Applied Materials Israel Limited

Tel Aviv University

Applied Materials Isreal Ltd

Ran Schleyen

Boaz Cohen

Irad Peleg

David Mendlovic

Dror Shemesh

Yuval Gronau

Ishai Schwarzband

Benzion Sender

Ariel Raz

Ore Shtalrid

Vladislav Kaplan

Ofir Greenberg

Ran Badanes

Denis Suhanov

Eyal Zakkay

Shachar Paz

Uri Eliezer Mendlovic

A system for classifying a pattern of interest (POI) on a semiconductor specimen is disclosed. The system comprises a processor and memory circuitry. The memory circuitry is configured to obtain a high-resolution image of the POI, and to generate data usable for classifying the POI in accordance with a defectiveness-related classification. To generate the data, a machine learning model is utilized that has been trained in accordance with training samples. The training samples include a high-resolution training image captured by scanning a respective training pattern on a specimen, the respective training pattern being similar to the POI. The training samples also include a label associated with the image, the label being derivative of low-resolution inspection of the respective training pattern.

Detecting defects in semiconductor specimens using weak labeling

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.

Machine learning-based defect detection of a specimen

A system of classifying a pattern of interest (POI) on a semiconductor specimen, where the system includes a processor and memory circuitry configured to obtain a high-resolution image of the POI, and generate data usable for classifying the POI in accordance with a defectiveness-related classification. Generating the data utilizes a machine learning model that has been trained in accordance with training samples. The training samples include a high-resolution training image captured by scanning a respective training pattern on a specimen, the respective training pattern being similar to the POI, and a label associated with the image. The label is derivative of low-resolution inspection of the respective training pattern.

There is provided a system comprising a processor configured to obtain a set of images of a semiconductor specimen, (1) for an image of the set of images, select at least one algorithmic module Mout of a plurality of algorithmic modules, (2) feed the image to Mto obtain data Drepresentative of one or more defects in the image, (3) obtain a supervised feedback regarding rightness of data D, (4) repeat (1) to (3) for a next image until a completion criterion is met, wherein an algorithmic module selected at (1) is different for at least two different images of the set of images, generate, based on the supervised feedback, a score for each of a plurality of the algorithmic modules, and use scores to identify one or more algorithmic modules Mas the most adapted for providing data representative of one or more defects in the set of images.

Automatic selection of algorithmic modules for examination of a specimen

A method, computer program product and a system for imaging an area that includes an upper surface and hole. The method may include acquiring, by a charged particle imager, a first image of a first type of electrons of the area while the charged particle imager is at a first configuration; acquiring, by the charged particle imager, a second image of the first type of electrons of the area and a first image of a second type of electrons of the area while the charged particle imager is at a second configuration that differs from the first configuration; and generating a hybrid image of the area based on (i) a first image of the first type of electrons of the upper surface, (ii) an inter-image offset, and (iii) a first image of the second type of electrons of the bottom of the hole.

Imaging an area that includes an upper surface and a hole

An imaging system and method are presents for use in reconstructing spectral data of an object. The imaging system comprises: an optical unit; a pixel array of a detector; and a data processor for receiving and processing image data indicative of light detected by the pixel array and generating reconstructed spectral data of the object being imaged. The optical unit is configured and operable for applying a predetermined coding to an input light field while creating an optical image thereof on a detection plane defined by the pixel array. Therefore, the image data is a function of the predetermined coding and a spectrum of the object to be determined.

Spectral imaging method and system

A light-field imaging system and a method for generating light-field image data are presented. The system comprising an imaging lens unit, a detector array and a polychromatic patterned filter located in optical path of collected light, being at an intermediate plane between the lens unit and the detector array. The method comprising: acquiring image data of a region of interest by passing input light coming from said region of interest through said imaging lens unit and said polychromatic patterned filter to be detected by said detector array to generate corresponding image data; and processing said image data to determined light components passing through different regions of said polychromatic patterned filter corresponding to different colors and different parts of the region of interest to provide light-field image data of said region of interest.

System and method for light-field imaging

A system for scanning an object, the system may include (a) charged particles optics that is configured to: scan, with a charged particle beam and at a first scan rate, a first region of interest (ROI) of an area of the object; detect first particles that were generated as a result of the scanning of the first ROI; scan, with the charged particle beam and at a second scan rate, a second ROI of the area of the object; wherein the second scan rate is lower than the first scan rate; wherein first ROI differs from the second ROI by at least one parameter; detect second particles that were generated as a result of the scanning of the second ROA; and (b) a processor that is configured to generate at least one image of the area in response to the first and second particles.

Growing community of inventors

Rehovot, Israel

Ran Schleyen