A fuzzy pattern matching method based on graph kernel for lithography hotspot detection

Nitta, Izumi; Kanazawa, Yuzi; Ishida, Tsutomu; Banno, Koji

doi:10.1117/12.2257654

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Hotspot Detection In the Verification Stages Of The Physical Design1

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2022

2025

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Cited by 3 publications

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“…The lithography hotspot detection based on a deep convolutional neural network was also presented [54] . A lithography hotspot detection method uses Delaunay triangulation and graph kernel-based machine learning, which could achieve high accuracy with a tolerable amount of false alarm [55] . The hotspot detection methods based on convolutional neural networks and other efforts to improve the performance, such as data augmentation, density-based scan clustering algorithm clustering, and modified batch normalization, were proposed [56] .…”

Section: Hotspot Detection In the Verification Stages Of The Physical Designmentioning

confidence: 99%

Advanced process and electron device technology

Zhang¹,

Su²,

Chang³

et al. 2022

Tsinghua Sci. Technol.

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This article reviews advanced process and electron device technology of integrated circuits, including recent featuring progress and potential solutions for future development. In 5 years, for pushing the performance of fin field-effect transistors (FinFET) to its limitations, several processes and device boosters are provided. Then, the three-dimensional (3D) integration schemes with alternative materials and device architectures will pave paths for future technology evolution. Finally, it could be concluded that Moore's law will undoubtedly continue in the next 15 years.

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Section: Hotspot Detection In the Verification Stages Of The Physical Designmentioning

confidence: 99%