KEYWORDS: Boron, Fin field effect transistors, Semiconducting wafers, Germanium, Silicon, 3D metrology, Epitaxy, Transistors, Process control, Metrology
The epitaxial growth of source/drain structures demands a process with tight control of boron and germanium composition to ensure consistent device performance. However, in-line monitoring of the epitaxial composition in FINFET structures has been one of the most difficult challenges for both process development and manufacturing. Traditional in-line monitoring schemes have relied heavily on critical dimension (CD) measurements, with no composition information. Instead, composition information was provided by offline analysis techniques such as secondary ion mass spectrometry (SIMS), which is destructive and does not measure the composition directly on the FinFET device structure. In this paper, we present results from in-line X-Ray Photoelectron Spectroscopy (XPS) measurements on FinFET structures. This technique is not only sensitive to individual element abundance but also gives information related to the local chemical environment. For this application we monitored silicon, germanium, and boron concentrations in SiGeB EPI source/drain 3D structure without interference from other structural features in the logic device. The in-line XPS measurement of PFET EPI boron and germanium performed in this way on the full structure transistor has been demonstrated to correlate with CMOS device performance, thus significantly reducing time to detect epitaxial composition drift or excursion.
Padraig Timoney, Taher Kagalwala, Edward Reis, Houssam Lazkani, Jonathan Hurley, Haibo Liu, Charles Kang, Paul Isbester, Naren Yellai, Michael Shifrin, Yoav Etzioni
KEYWORDS: Machine learning, Metrology, Metals, Copper, Back end of line, Semiconducting wafers, High volume manufacturing, Process control, Critical dimension metrology, Front end of line
In recent years, the combination of device scaling, complex 3D device architecture and tightening process tolerances have strained the capabilities of optical metrology tools to meet process needs. Two main categories of approaches have been taken to address the evolving process needs. In the first category, new hardware configurations are developed to provide more spectral sensitivity. Most of this category of work will enable next generation optical metrology tools to try to maintain pace with next generation process needs. In the second category, new innovative algorithms have been pursued to increase the value of the existing measurement signal. These algorithms aim to boost sensitivity to the measurement parameter of interest, while reducing the impact of other factors that contribute to signal variability but are not influenced by the process of interest. This paper will evaluate the suitability of machine learning to address high volume manufacturing metrology requirements in both front end of line (FEOL) and back end of line (BEOL) sectors from advanced technology nodes. In the FEOL sector, initial feasibility has been demonstrated to predict the fin CD values from an inline measurement using machine learning. In this study, OCD spectra were acquired after an etch process that occurs earlier in the process flow than where the inline CD is measured. The fin hard mask etch process is known to impact the downstream inline CD value. Figure 1 shows the correlation of predicted CD vs downstream inline CD measurement obtained after the training of the machine learning algorithm. For BEOL, machine learning is shown to provide an additional source of information in prediction of electrical resistance from structures that are not compatible for direct copper height measurement. Figure 2 compares the trench height correlation to electrical resistance (Rs) and the correlation of predicted Rs to the e-test Rs value for a far back end of line (FBEOL) metallization level across 3 products. In the case of product C, it is found that the predicted Rs correlation to the e-test value is significantly improved utilizing spectra acquired at the e-test structure. This paper will explore the considerations required to enable use of machine learning derived metrology output to enable improved process monitoring and control. Further results from the FEOL and BEOL sectors will be presented, together with further discussion on future proliferation of machine learning based metrology solutions in high volume manufacturing.
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