doi: 10.7763/IJET.2026.V18.1370
Limits on Resolution and Possibilities for Optimized Imaging of EUV Lithography in Connection with Advanced AI Chips
Fuyuan Yin
*
- School of Electronic Science Technology, Xi’an Jiaotong-Liverpool University, Jinzhou, China
- Manuscript received August 1, 2026
- accepted September 12, 2026
- published October 27, 2026
Abstract
Advancements in generative artificial intelligence and high-performance computing are driving improvements in logic chips, particularly in transistor density, interconnect bandwidth, and power efficiency, with critical layer patterning under 10 nm. Extreme ultraviolet (EUV) lithography employs a 13.5 nm wavelength and reflective projection, reducing the dependence on multiple patterning techniques used in 193 nm immersion lithography. The paper reviews known data and relates various parameters, using Rayleigh relations to estimate the imaging capacities of 0.33 NA and 0.55 NA systems. It also treats such matters as depth of field reduction, half-field of view, mask shading, and overlay control with regard to high NA conditions. The results indicate that 0.55 NA helps to raise the single-exposure resolution—from about 13 nm to roughly 8 nm—yet the yield of sub-10 nm patterns becomes increasingly unreliable because of photon shot noise, material dispersion, or local defects. Thus, when optimizing lithography for advanced AI chips one must not merely aim at minimal linewidths but also at a balance of resolution, dose, depth of field and defect rate.
Keywords
- extreme ultraviolet lithography
- High numerical aperture
- Resolution limit
- Random phenomena
- AI chip manufacturing
How to Cite
Fuyuan Yin, "Limits on Resolution and Possibilities for Optimized Imaging of EUV Lithography in Connection with Advanced AI Chips," International Journal of Engineering and Technology, vol. 18, no. 4, pp. 189-132, 2026. https://doi.org/ 10.7763/IJET.2026.V18.1370
Copyright & License
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).