Abstract: A defect image classification apparatus includes a control unit that selects images obtained from at least some detectors among a plurality of detectors, associated with kinds of defects to be a classification result of an automatic defect classification processing unit, as images displayed initially on a display unit. The control unit associates the kinds of the defects and the images displayed initially on the display unit, on the basis of a switching operation log when a user classifies images of defects determined previously as the same kinds as the kinds of the defects determined by the automatic defect classification processing unit.

Abstract: To quantify the degree of a defect, and provide information useful for yield management. Disclosed is a defect quantification method wherein: a defect image is classified; a measurement region and a measurement area are set to each of the defect image and a reference image on the basis of defect image classification results, said reference image corresponding to the defect image; and an evaluation value of a defect is calculated using each of the measurement values obtained from each of the measurement areas of the defect image and the reference image, and the defect is quantified.

Abstract: A defect observation apparatus includes a storage unit configured to store defect information about defects detected by an external inspection apparatus; a first imaging unit configured to capture an image of a defect using a first imaging condition and a second imaging condition; a control unit configured to correct positional information on the defect using the image captured with the first imaging unit; and a second imaging unit configured to capture an image of the defect based on the corrected positional information.

Abstract: To review minute defects that were buried in roughness scattered light with an observation device provided with a dark-field microscope, a scanning electron microscope (SEM), and a control unit, the present invention configures the dark-field microscope by installing a filter for blocking a portion of the scattered light, an imaging lens for focusing the scattered light that has passed through the filter, and a detector for dividing the image of the scattered light focused by the imaging lens into the polarization directions converted by a wavelength plate and detecting the resulting images, and the control has a calculation unit for determining the position of a defect candidate detected by another inspection device using the plurality of images separated into polarization directions and detected by the detector.

Abstract: A defect image classification apparatus includes a control unit that selects images obtained from at least some detectors among a plurality of detectors, associated with kinds of defects to be a classification result of an automatic defect classification processing unit, as images displayed initially on a display unit. The control unit associates the kinds of the defects and the images displayed initially on the display unit, on the basis of a switching operation log when a user classifies images of defects determined previously as the same kinds as the kinds of the defects determined by the automatic defect classification processing unit.

Abstract: In automatic defect classification, a classification recipe must be set for each defect observation device. If a plurality of devices operate at the same stage, the classification class in the classification recipes must be the same. Problems have arisen whereby differences occur in the classification class in different devices when a new classification recipe is created. This defect classification system has a classification recipe storage unit; an information specification unit, the stage of a stored image, and device information. A corresponding defect specification unit specifies images of the same type of defect from images obtained from different image pickup devices at the same stage. An image conversion unit converts the images obtained from the different image pickup devices at the same stage into comparable similar images; and a recipe update unit records the classification classes in the classification recipes corresponding to the specified images of the same type of defect.

Abstract: A method for reviewing defect, comprising the steps of: as an image acquisition step, imaging a surface of a sample using arbitrary image acquisition condition selected from a plurality of image acquisition conditions and obtaining a defect image; as a defect position calculation step, proceeding the defect image obtained by the image acquisition step and calculating a defect position on the surface of the sample; as a defect detection accuracy calculation step, obtaining a defect detection accuracy of the defect position calculated by the defect position calculation step; and as a conclusion determination step, determinating whether the defect detection accuracy obtained by the defect detection accuracy calculation step meets a predetermined requirement or not; wherein until it is determined that the defect detection accuracy obtained by the defect detection accuracy calculation step meets a predetermined in the conclusion determination step, the image acquisition condition is selected from the plurality of

Abstract: Disclosed is a technique wherein an object that requires adjustment in order to increase the reliability of automatic classification can be easily identified. A device (140) for adjusting classification classifies defects into a first class group according to the feature amount of the defects that are obtained from image data obtained from an electron microscope (110), and classifies the defects into a second class group according to the feature amount of the defects classified into the first class group. And, the device (140) for adjusting the classification calculates classification performance by comparing the defects that have been classified into the second class group, and outputs the calculated classification performance in a predetermined display format to an output unit (180).

Abstract: Provided is a GUI including: an unadded pane region that hierarchically displays folders which are sets of images having no class information added thereto; an image pane region that displays the images displayed in the unadded pane region, the displayed images having no classification added thereto; and a class pane region that displays images having classification added thereto, wherein by externally inputting class information for one image having the class information added thereto, the input class information is displayed.

Abstract: A defect observation device including an input-output unit supplied with information of a taught defect, and information of an ideal output of the taught defect, and configured to display a processing result based upon a determined image processing parameter set; and an automatic determination unit configured to: select image processing parameter sets which are less in number than the total number of all image processing parameter sets, out of all image processing parameter sets, calculate image processing results on an input defect image, by using the selected image processing parameter sets, calculate a coincidence degree for each of the selected image processing parameter sets, estimate distribution of an index value in all image processing parameter sets from distribution of the coincidence degree for the selected image processing parameter sets, and determine an image processing parameter set to have a high coincidence degree out of all image processing parameter sets.

Abstract: A method for reviewing defect, comprising the steps of: as an image acquisition step, imaging a surface of a sample using arbitrary image acquisition condition selected from a plurality of image acquisition conditions and obtaining a defect image; as a defect position calculation step, proceeding the defect image obtained by the image acquisition step and calculating a defect position on the surface of the sample; as a defect detection accuracy calculation step, obtaining a defect detection accuracy of the defect position calculated by the defect position calculation step; and as a conclusion determination step, determinating whether the defect detection accuracy obtained by the defect detection accuracy calculation step meets a predetermined requirement or not; wherein until it is determined that the defect detection accuracy obtained by the defect detection accuracy calculation step meets a predetermined in the conclusion determination step, the image acquisition condition is selected from the plurality of

Abstract: In automatic defect classification, a classification recipe must be set for each defect observation device. If a plurality of devices operate at the same stage, the classification class in the classification recipes must be the same. Problems have arisen whereby differences occur in the classification class in different devices when a new classification recipe is created. This defect classification system has a classification recipe storage unit; an information specification unit, the stage of a stored image, and device information. A corresponding defect specification unit specifies images of the same type of defect from images obtained from different image pickup devices at the same stage. An image conversion unit converts the images obtained from the different image pickup devices at the same stage into comparable similar images; and a recipe update unit records the classification classes in the classification recipes corresponding to the specified images of the same type of defect.

Abstract: A defect observation device supplied with a taught defect and an ideal output obtained by conducting image processing on the taught defect as its input and capable of conducting work of setting image processing parameters required to classify defect kinds easily and fast is provided.

Abstract: Disclosed is a technique wherein an object that requires adjustment in order to increase the reliability of automatic classification can be easily identified. A device (140) for adjusting classification classifies defects into a first class group according to the feature amount of the defects that are obtained from image data obtained from an electron microscope (110), and classifies the defects into a second class group according to the feature amount of the defects classified into the first class group. And, the device (140) for adjusting the classification calculates classification performance by comparing the defects that have been classified into the second class group, and outputs the calculated classification performance in a predetermined display format to an output unit (180).