Abstract: The present application provides a method for automatically detecting a wafer backside brightfield image anomaly, at least comprising: processing wafer backside brightfield images by means of histogram equalization, so as to obtain processed images; compiling statistics for a gray histogram of the processed images; calculating the number of abnormal pixels in each of the images; and providing a threshold, and highlighting the image with a score less than the threshold. In the present application, the wafer backside brightfield images are analyzed by means of image preprocessing and a specific calculation method, so as to quickly and automatically detect an abnormal wafer backside image.

Abstract: The present invention provides a classification method for automatically identifying wafer spatial pattern distribution, comprising the following steps: performing statistical analysis to distribution of defects on a wafer, the defects being divided into random defects, repeated defects and cluster defects; performing denoising and signal enhancement to the cluster defects; performing feature extraction to the cluster defects after denoising and signal enhancement; and performing wafer spatial pattern distribution classification to the cluster defects after feature extraction. By performing statistical analysis and neural network training to a great amount of wafer defect distribution, the spatial patterns in defect distribution can be automatically identified, the automatic classification of wafer spatial patterns can be realized, the workload of engineers is effectively reduced and the tracing of the root cause of such spatial pattern is facilitated.

Abstract: The present invention provides a classification method for automatically identifying wafer spatial pattern distribution, comprising the following steps: performing statistical analysis to distribution of defects on a wafer, the defects being divided into random defects, repeated defects and cluster defects; performing denoising and signal enhancement to the cluster defects; performing feature extraction to the cluster defects after denoising and signal enhancement; and performing wafer spatial pattern distribution classification to the cluster defects after feature extraction. By performing statistical analysis and neural network training to a great amount of wafer defect distribution, the spatial patterns in defect distribution can be automatically identified, the automatic classification of wafer spatial patterns can be realized, the workload of engineers is effectively reduced and the tracing of the root cause of such spatial pattern is facilitated.