Frank van der Merwe Manufacturing of Bilayer Graphene with ‘Smart’ Processes

While the number of exciting physical phenomena observed in bilayer graphene increases, a significant gap persists in transforming these discoveries into practical applications, owing to the small-scale samples obtained via top-down approaches. We realized a layer-by-layer (that is, Frank-van der Merwe) growth mode in large-scale bilayer graphene, with no island impurities, which is unprecedented in any van der Waals-stacked materials. Machine learning is adopted to assist spectroscopy, enabling the ‘smart’ characterization following the chemical vapor deposition. Rather than random sampling on large-area materials, we adapted K-means algorithms to analyze Raman mapping data for precise characterization of stacking order and layer number of graphene. After growth, a transfer is necessary to move bilayer graphene from the growth substrate to a destination substrate with a mandatory sacrificial support layer. This process induces residuals, wrinkles, and cracks, thus deteriorating 2D materials from their intrinsic properties. We utilized the Marangoni effect, also known as the ‘tears of wine’, to enable ‘smart’ transfer by building a surface tension gradient in transfer liquids. We demonstrate our autonomous Marangoni-flow transfer technique can transfer bilayer graphene without a support layer, resulting in residue-free bilayer graphene.