Abstract: A method includes, receiving a first digital image (FDI) to-be printed by a digital printing system (DPS) (10). In a in training phase: for first selected regions (111) in the FDI, a first set of synthetic images (SIs) (112A, 112B, 114A, 114B, 116A, 116B) having a defect caused by a defective part (DP) (99) in the first selected regions, is produced; a neural network (NN) (150) 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) (136, 146) acquired from an image produced by the DPS, suspected second regions (135, 145); for each of the second regions, a second set (137, 147) 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.

Abstract: There is provided a system and method of semiconductor specimen examination. The method includes obtaining a plurality of images of a semiconductor specimen acquired by an examination tool; processing the plurality of images using a first machine learning (ML) model for defect detection, thereby obtaining, from the plurality of images, a set of images labeled with detected defects, wherein the first ML model is previously trained using a first training set comprising a subset of synthetic defective images each containing one or more synthetic defects, and a subset of nominal images; and training a second ML model using a second training set comprising at least part of the set of images labeled with detected defects, wherein the second ML model, upon being trained, is usable for defect detection with improved detection performance with respect to the first ML model.

Abstract: There are provided systems and methods comprising, for each given overlay target of a plurality of different overlay targets to be manufactured on a semiconductor specimen, said given overlay target comprising a plurality of stacked semiconductor layers, obtaining a design image of the given overlay target, feeding the design image to a trained machine learning model, to simulate at least one image of the given overlay target that would have been acquired by an electron beam examination system, using the at least one image to determine, before actual manufacturing of the given overlay target, data informative of at least one simulated overlay in the image, and using the data informative of the at least one simulated overlay of each given overlay target to select at least one optimal overlay target among the plurality of different overlay targets, the optimal overlay target being usable to be manufactured on the semiconductor specimen.

Abstract: 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.

Abstract: There is provided a system and method of examination of semiconductor specimens. The method includes generating a sequence of anomaly scores corresponding to a sequence of specimens sequentially fabricated and examined during a fabrication process, comprising, for each given specimen: obtaining an image of the given specimen acquired by an examination tool; using a machine learning (ML) model to process the image and obtaining an anomaly map indicative of pattern variation in the image; and deriving, based on the anomaly map, an anomaly score indicative of level of pattern variation presented in the given specimen, wherein the anomaly score is correlated with a defectivity score related to defect detection in a correlation relationship, and has higher detection sensitivity than the defectivity score; and analyzing the sequence of anomaly scores to monitor on-going process stability, thereby providing defect related prediction along the fabrication process based on the correlation relationship.

Abstract: 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.

Abstract: There is provided a system and method for defect examination on a semiconductor specimen. The method comprises obtaining an original image of the semiconductor specimen, the original image having a first region annotated as enclosing a defective feature; specifying a second region in the original image containing the first region, giving rise to a contextual region between the first region and the second region; identifying in a target image of the specimen a set of candidate areas matching the contextual region in accordance with a matching measure; selecting one or more candidate areas from the set of candidate areas; and pasting the first region or part thereof with respect to the one or more candidate areas, giving rise to an augmented target image usable for defect examination on the semiconductor specimen.

Abstract: A method includes, receiving a first digital image (FDI) to-be printed by a digital printing system (DPS) (10). In a in training phase: for first selected regions (111) in the FDI, a first set of synthetic images (SIs) (112A, 112B, 114A, 114B, 116A, 116B) having a defect caused by a defective part (DP) (99) in the first selected regions, is produced; a neural network (NN) (150) 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) (136, 146) acquired from an image produced by the DPS, suspected second regions (135, 145); for each of the second regions, a second set (137, 147) 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 ƒ of spectral intensity I? and phase ?, PMD=ƒ(I?; ?), corresponding to a complex spectral response of the underneath layered structure, and second measured data Smeas indicative 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 Stheor of the sample and a modeled optical response Smodel of the patterned structure and the complex spectral response PMD of the underneath layered structure, such that Stheor=F(Smodel; PMD), where the general function is then utilized for comparing the second measured data Smeas and the theoretical optical response Stheor, and determining parameter(s) of interest of the top structure.

Abstract: 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 ƒ of spectral intensity I? and phase ?, PMD=ƒ(I?; ?), corresponding to a complex spectral response of the underneath layered structure, and second measured data Smeas indicative 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 Stheor of the sample and a modeled optical response Smodel of the patterned structure and the complex spectral response PMD of the underneath layered structure, such that Stheor=F(Smodel; PMD), where the general function is then utilized for comparing the second measured data Smeas and the theoretical optical response Stheor, and determining parameter(s) of interest of the top structure.

Abstract: 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 ƒ of spectral intensity I? and phase ?, PMD=ƒ(I?;?), corresponding to a complex spectral response of the underneath layered structure, and second measured data Smeas indicative 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 Stheor of the sample and a modeled optical response Smodel of the patterned structure and the complex spectral response PMD of the underneath layered structure, such that Stheor=F(Smodel; PMD).

Abstract: 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 ƒ of spectral intensity I? and phase ?, PMD=ƒ(I?;?), corresponding to a complex spectral response of the underneath layered structure, and second measured data Smeas indicative 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 Stheor of the sample and a modeled optical response Smodel of the patterned structure and the complex spectral response PMD of the underneath layered structure, such that Stheor=F(Smodel; PMD).

Abstract: A computerized method for continuous speech recognition using a speech recognition engine and a phoneme model. The computerized method inputs a speech signal into the speech recognition engine. Based on the phoneme model, the speech signal is indexed by scoring for the phonemes of the phoneme model and a time-ordered list of phoneme candidates and respective scores resulting from the scoring are produced. The phoneme candidates are input with the scores from the time-ordered list. Word transcription candidates are typically input from a dictionary and words are built by selecting from the word transcription candidates based on the scores. A stream of transcriptions is outputted corresponding to the input speech signal. The stream of transcriptions is re-scored by searching for and detecting anomalous word transcriptions in the stream of transcriptions to produce second scores.