Simple graphene film production by chemical vapour deposition and the use of AI for quality analysis

Abstract

The reproducible production of graphene films at low cost, while minimizing inter-batch variability, is essential for the development of affordable and reliable devices. By using a simple and reproducible methodology along with readily accessible low-cost equipment, graphene films can easily become accessible to non-graphene specialist research laboratories. This will help accelerate and encourage the development of graphene application research. Here, we describe in detail a simple hot-walled chemical vapour deposition (CVD) procedure to grow graphene with low-cost equipment, using non-processed commercially available copper foil. The quality of the film on copper foil and on Si/SiO2 substrates, after film transfer, was studied by scanning electron microscopy (SEM) and transmission electron microscopy (TEM) to confirm the predominantly monolayer nature of graphene. The quality of graphene was analysed by comparing against standardisation Raman metrics, full width at half maximum of the 2D peak and the ratio of the 2D:G peaks, proposed by the UK National Physical Laboratory showing the films produced were similar or better quality than commercially available monolayer graphene. The Random Forest machine learning method was used as a predictive tool for the classification of the quality of samples according to the batch and position on the copper substrate relative to the direction of gas flow, revealing that the position and full width half maximum of the G and 2D peaks are the important features in Raman for classification, supporting the idea that unintentional strain and doping are crucial to understand the inherent variations found in the production of monolayer graphene in hot-wall CVD setups.