Abstract: Disclosed herein is s computer-based method for obtaining and analyzing multi-die scan data of a patterned wafer. The method includes sequentially implementing an operation of scanning a respective plurality of sets of slices on a wafer, and, per each slice segment in a multiplicity of slice segments in the plurality of sets of slices, an operation of performing die-to-multi-die (D2MD) analysis of scan data of the slice segment in order to detect defects in the slice segment. Each set of slices may constitute a subset of the totality of slices on the respective die-column. Sets scanned in a same implementation are analogous to one another, thereby facilitating—in the die-to-multi-die analysis of scan data of a slice segment—taking into account, as reference, scan data of areas on other die-columns, which were scanned in the same implementation.

Abstract: Disclosed herein is a method for non-destructive hybrid acousto-optic and scanning electron microscopy-based metrology. The method includes: (i) obtaining acousto-optic and scanning electron microscopy measurement data of an inspected structure on a sample; (ii) processing the measurement data to extract values of key measurement parameters corresponding to the acousto-optic measurement data and the scanning electron microscopy measurement data, respectively; and (iii) obtaining estimated values of one or more structural parameters of the inspected structure by inputting the extracted values into an algorithm, which is configured to jointly process the extracted values to output estimated values of the one or more structural parameters.

Abstract: Disclosed is a computerized method for detecting defects on a sample. The method includes: (i) receiving scan data corresponding to a pixel on the sample; (ii) computing a difference vector d based on the scan data and corresponding reference data; (iii) computing a parameter D dependent on t=?d?(Glinear/??s?2)?s, wherein ?T?=K?1 with K being a covariance matrix corresponding to the pixel, s is a predetermined kernel characterizing a defect signal, and Glinear=s·(K?1 d) is a gaussian approximation of a likelihood ratio test expression for distinguishing the defect signal from noise, and wherein D substantially monotonically increases with ?t?; and (iv) computing a score q(g, D) indicative of whether the pixel is defective, wherein g is a parameter indicative of a value of Glinear and q(g, D) substantially monotonically increases with g and substantially monotonically decreases with D.

Abstract: Disclosed herein is s computer-based method for obtaining and analyzing multi-die scan data of a patterned wafer. The method includes sequentially implementing an operation of scanning a respective plurality of sets of slices on a wafer, and, per each slice segment in a multiplicity of slice segments in the plurality of sets of slices, an operation of performing die-to-multi-die (D2MD) analysis of scan data of the slice segment in order to detect defects in the slice segment. Each set of slices may constitute a subset of the totality of slices on the respective die-column. Sets scanned in a same implementation are analogous to one another, thereby facilitating—in the die-to-multi-die analysis of scan data of a slice segment—taking into account, as reference, scan data of areas on other die-columns, which were scanned in the same implementation.

Abstract: Disclosed is a computerized method for detecting defects on a sample. The method includes: (i) receiving scan data corresponding to a pixel on the sample; (ii) computing a difference vector d based on the scan data and corresponding reference data; (iii) computing a parameter D dependent on t=?d?(Glinear/??s?2)?s, wherein ?T?=K?1 with K being a covariance matrix corresponding to the pixel, s is a predetermined kernel characterizing a defect signal, and Glinear=s·(K?1 d) is a gaussian approximation of a likelihood ratio test expression for distinguishing the defect signal from noise, and wherein D substantially monotonically increases with ?t?; and (iv) computing a score q(g, D) indicative of whether the pixel is defective, wherein g is a parameter indicative of a value of Glinear and q(g, D) substantially monotonically increases with g and substantially monotonically decreases with D.