Large-Area Industrial-Scale Identification and Quality Control of Graphene
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Large-Area Industrial-Scale Identification and Quality Control of Graphene Craig M. Nolen1, Giovanni Denina2, Desalegne Teweldebrhan1, Bir Bhanu2, and Alexander A. Balandin1 1 Nano-Device Laboratory, Department of Electrical Engineering and Materials Science and Engineering Program, Bourns College of Engineering, University of California – Riverside, Riverside, California 92521, USA 2 Visualization and Intelligent Systems Laboratory, Department of Electrical Engineering, Bourns College of Engineering, University of California – Riverside, Riverside, California 92521, USA ABSTRACT A large-area graphene layer identification technique was developed for research and industrial applications. It is based on the analysis of optical microscopy images using computational image processing algorithms. The initial calibration is performed with the micro-Raman spectroscopy. The method can be applied to the wafer-scale graphene samples. The technique has the potential to be the gateway in the development of fully automated statistical process control methods for the next generation thin-film materials used by the semiconductor industry. The proposed technique can be applied to graphene on arbitrary substrates and used for other atomically thin materials. INTRODUCTION Discovery of graphene, an atomic monolayer of carbon [1] stimulated major interest within the scientific and engineering community owing to material’s exceptional intrinsic electronic [1] and thermal properties [2]. Research evolved quickly in the graphene field, with recent advancements in large-area growth methods of up to 30 inch rolls from chemical vapor deposition (CVD) on Cu [3] and transfer of grown graphene onto dielectric substrates [4]. Practical applications of graphene require a method for large-area identification of graphene and its quality control. CVD grown graphene wafers can have regions with different number of atomic planes and crystalline orientation of the grains [5]. The properties of graphene and fewlayer graphene samples depend strongly on the number of atomic layers. For example, thermal conductivity [2] and electron mobility differ significantly between graphene [6-8] and few-layer graphene [9, 10]. The large-area consistency of layering is critical for many graphene-based electronic devices due to their sensitivity to fluctuations in the intrinsic properties [11]. Optical transparency of single and few layer graphene complicate the large-scale recognition and quality control [12]. This is especially true when transferring grown or exfoliated graphene onto various dielectric substrates [13]. Available techniques are not suitable for the large-area graphene recognition [12, 14-20]. Here we present a method for graphene layer recognition with high accuracy, high throughput, complete automation, and a way for providing statistical analysis for quality control at the industrial level for large-scale integration [21, 22]. This technique is cheap, robust, timeeffective, and efficient at achieving automated high throughput large-area graphene laye
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