Source optimization based on compression sensing for plasmonic lithography

Jianfang He; Ziqi Li; Huwen Ding; Lisong Dong; Yayi Wei

doi:10.1117/12.3010575

10 April 2024 Source optimization based on compression sensing for plasmonic lithography

Jianfang He, Ziqi Li, Huwen Ding, Lisong Dong, Yayi Wei

Proceedings Volume 12953, Optical and EUV Nanolithography XXXVII; 1295316 (2024) https://doi.org/10.1117/12.3010575
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Conference Poster

Abstract

Plasmonic lithography can amplify the evanescent wave resonance at the mask and participate in imaging by exciting surface plasmon polaritons (SPPs), breaking the diffraction limit in traditional lithography. Source Optimization (SO) technology is widely used to compensate for imaging distortion in traditional lithography. This paper proposes an effective SO model for plasmonic lithography under the compressed sensing (CS) framework. To accelerate the algorithm, the SO is formulated as an underdetermined linear problem, where the number of equations is much smaller than the source variables. We selected lines, contacts, and complex test patterns to verify the imaging improvements and superiority of the model. The results indicate that compared to the annular sources, optimized sources can achieve better imaging results and higher imaging contrast. This provides favorable conditions for the large-scale application of plasmonic lithography.

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

Citation Download Citation

Jianfang He, Ziqi Li, Huwen Ding, Lisong Dong, and Yayi Wei "Source optimization based on compression sensing for plasmonic lithography", Proc. SPIE 12953, Optical and EUV Nanolithography XXXVII, 1295316 (10 April 2024); https://doi.org/10.1117/12.3010575

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