ABSTRACT
The thickness of the chalcogenide ovonic threshold switching (OTS) layer is one of the most critical parameters for the switch-only memory (SOM) process control. Traditionally, the OTS thickness and composition were measured by XRF
using the amounts of Ge, As, and Se. Still, XRF has a few limitations in delivering the required performance, especially for products with multilayer memory architecture. For these products, X-ray fluorescence (XRF) signals overlap and
cannot be used to measure the thickness of each layer. In the current paper, we have studied three new alternative approaches for measurements of the OTS thickness on-cell: Spectral Interferometry, Raman spectroscopy, and Hybrid
Machine Learning technique. The first method, Spectral interferometry with the Vertical Traveling Scatterometry approach (VTS), allowed OCD modeling of the top of the structure by blocking the complex underlayers and measuring only the
top OTS thickness on all targets, including within the chip. The second method, Raman spectroscopy, demonstrated oncell dimensional capabilities with an excellent correlation of the Ge-Se, As-Se, and Ge-Ge bonds of Raman active
chalcogenide to TEM OTS thickness values. Finally, the third method used Raman parameters calibrated with TEM as a reference thickness for the ML solution using the VTS spectra on-cell. This ML method is fast, model-free, and requires
minimal TEM samples for setup. All three methods have demonstrated capability for on-cell measurements and HVM process control.
Keywords: OTS thickness, Chalcogenide, Spectral Interferometry, Raman Spectroscopy, Hybrid Machine Learning
