Abstract: The presently disclosed subject matter includes a computer system and a computer-implemented method of generating synthetic examination output images, including synthetic fault images and synthetic fault-free images. The synthetic fault images comprise artificially generated 3D defects. The proposed technique enables fast, accurate, and efficient generation of a large and diverse collection of synthetic fault images and fault-free images, which can be implemented in runtime, as part of the examination process of semiconductor specimens.

Abstract: The presently disclosed subject matter includes a computer system and a computer-implemented method of generating synthetic examination output images, including synthetic fault images and synthetic fault-free images. The synthetic images comprise artificially generated 3D defects. A machine learning model is trained for transforming examination output images to height maps. The height maps are modified to include certain 3D features, and a second machine learning model is used for transforming the height maps to modified examination output images to exhibit the 3D features.

Abstract: The presently disclosed subject matter includes a method and a computer system dedicated for determining semiconductor specimen topography based on grayscale level (GL) imaging output. According to the disclosed approach, the semiconductor specimen surface is scanned from top-view using an examination tool such as a Scanning Electron Microscopy (SEM) to generate a grayscale level (GL) output image of the scanned surface. The GL output images are processed to deduce the corresponding height values of the scanned semiconductor specimen based on graphical features of the GL output images. The height values are then used for validating CMP hotspot predictions, which enhance the accuracy of hotspot detection and increase the reliability of the dummy fill results.

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 of generating a computational model for determining one or more aberration coefficients of a charged particle beam focused by a focusing lens towards a specimen is described. The method includes obtaining a plurality of input data associated with a plurality of beam spot information for a plurality of defocus settings; obtaining a plurality of one or more aberrations coefficients associated with corresponding input data of the plurality of input data; providing a training data set including the plurality of aberrations coefficients and the corresponding input data; and producing or generating the computational model by machine learning from the training data set. The computational model is also used for determining one or more aberration coefficients.

Abstract: A system and methods for OCD metrology are provided including receiving reference parameters, receiving multiple sets of measured scatterometric data, and receiving an optical model designed to generate one or more sets of model scatterometric data according to a set of pattern parameters, and training a machine learning model by applying, during the training, target features including the reference parameters, and by applying input features including the sets of measured scatterometric data and the sets of model scatterometric data, such that the trained machine learning model estimates new wafer pattern parameters from subsequently sets of measured scatterometric data.

Abstract: There are provided systems and methods comprising obtaining data Dpixel_intensity informative of a pixel intensity profile of a given specimen, feeding the data Dpixel_intensity to a machine learning model to determine, based on the data Dpixel_intensity, data informative of a depth of the given specimen, wherein the machine learning model has been trained with a training set, wherein at least part of the training set has been generated based on a model operative to predict, based on one or more parameters informative of a specimen, data informative of a pixel intensity profile of the 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: A system and methods for Advance Process Control (APC) in semiconductor manufacturing include: for each of a plurality of waiter sites, receiving a pre-process set of scatterometric training data, measured before implementation of a processing step, receiving a corresponding post-process set of scatterometric training data measured after implementation of the process step, and receiving a set of process control knob training data indicative of process control knob settings applied during implementation of the process step; and generating a machine learning model correlating variations in the pre-process sets of scatterometric training data and the corresponding process control knob training data with the corresponding post-process sets of scatterometric training data, to train the machine learning model to recommend changes to process control knob settings to compensate for variations in the pre-process scatterometric data.

Abstract: A system and methods for Advance Process Control (APC) in semiconductor manufacturing include: for each of a plurality of waiter sites, receiving a pre-process set of scatterometric training data, measured before implementation of a processing step, receiving a corresponding post-process set of scatterometric training data measured after implementation of the process step, and receiving a set of process control knob training data indicative of process control knob settings applied during implementation of the process step; and generating a machine learning model correlating variations in the pre-process sets of scatterometric training data and the corresponding process control knob training data with the corresponding post-process sets of scatterometric training data, to train the machine learning model to recommend changes to process control knob settings to compensate for variations in the pre-process scatterometric data.

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: A system and methods for Advance Process Control (APC) in semiconductor manufacturing include: for each of a plurality of waiter sites, receiving a pre-process set of scatterometric training data, measured before implementation of a processing step, receiving a corresponding post-process set of scatterometric training data measured after implementation of the process step, and receiving a set of process control knob training data indicative of process control knob settings applied during implementation of the process step; and generating a machine learning model correlating variations in the pre-process sets of scatterometric training data and the corresponding process control knob training data with the corresponding post-process sets of scatterometric training data, to train the machine learning model to recommend changes to process control knob settings to compensate for variations in the pre-process scatterometric data.

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 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 system and methods for OCD metrology are provided including receiving multiple first sets of scatterometric data, dividing each set into k sub-vectors, and training, in a self-supervised manner, k2 auto-encoder neural networks that map each of the k sub-vectors to each other. Subsequently multiple respective sets of reference parameters and multiple corresponding second sets of scatterometric data are received and a transfer neural network (NN) is trained. Initial layers include a parallel arrangement of the k2 encoder neural networks. Target output of the transfer NN training is set to the multiple sets of reference parameters and feature input is set to the multiple corresponding second sets of scatterometric data, such that the transfer NN is trained to estimate new wafer pattern parameters from subsequently measured sets of scatterometric data.

Abstract: A system and method are presented for controlling measurements of various sample's parameters. The system comprises a control unit configured as a computer system comprising data input and output utilities, memory, and a data processor, and being configured to communicate with a measured data provider to receive measured data indicative of measurements on the sample. The data processor is configured to perform model-based processing of the measured data utilizing at least one predetermined model, and determine, for each of one or more measurements of one or more parameters of interest of the sample, an estimated upper bound on an error value for the measurement individually, and generate output data indicative thereof.

Abstract: A system and methods for Advance Process Control (APC) in semiconductor manufacturing include: for each of a plurality of waiter sites, receiving a pre-process set of scatterometric training data, measured before implementation of a processing step, receiving a corresponding post-process set of scatterometric training data measured after implementation of the process step, and receiving a set of process control knob training data indicative of process control knob settings applied during implementation of the process step; and generating a machine learning model correlating variations in the pre-process sets of scatterometric training data and the corresponding process control knob training data with the corresponding post-process sets of scatterometric training data, to train the machine learning model to recommend changes to process control knob settings to compensate for variations in the pre-process scatterometric data.

Abstract: A system and methods for OCD metrology are provided including receiving multiple first sets of scatterometric data, dividing each set into k sub-vectors, and training, in a self-supervised manner, k2 auto-encoder neural networks that map each of the k sub-vectors to each other. Subsequently multiple respective sets of reference parameters and multiple corresponding second sets of scatterometric data are received and a transfer neural network (NN) is trained. Initial layers include a parallel arrangement of the k2 encoder neural networks. Target output of the transfer NN training is set to the multiple sets of reference parameters and feature input is set to the multiple corresponding second sets of scatterometric data, such that the transfer NN is trained to estimate new wafer pattern parameters from subsequently measured sets of scatterometric data.

Abstract: A system and method are presented for controlling measurements of various sample's parameters. The system comprises a control unit configured as a computer system comprising data input and output utilities, memory, and a data processor, and being configured to communicate with a measured data provider to receive measured data indicative of measurements on the sample. The data processor is configured to perform model-based processing of the measured data utilizing at least one predetermined model, and determine, for each of one or more measurements of one or more parameters of interest of the sample, an estimated upper bound on an error value for the measurement individually, and generate output data indicative thereof.