Abstract:
There is provided a defect inspection method including the steps of: acquiring image data sets of a sample under a plurality of imaging conditions; storing a plurality of image data sets acquired under the plurality of imaging conditions in an image storage unit; acquiring a defect candidate from each of the plurality of image data sets; cutting out, from the image data sets acquired under at least two imaging conditions and stored in the image storage unit, a partial images each including a position of the defect candidate detected in any of the plurality of image data sets and the periphery of the defect candidate position; and integrating the partial images acquired under at least two imaging conditions corresponding to the defect candidates, thereby classifying the defect candidates.

Description:
TECHNICAL FIELD 
       [0001]    The present invention relates to a defect inspection method for inspecting a minute defect existing on a surface of a sample with high sensitivity and a defect inspection device therefor. 
       BACKGROUND ART 
       [0002]    Thin-film devices such as a semiconductor wafer, a liquid crystal display, and a hard disk magnetic head are manufactured through a plurality of processing stages. In the manufacture of such thin-film devices, appearance inspection is performed for each of the series of several processes with the aim of improving and stabilizing a yield. In Patent Literature 1 (JP No. 3566589), there is disclosed “a method for detecting a defect such as a pattern defect or a foreign matter based on a reference image and an inspection image obtained by using lamp light, laser light, or electron beams in regions corresponding to two patterns formed so as to essentially have the same shape in an appearance inspection”. In Patent Literature 2 (JP-A-2006-98155), there is further disclosed “an inspection method for optimizing various inspection conditions, by effectively extracting a DOI and surely teaching it, in such a state that a small number of DOIs slip into a large number of Nuisances”. In Patent Literatures 3 (U.S. Pat. No. 7,221,992) and 4 (U.S. Pat. No. 2008/0285023), as a method for improving inspection sensitivity more, there is disclosed “a method for simultaneously detecting images under a plurality of different optical conditions, performing a comparison for each condition in brightness between the detected image and a reference image, and integrating comparison values to determine defects and noises”. Further, there are problems in that a high data transfer rate is needed for supplying a high-resolution defect image acquired under respective optical conditions to a defect determination unit, and in that a processor to exhibit high processing performance is needed in order to simultaneously process images under a plurality of conditions. In Patent Literature 5 (U.S. Pat. No. 7,283,659), there is disclosed “a method for efficiently performing a defect classification by using a two-tiered determination, namely, a classification of defect candidates through a non-image feature such as process information and that through a defect image feature”. 
       CITATION LIST 
     Patent Literature 
       [0000]    
       
         Patent Literature 1: JP No. 3566589 
         Patent Literature 2: JP-A-2006-98155 
         Patent Literature 3: U.S. Pat. No. 7,221,992 
         Patent Literature 4: U.S. Pat. No. 2008/0285023 
         Patent Literature 5: U.S. Pat. No. 7,283,659 
       
     
       SUMMARY OF INVENTION 
     Technical Problem 
       [0008]    Based on the above conventional techniques, when using a configuration in which images under different optical conditions are detected and integrated simultaneously, a high-rate data transfer unit and a memory or a storage medium of high capacity are needed to transfer and store images acquired under respective optical conditions. Further, the optical conditions for images to be integrated depend on a device configuration and are limited thereto. When images under the respective optical conditions for objects to be inspected are imaged in time series by scanning a stage, displacement due to a stage travel error occurs between images under different optical conditions. Therefore, positions between the images need to be corrected and integrated. However, when optical conditions are different, a pattern of a target object may look totally different. To calculate a positional correction amount, a detection image in a wide range is needed and there is a problem in that processing time and memory capacity are increased. 
         [0009]    To limit detection images to be processed, in the conventional technique, defect candidates are narrowed based on non-image features such as process information. 
       Solution to Problem 
       [0010]    The following is a brief description of the gist of the representative elements of the invention disclosed in this application. 
         [0011]    (1) There is provided a defect inspection method including the steps of: acquiring image data sets of a sample under a plurality of imaging conditions; storing the plurality of image data sets acquired under the plurality of imaging conditions in an image storage unit; acquiring a defect candidate from each of the plurality of image data sets; cutting out, from the image data sets acquired under at least two imaging conditions and stored in the image storage unit, partial images each including a position of the defect candidate detected in any of the plurality of image data sets and the periphery of the defect candidate position; and integrating the partial images acquired under at least two imaging conditions corresponding to the defect candidates, thereby classifying the defect candidates. 
       Advantageous Effects of Invention 
       [0012]    According to the present invention disclosed in this application, there are provided a defect inspection method for inspecting minute defects existing on a surface of a sample with high sensitivity and a defect inspection device therefor. 
     
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         [0013]      FIG. 1  illustrates one example of a configuration of a first embodiment of a defect inspection device according to the present invention; 
           [0014]      FIG. 2  illustrates one example of a configuration of an image acquisition unit in the first embodiment of the defect inspection device according to the present invention; 
           [0015]      FIG. 3  illustrates one example of a configuration of a defect candidate extraction unit in the first embodiment of the defect inspection device according to the present invention; 
           [0016]      FIG. 4  illustrates one example of a configuration of a defect candidate detection unit in the first embodiment of the defect inspection device according to the present invention; 
           [0017]      FIG. 5  illustrates one example of a configuration of a chip in the first embodiment of the defect inspection device according to the present invention; 
           [0018]      FIG. 6  illustrates one example of a conversion function for compressing a bit rate in the first embodiment of the defect inspection device according to the present invention; 
           [0019]      FIG. 7  illustrates one example of the number of teaching defects and classification performance of a defect candidate selection unit in the first embodiment of the defect inspection device according to the present invention; 
           [0020]      FIG. 8  illustrates one example of a feature space of the defect candidate selection unit in the first embodiment of the defect inspection device according to the present invention; 
           [0021]      FIG. 9  illustrates one example of a configuration of a post-processing unit in the first embodiment of the defect inspection device according to the present invention; 
           [0022]      FIG. 10  illustrates one example of a flow for determining a defect in the first embodiment of the defect inspection device according to the present invention; 
           [0023]      FIG. 11  illustrates one example of extended display of a GUI for teaching a defect candidate in the first embodiment of the defect inspection device according to the present invention; 
           [0024]      FIG. 12  illustrates one example of a configuration of a second embodiment of the defect inspection device according to the present invention; 
           [0025]      FIG. 13  illustrates one example of a configuration of an integration defect candidate extraction unit in the second embodiment of the defect inspection device according to the present invention; 
           [0026]      FIG. 14  illustrates one example of a configuration of a third embodiment of the defect inspection device according to the present invention; 
           [0027]      FIG. 15  illustrates one example of a configuration of an integration defect classification unit in the third embodiment of the defect inspection device according to the present invention; 
           [0028]      FIG. 16  illustrates one example of displacement detection and correction in the third embodiment of the defect inspection device according to the present invention; and 
           [0029]      FIG. 17  illustrates one example of a configuration of a SEM type inspection device in the first to third embodiments of the defect inspection device according to the present invention. 
       
    
    
     DESCRIPTION OF EMBODIMENTS 
       [0030]    Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. In all drawings for describing the embodiments, the same components are indicated by the same reference numerals in principle, and descriptions will not be repeated. 
       First Embodiment 
       [0031]    Hereinafter, a first embodiment of a defect inspection technique (a defect inspection method and a defect inspection device) of the present invention will be described in detail with reference to  FIGS. 1 to 11 . 
         [0032]    In the first embodiment of a pattern inspection technique of the present invention, a defect inspection device and a defect inspection method under dark-field illumination with respect to a semiconductor wafer will be described as an example. 
         [0033]      FIG. 1  illustrates one example of the configuration of the defect inspection device of the first embodiment. The defect inspection device according to the first embodiment includes image acquisition units  110  ( 110 - 1 ,  110 - 2 , and  110 - 3 ), image storage buffers  120  ( 120 - 1 ,  120 - 2 , and  120 - 2 ), defect candidate extraction units  130  ( 130 - 1 ,  130 - 2 , and  130 - 3 ), a defect candidate selection unit  140 , a control unit  150 , an integration post-processing unit  160 , and a result output unit  170 . The image acquisition units  110  acquire inspection image data of a semiconductor wafer, and transfer the image data to the image storage buffers  120  and the defect candidate extraction units  130 . The defect candidate extraction units  130  extract defect candidates from the image data transferred from the image acquisition units  110  through a process to be hereinafter described, and transfer the defect candidates to the defect candidate selection unit  140 . The defect candidate selection unit  140  eliminates, from the defect candidates, disinformation being false detection such as noises or Nuisance that a user does not want to detect, and transmits the left defect candidate information to the control unit  150 . From the control unit  150  to the image storage buffers  120 , coordinates of the left defect candidates are transmitted. From the image data stored in the image storage buffers  120 , an image including defect candidates is cut out and the defect candidate image is transferred to the integration post-processing unit  160 . The integration post-processing unit  160  extracts from the defect candidate image only a DOI (Defect of Interest) being a defect that the user wants to detect through a process to be hereinafter described, and supplies the DOI to the result output unit  170 . 
         [0034]    In  FIG. 1 , the defect inspection device has the image storage buffers  120 - 1 ,  120 - 2 , and  120 - 3 , and the defect candidate extraction units  130 - 1 ,  130 - 2 , and  130 - 3  with respect to the image acquisition units  110 - 1 ,  110 - 2 , and  110 - 3  which acquire images under three different acquisition conditions of inspection images. Here, the acquisition conditions of the inspection image include illumination conditions and detection conditions for samples, and inspection image acquisitions at different detection sensitivities. 
         [0035]      FIG. 2  illustrates one example of a configuration of the image acquisition unit  110  under a dark-field illumination in the first embodiment. The image acquisition unit  110  includes a stage  210 , a mechanical controller  230 , two illumination optical systems (illumination units)  240 - 1  and  240 - 2 , detection optical systems (upper detection system)  250 - 1  and (oblique detection system)  250 - 2 , and image sensors  260 - 1  and  260 - 2 . The detection optical system further has a spatial frequency filter  251  and an analyzer  252 . 
         [0036]    Examples of the sample  210  include an object to be inspected such as a semiconductor wafer. The sample  210  is mounted on the stage  220 , and a rotation (θ) and a movement in an X-Y plane and a movement in a Z direction are enabled. The mechanical controller  230  is a controller which drives the stage  220 . Light from the illumination unit  240  is irradiated on the sample  210  and scattered light from the sample  210  is imaged through the upper detection system  250 - 1  and the oblique detection system  250 - 2 . An optical image to be imaged is further received by the respective image sensors  260 , thus converting the optical image to an image signal. At this time, the sample  210  is mounted on the X-Y-Z-θ driven stage  220  and light scattered by foreign matters is detected while the stage  220  is moved in the horizontal direction, and as a result a detection result is acquired as a two-dimensional image. 
         [0037]    As an illumination light source for the illumination unit  240 , a laser may be used or a lamp may be used. Further, as a wavelength of light for each illumination light source, light of a short wavelength may be used, or light of a wideband wavelength (white light) may be used. In the case of using light of a short wavelength, for the purpose of raising the resolution of an image to be detected (detecting a minute defect), light (Ultra Violet Light: UV light) having a wavelength in an ultraviolet range may be used. In the case of using a laser as a light source, when it is a laser of a short wavelength, a unit (not illustrated) for reducing coherence can be provided on each of the illumination units  240 . 
         [0038]    Further, a time delay integrating type image sensor (Time Delay Integration Image Sensor: TDI image sensor) having a configuration in which a plurality of one-dimensional image sensors are two-dimensionally arrayed is adopted as the image sensor  260 , and each one-dimensional image sensor transfers the detected signals to the one-dimensional image sensor of a next stage and adds them in synchronization with a movement of the stage  220 , which permits a two-dimensional image to be acquired with high sensitivity at a relatively high speed. When a parallel output type sensor with a plurality of output taps is used as this TDI image sensor, an output from the sensor can be processed in parallel and detection can be performed at a higher speed. Further, when a backside illuminated sensor is used as the image sensor  260 , detection efficiency can be raised up as compared to a case where a frontside illuminated sensor is used. 
         [0039]    A detection result to be produced from the image sensors  260 - 1  and  260 - 2  is transferred via the control unit  270  to the image storage buffers  120 - 1  and  120 - 2  and the defect candidate extraction units  130 - 1  and  130 - 2 . 
         [0040]      FIG. 3  illustrates one example of the configuration of the defect candidate extraction unit in the first embodiment. The defect candidate extraction unit  130  includes a pre-processing unit  310 , an image memory unit  320 , a defect candidate detection unit  330 , a parameter setting unit  340 , a control unit  350 , a storage unit  360 , and an input and output unit  370 . 
         [0041]    At first, the pre-processing unit  310  performs image correction such as shading correction, dark level correction, and bit compression to image data produced from the image acquisition unit  110 , divides the image data to an image having a size of a fixed unit, and stores it in the image memory  320 . There is read out digital signals of an image (hereinafter, described as a reference image) in a region corresponding to an image (hereinafter, described as a detection image) in a region to be inspected stored in the image memory  320 . Here, as the reference image, an image of an adjacent chip may be used or an ideal image nondefective in an image and created from a plurality of adjacent chip images may be used. Further, the defect candidate detection unit  330  calculates a correction amount to align a plurality of adjacent chips and performs alignment between a detection image and a reference image by using a correction amount of the calculated position. Further, by using a feature amount of a corresponding pixel, the defect candidate detection unit  330  produces as a defect candidate a pixel being an outlier in a feature space. The parameter setting unit  340  sets an inspection parameter for a kind or threshold of a feature amount at the time of extracting a defect candidate supplied from the outside, and supplies it to the defect candidate detection unit  330 . The defect candidate detection unit  330  supplies an image and a feature amount of the extracted defect candidate to the defect candidate selection unit  140  via the control unit  350 . The control unit  350  includes a CPU which performs each type of control, and accepts a change in an inspection parameter (a kind and a threshold of a feature amount) from the user. The control unit  350  is further connected to an input and output unit  351  having an input unit and a display unit which displays detected defect information, and a storage unit  352  which stores a feature amount and an image of the detected defect candidate. 
         [0042]    Here, all of the control units  150 ,  270 , and  350  may be the same control unit, or configured by a different control unit, respectively, and connected to each other. 
         [0043]      FIG. 4  illustrates one example of the configuration of the defect candidate detection unit  330  in the first embodiment. The defect candidate detection unit  330  includes an alignment unit  430 , a feature amount operation unit  440 , a feature space formation unit  450 , and an outlier pixel detection unit  460 . The alignment unit  430  detects displacement produced from the image memory unit  320  between a detection image  410  and a reference image  420  for correction. The feature amount operation unit  440  calculates a feature amount based on pixels corresponding to the reference image  420  and the detection image  440  in which a displacement is corrected by the alignment unit  430 . The feature amount here calculated is defined as a brightness difference between the detection image  440  and the reference image  420 , and a summation or a variation of the brightness difference in a given region. The feature space formation unit  450  forms a feature space based on an arbitrarily selected feature amount, and the outlier pixel detection unit  460  produces a pixel in a position deviated in the feature space as a defect candidate. The feature space formation unit  450  may perform normalization based on the displacement of each defect candidate. Here, as a reference for determining a defect candidate, variation in data points in the feature space and a distance from a center of gravity in the data points may be used. At this time, and a determination reference may be determined be using a parameter produced from the parameter setting unit  340 . 
         [0044]      FIG. 5  illustrates one example of the configuration of a chip in the first embodiment of the defect inspection device according to the present invention, and detection of defect candidates in the defect candidate detection unit  330  will be described. On the sample (described as a semiconductor wafer, and also as a wafer)  210  to be inspected, a number of chips  500  having the same pattern and including a memory mat unit  501  and a peripheral circuit unit  502  are regularly arrayed. The control unit  270  continuously moves the semiconductor wafer  210  being a sample by using the stage  220  and sequentially takes in an image of a chip from the image sensors  2601  and  260 - 2  in synchronization with the above. With respect to a detection image, for example, a detection image in a region  530  of  FIG. 5 , the control unit  270  sets digital image signals in regions  510 ,  520 ,  540 , and  550  in the same position in the regularly arrayed chips as reference images. Further, the control unit  270  compares pixels in the detection image with corresponding pixels in the reference image or other pixels in the detection image, and detects pixels with a large difference as a defect candidate. 
         [0045]      FIG. 6  illustrates one example of a function for compression in the case of performing data compression with respect to the image data produced from the image acquisition unit  110  in the pre-processing unit  310 .  FIG. 6  illustrates an example where image data input in 12 bits is compressed to 10 bits. In an example of a function  610 , when a relationship between an input Iin and an output Iout is set to Iout=0.25×Iin, the same compression is performed in both of relatively dark and bright portions of the image data. On the other hand, in one example of functions  620  and  630 , a compression rate is reduced in a relatively dark portion of images and the compression rate is raised in a relatively bright portion thereof. When the data compression is performed, an image volume can be reduced in the defect candidate extraction unit  130 . Further, a memory capacity to be needed can be reduced and the image transfer efficiency can be improved. 
         [0046]      FIG. 7  illustrates one example of the configuration of the defect candidate selection unit  140  in the first embodiment of the defect inspection device according to the present invention. The defect candidate selection unit  140  includes a displacement detection/correction unit  710 , a defect candidate association unit  720 , and an outlier detection unit  730 . The displacement detection/correction unit  710  receives images and feature amounts of a plurality of defect candidates and detection positions on wafers from each of the defect candidate extraction units  130 - 1 ,  130 - 2 , and  130 - 3 , and detects displacement of wafer coordinates in each defect candidate for correction. 
         [0047]    By associating a defect candidate in which a detection position is corrected by the displacement detection/correction unit  710 , the defect candidate association unit  720  determines whether the defect candidate detected by each defect determination unit is a defect candidate (hereinafter, referred to as a single defect) detected by a single defect determination unit or a defect candidate (hereinafter, referred to as a common defect) in which the same defect is detected by a plurality of defect determination units. The defect candidate association unit  720  performs association by using a method for determining whether defect candidates are overlapped in the range previously set on wafer coordinates. 
         [0048]    The outlier detection unit  730  sets a threshold to the defect candidate associated by the defect candidate association unit  720 , detects a defect candidate in a position deviated in the feature space, and supplies a feature amount and a detection position of the defect candidate to the control unit  150 . At this time, for the common defect, a feature amount produced from each defect determination unit may be integrated by a linear or nonlinear function and an outlier may be determined. Suppose that as one example of the feature amount integration, feature amounts produced from each defect determination unit are set as x1, x2, and x3, and further arbitrarily set weights are set as w1, w2, and w3. In this case, a linear integration function is set as g=w1x1+w2x2+w3 and a nonlinear integration function is set as g=x1x2×3. Further, when the integration function g is greater than or equal to the set threshold, it is determined as an outlier. To the single defect and the common defect, respectively, different thresholds can be further set. A high threshold can be set to the single defect and a low threshold can be set to the common defect. An upper limit may be further set to the number of defect candidates supplied to the control unit  150 . In the case of exceeding the upper limit, a defect candidate may be supplied to the control unit  150  in the order corresponding to a defect in which likelihood from the threshold is large. 
         [0049]      FIG. 8  illustrates one example of the feature space treated by the defect candidate selection unit  140  and a threshold determined by the outlier detection unit  730 .  FIG. 8  illustrates an example of the two-dimensional feature space based on the feature amounts of the defect candidates produced from the two defect candidate extraction units  130 - 1  and  130 - 2  (acquisition conditions 1 and 2). Among the defect candidates which are greater than or equal to a threshold  830 - 1  in the defect candidate extraction unit  130 - 1 , a single defect  810 - 1  detected only by the defect candidate extraction unit  130 - 1  is determined as an outlier based on a threshold  840 - 1 . Among the defect candidates which are greater than or equal to a threshold  830 - 2  in the defect candidate extraction unit  130 - 2 , a single defect  810 - 2  detected only by the defect candidate extraction unit  130 - 2  is determined as an outlier based on a threshold  840 - 2 . A common defect  820  detected by the defect candidate extraction units  130 - 1  and  130 - 2  is determined as an outlier based on a threshold  850 . The defect candidates which are greater than or equal to each threshold are set as outliers (the defect candidates encircled in the drawing). 
         [0050]      FIG. 9  illustrates one example of configurations of the image storage buffers and the integration post-processing unit  160  in the first embodiment of the defect inspection device according to the present invention. The control unit  150  receives a detection position of the defect candidate determined as an outlier by the defect candidate selection unit  140  and sets an image cutout position. In the defect cutout, the detection image in a region to be inspected including a defect candidate and the reference image to be compared are cut out to each defect candidate. At this time, also in the defect candidates determined as a single defect by the defect candidate selection unit  140 , the same image cutout position is set to all the image storage buffers  120 - 1 ,  120 - 2 , and  120 - 3 . From the image storage buffers  120 - 1 ,  120 - 2 , and  120 - 3 , the integration post-processing unit  160  receives partial image data of the image cutout position determined by the control unit  150 . The integration post-processing unit  160  includes a pre-processing unit  910 , an image storage unit  920 , a defect classification unit  940 , and a user interface  950 . With respect to the supplied partial image data and the partial image data of each image storage buffer  120 , the pre-processing unit  910  performs an image alignment in units of sub-pixel and an adjustment of the brightness shift of the images between respective image data sets. From the pre-processing unit  910 , the feature amount extraction unit  920  receives partial image data of the detection image and the reference image under each image acquisition condition, and calculates the feature amount of the defect candidate. The feature amount to be calculated is (1) brightness, (2) contrast, (3) a contrast difference, (4) a brightness dispersion value of adjacent pixels, (5) a correlation coefficient, (6) increase and decrease in brightness of adjacent pixels, and (7) a secondary differential value of each defect candidate. The feature amount extraction unit  920  stores feature amounts in the feature amount storage unit  930  until the number of defect candidates becomes a fixed value or the defect candidates of a constant area in a wafer are extracted by the defect candidate extraction unit  130 . The defect classification unit  940  receives feature amounts of a fixed number of defect candidates stored in the feature amount storage unit  930 , creates a feature space, and performs a classification based on the distribution of the defect candidates in the feature space. The defect classification unit  940  performs a classification of the supplied defect candidates to an important defect (DOI) and an unimportant defect (Nuisance), a classification of in-film defect and on-film defect, a classification of defect kinds to foreign matters and scratches, and a separation of disinformation through real defects and noises. Here, the defect classification unit  940  is connected to the user interface  950 , and can input teaching from the user. Via the user interface, the user can teach a DOI that the user wants to detect. The result output unit  170  outputs results classified by the defect classification unit  940 . 
         [0051]      FIG. 10  illustrates one example of the process flow of defect inspection in the first embodiment of the defect inspection device according to the present invention, and here illustrates a process flow in the case where two image acquisition conditions are used. Images are acquired under each image acquisition condition ( 1000 - 1  and  1000 - 2 ), and stored in the image storage buffers  120 - 1  and  120 - 2  ( 1010 - 1  and  1010 - 2 ). A defect candidate is extracted from images acquired under each condition ( 1020 - 1  and  1020 - 2 ). The defect candidate selection unit  140  selects defect candidates through the association of the defect candidates under each image acquisition condition and the outlier calculation ( 1030 ). Then, the defect candidate selection unit  140  sets a partial image cutout position to each image storage buffer  120  ( 1040 ), and transfers partial image data to the integration post-processing unit  160  from each image storage buffer  120  ( 1050 - 1  and  1050 - 2 ). The integration post-processing unit integrates images under each condition and performs a defect classification ( 1060 ). The integration post-processing unit supplies classification results ( 1070 ). 
         [0052]      FIG. 11  illustrates one example of a graphic user interface in the first embodiment of the defect inspection device according to the present invention. By using the defect candidate extraction unit  130 , the user confirms a wafer map  1110  indicating results performed by the defect candidate extraction unit  130  based on images under each image acquisition condition. By using the defect candidate selection unit  140 , the user confirms a feature space  1120  for determining an outlier of the defect candidate and a wafer map  1130  indicating the defect candidate which is supplied to the integration post-processing unit  160  as a result of a selection of the defect candidates. By using the integration post-processing unit  160 , the user confirms a wafer map  1140  indicating results in the case of classifying real defects and disinformation, and a defect candidate image  1150  under each image acquisition condition. Further, the user can input a teaching. 
       Second Embodiment 
       [0053]    Hereinafter, a second embodiment of the defect inspection technique (the defect inspection method and the defect inspection device) of the present invention will be described with reference to  FIGS. 12 and 13 . 
         [0054]    In the defect inspection technique described in the first embodiment, there will be described an embodiment in which image data acquired by the image acquisition units  110 - 1 ,  110 - 2 , and  110 - 3  under a plurality of image acquisition conditions is supplied to an integration defect candidate extraction unit  180 . 
         [0055]      FIG. 12  illustrates one example of the configuration of the defect inspection device of the second embodiment. The defect inspection device according to the second embodiment includes the image acquisition units  110 , the image storage buffers  120 , the integration defect candidate extraction unit  180 , the defect candidate selection unit  140 , the control unit  150 , the integration post-processing unit  160 , and the result output unit  170 . Similarly to the first embodiment, the image acquisition units  110  acquire image data under a plurality of image acquisition conditions. The integration defect candidate extraction unit  180  integrates image data produced from the image acquisition units  110 - 1 ,  110 - 2 , and  110 - 3  and extracts defect candidates. 
         [0056]    The defect candidate selection unit  140  eliminates, from the defect candidates, disinformation being a false detection such as noises or Nuisance that a user does not want to detect, and transmits information about the left defect candidates to the control unit  150 . From the control unit  150  to the image storage buffers  120 , coordinates of the left defect candidates are transmitted. From the image data stored in the image storage buffers  120 , an image including defect candidates is cut out and the defect candidate image is transferred to the integration post-processing unit  160 . The integration post-processing unit  160  extracts as the defect candidate image only a DOI (Defect of Interest) being a defect that the user wants to detect through a process to be hereinafter described, and supplies the DOI to the result output unit  170 . 
         [0057]      FIG. 13  illustrates one example of the configuration of the integration defect candidate extraction unit  180  of the second embodiment. An integration image creation unit  1310  detects and corrects displacement of each image data produced from the image acquisition units  110 - 1 ,  110 - 2 , and  110 - 3  to create an integration image. In the integration image, a linear sum in which a weighted sum of both respective image data sets is calculated may be calculated and a nonlinear integration may be performed. The integration image creation unit  1310  supplies a created integration image to the pre-processing unit. Processes of the pre-processing unit  320  or later are set to be the same as that of the first embodiment. 
         [0058]    In the second embodiment, there is described an example where integration is performed by using a format in which an integration image is created from each image data. Further, there may be performed a method for extracting a feature amount from each image, creating a feature space based on the feature amount of a corresponding pixel, and extracting an outlier in the feature space as a defect candidate. 
       Third Embodiment 
       [0059]    Hereinafter, a third embodiment of the defect inspection technique (the defect inspection method and the defect inspection device) of the present invention will be described with reference to  FIGS. 14 to 16 . 
         [0060]    In the defect inspection technique described in the first embodiment, there will be described an embodiment in which image data sets are acquired by the image acquisition units  110 - 1 ,  110 - 2 , and  110 - 3  under a plurality of image acquisition conditions, defect candidates are extracted from each image data, and the extracted defect candidates are supplied to the integration defect classification unit  180 . 
         [0061]      FIG. 14  illustrates one example of the configuration of the defect inspection device of the third embodiment. The defect inspection device according to the third embodiment includes the image acquisition units  110 , the defect candidate extraction units  130 , an integration defect classification unit  190 , and the result output unit  170 . Similarly to the first embodiment, the image acquisition units  110 - 1 ,  110 - 2 , and  110 - 3  acquire image data sets under a plurality of image acquisition conditions. Similarly to the first embodiment, the defect candidate extraction units  130 - 1 ,  130 - 2 , and  130 - 3  extract defect candidates from the respective image data sets acquired by the image acquisition units  110 - 1 ,  110 - 2 , and  110 - 3 . 
         [0062]    The integration defect classification unit  190  receives the defect candidates acquired by the defect candidate extraction units  130 - 1 ,  130 - 2 , and  130 - 3 , and detects and corrects displacement of each defect candidate. Further, the integration defect classification unit  190  performs a defect classification, and supplies classification results to the result output unit  170 . 
         [0063]      FIG. 15  illustrates one example of the configuration of the integration defect classification unit  190  of the third embodiment. The integration defect classification unit  190  includes defect selection units  1510 , a displacement detection unit  1520 , a displacement correction unit  1530 , and a defect classification unit  1540 . 
         [0064]    The defect selection units  1510 - 1 ,  1510 - 2 , and  1510 - 3  select defect candidates for use in an alignment from the defect candidates produced from the defect candidate extraction units  130 - 1 ,  130 - 2 , and  130 - 3 . A reference for selecting the defect candidate includes a brightness difference between the detection image and the reference image, a size and a shape of a defect, and a combination thereof. 
         [0065]    The displacement detection unit  1520  calculates a displacement amount of the defect candidate based on the defect candidates selected by the defect selection units  1510 . Examples of the method for calculating the displacement amount include: 
         [0066]    (1) temporary association of both the closest points of each defect candidate, 
         [0067]    (2) calculation of such a displacement amount that the distance between both the temporarily associated defect candidates is minimized, 
         [0068]    (3) correction of the displacement, and 
         [0069]    (4) repetition of the above (1) to (3) until the displacement amount is converged. 
         [0070]    Based on the displacement amount produced from the displacement detection unit  1520 , the displacement correction unit  1530  performs a displacement correction to the defect candidates produced from the defect candidate extraction units  130 - 1 ,  130 - 2 , and  130 - 3 . 
         [0071]    The defect classification unit  1540  extracts a feature amount from the defect candidates corrected by the displacement correction unit  1530 , and classifies the defect candidates. The defect candidates are classified by using the same method as that of the first embodiment. The defect classification unit  1540  supplies the obtained classification results of the defect candidates to the result output unit  170 . 
         [0072]    Further, the displacement amount calculated by the displacement detection unit  1520  is stored in the storage unit  1550 , and the displacement correction unit  1530  reads in the displacement amount stored in the storage unit  1550  to thereby perform the displacement correction. 
         [0073]      FIG. 16  illustrates one example of the displacement correction of the defect candidates in the integration defect classification unit  190 . Defect candidates  1630  and  1640  for use in displacement detection are selected from defect candidates  1610  and  1620  under the image acquisition conditions 1 and 2, respectively, and a displacement amount is calculated based on the selected defect candidates. The displacement of the defect candidates  1610  and  1620  under the image acquisition conditions 1 and 2 is corrected based on the calculated displacement amount ( 1650 ). 
         [0074]    In the first to third embodiments, an example where the dark-field type inspection device is used as an inspection device is described. Further, the first to third embodiments are applicable to inspection devices of all systems such as the bright-field type inspection device and an SEM type inspection device. According to the inspection devices of a plurality of systems, images can be acquired under a plurality of image acquisition conditions and defects can be determined. 
         [0075]      FIG. 17  illustrates one example of the configuration of the SEM type inspection device. The same portions as those of the dark-field type inspection device described in the first embodiment or portions which perform the same operations as those of the dark-field type inspection device are indicated by the same reference numerals. After electron beams irradiated from an electron beam source  1410  pass through condenser lenses  1420  and  1430 , astigmatism or alignment deviation is corrected through an electron beam-axis adjuster  1440 . Scanning units  1450  and  1460  slant electron beams and control a position on which the electron beams are irradiated. The electron beams are converged by objective lenses  1470  and irradiated on an object to be imaged  1400  of the wafer  210 . As a result, secondary electrons and reflection electrons are emitted from the object to be imaged  1400 . The secondary electrons and the reflection electrons collide against a reflecting plate having a primary electron beam passing hole  1410  and secondary electrons generated thereon are detected by an electron detector  1490 . The secondary electrons and the reflection electrons detected by the primary electron beam passing hole  1410  are converted to digital signals by an A/D converter  1500 , and transferred to the control unit  270 . 
       REFERENCE SIGNS LIST 
       [0000]    
       
           110  Image acquisition unit 
           120  Image storage buffer 
           130  Defect candidate extraction unit 
           140  Defect candidate selection unit 
           150  Control unit 
           160  Integration post-processing unit 
           170  Result output unit 
           210  Wafer 
           220  Stage 
           230  Controller 
           240  Illumination system 
           250  Detection system 
           310  Pre-processing unit 
           320  Image memory unit 
           330  Defect candidate detection unit 
           340  Parameter setting unit 
           350  Control unit 
           410  Detection image 
           420  Reference image 
           430  Alignment unit 
           440  Feature amount operation unit 
           450  Feature space formation unit 
           460  Outlier pixel detection unit 
           710  Displacement detection/correction unit 
           720  Defect candidate association unit 
           730  Outlier detection unit 
           910  Pre-processing unit 
           920  Feature amount extraction unit 
           930  Feature amount storage unit 
           940  Defect classification unit 
           950  User interface