Abstract: A method of presenting defects data produced by inspection of semiconductor wafers or masks, the method including receiving defect data including a plurality of attributes per defect, using t-distributed Stochastic Neighbor Embedding to embed the defects attributes from a multi-dimensional attribute space into a lower-dimension space, and displaying the defect data embedded into the lower-dimension space on a 2D display as a scatter plot. A system for inspecting wafers or masks, the system including a user interface for presenting defect data produced by inspection of wafers or masks, the user interface implementing a method including receiving defect data including a plurality of attributes per defect, using t-distributed Stochastic Neighbor Embedding to embed the defects attributes from a multi-dimensional attribute space into a lower-dimension space, and displaying the defects data embedded into the lower-dimension space on a 2D display as a scatter plot. Related apparatus and methods are also described.

Abstract: There is provided a system and method of defect detection on a semiconductor specimen based on template matching or machine learning (ML). Template matching is performed between a set of template patches and a set of runtime images, by selectively performing at least two of the following: matching a defect template patch in an inspection image, matching a reference template patch in a reference image, or matching a difference template patch in a difference image, so as to provide likelihood of target of interest (TOI) presence in the inspection image. The ML-based approach includes feeding an inspection patch and a reference patch together to a trained ML model, to generate a feature vector representative of a given TOI candidate, and evaluating the feature vector of the given TOI candidate to provide a likelihood of the given TOI candidate being a TOI or non-TOI.

Abstract: A method for increasing Signal-to-Noise-Ratio (SNR) of defect detection in inspection of wafers or masks, the method including receiving a current image, receiving a reference image, receiving an indication for existence of a defect in the current image, producing a difference image between the current image and the reference image, performing singular value decomposition (SVD) on the difference image, removing one or more lower-valued singular values from a diagonal middle matrix produced by the SVD, thereby producing a reduced middle matrix, and producing an improved-SNR difference image by reconstructing the difference image using the reduced middle matrix. Related apparatus and methods are also described.

Abstract: There is provided a system and method of defect detection on a semiconductor specimen based on template matching or machine learning (ML). Template matching is performed between a set of template patches and a set of runtime images, by selectively performing at least two of the following: matching a defect template patch in an inspection image, matching a reference template patch in a reference image, or matching a difference template patch in a difference image, so as to provide likelihood of target of interest (TOI) presence in the inspection image. The ML-based approach includes feeding an inspection patch and a reference patch together to a trained ML model, to generate a feature vector representative of a given TOI candidate, and evaluating the feature vector of the given TOI candidate to provide a likelihood of the given TOI candidate being a TOI or non-TOI.