Verint Systems, Inc.

Verint Americas Inc.

Applied Materials Israel Limited

Boaz Dudovich

Oren Golan

Shahar Daliyot

Ron Schwartz

Shmuel Kiro

Itzik Horovitz

Eithan Goldfarb

Amir Bar

Gideon Hazzani

Lior Katz

Eran Wachman

Shiran Ben Israel

Paz Yabbo

Bhavna Ghai

Eli Oren Joni

Assaf Ariel

Lior Yehieli

Chen Itzikowitz

Eyal Rot

There is provided a system and method of optimizing an inspection recipe for inspecting a semiconductor specimen. The method includes obtaining test data from a test performed after inspection, the test data indicative of functional defectivity of the specimen with respect to at least one structural feature at a suspected layer; retrieving inspection data of the suspected layer including a set of inspection images and a set of defect maps of the plurality of processing steps of the suspected layer; correlating the test data and the set of defect maps of the suspected layer to identify one or more structural features of the suspected layer with unmatched defectivity; for each of the identified structural features, including at least part of the inspection images corresponding to the structural feature in a training set; and using the training set to train a machine learning (ML) model in the inspection recipe.

Inspection recipe optimization for semiconductor specimens

There is provided a system and method of defect detection of a semiconductor specimen. The method includes obtaining a first image of the specimen acquired at a first bit depth, converting by a first processor the first image to a second image with a second bit depth lower than the first bit depth, transmitting the second image to a second processor configured to perform first defect detection on the second image using a first defect detection algorithm to obtain a first set of defect candidates, and sending locations of the first set of defect candidates to the first processor, extracting, from the first image, a set of image patches corresponding to the first set of defect candidates based on the locations, and performing second defect detection on the set of image patches using a second defect detection algorithm to obtain a second set of defect candidates.

Defect detection of a semiconductor specimen

A video system for video surveillance or video monitoring includes a video source, a video control system, and a video management system. The video management system is configured to receive configuration data and generate maps based on the configuration data, wherein at least one of the maps is associated with the video source. The video management system displays the map associated with the video source and displays a graphical symbol on the map which indicates the location of the video source. The video management system then receives a selection of the video source, receives information associated with the selected video source, and displays the received information.

Systems and methods for managing and displaying video sources

A plurality of pairs of video cameras and interrogation devices may be placed in a public place along various paths that a person-of-interest might be expected to move. The person-of-interest is then located in multiple images acquired, collectively, by multiple video cameras. From each of the interrogation devices that are paired with these video cameras, a subset of the captured identifiers is obtained. Candidate identifiers are then restricted to those identifiers that are included in each of the subsets. A given identifier may be rejected as a candidate identifier. To automatically locate the person-of-interest in the images acquired by the 'paired' video cameras, a processor may utilize video-tracking techniques to automatically track the person-of-interest, such that the person-of-interest is not 'lost.' By virtue of utilizing such tracking techniques, the person-of-interest may be repeatedly located automatically, and with minimal chance of a false detection.

System and method for associating an identifier of a mobile communication terminal with a person-of-interest, using video tracking

Video is often used to count and identify people entering and exiting an area, such as a store. This information may be used to determine the number and amount of time people are in the area, and other statistics and characteristics. These variables may be skewed by the associates employed or working in the area, instead of patrons or others. The present disclosure is directed to method for eliminating associates from video by facial analysis and the application of analytics.

Counting and monitoring method using face detection

A method of operating a video system to detect fraudulent return transactions comprises receiving an indication of a return transaction from a point of sale system, wherein the indication of the return transaction indicates a time when the return transaction occurred. The method further comprises receiving video data of an area proximate to the point of sale system captured at the time when the return transaction occurred. The method further comprises processing the video data to determine whether a first person is located in a return area and whether a second person is located proximate to the point of sale system. The method further comprises determining whether the return transaction is legitimate or fraudulent based on whether the first person is located in the return area and whether the second person is located proximate to the point of sale system.

Point of sale integration with video analytics for fraud detection

Methods and systems for identifying and tracking individuals in a area-of-interest that may be covered by a video surveillance subsystem and by a communication location subsystem, and a correlation system correlates the outputs of the two subsystems. The communication location subsystem may monitor communication of mobile phones. The video subsystem captures video images of the area-of-interest, and processes the video images so as to identify individuals who are present in the area. The correlation system correlates a given mobile phone with a given individual who was identified by the video subsystem as being engaged in a phone conversation. After correlating the mobile phone with the individual using the phone, the correlation system outputs correlated information regarding the phone and its user to an operator.

System and method for video-assisted identification of mobile phone users

A system and method for the capture and storage of data relating to the movements of objects, in a specified area and enables this data to be displayed in a graphically meaningful and useful manner. Video data is collected and video metadata is generated relating to objects (persons) appearing in the video data and their movements over time. The movements of the objects are then analyzed to detect the movements within a region of interest. This detection of movement allows a user, such as a manager of a store, to make informed decisions as to the infrastructure and operation of the store. One detection method relates to the number of people that are present in a region of interest for a specified time period. A second detection method relates to the number of people that remain or dwell in a particular area for a particular time period. A third detection method determines the flow of people and the direction they take within a region of interest. A fourth detection method relates to the number of people that enter a certain area by crossing a virtual line, a tripwire.

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Rehovot, Israel

Boaz Dudovich