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- W4367186087 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." @default.
- W4367186087 created "2023-04-28" @default.
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- W4367186087 date "2023-04-28" @default.
- W4367186087 modified "2023-10-15" @default.
- W4367186087 title "Nondestructive monitoring of annealing and chemical–mechanical planarization behavior using ellipsometry and deep learning" @default.
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- W4367186087 doi "https://doi.org/10.1038/s41378-023-00529-9" @default.
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