Graphene fingerprint peak analysis method based on big data analysis

Abstract

The invention discloses a graphene fingerprint peak analysis method based on big data analysis. The method comprises the following steps: step 1, resampling m points in any region in q characteristicpeak neighborhoods on a graphene Raman spectrum; step 2, performing p-point fast Fourier transform on the resampled m point characteristic peak neighborhood characteristic spectrums to obtain a characteristic peak neighborhood complex track; step 3, normalizing the amplitude of each obtained complex track according to the maximum amplitude of the complex track; 4, dividing the complex track into aplurality of circles from outside to inside, and defining an identification feature set; 5, establishing a graphene Raman spectrum automatic identification model based on big data; and 6, automatically identifying the graphene Raman spectrum to be identified, and performing defect judgment and layer number judgment according to an identification result. Under the background of big data, graphenesingle-layer / multi-layer or defect-free automatic identification is carried out according to the Raman characteristic spectrum of graphene, so that the identification accuracy and efficiency are improved.

Description

technical field [0001] The present invention relates to the technical field of big data analysis, more specifically, the present invention relates to a graphene fingerprint peak analysis method based on big data analysis. Background technique [0002] As a two-dimensional crystal with a thickness of only one atom, graphene has very unique electrical properties, thermal conductivity and optical properties, and has begun to be widely used as transparent electrodes in electronic and optoelectronic devices such as rectifiers, photodetectors, and touch screens. application. At present, the preparation technology of large-area graphene is becoming more and more mature, especially the 12-inch graphene film prepared by chemical vapor deposition (CVD) technology has been reported. In this context, the quality of graphene films has become an important issue of concern. The distribution of electron energy levels in graphene with different layers is very different, and the scattering ...

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Problems solved by technology

Manual processing has become impossible, while the traditional non-intelligent algorithm is restricted by the interference phenomena that appear at random, such as the fluctuation of the fluorescent background of the sample substrate, the noise caused by the background radiation particles, and the fluctuation of the overall intensity of the spectral signal. It is also very difficult to deal with these huge amounts of data

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