Abstract: A method of evaluating a region of interest of a sample including: positioning the sample within in a vacuum chamber of an evaluation tool that includes a scanning electron microscope (SEM) column and a focused ion beam (FIB) column; acquiring a plurality of two-dimensional images of the region of interest by alternating a sequence of delayering the region of interest with a charged particle beam from the FIB column and imaging a surface of the region of interest with the SEM column; generating an initial three-dimensional data cube representing the region of interest by stacking the plurality of two-dimensional images on top of each other in an order in which they were acquired; identifying distortions within the initial three-dimensional data cube; and creating an updated three-dimensional data cube that includes corrections for the identified distortions.

Abstract: A method of evaluating a region of interest of a sample including: positioning the sample within in a vacuum chamber of an evaluation tool that includes a scanning electron microscope (SEM) column and a focused ion beam (FIB) column; acquiring a plurality of two-dimensional images of the region of interest by alternating a sequence of delayering the region of interest with a charged particle beam from the FIB column and imaging a surface of the region of interest with the SEM column; generating an initial three-dimensional data cube representing the region of interest by stacking the plurality of two-dimensional images on top of each other in an order in which they were acquired; identifying distortions within the initial three-dimensional data cube; and creating an updated three-dimensional data cube that includes corrections for the identified distortions.

Abstract: A method, a non-transitory computer readable medium, and a system for evaluating an inspection algorithm for inspecting a semiconductor specimen.

Abstract: Analyzing a sidewall of a hole milled in a sample to determine thickness of a buried layer includes milling the hole in the sample using a charged particle beam of a focused ion beam (FIB) column to expose the buried layer along the sidewall of the hole. After milling, the sidewall of the hole has a known slope angle. From a perspective relative to a surface of the sample, a distance is measured between a first point on the sidewall corresponding to an upper surface of the buried layer and a second point on the sidewall corresponding to a lower surface of the buried layer. The thickness of the buried layer is determined using the known slope angle of the sidewall, the distance, and the angle relative to the surface of the sample.

Abstract: A method of evaluating a region of interest of a sample including: positioning the sample within in a vacuum chamber of an evaluation tool that includes a scanning electron microscope (SEM) column and a focused ion beam (FIB) column; acquiring a plurality of two-dimensional images of the region of interest by alternating a sequence of delayering the region of interest with a charged particle beam from the FIB column and imaging a surface of the region of interest with the SEM column; generating an initial three-dimensional data cube representing the region of interest by stacking the plurality of two-dimensional images on top of each other in an order in which they were acquired; identifying distortions within the initial three-dimensional data cube; and creating an updated three-dimensional data cube that includes corrections for the identified distortions.

Abstract: A method of evaluating a region of a sample that includes alternating layers of different material. The method includes milling, with a focused ion beam, a portion of the sample that includes the alternating layers of different material; reducing the milling area; and repeating the milling and reducing steps multiple times during the delayering process until the process is complete.

Abstract: A method, a non-transitory computer readable medium, and a system for evaluating an inspection algorithm for inspecting a semiconductor specimen.

Abstract: A method of evaluating a region of a sample that includes alternating layers of different material. The method includes milling, with a focused ion beam, a portion of the sample that includes the alternating layers of different material; reducing the milling area; and repeating the milling and reducing steps multiple times during the delayering process until the process is complete.

Abstract: A modular neural network system comprising: a plurality of neural network modules; and a controller configured to select a combination of at least one of the neural network modules to construct a neural network, dedicated for a specific task.

Abstract: A modular neural network system comprising: a plurality of neural network modules; and a controller configured to select a combination of at least one of the neural network modules to construct a neural network, dedicated for a specific task.

Abstract: A modular neural network system comprising: a plurality of neural network modules; and a controller configured to select a combination of at least one of the neural network modules to construct a neural network, dedicated for a specific task.

Abstract: A modular neural network system comprising: a plurality of neural network modules; and a controller configured to select a combination of at least one of the neural network modules to construct a neural network, dedicated for a specific task.

Abstract: A robotic system (“new robot”) operative for performing at least one task in an environment, the system comprising: learn-from-predecessor functionality governed by a data exchange protocol, which controls short-range wireless knowledge transfer from a short-range wireless transmitter in a predecessor robot system (“old robot”) to a short-range wireless receiver in said robotic system, said knowledge comprising at least one environment-specific datum previously stored by the predecessor robot.

Abstract: A robotic system (“new robot”) operative for performing at least one task in an environment, the system comprising: learn-from-predecessor functionality governed by a data exchange protocol, which controls short-range wireless knowledge transfer from a short-range wireless transmitter in a predecessor robot system (“old robot”) to a short-range wireless receiver in said robotic system, said knowledge comprising at least one environment-specific datum previously stored by the predecessor robot.

Abstract: Disclosed are systems and devices for facilitating audiovisual communication. According to embodiments, there may be provided a communication device including or otherwise functionally associated with a video camera to acquire visual information from a scene and to convert the visual information into a video stream. A video processing circuit may (a) receive the video stream, (b) extract user related features from the video stream; and (c) detect and characterize one or more user actions. A controller to receive the video processing circuit output and responsive to an indication of one or more detected events within the video stream to trigger a communication session between said communication device and an addressee device, wherein different addressee devices may be associated with different detected actions.

Abstract: A system and method for accent invariant speech recognition comprising: maintaining a database scoring a set of language units in a given language, and for each of the language units, scoring audio samples of pronunciation variations of the language unit pronounced by a plurality of speakers; extracting and storing m the database a feature vector for locating each of the audio samples in a feature space; identifying pronunciation variation distances, which are distances between locations of audio samples of the same language unit in the feature space, and inter-unit distances, which are distances between locations of audio samples of different language units in the feature space; calculating a transformation applicable on the feature space to reduce the pronunciation variation distances relative to the inter-unit distances; and based on the calculated transformation, training a processor to classify as a same language unit pronunciation variations of the same language unit.

Abstract: This application discloses systems for generating a searchable real-scene database that includes records indicating entities and physical events occurring within the real-scene. The system includes (i) a camera feed interface to receive a camera feed from the cameras observing the real-scene; (ii) an image processing circuitry including a static scene analysis unit to: (a) extract features of entities appearing in a camera feed, (b) recognize entity types of entities appearing in the camera feeds, (c) assign an entity designator to a specific entity appearing in the camera feeds, (d) generate an entity designator record for a uniquely identifiable entity, and generate an attribute record for a uniquely identifiable entity; and (iii) a scene dynamics analysis unit to: (a) detect entity movements, (b) detect entity speech, (c) characterize entity actions based on detected entity movement or entity speech, and (d) generate a record indicting an action taken by an entity.

Abstract: A method of training a heterogeneous sensory system, including: accepting data of an environment from a first sensor, identifying objects in the data from the first sensor by a first processing unit, accepting data of the environment from a second sensor corresponding to the data of the first sensor, identifying objects in the data from the second sensor by a second processing unit, comparing the identified objects from the first processing unit and the corresponding objects from the second processing unit with a system processing unit, determining cases of discrepancies, between the objects identified in the data of the first sensor and not in the data of the second sensor or vice versa, tagging the data of the cases with discrepancies, storing the tagged data in a training buffer; and training the first processing unit and/or the second processing unit with the data stored in the training buffer.

Abstract: This application discloses to methods, circuits, devices, assemblies and systems for generating a searchable real-scene database including records indicating entities and physical events occurring within the real-scene. The said system may include a camera feed interface to receive a camera feed from each of one or more cameras observing the real-scene. It may also include image processing circuitry including a static scene analysis unit to: (a) extract features of entities appearing in a camera feed, (b) recognize entity types of entities appearing in the one or more camera feeds, (c) assign an entity designator to a specific entity appearing in the camera feeds, (d) generate an entity designator record for a uniquely identifiable entity, and generate an attribute record for a uniquely identifiable entity.

Abstract: A computerized method for providing no-configuration object recognition video analytics, the method comprising generating a database stored in computer memory and including labelled images of recognized objects imaged at an initial set of sites including at least one site; using the database as a training set to train a pattern recognition engine; uploading images of additional site/s including at least one additional site which is not a member in the initial set of sites; providing object recognition results for the additional site/s by using the pattern recognition engine on the images as uploaded; verifying the object recognition results; and using a processor for operating the pattern recognition engine on images generated at each the additional site/s including modifying the pattern recognition engine according to and when indicated by the verifying, thereby to provide no-configuration object recognition video analytics at the additional site/s.