Correlation analysis of sampled wafer profile maps based on a deep reconstruction model

• Published in: Applied soft computing Vol. 159; p. 111634
• Main Authors: Kong, Yuting, Ni, Dong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2024
• Subjects: Correlation analysis; Deep learning; Multi-type wafer maps; Semiconductor manufacturing; Wafer map reconstruction; Deep learning; Wafer map reconstruction; Multi-type wafer maps; Semiconductor manufacturing; Correlation analysis
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2024.111634

In the semiconductor manufacturing process, various kinds of metrology and test data can form different types of wafer maps. By analyzing the correlation of multiple types of wafer maps, especially for the wafer bin maps (WBMs) and the wafer profile maps, the faulty inline manufacturing steps strongly correlated with end-of-line yield can be found for yield improvement. This paper proposes a correlation analysis method based on a deep reconstruction model to integrate the knowledge among multiple types of wafer maps for root cause analysis. First, the sparsely sampled wafer profile maps are restored to original wafer profile maps through the deep reconstruction model for obtaining more information. And then, the correlation between the WBMs and the reconstructed profile maps is calculated. The process step whose wafer profile maps have the highest correlation with WBMs is highly related to the fault. Experiments on the real-world dataset demonstrate that the proposed method can restore the wafer profile map well and has high accuracy in matching the relevant process wafer profile map based on the WBMs. •The deep reconstruction model reconstructs full wafer profile maps.•The correlation analysis framework correlates wafer maps from different domains.•The deep reconstruction model’s effectiveness is proven compared to existing methods.