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

Kwon, Hyeokjin; Huh, Hyunsuk; Seo, Hwiwon; Han, Songhee; Won, Imhee; Sue, J.A.; Oh, Dongyean; Iza, Felipe; Lee, Seung−Chul; Park, Sung Kye; Cha, Seon-Yong

doi:10.1063/5.0093076

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…In this paper, we will use a DG model based on GANs and a generator consisting of RNN models, along with improvements to the data model [20][21][22], so that the limitations of the time-series data module can be effectively addressed.…”

Section: Gans-based Alarm Data Enhancement For Communication Networkmentioning

confidence: 99%

Neural network-based fiber optic cable fault prediction study for power distribution communication network

Zhang,

Yan,

Shen

et al. 2023

Applied Mathematics and Nonlinear Sciences

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As the foundation of communication networks, optical fiber carries huge network traffic, so the prediction of fiber optic cable faults is an important guarantee for the operation of communication networks. Based on the combination of fiber optic system networking technology and network management data, this study constructs an alarm correlation analysis method by using data mining technology to obtain the data set of the fault prediction model for the problem of low fault prediction accuracy of traditional communication networks. The dataset is used to balance the sample data by generating a small number of new samples through the generative adversarial network. The memory-based feature generation convolutional network is proposed to enhance the feature interaction to realize fault prediction in communication networks. The prediction model has a high prediction accuracy of 98.68%, which saves about 160 min for repair work through the application of fiber optic cable fault prediction, which compares well with other models. Fault prediction based on neural networks can provide assistance in the operation and maintenance of distribution communication networks.

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Section: Gans-based Alarm Data Enhancement For Communication Networkmentioning

confidence: 99%

Neural network-based fiber optic cable fault prediction study for power distribution communication network

Zhang,

Yan,

Shen

et al. 2023

Applied Mathematics and Nonlinear Sciences

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Effect of heavy inert ion strikes on cell density-dependent profile variation and distortion during the etching process for high-aspect ratio features

et al. 2022

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Vertical scaling technique faces a physical limitation in 3D NAND device fabrication, even assuming superior etching technology. Another promising scaling technique to increase the storage density is lateral scaling, which increases the number of holes between slit and slit from four to nine and above. However, unpredictable small critical dimension, feature-to-feature variation, and distortion occur. To elucidate the profile deteriorations induced by the lateral scaling, we analyzed the effect of the angular etching yield dependency of the incident ion fluxes into a given feature using the multiscale technology computer-aided design methodology. As one of the inherent features of the gas, incident angle θmax in which the sputtering yield achieves its maximum value is a crucial factor for analyzing and modeling etching profiles. Moreover, the impact of the heavy inert ion strikes on the unpreferred etching profiles was investigated. In this study, the synergy of lower energy ions, larger fluxes, and larger θmax of heavy inert ions decrease the feature-to-feature variation, reducing hard mask distortion without the etch rate reduction.

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Asynchronous pulse-modulated plasma effect on the generation of abnormal high-energetic electrons for the suppression of charge-up induced tilting and cell density-dependent etching profile variation

et al. 2023

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The formation of high-energy electrons and ion fluxes induced by an abnormal electron heating mode in asynchronous pulse-modulated plasma was investigated using particle-in-cell simulation. We demonstrate that the abnormally high electron heating mode was induced only for a short time in the asynchronous pulsed plasmas. Furthermore, enhanced production of energetic electrons accompanies this electron heating. In particular, the higher energy electrons ( ε > 20 eV) are mainly produced by the abnormal electron heating during the first period of the abrupt sheath expansion phase in the asynchronous pulsed plasma with α1 = α3 = 0.05. These high-energy electrons are crucial for tailoring the expansion of plasma density and neutralizing the surface charging for the HARC etching process. A synergy of higher energy electrons and higher density ion fluxes in asynchronous pulsed plasma can be a promising solution to reduce statistical variation and charging-induced profile deterioration without the etch rate reduction in 3D NAND fabrication.

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

Cited by 4 publications

References 23 publications

Neural network-based fiber optic cable fault prediction study for power distribution communication network

Neural network-based fiber optic cable fault prediction study for power distribution communication network

Effect of heavy inert ion strikes on cell density-dependent profile variation and distortion during the etching process for high-aspect ratio features

Asynchronous pulse-modulated plasma effect on the generation of abnormal high-energetic electrons for the suppression of charge-up induced tilting and cell density-dependent etching profile variation

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