摘要(英) |
In this paper, we use two methods to analysis wafer map. The first method uses deep learning to train a neural network model by actual wafer map data, which is the WM-811K wafer database released by TSMC. The failure patterns can be divided into the following nine categories: Center, Donut, Scratch, Edge-Ring, Edge-Loc, Loc, Near-full, Random, none. Therefore, the trained neural network model can be used to recognize these nine error patterns.
The second method establish a model for judging the randomness of wafer map spatial pattern. First, obtain the wafer format, and then use Matlab simulation to randomly generate the synthetic wafer map. We extract two parameters NBD (Number of Bad Die), NCL (Number of Contiguous Line), and normalize these two parameters to obtain BD and CL. According to different BD, simulate a large number of synthetic wafer maps, generate a complete Boomerangs chart, and use B-score as the criterion for identifying the randomness of the wafer map.
Finally, the two types of methods are combined. The pattern recognition model is used to identify the failure pattern. The B-score and the yield are used to further analyze the wafer after partition from the viewpoint of randomness. In addition, the superposition analysis of the wafer map can be used to determine whether the product has common features. |
參考文獻 |
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