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

    公开(公告)号:US11360030B2

    公开(公告)日:2022-06-14

    申请号:US16782005

    申请日:2020-02-04

    Abstract: 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.

    SYSTEM AND METHODS OF GENERATING COMPARABLE REGIONS OF A LITHOGRAPHIC MASK

    公开(公告)号:US20210233220A1

    公开(公告)日:2021-07-29

    申请号:US16752353

    申请日:2020-01-24

    Abstract: Implementations of the disclosure provide methods for generating an in-die reference for die-to-die defect detection techniques. The inspection methods using in-die reference comprise finding similar blocks of a lithographic mask, the similar blocks are defined by similar CAD information. A comparison distance is selected based on (i) areas of the similar blocks and (ii) spatial relationships between the similar blocks. The similar blocks are aggregated, based on the comparison distance, to provide multiple aggregated areas; and comparable regions of the lithographic mask are defined based on the multiple aggregate blocks. Images of at least some of the comparable regions of the lithographic mask are acquired using an inspection module. The acquired images are compared.

    Machine learning-based defect detection of a specimen

    公开(公告)号:US11449711B2

    公开(公告)日:2022-09-20

    申请号:US16733219

    申请日:2020-01-02

    Abstract: There is provided a method of defect detection on a specimen and a system thereof. The method includes: obtaining a runtime image representative of at least a portion of the specimen; processing the runtime image using a supervised model to obtain a first output indicative of the estimated presence of first defects on the runtime image; processing the runtime image using an unsupervised model component to obtain a second output indicative of the estimated presence of second defects on the runtime image; and combining the first output and the second output using one or more optimized parameters to obtain a defect detection result of the specimen.

    Method of classifying defects in a specimen semiconductor examination and system thereof

    公开(公告)号:US11321633B2

    公开(公告)日:2022-05-03

    申请号:US16228676

    申请日:2018-12-20

    Abstract: There are provided a classifier and method of classifying defects in a semiconductor specimen. The method comprises receiving defects classified into a majority class, each having values for plurality of attributes, some defects belonging to a minority class, and some to the majority; selecting an attribute subset and defining differentiators for attributes wherein a second classifier using the subset and differentiators classifies correctly to minority and majority classes at least part of the defects; generating a training set comprising: defects of the majority and minority classes, and additional defects which the second classifier classifies as minority; training, upon the training set, subset, and differentiators, an engine obtaining a confidence level that a defect belongs to the majority class; applying the engine to second defects classified to the majority class, to obtain a confidence level of classifying each defect to the majority class; and outputting defects having a low confidence level.

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