Konica Minolta Laboratory U.s.a., Inc.

Kla Tencor Corporation

Chaohong Wu

Wei Ming

Lisheng Gao

Yong Zhang

Tao Luo

Stephanie Chen

Methods and systems for detecting defects on a wafer are provided. One method includes altering one or more design clips based on how the one or more design clips will appear in output generated by a wafer inspection process for a wafer. The method also includes aligning the one or more altered design clips to the output generated for the wafer during the wafer inspection process. In addition, the method includes detecting defects on the wafer based on the output aligned to the one or more altered design clips.

Context-based inspection for dark field inspection

A method for removing horizontal and vertical lines in a document image while preserving integrity of the character strokes that intersect the lines. For each detected horizontal line, a vertical run length profile is calculated. Areas of the run length profile having two adjacent peaks with a valley in between are detected, which correspond to intersections of the horizontal line with non-vertical lines. A first derivative curve may be used to detect such peaks and valleys. Areas of the run length profile with large run length value for consecutive pixel locations are also detected, which corresponds to intersections of the horizontal line with near vertical lines. The horizontal line is removed in areas outside of the intersection areas, while preserving pixels within the intersection areas. Vertical line removal may be done similarly. This template-free method can remove lines in tables, forms, and underline and extract handwriting or printed characters.

Removal of underlines and table lines in document images while preserving intersecting character strokes

A text line segmentation method for a document image containing printed text and handwriting, or document image containing skewed lines or printed text. Connected component (CC) are obtained for the document, and their bounding boxes and centroids are calculated. The CCs are categorized into three categories based on bounding box sizes: small objects, regular text objects, and large objects involving handwriting. The centroids of regular text objects are used in a cluster analysis to find the vertical centers of the N text lines. Then, each CC is classified into one of the N lines based on the vertical distance between its centroid and the vertical centers of text lines, and copied into to a corresponding object board. Extra spaces are removed from the object boards to obtain the line segments. The large object involving handwriting will be classified into one of the lines but absent from other lines.

Line segmentation method applicable to document images containing handwriting and printed text characters or skewed text lines

A word segmentation method for processing a document image applies clustering analysis to the spacing segments of a line. The spacing segments are generated by thresholding a one-dimensional vertical projection profile of the line. Taking advantage of the bimodal distribution of spacing length distribution of text lines, a k-means clustering algorithm is used, with the number of clusters pre-set to two, to classify the spacing segments as either character spacing or word spacing. Moreover, k-means++ initialization is used to enhance performance of cluster analysis. The clustering result such as cluster centers and compactness is used to prune single-word text line, single table item, etc. The locations of the word spacing segments are then used to segment the line of text into words.

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Mountain View, CA, United States of America

Chaohong Wu