摘要(英) |
In this paper, we analyze the similarities between wafers within the TSMC WM-172K wafer database. We employ a similarity formula to calculate similarity values, which reflect the degree of similarity between two wafers. Next, we establish a similarity threshold, which serves as a reference value for determining whether wafers are similar.
When establishing the similarity threshold, we consider two factors: yield and chip size. We create two new similarity thresholds in addition to the previous one. Using these thresholds, we identify sets of dissimilar wafers within a lot. Furthermore, we define absolute similarity, meaning finding a group of wafers that are identical within the same batch.
Finally, we discuss the practical application of similarity thresholds on actual wafer images. We select the real wafer data set provided by a certain company as the object of the experiment. We use similarity threshold and chamber table to identify batches with similar characteristics and relationships, rapidly determining the presence and location of chamber effects, and early detecting anomalous machines. This leads to improved production efficiency and yield.
Through the analysis of similarity, we can assess the degree of similarity between wafers, aiding in the identification of error patterns and issues that occur within wafers. |
參考文獻 |
[1] Ming-Ju Wu, Jyh-Shing R. Jang, and Jui-Long Chen, “Wafer map failure pattern recognition and similarity ranking for large-scale data sets”, IEEE Transactions on Semiconductor Manufacturing, Vol. 28, No. 1, pp.1-12, Feb. 2015.
[2] CORTES, Corinna; VAPNIK, Vladimir, “Support-vector networks”, Machine learning, Vol. 20, pp.273-297, 1995.
[3] Chung-Shou LIAO, et al. “Similarity searching for defective wafer bin maps in semiconductor manufacturing”, IEEE Transactions on Automation Science and Engineering, Vol. 11, No. 3, pp.953-960, July 2014.
[4] Rui Wang and Songhao Wang, “Tensor Voting Based Similarity Matching of Wafer Bin Maps in Semiconductor Manufacturing”, 2022 5th International Conference on Data Science and Information Technology (DSIT). IEEE, 6 pages, 2022.
[5] Chia-Jui Wen, Jwu-E Chen, “Application of Similarity Cluster Analysis to the Real-world Wafer Lots”, National Central University, Taoyuan, Taiwan (R.O.C.), Oct. 2021.
[6] Han Wei Huang, Jwu-E Chen, “Application of Similarity Threshold for Lot Analysis to Real-world Wafer Maps”, National Central University, Taoyuan, Taiwan (R.O.C.), Aug. 2022.
[7] Jea-Hoon LEE, Il-Chul MOON, and Rosy OH. “Similarity search on wafer bin map through nonparametric and hierarchical clustering”, IEEE Transactions on Semiconductor Manufacturing, Vol. 34, No. 4, pp.464-474, 2021.
[8] Chang Xu, Qi-Shi Shi, and Ping-fen Shi, “A Novel Wafer-Map Similarity Search System with High Speed and Accuracy”, in Proc. 2021 China Semiconductor Technology International Conference (CSTIC), 3 pages.
[9] Ray Lung, 2022 |