A Novel Approach to Test-Induced Defect Detection in Semiconductor Wafers, Using Graph-Based Semi-Supervised Learning (GSSL)
The semiconductor industry plays a vital role in modern technology, with semiconductor devices embedded in almost all electronic products. As these devices become increasingly complex, ensuring quality and reliability poses significant challenges. Electrical testing on semiconductor wafers for defec...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10855443/ |
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| author | Pedram Aridas Narendra Kumar Anis Salwa Mohd Khairuddin Daniel Ting Vivek Regeev |
| author_facet | Pedram Aridas Narendra Kumar Anis Salwa Mohd Khairuddin Daniel Ting Vivek Regeev |
| author_sort | Pedram Aridas |
| collection | DOAJ |
| description | The semiconductor industry plays a vital role in modern technology, with semiconductor devices embedded in almost all electronic products. As these devices become increasingly complex, ensuring quality and reliability poses significant challenges. Electrical testing on semiconductor wafers for defects is crucial, but paradoxically, the testing process itself can introduce defects. These test-induced defects could remain undetected on the wafer, proceed through assembly, and may only be discovered later by customers, leading to returns and significant yield loss. This study proposes a novel graph-based semi-supervised learning (GSSL) algorithm to identify these test-induced hidden defects on the semiconductor wafer that escape conventional methods. The algorithm, which incorporates domain knowledge in creating a graph representation of wafer, and utilizing a weighted edge label propagation model, has demonstrated its effectiveness by achieving a 68% accuracy on a real-world dataset, offering a promising approach to enhance quality control in semiconductor manufacturing. |
| format | Article |
| id | doaj-art-5f1e5ac7e31f4fb49f59ef8dda39a032 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5f1e5ac7e31f4fb49f59ef8dda39a0322025-02-06T00:00:35ZengIEEEIEEE Access2169-35362025-01-0113216782169410.1109/ACCESS.2025.353510310855443A Novel Approach to Test-Induced Defect Detection in Semiconductor Wafers, Using Graph-Based Semi-Supervised Learning (GSSL)Pedram Aridas0https://orcid.org/0000-0001-8239-1823Narendra Kumar1https://orcid.org/0000-0001-9999-8971Anis Salwa Mohd Khairuddin2https://orcid.org/0000-0002-9873-4779Daniel Ting3Vivek Regeev4https://orcid.org/0009-0008-6997-6622Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, MalaysiaDepartment of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, MalaysiaDepartment of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, MalaysiaNXP Malaysia, Petaling Jaya, Selangor, MalaysiaNXP Malaysia, Petaling Jaya, Selangor, MalaysiaThe semiconductor industry plays a vital role in modern technology, with semiconductor devices embedded in almost all electronic products. As these devices become increasingly complex, ensuring quality and reliability poses significant challenges. Electrical testing on semiconductor wafers for defects is crucial, but paradoxically, the testing process itself can introduce defects. These test-induced defects could remain undetected on the wafer, proceed through assembly, and may only be discovered later by customers, leading to returns and significant yield loss. This study proposes a novel graph-based semi-supervised learning (GSSL) algorithm to identify these test-induced hidden defects on the semiconductor wafer that escape conventional methods. The algorithm, which incorporates domain knowledge in creating a graph representation of wafer, and utilizing a weighted edge label propagation model, has demonstrated its effectiveness by achieving a 68% accuracy on a real-world dataset, offering a promising approach to enhance quality control in semiconductor manufacturing.https://ieeexplore.ieee.org/document/10855443/Defect detectiongraphsemi-supervised learningsemiconductor wafertest-induced |
| spellingShingle | Pedram Aridas Narendra Kumar Anis Salwa Mohd Khairuddin Daniel Ting Vivek Regeev A Novel Approach to Test-Induced Defect Detection in Semiconductor Wafers, Using Graph-Based Semi-Supervised Learning (GSSL) IEEE Access Defect detection graph semi-supervised learning semiconductor wafer test-induced |
| title | A Novel Approach to Test-Induced Defect Detection in Semiconductor Wafers, Using Graph-Based Semi-Supervised Learning (GSSL) |
| title_full | A Novel Approach to Test-Induced Defect Detection in Semiconductor Wafers, Using Graph-Based Semi-Supervised Learning (GSSL) |
| title_fullStr | A Novel Approach to Test-Induced Defect Detection in Semiconductor Wafers, Using Graph-Based Semi-Supervised Learning (GSSL) |
| title_full_unstemmed | A Novel Approach to Test-Induced Defect Detection in Semiconductor Wafers, Using Graph-Based Semi-Supervised Learning (GSSL) |
| title_short | A Novel Approach to Test-Induced Defect Detection in Semiconductor Wafers, Using Graph-Based Semi-Supervised Learning (GSSL) |
| title_sort | novel approach to test induced defect detection in semiconductor wafers using graph based semi supervised learning gssl |
| topic | Defect detection graph semi-supervised learning semiconductor wafer test-induced |
| url | https://ieeexplore.ieee.org/document/10855443/ |
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