Machine learning approaches for nuisance filtering in inline defect inspection

Improved color defect detection with machine learning for after develop inspections in lithography

MP McLaughlin, P Mennell, A Stamper… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Color defect detection was improved with the development of a Machine Learning (ML)
method for after develop inspections in lithography. For coating defects, the method …
被引用次数：5 相关文章

Enhanced defect detection in after develop inspection with machine learning disposition

MP McLaughlin, A Stamper, G Barber… - 2021 32nd Annual …, 2021 - ieeexplore.ieee.org
A complementary Machine Learning disposition method was generated and tested for after
develop inspections in lithography. For lithography coating defects, this new method showed …
被引用次数：5 相关文章

The Design-Based Inspection Strategy for CU Void Defects Reduction

X Zhang, H Chen, Y Long… - 2023 China Semiconductor …, 2023 - ieeexplore.ieee.org
With the decrease of line width, it is a big challenge for Cu gap-filling in Back-end-of-line
(BEOL) which can induce Cu void defects. Poor Cu gap-filling can cause yield loss and …
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The Strong Effect of Spectral Mode and Directional Electrical Field for Nuisance Filtering In Defect Inspection

X Zhang, H Chen, Y Long… - 2021 China Semiconductor …, 2021 - ieeexplore.ieee.org
As shrinkage of design nodes and increase of pattern density, defects become more and
more critical in the integrated circuit (IC) manufacturing. Capturing more defects is essential …
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