Abstract: The present disclosure provides a system and a method for quickly diagnosing, classifying, and sampling in-line defects based on a CAA pre-diagnosis database. The method includes the steps of obtaining a design layout of an object and a defect data of an important process stage of the object, obtaining a pre-diagnosis data group related to the design layout from a CAA pre-diagnosing database, and judging a killer defect index and a failure risk level of the defect data according to the pre-diagnosis data group.

Abstract: The instant disclosure provides an intelligent CAA (Critical Area Analysis) failure pre-diagnosis system and method for a design layout. The intelligent CAA failure pre-diagnosis method includes the steps of obtaining a design layout of an object and defining at least one layout region having a layout pattern thereon, obtaining a plurality of defects, comparing the defects one-by-one to a predetermined portion of the layout pattern in the order of their sizes, and calculating a CAA failure risk level of the layout region according to the comparison result.

Abstract: Disclosure herein is related to a method and a system for intelligent weak pattern diagnosis for semiconductor product, and a related non-transitory computer-readable storage medium. In the method, a weak pattern layout is firstly retrieved from a defect pattern library and a frequent failure defect pattern library; defect data is retrieved from fab defect inspection tool; a design layout is then received and weak defect pattern screen is performed to extract known and unknown weak defect patterns. In addition to updating the weak pattern library, the weak pattern contour can be made upon SEM image data, and then the true systematic weak pattern can be justified.

Abstract: Disclosure is related to a method and a system for intelligent defect classification and sampling, and a computer-readable storage device. The computer-implemented method acquires in-line defect inspection file, and retrieves the defect patterns over a device under test, e.g. a wafer from a fab. The system incorporates a defect pattern recognition engine to recognize the defect signature patterns from the defect patterns. A sampling scheme is performed to acquire weak defect patterns. A critical area analysis based on failure probability of weak patterns is incorporated to performing the sampling. The defect layout pattern groups probably causing the open or short failure can be obtained. The defect signature patterns through sampling are then displayed using a browsing system. Through a user interface, the user can perform functions, such as filtering, selection and merging, onto the defect patterns.