Training dataset optimization for deep learning freeform grayscale photolithography mask design

Merlin Moreau; Jean-Baptiste Henry; Gaby Bélot; Stéphane Bonnet

doi:10.1117/12.3026880

18 September 2024 Training dataset optimization for deep learning freeform grayscale photolithography mask design

Merlin Moreau, Jean-Baptiste Henry, Gaby Bélot, Stéphane Bonnet

Proceedings Volume 13273, 39th European Mask and Lithography Conference (EMLC 2024); 1327305 (2024) https://doi.org/10.1117/12.3026880
Event: 39th European Mask and Lithography Conference (EMLC 2024), 2024, Grenoble, France

Abstract

This paper presents the optimization of the grayscale photolithography mask design through a deep-learning workflow, in particular the training dataset optimization thanks to binarization-based mask generation method is studied. To do that, the dithering approach, the new barycentric approach are investigated along with the impact of dataset composition in terms of 3D shapes and mask chromium pattern. The evaluation method to know which dataset provide the best learning ability and at the end 3D shapes close to the targets shapes is presented. Finally, this paper shows that an optimized dataset composed of heterogeneous shapes and mask generated through the proposed barycentric approach giving good results in terms of obtained 3D shape at the end of the workflow in comparison to the homogeneous dataset. Moreover, thanks to these optimized datasets, the obtained 3D shapes at the end of the deep learning workflow is comparable to physics-based mask design method for objects like hemispheres.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Merlin Moreau, Jean-Baptiste Henry, Gaby Bélot, and Stéphane Bonnet "Training dataset optimization for deep learning freeform grayscale photolithography mask design", Proc. SPIE 13273, 39th European Mask and Lithography Conference (EMLC 2024), 1327305 (18 September 2024); https://doi.org/10.1117/12.3026880

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