Landa Corporation Ltd.

Applied Materials Israel Limited

Nova Measuring Instruments Ltd.

Nova Corporation

Boris Levant

Gilad Barak

Yanir Hainick

Vladimir Machavariani

Noam Tal

Roy Koret

Dror Shafir

Elad Schleifer

Alon Siman Tov

Shai Silberstein

Avraham Guttman

Michael Chemama

Bobin Mathew Skaria

Tomer Haim Peled

Yuri Shirman

Tal Frank

Smadar Ferber

Bar Dubovski

Assaf Ariel

Jasmin Sonia Linshiz

Ze'Ev Lindenfeld

Shaul Pres

Lilach Choona

Ran Yacoby

Shay Yogev

Tomer Yanir

There is provided a system and method for examining a semiconductor specimen. The method includes obtaining a runtime image of the specimen, and providing the runtime image as an input to an end-to-end (E2E) learning model to process, thereby obtaining, as an output of the E2E learning model, runtime measurement data specific for a metrology application. The E2E learning model is previously trained for the metrology application using a training set comprising a plurality of training images of the specimen and respective ground truth measurement data associated therewith, and one or more cost functions specifically configured to evaluate, for the plurality of training images and corresponding training measurement data outputted by the E2E learning model, one or more metrology benchmarks from a group comprising precision, correlation, and matching.

End-to-end measurement for semiconductor specimens

There is provided a system and method of examination of a semiconductor specimen. The method includes obtaining an e-beam image representative of a given layer of a given structure on the specimen in runtime, processing at least the e-beam image using a ML model, and obtaining yield related prediction with respect to the given structure prior to performing an electrical test. The ML model is previously trained using a training set comprising multiple stacks of e-beam images corresponding to multiple sites of the given structure on one or more training specimens, each stack of e-beam images representative of the at least given layer of a respective site; and test data acquired from an electrical test performed at the multiple sites and related to actual yield of the training specimens, the test data respectively correlated with the stacks of e-beam images and used as ground truth thereof.

Machine learning based yield prediction

A method includes, receiving a first digital image (FDI) to-be printed by a digital printing system (DPS) (). In a in training phase: for first selected regions () in the FDI, a first set of synthetic images (SIs) (A,B,A,B,A,B) having a defect caused by a defective part (DP) () in the first selected regions, is produced; a neural network (NN) () is trained to detect the defect using the first set SIs. In a subsequent detection phase: the NN is applied for identifying, in a second digital image (SDI) () acquired from an image produced by the DPS, suspected second regions (); for each of the second regions, a second set () of SIs having DPs that form the defects, is produced; and the DP is identified by comparing, in each of the second regions, between the SDI and the second set SIs.

Detecting a defective nozzle in a digital printing system

A method for correcting an error in image printing, the method includes receiving a reference digital image (RDI). Based on a predefined selection criterion, one or more regions in the RDI that are suitable for use as anchor features for sensing the error, are automatically selected. A digital image (DI) acquired from a printed image of the RDI, is received and the one or more regions are automatically identified in the DI. Based on the anchor features of the DI, the error is automatically estimated in the printed image. A correction that, when applied to the DI, compensates for the estimated error, is calculated. The estimated error is corrected in a subsequent digital image (SDI) to be printed, and the SDI having the corrected error, is printed.

Correcting registration errors in digital printing

Semiconductor device metrology including creating a time-domain representation of wavelength-domain measurement data of light reflected by a patterned structure of a semiconductor device, selecting an earlier-in-time portion of the time-domain representation that excludes a later-in-time portion of the time-domain representation, and determining one or more measurements of one or more parameters of interest of the patterned structure by performing model-based processing using the earlier-in-time portion of the time-domain representation.

Time-domain optical metrology and inspection of semiconductor devices

a method for correcting an error in image printing, the method includes receiving a reference digital image (RDI). Based on a predefined selection criterion, one or more regions in the RDI that are suitable for use as anchor features for sensing the error, are selected. A digital image (DI) acquired from a printed image of the RDI, is received and the one or more regions are identified in the DI. Based on the anchor features of the DI, the error is estimated in the printed image. A correction that, when applied to the DI, compensates for the estimated error, is calculated. The estimated error is corrected in a subsequent digital image (SDI) to be printed, and the SDI having the corrected error, is printed.

Determining parameters of a patterned structure located on top of an underneath layered structure, where input data is provided which includes first measured data PMD being a function &#x192; of spectral intensity Iand phase &#x3d5;, PMD=&#x192;(I; &#x3d5;), corresponding to a complex spectral response of the underneath layered structure, and second measured data Sindicative of specular reflection spectral response of a sample formed by the patterned structure and the underneath layered structure, and where a general function F is also provided describing a relation between a theoretical optical response Sof the sample and a modeled optical response Sof the patterned structure and the complex spectral response PMD of the underneath layered structure, such that S=F(S; PMD), where the general function is then utilized for comparing the second measured data Sand the theoretical optical response S, and determining parameter(s) of interest of the top structure.

Method and system for optical metrology in patterned structures

A data analysis method and system are presented for use in determining one or more parameters of a patterned structure located on top of an underneath layered structure. According to this technique, input data is provided which includes first measured data PMD being a function &#x192; of spectral intensity I&#x3bb; and phase &#x3c6;, PMD=&#x192;(I;&#x3c6;), corresponding to a complex spectral response of the underneath layered structure, and second measured data Sindicative of specular reflection spectral response of a sample formed by the patterned structure and the underneath layered structure. Also provided is a general function F describing a relation between a theoretical optical response Sof the sample and a modeled optical response Sof the patterned structure and the complex spectral response PMD of the underneath layered structure, such that S=F(S; PMD). The general function is then utilized for comparing the second measured data Sand the theoretical optical response S, and determining parameter(s) of interest of the top structure.

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Rehovot, Israel

Boris Levant