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: 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 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: In an automatic defect classifying method, defects not reviewed are assigned with defect classes having the same definitions as those of reviewed defects in order to effectively use information on defects not reviewed, the defects not reviewed occupying most of defects on a wafer. Defects not reviewed are assigned defect classes having the same definitions, by using defect data of defects detected with an inspection equipment and defect classes of already reviewed defects given by ADC of a review equipment. Since all defects are assigned the defect classes having the same definitions, more detailed analysis is possible in estimating the generation reasons of defects.

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: In order to be able to detect an irradiation position of an electron beam matching a defect position and conduct composition analysis of a defect with high precision and high efficiency, in the present invention, when a composition analysis target defect is selected and irradiation conditions of the electron beam are set for EDX analysis, a low-resolution reference image of low resolution is acquired using the electron beam at a defect corresponding position corresponding to the position of this defect on a chip in the vicinity of a target chip including defects, and a low-resolution defect image of the same low resolution is next acquired at the defect position of the target chip. Then, by comparing these low-resolution images, the defect position is acquired, the electron beam is slanted and irradiated on this defect position to acquire a composition spectrum of the defect.

Abstract: In a wafer inspection and sampling system, wafer defects are detected and stored in a data store as defect data. Information is also provided, representative of clusters of defects on the wafer. A statistically based sampling of the defects is made to obtain a set of sampled defects. Subsequent detailed inspection and analysis of the sampled defects produces additional data which facilitate an understanding of process errors.

Abstract: In a wafer inspection and sampling system, wafer defects are detected and stored in a data store as defect data. Information is also provided, representative of clusters of defects on the wafer. A statistically based sampling of the defects is made to obtain a set of sampled defects. Subsequent detailed inspection and analysis of the sampled defects produces additional data which facilitate an understanding of process errors.

Abstract: In order to be able to detect an irradiation position of an electron beam matching a defect position and conduct composition analysis of a defect with high precision and high efficiency, in the present invention, when a composition analysis target defect is selected and irradiation conditions of the electron beam are set for EDX analysis, a low-resolution reference image of low resolution is acquired using the electron beam at a defect corresponding position corresponding to the position of this defect on a chip in the vicinity of a target chip including defects, and a low-resolution defect image of the same low resolution is next acquired at the defect position of the target chip. Then, by comparing these low-resolution images, the defect position is acquired, the electron beam is slanted and irradiated on this defect position to acquire a composition spectrum of the defect.

Abstract: To efficiently extract identification of apparatuses causing problems in a thin-film device manufacturing process, candidates for the problem-generating manufacturing apparatus are extracted by evaluating data obtained in relation to produced inspections and data indicating the states of the manufacturing apparatus, with respect to products that enable efficient extraction of problem-generated apparatuses in a thin-film devise manufacturing process. This facilitates inferring the identification of the problem-generating apparatus.

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 wafer inspection and sampling system, wafer defects are detected and stored in a data store as defect data. Information is also provided, representative of clusters of defects on the wafer. A statistically based sampling of the defects is made to obtain a set of sampled defects. Subsequent detailed inspection and analysis of the sampled defects produces additional data which facilitate an understanding of process errors.

Abstract: In a wafer inspection and sampling system, wafer defects are detected and stored in a data store as defect data. Information is also provided, representative of clusters of defects on the wafer. A statistically based sampling of the defects is made to obtain a set of sampled defects. Subsequent detailed inspection and analysis of the sampled defects produces additional data which facilitate an understanding of process errors.

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: 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.