Multi-source wafer map retrieval based on contrastive learning for root cause analysis in semiconductor manufacturing

Wei-Jyun Hong, Chia-Yu Shen and Pei-Yuan Wu ()
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Wei-Jyun Hong: National Taiwan University
Chia-Yu Shen: National Taiwan University
Pei-Yuan Wu: National Taiwan University

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 1, No 13, 259-270

Abstract: Abstract In semiconductor manufacturing, wafer yield is a key success factor to determine profits. Since yield improvement involves capturing the faulty manufacturing steps from hundreds of wafer manufacturing steps, which requires a lot of time and manpower, previous studies employ wafer bin map fault type recognition model or wafer bin map retrieval model for guessing which manufacturing steps likely to fail and examine them first. However, existing methods still lack of the ability to directly capture the faulty manufacturing steps. Capturing the faulty manufacturing steps is critical as engineers can correct the erroneous step among hundreds of manufacturing steps to increase yields at minimal cost. The objective of our study is to find out the historical wafer defect maps that are highly reflective of erroneous manufacturing steps by querying a wafer bin map. A retrieval model emerges to directly rank the wafer defect maps to capture the most possible faulty manufacturing step, namely multi-source wafer map retrieval model. Although existing multi source image retrieval models can eliminate the visual gap between wafer maps from different sources, most of these methods focus on finding semantically related samples (e.g. of the same defect fault type) and ignore the spatial significance that should be considered in multi-source wafer map retrieval, such as various sizes, shapes and locations of defect patterns on wafer maps. This study applies a contrastive learning based approach to address the visual gap between wafer maps from different sources, which also takes spatial information into account. This study also adapts a mixed sample strategy to address the visual gap and applies pixel-wise multiplication between wafer maps from different sources to indicate common defect locations. The effectiveness of our proposed retrieval approach is supported by testing results on in-line production wafer map datasets.

Keywords: Root cause analysis; Wafermap; Image retrieval; Contrastive learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-023-02233-x

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