Scalable and adaptable structural metrology for AR/VR device images based on deep learning

Subhei Shaar; Maclean Harned; Bo Zhao; Kundan Chaudhary; Raja Muthinti; Ali Hallal; Martin Jacob; Julien Baderot; Sergio Martinez; Johann Foucher

doi:10.1117/12.3034677

12 November 2024 Scalable and adaptable structural metrology for AR/VR device images based on deep learning

Subhei Shaar, Maclean Harned, Bo Zhao, Kundan Chaudhary, Raja Muthinti, Ali Hallal, Martin Jacob, Julien Baderot, Sergio Martinez, Johann Foucher

Author Affiliations +

Proceedings Volume 13216, Photomask Technology 2024; 132161H (2024) https://doi.org/10.1117/12.3034677
Event: SPIE Photomask Technology + EUV Lithography, 2024, Monterey, California, United States

Abstract

AR/VR device technology is evolving fast thus metrology and measurements must be adapted at the same pace as the process development in closed loop analysis. To answer such constraint, we propose a pipeline based on deep learning able to measure gratings structures from cross section and top-down views. This pipeline was updated in parallel to the process development includes additional measurements, new variations of the structures not covered by the algorithm and user feedback for improved metrology. Deep learning can account for such variability, where classic approaches are not able to handle complex 2D structures displaying many degrees of freedom such as size, material and design among others. We illustrate all these challenges with the performances of the pipeline alongside the development cycle requiring modifications of the pipeline. Large batches of data can be processed thanks to the robustness and the speed of the analysis.

Conference Presentation

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

Citation Download Citation

Subhei Shaar, Maclean Harned, Bo Zhao, Kundan Chaudhary, Raja Muthinti, Ali Hallal, Martin Jacob, Julien Baderot, Sergio Martinez, and Johann Foucher "Scalable and adaptable structural metrology for AR/VR device images based on deep learning", Proc. SPIE 13216, Photomask Technology 2024, 132161H (12 November 2024); https://doi.org/10.1117/12.3034677

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