Large-Scale Automated Identification and Quality Control of Exfoliated and CVD Graphene via Image Processing Technique

摘要

Graphene, a mono layer of carbon atoms, is a high-interest material in the research community and semiconductor industry due to its extraordinary electronic, thermal, and mechanical properties. Graphene layer identification is very important since its intrinsic properties change drastically between each 0.34-nm thick layer. Current methods of identification rely on restrictive small-area microscopy techniques, the most robust being micro-Raman spectroscopy. Here we present a new method for a large-area graphene layer identification characterized by low cost, high accuracy, high throughput, complete automation, and scalability. Our metrology tool is based on a fast image processing algorithm, which analyzes optical contrasts between single-layer, bi-layer, and few-layer graphene used for exfoliated, transferred, or grown graphene flakes on large wafers verified by micro-Raman spectroscopy.