# Special Issue \"Fundamentals and Recent Advances in Epitaxial Graphene on SiC\"
## 2 Critical Aspects of Epitaxial Graphene Growth: Recipes, Properties, and Quality  
The quality of the buffer layer (also known as C-rich surface reconstruction of SiC and zero graphene layer) is identified as one of the most important factors determining the quality of the epitaxial graphene monolayer on SiC grown via Si sublimation approach. In other words, the fabrication of a large-area epitaxial graphene layer with high thickness uniformity requires pre-formation of high-quality continuous buffer layer on large areas. Thus, an optimization of the growth regime with respect to the buffer layer formation during early stage graphenization process as well as a complete understanding of the growth mechanism are key ingredients to fabricate device-quality graphene. In fact, a successful graphenization process may occur only in a very narrow operational temperature/pressure window which imposes significant restrictions on the growth regime tunability. This makes the optimization of growth conditions quite a challenging task. Despite the large research efforts to tackle this task, it still requires more systematic consideration. In this regard, the critical study by the authors of [1] on optimizing the formation conditions of buffer layers through control of the graphite crucible temperature and varying the Ar gas pressure is a recent contribution to the process. It was revealed that the buffer layer coverage is strongly dependent on the temperature at which Ar gas is introduced, demonstrating a gradual decrease as the temperature increases. The mechanism behind this behavior has been discussed. In the same paper, the relationship between the growth temperature and electronic properties (carrier mobility, carrier density) of quasi-freestanding graphene monolayer and epitaxial graphene monolayer, respectively, were discussed. It was also illustrated that the conductivity type and free carrier density for graphene are extremely sensitive to ambient conditions which was observed by many researchers earlier. In line with this, the review paper by [PERSON] et al. [6] provides a more general picture of the effect of epitaxial graphene-ambient interaction on the carrier transport in SiC-supported epitaxial graphene. The authors highlight the adsorption of different molecules (O\({}_{2}\), H\({}_{2}\)O, NO\({}_{2}\), H\({}_{2}\)O\({}_{2}\), CO\({}_{2}\), NH\({}_{3}\), CO, NO, N\({}_{2}\)O\({}_{4}\)) as a main reason that underlie conductivity type flipping, transport properties fluctuations and carrier density saturation. Apart from the unintentional doping of epitaxial graphene by environmental gases and other molecules, the electronic properties of epitaxial graphene on SiC can be modulated by the intentional incorporation of external dopants, as was demonstrated in another study [3]. Nitrogen ion implantation was proposed as an instrumental approach to stabilize the \(n\)-type conductivity in epitaxial graphene without serious structural damage. However, a balance between graphene quality and implantation dose must be reached. In that light, the mentioned paper dealt with finding the correlation between the fluence value and epitaxial graphene properties such as fragmentation degree, and defect density. It is instructive that all mentioned works exploit Raman Spectroscopy to estimate the quality of epitaxial graphene. More specifically, the relationship between the intensities of \(2D\) and \(G\) characteristic peaks is used to determine the number of graphene layers, while \(D/G\) amplitude ratio is employed to calculate the defect density. Although the graphene Raman spectroscopy is a mature field, it continues to evolve especially in the direction of signal processing (for example, peak fitting quality). In this regard, the work by [PERSON] and [PERSON] [2] originally offers a Voigt line shape fitting approach for analysis of \(2D\) peak line shape for epitaxial graphene, which includes both the inhomogeneous and homogeneous broadening.