United Microelectronics Corp.

Yung-Yu Yang

Chung-Hsuan Wu

Chen-Hui Huang

Nai-Ying Lo

Chia-Feng Hsiao

Ching-Hsing Hsieh

Chia-Chi Chang

Feng-Chi Chung

Lian-Hua Shih

Cheng-Hsien Chen

Tzu-Ping Kao

Chen-Hsuan Hung

Meng-Chih Chang

En-Wei Tsui

Li-Ting Lin

Ming-Tung Wang

Chiu-Ping Chang

Yi-Hui Tseng

Shuo-Yu Chen

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.

Wafer backside defect detection method and wafer backside defect detection apparatus

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.

Automatic detection method and automatic detection system for detecting crack on wafer edges

A method of monitoring a processing system for processing a substrate is provided. The method includes the following steps: acquiring data from the processing system for a plurality of parameters, the data including a plurality of data values; grouping the parameters into a plurality of sub-groups, each of the sub-groups including a plurality of correlated parameters; constructing a principle components analysis (PCA) model from the data values for the correlated parameters in a first one of the sub-groups, including normalizing the data values in the first one of the sub-groups with a first weighting factor and a second weighting factor, wherein the first weighting factor is different from the second weighting factor; and determining a statistical quantity using the PCA model.

Method of monitoring processing system for processing substrate

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Tainan, Taiwan

Yung-Yu Yang