Abstract: A context-based inspection system is disclosed. The system may include an optical imaging sub-system. The system may further include one or more controllers communicatively coupled to the optical imaging system. The one or more controllers may be configured to: receive one or more reference images; receive one or more test images of a sample; generate one or more probabilistic context maps during inspection runtime using an unsupervised classifier; provide the generated one or more probabilistic context maps to a supervised classifier during the inspection runtime; and apply the supervised classifier to the received one or more test images to identify one or more DOIs on the sample.

Abstract: A context-based inspection system is disclosed. The system may include an optical imaging sub-system. The system may further include one or more controllers communicatively coupled to the optical imaging system. The one or more controllers may be configured to: receive one or more reference images; receive one or more test images of a sample; generate one or more probabilistic context maps during inspection runtime using an unsupervised classifier; provide the generated one or more probabilistic context maps to a supervised classifier during the inspection runtime; and apply the supervised classifier to the received one or more test images to identify one or more DOIs on the sample.

Abstract: A system may be configured for joint defect discovery and optical mode selection. Defects are detected during a defect discovery step. The discovered defects are accumulated into a mode selection dataset. The mode selection dataset is used to perform mode selection to determine a mode combination. The mode combination may then be used to train the defect detection model. Additional defects may then be detected by the defect detection model. The additional defects may then be provided to the mode selection dataset, for further performing mode selection and training the defect detection model. One or more run-time modes may then be determined. The system may be configured for mode selection and defect detection at an image pixel level.

Abstract: Methods and systems for accelerated training of a machine learning based model for semiconductor applications are provided. One method for training a machine learning based model includes acquiring information for non-nominal instances of specimen(s) on which a process is performed. The machine learning based model is configured for performing simulation(s) for the specimens. The machine learning based model is trained with only information for nominal instances of additional specimen(s). The method also includes re-training the machine learning based model with the information for the non-nominal instances of the specimen(s) thereby performing transfer learning of the information for the non-nominal instances of the specimen(s) to the machine learning based model.

Abstract: An inspection system is disclosed. The inspection system includes a shared memory configured to receive image data from a defect inspection tool and a controller communicatively coupled to the shared memory. The controller includes a host image module configured to apply one or more general-purpose defect-inspection algorithms to the image data using central-processing unit (CPU) architectures, a results module configured to generate inspection data for defects identified by the host image module, and secondary image module(s) configured to apply one or more targeted defect-inspection algorithms to the image data. The secondary image module(s) employ flexible sampling of the image data to match a data processing rate of the host image module within a selected tolerance. The flexible sampling of the image data is adjusted responsive to the inspection data generated by the results module and the host image module.

Abstract: Some embodiments of the present disclosure relate to methods and roofing systems including a hybrid layered structure. In some embodiments, the hybrid layered layer structure, method, and system are used for waterproofing at least one steep slope roof substrate. In some embodiments, the at least one steep slope roof substrate at least one protruding member protruding from the at least one steep slope roof substrate. In some embodiments, the hybrid layered structure comprises a first coating layer, a roofing membrane, and a second coating layer.

Abstract: A system may be configured for joint defect discovery and optical mode selection. Defects are detected during a defect discovery step. The discovered defects are accumulated into a mode selection dataset. The mode selection dataset is used to perform mode selection to determine a mode combination. The mode combination may then be used to train the defect detection model. Additional defects may then be detected by the defect detection model. The additional defects may then be provided to the mode selection dataset, for further performing mode selection and training the defect detection model. One or more run-time modes may then be determined. The system may be configured for mode selection and defect detection at an image pixel level.

Abstract: An inspection system is disclosed. The inspection system includes a shared memory configured to receive image data from a defect inspection tool and a controller communicatively coupled to the shared memory. The controller includes a host image module configured to apply one or more general-purpose defect-inspection algorithms to the image data using central-processing unit (CPU) architectures, a results module configured to generate inspection data for defects identified by the host image module, and secondary image module(s) configured to apply one or more targeted defect-inspection algorithms to the image data. The secondary image module(s) employ flexible sampling of the image data to match a data processing rate of the host image module within a selected tolerance. The flexible sampling of the image data is adjusted responsive to the inspection data generated by the results module and the host image module.

Abstract: A pull tool is provided having a tool body with at least one tool arm extending transverse from a main body and a pointer tip formed at a distal end of the main body. A deformable tip is formed of an elastomer having a conductive additive. The deformable tip secured to and covers the pointer tip of the tool body to provide conductive touch with the deformable tip.

Abstract: Some embodiments of the present disclosure relate to methods and roofing systems including a hybrid layered structure. In some embodiments, the hybrid layered layer structure, method, and system are used for waterproofing at least one steep slope roof substrate. In some embodiments, the at least one steep slope roof substrate at least one protruding member protruding from the at least one steep slope roof substrate. In some embodiments, the hybrid layered structure comprises a first coating layer, a roofing membrane, and a second coating layer.

Abstract: Some embodiments of the present disclosure relate to methods and roofing systems including a hybrid layered structure. In some embodiments, the hybrid layered layer structure, method, and system are used for waterproofing at least one steep slope roof substrate. In some embodiments, the at least one steep slope roof substrate at least one protruding member protruding from the at least one steep slope roof substrate. In some embodiments, the hybrid layered structure comprises a first coating layer, a roofing membrane, and a second coating layer.

Abstract: Methods and systems for setting up inspection of a specimen with design and noise based care areas are provided. One system includes one or more computer subsystems configured for generating a design-based care area for a specimen. The computer subsystem(s) are also configured for determining one or more output attributes for multiple instances of the care area on the specimen, and the one or more output attributes are determined from output generated by an output acquisition subsystem for the multiple instances. The computer subsystem(s) are further configured for separating the multiple instances of the care area on the specimen into different care area sub-groups such that the different care area sub-groups have statistically different values of the output attribute(s) and selecting a parameter of an inspection recipe for the specimen based on the different care area sub-groups.

Abstract: Methods and systems for generating a high resolution image for a specimen from a low resolution image of the specimen are provided. One system includes one or more computer subsystems configured for acquiring a low resolution image of a specimen. The system also includes one or more components executed by the one or more computer subsystems. The one or more components include a deep convolutional neural network that includes one or more first layers configured for generating a representation of the low resolution image. The deep convolutional neural network also includes one or more second layers configured for generating a high resolution image of the specimen from the representation of the low resolution image. The second layer(s) include a final layer configured to output the high resolution image and configured as a sub-pixel convolutional layer.

Abstract: Methods and systems for performing active learning for defect classifiers are provided. One system includes one or more computer subsystems configured for performing active learning for training a defect classifier. The active learning includes applying an acquisition function to data points for the specimen. The acquisition function selects one or more of the data points based on uncertainty estimations associated with the data points. The active learning also includes acquiring labels for the selected one or more data points and generating a set of labeled data that includes the selected one or more data points and the acquired labels. The computer subsystem(s) are also configured for training the defect classifier using the set of labeled data. The defect classifier is configured for classifying defects detected on the specimen using the images generated by the imaging subsystem.

Abstract: Methods and systems fir identifying nuisances and defects of interest (DOIs) in defects detected on a wafer are provided. One method includes acquiring metrology data for the wafer generated by a metrology tool that performs measurements on the wafer at an array of measurement points. In one embodiment, the measurement points are determined prior to detecting the defects on the wafer and independently of the defects detected on the wafer. The method also includes determining locations of defects detected on the wafer with respect to locations of the measurement points on the wafer and assigning metrology data to the defects as a defect attribute based on the locations of the defects determined with respect to the locations of the measurement points. In addition, the method includes determining if the defects are nuisances or DOIs based on the defect attributes assigned to the defects.

Abstract: Methods and systems for determining parameter(s) of a metrology process to be performed on a specimen are provided. One system includes one or more computer subsystems configured for automatically generating regions of interest (ROIs) to be measured during a metrology process performed for the specimen with the measurement subsystem based on a design for the specimen. The computer subsystem(s) are also configured for automatically determining parameter(s) of measurement(s) performed in first and second subsets of the ROIs during the metrology process with the measurement subsystem based on portions of the design for the specimen located in the first and second subsets of the ROIs, respectively. The parameter(s) of the measurement(s) performed in the first subset are determined separately and independently of the parameter(s) of the measurement(s) performed in the second subset.

Abstract: Methods and systems for setting up inspection of a specimen with design and noise based care areas are provided. One system includes one or more computer subsystems configured for generating a design-based care area for a specimen. The computer subsystem(s) are also configured for determining one or more output attributes for multiple instances of the care area on the specimen, and the one or more output attributes are determined from output generated by an output acquisition subsystem for the multiple instances. The computer subsystem(s) are further configured for separating the multiple instances of the care area on the specimen into different care area sub-groups such that the different care area sub-groups have statistically different values of the output attribute(s) and selecting a parameter of an inspection recipe for the specimen based on the different care area sub-groups.

Abstract: A tool for supporting fire wardens during a building evacuation event is provided. The tool includes a mobile computing device comprising a display unit, a memory and a processor. The memory has executable instructions of an application stored thereon, which, when executed, cause the processor to interface with monitoring and control systems of a building and to control the display unit to display an interactive floor plan of the building with scene selection capability, to display evacuation, evacuation re-routing and action guidance overlaid on the interactive floor plan and to display real-time monitoring and building control options overlaid on the interactive floor plan.

Abstract: An inspection system includes a controller communicatively coupled to a physical inspection device (PID), a virtual inspection device (VID) configured to analyze stored PID data, and a defect verification device (DVD). The controller may receive a pattern layout of a sample including multiple patterns fabricated with selected lithography configurations defining a process window, receive locations of PID-identified defects identified through analysis of the sample with the PID, wherein the PID-identified defects are verified by the DVD, remove one or more lithography configurations associated with the locations of the PID-identified defects from the process window, iteratively refine the process window by removing one or more lithography configurations associated with VID-identified defects identified through analysis of selected portions of stored PID data with the VID, and provide, as an output, the process window when a selected end condition is met.

Abstract: A voltage contrast imaging defect detection system includes a voltage contrast imaging tool and a controller coupled to the voltage contrast imaging tool. The controller is configured to generate one or more voltage contrast imaging metrics for one or more structures on a sample, determine one or more target areas on the sample based on the one or more voltage contrast imaging metrics, receive a voltage contrast imaging dataset for the one or more target areas on the sample from the voltage contrast imaging tool, and detect one or more defects based on the voltage contrast imaging dataset.