Applied Materials Israel Limited

Efrat Rozenman

Moshe Amzaleg

Idan Kaizerman

Yehuda Cohen

Gilad Almogy

Evgeni Levin

Ishai Schwarzband

Amit Batikoff

Gadi Greenberg

Nir Ben-David Dodzin

Zvi Goren

Ron Katzir

Imry Kissos

Michele Dalla-Torre

Vladimir Shlain

Nir Ben-David Dodzen

Ariel Shkalim

Tuvia Dror Kutscher

Mark Geshel

Rami Elichai

Vered Gatt

Nir Ben David Dodzin

Yair Hanani

There are provided a method of generating an inspection recipe usable for inspecting an inspection area of a specimen and a recipe generating unit. The recipe generating unit is configured: upon obtaining design data informative of design structural elements comprised in a design PoI corresponding to the at least one PoI, to provide global segmentation of a test image captured by an inspection tool unit from the inspection area and comprising at least one test PoI of substantially the same design as the at least one PoI, thereby to obtain segmented structural elements comprised in the test PoI and segmentation configuration data; to associate the segmented structural elements comprised in the test PoI with the design structural elements comprised in the design PoI, thereby to obtain design association data; and to generate an inspection recipe comprising, at least, segmentation configuration data and design association data.

Method of inspecting a specimen and system thereof

A method for defect classification includes storing definitions of defect classes in terms of a classification rules in a multi-dimensional feature space. Inspection data associated with defects detected in one or more samples under inspection is received. A plurality of first classification results is generated by applying an automatic classifier to the inspection data based on the definitions, the plurality of first classification results comprising a class label and a corresponding confidence level for a defect. Upon determining that a confidence level for a defect is below a predetermined confidence threshold, a plurality of second classification results are generated by applying at least one inspection modality to the defect. A report is generated comprising a distribution of the defects among the defect classes by combining the plurality of first classification results and the plurality of second classification results.

Integration of automatic and manual defect classification

A method for classification includes receiving an image of an area of a semiconductor wafer on which a pattern has been formed, the area containing an image location of interest, and receiving computer-aided design (CAD) data relating to the pattern comprising a CAD location of interest corresponding to the image location of interest. At least one value for one or more attributes of the image location of interest is computed based on a context of the CAD location of interest with respect to the CAD data.

Defect classification using CAD-based context attributes

There are provided a method of inspecting the inspection area and an inspection system thereof. The inspection system comprises an inspection control unit operatively coupled to an inspection tool unit and to a recipe generating unit. The inspection control unit is configured to obtain the design data and the inspection recipe; to provide local segmentation of at least one inspection PoI comprised in an inspection image captured from the inspection area by the inspection tool unit, thereby obtaining inspection structural elements comprised in the at least one inspection PoI, the local segmentation is provided using segmentation configuration data specified in the inspection recipe; to identify one or more target structural elements and design structural elements corresponding thereto, identifying is provided using design association data specified in the inspection recipe; and to enable metrology measurements for the one or more target structural elements using the identified design structural elements.

A system including an interface for receiving inspection image data of an inspection image of an inspection object. The inspection image data includes information of an analyzed pixel of the inspected image and of reference pixels of the inspected image. The system further includes a memory and a processor device operatively coupled to the interface and the memory to obtain an inspected value representative of the analyzed pixel of the inspected image, and a reference value for each of the reference pixels of the inspected image. For each reference pixel, the processor devices calculates a difference between the reference value of a respective reference pixel and the inspected value of the analyzed pixel, computes a representative difference value based on the differences and determines a presence of a defect in the analyzed pixel based on the representative difference value.

System, method and computer program product for defect detection based on multiple references

A method for defect classification includes storing, in a computer system, a definition of a region in a feature space. The definition is associated with a class of defects and comprises a kernel function comprising a parameter. The parameter determines a shape of the region. A confidence threshold for automatic classification of at least one defect associated with the class is received. A value of the parameter associated with the confidence threshold is selected. Inspection data for a plurality of defects detected in one or more samples under inspection is received. The plurality of defects for the class are automatically classified using the kernel function and the selected value of the parameter.

Optimization of unknown defect rejection for automatic defect classification

Methods, systems, and computer program products for signature detection. One example of a method includes: acquiring an article defect density map comprising a plurality of sections corresponding to a first resolution level which is indicative of defect numbers for the sections, and determining a distribution representative of the defect numbers or function thereof; determining a threshold in accordance with said distribution, and identifying sections, out of said plurality of sections in the article defect density map, with defect numbers or function thereof above the threshold; and clustering at least part of adjoining identified sections, into one or more signatures, thus detecting said one or more signatures.

Systems, methods and computer program products for signature detection

A method for classification includes receiving inspection data associated with a plurality of defects found in one or more samples and receiving one or more benchmark classification comprising a class for each of the plurality of defects. A readiness criterion for one or more of the classes is evaluated based on the one or more benchmark classification results, wherein the readiness criterion comprises for each class, a suitability of the inspection data for training an automatic defect classifier for the class. A portion of the inspection data is selected corresponding to one or more defects associated with one or more classes that satisfy the readiness criterion. One or more automatic classifiers are trained for the one or more classes that satisfy the readiness criterion using the selected portion of the inspection data.

Classifier readiness and maintenance in automatic defect classification

A method for classification includes receiving an image of an area of a semiconductor wafer on which a pattern has been formed, the area containing a location of interest. At least one value for one or more attributes of the location of interest are computed based upon topographical features of the location of interest in a three-dimensional (3D) map of the area.

Defect classification using topographical attributes

A system includes a memory and a processor device operatively coupled to the memory to obtain an inspected noise-indicative value representative of an analyzed pixel of an inspected image of an inspected object, and a reference noise-indicative value representative for each of multiple reference pixels of the inspected image. The processor device computes a representative noise-indicative value based on the inspected noise-indicative value and multiple reference noise-indicative values, calculates a defect-indicative value based on an inspected value representative of the analyzed pixel and determines a presence of a defect in the analyzed pixel based on the representative noise-indicative value and the defect-indicative value.

Method, system, and computer program product for detection of defects based on multiple references

There is provided an inspection method capable of classifying defects detected on a production layer of a specimen. The method comprises: obtaining input data related to the detected defects; processing the input data using a decision algorithm associated with the production layer and specifying two or more classification operations and a sequence thereof; and sorting the processed defects in accordance with predefined bins, wherein each bin is associated with at least one classification operation, wherein at least one classification operation sorts at least part of the processed defects to one or more classification bins to yield finally classified defects, and wherein each classification operation, excluding the last one, sorts at least part of the processed defects to be processed by one or more of the following classification operations.

Method of design-based defect classification and system thereof

A system capable of inspecting an article for defects, the system including: a patch comparator, configured to determine with respect to each of a plurality of reference patches in a reference image a similarity level, based on a predefined patch-similarity criterion and on a source patch defined in the reference image; an evaluation module, configured to rate each inspected pixel out of multiple inspected pixels of the inspection image with a representative score which is based on the similarity level of a reference patch associated with a reference pixel corresponding to the inspected pixel; a selection module, configured to select multiple selected inspected pixels based on the representative scores of the multiple inspected pixels; and a defect detection module, configured to determine a presence of a defect in the candidate pixel based on an inspected value of the candidate pixel and inspected values of the selected inspected pixels.

System, a method and a computer program product for patch-based defect detection

A defect detection system for computerized detection of defects in an inspected object based on processing of an inspection image generated by collecting signals arriving from the inspected object, the system including: an interface for obtaining an inspected noise-indicative value and multiple reference noise-indicative values, the inspected noise-indicative value representative of an analyzed pixel and each of the reference noise-indicative values representative of a reference pixel among a plurality of reference pixels; and a processor, including: a noise analysis module, configured to compute a representative noise-indicative value based on a plurality of noise-indicative values which includes the inspected noise-indicative value and the multiple reference noise-indicative values; and a defect analysis module, configured to calculate a defect-indicative value based on an inspected value representative of the analyzed pixel, and to determine a presence of a defect in the analyzed pixel based on the representative noise-indicative value and the defect-indicative value.

A defect detection system for computerized detection of defects, the system including: an interface for receiving inspection image data including information of an analyzed pixel and of a plurality of reference pixels; and a processor, including: a differences analysis module, configured to: (a) calculate differences based on an inspected value representative of the analyzed pixel and on multiple reference values, each of which is representative of a reference pixel among the plurality of reference pixels; wherein the differences analysis module is configured to calculate for each of the reference pixels a difference between the reference value of the reference pixel and the inspected value; and (b) compute a representative difference value based on a plurality of the differences; and a defect analysis module, configured to determine a presence of a defect in the analyzed pixel based on the representative difference value.

A method for inspecting a plurality of dies, that are typically disposed on a surface of a semiconducting wafer. Each of the dies includes respective functional features within the die. The method consists of identifying within a first die a first multiplicity of the functional features having respective characteristics, and measuring respective first locations of the first multiplicity with respect to an origin of the first die. Within a group of second dies a second multiplicity of the functional features having the respective characteristics is identified, respective second locations of the second multiplicity are measured. The second locations are compared to the first locations to determine a location of an origin of the group of the second dies.

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Aseret, Israel

Efrat Rozenman