A deep learning workflow to generate free-form masks for grayscale lithography

Merlin Moreau; Jean-Baptiste Henry; Stéphane Bonnet

doi:10.1117/12.3009759

10 April 2024 A deep learning workflow to generate free-form masks for grayscale lithography

Merlin Moreau, Jean-Baptiste Henry, Stéphane Bonnet

Proceedings Volume 12954, DTCO and Computational Patterning III; 129540Y (2024) https://doi.org/10.1117/12.3009759
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Abstract

Grayscale lithography (GL) is a well-suited technique to manufacture 3D micro objects, such as micro-lens, in a single lithography step. The current method to realize GL masks is limited to square pattern masks and suffers from a high computational cost. This article introduces a deep learning workflow to generate free-form masks for GL. The proposed workflow is composed of five main steps: the dataset generation, the neural network training and inference, the post-treatment and its evaluation. With this method, quality index for 3D simulated objects is equivalent to the current iterative computational method and the computation time is reduced at the same time.

Conference Presentation

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

Citation Download Citation

Merlin Moreau, Jean-Baptiste Henry, and Stéphane Bonnet "A deep learning workflow to generate free-form masks for grayscale lithography", Proc. SPIE 12954, DTCO and Computational Patterning III, 129540Y (10 April 2024); https://doi.org/10.1117/12.3009759

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