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10 August 2023 RCWA acceleration for channel-hole structures with a neural network
Hyomin Ahn, Yoonsung Bae, Jeung Song, Namyoon Kim, Joonyoung Ahn, Sungmin Jo, Wookrae Kim, Myungjun Lee
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Proceedings Volume 12619, Modeling Aspects in Optical Metrology IX; 126190G (2023) https://doi.org/10.1117/12.2680983
Event: SPIE Optical Metrology, 2023, Munich, Germany
Abstract
The rigorous coupled-wave analysis (RCWA) is a semi-analytic solver to Maxwell's equation, which is one of the most successful methods for modeling periodic optical structure. The repetitive nature of semiconductors has made RCWA widely applied in the semiconductor metrology industry. However, devices with high aspect ratio units, such as vertical NANDs(V-NANDs), require lengthy computation times, making them difficult to model in practice even with fully parallelized RCWA applications. This is because RCWA involves a time-consuming process of eigendecomposition and matrix inversion for each layer sliced along the vertical axis. In order to circumvent such computations, we propose a neural network based approach: channel-hole approximating network in the electromagnetic aspect (CHANEL). Based on the characteristic that the horizontal cutting plane is topologically consistent along the vertical axis of the channel-hole, CHANEL directly predicts the scattering matrix of each layer from its structural and optical parameters. In the scattering matrix of each layer, we found salient regions for Jones matrix calculation, which enhanced the accuracy of Jones matrix prediction with intensive learning on that area. In this paper, we demonstrate that CHANEL outperforms the traditional CPU-based RCWA implementations in terms of time, performing diffraction simulation more than 10 times faster.
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Hyomin Ahn, Yoonsung Bae, Jeung Song, Namyoon Kim, Joonyoung Ahn, Sungmin Jo, Wookrae Kim, and Myungjun Lee "RCWA acceleration for channel-hole structures with a neural network", Proc. SPIE 12619, Modeling Aspects in Optical Metrology IX, 126190G (10 August 2023); https://doi.org/10.1117/12.2680983
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KEYWORDS
Scattering
Neural networks
Diffraction
Light scattering
Data modeling
Machine learning
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