Wafer Defect Root Cause Analysis with Partial Trajectory Regression DM: Big Data Management and Machine Learning Article Swipe
Kohei Miyaguchi
,
Masao Joko
,
Rebekah Sheraw
,
Tsuyoshi Idé
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/asmc64512.2025.11010733
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/asmc64512.2025.11010733
Identifying upstream processes responsible for wafer defects is challenging due to the combinatorial nature of process flows and the inherent variability in processing routes, which arises from factors such as rework operations and random process waiting times. This paper presents a novel framework for wafer defect root cause analysis, called Partial Trajectory Regression (PTR). The proposed framework is carefully designed to address the limitations of conventional vector-based regression models, particularly in handling variable-length processing routes that span a large number of heterogeneous physical processes. To compute the attribution score of each process given a detected high defect density on a specific wafer, we propose a new algorithm that compares two counterfactual outcomes derived from partial process trajectories. This is enabled by new representation learning methods, proc2vec and route2vec. We demonstrate the effectiveness of the proposed framework using real wafer history data from the NY CREATES fab in Albany.
Related Topics
Concepts
Root cause analysis
Wafer
Trajectory
Big data
Root cause
Regression analysis
Computer science
Wafer fabrication
Root (linguistics)
Regression
Artificial intelligence
Reliability engineering
Data mining
Statistics
Machine learning
Engineering
Mathematics
Electrical engineering
Physics
Linguistics
Philosophy
Astronomy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/asmc64512.2025.11010733
- OA Status
- green
- Cited By
- 1
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410771619
All OpenAlex metadata
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https://openalex.org/W4410771619Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/asmc64512.2025.11010733Digital Object Identifier
- Title
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Wafer Defect Root Cause Analysis with Partial Trajectory Regression DM: Big Data Management and Machine LearningWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-05Full publication date if available
- Authors
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Kohei Miyaguchi, Masao Joko, Rebekah Sheraw, Tsuyoshi IdéList of authors in order
- Landing page
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https://doi.org/10.1109/asmc64512.2025.11010733Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2507.20357Direct OA link when available
- Concepts
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Root cause analysis, Wafer, Trajectory, Big data, Root cause, Regression analysis, Computer science, Wafer fabrication, Root (linguistics), Regression, Artificial intelligence, Reliability engineering, Data mining, Statistics, Machine learning, Engineering, Mathematics, Electrical engineering, Physics, Linguistics, Philosophy, AstronomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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35Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2507812949, https://openalex.org/W2521071558, https://openalex.org/W2901215507, https://openalex.org/W7077083638, https://openalex.org/W4226110010, https://openalex.org/W4385484909, https://openalex.org/W4388917710, https://openalex.org/W2884425876, https://openalex.org/W4387911366, https://openalex.org/W3083380051, https://openalex.org/W4200224444, https://openalex.org/W4321374432, https://openalex.org/W4403024174, https://openalex.org/W1481376060, https://openalex.org/W142583486, https://openalex.org/W4404919461, https://openalex.org/W4401368171, https://openalex.org/W4205893379, https://openalex.org/W4403024242, https://openalex.org/W4407411996, https://openalex.org/W6636510571, https://openalex.org/W6739901393, https://openalex.org/W4226463366, https://openalex.org/W4317792685, https://openalex.org/W2975495759, https://openalex.org/W6737947904, https://openalex.org/W2516809705, https://openalex.org/W2616247523, https://openalex.org/W6810032775, https://openalex.org/W6793797138, https://openalex.org/W6790587931, https://openalex.org/W4385567719, https://openalex.org/W6676236601, https://openalex.org/W1510073064, https://openalex.org/W1516659296 |
| referenced_works_count | 35 |
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