Patent Publication Number: US-2012045115-A1

Title: Defect inspection device and defect inspection method

Description:
TECHNICAL FIELD 
     The present invention relates to a device and a method for detecting a defect in an inspection region. The present invention relates to a device and a method for detecting a defect of a fine pattern, a defect due to a foreign matter, and the like in an inspection region by using, for example, electron beams, lamp light, laser light or the like, on the basis of an image (a taken image) acquired from the inspection region. 
     BACKGROUND ART 
     An electron beam inspection device is used for a defect inspection of a wafer, for example. The electron beam inspection device of this type inspects the wafer for a defect roughly in the following steps. Firstly, the electron beam inspection device scans an electron beam in synchronization with the movement of a stage to acquire a secondary electronic image (hereinafter, referred to as a “detected image”) of a circuit pattern formed on the wafer. Next, the electron beam inspection device makes a comparison between the detected image and a reference image and determines that a portion exhibiting a large difference is defective. When a statistically significant method is used for the detected defect to obtain defect information, analysis of distribution of such defects or detailed information of the defects is used to analyze a problem with a wafer manufacturing process. 
     Meanwhile, in a conventional defect inspection, a defect is determined by applying a certain threshold to a difference image acquired from a detected image and a reference image. In other words, a portion, in the extracted difference image, which exhibits a difference larger than the threshold in intensity between the images is determined to be defective. 
     However, applying the same threshold to all the regions in an inspected object causes a problem that a lot of false detections (misreports) occur due to an influence of the density or dimensions of a pattern. Hence, a method has been proposed in which the magnitude of a used threshold is optimized according to the density or dimensions of a pattern present in an inspected portion of an inspected object (see Patent Document 1). In addition, a method has been proposed in which an inspected object is divided into multiple regions by using design data, and adjustment parameters are applied to thresholds and gray-scale transformation processing of detected images corresponding to the respective regions (see Patent Document 2). 
     PRIOR ART DOCUMENTS 
     Patent Documents 
     Patent Document 1: Japanese Patent Application Publication No. 63-126242 
     Patent Document 2: Japanese Patent Application Publication No. 11-135583 
     SUMMARY OF THE INVENTION 
     Problem to be Solved by the Invention 
     In fact, use of these methods enables optimization of a threshold difference between the inspection regions (detected images). However, a threshold of only one type is still applied to a single inspection region (inspected image). This means that as long as defects are in a single inspection region (inspected image), not only a significant defect but also an insignificant defect is influenced similarly by the set threshold. Consequently, the significant defect and the insignificant defect cannot be discriminated from each other. 
     Means for Solving the Problem 
     Hence, the present invention proposes a mechanism in which multiple sensitivity regions are set in a single inspection region, and a defect only in a region where a DOI (Defect of interesting) is present in the single inspection region can be detected while being discriminated from the other ones. Specifically, multiple sensitivity regions are set in the inspection region based on features of an image in the inspection region, and set sensitivities for the respective sensitivity regions are applied to a detected image, a difference image, and a determination threshold for a defect determination unit. 
     EFFECTS OF THE INVENTION 
     The present invention makes it possible to selectively extract, among defects in a single inspection region, only a significant defect in a partial region designated by an operator. This can considerably reduce time required for a defect inspection. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram for explaining an example of an overall structure of a semiconductor wafer inspection device according to Embodiment 1. 
         FIG. 2  is a diagram for explaining a structural layout of a wafer to be used as an inspection target in Embodiment 1. 
         FIG. 3  is a diagram for explaining a recipe creation step and an inspection step according to Embodiment 1. 
         FIG. 4  is a diagram showing an example of a GUI screen for a trial inspection according to Embodiment 1. 
         FIG. 5  is a diagram showing an example of a GUI screen for sensitivity setting according to Embodiment 1. 
         FIG. 6  is a diagram showing an example of a wiring pattern as an inspection target. 
         FIG. 7  is a diagram for explaining an overview of inspection processing according to Embodiment 1. 
         FIG. 8  is a diagram for explaining an example of an operation of generating a sensitivity table according to Embodiment 1. 
         FIG. 9  is a diagram for explaining an example of an operation of applying a sensitivity table according to Embodiment 1. 
         FIG. 10  is a diagram for explaining an example of an overall structure of a semiconductor wafer inspection device according to Embodiment 7. 
         FIG. 11  is a diagram for explaining a step of generating a background feature table. 
         FIG. 12  is a diagram for explaining an example of an overall structure of a semiconductor wafer inspection device according to Embodiment 8. 
         FIG. 13  is a diagram showing an example of a GUI screen for sensitivity setting according to Embodiment 9. 
         FIG. 14  is a diagram for explaining a sensitivity adjustment method according to Embodiment 10. 
         FIG. 15  is a diagram for explaining a sensitivity adjustment method according to Embodiment 11. 
         FIG. 16  is a diagram for explaining feature information detection processing according to Embodiment 12. 
     
    
    
     MODES FOR CARRYING OUT THE INVENTION 
     Hereinafter, descriptions are given in turn of embodiments of a defect inspection device and a defect inspection method according to the invention. Note that the drawings used in describing the embodiments are drawn from the standpoint of explaining the embodiments. Thus, the invention is not limited to the drawings to be described later. In addition, the embodiments to be described later can be combined as appropriate. 
     (1) Embodiment 1  
     (1-1) Device Configuration 
       FIG. 1  shows a schematic configuration of a circuit pattern inspection device according to Embodiment 1. Note that  FIG. 1  shows a vertical cross-sectional structure and a signal processing system of the circuit pattern inspection device. The circuit pattern inspection device is a device to which a scanning electron microscope is applied and emits electron beams to a semiconductor device substrate such as a wafer. Hence, a chief part thereof is accommodated in a vacuum chamber. 
     The circuit pattern inspection device emits an electron beam  102  generated from an electron source  101  to a wafer  106  placed on a sample stage  109 , detects secondary signals  110  which are generated secondary electrons, reflected electrons, or the like, by using a detector  113 , and forms an image. The image is a detected image. Note that the detected image is compared with a reference image, and a pixel region having an intensity difference equal to or larger than a determination threshold is extracted as a defect candidate. 
     An object lens  104  is arranged on an irradiation path of the electron beam  102  to converge energy of the electron beam  102  on the wafer  106 . This object lens  104  narrows a beam diameter. Thus, the diameter of the electron beam  102  on the wafer  106  is made very small. 
     In order to acquire a detected image in a certain range (inspection region), a deflector  103  deflects the electron beam  102  to scan the wafer  106 . At this time, a movement position of the electron beam  102  on the wafer  106  in the scanning and timing of sampling a corresponding one of the secondary signals  110  by the detector  113  are synchronously controlled. Thereby, a two-dimensional image (that is, a detected image) corresponding to an inspection region is acquired or taken. 
     The wafer  106  has various circuit patterns formed on a surface thereof These circuit patterns are formed of various materials. Thus, the irradiation of the circuit patterns with the electron beam  102  might cause a charge accumulation phenomenon (a charge phenomenon). The charge phenomenon causes lightness of an image to be changed and causes a trajectory of the electron beam  102  made incident to be deflected. Thus, a charge control electrode  105  is arranged in front of the wafer  106  to control an electric field strength. 
     Incidentally, an image is formed by irradiating a standard sample piece  121  with the electron beam  102  and calibration of coordinates and calibration of a focus point are performed prior to execution of the aforementioned inspection. Meanwhile, since the diameter of the electron beam  102  is very small, the width of scanning by the deflector  103  is much smaller than the wafer  106 . In other words, an image (detected image) acquired with the electron beam  102  is very small. Hence, prior to the execution of the inspection, the wafer  106  is firstly placed on an XY stage  107 , and a light microscope  120  detects an alignment mark for coordinate calibration formed on the wafer  106 . The detection is performed at a comparatively low magnification. Next, the XY stage  107  is moved so that the alignment mark is positioned to be irradiated with the electron beam  102 , and thereby the coordinates are calibrated. 
     In addition, the focal point is calibrated in the following manner. Specifically, the height of the standard sample piece  121  is measured by a Z sensor  108  configured to measure the height of the wafer  106 . Next, the height of the alignment mark provided on the wafer  106  is measured. By using a value of the measurement, the excitation strength of the object lens  104  is adjusted so that a focal range of the electron beam  102  narrowed by the object lens  104  includes the alignment mark. 
     Besides, a deflector  112  is arranged in the circuit pattern detection device so as to detect as many secondary signals  110  generated from the wafer  106  as possible. The deflector  112  is used to deflect the electron beam  102  so that many secondary signals  110  hit a reflector  111 . Consequently, many secondary electrons reflected by the reflector  111  are detected by the detector  113 . 
     An overall controller  118  transmits a control signal a to the deflector  103 , transmits a strength control signal b to the object lens  104 , receives a value c of measurement in a height direction of the wafer  106  from the Z sensor  108 , and transmits a control signal d to the XY stage  107 . 
     Each detection signal detected by the detector  113  is converted into a detected image h by a digital image generator  114 . The detected image h is stored in an image storage memory  115 . In this embodiment, the detected image h is stored in a memory region  115   b.  Note that a reference image is stored in a memory region  115   a.  As the reference image, for example, another detected image determined to be normal, a synthesis image generated by synthesizing multiple detected images, an image generated from design data, or the like is used. 
     A feature detector  122  detects image feature information k from the detected image stored in the memory region  115   b.  A sensitivity adjuster  123  creates a sensitivity adjustment table l by using the feature information k detected by the feature detector  122  and sensitivity adjustment parameters (sensitivity coefficients) g given by a console  119  through the overall controller  118 . The sensitivity adjustment table l includes registered sensitivity values different according to respective multiple regions defined in the inspection region. An example of setting the sensitivity values will be described later. The created sensitivity adjustment table l is given to a sensitivity corrector  125 . 
     The detected image h stored in the memory region  115   b  and the reference image stored in the memory region  115   a  are given to a difference image generator  124 . The difference image generator  124  generates a difference image i between the detected image h and the reference image by subtracting one from the other. The sensitivity corrector  125  applies the sensitivity adjustment table l to the difference image i and corrects the image in such a manner as to increase the sensitivity in a part of the difference image i and lower the sensitivity in another part thereof The difference image i subjected to the correction (that is, an adjusted difference image j) is stored in a difference image storage memory  116 . 
     A defect determination unit  117  extracts a pixel whose intensity value is larger than zero as a defect candidate among the adjusted difference images j, and sends the overall controller  118  an image signal thereof and corresponding coordinates on the wafer  106 , as a defect information signal e. The overall controller  118  and the console  119  are connected with each other to perform two-way communications. For example, a defect image signal based on the defect information signal e is transmitted from the overall controller  118  to the console  119 . This displays a defect image on the screen of the console  119 . On the other hand, an inspection condition f inputted by the operator is transmitted from the console  119  to the overall controller  118 . Based on the inspection condition f, the overall controller  118  performs computing on the control signal a to the deflector  103 , the control signal b to the object lens  104 , and the control signal d to the XY stage  107 . 
     (1-2) Inspection Target Example 
       FIG. 2  shows an example of a pattern structure of the wafer  106  which is an example of an inspection target. The wafer  106  has a disc shape having, for example, a diameter of 200 mm to 300 mm and a thickness of 1 mm, approximately. The wafer  106  has circuit patterns formed on a surface thereof, the circuit patterns corresponding to several hundred to several thousand products. Each circuit pattern is formed by a rectangular circuit pattern called a die  201 . The die  201  corresponds to a single product. For example, in a case of a general memory device, the die  201  includes four memory mat groups  202  in a pattern layout thereof Further, each memory mat group  202  includes, for example, 100×100 memory mats  203 . Each memory mat  203  includes several million memory cells  204  repeated two-dimensionally. 
     (1-3) Inspection Recipe Creation Step and Inspection Step 
     Descriptions are given of an inspection recipe creation step and an inspection step by using  FIG. 3 . Part (a) of  FIG. 3  shows the inspection recipe creation step, and Part (b) of  FIG. 3  shows the inspection step. An inspection recipe is created before execution of an inspection. 
     (a) Inspection Recipe Creation Step 
     Descriptions are given of an inspection recipe creation step by using  FIG. 3(A) . Firstly, the operator instructs the overall controller  118  through the console  119  to read a standard recipe and mount the wafer  106  on the sample stage  109  (Step  301 ). At this time, the wafer  106  is loaded on the sample stage  109  from an unillustrated cassette by an unillustrated loader. 
     Next, the operator sets general inspection conditions in the overall controller  118  through the console  119  (Step  302 ). For example, various conditions are set for, for example, the electron source  101 , the deflector  103 , the object lens  104 , the charge control electrode  105 , the reflector  111 , the deflector  112 , the detector  113 , the digital image generator  114 , and the like. At this time, an image of the standard sample piece  121  is detected, and set values of regions are corrected to appropriate values according to the detection result. In addition, a pattern layout of the wafer  106  is set. At this time, the operator designates a rectangle for a layout of the memory mat  203  which is a region having the memory cell  204  repeated therein, and sets the memory mat group  202  having the rectangle of the memory mat  203  repeated therein. Moreover, an alignment pattern and coordinates thereof are registered, and thereby an alignment condition is set. Further, information on an inspection region to be inspected is registered. For example, pixel dimensions, the number of additions to be used in image processing, and the like are set. Still further, a calibration condition is set so that the inspection can be made on a constant condition even if amounts of detection light amount vary with the wafer. For example, a coordinate point and an initial gain are set, the coordinate point and the initial gain being used for acquiring an image suitable for light amount calibration. 
     Upon completion of setting the general inspection conditions, the overall controller  118  executes a temporary inspection, and a detected image is stored in the image storage memory  115  (Step  303 ). At this time, two detected images are read from the image storage memory  115 , and a difference image i therebetween is generated. At the stage of the temporary inspection, the sensitivity adjustment table l including only one sensitivity value registered therein is used. Thus, the difference image i is stored in the difference image storage memory  116 . 
     Thereafter, the feature detector  122  detects the feature information k from the detected image stored in the image storage memory  115  (Step  304 ). 
     Next, the operator sets the sensitivity coefficients g for feature amounts through the console  119  (Step  305 ). The sensitivity coefficients g are given to the sensitivity adjuster  123  through the overall controller  118 . The sensitivity adjuster  123  creates the sensitivity adjustment table l by using the sensitivity coefficients g set by the operator. The sensitivity adjustment table l includes sensitivity values for feature amounts detected in the inspection region and sensitivity values for the other regions. The sensitivity adjustment table l is applied to the difference image i by the sensitivity corrector  125 . The adjusted difference image j generated by this processing is stored in the difference image storage memory  116 . 
     Thereafter, a trial inspection (Step  306 ), a defect check (Step  307 ), an inspection condition check (Step  308 ), and a recipe creation termination determination (Step  309 ) are executed by using, for example, a GUI (graphical user interface) screen shown in  FIG. 4 . Note that while the inspection condition is not established, a series of aforementioned steps  302  to  309  is repeatedly executed. 
     Here, a screen configuration of the GUI screen is briefly explained by taking  FIG. 4  as an example. The GUI screen shown in  FIG. 4  is a GUI screen used at the time of execution of the trial inspection (Step  306 ). The GUI screen includes: a region (map display portion)  401  in which stored images are displayed in a map form; a region (image display portion)  402  in which a detected image clicked in the map display portion  401  and an image having a defect  407  in the map display portion  401  are displayed; a region (defect information display portion)  403  in which defect information on the defect  407  displayed in the image display portion  402  is displayed; a sensitivity setting button  404 ; a comparison start button  405 ; and a tool bar  406  for adjusting defect display threshold. 
     In  FIG. 4 , a display region  408  located in an upper left portion of the image display portion  402  is for displaying a detected image, a display region  409  located in an upper right portion thereof is for displaying a reacquired image, the reference image, a synthesis model image, and the like, and a display region  410  located in a lower left portion thereof is for displaying an image checked for defect. 
     The trial inspection in Step  306  is performed in the following manner. Firstly, the operator sets an appropriate threshold by operating the tool bar  406  for adjusting defect display threshold, and then clicks the comparison start button  405 . This executes a comparison between actual patterns based on images stored in advance. When the trial inspection is executed and when the defect  407  having difference equal to or larger than the threshold is found, the defect  407  appears in the map display portion  401 . When the operator clicks on the defect  407 , the defect image appears in the image display portion  402  ( 410 ), and defect information appears in the defect information display portion  403 . 
     When the operator clicks the sensitivity adjustment button  404 , a GUI screen shown in  FIG. 5  appears. Sensitivity adjustment through this GUI screen corresponds to the sensitivity adjustment table creation processing (Step  305 ). A sensitivity adjustment display portion  501  shown in  FIG. 5  includes: an image display portion  502  in which the image recorded in advance and the sensitivities for the shapes are displayed; a sensitivity adjustment portion  503  which displays shape information detected from the image displayed in the image display portion  502  and is used for inputting the sensitivity coefficients g; a shape detection button  504  for instructing for execution of processing of detecting feature information from the image displayed in the image display portion  502 ; an application button  505  for applying numerical values set in the sensitivity adjustment portion  503  to the trial inspection or an actual inspection; a cancellation button  506  for cancelling the numerical values set in the sensitivity adjustment portion  503 ; and a review button  507  for checking, by using an image, the numerical values set in the sensitivity setting portion  503 . 
     Note that the sensitivity setting (generation of the sensitivity adjustment table l) is performed in the following manner. Firstly, the operator performs an operation of displaying an image stored in advance on the image display portion  502 . Next, when the operator clicks the shape detection button  504 , a rectangle, a circle, an edge, and the like are detected from the detected image displayed in the image display portion  502 , and the various shapes appear in the sensitivity setting portion  503 . The operator inputs sensitivity coefficients g for a region inside and a region outside each shape based on the display. To put it differently, the operator sets the multiple sensitivity coefficients g in a single detected image. 
     For example, in a case of a semiconductor manufacturing process, various patterns as shown in Parts (A) to (D) of  FIG. 6  can be inspected. In this embodiment, multiple sensitivities optimum for the respective patterns can be set in the single detected image. Incidentally, Parts (A) to (C) of  FIG. 6  show examples of hole patterns  601 ,  602 , and  603  made in a drilling step. For example, the hole pattern  601  is a pattern formed by circles  611  having the same diameter. For example, the hole pattern  602  is a pattern formed by circles  612  and circles  613  having different diameters. The hole pattern  603  is a pattern formed by circles  614  having the same diameter and two wiring patterns  615  having the same line width. Part (D) of  FIG. 6  shows an example of a wiring pattern  604 . The wiring pattern  604  is a pattern formed by three wirings  616  each having a certain line width. 
     Generally, an operator is interested in defect detection in a particular pattern portion in patterns generated in a wafer. Thus, this sensitivity setting enables the sensitivity of a portion of operator&#39;s interest to be set higher than the surrounding portions. Note that  FIG. 5  shows a case where each sensitivity coefficient g is selected from “high,” “middle,” and “low.” However, the sensitivity coefficient g can be inputted as a numeric value. In addition, the case of  FIG. 5  allows an extended width to be set as “extend” or “not extend.” The extended width is used to provide whether or not to extend the setting range, applied to the line width, of the sensitivity coefficient g for a detected line. For example, the extended width is used to increase the sensitivity for a defect on a line. In the case of  FIG. 5 , the selection is made only on whether or not to extend the line width, but a configuration can be employed in which a selection is made from multiple degrees. Moreover, the sensitivity setting portion  503  enables selection of a display of a detected shape in the image display portion  502 . This is a function for facilitating checking of a shape detected by a click operation on the shape detection button  504  and for preventing unintended sensitivity setting. 
     Next, when the operator clicks the review button  507 , the shapes and the sensitivities therefor set in the image display portion  503  appear in the image display portion  502 . Lastly, when the operator clicks the application button  505 , a feature table is generated in the sensitivity adjuster  123  based on the sensitivity coefficients set in the sensitivity setting portion  503 . This completes the sensitivity setting using the sensitivity setting screen  501 , and the individual sensitivity coefficients g set in the sensitivity setting portion  503  are ready for application to an inspection. Note that the operator can revoke the numerical values set in the sensitivity setting portion  503  by clicking the cancellation button  506 . In addition, the sensitivity setting screen  501  can be forcedly closed by merely clicking the cancellation button  506 . 
     After the setting of the sensitivities (generation of the sensitivity adjustment table l), the aforementioned GUI screen shown in  FIG. 4  appears, and the trial inspection is executed. As described above, the trial inspection is started by the operation of clicking the comparison start button  405 . This click operation triggers reading the sensitivity coefficients g set on the sensitivity setting screen  501  from the sensitivity adjustment table l, and execution of the comparison between the actual patterns stored in advance. Thereafter, the operator checks for the defect detected through the image display portion  402  (Step  307 ). If there is no problem (an affirmative result in Step  309 ), the recipe creation is terminated. After the termination of the recipe creation, the wafer is unloaded, and the features, the sensitivity coefficients g, and the like of the shapes set in the sensitivity adjuster  123  are stored in the recipe (Step  310 ). If there is a problem with the detected defect (a negative result in Step  309 ), the processing returns to the general inspection condition setting, and then executes the aforementioned series of processing steps. 
     (b) Inspection Step 
     The description is given of an actual inspection step on the basis of Part (B) of  FIG. 3 . The operator designates a wafer to be inspected and a recipe therefor to start an inspection operation (Step  311 ). Then, the designated wafer is loaded (Step  312 ), and optical conditions for units of an electronic optical system and the like are set (Step  313 ). Subsequently, an alignment (Step  314 ) and a calibration (Step  315 ) are executed. With the aforementioned processing, preparation work for an inspection is completed. 
     Thereafter, an image detection and a defect determination are executed (Step  316 ). Firstly, an image (detected image) is acquired from a set region. The detected image has been stored in the image storage memory  115  as shown in  FIG. 7 . The feature detector  122  reads the detected image from the memory region  115   b  and extracts feature information k (shape features) from the detected image. The extracted feature information k is given to the sensitivity adjuster  123 . 
     Next, the sensitivity adjuster  123  collates the feature information k with the feature table of the recipe. At this time, the sensitivity adjuster  123  creates a sensitivity adjustment table l unique to the detected image so that the sensitivity coefficient in the recipe is applied to a matching region. However, if the detected feature information k is not present in the recipe, the sensitivity adjuster  123  writes the sensitivity coefficient individually set by the operator to the sensitivity adjustment table l. 
     Thereafter, the difference image generator  124  creates a difference image i between the detected image ( 115   b ) and a reference image ( 115   a ). The created difference image i is given to the sensitivity corrector  125 . The sensitivity corrector  125  applies the sensitivity adjustment table l to the difference image i, and outputs an adjusted difference image j to which the sensitivities set for feature portions in the detected image is applied. This adjusted difference image j is stored in the difference image storage memory  116 . 
     This adjusted image j is read by the defect determination unit  117 . The defect determination unit  117  includes a threshold setting function unit  701  and a defect determination function unit  702 . The threshold setting function unit  701  is used by the operator to set threshold information. A threshold thus set is used as a threshold table  703 . The defect determination function unit  702  applies the threshold table  703  to the adjusted difference image j read from the difference image storage memory  116 . In sum, the defect determination function unit  702  compares the intensities of the adjusted difference image j with the threshold in the threshold table  703  and outputs a region having an intensity larger than the threshold therefor as a defect information signal e. 
     Upon determination of the defect as described above, a review of the defect by the operator is executed (Step  317 ). The operator checks a defect type based on the detected image acquired at the inspection and displayed in the image display portion  402 , a reacquired image reacquired when the stage is moved to the defect coordinates, a synthesis model image, an image checked for defect, and the like. 
     Upon completion of the defect review, necessities for wafer quality determination based on defect distribution on a defect type basis and for additional analysis are determined After these results are stored in the overall controller  118  (Step  318 ), the wafer is unloaded, and the inspection is terminated (Step  319 ). 
     (1-4) Detailed Operations of Detecting Features and Adjusting Sensitivities 
     Subsequently, a description is given of details of aforementioned operations performed by the feature detector  122  and the sensitivity adjuster  123 . Firstly, a flow of processing operations in the trial inspection is described by using Part (A) of  FIG. 8 . Note that a circle pattern  808  is formed in the detected image  408 . In the trial inspection, the feature detector  122  executes noise elimination processing and edge detection processing on the image  408 . As the result, an edge image  803  is generated. Based on the edge detection, an edge portion  809  of the pattern  808  is extracted. Thereafter, the feature detector  122  applies a Hough transform and a circular Hough transform to the edge image  803  to detect feature information on a circle, a line, and the like from the edge image  803 . 
     The sensitivity adjuster  123  registers the detected feature information in a feature table  804 . Subsequently, the operator checks the features registered in the feature table  804  on the screen. The operator sets a high sensitivity coefficient g for a portion having feature information recognized as a DOI defect by the operator, while setting a low sensitivity coefficient g for the other portions. In sum, the feature table  804  including sets of information is added to the recipe, the sets of information each having (i) feature information, (ii) a region corresponding to the feature information, and (iii) a sensitivity coefficient g corresponding to the feature information. 
     Next, a flow of processing operations in the actual inspection is described by using Part (B) of  FIG. 8 . Also in the actual inspection, the feature detector  122  executes the noise elimination processing and the edge detection processing on a detected image  805 . Thereby, an edge image  806  is acquired. Thereafter, a Hough transform and a circular Hough transform are applied to the edge image  806  to detect circle pattern regions and line pattern regions included in the edge image  806 . The processing performed so far are the same as those in the trial inspection. 
     The sensitivity adjuster  123  executes processing of collating detected feature information with the feature information in the feature table  804 . If there is a match in the feature information, the sensitivity adjuster  123  writes a corresponding one of the sensitivity coefficients g set in the feature table  804  in an adjustment region  810  in a sensitivity adjustment table  807 . If no matching feature information is present, the sensitivity adjuster  123  writes a default sensitivity coefficient g set by the operator in the adjustment region  810  in the sensitivity adjustment table  807 . 
     (1-5) Effects of Sensitivity Adjustment to Partial Regions 
     By using  FIG. 9 , a description is given of effects due to the capability of setting the multiple sensitivities on the single detection screen in this embodiment. Note that Part (A) of  FIG. 9  shows a difference image  901  created by a detected image and a reference image. Part (B) of  FIG. 9  shows an enlarged image  902  in which a part of the difference image  901  is enlarged. Part (C) of  FIG. 9  shows the sensitivity adjustment table  807  generated by collating detected feature information with feature information in a feature table. Part (D) of  FIG. 9  shows an enlarged image  903  of an adjusted difference image j obtained after application of a sensitivity adjustment table  907  to the difference image  901 . 
     In the actual inspection, the sensitivity adjustment table  807  is applied to the difference image  901  created from the inspected image and the reference image, as described above. As shown in Part (B) of  FIG. 9  in an enlarged manner, a defect  905   a  and a defect  905   b  which have different intensities are present in a circular region  906  in the difference image  901 , and multiple defects  904  having different intensities are present outside the circular region  906 . 
     In a case of a conventional method, all the defects are not discriminated from each other, and a single sensitivity is set for one entire detected image, and a comparison with a single threshold is made, whereby a defect is determined 
     However, in this embodiment, by using the sensitivity adjustment table  807 , a region  907  having a high sensitivity value is written to the circular region  906 , and a region  908  having a low sensitivity value is written to an outside region thereof, as shown in Part (C) of  FIG. 9 . Thus, when the sensitivity adjustment table  807  is applied to the difference image  901 , an adjusted difference image  903  including only the defects  905   a  and  905   b  in the circular region  906  can be obtained, as shown in Part (D) of  FIG. 9  which is an enlarged view of the difference image  901 . That is, the defects  906  present in the region having a low sensitivity value  908  set therefor are eliminated from the adjusted difference image  903 . 
     As described above, application of a technique according to the embodiment makes it possible to show the operator only the defects  905   a  and  905   b  present in a DOI shape region. In other words, it is possible to omit work in which the operator himself/herself differentiates a significant defect, from the other defects, included in defects detected from an inspection region based on the same sensitivity. Consequently, the operator can review only defects present in the DOI region on the screen at the time of defect review. Thus, the efficiency of a review by the operator can be enhanced. Moreover, since a region can be designated by using feature information in this embodiment, the operator does not have to accurately calculate the location of the DOI region, and can easily designate a region to be adjusted. As the result, it is possible to prevent an occurrence of a misreport without lowering the defect capture efficiency of the entire wafer or the entire die. Furthermore, since the sensitivity only for a particular region can be increased, a high-sensitivity inspection can substantially be achieved. 
     Besides, this embodiment employs a method in which the sensitivities are set through the designation of the feature amounts included in an image. Thus, even at a stage of design data in which no detected image is present, the sensitivity adjustment regions can be set. Consequently, time to acquire a detected image for setting the sensitivity adjustment regions can be saved. Also in this point, the technique according to the embodiment is effective to enhance the production efficiency. 
     Still further, in this embodiment, multiple sensitivities can be set in a single detected image. Thus, the sensitivities to detected defects can be applied respectively. As the result, the ratio of misreports can be made small enough, and thus the work efficiency in an inspection process can be enhanced. 
     (2) Embodiment 2  
     Next, a description is given of a circuit pattern inspection device according to Embodiment 2. Note that a basic device configuration of the circuit pattern inspection device according to Embodiment 2 and basic processing steps thereof are the same as those in Embodiment 1. Hereinafter, only a difference from Embodiment 1 will be described. 
     In Embodiment 1 described above, a Hough transform is applied in the feature information detection processing performed by the feature detector  122 . However, the feature information can be detected by another method. For example, template matching is applicable thereto. 
     In this embodiment, the feature detector  122  executes noise elimination on the detected image  805 , and executes processing of matching with a template image prepared in advance on the image subjected to the noise elimination. Such a processing method can also detect necessary feature information in the detected image  805 . Note that since the template matching is a known technique, a detailed description thereof is omitted. In addition, it is possible to apply a method in which the operator selects a used template from multiple templates in advance. 
     In Embodiment 2, the following effects can be expected in addition to the effects of Embodiment 1. In sum, the operator can dynamically designate a region of his/her interest through a template selection operation. Thus, the review efficiency can be enhanced over that in Embodiment 1. Further, the template selection by the operator can be executed intuitively. Consequently, stress on the operator can be reduced. 
     (3) Embodiment 3  
     Next, a description is given of a circuit pattern inspection device according to Embodiment 3. Note that the circuit pattern inspection device according to Embodiment 3 also has a basic device configuration and basic processing steps as the same as those in Embodiment 1. Hereinafter, only a difference from Embodiment 1 will be described. 
     In Embodiment 1 described above, the description has been given of the case where the feature detector  122  has feature information of the detected image, which is a circle, a rectangle, and a line. However, it goes without saying that the feature information included in the detected image  408  is not limited thereto. For example, a case where the feature information is a polygon is conceivable. A polygon includes a combination of all or some of a circle, a rectangle, and a line. The polygon also includes a combination of multiple same-type shapes. 
     The feature detector  122  in this embodiment can be implemented by adding a function of detecting or determining a polygon to the feature detector  122  in Embodiment 1. 
     Compared with Embodiment 1, a range of a figure detectable as feature information can be extended by using Embodiment 3. 
     (4) Embodiment 4  
     Next, a description is given of a circuit pattern inspection device according to Embodiment 4. Note that the circuit pattern inspection device according to Embodiment 4 also has a basic device configuration and basic processing steps as the same as those in Embodiment 1. Hereinafter, only a difference from Embodiment 1 will be described. 
     In Embodiment 1 described above, the description has been given of the case where shapes registered in the feature table by the sensitivity adjuster  123  are a circle, a line, and a rectangle. However, it goes without saying that the shapes registered in the feature table are not limited thereto. For example, a polygon may be registered in the feature table. The polygon in this embodiment also includes a combination of all or some of the circle, the rectangle, and the line. The polygon also includes a combination of multiple same-type shapes. 
     The sensitivity adjuster  123  in this embodiment can be implemented by adding a function of detecting or determining a polygon to the sensitivity adjuster  123  in Embodiment 1. 
     Compared with Embodiment 1, a range of sensitivity adjustment can be extended by using Embodiment 4. 
     (5) Embodiment 5 
     Next, a description is given of a circuit pattern inspection device according to Embodiment 5. Note that the circuit pattern inspection device according to Embodiment 5 also has a basic device configuration and basic processing steps as the same as those in Embodiment 1. Hereinafter, only a difference from Embodiment 1 will be described. 
     In Embodiment 1 described above, the description has been given of the case where the feature detector  122  detects feature information from the detected image  408 . However, the feature information can be detected from an image other than the detected image  408 . For example, an image to be used for defect determination, a synthesis image created from multiple images, and an image generated from design data can be used for detecting the feature information. 
     (6) Embodiment 6  
     Next, a description is given of a circuit pattern inspection device according to Embodiment 6. Note that the circuit pattern inspection device according to Embodiment 6 also has a basic device configuration and basic processing steps as the same as those in Embodiment 1. Hereinafter, only a difference from Embodiment 1 will be described. 
     In Embodiment 1 described above, the description has been given of the case where the sensitivity adjuster  123  sets sensitivities for shapes detected from the detected image. However, the sensitivity setting is not limited to this step. For example, by using a feature information database prepared in advance, the sensitivities for the feature information may be set before the detected image is acquired or before the feature information is acquired from the detected image. 
     (7) Embodiment 7  
     (7-1) Device Configuration 
     Next, a description is given of a circuit pattern inspection device according to Embodiment 7. Note that the circuit pattern inspection device according to Embodiment 6 also has a basic device configuration and basic processing steps as the same as those in Embodiment 1 Hereinafter, only a difference from Embodiment 1 will be described. 
       FIG. 10  shows a schematic configuration of a circuit pattern inspection device according to this embodiment. Note that portions in  FIG. 10  corresponding to those in  FIG. 1  are illustrated while being assigned the same reference signs. Embodiment 7 is different from Embodiment 1 in that a background feature analyzer  1001  and a background feature adding unit  1002  are added to the device configuration in Embodiment 1. 
     The background feature analyzer  1001  executes processing of analyzing a region having a white back ground and a region having a black background and of recording an analysis result in a background feature table m. The analysis is made by comparing a detected image stored in the image storage memory  115  and a reference image (such as a synthesis image obtained by synthesizing multiple detected images or an image generated from design data). 
     In this specification, the white background and the black background are used in the following sense. An image  1  and an image  2  are herein present. The image  1  is the detected image, while the image  2  is the reference image. An image obtained by subtracting an intensity value of the reference image from an intensity value of the detected image is referred to as a difference image (=the image  1 −the image  2 ). In this specification, in the difference image, a region in which an intensity value larger than 0 (zero) is present is referred to as the black background, while a region in which an intensity value equal to or smaller than 0 (zero) is present is referred to as the white background. 
     The background feature analyzer  1001  in this embodiment registers a region detected as the white background in a white background sensitivity adjustment table ma, and registers a region detected as the black background in a black background sensitivity adjustment table mb. Note that the background feature table m is additionally formed in a partial region of the sensitivity adjustment table l created by the sensitivity adjuster  123 . However, the background feature table m may be created as a table independent from the sensitivity adjustment table l. 
     The details of the background feature table m created by the background feature analyzer  1001  are described by using  FIG. 11 . Part (A) of  FIG. 11  shows a detected image  408  stored in the image storage memory  115 . Part (B) of  FIG. 11  shows a reference image  1101  for comparison with the detected image  408 . Part (C) of  FIG. 11  shows a background feature table  1102 . In Part (A) of  FIG. 11 , in the detected image  408 , a region  1103  having a higher intensity than the reference image  1101  is the black background, while a region  1104  having a lower intensity than the reference image  1101  is the white background. Thus, as shown in Part (C) of  FIG. 11 , a region  1106  is registered in the white background sensitivity adjustment table ma, and a region  1105  is registered in the background feature table mb. 
     The background feature adding unit  1002  applies the background feature table m to an adjusted difference image j outputted from the sensitivity corrector  125  and adjusts the sensitivities according to whether the detected defect image belongs to the white background or the black background. To put it differently, in this embodiment, background information is used for readjusting the sensitivities of an adjusted difference image j. Note that the sensitivities to be applied to the white background and the sensitivities to the black background are set by the sensitivity adjuster  123 , for example. An adjusted difference image j subjected to the readjustment is stored in the difference image storage memory  116 . 
     (7-2) Characteristics of Processing Operation 
     Next, a description is given of a processing operation portion particular to this embodiment. Thus, the description is mainly given of operations of the background feature analyzer  1001  and the background feature adding unit  1002 . The same processing as that in Embodiment 1 is executed in the other circuit portions, as a matter of course. 
     The background feature analyzer  1001  calls the detected image  408  and the reference image  1101  from the image storage memory  115  to acquire intensity differences on a pixel basis. Here, an intensity of a certain pixel in the detected image  408  is A(x, y), and an intensity of a certain pixel in the reference image  1101  is B(x, y). At this time, if the intensity difference (A−B) of the certain pixel in the detected image  408  from that in the reference image  1101  is a value equal to or larger than zero, it is determined that the pixel has the black background. On the other hand, the intensity difference (A−B) is a value smaller than zero, it is determined that the pixel has the white background. The background feature analyzer  1001  calculates the intensity difference for all the pixels to create the background feature adding table  1102 . In the case of Part (C) of  FIG. 11 , the region  1105  having the black background and the region  1106  having the white background which are outside circle patterns are present. 
     Information on the background feature table m is applied to the adjusted difference image j by the background feature adding unit  1002 . Note that the sensitivity of the outside of the circle patterns is set high in this Embodiment 7, contrary to Embodiment 1. In other words, in the adjusted difference image j outputted from the sensitivity corrector  125 , sensitivities for the region  1103  and the  1104  are both set high. 
     Thus, the background feature adding unit  1002  corrects the sensitivities according to whether each of the region  1103  and the region  1104  is the white background or the black background. For example, the background feature adding unit  1002  operates in a manner that the sensitivities for the white background and the black background are collectively increased or lowered, or in a manner that the sensitivity for one is increased and the sensitivity for the other is lowered. 
     Consequently, the operator can find both or only one of the region  1103  and the region  1104  as a DOI defect in the detected image on the basis of the sensitivities set according to the background type. In other words, the operator can not only execute the defect review easily but also execute a defect analysis and classification of the DOI region simultaneously. 
     Note that differently in the description above, the background feature adding unit  1002  can be used as a function unit configured to give the background information as added information to the adjusted difference image j. In this case, the background information can be used at the stage of the determination processing performed by the defect determination unit  117 . For example, the background information can be used for adjusting a determination threshold. In addition, a method can be employed in which the background information is not used for the processing by the defect determination unit  117  but is outputted to the overall controller  118  as added information as it is. In this case, the background information can be used for various post-processing to be executed by the overall controller  118 . 
     (8) Embodiment 8  
     Next, a description is given of a circuit pattern inspection device according to Embodiment 8. Note that a basic device configuration of the circuit pattern inspection device according to Embodiment 8 and basic processing steps thereof are similar to those in Embodiment 7 (or Embodiment 1). To put it differently, Embodiment 8 corresponds to a modification of Embodiment 7. 
     In Embodiment 7 described above, the description has been given of the case where the background information is applied to the adjusted difference image j. In this embodiment in contrast, the background information is applied to the defect information signal e outputted from the defect determination unit  117 . 
       FIG. 12  shows a schematic configuration of the circuit pattern inspection device according to Embodiment 8. Note that portions in  FIG. 12  corresponding to those in  FIG. 10  are illustrated while being denoted by the same reference signs. In Embodiment 8, a background feature adding unit  1201  is used instead of the background feature adding unit  1002  in Embodiment 7. The background feature adding unit  1201  is arranged between the defect determination unit  117  and the overall controller  118 . Thus, processing operations up to the operation of the defect determination unit  117  in this Embodiment 8 are the same as those in Embodiment 1. 
     In this embodiment, the background feature adding unit  1201  can function as a function unit, for example, configured to add the background information as added information to the defect information signal e outputted from the defect determination unit  117 . In this case, the overall controller  118  can determine the ratio of the white background to the black background and tendencies of defects according to each type later. 
     In addition, in this embodiment, the background feature adding unit  1201  can function as a function unit configured to correct a sensitivity for each defect included in the corresponding defect information signal e outputted from the defect determination unit  117 . In this case, only a DOI of the operator&#39;s interest can be stored in the overall controller  118 . 
     (9) Embodiment 9  
     Next, a description is given of a circuit pattern inspection device according to Embodiment 9. Note that a basic device configuration of the circuit pattern inspection device according to Embodiment 9 and basic processing steps thereof are the same as those in Embodiment 1. Hereinafter, only a difference from Embodiment 1 will be described. 
     In Embodiment 1 described above, the description has been given of the case where the feature detector  122  detects the feature information while focusing on shapes in the detected image  408 . However, the feature information can be detected by using an intensity or a color. 
       FIG. 13  shows a display example of the sensitivity setting screen  501  according to Embodiment 9. Note that portions in  FIG. 13  corresponding to those in  FIG. 5  are illustrated while being denoted by the same reference signs. The sensitivity adjustment display portion  501  shown in  FIG. 13  includes: the image display portion  502  configured to display sensitivities for images and shapes which are recorded in advance; a statistic calculation button  1301  for instructing for execution of processing of statistically calculating intensity distribution of an image displayed in the image display portion  502 ; a statistic amount display portion  1302  configured to display the statistic intensity distribution calculated from the image displayed in the image display portion  502 ; a sensitivity setting portion  1303  for setting a range of the statistic amount and sensitivities to be applied to the range; an application button  505  for applying numerical values set in a sensitivity adjustment portion  1303  to an trial inspection or an actual inspection; a cancellation button  506  for cancelling the numerical values set in the sensitivity adjustment portion  1303 ; and a review button  507  for checking, by using an image, the numerical values set in the sensitivity setting portion  1303 . 
     Hereinafter, processing operations particular to this embodiment will be described. The sensitivity adjustment display portion  501  shown in  FIG. 13  is displayed by clicking the sensitivity setting button  404  in the trial inspection of Part (A) of  FIG. 3 . Next, by using the image display portion  502 , the operator checks a detected image stored in advance. Subsequently, the operator operates the statistic calculation button  1301  by clicking thereon to obtain statistic values. The overall controller  118  detects this clicking operation to calculate the statistic values. A calculated statistic amount is displayed in the statistic amount display portion  1302  in a graph, a character string or a three-dimensional form or another form.  FIG. 13  shows the intensity distribution with a curved line. 
     The operator checks the values of the statistic amount and sets intensities and sensitivities to be used as boundaries. In the case of  FIG. 13 , the operator sets a sensitivity for a high intensity (a range from an intensity g to an intensity h) to be high and sets a sensitivity for a low intensity (a range from an intensity n to an intensity m) to be low. 
     Thereafter, the operator clicks the review button  507  to check the detected image subjected to sensitivity correction which is displayed in the image display portion  502 . When being satisfied with the detected image subjected to the sensitivity correction, the operator clicks the application button  505 . On the other hand, if the operator determines that readjustment is needed, the operator sets values in the sensitivity setting portion  1303  again, clicks the review button  507 , and thereby checks a sensitivity image. Note that when wishing to stop the sensitivity adjustment, the operator clicks the cancellation button  506 . 
     As described above, when the application button  505  is clicked, setting information therefor is reflected on a corresponding inspection condition. Thereafter, the operator checks the result of the trial inspection executed according to the reset sensitivity conditions. If there is no problem, the operator terminates the creation of a recipe. Upon termination of the creation of the recipe, the wafer is unloaded. In addition, the intensities set by the sensitivity adjuster  123  and information on the sensitivity coefficients are stored in the recipe. 
     Next, in the actual inspection, a wafer to be inspected and recipe information thereon are designated to start an inspection operation. As shown in Part (B) of  FIG. 3 , after the start of the inspection, the designated wafer is loaded, and optical conditions for units of the electronic optical system and the like are set. Subsequently, an alignment and a calibration are executed. Upon completion of the preparation work, an image in a set region is acquired, and intensity distribution is statistically calculated from the detected image. 
     Thereafter, the sensitivity adjuster  123  collates intensity values of the detected image with regions of the intensity values set in the recipe, and registers a sensitivity coefficient g for a region having a matching intensity value range. That is, a sensitivity adjustment table l is generated. In the meantime, a difference image i is created from the detected image and a reference image which are stored in the image storage memory  115 . The sensitivity corrector  125  applies the sensitivity adjustment table l to the difference image i, and stores the difference image i as an adjusted difference image j in the difference image storage memory  116 . Thereafter, the defect determination unit  117  executes a defect determination on the adjusted difference image j stored in the difference image storage memory  116 . 
     After the defect determination, a defect review is executed. In the defect review, the operator executes checking of a defect type on a detected image  408  acquired in the inspection, a reacquired image reacquired when the stage is moved to correspond to the defect coordinates, a synthesis image generated by synthesizing multiple detected images, an image checked for defect, and the like. Upon completion of the defect review, necessities for wafer quality determination based on defect distribution on a defect type basis and for additional analysis are determined These results are stored in the overall controller  118 , the wafer is unloaded and the inspection is terminated. 
     As in this embodiment, even if information on a shape is not necessarily present, use of the intensity distribution enables detection of the feature information on any shape, for example, and setting of the inspection sensitivities therefor. In addition, in this embodiment, the description has been mainly given of the case where the intensity distribution is used, but distribution of colors (color phases) or chromas (saturations) can be used to detect the feature information and set the inspection sensitivities. A method using colors is effective when being applied to a general-purpose inspection and the like of a product other than a semiconductor manufacturing device. 
     (10) Embodiment 10  
     Next, a description is given of a circuit pattern inspection device according to Embodiment 10. Note that a basic device configuration of the circuit pattern inspection device according to Embodiment 10 and basic processing steps thereof are the same as those in Embodiment 1. Hereinafter, only a difference from Embodiment 1 will be described. The difference between Embodiment 10 and Embodiment 1 is the content of processing up to generation of an adjusted difference image from a detected image. 
       FIG. 14  shows processing steps employed in Embodiment 10. Note that portions in  FIG. 14  corresponding to those in  FIG. 7  are illustrated while being assigned the same reference signs. 
     Also in this embodiment, the feature detector  122  detects feature information from a detected image ( 115   b ), and the sensitivity adjuster  123  sets sensitivity coefficients g according to regions based on the detected feature information. However, in this embodiment, a sensitivity adjustment table l generated by the sensitivity adjuster  123  is applied to the detected image ( 115   b ). In other words, in this embodiment, the sensitivities of the detected image ( 115   b ) are adjusted by a sensitivity corrector  1401 , before a difference image i is calculated. Thereby, only a DOI defect is extracted from the detected image ( 115   b ). 
     Thereafter, the detected image ( 115   b ) whose sensitivities are adjusted is given to a difference image generator  1402  to calculate a difference from a reference image ( 115   a ). Such processing steps also make it possible to obtain a difference image including only a DOI defect. In this embodiment, output from the difference image generator  1402  is stored in the difference image storage memory  116 . Since processing operations after the operation of the defect determination unit  117  are the same as those in Embodiment 1, a description thereof will be omitted. 
     (11) Embodiment 11  
     Next, a description is given of a circuit pattern inspection device according to Embodiment 11. Note that a basic device configuration of the circuit pattern inspection device according to Embodiment 11 and basic processing steps thereof are the same as those in Embodiment 1. Hereinafter, only a difference from Embodiment 1 will be described. The difference between Embodiment 11 and Embodiment 1 is an application target of a sensitivity adjustment table l. In Embodiment 1, the description has been given of the case where the sensitivity adjustment table l is given to the sensitivity corrector  125 . Meanwhile, in Embodiment 10 described above, the description has been given of the case where the sensitivity adjustment table l is given to the sensitivity corrector  1401 . However, in Embodiment 11, the sensitivity adjustment table l is used for correcting thresholds used by the defect determination unit  117 . 
       FIG. 15  shows processing steps employed in Embodiment 11. Note that portions of  FIG. 15  corresponding to  FIG. 7  are illustrated while being denoted by the same reference signs. Also in this embodiment, the feature detector  122  detects feature information from a detected image ( 115   b ), and the sensitivity adjuster  123  sets sensitivity coefficients g according to regions based on the detected feature information. Still also in this embodiment, the difference image generator  124  generates a difference image between a detected image ( 115   b ) and a reference image ( 115   a ) which are stored in the image storage memory  115 . 
     However, in the embodiment, the difference image generated by the difference image generator  124  is stored in the difference image storage memory  116 , and a sensitivity adjustment table l generated by the sensitivity adjuster  123  is given to a threshold corrector  1501  of the defect determination unit  117 . The threshold corrector  1501  applies the sensitivity adjustment table l to the threshold table  703  given by the threshold setting function unit  701 , and sets multiple thresholds in a single region of the detected image. For example, the threshold corrector  1501  sets a threshold of a region corresponding to a DOI defect to be low, and sets thresholds of the other regions to be high. Specifically, the defect determination function unit  702  determines a portion having an intensity larger than a threshold provided therefor as a defect, and determines a portion having an intensity smaller than a threshold provided therefor as not a defect. Thus, output from this defect determination function unit  702  is the same as the output from the defect determination function unit  702  in Embodiment 1. 
     (12) Embodiment 12  
     Next, a description is given of a circuit pattern inspection device according to Embodiment 12. Note that a basic device configuration of the circuit pattern inspection device according to Embodiment 12 and basic processing steps thereof are the same as those in Embodiment 1. Hereinafter, only a difference from Embodiment 1 will be described. 
     In Embodiment 1 described above, the Hough transform has been used in the processing of detecting the feature information by the feature detector  122 . 
     However, the feature information can be detected also by a method as shown in  FIG. 16 .  FIG. 16  includes an inspected image  1601 , a first accumulated intensity graph  1604 , and a second accumulated intensity graph  1605 . Note that the inspected image  1601  has a light region  1602  and a dark region  1603 . 
     In this case, the feature detector  122  generates the first accumulated intensity graph  1604  in which intensity values of pixels having the same X coordinate are added in a Y coordinate direction. Next, the feature detector  122  generates the second accumulated intensity graph  1605  in which intensity values of pixels having the same Y coordinate are added in an X coordinate direction. 
     Since the inspected image  1601  has the light region  1602  and the dark region  1603 , it is possible to find a region ranging from XA to XB having a low accumulated intensity value in the first accumulated intensity graph  1604 . Similarly, it is possible to find a region ranging from YA to YB having a low accumulated intensity value in the second accumulated intensity graph  1605 . Utilization of these results makes it possible for the feature detector  122  to detect a positional range of the dark region  1603  as a region surrounded by four points (XA, YA), (XB, YA), (XB, YB) and (XA, YB). It goes without saying that the light region  1602  can be detected as a region outside the dark region  1603 . 
     In a case where the shape of a feature region is comparatively simple, such a method is applied thereto, and thereby the feature region can be detected by using a comparatively small amount of calculation. 
     (13) Other Embodiment 
     In the aforementioned embodiments, the descriptions have been mainly given of the cases where a thin film device such as a wafer, a thin film transistor (TFT), or a photo mask is inspected. However, the technique according to the invention is not necessarily limited to an electron beam inspection device, but applicable to an appearance inspection device using lamp light, laser light or the like. In addition, as long as a device is intended for an inspection for a defect on the basis of a comparison between patterns which should to be originally identical with each other, the invention is applicable to the device without limiting an inspection target. 
     EXPLANATION OF THE REFERENCE NUMERALS 
       102  . . . electron beam,  105  . . . charge control electrode,  106  . . . wafer,  110  . . . secondary signal,  113  . . . detector,  114  . . . digital image generator,  115  . . . image storage memory,  116  . . . difference image storage memory,  117  . . . defect determination unit,  118  . . . overall controller,  119  . . . console,  120  . . . light microscope,  121  . . . standard sample piece,  122  . . . feature detector,  123  . . . sensitivity adjuster,  124  . . . difference image generator,  125  . . . sensitivity corrector,  201  . . . die,  202  . . . memory mat group,  203  . . . memory mat,  204  . . . memory cell,  401  . . . map display portion,  402  . . . image display portion,  403  . . . defect information display portion,  404  . . . sensitivity setting button,  405  . . . comparison start button,  406  . . . tool bar for defect display threshold adjustment,  407  . . . defect,  408 ,  409 ,  410  . . . display region,  501  . . . sensitivity adjustment display portion,  502  . . . image display portion,  503  . . . sensitivity adjuster,  504  . . . shape detection button,  505  . . . application button,  506  . . . cancellation button,  507  . . . review button,  601 ,  602 ,  603  . . . hole pattern,  604  . . . wiring pattern,  803  . . . edge image,  804  . . . feature table,  805  . . . detected image,  806  . . . edge image,  807  . . . sensitivity adjustment table,  901  . . . difference image,  902 ,  903  . . . enlarged image,  904 ,  905   a,    905   b  . . . defect,  906  . . . circular region,  907  . . . region having high sensitivity value,  908  . . . region having low sensitivity value,  1001  . . . background feature analyzer,  1102  . . . background feature adding unit,  1201  . . . background feature adding unit,  1301  . . . statistic calculation button,  1302  . . . statistic amount display portion,  1303  . . . sensitivity setting portion,  1401  . . . sensitivity corrector,  1402  . . . difference image generator,  1501  . . . threshold corrector, e . . . defect information signal, g . . . sensitivity coefficient, h . . . detected image, i . . . difference image, j . . . adjusted difference image, k . . . feature information, l . . . sensitivity adjustment table, m . . . background feature table, ma . . . white background sensitivity adjustment table, mb . . . black background sensitivity adjustment table