Litho-NeuralODE 2.0: Improving hotspot detection accuracy with advanced data augmentation, DCT-based features, and neural ordinary differential equations

Zhang, Qing; Zhang, Yuhang; Li, Jizuo; Lü, Wei; Li, Yongfu

doi:10.1016/j.vlsi.2022.02.010

Cited by 4 publications

(2 citation statements)

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“…13 dataset post-processed by discrete cosine transform (DCT) on the ICCAD2012 CAD competition data 15 . Each benchmark set consists of tens of thousands of layout segments, each with input segments sized at 12 pixels×12 pixels and 32 channels 22 Table 1. lists the detailed information of the ICCAD 2012 contest dataset, consisting of 32 nm and four sets of 28 nm benchmarks.…”

Section: Methodsmentioning

confidence: 99%

“…15 Each benchmark set consists of tens of thousands of layout segments, each with input segments sized at 12 pixels × 12 pixels and 32 channels. 22 Table 1 lists the detailed information of the ICCAD 2012 contest dataset, consisting of 32 nm and four sets of 28 nm benchmarks. Columns "#HS" and "#NHS" show the total number of hotspots and nonhotspots in the training and test sets.…”

Section: Benchmark Information and Training Configurationsmentioning

confidence: 99%

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Lithography hotspot detection through multi-scale feature fusion utilizing feature pyramid network and dense block

Xu,

Yuan,

et al. 2024

J. Micro/Nanopattern. Mats. Metro.

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Lithography hotspot (LHS) detection is crucial for achieving manufacturability design in integrated circuits (ICs) and ensuring the final yield of ICs chips. Recognizing the challenges posed by conventional deep learning-based methods for lithographic hotspot detection in meeting the demands of advanced IC manufacturing accuracy, this study introduces an LHS detection approach. This approach leverages multiscale feature fusion to identify defects in lithographic layout hotspots accurately. This method incorporates the convolutional block attention module into the backbone network to enhance the focus of the model on the layout area. Additionally, a feature pyramid is employed to merge deep and shallow features from the layout pattern, significantly enhancing the capability of hotspot detection network to extract both image and semantic features. Concurrently, by utilizing a dense block that directly interconnects various layers, the network gains the capacity to capture the correlation between low-level and high-level features, thereby enhancing the perceptual capabilities of the model. Experimental results demonstrate the superiority of the algorithm across accuracy, false alarm, F1 score, and overall detection simulation time compared to alternative lithographic hotspot detection algorithms.

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