Abstract: There is provided a method of examination of a semiconductor specimen. The method comprises: upon obtaining by a computer a Deep Neural Network (DNN) trained for a given examination-related application within a semiconductor fabrication process, processing together one or more fabrication process (FP) images using the obtained trained DNN, wherein the DNN is trained using a training set comprising synthetic images specific for the given application; and obtaining, by the computer, examination-related data specific for the given application, and characterizing at least one of the processed one or more FP images. Generating the training set can comprise: training an auxiliary DNN to generate a latent space, generating a synthetic image by applying the trained auxiliary DNN to a point selected in the generated latent space, and adding the generated synthetic image to the training set.

Abstract: There are provided a classifier and method of classifying defects in a semiconductor specimen. The method comprises receiving defects classified into a majority class, each having values for plurality of attributes, some defects belonging to a minority class, and some to the majority; selecting an attribute subset and defining differentiators for attributes wherein a second classifier using the subset and differentiators classifies correctly to minority and majority classes at least part of the defects; generating a training set comprising: defects of the majority and minority classes, and additional defects which the second classifier classifies as minority; training, upon the training set, subset, and differentiators, an engine obtaining a confidence level that a defect belongs to the majority class; applying the engine to second defects classified to the majority class, to obtain a confidence level of classifying each defect to the majority class; and outputting defects having a low confidence level.

Abstract: A candidate defect may be identified at a semiconductor wafer. A determination may be made as to whether the candidate defect at the semiconductor wafer corresponds to a systematic defect or a random defect. In response to determining that the candidate defect at the semiconductor wafer corresponds to a systematic detect, the candidate defect at the semiconductor wafer may be provided to a defect review tool for review by the defect review tool.

Abstract: A candidate defect may be identified at a semiconductor wafer. A determination may be made as to whether the candidate defect at the semiconductor wafer corresponds to a systematic defect or a random defect. In response to determining that the candidate defect at the semiconductor wafer corresponds to a systematic detect, the candidate defect at the semiconductor wafer may be provided to a defect review tool for review by the defect review tool.

Abstract: An examination system, a method of obtaining a training set for a classifier, and a non-transitory computer readable medium, the method comprising: upon receiving in a memory device object inspection results comprising data indicative of potential defects, each potential defect of the potential defects associated with a multiplicity of attribute values defining a location of the potential defect in an attribute space: sampling by the processor a first set of defects from the potential defects, wherein the defects within the first set are dispersed independently of a density of the potential defects in the attribute space; and obtaining by the processor a training defect sample set comprising the first set of defects and data or parameters representative of the density of the potential defects in the attribute space.

Abstract: There is provided a system and method of generating a training set usable for examination of a semiconductor specimen. The method comprises: obtaining a simulation model capable of simulating effect of a physical process on fabrication process (FP) images depending on the values of parameters of the physical process; applying the simulation model to an image to be augmented for the training set and thereby generating one or more augmented images corresponding to one or more different values of the parameters of the physical process; and including the generated one or more augmented images into the training set. The training set can be usable for examination of the specimen using a trained Deep Neural Network, automated defect review, automated defect classification, automated navigation during the examination, automated segmentation of FP images, automated metrology based on FP images and other examination processes that include machine learning.

Abstract: A system, method and computer software product, the system capable of classifying defects and comprising: an hardware-based GUI component; and a processing and memory circuitry configured to: a. upon obtaining data informative of a plurality of defects and attribute values thereof, using the attribute values to create initial classification of the plurality of defects into a plurality of classes; b. for a given class, presenting to a user, by the hardware-based GUI component, an image of a defect initially classified to the given class with a low likelihood, wherein the image is presented along with images of one or more defects initially classified to the given class with the highest likelihood; and c. subject to confirming by the user, using the hardware-based GUI component, that the at least one defect is to be classified to the given class, indicating the at least one defect as belonging to the given class.

Abstract: There are provided a classifier and a method of classifying defects in a semiconductor specimen. The classifier enables assigning each class to a classification group among three or more classification groups with different priorities. Classifier further enables setting purity, accuracy and/or extraction requirements separately for each class, and optimizing the classification results in accordance with per-class requirements. During training, the classifier is configured to generate a classification rule enabling the highest possible contribution of automated classification while meeting per-class quality requirements defined for each class.

Abstract: According to an embodiment of the invention there may be provided a system for assigning lithographic mask inspection process parameters. The system may include a search module, a decision module and a memory module. The memory module may be configured to store an expected image expected to be formed on a photoresist during a lithographic process that involves illuminating the lithographic mask. The search module may be configured to search in the expected image for printable features. The decision module may be configured to assign a first lithographic mask inspection process parameter to lithographic mask areas related to printable features and assign a second lithographic mask inspection process parameter to at least some lithographic mask areas that are not associated with printable features. The second lithographic mask inspection process parameter may differ from the first lithographic mask inspection process parameter.

Abstract: An inspection system that may include a processor and a memory module; wherein the memory module is configured to store a first image of an area of an object and a second image of the area of the object; wherein the processor is configured to generate a synthetic image of the area of the object, and to compare the synthetic image to the second image to provide defect detection results.

Abstract: There are provided system and method of classifying defects in a semiconductor specimen. The method comprises: upon obtaining by a computer a Deep Neural Network (DNN) trained to provide classification-related attributes enabling minimal defect classification error, processing a fabrication process (FP) sample using the obtained trained DNN; and, resulting from the processing, obtaining by the computer classification-related attributes characterizing the at least one defect to be classified, thereby enabling automated classification, in accordance with the obtained classification-related attributes, of the at least one defect presented in the FP image.

Abstract: There are provided system and method of segmentation a fabrication process (FP) image obtained in a fabrication of a semiconductor specimen. The method comprises: upon obtaining a Deep Neural Network (DNN) trained to provide segmentation-related data, processing a fabrication process (FP) sample using the obtained trained DNN and, resulting from the processing, obtaining by the computer segments-related data characterizing the FP image to be segmented, the obtained segments-related data usable for automated examination of the semiconductor specimen. The DNN is trained using a segmentation training set comprising a plurality of first training samples and ground truth data associated therewith, each first training sample comprises a training image; FP sample comprises the FP image to be segmented.

Abstract: According to an embodiment of the invention there may be provided a system for assigning lithographic mask inspection process parameters. The system may include a search module, a decision module and a memory module. The memory module may be configured to store an expected image expected to be formed on a photoresist during a lithographic process that involves illuminating the lithographic mask. The search module may be configured to search in the expected image for printable features. The decision module may be configured to assign a first lithographic mask inspection process parameter to lithographic mask areas related to printable features and assign a second lithographic mask inspection process parameter to at least some lithographic mask areas that are not associated with printable features. The second lithographic mask inspection process parameter may differ from the first lithographic mask inspection process parameter.

Abstract: There are provided system and method of examining a semiconductor specimen. The method comprises: upon obtaining a Deep Neural Network (DNN) trained for a given examination-related application within a semiconductor fabrication process, processing together one or more fabrication process (FP) images using the obtained trained DNN, wherein the DNN is trained using a training set comprising ground truth data specific for the given application; and obtaining examination-related data specific for the given application and characterizing at least one of the processed one or more FP images. The examination-related application can be, for example, classifying at least one defect presented by at least one FP image, segmenting the at least one FP image, detecting defects in the specimen presented by the at least one FP image, registering between at least two FP images, regression application enabling reconstructing the at least one FP image in correspondence with different examination modality, etc.

Abstract: An inspection system that may include a processor and a memory module; wherein the memory module is configured to store a first image of an area of an object and a second image of the area of the object; wherein the processor is configured to generate a synthetic image of the area of the object, and to compare the synthetic image to the second image to provide defect detection results.

Abstract: Provided is an RPG disruption system (RDS) for protecting an enclosure against RPG's, the system including a casing fixed in close proximity to a wall of the enclosure facing an anticipated RPG threat. The casing includes an anvil accommodating a propelling mechanism for propelling the disrupting element, and an activating system for activating the propelling mechanism so as to propel the disrupting element towards an approaching RPG, to thereby neutralize it.

Abstract: Provided is an RPG disruption system (RDS) for protecting an enclosure against RPG's, the system including a casing fixed in close proximity to a wall of the enclosure facing an anticipated RPG threat. The casing includes an anvil accommodating a propelling mechanism for propelling the disrupting element, and an activating system for activating the propelling mechanism so as to propel the disrupting element towards an approaching RPG, to thereby neutralize it.

Abstract: A method and a device for the acoustic detection of a one lung intubation situation in a human subject are disclosed. According to some embodiments, the disclosed method includes computing an autoregressive moving average (ARMA) or an autoregressive function of electrical signals received from acoustic detectors placed at different locations on the body of the subject. Appropriate locations for acoustic detectors include the back region and the chest region. The disclosed method and apparatus are insensitive to uncancelled, random background noise with a loudness associated with an operating room or intensive care ward. The disclosed device is configurable so that the relative occurrence rate of missed detections or false negatives and false positive alarms can be modified. In one exemplary embodiment, the device is adapted such that at most 9% of identifications are false positive identifications, and at most 2% of identifications are false negative identifications.

Abstract: A rotary sprinkler comprising a rotor (16) with an axle (38) having a tip, and a thrust hearing (14) with a socket (26) having a bottom (30), the socket being adapted to receive for rotation the axle so that the tip abuts the bottom in a contact zone. The sprinkler further comprises a hard element (42) constituting a part of the bottom or of the tip. The hard element (42) is made of harder material than the axle or the socket, thereby reducing their wear.

Abstract: Briefly, some embodiments of the invention may provide devices, systems and methods for modifying video signals. In accordance with some embodiments of the invention, a device may include a video adaptor to receive a first video signal having an image rendering code embedded therein, and to produce a second video signal based on the first video signal and the image rendering code.