Applied Materials Israel Limited

Applied Materials Isreal Ltd

Yotam Sofer

Boaz Cohen

Idan Kaizerman

Saar Shabtay

Ariel Hirszhorn

Amir Bar

Shaul Engler

Marcelo Gabriel Bacher

Eli Buchman

A system, method and computer readable medium for examining a specimen, the method comprising: obtaining defects of interest (DOIs) and false alarms (FAs) from a review subset selected from a group of potential defects received from an inspection tool, each potential defect is associated with attribute values defining a location of the potential defect in an attribute space; generating a representative subset of the group, comprising potential defects selected in accordance with a distribution of the potential defects within the attribute space, and indicating the potential defects in the representative subset as FA; and training a classifier using data informative of the attribute values of the DOIs, the potential defects of the representative subset, and respective indications thereof as DOIs or FAs, wherein the trained classifier is to be applied to at least some of the potential defects to obtain an estimation of a number of expected DOIs.

Selecting a representative subset of potential defects to improve defect classifier training and estimation of expected defects of interest

Disclosed is a system, method and computer readable medium for selecting a coreset of potential defects for estimating expected defects of interest. An example method includes obtaining a plurality of defects of interest (DOIs) and false alarms (FAs) from a review subset selected from a group of potential defects received from an inspection tool. The method further includes generating a representative subset of the group of potential defects. The representative subset includes potential defects selected in accordance with a distribution of the group of potential defects within an attribute space. The method further includes, upon training a classifier using data informative of the attribute values of the DOIs, the potential defects of the representative subset, and respective indications thereof as DOIs or FAs, applying the classifier to at least some of the potential defects to obtain an estimation of a number of expected DOIs in the specimen.

Selecting a coreset of potential defects for estimating expected defects of interest

Samples at a semiconductor wafer that have been reviewed by a review tool may be identified. Furthermore, a candidate sample at the semiconductor wafer that has not been reviewed by the review tool may be identified. A location of the candidate sample at the semiconductor wafer may and a number of the samples that have been reviewed that are at locations proximate to the location of the candidate sample may be determined. The candidate sample may be selected for review by the review tool based on the number of the plurality of samples that are at locations proximate to the location of the candidate sample.

Guided inspection of a semiconductor wafer based on spatial density analysis

A first defect map representing defects in a first semiconductor specimen in an attribute hyperspace may be received. Scores may be assigned to classified defects in the first defect map where an assigned score of a given defect of the classified defects in the first defect map is indicative of a number of defects within a threshold distance in the attribute hyperspace to the given defect in the first defect map that are classified to a same defect class as the given defect. A second defect map representing defects in a second semiconductor specimen in the attribute hyperspace may be received. Defects in the second defect map may be selected for review based on the scores assigned to the classified defects in the first defect map. The selected defects in the second defect map may be selected for classification.

Method of examining defects in a semiconductor specimen and system thereof

A method for process analysis includes acquiring first inspection data, using a first inspection modality, with respect to a substrate having multiple instances of a predefined pattern of features formed thereon using different, respective sets of process parameters. Characteristics of defects identified in the first inspection data are processed so as to select a first set of defect locations in which the first inspection data are indicative of an influence of the process parameters on the defects. Second inspection data are acquired, using a second inspection modality having a finer resolution than the first inspection modality, of the substrate at the locations in the first set. The defects appearing in the second inspection data are analyzed so as to select, from within the first set of the locations, a second set of the locations in which the second inspection data are indicative of an optimal range of the process parameters.

Process window analysis

A candidate defect may be identified at a semiconductor wafer. A determination may be made as to whether the candidate defect at the semiconductor wafer corresponds to a systematic defect or a random defect. In response to determining that the candidate defect at the semiconductor wafer corresponds to a systematic detect, the candidate defect at the semiconductor wafer may be provided to a defect review tool for review by the defect review tool.

Guided inspection of a semiconductor wafer based on systematic defects

There is provided a method of examining defects in a semiconductor specimen and a system thereof. The method comprises: processing a first defect map obtained for a first semiconductor specimen to assign cluster-seed (CS) scores to at least some of the classified defects presented therein, wherein a CS score of a given defect is indicative of a number of neighboring defects classified to the same class as the given defect; upon obtaining a second defect map for a second semiconductor specimen, using the CS scores assigned to the at least some of the classified defects presented in the first defect map to select, among defects presented in the second defect map, defects for review, wherein the first defect map and the second defect map present respective defects in an attribute hyperspace; and reviewing defects selected in the second defect map, thereby obtaining classified defects presented in the second semiconductor specimen.

Examining an object, comprising: receiving potential defects, each associated with a location; performing first clustering of the potential defects to obtain first and second subsets, the clustering performed such that potential defects in the first subset are denser in a physical area than potential defects in the second subset; automatically assigning first validity probabilities to potential defects in the first and second subsets; selecting for review potential defects from the first and second subsets, according to a third policy, and in accordance with a strategy for combining top elements and randomly selected elements from the merged list; receiving indications for potential defects in part of the potential defect lists, subsequent to potential defects being reviewed; updating the policies in accordance with validation or classification of items in the first and second subsets; and repeating said assigning, selecting, receiving and updating with the updated policies, until a stopping criteria is observed.

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Givatayim, Israel

Yotam Sofer