The invention includes a pulse oscillated light source, an illumination unit that guides light output from the light source to a sample, a scanning unit that controls a position at which the sample is scanned by the illumination unit, a light converging unit that converges light reflected from the sample, a first photoelectric conversion unit that outputs an electric signal corresponding to the light converged by the light converging unit, an AD conversion unit that converts the electric signal output from the first photoelectric conversion unit into a digital signal in synchronization with pulse oscillation of the light source, a linear restoration unit that processes a digital signal converted by the AD conversion unit in synchronization with a pulse oscillation output by the AD conversion unit and corrects nonlinearity of the first photoelectric conversion unit, a defect detection unit that detects a defect of the sample based on an output of the linear restoration unit, and a processing unit that obtains and outputs a position and a size of the defect detected by the defect detection unit.

A system has detectors configured to receive a beam of light reflected from a wafer. For example, three detectors may be used. Each of the detectors is a different channel. Images from the detectors are combined into a pseudo-color RGB image. A convolutional neural network unit (CNN) can receive the pseudo-color RGB image and determine a size of a defect in the pseudo-color RGB image. The CNN also can classify the defect into a size category.

Disclosed is a method for estimating twin defect density in a single-crystal sample, including: (A) etching the observed surface of a single crystal to form etch pits; (B) selecting bar-shaped etch pits caused by twin defect; (C) from the long-axis direction lengths of the etch pits caused by twin defect, estimating the twin defect density by using the following equation: twin defect density=Σkx′.sub.i/area of sample, wherein 2≤k≤3, and x′.sub.i is the long-axis direction length of an etch pit caused by the i-th twin.

The present invention relates to a smart conversion and calibration of the defect coordinate, diagnosis, sampling system and the method thereof for manufacturing fab is provided. The intelligent defect diagnosis method comprises: receiving pluralities of defect data, design layout data, analyzing the defect data, design layouts, by a Critical Area Analysis (CAA) system, wherein the analyzing step further contains the sub-steps: superposing the defect contour pattern and the design layout, performing CAA to identify a killer or non-killer defect based on the open or short failure probability, defects are classified as high, medium, low, or negligible risk defect based on the Killer Defect Index, defect signal parameters, selecting defect samples based on the defect classification data, selecting alarm defect and filtering false defect with pattern match with defect pattern library and frequent failure defect library, performing coordinate conversion and pattern match between image contour and design layout for coordinate correction, creating a CAA accuracy correction system and defect size calibration system by analyzing original defect size data and defect contour size from image analysis, evaluating the defect size using measurement uncertainty analysis with statistical analysis methods to reach the purposes of increasing CAA accuracy and Killer Defect identification rate.

The present invention relates to a smart defect calibration, diagnosis, sampling system and the method thereof for manufacturing fab is provided. The intelligent defect diagnosis method comprises: receiving pluralities of defect data, design layout data, analyzing the defect data, design layouts, by a Critical Area Analysis (CAA) system, wherein the analyzing step further contains the sub-steps: superposing the defect contour pattern and the design layout, performing CAA to identify a killer or non-killer defect based on the open or short failure probability, defects are classified as high, medium, low, or negligible risk defect based on the Killer Defect index, defect signal parameters, selecting defect samples based on the defect classification data, selecting alarm defect and filtering false defect with pattern match with defect pattern library and frequent failure defect library, performing coordinate conversion and pattern match between image contour and design layout for coordinate correction, creating a CAA accuracy correction system and defect size calibration system by analyzing original defect size data and defect contour size from image analysis, evaluating the defect size using measurement uncertainty analysis with statistical analysis methods to reach the purposes of increasing CAA accuracy and Killer Defect identification rate.

The present invention relates to a smart conversion and calibration of the defect coordinate, diagnosis, sampling system and the method thereof for manufacturing fab is provided. The intelligent defect diagnosis method comprises: receiving pluralities of defect data, design layout data, analyzing the defect data, design layouts, by a Critical Area Analysis (CAA) system, wherein the analyzing step further contains the sub-steps: superposing the defect contour pattern and the design layout, performing CAA to identify a killer or non-killer defect based on the open or short failure probability, defects are classified as high, medium, low, or negligible risk defect based on the Killer Defect Index, defect signal parameters, selecting defect samples based on the defect classification data, selecting alarm defect and filtering false defect with pattern match with defect pattern library and frequent failure defect library, performing coordinate conversion and pattern match between image contour and design layout for coordinate correction, creating a CAA accuracy correction system and defect size calibration system by analyzing original defect size data and defect contour size from image analysis, evaluating the defect size using measurement uncertainty analysis with statistical analysis methods to reach the purposes of increasing CAA accuracy and Killer Defect identification rate.

The present invention relates to a smart conversion and calibration of the defect coordinate, diagnosis, sampling system and the method thereof for manufacturing fab is provided. The intelligent defect diagnosis method comprises: receiving pluralities of defect data, design layout data, analyzing the defect data, design layouts, by a Critical Area Analysis (CAA) system, wherein the analyzing step further contains the sub-steps: superposing the defect contour pattern and the design layout, performing CAA to identify a killer or non-killer defect based on the open or short failure probability, defects are classified as high, medium, low, or negligible risk defect based on the Killer Defect Index, defect signal parameters, selecting defect samples based on the defect classification data, selecting alarm defect and filtering false defect with pattern match with defect pattern library and frequent failure defect library, performing coordinate conversion and pattern match between image contour and design layout for coordinate correction, creating a CAA accuracy correction system and defect size calibration system by analyzing original defect size data and defect contour size from image analysis, evaluating the defect size using measurement uncertainty analysis with statistical analysis methods to reach the purposes of increasing CAA accuracy and Killer Defect identification rate.

The present invention relates to a smart defect calibration, diagnosis, sampling system and the method thereof for manufacturing fab is provided. The intelligent defect diagnosis method comprises: receiving pluralities of defect data, design layout data, analyzing the defect data, design layouts, by a Critical Area Analysis (CAA) system, wherein the analyzing step further contains the sub-steps: superposing the defect contour pattern and the design layout, performing CAA to identify a killer or non-killer defect based on the open or short failure probability, defects are classified as high, medium, low, or negligible risk defect based on the Killer Defect Index, defect signal parameters, selecting defect samples based on the defect classification data, selecting alarm defect and filtering false defect with pattern match with defect pattern library and frequent failure defect library, performing coordinate conversion and pattern match between image contour and design layout for coordinate correction, creating a CAA accuracy correction system and defect size calibration system by analyzing original defect size data and defect contour size from image analysis, evaluating the defect size using measurement uncertainty analysis with statistical analysis methods to reach the purposes of increasing CAA accuracy and Killer Defect identification rate.

The present invention relates to a smart conversion and calibration of the defect coordinate, diagnosis, sampling system and the method thereof for manufacturing fab is provided. The intelligent defect diagnosis method comprises: receiving pluralities of defect data, design layout data, analyzing the defect data, design layouts, by a Critical Area Analysis (CAA) system, wherein the analyzing step further contains the sub-steps: superposing the defect contour pattern and the design layout, performing CAA to identify a killer or non-killer defect based on the open or short failure probability, defects are classified as high, medium, low, or negligible risk defect based on the Killer Defect Index, defect signal parameters, selecting defect samples based on the defect classification data, selecting alarm defect and filtering false defect with pattern match with defect pattern library and frequent failure defect library, performing coordinate conversion and pattern match between image contour and design layout for coordinate correction, creating a CAA accuracy correction system and defect size calibration system by analyzing original defect size data and defect contour size from image analysis, evaluating the defect size using measurement uncertainty analysis with statistical analysis methods to reach the purposes of increasing CAA accuracy and Killer Defect identification rate.

A smart defect calibration, diagnosis, sampling system and the method thereof for manufacturing fab is provided. The intelligent defect diagnosis method includes: receiving pluralities of defect data, design layout data, analyzing the defect data, design layouts, by a Critical Area Analysis (CAA) system, selecting defect samples based on the defect classification data, selecting alarm defect and filtering false defect with pattern match with defect pattern library and frequent failure defect library, performing coordinate conversion and pattern match between defect image contour, defect image pattern, and design layout for coordinate correction, creating a CAA accuracy correction system and defect size calibration system by analyzing original defect size data and defect contour size from image analysis, evaluating the defect size using measurement uncertainty analysis with statistical analysis methods to reach the purposes of increasing CAA accuracy and Killer Defect identification rate.