Abstract:
The present invention relates to a device ( 303 ) for setting image acquisition conditions for charged particle beam devices or the like. An image integration unit ( 402 ) forms a plurality of images with a number of different integrations (number of integrations 2, 4 . . . N) from one image (number of integrations N) acquired in advance. A pattern matching unit ( 403 ) matches the patterns of each of the plurality of images having a number of different integrations with template images registered in advance and then finds a score that shows the degree of matching between images. A selection unit ( 407 ) selects a number of integrations such that any variation in the scores is contained within a prescribed allowable range. The selected number of integrations is stored in a recipe of the device. Thus, it is possible to determine the number of integrations in the recipes without having to operate the device, and to set image acquisition conditions so as to allow a minimization of the processing time while maintaining a sufficient S/N ratio.

Description:
RELATED APPLICATIONS 
     This application is the U.S. National Phase under 35 U.S.C. §371 of International Application No. PCT/JP2011/003215, filed on Jun. 8, 2011, which in turn claims the benefit of Japanese Application No. 2010-168778, filed on Jul. 28, 2010, the disclosures of which Applications are incorporated by reference herein. 
     TECHNICAL FIELD 
     The present invention relates to a device for setting image acquisition conditions, such as a charged particle beam device, typified by a scanning electron microscope, and a computer program. In particular, the present invention relates to a device and a computer program that set device conditions of a device of forming an image by integrating signals. 
     BACKGROUND ART 
     Recently, semiconductor processes have become further finer. Fine patterns are measured and inspected using a microscope. Images of processed patterns are taken by the microscope and displayed on a display, and inspection and measurement (which hereinafter may be simply referred to as “inspection”) are performed using an image processing technique. 
     In the case of inspecting processed circuit patterns in the middle of semiconductor processes, inspection of all the patterns on a semiconductor chip is ineffective. Accordingly, areas where a malfunction tends to occur in the process or occurred in the past are specified, and inspection is performed. 
     At this time, it is significantly difficult to find the specified areas to be subjected to the inspection using a microscope having a high resolution. A method that is referred to as template matching disclosed in Patent Literature 1 is used. 
     The template matching is a method of specifying a desired target pattern in a search region on a sample. On each position in the search region, a degree of matching with a pattern image, which is referred to as a template, is determined, and a position indicating the highest degree of matching with the template in the search region is identified, thereby specifying the position. The operation is performed by a computer. More specifically, a plurality of gradation values representing the unevenness of a pattern in the microscope image are compared with a template diagram in a certain region. If the degree of matching is high, it is determined that matching is achieved. Preliminary registration of position information and the template allows automatic measurement. 
     CITATION LIST 
     Patent Literature 
     
         
         Patent Literature 1: JP-A-2002-328015 
       
    
     SUMMARY OF INVENTION 
     Technical Problem 
     A charged particle beam device, such as scanning electron microscope (SEM), is a device that forms an image on the basis of detection of charged particles (electrons or ions) emitted from a sample, and synchronizes a beam scanning signal and scanning of a display device with each other to thereby form a two-dimensional image. In this case, an image having a high S/N ratio is formed by integrating a plurality of image signals (frames). The number of frames is proportional to the number of scanning by a scanning deflector. As the number of frames increases, the amount of signals supplied to form an image increases. Accordingly, a large number of frames to be integrated enables an image having a high S/N ratio to be formed. 
     In a scanning electron microscope or the like that automatically measures and inspects a sample of a semiconductor device or the like, image acquisition conditions including the number of frames are required to be preset. Conditions of image signals provided for template matching are also required to be preset. A large number of integrations enables an image having a high S/N ratio to be formed as described above. However, unnecessary times of beam scanning increases processing time accordingly. This also causes a possibility that contaminates a sample, shrinks a pattern, or increases charging or the like. Accordingly, in order to maintain a high S/N ratio while suppressing processing time and the like, the optimum number of frames is required to be selected. However, Patent Literature 1 does not describe selection of an optimal number of frames for pattern matching. 
     Hereinafter, a device for setting image acquisition conditions and a computer program that have an object to set image acquisition conditions maintaining a high S/N ratio while suppressing processing time and the like will be described. 
     Solution to Problem 
     As an aspect to achieve the object, a device for setting image acquisition conditions and a computer program causing a computer to execute the processes are hereinafter proposed that include: an image integration unit that integrates a plurality of image signals and forms an image; and a pattern matching unit that performs pattern matching on the image integrated by the image integration unit using a preliminarily registered template, wherein the image integration unit changes the number of integrations on each of a plurality of preliminarily acquired integrated images, and forms a plurality of images with the different numbers of integrations, the pattern matching unit performs pattern matching on the plurality of images with the different numbers of integrations, and acquires a score representing a degree of matching between the template and a position specified by the template, and the device further comprises a selection unit that selects the number of integrations where variation in the scores is within a prescribed allowable range, the number of integrations where all the plurality of integrated images represent a score of at least a prescribed value, the number of integrations where variation in dimensions of a pattern specified by the pattern matching is within a prescribed allowable range, or the number of integrations where an average of dimensions of the pattern is within a prescribed range. 
     ADVANTAGEOUS EFFECTS OF INVENTION 
     The configuration can select the appropriate number of integrations for image acquisition using integrated images having already been acquired. Accordingly, appropriate image acquisition conditions can be set in accordance with process variation or the like. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a schematic configuration diagram of a scanning electron microscope. 
         FIG. 2  is a diagram showing examples of images acquired by the scanning electron microscope. 
         FIG. 3  is a schematic configuration diagram of the scanning electron microscope and a device for setting image acquisition conditions. 
         FIG. 4  is a diagram illustrating the device for setting image acquisition conditions in detail. 
         FIG. 5  is a diagram illustrating an example of an image formed on the basis of integrations of plural pieces of image data. 
         FIG. 6  is a diagram illustrating an example of forming images with different number of integrations on the basis of a plurality of integrated images. 
         FIG. 7  is a diagram showing a graph of scores in the case where a plurality of images according to different numbers of integrations are formed on the basis of a plurality of evaluation target images, and template matching is performed on the images. 
         FIG. 8  is a diagram showing an example of a table representing an average and variation of scores of the numbers of integrations. 
         FIG. 9  is a flowchart illustrating a step of determining the optimal number of integrations on the basis of the scores of pattern matching. 
         FIG. 10  is a flowchart illustrating a step of determining the optimal number of integrations on the basis of the scores of pattern matching. 
         FIG. 11  is a graph showing a relationship between the numbers of integrations and measured lengths. 
         FIG. 12  is an average and variation of measured lengths according to the numbers of integrations. 
         FIG. 13  is a flowchart illustrating a step of determining the optimal number of integrations on the basis of measurement result. 
         FIG. 14  is a flowchart illustrating a step of determining the optimal number of integrations on the basis of measurement result. 
         FIG. 15  is a diagram illustrating a step of determining an acceptance of a template. 
         FIG. 16  is a flowchart illustrating a step of setting the optimal acceptance on the basis of the score of pattern matching. 
         FIG. 17  is a flowchart illustrating a step of setting the optimal acceptance on the basis of the score of pattern matching. 
         FIG. 18  is a flowchart illustrating a step of setting the appropriate number of integrations on the basis of pattern matching. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Microscopes include a type of emitting light and a type of emitting electron beams. The types are different in resolution. However, in some cases, both types can adopt the same image processing. 
     This is because, although an optical microscope with a high resolution causes a CCD sensor or the like to detect light reflected by a sample and an electron microscope having a higher resolution detects electrons generated on the sample, both types perform digital signal processing for imaging. 
     Image acquisition conditions (presence and absence of focus, the number of integrations, magnification, etc.) of an electron microscope and the like automatically measuring and inspecting a sample are to be preliminarily registered. However, a semiconductor manufacturing process includes process variation. Accordingly, conditions at the time of registration are sometimes incapable of appropriately extracting a pattern to cause a matching error. 
     In order to solve the problem, the number of integrations (the number of frames), which is one of the image acquisition conditions, may be set larger to sharpen the pattern. Unnecessary increase in the number of integrations unfortunately reduces the throughput. Furthermore, there is a possibility of increasing adverse effects of contamination, shrinkage, and charging. 
     Accordingly, measures to appropriately set the number of integrations according to process variation and the like are preferable to realize achievement of a matching success rate and reduction in processing time and the like. However, there are no clear indicator for appropriate setting, and is a possibility of erroneous setting. Accordingly, it is difficult to reset the number of integrations. Furthermore, erroneous setting may affect measurement results and the like, which is a cause of giving up resetting. 
     In many cases, an unnecessary number of integrations, which is one of image acquisition conditions requiring preliminary setting in the template matching method, are set. This setting is largely affected by contamination, shrinkage, charging and the like, while throughput is reduced. In order to solve the problem, it is preferred to correct the number of integrations to an appropriate number. The correction requires verification using a device and a sample. This verification prevents the device from being used for inspection, which is the original object, and results in reducing the operating ratio of the device. That is, once operation on a mass production line has started, it is significantly difficult to change the image acquisition conditions. 
     Hereinafter, a device for setting image acquisition conditions and a computer program that can find setting conditions of the device without using a device or a sample will be described in further detail. 
     In order to set device conditions without using a device or a sample, a method is proposed that sets conditions using images acquired by automatic measurement or automatic inspection. A critical dimension SEM (CD-SEM) is a device for continuously measuring many samples formed through the same manufacturing process. That is, as the measurement process progresses, image information is accumulated. The accumulation of the image information and use thereof for determination for setting image acquisition conditions negate the need to operate the device only for setting the device conditions. 
     The previously acquired images include variation in pattern size, brightness at pattern edges, and pattern noise due to process variation. Use of data of the image group can verify variation in the case of changing the image acquisition conditions without using a device or a sample. In the device of this embodiment, which will be described below, the image acquisition conditions are changed using the data of the image group including process variation. 
     Use of the image group including process variation acquired by automatic measurement negates the need to use the device or a sample again for verifying change of image acquisition conditions, thereby allowing changing off-line. Furthermore, mass production can be developed in a short time period. Accordingly, even a user who cannot have tried correction concerning matching error can easily perform change. 
     Embodiment 1 
       FIG. 1  is a schematic configuration diagram of a CD-SEM. A primary electron beam  104  drawn from a negative pole  101  by a voltage V 1  applied to a first positive pole  102  is accelerated by a voltage Vacc applied to a second positive pole  103  and travels toward a lens system on a succeeding stage. The primary electron beam  104  is converged as a minute spot on a sample  107  by a converging lens  105  and an objective lens  106  controlled by a lens control power source  114 , and two-dimensionally scans the sample  107  by means of two stages of deflection coils  108 . 
     Scanning signals for the deflection coils  108  are controlled by a deflection controller  109  in conformity with an observation magnification. A secondary electron  110  emitted from the sample by the primary electron beam  104  scanning the sample  107  is detected by a secondary electron detector  111 . Information on the secondary electron detected by the secondary electron detector  111  is amplified by an amplifier  112  and displayed on a display of a computer  113 . 
     The process of manufacturing a semiconductor device processes a silicon wafer to manufacture a semiconductor device. The wafer is adopted as the sample  107 . A circuit pattern in the middle of manufacturing is displayed on a screen of the display of the computer  113 , which allows an operator to observe a manufacturing failure of the circuit pattern and an adhering foreign body. Some CD-SEMs have a function of automatically measuring the width of a circuit pattern using secondary electron information. The process using the image information and the template matching identifying a desired pattern from the image are performed in an operation unit in the computer  113 . Images used for template matching and an automatic measurement file for automatic measurement are registered in a storage in the computer  113 . The automatic measurement is performed on the basis of the registered information. The acquired image is registered in a data accumulation unit in the computer  113 . 
       FIG. 2  schematically shows images acquired by automatic measurement by a CD-SEM. In the case of adopting  FIG. 2  (a) as a standard, (b) has a large pattern size and (c) has a small pattern size. There is what has a large noise as with (d). These images include an image having a strong pattern edge as with (e) and an image having a weak pattern edge as with (f). The differences between images (a) to (f) occur owing to effects of process variation or differences in layer, even in the case of automatic measurement with the identical image acquisition conditions. The differences of images (d) to (f) also appear owing to differences in the number of integrations, which is one of the image acquisition conditions. The larger the number of integrations, the stronger the pattern edge is and the lower the noise is. The smaller the number of integrations, the weaker the pattern edge is and the higher the noise is. The differences of images (a) to (f) affect scores acquired by matching with the template. 
     In particular, in the case of an image with high noise and a weak pattern edge, the pattern is not successfully detected, which tends to cause a matching error. The score will now be described. For instance, the score represents a degree of matching with the template from 0 to 1000. Complete matching is represented as 1000. The lower the degree of matching, the lower the score becomes. In the CD-SEM, in order to prevent erroneous detection, an acceptance is set as a reference value for determining presence or absence of a target pattern. The acceptance is a threshold set automatically or by a user. If the score is at least the acceptance, it is determined that the pattern is successfully detected. Instead, if the score is equal to or less than the acceptance, it is determined that matching error occurs. 
       FIG. 3  is a schematic configuration diagram of the CD-SEM and image acquisition conditions optimization function/device. An image acquired by a CD-SEM  301  is accumulated in a data accumulation unit  302  residing in the computer  113  in  FIG. 1 . As necessary, the image is transmitted to an image acquisition conditions optimization function/device  303 . The image acquisition conditions optimization function/device  303  optimizes the image acquisition conditions. A result calculated here is transmitted to a storage  304  residing in the computer  113  in  FIG. 1 , and registered again as new setting content of automatic measurement. A CD-SEM  305  performs automatic measurement with the new setting content. 
     The image acquisition conditions optimization function/device  303  in  FIG. 3  will now be described in detail with reference to  FIG. 4 . First, image data accumulated in a data accumulation unit  401  is transmitted to an image acquisition conditions optimization function/device  409 . A conversion unit  402  of the number of integrations converts the data into a plurality of images with the number of integrations. The converted images are transmitted to a pattern matching unit  403  at any time, which matches each image with the number of integration with the template to thereby acquire the score. 
     The acquired score is temporarily accumulated in a matching result (score) accumulation unit  404 . Meanwhile, in the case where the optimization target is a length measurement image, there is a possibility where the measured length varies owing to contamination or variation in shrinkage according to the number of integrations. Accordingly, it is required to verify that the measured length is within a control value, in addition to the acceptance. Thus, while the image converted by the conversion unit  402  of the number of integrations is transmitted to the pattern matching unit  403 , the image is transmitted to a length measurement unit  405  in parallel. Length measurement with each image with the number of integrations acquires the measured length. The measurement result is accumulated in a length measurement result accumulation unit  406 . 
     Accordingly, verification of the results accumulated in the matching result accumulation unit  404  and the length measurement result accumulation unit  406  allows a simulation of the case of changing the number of integrations, which is one of the image acquisition conditions. A score average, variation in the scores (3σ), a length measurement average, and a length measurement reproducibility (3σ) are calculated from the accumulated information. A filter unit  407  automatically determines the optimal number of integrations from the calculated result, thereby determining optimum conditions. 
     The conversion unit  402  of the number of integrations in  FIG. 4  will now be described in detail with reference to  FIG. 5 . Image data in the data accumulation unit  401  includes gradation values of respective pixels per scan. For instance, in the case of the image with the number of integrations N (N frames), N times of scans are repeatedly performed as shown in  FIG. 5 , and one image is generated from the data. Thus, N pieces of data exist. Use of the N pieces of data can generate images with 2 frames, 4 frames, 6 frames to N frames at the maximum as shown in  FIG. 6 . The number of generated frames can be automatically or arbitrarily set. 
     The pattern matching unit  403  in  FIG. 4  will now be described in detail with reference to  FIG. 7 . After the images with the respective numbers of integrations are transmitted from the conversion unit  402  of the number of integrations to the pattern matching unit  403 , matching with the template registered in the automatic length measurement file in the storage of the computer  113  in  FIG. 1  is performed on each image. As exemplified in  FIGS. 5 and 6 , a plurality of images with the numbers of integrations can be generated from one image. Accordingly, a plurality of scores per image can be acquired as shown in  FIG. 7 . Likewise, acquisition of scores of images 2 to X allows the score average and the variation in the scores (3σ) of each number of integrations to be calculated. Here, it is provided that the calculated result is shown in  FIG. 8 . 
     Meanwhile, the filter unit  407  in  FIG. 4  determines the optimal number of integrations on the basis of the acceptance registered in the automatic measurement file and an arbitrarily set allowable value of the variation in the scores. That is, the filter unit  407  functions as a selection unit that selects a candidate of the number of integrations or the final number of integrations. 
     Here,  FIG. 9  exemplifies a matching result determination flowchart. In a matching result accumulation unit  701 , the score average and the variation in the scores (3σ) with each number of integrations are calculated as described above. The information is transmitted to a filter unit  702 , and, first, it is determined whether the variation in the scores (3σ) is within the allowable range or not. Here, it is provided that, if a value where 3σ is within R is the allowable value, a plurality of applicable conditions exist. Next, it is determined whether the score average is higher than the acceptance set in the automatic length measurement file by at least a certain amount U or not. 
     This is because errors may frequently occur if the difference from the acceptance is not at least the certain amount. If a plurality of applicable conditions exist at this stage, a condition with the smallest number of integrations is determined as the optimum condition. Here,  FIG. 10  shows a determination example in the case of  FIG. 8 . For instance, provided that the acceptance Q set in the automatic length measurement file is 300, the allowable value R of the variation in the scores (3σ) is 100, and the certain amount U is 100, it is determined that 8 frames or more is optimal. 
     Next, the length measurement unit  405  in  FIG. 4  will be described in detail with reference to  FIGS. 11 and 12 . After the images with the respective numbers of integrations are transmitted from the conversion unit  402  of the number of integrations to the length measurement unit  405 , length measurement with the same condition as the length measurement method registered in the automatic length measurement file is performed on each image. As described above, plural images with the numbers of integrations can be generated from one image. Accordingly, a plurality of measured lengths can be acquired on the basis of one image as in  FIG. 11 . Likewise, acquisition of measured lengths with the respective numbers of integrations on images 2 to X allows the length measurement average and the length measurement reproducibility (3σ) to be calculated. Here, it is provided that the calculated result is shown in  FIG. 12 . 
     Meanwhile, the filter unit  407  determines the optimal number of integrations on the basis of the length measurement managing value set in the automatic measurement file and the automatically or arbitrarily set length measurement variation (3σ). Here,  FIG. 13  shows a measurement length result determination flowchart. In a length measurement result accumulation unit  901 , the length measurement average and the length measurement reproducibility (3σ) with each number of integrations are calculated, as described above. The information is transmitted to a filter unit  902 , and, first, it is determined whether the length measurement reproducibility (3σ) is within the allowable range or not. Here, it is provided that, if a value where the allowable value is 3σ is within M, a plurality of applicable conditions exist. Next, it is verified whether the length measurement average falls within the length measurement managing value set in the automatic length measurement file. This is because, change in the number of integrations prevents the measured length from being significantly changed. If a plurality of applicable conditions exist at this stage, a condition with the smallest number of integrations is determined as the optimum condition. 
     Here, a determination example in the case of  FIG. 12  is shown in  FIG. 14 . For instance, provided that the set length measurement managing value (N±P) is within 51.5 nm±1 nm and the length measurement reproducibility M is within 3 nm, 16 frames or more is determined optimal. Even if the pattern matching result is determined such that 8 frames is optimal, determination of the measurement result has precedence in the case of the length measurement image. 
       FIG. 18  is a flowchart illustrating a process of determining image acquisition conditions. After processing is started, image data is read from a storing medium accumulating the images (step  1201 ). Image data for forming one image includes plural pieces of data of images to be integrated. Accordingly, a plurality of images including the different numbers of integrations are formed using these pieces of image data (step  1202 ). For instance, a plurality of images including the different numbers of integrations, for instance, one, two, three, . . . , N are formed. In the case where a operation device preliminarily forming the images with the different numbers of integrations, and a storing medium accumulating the differently integrated images are provided, step  1202  is not required. 
     Next, template matching is performed on the integrated image, thereby acquiring the score (step  1203 ). The template matching process is performed on each image with the different number of integrations, thereby acquiring a plurality of scores (degree of matching between the template and the position specified by the template) on one evaluation target image (step  1204 ). Next, the process of acquiring a plurality of scores is performed even on a different evaluation target image, thereby acquiring a plurality of scores on the evaluation target images (step  1205 ). The scores acquired as described above are compared with the preset acceptance (step  1206 ). The number of integrations having a score of at least the acceptance on every evaluation target image is selected (step  1207 ). The acceptance is a threshold for determining whether matching succeeds or not. Accordingly, it can be considered that the number of integrations where all the scores exceed the acceptance is the image acquisition condition where success of matching is compensated on a plurality of images acquired from the different samples (different samples acquired under the same manufacturing conditions). Thus, selection of the number of integrations from there among at least allows the matching success rate to be maintained high. 
     Next, the minimum one is selected from among the selected numbers of integrations (step  1208 ). The selection of the minimum one from among the numbers of integrations where the high matching success rate is compensated can minimize beam irradiation on the sample while maintaining the matching success rate in a high state. 
     Finally, the selected number of integrations is stored in a recipe as the image acquisition conditions (step  1209 ). The above steps allow appropriate image acquisition conditions to be set without reducing the operating ratio of the device. 
     Embodiment 2 
     On the automatic length measurement file in the CD-SEM, it is determined whether template matching succeeds or not according to the acceptance, as described above. In the case where the matching success rate is set to have priority, the acceptance may sometimes become higher than necessary. Automatic determination of the acceptance will be described in detail with reference to  FIG. 15 . First, an image  1001  to be registered as a template is acquired. Next, a target pattern  1002  to be a target of image recognition in the acquired image is registered. At the same time of registering the target pattern, image recognition  1003  is performed with the target pattern in the acquired image. A first candidate score S 1  with the highest score and a second candidate score S 2  with the second highest score are extracted. The acceptance is calculated from S 1  and S 2 . The acceptance is the average between S 1  and S 2 . In the case where S 1  is 800 and S 2  is 300, the acceptance is 550. 
     Here, the acceptance is a value that can be set to an arbitrary value. The value can be rewritten in the case where it is determined that the value automatically determined on registration of the template is high. Thus, a method is proposed that automatically determines whether the automatically determined acceptance is optimal or not and, if the value is determined inappropriate, automatically or arbitrarily rewrites the value to an appropriate value. It should be noted that, setting the acceptance too low causes a possibility of erroneously recognizing a pattern different from the target pattern as the target pattern. In order to prevent erroneous recognition, a minimum value Qmin with an acceptance that can be preset is determined. 
     Even without difference between the acceptance and the score average, errors frequently occur. Accordingly, the acceptance is required to be set such that at least a certain difference is secured.  FIG. 16  exemplifies an acceptance optimization flowchart in consideration therewith. If the acceptance is high, the variation in the scores (3σ) is small. Accordingly, even with a high score average, the verification of score average  1103  unfortunately processes the condition as an inapplicable condition. Here, it is verificated whether the score average of the inapplicable conditions is at least a minimum acceptance Qmin and the certain amount U of difference is secured or not. If the condition is met, reduction in acceptance allows adoption of the condition where the number of integrations is further reduced. 
     It is provided that the optimal acceptance value is a value acquired by subtracting the certain amount U from the score average of the inapplicable condition. However, it can be automatically or arbitrarily selected whether to adopt a condition with a reduced acceptance or not. That is, the optimum condition is selected from between two cases, or the case where the acceptance is not optimized and the case where the acceptance is optimized (a plurality of conditions if, on optimization, a plurality of applicable conditions exist). In the case of setting optimization to be automatic, it can be preset whether to select acceptance-optimized one or not. 
     Here,  FIG. 17  shows an example of simulation based on the result in  FIG. 8 . In the case where the acceptance Q registered in the automatic length measurement file is 550, the allowable value R of the variation in the scores (3σ) is 100, the certain amount U is 100, and the minimum acceptance Qmin is 300, it is determined that the number of integrations is required to be at least 16 unless the acceptance is optimized. In the case where the optimization of the acceptance is valid, the number of integrations can be reduced to 8 frames. 
     REFERENCE SIGNS LIST 
     
         
           101  negative pole 
           102  first positive pole 
           103  second positive pole 
           104  primary electron beam 
           105  converging lens 
           106  objective lens 
           107  sample 
           108  deflection coils 
           109  deflection controller 
           110  secondary electron 
           111  secondary electron detector 
           112  amplifier 
           113  computer 
           114  lens control power source
         301 ,  305  CD-SEM   
     
           302 ,  401  data accumulation unit 
           303 ,  409  image acquisition conditions optimization function/device 
           304  storage 
           402  conversion unit of the number of integrations 
           403  pattern matching unit 
           404  matching result (score) accumulation unit 
           405  length measurement unit 
           406 ,  901 ,  903  length measurement result accumulation unit 
           407 ,  702 ,  704 ,  902 ,  904 ,  1102 ,  1105  filter unit 
           701 ,  703 ,  1101 ,  1104  matching result accumulation unit 
           1001  template registration image 
           1002  target pattern registration 
           1103  verification of score average