Proceedings Article | 10 April 2024
Mor Baram, Ran Alkoken, Noam Teomim, Noam Tal, Bobin Mathew, Ilan Ben Harush, Eyal Angel, Shmuel Mizrachi, Liraz Gershtein, David Ulliel, Gadi Oron, Anna Levant
KEYWORDS: Data modeling, Metrology, Process control, Scanning electron microscopy, Deep learning, Neural networks, Mathematical optimization, Machine learning, Critical dimension metrology
Technology nodes shrink leads to a very tight process control window. As an inline metrology tool, CD-SEM (Critical Dimension Scanning Electron Microscope) matching error for the entire fleet should be less than 10% of the process control window. The tight matching is also obligated to be coupled with HVM (High-Volume Manufacturing) requirements of high tool availability and fast recipe creation. To meet these challenging restrictions, iterative improvements on existing methodologies are no longer sufficient and a complimentary approach needs to be adopted. The operation of any fab tool, and specifically CD-SEM holds an enormous amount of information. The tool’s various modules parameters such as: currents and voltages and environmental status (temperature, noise etc.), the working point and beam parameters: scan rate, beam current, pixel size, landing energy, beam shape and size, etc., and the specific wafer characteristics: materials, pattern, charging effects etc. Encapsulating all these together contributes to the final matching error sensitivity. As we are moving towards a data-driven era, this information can be utilized to better predict, correct, and improve matching (i.e., by neural network, machine learning etc.). Furthermore, today’s matching metrics, which are based on 1-D CD measurements from SEM images (a convolution of the final SEM image and algorithm), are indirect and are not sufficient for understanding the whole story. In this paper, we propose a complementary approach that combines iterative improvements with data-driven methodologies that can enable a matching level suitable for EUV requirements in the Å era.
