Applied Materials Israel Limited

Kenshoo Ltd.

Applied Materials Isreal Ltd

Amir Bar

Boaz Cohen

Boaz Dudovich

Yotam Sofer

Saar Shabtay

Nir Cohen

Shaul Engler

Marcelo Gabriel Bacher

Michael Aronowich

Lior Katz

Shiran Ben Israel

Paz Yabbo

Bhavna Ghai

Eli Oren Joni

Tzach Zohar

Assaf Ariel

Lior Yehieli

Chen Itzikowitz

Eyal Rot

There is provided a system and method of optimizing an inspection recipe for inspecting a semiconductor specimen. The method includes obtaining test data from a test performed after inspection, the test data indicative of functional defectivity of the specimen with respect to at least one structural feature at a suspected layer; retrieving inspection data of the suspected layer including a set of inspection images and a set of defect maps of the plurality of processing steps of the suspected layer; correlating the test data and the set of defect maps of the suspected layer to identify one or more structural features of the suspected layer with unmatched defectivity; for each of the identified structural features, including at least part of the inspection images corresponding to the structural feature in a training set; and using the training set to train a machine learning (ML) model in the inspection recipe.

Inspection recipe optimization for semiconductor specimens

There is provided a system and method of defect detection of a semiconductor specimen. The method includes obtaining a first image of the specimen acquired at a first bit depth, converting by a first processor the first image to a second image with a second bit depth lower than the first bit depth, transmitting the second image to a second processor configured to perform first defect detection on the second image using a first defect detection algorithm to obtain a first set of defect candidates, and sending locations of the first set of defect candidates to the first processor, extracting, from the first image, a set of image patches corresponding to the first set of defect candidates based on the locations, and performing second defect detection on the set of image patches using a second defect detection algorithm to obtain a second set of defect candidates.

Defect detection of a semiconductor specimen

A method of automatic optimization of an examination recipe includes obtaining inspection data of a given layer of a semiconductor specimen acquired by an inspection tool during runtime examination, the inspection data including inspection images representative of defect candidates from a defect map of the given layer, extracting inspection features characterizing the inspection images, and using a classifier to classify the defect candidates based on the inspection features, giving rise to a list of defect candidates having a higher probability of being defects of interest (DOIs). The semiconductor specimen includes multiple layers, and the classifier is a general-purpose classifier (GPC) usable for runtime classification of inspection data from any layer of the multiple layers of the semiconductor specimen, the GPC being previously trained using training data including inspection features characterizing training inspection images of various types of DOIs and nuisances collected from the multiple layers and label data associated therewith.

Automatic optimization of an examination recipe

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

There is provided a system and method of automatic optimization of an examination recipe. The method includes obtaining one or more inspection images each representative of at least a portion of the semiconductor specimen, the one or more inspection images being indicative of respective defect candidates selected from a defect map using a first classifier included in the examination recipe; obtaining label data respectively associated with the one or more inspection images and informative of types of the respective defect candidates; extracting inspection features characterizing the one or more inspection images; retraining the first classifier using the first features and the label data, giving rise to a second classifier; and optimizing the examination recipe by replacing the first classifier with the second classifier; wherein the optimized examination recipe is usable for examining a subsequent semiconductor specimen.

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

A system for identifying keywords which may include a non-acquiescence information collector operative to collect information indicating, for each individual website from among a multiplicity of websites, at least one keyword which brought at least one user into contact with said individual website but did not result in a defined criterion for acquiescence of a user to at least one suggestion presented by said website; and a negative keyword definer operative to define at least one specific keyword which brought at least one user into contact with a specific website but did not result in a transaction between said user and said website, as a negative keyword for said specific website. Positive keyword extraction based on aggregation of visitor acquiescence information may be provided. Paradigm structuring may include template generation of invitations and/or attribute based generation of invitation cueing sets.

System for finding website invitation cueing keywords and for attribute-based generation of invitation-cueing instructions

A computerized system which implements a method for obtaining an improved set of relevant keywords from an initial set of relevant keywords and their associated relative ratings, utilizing the proven expertise and reliability of existing keyword expansion tools and the accumulated experience of search engines. The method comprises iterating over keyword seeds, search engines' results and keyword suggestion sources' results, merging lists of keywords and combining their scores by means of an optimization method and learned effectiveness of result sources. Keyword relevance scores may be effectively used to distribute a content provider's resources amongst keywords thereby to achieve inclusion and optimization in search engines' results. The system input is a set of one or more seed keywords and the system produces an expanded list of unique keywords, each keyword typically accompanied by a computed relevance score.

Growing community of inventors

Rehovot, Israel

Amir Bar