Abstract: A method provides a design layout having a pattern of features. The design layout is transferred onto a substrate on a semiconductor substrate using a mask. A scanning parameter is determined based on the design layout. An image of the substrate is generated using the determined scanning parameter. A substrate defect is identified by comparing a first number of closed curves in a region of the image and a second number of polygons in a corresponding region of the design layout.

Abstract: A method provides a design layout having a pattern of features. The design layout is transferred onto a substrate on a semiconductor substrate using a mask. A scanning parameter is determined based on the design layout. An image of the substrate is generated using the determined scanning parameter. A substrate defect is identified by comparing a first number of closed curves in a region of the image and a second number of polygons in a corresponding region of the design layout.

Abstract: An adaptable classification method is provided. The method performs the classification by using a classification standard having a plurality of categories. The classification standard is classified into different categories based on probability ranges. The adaptable classification method includes training a classifying device with a plurality of samples and using the trained classifying device to determine the categories of the samples to obtain classification model scores of the samples, transferring, by using logistic-like functions, the classification model scores into probability values; and adjusting parameters of logistic-like functions to iterate the training of the classifying device such that the probability values conform to value ranges corresponding to categories of the classification standard.

Abstract: An adaptable classification method is provided. The method performs the classification by using a classification standard having a plurality of categories. The classification standard is classified into different categories based on probability ranges. The adaptable classification method includes training a classifying device with a plurality of samples and using the trained classifying device to determine the categories of the samples to obtain classification model scores of the samples, transferring, by using logistic-like functions, the classification model scores into probability values; and adjusting parameters of logistic-like functions to iterate the training of the classifying device such that the probability values conform to value ranges corresponding to categories of the classification standard.