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.