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Contrastive deep clustering for detecting new defect patterns in wafer bin maps

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Abstract

Wafer bin maps (WBMs) data, presented as images, play a critical role in identifying defects in the semiconductor industry. Thus, accurately classifying WBM defect patterns is essential to maintain high quality and enhance the overall yield. However, the task of labeling and classifying WBM data, which are generated daily in the tens of thousands or more, presents a challenge for experts. Recently, with advancements in artificial intelligence research, there has been a surge in efforts to automatically classify WBM defect patterns. Nevertheless, existing studies have primarily focus on classifying known defect patterns using labels. However, in the real-world semiconductor industry, new defect patterns are constantly emerging in addition to the known patterns. In this study, we propose the contrastive deep clustering (CODEC) for wafer bin maps that identifies new defective patterns in WBMs while simultaneously clustering these patterns into multiple defects without using labels. We use a contrastive loss function to address the challenges associated with a limited number of novel defect patterns. We demonstrate the effectiveness of our proposed methodology in accurately classifying new defect patterns using open data WM-811 k.

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Data availability

All data are fully available without restriction. The dataset used in this study is available from the following website: https://www.kaggle.com/datasets/qingyi/wm811k-wafer-map.

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Funding

This work was supported in part by the Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) (The Competency Development Program for Industry Specialist) under Grant P0008691 and the National Research Foundation of Korea grant funded by the Korea government (RS-2022–00144190).

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Authors and Affiliations

  1. School of Industrial and Management Engineering, Korea University, Seoul, 02841, Republic of Korea

    Insung Baek & Seoung Bum Kim

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  1. Insung Baek
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  2. Seoung Bum Kim
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All authors equally contributed to this study: design, experiment, and data analysis.

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Correspondence to Seoung Bum Kim.

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Baek, I., Kim, S.B. Contrastive deep clustering for detecting new defect patterns in wafer bin maps. Int J Adv Manuf Technol 130, 3561–3571 (2024). https://doi.org/10.1007/s00170-023-12939-0

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