The present invention relates to a smart conversion and calibration of the defect coordinate, diagnosis, sampling system and the method thereof for manufacturing fab is provided. The intelligent defect diagnosis method comprises: receiving pluralities of defect data, design layout data, analyzing the defect data, design layouts, by a Critical Area Analysis (CAA) system, wherein the analyzing step further contains the sub-steps: superposing the defect contour pattern and the design layout, performing CAA to identify a killer or non-killer defect based on the open or short failure probability, defects are classified as high, medium, low, or negligible risk defect based on the Killer Defect Index, defect signal parameters, selecting defect samples based on the defect classification data, selecting alarm defect and filtering false defect with pattern match with defect pattern library and frequent failure defect library, performing coordinate conversion and pattern match between image contour and design layout for coordinate correction, creating a CAA accuracy correction system and defect size calibration system by analyzing original defect size data and defect contour size from image analysis, evaluating the defect size using measurement uncertainty analysis with statistical analysis methods to reach the purposes of increasing CAA accuracy and Killer Defect identification rate.

The present invention relates to a smart defect calibration, diagnosis, sampling system and the method thereof for manufacturing fab is provided. The intelligent defect diagnosis method comprises: receiving pluralities of defect data, design layout data, analyzing the defect data, design layouts, by a Critical Area Analysis (CAA) system, wherein the analyzing step further contains the sub-steps: superposing the defect contour pattern and the design layout, performing CAA to identify a killer or non-killer defect based on the open or short failure probability, defects are classified as high, medium, low, or negligible risk defect based on the Killer Defect Index, defect signal parameters, selecting defect samples based on the defect classification data, selecting alarm defect and filtering false defect with pattern match with defect pattern library and frequent failure defect library, performing coordinate conversion and pattern match between image contour and design layout for coordinate correction, creating a CAA accuracy correction system and defect size calibration system by analyzing original defect size data and defect contour size from image analysis, evaluating the defect size using measurement uncertainty analysis with statistical analysis methods to reach the purposes of increasing CAA accuracy and Killer Defect identification rate.

The present invention relates to a smart conversion and calibration of the defect coordinate, diagnosis, sampling system and the method thereof for manufacturing fab is provided. The intelligent defect diagnosis method comprises: receiving pluralities of defect data, design layout data, analyzing the defect data, design layouts, by a Critical Area Analysis (CAA) system, wherein the analyzing step further contains the sub-steps: superposing the defect contour pattern and the design layout, performing CAA to identify a killer or non-killer defect based on the open or short failure probability, defects are classified as high, medium, low, or negligible risk defect based on the Killer Defect Index, defect signal parameters, selecting defect samples based on the defect classification data, selecting alarm defect and filtering false defect with pattern match with defect pattern library and frequent failure defect library, performing coordinate conversion and pattern match between image contour and design layout for coordinate correction, creating a CAA accuracy correction system and defect size calibration system by analyzing original defect size data and defect contour size from image analysis, evaluating the defect size using measurement uncertainty analysis with statistical analysis methods to reach the purposes of increasing CAA accuracy and Killer Defect identification rate.

Systems, methods, and computer-readable media to reconstruct thin wall features of domain features in images, which are marginally resolved by a multi-dimensional image, using geometrical continuity. The technique permits reconstruction of features such as structure walls that have a variable thickness, where a thin portion of the wall feature is marginally resolved by an image due to resolution limitations, and the marginally resolved portion of the feature is incompletely segmented. Using the systems, methods, and computer-readable media, such a wall feature can be recognized as a broken wall that misrepresents the completeness of the feature, and can be reconstructed as a completed wall feature.

Provided are a damage figure creation supporting apparatus, a damage figure creation supporting method, a damage figure creation supporting program, and a damage figure creation supporting system that enable efficient creation of a damage figure using marking colors. The damage figure creation supporting method includes a step of acquiring an image obtained by photographing a surface of a structure in color, a step of analyzing the acquired image and detecting markings applied to damaged portions on the surface of the structure for respective colors, and a step of creating a damage figure, based on detection results of the markings for the respective colors. The damage figure is configured as a diagram of tracing the markings and has a layered structure with layers each corresponding to a color.

A smart defect calibration, diagnosis, sampling system and the method thereof for manufacturing fab is provided. The intelligent defect diagnosis method includes: receiving pluralities of defect data, design layout data, analyzing the defect data, design layouts, by a Critical Area Analysis (CAA) system, selecting defect samples based on the defect classification data, selecting alarm defect and filtering false defect with pattern match with defect pattern library and frequent failure defect library, performing coordinate conversion and pattern match between defect image contour, defect image pattern, and design layout for coordinate correction, creating a CAA accuracy correction system and defect size calibration system by analyzing original defect size data and defect contour size from image analysis, evaluating the defect size using measurement uncertainty analysis with statistical analysis methods to reach the purposes of increasing CAA accuracy and Killer Defect identification rate.

A non-transitory computer-readable medium storing a program code including an image generation model, which when executed, causes a processor to input input data including sampling data of some of a plurality of semiconductor dies of a wafer to a generator network of the image generation model and output a wafer map indicating the plurality of semiconductor dies, and to input the wafer map output from the generator network to a discriminator network of the image generation model and discriminate the wafer map.

A system and method for monitoring a track and/or rail employs one or more distance measuring imagers mounted to a railcar and directed to image the track and/or rails wherein the images are geo-tagged with location data. Geo-tagged distance measurements are processed to determine at least track gauge and/or at least rail fastener integrity. The system and method may also determine other track and/or rail integrity issues including, e.g., rail profile, rail alignment, center point dip, cross level, rail cant, wheel wear, wheel integrity, rail wear, rail defects, and/or rail temperature. The system and method may also determine when inspection and/or maintenance of the track is indicated, and provide selected records of where and/or when such inspection and/or maintenance is indicated.

Disclosed herein are optical near-field systems and methods that provide a noninvasive and fast approach to detect and characterize dislocation defects in semiconductors films caused by a mismatched film-substrate, such as found in GaAsSi. The embodiments disclosed utilize optical cavities formed by the dislocation defects. The optical cavities act to localize a beam excitation light, which elicits second harmonic generated (SHG) light from the same region. The SHG light can be probed and mapped to provide information regarding the defects. The information derived from the map includes defect location, defect density, and defect orientation.

A system and method for monitoring a track and/or rail employs one or more imagers mounted to a railcar and directed to image the track and/or rails wherein the images are geo-tagged with location data. Geo-tagged images are processed to determine at least track gauge and/or at least rail fastener integrity. The system and method may also determine other track and/or rail integrity issues including, e.g., rail fastener integrity, rail profile, rail alignment, center point dip, cross level, rail cant, wheel wear, wheel integrity, rail wear, rail defects, and/or rail temperature. The system and method may also determine when inspection and/or maintenance of the track is indicated, and provide selected records of where and/or when such inspection and/or maintenance is indicated.