Importance sampling in Gaussian Random Field EUV stochastic model for quantification of stochastic variability of EUV vias

Citation:

Zexin Pan, Azat Latypov, Chih-I Wei, Peter De Bisschop, Germain Fenger, John Sturtevant, "Importance sampling in Gaussian random field EUV stochastic model for quantification of stochastic variability of EUV vias," Proc. SPIE 12494, Optical and EUV Nanolithography XXXVI, 1249419 (28 April 2023); https://doi.org/10.1117/12.2658651

What you'll learn:

1. What importance sampling methods are and how they can be used to substantially reduce the number of trials required to estimate rare failure probabilities or other stochastic metrics in Gaussian Random Field (GRF) models of EUV lithography processes.
2. How the Separation of Variables Importance Sampling estimator can be applied to efficiently calculate the probability of "missing via" failures, which are very rare events in typical EUV via patterning processes.
3. How an importance sampling-based efficient Monte Carlo sampling method can be used to accurately calculate the local CD uniformity (LCDU) of vias patterned by EUV lithography, requiring much fewer computational trials compared to brute force Monte Carlo.
4. The challenges and potential approaches for applying importance sampling methods to experimental measurements of EUV stochastic variability, such as relating the likelihood ratios to measurable quantities like the number of absorbed photons.

Who should read this:

• Lithography and process simulation researchers and engineers working on advanced EUV lithography processes
• Process integration and device engineers