Applied Materials Israel Limited

Eyal Neistein

Elad Cohen

Moshe Amzaleg

Haim Feldman

Ido Almog

Harel Ilan

Lior Katz

Shahar Arad

Yuri Feigin

Ariel Shkalim

Mark Geshel

Ori Golani

Eyal Bassa

Limor Martin

Shlomo Tubul

Karen Pomeranz

Vivek Balasubramanian

Disclosed herein is a method for detecting defects on a sample. The method includes obtaining scan data of a region of a sample in a multiplicity of perspectives, and performing an integrated analysis of the obtained scan data. The integrated analysis includes computing, based on the obtained scan data, and/or estimating cross-perspective covariances, and determining presence of defects in the region, taking into account the cross-perspective covariances.

Multi-perspective wafer analysis

A system and method for specimen examination, the system comprising a processing and memory circuitry (PMC) for: obtaining an image of at least a part of a specimen, the image acquired by an examination tool; receiving one or more characteristics of a defect of interest and a location of interest associated therewith; modifying within the image one or more pixels corresponding to the location of interest, wherein the modification is provided in accordance with a characteristic of the defect of interest, thereby planting the defect of interest into the image; processing the modified image to detect locations of potential defects of the specimen in accordance with a detection recipe; and determining whether the detected locations include the location of interest. Subject to the location of interest not being detected, modifying the detection recipe to enable detecting the planted defect of interest at the location of interest.

Detecting defects in a semiconductor specimen

A method and system for detecting defects in a specimen, the method comprising: obtaining an image comprising a plurality of pixels of a specimen part; processing the image according to a detection recipe to derive information related to potential defects in the specimen, the information comprising a first data set informative of first locations identified, in accordance with the detection recipe as locations of potential defects, and a second data set informative of second locations not identified as locations of potential defects; receiving data specifying targeted locations of interest within the part of the specimen; when the first data set is not informative of each targeted location, generating a third data set by adding to the first data set information related to the missing targeted location from the second data set, the information bearing an indication that it corresponds to a targeted location; and outputting the third data set.

Detecting targeted locations in a semiconductor specimen

A method, system and computer readable medium for providing information about a region of a sample. The method includes (i) obtaining, by an imager, multiple images of the region; wherein the multiple images differ from each other by at least one parameter (ii) receiving or generating multiple reference images; (iii) generating multiple difference images that represent differences between the multiple images and the multiple reference images; (iv) calculating a set of region pixel attributes, (v) calculating a set of noise attributes, based on multiple sets of region pixels attributes of the multiple region pixels; and (vi) determining for each region pixel, whether the region pixel represents a defect based on a relationship between the set of noise attributes and the set of region pixel attributes of the pixel.

Computerized system and method for obtaining information about a region of an object

There are provided system and method of detecting defects on a specimen, the method comprising: capturing a first image from a first die and obtaining one or more second images; receiving: i) a first set of predefined first descriptors each representing a type of DOI, and ii) a second set of predefined second descriptors each representing a type of noise; generating at least one difference image based on difference between pixel values of the first image and pixel values derived from the second images; generating at least one third image, comprising: computing a value for each given pixel of at least part of the at least one difference image based on the first and second sets of predefined descriptors, and surrounding pixels centered around the given pixel; and determining presence of defect candidates based on the at least one third image and a predefined threshold.

Method of defect detection and system thereof

There are provided system and method of generating an examination recipe usable for examining a specimen, the method comprising: capturing images from dies and obtaining noise map indicative of noise distribution on the images; receiving design data representative of a plurality of design groups each having the same design pattern; calculating a group score for each given design group, the group score calculated based on the noise data associated with the given design group and a defect budget allocated for area of the given design group; providing segmentation related to the dies, comprising: associating design groups with segmentation labels indicative of different noise levels based on the group score, thereby obtaining a set of die segments each corresponding to one or more design groups associated with the same segmentation label and segmentation configuration data; and generating an examination recipe using the segmentation configuration data.

Method of generating an examination recipe and system thereof

There are provided system and method of detecting repeating defects on a specimen, the specimen obtained by printing two or more mask fields thereon, each of mask field comprising multiple dies, the method comprising: scanning the specimen to capture a plurality of first images from first dies located at the same position in the mask fields, and, for each first image, capture two or more second images from dies located in different positions from the first dies; generating a plurality of third images corresponding to the plurality of first images; generating, an average third image constituted by pixels with values computed as accumulated pixel values of corresponding pixels in the plurality of third images divided by the number of the two or more mask fields; and determining presence of repeating defects on the specimen based on the average third image and a predefined defect threshold.

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Herzliya, Israel

Eyal Neistein