Abstract: With the objective of achieving defect kind training in a short period of time to teach classification conditions of defects detected as a result of inspecting a thin film device, according to one aspect of the present invention, there is provided a visual inspection method, and an apparatus therefor, comprising the steps of: detecting defects based on inspection images acquired by optical or electronic defect detection means, and at the same time calculating features of the defects; and classifying the defects according to classification conditions set beforehand, wherein said classification condition setting step further includes the steps of: collecting defect features over a large number of defects acquired beforehand from the defect detection step; sampling defects based on the distribution of the collected defect features over the large number of defects; and setting defect classification conditions based on the result of reviewing the sampled defects.

Abstract: In a process for manufacturing a semiconductor wafer, defect distribution state analysis is performed so as to facilitate identification of the defect cause including a device cause and a process cause by classifying the defect distribution state according to the defect position coordinates detected by the inspection device, into one of the distribution characteristic categories: repeated defects, clustered defects, arc-shaped regional defects, radial regional defects, line type regional defects, ring and blob type regional defects, and random defects.

Abstract: In a pattern inspection apparatus, influences of pattern brightness variations that is caused in association with, for example, a film thickness difference or a pattern width variation can be reduced, high sensitive pattern inspection can be implemented, and a variety of defects can be detected. Thereby, the pattern inspection apparatus adaptable to a broad range of processing steps is realized. In order to realize this, the pattern inspection apparatus of the present invention performs comparison between images of regions corresponding to patterns formed to be same patterns, thereby determining mismatch portions across the images to be defects. The apparatus includes multiple sensors capable of synchronously acquiring images of shiftable multiple detection systems different from one another, and an image comparator section corresponding thereto.

Abstract: When size of a defect on an increasingly miniaturized pattern is obtained by defect inspection apparatus in the related art, a value is inconveniently given, which is different from a measured value of the same defect by SEM. Thus, a dimension value of a defect detected by defect inspection apparatus needs to be accurately calculated to be approximated to a value measured by SEM. To this end, size of the defect detected by the defect inspection apparatus is corrected depending on feature quantity or type of the defect, thereby defect size can be accurately calculated.

Abstract: In a process for manufacturing a semiconductor wafer, defect distribution state analysis is performed so as to facilitate identification of the defect cause including a device cause and a process cause by classifying the defect distribution state according to the defect position coordinates detected by the inspection device, into one of the distribution characteristic categories: repeated defects, clustered defects, arc-shaped regional defects, radial regional defects, line type regional defects, ring and blob type regional defects, and random defects.

Abstract: With the objective of achieving defect kind training in a short period of time to teach classification conditions of defects detected as a result of inspecting a thin film device, according to one aspect of the present invention, there is provided a visual inspection method, and an apparatus therefor, comprising the steps of: detecting defects based on inspection images acquired by optical or electronic defect detection means, and at the same time calculating features of the defects; and classifying the defects according to classification conditions set beforehand, wherein said classification condition setting step further includes the steps of: collecting defect features over a large number of defects acquired beforehand from the defect detection step; sampling defects based on the distribution of the collected defect features over the large number of defects; and setting defect classification conditions based on the result of reviewing the sampled defects.

Abstract: When size of a defect on an increasingly miniaturized pattern is obtained by defect inspection apparatus in the related art, a value is inconveniently given, which is different from a measured value of the same defect by SEM. Thus, a dimension value of a defect detected by defect inspection apparatus needs to be accurately calculated to be approximated to a value measured by SEM. To this end, size of the defect detected by the defect inspection apparatus is corrected depending on feature quantity or type of the defect, thereby defect size can be accurately calculated.

Abstract: With the objective of achieving defect kind training in a short period of time to teach classification conditions of defects detected as a result of inspecting a thin film device, according to one aspect of the present invention, there is provided a visual inspection method, and an apparatus therefore, comprising the steps of: detecting defects based on inspection images acquired by optical or electronic defect detection means, and at the same time calculating features of the defects; and classifying the defects according to classification conditions set beforehand, wherein said classification condition setting step further includes the steps of: collecting defect features over a large number of defects acquired beforehand from the defect detection step; sampling defects based on the distribution of the collected defect features over the large number of defects; and setting defect classification conditions based on the result of reviewing the sampled defects.

Abstract: A method for evaluating a color picture tube includes displaying on a display surface of a color picture tube a measurement pattern including a plurality of first patterns arranged at different positions relative to fluophor dots of said color picture tube and a plurality of second patterns near said first patterns and sufficiently large relative to said fluophor dots. A first image is obtained using an imaging element to image said displayed measurement pattern. A second image is obtained using said imaging element to image while controlling light intake to allow brightness components of no more than about 1% of maximum luminance from said first image to be separated from noise and imaged. A third image is created by combining said first image and said second image while adjusting scales according to a light intake ratio. From said third image, display center positions of said plurality of first patterns is calculated using said second pattern positions.

Abstract: To inspect a substrate such as a semiconductor substrate for surface roughness at high precision. The surface roughness of the substrate is measured in each frequency band of the surface roughness by applying a light to the substrate surface and detecting a scattered light or reflected light at a plurality of azimuth or elevation angles.

Abstract: When size of a defect on an increasingly miniaturized pattern is obtained by defect inspection apparatus in the related art, a value is inconveniently given, which is different from a measured value of the same defect by SEM. Thus, a dimension value of a defect detected by defect inspection apparatus needs to be accurately calculated to be approximated to a value measured by SEM. To this end, size of the defect detected by the defect inspection apparatus is corrected depending on feature quantity or type of the defect, thereby defect size can be accurately calculated.

Abstract: The distribution states of defects are analyzed on the basis of the coordinates of defects detected by an inspection apparatus to classify them into a distribution feature category, or any one of repetitive defect, congestion defect, linear distribution defect, ring/lump distribution defect and random defect. In the manufacturing process for semiconductor substrates, defect distribution states are analyzed on the basis of defect data detected by an inspection apparatus, thereby specifying the cause of defect in apparatus or process.

Abstract: In a defect inspection apparatus for performing an inspection with an optical system, the dimension of a defect is measured substantially concurrently with detection of the defect. In order to promote the accuracy of measurement of the defect dimension, a collation unit is provided which collates the defect dimension by using a standard sample such as a standard grain.

Abstract: When size of a defect on an increasingly miniaturized pattern is obtained by defect inspection apparatus in the related art, a value is inconveniently given, which is different from a measured value of the same defect by SEM. Thus, a dimension value of a defect detected by defect inspection apparatus needs to be accurately calculated to be approximated to a value measured by SEM. To this end, size of the defect detected by the defect inspection apparatus is corrected depending on feature quantity or type of the defect, thereby defect size can be accurately calculated.

Abstract: An image feature is calculated based on the image of a detected defect, a coordinate feature is calculated based on position coordinates of the detected defect, and false alarm judgment is performed according to a decision tree constructed by threshold processing to the image feature or the coordinate feature.

Abstract: A defect classification method to classify defects by using a classifier having a binary tree structure based on features of defects extracted from detected signals acquired from a defect inspection apparatus includes a classifier construction process for constructing the classifier by setting a branch condition including defect classes respectively belonging to groups located on both sides of the branch point, a feature to be used for branching, and a discriminant reference, for each branch point in the structure based on instruction of defect classes and feature data respectively associated therewith beforehand. The process includes a priority order specification process for previously specifying target classification performance of purity and accuracy for each defect class, whole and in worst case, with priority order, and an evaluation process for evaluating whether the specified target classification performance under the branching condition is satisfied and displaying a result of evaluation, every item.

Abstract: When size of a defect on an increasingly miniaturized pattern is obtained by defect inspection apparatus in the related art, a value is inconveniently given, which is different from a measured value of the same defect by SEM. Thus, a dimension value of a defect detected by defect inspection apparatus needs to be accurately calculated to be approximated to a value measured by SEM. To this end, size of the defect detected by the defect inspection apparatus is corrected depending on feature quantity or type of the defect, thereby defect size can be accurately calculated.

Abstract: The distribution states of defects are analyzed on the basis of the coordinates of defects detected by an inspection apparatus to classify them into a distribution feature category, or any one of repetitive defect, congestion defect, linear distribution defect, ring/lump distribution defect and random defect. In the manufacturing process for semiconductor substrates, defect distribution states are analyzed on the basis of defect data detected by an inspection apparatus, thereby specifying the cause of defect in apparatus or process.

Abstract: The distribution states of defects are analyzed on the basis of the coordinates of defects detected by an inspection apparatus to classify them into a distribution feature category, or any one of repetitive defect, congestion defect, linear distribution defect, ring/lump distribution defect and random defect. In the manufacturing process for semiconductor substrates, defect distribution states are analyzed on the basis of defect data detected by an inspection apparatus, thereby specifying the cause of defect in apparatus or process.

Abstract: This invention relating to an inspection apparatus capable of classifying defects at high accuracy makes it possible to accurately extract various characteristic quantities of each defect by using the images obtained by imaging a semiconductor wafer under dark-field illumination, and providing, with respect to a differential image signal between the image signals obtained from dies near to each other in image brightness, a defect detection threshold and a characteristic quantity extraction threshold lower than the defect detection threshold.