Attention Mechanism-Based Root Cause Analysis for Semiconductor Yield Enhancement Considering the Order of Manufacturing Processes

• Published in: IEEE transactions on semiconductor manufacturing 2022-05, Vol.35 (2), p.282-290
• Main Authors: Lee, Min Yong, Choi, Yeoung Je, Lee, Gyeong Taek, Choi, Jongkwan, Kim, Chang Ouk
• Format: Article
• Language: English
• Subjects: Attention mechanism; Chemicals; Data models; Etching; interaction effects; Ions; Logic gates; long short-term memory; Manufacturing; Root cause analysis; Search methods; Semiconductor device manufacture; Semiconductor device modeling; semiconductor manufacturing; variable selection; yield analysis
• ISSN: 0894-6507; 1558-2345
• DOI: 10.1109/TSM.2022.3156600

In semiconductor manufacturing processes, yield analysis aims to increase the yield by determining and managing the causes of low yield. The variable data collected from semiconductor manufacturing processes, in which hundreds of unit processes are implemented according to specific conditions and sequences, are interdependent, and the variables related to previous processes influence the variables in subsequent processes. Therefore, the order of processes should be considered when building a model that searches for the causes of low yield. However, there have been few studies in this area. This paper proposes a low-yield root cause search method considering the order of processes using a long short-term memory with attention mechanism (LSTM-AM) model. Specifically, the LSTM-AM model is applied to data classified according to the process structure of semiconductor products, and the causes of low yield are determined considering the order of processes by extracting attention weights. Experiments are conducted to verify the suitability of the proposed method using real yield data from a semiconductor company. The experimental results confirm that the proposed method outperforms the existing low yield root cause search methods in terms of low yield prediction.