Quantifying Process Sensitivities for EUV and High-Na Using Machine Learning Based Analytics

The identification of process bottlenecks for emerging nodes is becoming critical in early technology pathfinding. This is chiefly due to the impact of many process parameters on scaling performance. Moreover, quantifying impact of process parameters on scaling performance is of utmost importance since that will determine the ultimate patterning pitches. Edge placement error (EPE) budget is a key limiter for scaling. Previously we introduced a Machine learning based analytics framework to perform impact analysis of various process assumptions on EPE. Here, we extend this framework to forecast key limitations of EUV double patterning for 2025 nodes and beyond. Following the adoption of EUV lithography, the industry is exploring increasing the numerical aperture (NA) to enable high-NA EUV processes. We apply our simulation framework to predict key process sensitivities for controlling EPE for high-NA EUV lithography.