnification defect image is demanded. 
         [0061]    According to the technology disclosed in Patent Literature 1, the reference image becoming the comparison target is combined with the defect image. For this reason, if the technology is compared with the method described in  FIG. 5 , an advantage of the technology is that a difference of the visibility of the defect image and the reference image, for example, a variation of a brightness distribution decreases. However, there is a problem in the defection detection precision and the throughput. 
         [0062]    First, for the defect detection precision, there is a problem in that precision of reference image combination using periodicity is bad, when a pattern does not exist in a view of an image. If a defect area remains in the combined reference image, the possibility of the defect being missed or an area other than the defect being erroneously detected becomes high, as compared with the defect image. 
         [0063]    Next, for the throughput, there is a problem in that it is necessary to calculate the periodicity of the defect image, divide an image according to the periodicity, and recombine the divided images and an operation cost is high to combine the reference image and the throughput is deteriorated. The processing for combining the reference image using the pattern in the view of the observation is repetitively executed for each defect image, that is, by the number of observation targets. For this reason, when a total of processing time is longer than acquisition time of the representative reference image described in  FIG. 4 , a method of acquiring the representative low-magnification reference image once in the first of the ADR sequence is at an advantage in terms of the throughput. 
         [0064]    Therefore, a processing method to resolve a problem when the technology described in  FIG. 4  or  5  or the technology disclosed in Patent Literature 1 is applied to the case in which the pattern does not exist in the view of the image will be described.  FIG. 6  is an example of a flowchart of the ADR according to this embodiment. 
         [0065]    First, the low-magnification defect image is acquired ( 601 ). As compared with  FIG. 4 , because it is not necessary to acquire the representative low-magnification reference image in the first of the sequence, processing time for acquisition processing of the representative low-magnification reference image can be decreased. 
         [0066]    Next, a low-magnification reference image is generated using an average brightness value of the acquired low-magnification defect image ( 602 ). The processing for generating the low-magnification reference image needs to be executed for each low-magnification defect image. However, as compared with Patent Literature 1, because an average value is only calculated simply and the processing is simple, the low-magnification reference image can be generated in short time. In addition, processing for calculating an average brightness value can be generally executed at a speed higher than a speed when noise reduction processing is executed on the low-magnification defect image, the low-magnification reference image, and the difference image. Here, the “average brightness value” is an average brightness value of entire pixels configuring the low-magnification defect image or a representative brightness value. For example, when the low-magnification defect image is configured by 500×500 pixels, an image of 500×500 pixels is configured such that an average of entire pixel values of a total of 250,000 pixels is used as an average brightness value and all of the pixels have the average brightness value. In the SEM image, because the brightness values are represented often by 256 gradations from 0 to 255 in gray scale, the average brightness value takes a value between 0 and 255 in that case. 
         [0067]    Finally, the low-magnification reference image generated by  602  and the low-magnification defect image acquired by  601  are compared, a difference is detected as a defect area, and the defect coordinates are detected ( 603 ). At the detected defect coordinates, a high-magnification defect image is acquired with a magnification suitable for the observation, generally, a magnification higher than a magnification of the low-magnification defect image ( 604 ). Processing of  601  to  604  is executed on entire samples and entire inspection target points of the observation target ( 605 ). 
         [0068]      FIG. 7  is an example of a schematic diagram illustrating defect detection processing using the low-magnification reference image generated using the average brightness value of the low-magnification defect image described in  FIG. 6 . A low-magnification defect image  701  and a low-magnification reference image  702  generated using an average brightness value of the low-magnification defect image are compared and it is determined that a significant difference exists when a difference of the gradation values is equal to or more than a threshold value. In a difference image  703 , pixels in which it is determined that the significant difference exists are shown by white and pixels in which it is determined that the significant difference does not exist are shown by black. 
         [0069]    Similar to the case of  FIG. 5 , in the case of  FIG. 7 , a defect  704  having a curved line shape exists on the low-magnification defect image  701  and a defect area  705  is detected on the difference image  703 . As compared with the defect area  505  of  FIG. 5 , because noises do not exist in the low-magnification reference image  702 , the defect area is extracted more correctly. In addition to the defect area  705  to be detected, a part of the noises existing on the defect image  701  remains on the difference image  703 . However, as compared with the difference image  503  of  FIG. 5 , because the noises do not exist in the low-magnification reference image  702 , the difference area other than the defect decreases. 
         [0070]    As such, the low-magnification defect image  301  is used as an input, the low-magnification reference image  307  is generated using the average brightness value of the low-magnification defect image  301  by the reference image generation processing unit  306 , the low-magnification defect image  301  and the low-magnification reference image  307  are compared by the defect detection processing unit  308 , and a difference can be output as the defect coordinates  309 . According to this method, because the low-magnification reference image generated using the average brightness value of the low-magnification defect image is used, stable defect detection can be realized for the change in the noises or the visibility of the representative low-magnification reference image, as compared with the method using the representative low-magnification reference image. In addition, because the low-magnification reference image can be generated by a simple operation like calculation of the average brightness value of the low-magnification defect image, high-speed defect detection processing can be realized. 
       Embodiment 2 
       [0071]    In this embodiment, an example of a defect detection method in which great importance is attached to stability of defect detection rather than a processing speed as compared with the method described in Embodiment 1 will be described. Because the configurations described in  FIGS. 1 to 3  and the content described in  FIGS. 4 to 7  are the same as those in this embodiment, explanation thereof is omitted. 
         [0072]      FIG. 8  is a schematic diagram illustrating a problem of the defect detection method using the low-magnification reference image generated using the average brightness value of the low-magnification defect image, described in  FIGS. 6 and 7 . The problem when the low-magnification reference image is generated using the average brightness value of the low-magnification defect image is that, when a size of a defect area occupied in the low-magnification defect image is relatively large, a calculated average brightness value is affected by a brightness value of the defect area. A “defect occupation rate” is a ratio of a defect area occupied in an entire view of an image. Because the defect area is normally small in the view of the image, the defect occupation rate is relatively small. However, when a defect to be detected is a so-called enormous defect, the defect occupation rate increases. 
         [0073]    As illustrated in  FIG. 8 , when a defect  804  in which a defect occupation rate for the view of the observation is high exists in a low-magnification defect image  801 , a low-magnification reference image  802  generated using an average brightness value of the low-magnification defect image  801  is affected by a pixel value of a portion of the defect  804  having the high occupation rate. It is ideal that the low-magnification reference image is generated using an average brightness of an area other than the defect area. However, in the case of  FIG. 8 , because the brightness of the defect area  804  in the low-magnification defect image  801  is low (dark) and the brightness of the area other than the defect area is high (bright), the brightness of the generated low-magnification reference image  802  is low (dark) as compared with the ideal low-magnification reference image. For this reason, in a difference image  803  obtained by comparing the low-magnification defect image  801  and the low-magnification reference image  802  and calculating a difference as the defect area, a defect area  805  for detection is determined as a non-defect area (in the drawing, a black portion). Meanwhile, a background portion to be determined as the non-defect area is erroneously detected as a defect area (in the drawing, a white portion). 
         [0074]    As such, the method of generating the low-magnification reference image using the average brightness value of the low-magnification defect image described in Embodiment 1 has a problem in that defect detection precision decreases, when a defect occupation rate is large in the low-magnification defect image. 
         [0075]    A method to resolve a problem in that defect detection precision decreases when a defect occupation rate in the low-magnification defect image described in  FIG. 8  is high will be described using  FIG. 9 .  FIG. 9  is an example of a flowchart of ADR to change a low-magnification reference image according to a defect occupation rate of a defect image. That is,  FIG. 9  is an example of the case in which, when the defect occupation rate is high, the reference image is acquired and when the defect occupation rate is low, the low-magnification reference image is generated by the method according to Embodiment 1. 
         [0076]    First, a low-magnification defect image is acquired ( 901 ). Next, an occupation rate (defect occupation rate) of a defect area in the acquired low-magnification defect image is calculated and the magnitude with a predetermined threshold value is determined ( 902 ). On the basis of a determination result, it is determined whether a low-magnification reference image is generated using an average brightness value of the low-magnification defect image. Specifically, when a defect occupation rate is low, it is determined that a method of generating the low-magnification reference image using the average brightness value of the low-magnification defect image is effective and the low-magnification reference image is generated from the average brightness value of the low-magnification defect image, using the same method as the method described in  FIG. 6  or  7  ( 904 ). Meanwhile, in the case in which the defect occupation rate is high, if the low-magnification reference image is generated from the average brightness value of the low-magnification defect image, the possibility of the low-magnification reference image not being generated using the desired brightness value due to an influence of the defect area having the high defect occupation rate is high. For this reason, an image of the coordinates where it can be expected that the defect does not exist is acquired newly as the low-magnification reference image ( 903 ). Here, the magnitude of the defect occupation rate may be determined by a certain threshold value, as in the method illustrated in Embodiment 8. 
         [0077]    Next, the low-magnification reference image acquired by  903  or the low-magnification reference image generated by  904  and the low-magnification defect image acquired by  901  are compared and a difference thereof is detected as a defect area ( 905 ). From the detected defect area, a high-magnification defect image is acquired with a magnification higher than a magnification suitable for the observation, generally, a magnification higher than a magnification of the low-magnification defect image ( 906 ). Works of  901  to  906  are executed on the entire samples and inspection target points ( 907 ). 
         [0078]      FIG. 10  is an example of a schematic diagram illustrating the defect detection method described in  FIG. 9 . When it is determined that a defect area  1004  is large and a defect occupation rate is high, in a low-magnification defect image  1001 , an image of the coordinates where it can be expected that the defect does not exist is acquired without generating the low-magnification reference image from the low-magnification defect image and the image is used as a low-magnification reference image  1002 . Meanwhile, when it is determined that the defect area is small and the defect occupation rate is low, using the same method as the method described in  FIG. 6  or  7 , the image generated from the average brightness value of the low-magnification defect image is used as the low-magnification reference image. 
         [0079]    Next, the low-magnification defect image  1001  and the low-magnification reference image  1002  are compared and a difference image  1003  is acquired. When the low-magnification defect image  1001  and the low-magnification reference image  1002  are compared in a unit of a pixel and a difference of gradation values is equal to or more than a threshold value, it is determined that a significant difference exists. In the difference image  1003  of  FIG. 10 , a portion in which it is determined that the significant difference exists is shown by white and a portion in which it is determined that the significant difference does not exist is shown by black. 
         [0080]    As such, the defect occupation rate is calculated by the defect occupation rate determination processing unit  305  using the low-magnification defect image  301  as an input. When the defect occupation rate is high, the reference image generation processing unit  306  newly images the low-magnification reference image  302  and when the defect occupation rate is low, the reference image generation processing unit  306  generates the low-magnification reference image  307  from the average brightness value of the low-magnification defect image. The defect detection processing unit  308  compares the low-magnification defect image  301  and the low-magnification reference image  307  output from the reference image generation processing unit  306  and outputs a difference as the defect coordinates  309 . 
         [0081]    According to this method, when the defect occupation rate of the low-magnification defect image is low, the low-magnification reference image generated using the average brightness value of the low-magnification defect image is used for the defect detection, so that stable defect detection can be realized for the change in the brightness due to the noises or the material quality or structure at the observation coordinates. When the defect occupation rate of the low-magnification defect image is high, an image of the coordinates where it is can be expected that the defect does not exist is acquired and used as the low-magnification reference image. Therefore, a defect detection rate can be prevented from decreasing. From the viewpoint of the processing speed, if the number of times of acquiring the low-magnification reference image increases, the throughput of the ADR decreases. However, the observation condition of the low-magnification defect image is set without excessively increasing the magnification of the low-magnification defect image (excessively narrowing the view of the observation), so that the defect image in which the defect occupation rate is high can be suppressed from being generated. 
       Embodiment 3 
       [0082]    In this embodiment, an embodiment of a defect observation method in which a decrease in processing speed is minimally suppressed while great importance is attached to stability of defect detection as compared with the method described in Embodiment 1 will be described. As compared with Embodiment 2, the stability of the defect detection is the same and the processing speed is high. Because the configurations described in  FIGS. 1 to 3  and the content described in  FIGS. 4 to 7  are the same as those in this embodiment, explanation thereof is omitted. 
         [0083]    Similar to the method of acquiring the representative reference image described in  FIG. 5 , the method described in Embodiment 2 has a problem in that an influence of noises of a low-magnification reference image is easily received and defect detection precision is deteriorated. Control is enabled by magnification adjustment at the time of acquiring the low-magnification defect image. However, as described in  FIG. 9 , if the case in which it is determined that a defect occupation rate is high increases, processing for acquiring the low-magnification reference image is necessary. For this reason, the throughput decreases. 
         [0084]      FIG. 11  is an example of a flowchart of ADR to generate a low-magnification reference image using an average brightness value of a representative low-magnification reference image, when the defect occupation rate of the low-magnification defect image is high. A method to resolve the problem in the method in which the image of the coordinates where it can be expected that the defect does not exist is acquired and is used as the low-magnification reference image, when the defect occupation rate of the low-magnification defect image is high, described in Embodiment 2, will be described. 
         [0085]    First, an image of the coordinates in which it can be expected that the defect does not exist is acquired in the first of an ADR sequence and the image is set as a representative low-magnification reference image ( 1101 ). Next, a low-magnification defect image is acquired ( 1102 ) and a defect occupation rate of a defect area in the acquired low-magnification defect image is calculated and the magnitude with a predetermined threshold value is determined ( 1103 ). On the basis of a determination result, it is determined whether a low-magnification reference image is generated using an average brightness value of the low-magnification defect image. Specifically, when a defect occupation rate is low, it is determined that a method of generating the low-magnification reference image using the average brightness value of the low-magnification defect image is effective and the low-magnification reference image is generated from the average brightness value of the low-magnification defect image ( 1105 ). Meanwhile, when the defect occupation rate is high, the low-magnification reference image is generated using the average brightness value of the representative low-magnification reference image acquired by  1101  ( 1104 ). 
         [0086]    Finally, the low-magnification reference image generated using the average brightness value of the representative low-magnification reference image or the low-magnification reference image generated using the average brightness value of the low-magnification defect image and the low-magnification defect image acquired by  1102  are compared and a difference is detected as a defect area ( 1106 ). Next, an enlarged high-magnification defect image of the detected defect area is acquired ( 1107 ) and processing of  1102  to  1107  is executed on entire samples and inspection target points ( 1108 ). 
         [0087]      FIG. 12  is an example of a schematic diagram illustrating the defect detection method described in  FIG. 11 . First, a low-magnification reference image  1202  is generated from the average brightness value of the representative low-magnification reference image  1206  acquired in the first of the ADR sequence. The defect occupation rate of the acquired low-magnification defect image  1201  is calculated. When the defect occupation rate is high, the low-magnification defect image  1201  and the low-magnification reference image  1202  are compared and a difference image  1203  is acquired. Meanwhile, when it is determined that the defect area is small and the defect occupation rate is low, using the same method as the method described in  FIG. 6  or  7 , the image generated from the average brightness value of the low-magnification defect image is used as the low-magnification reference image. 
         [0088]    In the difference image  1203  of  FIG. 12 , it is determined that a significant difference exists, when the low-magnification defect image  1201  and the low-magnification reference image  1202  are compared in a unit of a pixel and a difference of gradation values is equal to or more than a threshold value. In the difference image  1203 , pixels in which it is determined that the significant difference exists are shown by white and images in which it is determined that the significant difference does not exist are shown by black. 
         [0089]    As such, the defect occupation rate is calculated by the defect occupation rate determination processing unit  305  using the low-magnification defect image  301  as an input. When the defect occupation rate is high, the reference image generation processing unit  306  generates the low-magnification reference image  307  from the average brightness value of the representative low-magnification reference image and when the defect occupation rate is low, the reference image generation processing unit  306  generates the low-magnification reference image  307  from the average brightness value of the low-magnification reference image. The defect detection processing unit  308  compares the low-magnification defect image  301  and the low-magnification reference image  307  output from the reference image generation processing unit  306  and outputs a difference as the defect coordinates  309 . 
         [0090]    According to this method, when the defect occupation rate is low, the low-magnification reference image generated using the average brightness value of the low-magnification defect image is used for the defect detection, so that stable defect detection can be realized for the change in the brightness due to the noises or the material quality or structure at the observation coordinates. When the defect occupation rate of the low-magnification defect image is high, the low-magnification reference image is generated using the average brightness value of the representative low-magnification reference image. Therefore, the defect detection is enabled without being affected by the defect area of the low-magnification defect image and the defect detection rate can be prevented from decreasing. In addition, the low-magnification reference image is generated from the average brightness of the representative low-magnification reference image, so that it is difficult to receive an influence of the noises of the representative low-magnification reference image, as compared with the case in which the representative low-magnification reference image is used as it is, and high-precision defect detection is enabled. In addition, the representative reference image is acquired once in the first of the sequence, so that a decrease in the throughput by the low-magnification reference image acquisition can be minimally suppressed, as compared with the method of acquiring the low-magnification reference image whenever the defect occupation rate is high. 
       Embodiment 4 
       [0091]    In this embodiment, an example of a defect observation method for realizing stable defect detection by using a reference image acquired or generated in the past will be described. Because the configurations described in  FIGS. 1 to 3  and the content described in  FIGS. 4 to 7  are the same as those in this embodiment, explanation thereof is omitted. 
         [0092]    A defect detection method suitable for an observation target can be selected by the methods described in Embodiments 1 to 3, in consideration of a balance of the defect detection precision and the throughput. However, when one recipe is repetitively used for different processes, the possibility of visibility of a background portion being different due to a difference of a material quality or a structure of a sample is high. For this reason, a stable correspondence method is demanded for this case. 
         [0093]    In addition, in a method of acquiring one representative reference image in the first of a sequence, like the ADR sequence described in Embodiment 3, it is not guaranteed that the representative reference image is an ideal reference image. The ideal reference image is an image in which a manufacturing pattern or a structure of a lower layer does not appear in a view and brightness irregularities by noises or charging of a sample do not occur at the same brightness as a brightness of the background portion of the defect image. It is rare, but there is the case in which the representative reference image is not the ideal reference image. For this reason, a stable method is demanded for this case. 
         [0094]      FIG. 13  is a flowchart of ADR to generate a low-magnification reference image using an average brightness value of an acquired or generated reference image, when the defect occupation rate of the low-magnification defect image is high. First, a low-magnification defect image is acquired ( 1301 ) and an occupation rate of a defect area in the acquired low-magnification defect image is calculated and the magnitude with a predetermined threshold value is determined ( 1302 ). On the basis of a determination result, it is determined whether a low-magnification reference image is generated using an average brightness value of the low-magnification defect image. Specifically, when a defect occupation rate is low, it is determined that a method of generating the reference image using the average brightness value of the low-magnification defect image is effective and the low-magnification reference image is generated from the average brightness value of the low-magnification defect image ( 1304 ). Meanwhile, when the defect occupation rate is high, the low-magnification reference image is generated using an average brightness value of the acquired reference image or the generated reference image ( 1303 ). Here, the acquired reference image or the generated reference image is not limited to a low-magnification reference image acquired or generated in the ADR sequence at the time of the observation and may include a reference image acquired or generated in the past, in a recipe considered as having the same image quality. The reference image may be automatically selected and may be designated by a recipe by selection of an operator. A plurality of acquired or generated low-magnification reference images are selected and a low-magnification reference image can be generated using an average brightness value of the entire images. 
         [0095]    In  FIG. 13 , because the low-magnification reference image is generated using the average brightness value of the acquired or generated reference image ( 1303 ) after defect occupation rate determination processing ( 1302 ), a newest low-magnification reference image generated in the ADR sequence can be used. Thereby, stable defect detection according to a change in visibility by a material quality or a structure at an observation point can be expected. 
         [0096]    However, if a low-magnification reference image is generated every time, an operation cost increases. For this reason, the low-magnification reference image may be generated in advance at an average brightness of an acquired or generated reference image, before the ADR sequence starts. The generated low-magnification reference image may be registered in a recipe in which an execution condition of the ADR is described, for example. In this way, the low-magnification reference image having considered the reference image acquired or generated in the past can be used without deteriorating the throughput of the ADR. Therefore, stable defect detection can be realized. 
         [0097]    Next, the low-magnification reference image generated by  1303  or the low-magnification reference image generated by  1304  and the low-magnification defect image acquired by  1301  are compared and a difference is detected as a defect area ( 1305 ). Finally, an enlarged image of the detected defect area is acquired as a high-magnification defect image ( 1306 ). Processing of  1301  to  1306  is executed on entire samples and inspection target points ( 1307 ). 
         [0098]      FIG. 14  is an example of a schematic diagram illustrating the defect detection method described in  FIG. 13 . 
         [0099]    First, a low-magnification reference image  1402  is generated using an average brightness value of one or more acquired or generated reference images  1406 . A defect occupation rate of the acquired low-magnification defect image  1401  is calculated. When the defect occupation rate is high, the low-magnification defect image  1401  and the low-magnification reference image  1402  are compared in a unit of a pixel. Meanwhile, when it is determined that the defect area is small and the defect occupation rate is low, using the same method as the method described in  FIG. 6  or  7 , the image generated from the average brightness value of the low-magnification defect image is used as the low-magnification reference image and the low-magnification reference image and the low-magnification defect image are compared. When a difference of gradation values is equal to or more than a threshold value by comparison, it is determined that a significant difference exists. In the difference image  1403 , pixels in which it is determined that the significant difference exists are shown by white and pixels in which it is determined that the significant difference does not exist are shown by black. 
         [0100]    As such, the defect occupation rate is calculated by the defect occupation rate determination processing unit  305  using the low-magnification defect image  301  as an input. When the defect occupation rate is high, the reference image generation processing unit  306  generates the low-magnification reference image  307  using the average brightness value of the acquired low-magnification reference image  304  and when the defect occupation rate is low, the reference image generation processing unit  306  generates the low-magnification reference image  307  from the average brightness value of the low-magnification defect image. The defect detection processing unit  308  compares the low-magnification defect image  301  and the low-magnification reference image  307  output from the reference image generation processing unit  306  and outputs a difference as the defect coordinates  309 . 
         [0101]    According to this method, when the defect occupation rate is low, the low-magnification reference image generated using the average brightness value of the low-magnification defect image is used for the defect detection, so that stable defect detection can be realized for the change in the brightness due to the noises or the material quality or structure at the observation coordinates. When the defect occupation rate of the low-magnification defect image is high, the average brightness value is calculated from one or more acquired reference images and the low-magnification reference image is generated using the average brightness value. Therefore, stable generation of the low-magnification reference image is enabled for the change in the brightness due to the noises or the material quality or structure at the observation coordinates. The case in which the representative reference image is not the ideal reference image, which rarely occurs, can be avoided. 
       Embodiment 5 
       [0102]    In this embodiment, an example of a defect observation method that generates a reference image using an average brightness value of an area other than a defect area from a defect image acquired in the past and realizes stable defect detection using the reference image will be described. Because the configurations described in  FIGS. 1 to 3  and the content described in  FIGS. 4 to 7  are the same as those in this embodiment, explanation thereof is omitted. 
         [0103]      FIG. 15  is an example of a flowchart of ADR illustrating a method, different from Embodiment 4, to avoid the case in which a representative low-magnification reference image is not an ideal reference image, which rarely occurs. First, a low-magnification defect image is acquired ( 1501 ) and a defect occupation rate of a defect area in the acquired low-magnification defect image is calculated and the magnitude with a predetermined threshold value is determined ( 1502 ). On the basis of a determination result, it is determined whether a low-magnification reference image is generated using an average brightness value of the low-magnification defect image. Specifically, when a defect occupation rate is low, it is determined that a method of generating the low-magnification reference image using the average brightness value of the low-magnification defect image is effective and the low-magnification reference image is generated from the average brightness value of the low-magnification defect image ( 1504 ). Meanwhile, when the defect occupation rate is high, an average brightness value of an area other than a defect area is calculated from an image including an acquired defect to imaging of the low-magnification defect image and a low-magnification reference image is generated using the average brightness value ( 1503 ). At this time, the defect area included in the image including the acquired defect is already calculated by ADR or ADC processing using the defect image as an inspected target. 
         [0104]    Next, the low-magnification reference image generated by  1503  or the low-magnification reference image generated by  1504  and the low-magnification defect image acquired by  1501  are compared and a difference is detected as a defect area ( 1505 ). Finally, an enlarged image of the detected defect area is acquired as a high-magnification defect image ( 1506 ). Processing of  1501  to  1506  is executed on entire inspection target samples ( 1507 ). 
         [0105]    In  FIG. 15 , because the low-magnification reference image is generated using the average brightness value from the area other than the defect area in the acquired defect image ( 1503 ) after the defect occupation rate determination processing ( 1502 ), a newest low-magnification defect image acquired in the ADR sequence can be used. Thereby, stable defect detection according to a change in visibility by a material quality or a structure at an observation point can be expected. 
         [0106]    In addition, if a low-magnification reference image is generated every time, an operation cost increases. For this reason, before the ADR sequence starts, the defect area may be excluded from the acquired defect image in advance and the low-magnification reference image may be generated using the average brightness. The generated low-magnification reference image may be registered in a recipe in which an execution condition of the ADR is described, for example. In this way, the low-magnification reference image generated by considering the reference image acquired in the past can be used without deteriorating the throughput of the ADR. Therefore, stable defect detection can be realized. 
         [0107]    When the low-magnification reference image is generated by  1503 , an extraction result of the defect area by the ADR or the ADC remains on the acquired defect image. For this reason, the average brightness value of the area other than the defect area can be calculated while an operation cost is suppressed. When the extraction result of the defect area does not remain, the low-magnification defect image and the low-magnification reference image may be compared again and the defect area may be extracted. However, because the operation cost increases, an optimal method may be selected by comparing the methods described to  FIG. 14  and considering a balance of the acquired defect detection precision and the operation cost. 
         [0108]      FIG. 16  is an example of a schematic diagram illustrating the defect detection method described in  FIG. 15 . First, an average brightness of an area other than a defect area  1607  is calculated from one or more acquired defect images  1606  and a low-magnification reference image  1602  is generated using an average brightness value. When a defect occupation rate of an acquired low-magnification defect image  1601  is calculated and the defect occupation rate is high, the low-magnification defect image  1601  and the low-magnification reference image  1602  are compared in a unit of a pixel. Meanwhile, when it is determined that the defect area is small and the defect occupation rate is low, using the same method as the method described in  FIG. 6  or  7 , the image generated from the average brightness value of the low-magnification defect image is used as the low-magnification reference image and the low-magnification reference image and the low-magnification defect image are compared. When a difference of gradation values is equal to or more than a threshold value by comparison, it is determined that a significant difference exists. In the difference image  1603 , pixels in which it is determined that the significant difference exists are shown by white and pixels in which it is determined that the significant difference does not exist are shown by black. 
         [0109]    As such, the defect occupation rate is calculated by the defect occupation rate determination processing unit  305  using the low-magnification defect image  301  as an input. When the defect occupation rate is high, the reference image generation processing unit  306  generates the low-magnification reference image  307  using the average brightness value of the area other than the defect area in the acquired low-magnification defect image  303  and when the defect occupation rate is low, the reference image generation processing unit  306  generates the low-magnification reference image  307  from the average brightness value of the low-magnification defect image. The defect detection processing unit  308  compares the low-magnification defect image  301  and the low-magnification reference image  307  output from the reference image generation processing unit  306  and outputs a difference thereof as the defect coordinates  309 . 
         [0110]    According to this method, when the defect occupation rate is low, the low-magnification reference image generated using the average brightness value of the low-magnification defect image is used for the defect detection, so that stable defect detection can be realized for the change in the brightness due to the noises or the material quality or structure at the observation coordinates. When the defect occupation rate of the low-magnification defect image is high, the average brightness value of the area other than the defect area is calculated from one or more acquired defect images and the low-magnification reference image is generated using the average brightness value. Therefore, stable generation of the low-magnification reference image is enabled for the change in the brightness due to the noises or the material quality or structure at the observation coordinates. The case in which the representative low-magnification reference image is not the ideal reference image, which rarely occurs, can be avoided. 
       Embodiment 6 
       [0111]    In this embodiment, a method of calculating a defect occupation rate will be described. The method of calculating the defect occupation rate according to this embodiment can be applied to any method of Embodiments 1 to 5. Because the configurations described in  FIGS. 1 to 3  and the content described in  FIGS. 4 to 16  are the same as those in this embodiment, explanation thereof is omitted. 
         [0112]      FIG. 17  is an example of a schematic diagram illustrating a method of calculating a defect occupation rate in a defect image.  FIG. 17  illustrates an example of the case of calculating the defect occupation rate, on the basis of a brightness distribution of pixels included in the defect image. 
         [0113]    An example of the case in which a defect area is small is shown in a defect image  1701 , an example of the case in which a defect area is middle is shown in a defect image  1702 , and an example of the case in which a defect area is large is shown in a defect image  1703 . At a right side of the image, a histogram of a brightness distribution calculated for each image and a standard deviation σ of the brightness distribution are displayed. The standard deviation of the brightness distribution corresponding to the defect image  1701  in which the defect area is small is σ=30 ( 1704 ). The standard deviation of the brightness distribution corresponding to the defect image  1702  in which the defect area is middle is σ=40 ( 1705 ) and has a large value as compared with  1704  in which the defect area is small. In addition, the standard deviation of the brightness distribution corresponding to the defect image  1703  in which the defect area is large is σ=70 ( 1706 ) and has a large value as compared with  1704  in which the defect area is small and  1705  in which the defect area is middle. As such, because the standard deviation of the brightness distribution and the defect occupation rate are in a monotonous correlation, it can be determined that the defect occupation rate is large, when the standard deviation of the brightness distribution is more than a certain threshold value. 
         [0114]    The calculation of the defect occupation rate can be executed by the entire control unit and analysis unit  113  of  FIG. 1 , the operation/analysis unit  201  of  FIG. 2 , and the defect occupation rate determination processing unit  305  of  FIG. 3 . Of course, the calculation may be executed by other processing unit. 
         [0115]    As such, the defect occupation rate of the defect image can be calculated simply by using the standard deviation of the brightness distribution of the defect image. 
         [0116]    Here, the method of calculating the defect occupation rate using the standard deviation of the brightness distribution of the defect image has been described. However, parameters other than the standard deviation may be used. In addition, the method of calculating the defect occupation rate is not limited to the above method. 
       Embodiment 7 
       [0117]    In this embodiment, a method of calculating a defect occupation rate, which is different from the method according to Embodiment 6, will be described. The method of calculating the defect occupation rate according to this embodiment can be applied to any method of Embodiments 1 to 5. Because the configurations described in  FIGS. 1 to 3  and the content described in  FIGS. 4 to 16  are the same as those in this embodiment, explanation thereof is omitted. 
         [0118]      FIG. 18  is an example of a schematic diagram illustrating a method of calculating a defect occupation rate from a brightness distribution of a defect area/background of a defect image. First, an image  1801  including one or more defects acquired before acquisition of an inspected image is separated into a defect area  1802  and a background portion other than the defect area and a brightness distribution is calculated. If a result obtained by ADR or ADC with respect to the image  1801  including the defect is used, a defect area can be easily calculated while an operation cost is suppressed. 
         [0119]    In an example of  FIG. 18 , because a brightness of the defect area is low (dark), a state in which the brightness is distributed greatly in the region having the low brightness can be confirmed in a brightness portion  1803  of the defect area. Meanwhile, in a brightness distribution  1804  of the background portion other than the defect area, the distribution is concentrated in a range of brightness values from 100 to 220. From a result, the number of pixels in which the brightness value is beyond the range of 100 to 220 is counted as defect candidates and can be converted into a defect occupation rate. The range of the brightness values used for calculation of the defect occupation rate may be set by a recipe by an operator and may be automatically updated when the defect area is calculated. 
         [0120]    As such, the range of the brightness values to be determined as the defect candidates is calculated previously from the brightness distribution of the image including the defect acquired in the past, the pixels corresponding to the range in the defect image to calculate the defect occupation rate are counted as the defect area, and the defect occupation rate can be calculated easily. 
         [0121]    If the method according to this embodiment is compared with the method of calculating the defect occupation rate according to Embodiment 6, it is necessary to set the range of the brightness values counted as the defect candidates in advance. However, if the result calculated by the ADR or the ADC is used, the range setting is easily automated and thus, the workload of the operator is small. Because range setting optimal to the focused defect kind is enabled, detailed setting is enabled. By executing the detailed setting, a defect kind to be ignored, called a nuisance defect, can be excluded. For example, as illustrated in  FIG. 18 , when a defect kind to be recognized as the defect takes a characteristic brightness distribution, a brightness range corresponding to the brightness distribution is counted as the defect area. Meanwhile, when the defect kind to be ignored without being recognized as the defect, such as the nuisance defect, takes a characteristic brightness distribution, the brightness range corresponding to the brightness distribution is not counted as the defect area, so that a defect occupation rate can be calculated for only desired defect kinds. 
       Embodiment 8 
       [0122]    In this embodiment, a method of determining a threshold value when a standard deviation of a brightness distribution of a defect image is used as a threshold value of a defect occupation rate determination processing will be described. The method of determining the threshold value according to this embodiment can be applied to any method of Embodiments 1 to 5. Because the configurations described in  FIGS. 1 to 3  and the content described in  FIGS. 4 to 18  are the same as those in this embodiment, explanation thereof is omitted. 
         [0123]      FIG. 19  is an example of a schematic diagram of a method using a standard deviation of a brightness distribution of a defect image as a threshold value of defect occupation rate determination processing. First, a plurality of images are classified into a plurality of groups according to magnitudes of defects. Specifically, the plurality of images are classified into a group to be determined as an enormous defect and a group other than the group. An image in which it is determined that a defect occupation rate is low is selected in  1901  and an image in which it is determined that the defect occupation rate is high is selected in  1904 . The image can be selected by an operator through a GUI displayed on a display. However, candidates of defect images are automatically selected in advance on the basis of a defect detection result of ADR or ADC, so that the workload of the operator can be reduced. 
         [0124]    In  FIG. 19 , an average brightness distribution and a standard deviation are calculated for each group, with respect to defect image sets of each group classified on the basis of the defect occupation rate. A variation of a brightness distribution  1902  of a defect image set  1901  in which it is determined that the defect occupation rate is low is small and a standard deviation  1903  becomes σ=30. Meanwhile, a variation of a brightness distribution  1905  of a defect image set  1904  in which it is determined that the defect occupation rate is low is relatively large and a standard deviation becomes σ=80. 
         [0125]    The calculation of the standard deviation of the brightness distribution of the defect image can be realized by the entire control unit and analysis unit  113  of  FIG. 1 , the operation/analysis unit  201  of  FIG. 2 , and the defect occupation rate determination processing unit  305  of  FIG. 3 . Of course, the standard may be executed by other processing unit. 
         [0126]    The standard deviation showing the variation of the brightness distribution of the defect image selected as described above is displayed, so that the operator can easily determine a determination threshold value of the defect occupation rate. 
         [0127]    If an image in which a defect having a size equal to or more than a specific size is detected is automatically selected on the basis of a defect detection result of ADR or ADC and a standard deviation of a brightness distribution of a selected defect image set is calculated, the determination threshold value of the defect occupation rate can be automatically set or automatically updated. 
         [0128]    Even in which the operator manually selects the defect image, instead of the automatic selection, if the defect image of the selection candidate is previously selected as a default in ADR or ADC on a selection screen, efficiency of selection work of the operator can be improved. 
       Embodiment 9 
       [0129]    In this embodiment, a method of selecting the plurality of defect detection methods described in Embodiments 1 to 8 will be described. Because the configurations described in  FIGS. 1 to 3  and the content described in  FIGS. 4 to 19  are the same as those in this embodiment, explanation thereof is omitted. 
         [0130]      FIG. 20  is an example of a GUI that can select a defect detection method. A sample of a low-magnification defect image is displayed in  2001  and a defect occupation rate in the low-magnification defect image is displayed in  2003 . The method according to Embodiment 6 or 7 can be applied to the calculation of the defect occupation rate. However, the present invention is not limited thereto. When a plurality of images can be selected, the plurality of images and a defect occupation rate of each image or an average value of defect occupancies of each image may be displayed. 
         [0131]    A value of 2004 becomes a determination threshold value of the defect occupation rate. The determination threshold value of the defect occupation rate can be input by an operator. However, the determination threshold value can be automatically set by the method described in Embodiment 8. Even in the case in which the operator inputs and sets the determination threshold value, if a value automatically set as an initial value is displayed, the value can become one of determination standards when the operator sets the threshold value. In an example of  FIG. 20 , because the defect occupation rate of the low-magnification defect image is equal to or smaller than the threshold value, it is determined that the defect occupation rate of the low-magnification defect image is low and defect detection processing of a rear step is executed. 
         [0132]    The plurality of methods of generating or acquiring the reference image, described in the embodiments, are written together in  2006  and the operator can select the method. As displayed in  2005 , if each defect detection method is arranged in order of the high processing speeds, each defect detection method becomes one of determination standards when the operator selects the defect detection method. In  2002 , a low-magnification reference image is displayed to correspond to the defect detection method selected in  2006 . 
         [0133]    When an ADR check box  2007  is set to ON, it is effective to display a result obtained by detecting the defect by the selected defect detection method. 
         [0134]    As such, the low-magnification defect image of the observation target, the defect occupation rate thereof, the threshold value of the defect occupation rate, the low-magnification reference image corresponding to the selected defect detection method, and the defect detection method are displayed, so that propriety of the selected defect detection method can be easily confirmed. The GUI can be mounted on the entire control unit and analysis unit  113  of  FIG. 1  and the operation/analysis unit  201  of  FIG. 2 . Of course, the GUI may be mounted to other processing unit. 
         [0135]    The present invention is not limited to the embodiments described above and various modifications are included. For example, the embodiments are described in detail to facilitate the description of the present invention and are not limited to embodiments in which all of the described configurations are included. In addition, a part of the configurations of the certain embodiment can be replaced by the configurations of another embodiment or the configurations of another embodiment can be added to the configurations of the certain embodiment. In addition, for a part of the configurations of the individual embodiments, other configurations can be added, removed, or replaced. 
         [0136]    In addition, a part or all of the individual configurations, functions, processing units, and processing mechanisms may be designed by integrated circuits and may be realized by hardware. In addition, the individual configurations and functions may be realized by software by analyzing programs for realizing the functions by a processor and executing the programs by the processor. Information such as the programs, the tables, and the files for realizing the individual functions may be stored in a recording device such as a memory, a hard disk, and a solid state drive (SSD) or a recording medium such as an IC card, an SD card, and an optical disk. 
         [0137]    In addition, only control lines or information lines necessary for explanation are illustrated and the control lines or information lines do not mean all control lines or information lines necessary for a product. In actuality, almost all configurations may be connected to each other. 
       REFERENCE SIGNS LIST 
       [0000]    
       
         
           
               101 : electron gun 
               102 : lens 
               103 : scanning deflector 
               104 : objective lens 
               105 : sample 
               106 : stage 
               107 : primary electron beam 
               108 : secondary particle 
               109 : secondary particle detector 
               110 : electronic optical system control unit 
               111 : A/D conversion unit 
               112 : stage control unit 
               113 : entire control/analysis unit 
               114 : image processing unit 
               115 : operation unit 
               116 : storage device 
               117 : optical microscope 
               118 : SEM-type defect observation device 
               119 : LAN 
               120 : recipe management device 
               121 : defect information database 
               201 : operation/analysis unit 
               202 : defect data storage unit 
               203 : image data storage unit 
               204 : analysis parameter storage unit 
               205 : analysis result storage unit 
               301 : low-magnification defect image 
               302 : low-magnification reference image 
               303 : enormous defect determination processing unit 
               304 : reference image generation processing unit 
               305 : low-magnification reference image 
               306 : defect detection processing unit 
               307 : defect coordinates 
               501 : defect image 
               502 : reference image 
               503 : difference image 
               504 : defect 
               505 : detected defect area 
               701 : defect image 
               702 : reference image generated using average brightness value of defect image 
               703 : difference image 
               704 : defect 
               705 : detected defect area 
               801 : defect image 
               802 : reference image generated using average brightness value of defect image 
               803 : difference image 
               804 : enormous defect 
               805 : detected defect area 
               1001 : defect image 
               1002 : reference image 
               1003 : difference image 
               1004 : defect 
               1005 : detected defect area 
               1201 : defect image 
               1202 : reference image generated using average brightness value of representative reference image 
               1203 : difference image 
               1204 : defect 
               1205 : detected defect area 
               1206 : representative reference image 
               1401 : defect image 
               1402 : reference image generated using average brightness value of acquired reference image 
               1403 : difference image 
               1404 : defect 
               1405 : detected defect area 
               1406 : acquired reference image 
               1601 : defect image 
               1602 : reference image generated using average brightness value of area other than defect from acquired defect image 
               1603 : difference image 
               1604 : defect 
               1605 : detected defect area 
               1606 : acquired defect image 
               1607 : defect area 
               1701 : image having low defect occupation rate 
               1702 : image having middle defect occupation rate 
               1703 : image having high defect occupation rate 
               1704 : brightness distribution histogram of image having low defect occupation rate and standard deviation 
               1705 : brightness distribution histogram of image having middle defect occupation rate and standard deviation 
               1706 : brightness distribution histogram of image having high defect occupation rate and standard deviation 
               1801 : defect image 
               1802 : defect area 
               1803 : brightness distribution of defect area 
               1804 : brightness distribution of background portion other than defect area 
               1901 : defect image set 
               1902 : brightness distribution 
               1903 : standard deviation 
               1904 : defect image group 
               1905 : brightness distribution 
               1906 : standard deviation 
               2001 : defect image 
               2002 : reference image corresponding to selected defect detection method 
               2003 : defect occupation rate 
               2004 : defect occupation rate determination threshold value 
               2005 : defect detection method arranged in order of throughput 
               2006 : selectable defect detection method 
               2007 : ADR execution check box