Nondestructive monitoring of annealing and chemical–mechanical planarization behavior using ellipsometry and deep learning
Abstract The Cu-filling process in through-silicon via (TSV-Cu) is a key technology for chip stacking and three-dimensional vertical packaging. During this process, defects resulting from chemical–mechanical planarization (CMP) and annealing severely affect the reliability of the chips. Traditional...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2023-04-01
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| Series: | Microsystems & Nanoengineering |
| Online Access: | https://doi.org/10.1038/s41378-023-00529-9 |
| _version_ | 1797836415770296320 |
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| author | Qimeng Sun Dekun Yang Tianjian Liu Jianhong Liu Shizhao Wang Sizhou Hu Sheng Liu Yi Song |
| author_facet | Qimeng Sun Dekun Yang Tianjian Liu Jianhong Liu Shizhao Wang Sizhou Hu Sheng Liu Yi Song |
| author_sort | Qimeng Sun |
| collection | DOAJ |
| description | Abstract The Cu-filling process in through-silicon via (TSV-Cu) is a key technology for chip stacking and three-dimensional vertical packaging. During this process, defects resulting from chemical–mechanical planarization (CMP) and annealing severely affect the reliability of the chips. Traditional methods of defect characterization are destructive and cumbersome. In this study, a new defect inspection method was developed using Mueller matrix spectroscopic ellipsometry. TSV-Cu with a 3-μm-diameter and 8-μm-deep Cu filling showed three typical types of characteristics: overdishing (defect-OD), protrusion (defect-P), and defect-free. The process dimension for each defect was 13 nm. First, the three typical defects caused by CMP and annealing were investigated. With single-channel deep learning and a Mueller matrix element (MME), the TSV-Cu defect types could be distinguished with an accuracy rate of 99.94%. Next, seven effective MMEs were used as independent channels in the artificial neural network to quantify the height variation in the Cu filling in the z-direction. The accuracy rate was 98.92% after training, and the recognition accuracy reached 1 nm. The proposed approach rapidly and nondestructively evaluates the annealing bonding performance of CMP processes, which can improve the reliability of high-density integration. |
| first_indexed | 2024-04-09T15:08:32Z |
| format | Article |
| id | doaj.art-69fd8858146a490a8f49227ed414e12c |
| institution | Directory Open Access Journal |
| issn | 2055-7434 |
| language | English |
| last_indexed | 2024-04-09T15:08:32Z |
| publishDate | 2023-04-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Microsystems & Nanoengineering |
| spelling | doaj.art-69fd8858146a490a8f49227ed414e12c2023-04-30T11:18:58ZengNature Publishing GroupMicrosystems & Nanoengineering2055-74342023-04-019111010.1038/s41378-023-00529-9Nondestructive monitoring of annealing and chemical–mechanical planarization behavior using ellipsometry and deep learningQimeng Sun0Dekun Yang1Tianjian Liu2Jianhong Liu3Shizhao Wang4Sizhou Hu5Sheng Liu6Yi Song7The Institute of Technological Sciences, Wuhan UniversityThe Institute of Technological Sciences, Wuhan UniversitySchool of Mechanical Science and Engineering, Huazhong University of Science and TechnologyHongyi Honor College of Wuhan UniversitySchool of Power and Mechanical Engineering, Wuhan UniversityHongyi Honor College of Wuhan UniversityThe Institute of Technological Sciences, Wuhan UniversityThe Institute of Technological Sciences, Wuhan UniversityAbstract The Cu-filling process in through-silicon via (TSV-Cu) is a key technology for chip stacking and three-dimensional vertical packaging. During this process, defects resulting from chemical–mechanical planarization (CMP) and annealing severely affect the reliability of the chips. Traditional methods of defect characterization are destructive and cumbersome. In this study, a new defect inspection method was developed using Mueller matrix spectroscopic ellipsometry. TSV-Cu with a 3-μm-diameter and 8-μm-deep Cu filling showed three typical types of characteristics: overdishing (defect-OD), protrusion (defect-P), and defect-free. The process dimension for each defect was 13 nm. First, the three typical defects caused by CMP and annealing were investigated. With single-channel deep learning and a Mueller matrix element (MME), the TSV-Cu defect types could be distinguished with an accuracy rate of 99.94%. Next, seven effective MMEs were used as independent channels in the artificial neural network to quantify the height variation in the Cu filling in the z-direction. The accuracy rate was 98.92% after training, and the recognition accuracy reached 1 nm. The proposed approach rapidly and nondestructively evaluates the annealing bonding performance of CMP processes, which can improve the reliability of high-density integration.https://doi.org/10.1038/s41378-023-00529-9 |
| spellingShingle | Qimeng Sun Dekun Yang Tianjian Liu Jianhong Liu Shizhao Wang Sizhou Hu Sheng Liu Yi Song Nondestructive monitoring of annealing and chemical–mechanical planarization behavior using ellipsometry and deep learning Microsystems & Nanoengineering |
| title | Nondestructive monitoring of annealing and chemical–mechanical planarization behavior using ellipsometry and deep learning |
| title_full | Nondestructive monitoring of annealing and chemical–mechanical planarization behavior using ellipsometry and deep learning |
| title_fullStr | Nondestructive monitoring of annealing and chemical–mechanical planarization behavior using ellipsometry and deep learning |
| title_full_unstemmed | Nondestructive monitoring of annealing and chemical–mechanical planarization behavior using ellipsometry and deep learning |
| title_short | Nondestructive monitoring of annealing and chemical–mechanical planarization behavior using ellipsometry and deep learning |
| title_sort | nondestructive monitoring of annealing and chemical mechanical planarization behavior using ellipsometry and deep learning |
| url | https://doi.org/10.1038/s41378-023-00529-9 |
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