TCAD augmented generative adversarial network for hot-spot detection and mask-layout optimization in a large area HARC etching process

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Cost-effective vertical etching of plug holes and word lines is crucial in enhancing 3D NAND device manufacturability. Even though multiscale technology computer-aided design (TCAD) methodology is suitable for effectively predicting etching processes and optimizing recipes, it is highly time-consuming. This article demonstrates that our deep learning platform called TCAD-augmented Generative Adversarial Network can reduce the computational load by 2 600 000 times. In addition, because well-calibrated TCAD data based on physical and chemical mutual reactions are used to train the platform, the etching profile can be predicted with the same accuracy as TCAD-only even when the actual experimental data are scarce. This platform opens up new applications, such as hot spot detection and mask layout optimization, in a chip-level area of 3D NAND fabrication. Published under an exclusive license by AIP Publishing.
Publisher
AIP Publishing
Issue Date
2022-07
Language
English
Article Type
Article
Citation

PHYSICS OF PLASMAS, v.29, no.7

ISSN
1070-664X
DOI
10.1063/5.0093076
URI
http://hdl.handle.net/10203/312543
Appears in Collection
ME-Journal Papers(저널논문)
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