A Novel Computer Algorithm for Automating Nano-Scale Thickness Identification of 2D Materials

Atomically thin 2D materials such as graphene promise great innovation in the electronics industry. However, these promises have stayed unfulfilled due to the lack of commercial 2D material production. There is currently no scalable and no economical way to identify the thicknesses of 2D materials reliably. Traditionally, Raman spectroscopy or atomic force microscopy (AFM) is used to measure the nanoscale (1-10 atom) thicknesses of 2D materials. These processes have many limitations because they require expensive equipment, are highly time-consuming, and only provide data for a single point on the sample. This paper presents a cheap and scalable solution that uses optical microscopy images to identify thicknesses. Two significant challenges need to be overcome to make this method feasible. Firstly, color temperatures and lighting intensities vary between different cameras and microscope setups. Secondly, the atomic-scale thicknesses of 2D materials translate into a very low contrast ratio between the 2D material and the substrate. This paper addresses these two challenges with novel computer vision and color normalization techniques. The methods accuracy was verified by comparing its results with measurements from Raman spectroscopy. It reliably calculates thicknesses with a single compressed jpg image, making it drastically faster and cheaper than traditional methods. The algorithm can be applied to any 2D material stack combination and adapt to any microscope setup, regardless of camera type and light color temperature. Furthermore, while the Raman spectrometer uses a laser to measure a single points thickness, the new method provides insight into the topography of the entire material surface. All these advancements pave the way for industrial-scale production of 2D materials.