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- W4372263884 abstract "Due to the complicated nanoscale structures of current integrated circuits(IC) builds and low error tolerance of IC image segmentation tasks, most existing automated IC image segmentation approaches require human experts for visual inspection to ensure correctness, which is one of the major bottlenecks in large-scale industrial applications. In this paper, we present the first data-driven automatic error detection approach that targets two types of IC segmentation errors: wire and via errors. On an IC image dataset collected from real industry, we demonstrate that, by adapting existing CNN-based approaches of image classification and image translation with additional pre-processing and post-processing techniques, we are able to achieve recall/precision of 0.92/0.93 in wire error detection and 0.96/0.90 in via error detection, respectively." @default.
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- W4372263884 date "2023-06-04" @default.
- W4372263884 modified "2023-10-14" @default.
- W4372263884 title "Automatic Error Detection in Integrated Circuits Image Segmentation: A Data-Driven Approach" @default.
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- W4372263884 doi "https://doi.org/10.1109/icassp49357.2023.10095703" @default.
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