| 摘要(英) |
The general wafer map is composed of random and systematic errors. In this paper, we analyze the six kinds of symptomatic errors among the nine kinds of systematic errors. After observing the characteristics of the six kinds of errors, the wafer map is partitioned into wafer maps of the feature graphics so that systematic errors of the feature can be enhanced and the part of random errors can be separated. Then we observe the effect of improving system errors resolution after partition.
However, after the partition into various shapes, it is an issue whether all the characteristic parameters of our research will not be applicable due to the different shapes. Therefore, this study also verifies the parameters of all the partition patterns. In addition, the use of the Poisson Yield can determine the randomness of the wafer map, in the past we use the number of bad dice (NBD) and the number of contiguous line(NCL) can describe a part of Poisson Yield. In this paper, we propose to correct the defects of the previous experimental parameters so that future research methods can describe the entire Poisson Yield to facilitate the determination of randomness.
In addition to partitioning the continuation of the previous use of Boomerang to determine the clustering situation, and then proposed two methods to determine the systematic error partition, namely sector-shaped and donut-shaped bad dice distribution detection. The two methods are to predict the distribution of bad dice to discriminate. Finally, the three methods are combined to complement each other, so as to improve the ability of discrimination systematic errors promptly, so as to improve the yield, test efficiency and reduce costs. |
| 參考文獻 |
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[9] 蕭寶威,“Wafer Map Analysis from Random Distributed Defects”, 碩士論文﹐中央大學﹐2016.
[10] 葉昱緯,“Application of Boomerang Chart to Real-World Mass Production Wafer Maps”, 碩士論文﹐中央大學﹐2016. |
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