Abstract:
A wafer backside defect detection method and a wafer backside defect detection apparatus are provided. The wafer backside defect detection method includes the following steps. A peripheral edge area of a wafer backside image that at least one notch is located is cropped off. Adjacent white pixels on the wafer backside image are connected to obtain a plurality of abnormal regions. If a total area of top N of the abnormal regions is more than 10% of an area of the wafer, it is deemed that the wafer has a roughness defect. N is a natural number. If the total area of the top N of the abnormal regions is less than 1% of the area of the wafer and a largest abnormal region of the abnormal regions is longer than a predetermined length, it is deemed that the wafer has a scratch defect.
Abstract:
A method of a fault detection and classification (FDC) may be used to determine outlier tools from a plurality of tools. The method includes generating a plurality of parameter charts, generating a plurality of group charts according to the plurality of parameter charts, generating a score table according to the plurality of group charts, determining outlier tools according to the score table, and performing tool correction on the outlier tools.
Abstract:
A fault detection method, includes the following steps. A target sequence is received, the target sequence includes several data. A first moving average operation is performed on the target sequence to establish a first moving average sequence. A second moving average operation is performed on the target sequence to establish a second moving average sequence. A difference operation between the first moving average sequence and the second moving average sequence is performed to obtain a difference sequence, the difference sequence includes several difference values. An upper limit value is set. When one of the difference values is greater than the upper limit value, the target sequence is determines as abnormal.
Abstract:
An automatic detection method and an automatic detection system for detecting any crack on wafer edges are provided. The automatic detection method includes the following steps. Several wafer images of several wafers are obtained. The wafer images are integrated to create a templet image. Each of the wafer images is compared with the templet image to obtain a differential image. Each of the differential images is binarized. Each of the differential images which are binarized is de-noised. Whether each of the differential images has an edge crack is detected according to pattern of each of the differential images which are de-noised.
Abstract:
An automatic detecting method and an automatic detecting apparatus using the same are provided. The automatic detecting apparatus includes an inputting unit, a dividing unit, a contouring unit, a range analyzing unit, a boundary analyzing unit, an edge detecting unit, an expanding unit and an overlapping unit. The dividing unit is used for dividing an overlooking image into four clusters via a clustering algorithm. The contouring unit is used for obtaining a contour. The range analyzing unit is used for obtaining a detecting range. The boundary analyzing unit is used for obtaining a circular boundary in the detecting range. The edge detecting unit is used for obtaining a plurality of edges in the circular boundary. The expanding unit is used for expanding the edges to obtain a plurality of expanded edges. The overlapping unit is used for overlapping the expanded edges and the contour to obtain a defect pattern.