Calibration of Gaussian random field stochastic EUV models
This paper delves into the calibration of Gaussian random field (GRF) stochastic models for Extreme Ultraviolet (EUV) lithography. These models are crucial for predicting and mitigating random variations in feature dimensions, a significant challenge in modern semiconductor manufacturing.
We discuss the conventional calibration method based on matching line edge roughness (LER) and line width roughness (LWR) to experimental data. Additionally, we explore a more advanced approach utilizing power spectral density (PSD) analysis of feature edges. This approach provides a deeper understanding of the underlying stochastic processes and enables more precise model calibration.
The paper also addresses the concept of spatial ergodicity, which is essential for accurate statistical estimation of feature roughness. We analyze how sampling length and step size influence the convergence of LER and LWR estimates and provide guidelines for ensuring reliable measurements.
By advancing the calibration techniques for GRF stochastic models, we aim to improve the accuracy of predicting and mitigating random variations in EUV lithography, ultimately leading to more precise and reliable semiconductor manufacturing.
What you’ll learn:
- Advanced stochastic modeling for EUV lithography: Learn how to improve the accuracy of predicting and mitigating random variations in feature dimensions.
- Calibration techniques: Explore traditional and advanced calibration methods for Gaussian random field (GRF) models.
- Spatial ergodicity: Understand the concept of spatial ergodicity and its impact on accurate statistical estimation of feature roughness.
- Practical applications: Discover how to apply these techniques to enhance semiconductor manufacturing processes and improve product yield.
"By leveraging advanced stochastic modeling techniques and rigorous calibration methods, we can significantly improve the precision and reliability of EUV lithography, enabling the fabrication of cutting-edge semiconductor devices."– Azat Latypov, author
Who should read this:
- Semiconductor manufacturing engineers
- EUV lithography engineers
- Process integration engineers
- Device design and modeling engineers