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This paper presents a deep learning model utilizing Fully Convolutional Networks to predict full-chip post-CMP nanotopography with nanometer-scale accuracy, addressing the challenges of traditional CMP modeling. By integrating White Light Interferometry and Atomic Force Microscopy techniques, the model significantly reduces computational costs and inference time compared to existing physics-based models. The results demonstrate improved accuracy and efficiency in predicting surface topography, paving the wa

Author

adolffilterpainter

Language

EN