Graphene islands were grown on monolayer graphene via the CVD method[2], and then transferred to a Si/SiO2 substrate. Micro-Raman hyperspectral maps were taken from bi- and tri- layer islands with a wide variety of shapes and sizes. Using homemade code we fit the spectra from each pixel in the maps and extract the peak parameters for all of the major graphene Raman features. These pixels define a coordinate manifold, with the peak parameters building a hyperdimensional space over these coordinates. Principal Component Analysis was used to find the vectors where the spectra vary most widely, and data clusters were identified along these principal vectors. These clusters were further decomposed using this method iteratively.
When all reasonable clusters are identified, the clusters can be remapped to the individual islands, allowing one to classify distinct regions on the islands with similar spectral features. Correlations between the peak parameters can be extracted from each cluster, and then fitted to determine the relationships between them. These relationships reflect the unique physics of the few-layer graphene system, such as biaxial strain or doping level. By classifying regions with similar behavior, we can determine how this physics is influenced by the geometry of the system.
Acknowledgement: This work was supported by: NSF ECCS-1509786
References:
[1]Ado Jorio, Luiz Gustavo Cançado, Raman spectroscopy of twisted bilayer graphene, Solid State Communications, Volumes 175–176, December 2013, Pages 3-12, ISSN 0038-1098, http://dx.doi.org/10.1016/j.ssc.2013.08.008.
[2]Huy Q. Ta, David J. Perello, Dinh Loc Duong, Gang Hee Han, Sandeep Gorantla, Van Luan Nguyen, Alicja Bachmatiuk, Slava V. Rotkin, Young Hee Lee, and Mark H. Rümmeli, Stranski–Krastanov and Volmer–Weber CVD Growth Regimes To Control the Stacking Order in Bilayer Graphene, Nano Letters 2016 16 (10), 6403-6410, DOI: 10.1021/acs.nanolett.6b02826