This work addresses current limitations of X-ray reflectometry (XRR) for modeling thin films and provides a basis for their improvement. Better accuracy in the characterization of novel thin film structures requires better model selection techniques and better knowledge of the theorectical limitiations of current XRR analysis techniques. We use hafnium dioxide (HfO2) nanoscale (>>1nm) thin films deposited by atomic layer deposition (ALD) to study the limitations of current techniques. These structures are of strategic importance as CMOS gate and barrier materials. We show that XRR modeling-for our measured data range and counting statistics-will fail for thickness less than 1 nm. We also show that a 2-layer model (HfO2/SiOxHfy/Si substrate) is more plausible than a 1-layer model (HfO2/Si substrate) for the measured data.
Proceedings Title: Proceedings| 2005
Conference Dates: March 15-18, 2005
Conference Title: International Conference on Characterization and Metrology
Pub Type: Conferences
atomic layer deposition, bayesian statistics, hafnium oxide, thin films, x-ray reflectrometry