TWI456190B - Method of chip detects inspecting, system therefor, and computer program product thereof - Google Patents

Claims (10)

A method for detecting a wafer image includes the following steps: a first step of capturing a wafer image; a second step of correcting the wafer image in a horizontal direction; and a third step of extracting the wafer image by using a binarization module a candidate for medium-high intensity; in the fourth step, the two-phase heteromorphic module is used to remove a small flaw; in the fifth step, a machine learning module is used to classify the support vector machine by a feature. At least one spot and at least one high-intensity flaw in the wafer image; in the sixth step, the wafer boundary is converted by using a Hough transform module, and the wafer image is divided into a wafer portion and a background portion by using the average image intensity of the wafer To calculate an energy map; in a seventh step, the contour of the wafer image is filled incompletely by the patterning method, and the fine flaw in the wafer image is removed to obtain a complete wafer boundary contour; and an eighth step is Using the Hough transform module to find a straight line representing the boundary of the wafer, and calculating the difference between the straight line and the boundary contour of the complete wafer for detecting Low intensity flaws. The method for detecting a wafer image according to claim 1, wherein the second step detects the edge of the image by a Kenny edge detector module, and uses the Hough transform module to find the longest image in the wafer. a straight line and find its angle, and then use a two-dimensional rotation formula to rotate the wafer image positive. The method for detecting a wafer image according to claim 1, wherein the pattern modules comprise a disconnect operation and a close operation. The method for detecting a wafer image according to claim 1, wherein the 瑕疵 feature comprises 瑕疵 intensity smoothness, 瑕疵 texture complexity, and 瑕疵 texture structure. The method for detecting a wafer image according to claim 1, further comprising an adjusting step performed between the fifth step and the sixth step, wherein removing the pixel intensity value in the wafer image is greater than the image intensity of the wafer A pixel of the average for the energy map to effectively separate the wafer portion from the background portion. The method for detecting a wafer image according to claim 1, wherein the energy map is divided into two parts of a larger and a smaller part by an average value of the image intensity of the wafer, and the portion with a higher intensity is represented by a first color, and the intensity is smaller. The part is represented by the second color. The method for detecting a wafer image according to claim 1, wherein in the eighth step, the number of image pixels having a large gap between the line and the boundary contour of the wafer is less than a threshold value, and the line is determined to be absent. When the number of the image pixels having a large difference from the boundary contour of the wafer is greater than the threshold value, it is determined as a single image. A method for detecting a wafer image, the method of claim 1, wherein the wafer image detection system comprises: a carrier platform for placing a wafer; a camera mechanism for capturing a wafer image; and a The computer device comprises: an image display module; a plurality of computing modules for analyzing and determining the image of the wafer; a database for storing an analysis result and establishing machine learning data to speed up the backward comparison speed; and an output The device is used to display the analysis result. The method for detecting a wafer image defect according to claim 8, wherein the computing module comprises a Kenny edge detector module, a Hough transform module, a binarization module, and a type Module and a machine learning module. a computer program product for detecting wafer images, when the computer is loaded with a Kenny edge detector module, a Hough conversion module, a binary module, a type module and After a machine learning module is executed, the method described in claim 1 can be completed.