Identifying Good-Dice-in-Bad-Neighborhoods Using Artificial Neural Networks

Chia Heng Yen*, Ting Rui Wang, Ching Min Liu, Cheng Hao Yang, Chun Teng Chen, Ying Yen Chen, Jih Nung Lee, Shu Yi Kao, Kai Chiang Wu, Mango Chia Tso Chao

*此作品的通信作者

研究成果: Article同行評審

摘要

It is known that the determination of the good-dice-in-bad-neighborhoods (GDBNs) has been regarded as an effective technique to reduce the value of the defect parts per million (DPPM) by identifying and rejecting the suspicious dice even though they are good in testing. Instead of examining eight immediate neighbors in a small-sized 3\times 3 window or exploiting simple linear regression, a large-sized window can be used to recognize the broad-sighted neighborhoods and accurately infer the suspiciousness level for any given die. In this paper, the artificial neural networks (ANN)-based method can be proposed to solve the GDBN identification. Furthermore, two enhanced techniques can be further presented to improve the inference accuracy of the original ANN-based method by considering the variation of the time-dependent wafer patterns and the wafer-to-wafer relationship between two adjacent wafers. After applying the two enhanced techniques, the business profits can be improved in the new ANN-based method. Various experiments on two datasets clearly reveal the superiority of the proposed ANN-based method over the other existing methods. In addition to the reduction of the DPPM value, the new ANN-based method can achieve the 1.5X-2X better reduction in the cost of the return merchandise authorization (RMA). On the other hand, the experimental results show that the similar result can also be obtained in the other lower-yield products. By using the new ANN-based method, the relationships on bad dice cross wafers can be captured and the highly-accurate inference results can be simultaneously maintained.

原文English
頁(從 - 到)280-292
頁數13
期刊IEEE Transactions on Semiconductor Manufacturing
37
發行號3
DOIs
出版狀態Published - 2024

指紋

深入研究「Identifying Good-Dice-in-Bad-Neighborhoods Using Artificial Neural Networks」主題。共同形成了獨特的指紋。

引用此