TCAD augmented generative adversarial network for hot-spot detection and mask-layout optimization in a large area HARC etching process
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-consumi...
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| Veröffentlicht in: | Physics of plasmas 2022-07, Vol.29 (7) |
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| container_title | Physics of plasmas |
| container_volume | 29 |
| creator | Kwon, Hyoungcheol Huh, Hyunsuk Seo, Hwiwon Han, Songhee Won, Imhee Sue, Jiwoong Oh, Dongyean Iza, Felipe Lee, Seungchul Park, Sung Kye Cha, Seonyong |
| description | 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. |
| doi_str_mv | 10.1063/5.0093076 |
| format | Article |
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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. 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| language | eng |
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| source | AIP Journals Complete; Alma/SFX Local Collection |
| subjects | CAD Chemical reactions Computer aided design Etching Generative adversarial networks Layouts Manufacturability Optimization Plasma physics |
| title | TCAD augmented generative adversarial network for hot-spot detection and mask-layout optimization in a large area HARC etching process |
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