Fast 3D lithography simulation by convolutional neural network: POC study

Hiroyoshi Tanabe; Shimpei Sato; Atsushi Takahashi

doi:10.1117/12.2575971

20 September 2020 Fast 3D lithography simulation by convolutional neural network: POC study

Hiroyoshi Tanabe, Shimpei Sato, Atsushi Takahashi

Author Affiliations +

Proceedings Volume 11518, Photomask Technology 2020; 115180L (2020) https://doi.org/10.1117/12.2575971
Event: SPIE Photomask Technology + EUV Lithography, 2020, Online Only

Abstract

Thin mask model has been conventionally used in optical lithography simulation. In this model the diffracted waves from the mask are assumed to be Fourier transform of the mask pattern. This assumption is the basis of Hopkins' method and sum of coherent system model. In EUV (Extreme UltraViolet) lithography thin mask model is not valid because the absorber thickness is comparable to the mask pattern size. Fourier transformation cannot be applied to calculate the diffracted waves from thick masks. Rigorous electromagnetic simulations such as finite-difference time-domain method, rigorous coupled wave analysis and 3D waveguide method are used to calculate the diffracted waves from EUV masks. However, these simulations are highly time consuming. We reduce the calculation time by adapting a convolutional neural network. We construct a convolutional network which can predict the diffracted waves from 1D EUV mask patterns. We extend the TCC method to include the off-axis mask 3D effects. Our model is applicable to arbitrary source shapes and defocus.

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Citation Download Citation

Hiroyoshi Tanabe, Shimpei Sato, and Atsushi Takahashi "Fast 3D lithography simulation by convolutional neural network: POC study", Proc. SPIE 11518, Photomask Technology 2020, 115180L (20 September 2020); https://doi.org/10.1117/12.2575971

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