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: Anomaly sign detection methods such as those found in plants cannot detect anomalies when relevant sensor information is not acquired, and while it is possible to detect anomalies through changes in sensor output when manual operations are performed, it is difficult to distinguish between anomalies such as those caused only by the sensor signal and actual anomalies which should be detected. The disclosed method uses event signals, which contain a signal based on the status of a unit unable to acquire sensor information and a signal based on human operations. An event sequence is extracted from an event signal outputted from a piece of equipment and grouped by clustering, then a frequency matrix is created for the alarms generated within a prescribed interval of an event sequence, and a prediction of alarms with a high probability of occurring for an event sequence is output on the basis of the frequency matrix.

Abstract: An inspecting method and apparatus for inspecting a substrate surface includes application of a light to the substrate surface, detection of scattered light or reflected light from the substrate surface due to the applied light at a plurality of positions to obtain a plurality of electrical signals, extraction of a signal in a mutually different frequency band from each of the plurality of electrical signals, and calculation of a value regarding a state of film of the substrate through an arithmetical operation process of a plurality of extracted signals in the frequency bands.

Abstract: Provided is an anomaly detection method and system capable of constructing determination condition rules of anomaly detection from case-based anomaly detection by way of multivariate analysis of a multi-dimensional sensor signal, applying the rules to design-based anomaly detection of individual sensor signals, and also appropriately executing setting and control of threshold values for highly sensitive, early, and clearly visible detection of anomalies. Anomaly detection on the basis of a case base by way of multivariate analysis controls design-based anomaly detection. That is to say, (1) anomaly detection on the basis of a case base performs selection of sensor signals and anomaly detection according to various types of anomalies. Specifically, anomaly detection (characteristic conversion), evaluation of level of effect of each signal, construction of determination conditions (rules), and display and selection of sensor signals corresponding to the anomaly are performed.

Abstract: A visual inspection method and apparatus detecting a defect with the use of a detected signal obtained by illuminating one of a light and an electron beam onto a substrate to be inspected. The visual inspection method and apparatus includes calculation of an image feature based on an image of the detected defect, calculation of a coordinate feature based on position information of the detected defect, and outputting of real defect information by performing false alarm judgment by processing with respect to one of the image feature and the coordinate feature.

Abstract: A defect inspection apparatus for inspecting defects on an inspecting object includes an illuminator which irradiates a beam of light on the inspecting object, a photo-detector which detects rays of light from the inspecting object due to the irradiation of the light beam by the illuminator, a defect detector which detects a defect by processing a signal obtained through detection by the photo-detector, a characteristic quantity calculator which calculates a characteristic quantity related to a size of the defect, and a defect size calculator which uses a relation between size and characteristic quantity which is calculated by an optical simulation and calculates a size of the detected defect.

Abstract: A defect inspection apparatus for inspecting defects on an inspecting object includes an illuminator which irradiates a beam of light on the inspecting object, a photo-detector which detects rays of light from the inspecting object due to the irradiation of the light beam by the illuminator, a defect detector which detects a defect by processing a signal obtained through detection by the photo-detector, a characteristic quantity calculator which calculates a characteristic quantity related to a size of the defect, and a defect size calculator which uses a relation between size and characteristic quantity which is calculated by an optical simulation and calculates a size of the detected defect.

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: 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: (1) A compact set of learning data about normal cases is created using the similarities among data as key factors, (2) new data is added to the learning data according to the similarities and occurrence/nonoccurrence of an anomaly, (3) the alarm occurrence section of a facility is deleted from the learning data, (4) a model of the learning data updated at appropriate times made by the subspace method, and an anomaly candidate is detected on the basis of the distance between each piece of the observation data and a subspace, (5) analyses of event information are combined and an anomaly is detected from the anomaly candidates, and (6) the deviance of the observation data is determined on the basis of the distribution of histograms of use of the learning data, and the abnormal element (sensor signal) indicated by the observation data is identified.

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: 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: 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: A defect inspection apparatus for inspecting defects on an inspecting object includes an illuminator which irradiates a beam of light on the inspecting object, a photo-detector which detects rays of light from the inspecting object due to the irradiation of the light beam by the illuminator, a defect detector which detects a defect by processing a signal obtained through detection by the photo-detector, a characteristic quantity calculator which calculates a characteristic quantity related to a size of the defect, and a defect size calculator which uses a relation between size and characteristic quantity which is calculated by an optical simulation and calculates a size of the detected defect.

Abstract: Provided are a method which permits complete training data and data with added errors, and enables the early and accurate discovery of errors in facilities such as a plant, and a system thereof. To achieve the objectives, (1) the behavior of temporal data is observed over time, and the trace is divided into clusters; (2) the divided cluster groups are modeled in sub spaces, and the discrepancy values are calculated as errors candidates; (3) the training data are used (compare, reference, etc.) for reference to determine the state transitions caused by the changes over time, the environmental changes, the maintenance (parts replacement), and the operation states; and (4) the modeling is a sub space method such as regression analysis or projection distance method of every N data removing N data items, (N=0, 1, 2, . . . ) (for example, when N=1, one error data item is considered to have been added, this data is removed, then the modeling is performed), or a local sub space method.

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: 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 correction unit is provided which corrects the defect dimension by using a standard sample such as a standard particle.