TW201430330A - System and method for the detection of the number of graphene layers - Google Patents

Abstract

The present invention relates to a system employing image analysis for detecting the number of graphene layers and having an acquisition image module, an image enlarged module and a multispectral color image reproduction module. An aspect of the present invention relates to a method having a procedure for constructing a database to store information of graphene layers spectra and a procedure for detecting grapheme layers with multi-spectral color reproduction image, whereby the former has steps of analyzing grapheme layers spectra, performing principal component analysis (PCA), classifying grapheme layers, and building a database, and the latter has steps of retrieving an image, analyzing graphene spectra, categorizing grapheme layers, performing color enhancement, reproducing a color image and detecting the number of grapheme layers. The system and method in accordance with the present invention, using simple device, facilitate and simplify the detection of the number of graphene layers.

Description

Graphene film layer number detection system and detection method

The invention relates to a graphene film detecting system and a detecting method, in particular to an image analyzing system and method applied to the graphene layer layer number detecting.

With the development of graphite technology, the visualization of graphene film, that is, how to determine the thickness of graphene film and its number of layers, especially how to determine the number of layers of low-layer graphene film (FLG), is extremely Important topic. Existing techniques for discriminating graphene film layers include: Raman spectroscopy, penetration spectroscopy, techniques using atomic force microscopy (AFM), and techniques using optical microscopy.

(1) Raman spectrum technology and penetration spectrum technology

As described in reference 17, the Raman spectrum technique can detect the number of layers of the graphene film by discriminating the intensity change of the G-band and the Raman frequency change of the 2D-band.

Reference 19 discloses a technique for calculating an atomic number of layers of graphite on a ceria/ytterbium (SiO ₂ /Si) substrate by Raman spectrum. The Raman spectrum is used to count the atomic plane layer graphite on the cerium oxide/cerium (SiO ₂ /Si) substrate, and the comprehensive intensity ratio of the optical phonon peak of the graphite peak and the ytterbium is measured, and the number of layers of the graphene film is larger than The range of 4 layers (N>4) is used to obtain the information of the number of layers of graphene film.

As described in Reference 18, the transmittance of the single-layer graphene film is 97.7%, and the penetration spectrum technique can obtain the graphene layer number information by measuring the transmittance.

When the aforementioned Raman spectrum technique and the penetration spectrum technique are employed, a Raman spectrum and a penetration spectrum verification process are required, which is extremely time consuming and labor intensive. For example, using the imaging technique of the Raman spectrum, it takes 3 hours to determine the completion of a graphene film detection of about 50 μm ² ; in addition, the detection method of the Raman spectrum still has a number of blurred regions, which makes it impossible to determine the true The number of layers of graphene film.

(2) Technology using atomic force microscopy

As shown in reference 19, the thickness of the single-layer graphene film is about 0.34 nm, and the technique of atomic force microscopy can be used to determine the roughness of the surface to obtain the graphene layer number information. However, the technique of using atomic force microscopy also requires extremely long measurement times.

(3) Technology using optical microscopy

In addition to the prior art using optical microscopy, the disclosure of reference 14 is based on, for example, a cerium oxide/cerium substrate, cerium oxide (SiO ₂ ), cerium nitride (Si ₃ N ₄ ), and trioxide. Different dielectric layers such as aluminum (Al ₂ O ₃ ) are used to more thoroughly analyze the color of the multilayer graphene film to optically identify the difference in the number of layers by the film.

Reference 12 discloses a method for detecting the number of layers of graphene film by microscopic imaging technology on a specific substrate. From the research, it can be found that a layer of 300 nm of SiO _{2 is} plated on the substrate, and the graphene film can be distinguished. The graphene film is observed on the nanometer cerium oxide and has the best visual effect. This paper points out that the cerium oxide/germanium substrate in the 285 nm oxide layer can greatly improve the visibility of the graphene film, and the rationality of the theory is verified by the method of calculating the color difference.

Reference 13 discloses an optical method of a multi-spectral technique, which proposes a nanometer thickness for identification and measurement and an effective optical characteristic of graphite as a base material, which is a substrate made of a thin insulating layer. The height between the base material and the substrate is compared by selecting a graphene film of appropriate optical properties and thickness of the insulating layer. The effective refractive index and light absorption coefficient of graphite oxide, thermally reduced graphite oxide and graphite can be obtained by comparing the predicted and measured differences.

Reference 15 discloses that for different substrates such as tantalum carbide (SiC), ruthenium dioxide/ruthenium, quartz, tantalum, glass, etc., since different materials have different lattice constants and electronic structures, the observation of graphene films is affected. The influence of the substrate effect causes a difference in the number of layers of the graphene film observed. This document points out that the number of layers of graphene film is observed on a quartz substrate and a ceria/germanium substrate, which has a high contrast of color difference.

The prior art disclosed in Reference 14 uses different wavelengths of light sources to simulate the erbium dioxide/germanium substrate, tantalum nitride, and aluminum oxide as different layers of graphene film. The results indicate that the tantalum nitride is suitable for Observing the low-layer graphene film, the ceria/矽 is suitable for observing the middle layer graphene film, and the aluminum oxide is suitable for observing the high-order graphene film.

Reference 16 discloses the analysis of the color difference between different substrates of cerium oxide/cerium and cerium oxide/air (SiO ₂ /air), which describes the color of various layers of graphite and graphene thin film oxide deposited on various insulating layers. And analysis of the effect on the thickness of the material, the type of insulation layer and the presence of the back of the ruthenium substrate were analyzed. Although it is pointed out that the color of the graphite oxide layer on the ceria/germanium substrate is found to change periodically as the thickness of the material increases, however, the color of the graphite layer on the same substrate is saturated without similar periodic changes. .

In summary, the prior art uses optical microscopy to provide fast imaging speeds, particularly with multi-spectral techniques, as a quick and intuitive way to detect the number of layers of a multilayer graphene film. However, such a technique using an optical microscope requires a substrate of a specific thickness, and on a transparent substrate, the prior art using an optical microscope cannot be implemented.

In addition, there is a technique of detecting the number of graphene film layers by using an optical microscope. Although the number of layers of the graphene film can be visually recognized through a microscope under a specific substrate thickness structure, for example, 300 nm is plated on the tantalum substrate. The ruthenium dioxide dielectric layer is then plated with a graphene film under the substrate of the structure, and the number of layers of the graphene film is detected. However, the disadvantages of the prior art using optical microscopy have increased the steps and processes required to fabricate graphene thin film electronic components.

In addition, the prior art using optical microscopy has yet to be strengthened in visual comparison of the number of layers of graphene films. For example, for a five-layer graphene film transferred onto a glass substrate, the prior art using an optical microscope can be found to have insufficient visual contrast under an optical microscope, and it is almost impossible to observe a graphene film, thereby failing to detect a graphene film. The number of layers.

In view of the time-consuming and labor-intensive verification process required to use the Raman spectrum and the penetrating spectrum, there is a need for the use of atomic force microscopy. Long-term measurement of expensive instruments, the use of optical microscopy technology requires a certain thickness of the substrate to cause the failure to implement on the transparent substrate, plus the use of optical microscopy technology to detect low-layer graphene film visual contrast Disadvantages of the present invention are to improve the disadvantages, and to use a multi-spectral color image reproduction technology with a microscope, a photosensitive coupling device (CCD), or the like, to use a multi-spectral color image reproduction technique on a transparent substrate or an opaque substrate to lower the number of layers of graphite. The olefin film is rapidly detected and can be applied to related industries such as detection of yield and quality, thereby providing a rapid method for judging and verifying the number of layers of graphene film.

In order to achieve the foregoing objectives, the technical means adopted by the present invention uses a multi-spectral image technique of chromaticity, based on different spectral expressions such as destructive interference or constructive interference between different layers of graphene films, combined with spindle component analysis, Quantifying the spectral data, defining a community between different layers by the specific gravity coefficient of the first main axis and the second main axis, and detecting the optical microscope image of the number of layers of the low-layer graphene film by the distinction of the community, thereby providing the application to the graphene film Image analysis system and analysis method for layer number detection.

Specifically, the present invention can use the multi-spectral technique to perform spindle component analysis and determine the threshold value, and obtain an image image of the simulated reconstruction between different layers to achieve the effect of rapidly detecting the number of layers (N) of the graphene film having a low number of layers.

The invention overcomes the problems of the prior art, not only breaks through the limitation of the thickness and structure of the substrate, but also can detect the graphene film more timely and correctly. The multi-spectral technology of the present invention achieves color reproduction of cells through a spindle component analysis method combined with color adaptive conversion and linear regression. It is not necessary to control the color change of the illumination source in the microscope, and the detection and analysis of the graphene film can be effectively realized by using clear and simple instruments and algorithm parameters. Therefore, the invention can save the traditional time-consuming and labor-intensive Raman spectrum and the penetration spectrum verification process, and does not need to use an expensive instrument such as an atomic force microscope for long-term measurement, and can indeed provide efficient, low-cost, low-time technology. In order to facilitate the invention of the effect of detecting the number of layers of the graphene film in the lower layer.

10‧‧‧Observation module

11‧‧‧stage unit

12‧‧‧Lighting unit

13‧‧‧Image magnification unit

14‧‧‧ Filter unit

20‧‧‧Image capture module

21‧‧‧ lens unit

22‧‧‧Photosensitive coupling unit

23‧‧‧Capture unit

30‧‧‧Multi-spectral color image reproduction module

31‧‧‧ Spectrum analysis steps

32‧‧‧Color gain step

33‧‧‧Color image reproduction steps

40‧‧‧ Graphene film samples

1 is a schematic diagram of an image analysis system of the present invention applied to layer number detection of graphene films.

2 is another schematic diagram of an image analysis system of the present invention applied to layer number detection of graphene films.

Figure 3 is a color diagram of a glass substrate grown with a low number of graphene films by Raman spectrum analysis. The numerical symbols 0 to 5 in the figure respectively indicate the color gradation corresponding to the number of layers of the graphene film.

Fig. 4 is a gray scale diagram of a glass substrate on which a low number of graphene films are grown by Raman spectrum analysis. The numerical symbols 0 to 5 in the figure respectively indicate the color gradation corresponding to the number of layers of the graphene film.

Figure 5 is a color diagram of a glass substrate having a low number of graphene films grown by the present invention. The numerical symbols 0 to 5 in the figure respectively indicate the color gradation corresponding to the number of layers of the graphene film.

Figure 6 is a gray scale diagram of a glass substrate on which a low number of graphene films are grown by the present invention. The numerical symbols 0 to 5 in the figure respectively indicate the color gradation corresponding to the number of layers of the graphene film.

Figure 7 is an image of a three-layer graphene film grown on a ceria/ruthenium substrate.

Fig. 8 is an external view of a glass substrate on which a five-layer graphene film is grown.

Fig. 9 is a schematic view showing the structure of a five-layer graphene film grown on a glass substrate.

Fig. 10 is a sectional view of a glass substrate on which a five-layer graphene film is grown.

Fig. 11 is a schematic view showing the selection of regions across different layers of a glass substrate on which five layers of graphene film are grown, and an enlarged view of each region.

Figure 12 is a graph showing the color difference between the analog patch and the microscope patch in a 24-color block for measuring the reflected spectrum.

Fig. 13 is a flow chart for finding a conversion matrix between the spectrum analyzer and the image capturing device.

Fig. 14 is a flow chart for obtaining an analog spectrum.

Fig. 15 is a graph showing the principal component analysis of the spectrum of the three-layer graphene film grown on the ceria/germanium substrate.

Fig. 16 is a graph showing the principal component analysis of the spectrum of the five-layer graphene film grown on a glass substrate.

Figure 17 is a graph of Raman spectrum analysis of a three-layer graphene film grown on a ceria/ruthenium substrate.

Figure 18 is a graph of Raman spectrum analysis of a five-layer graphene film grown on a glass substrate.

Fig. 19 is a 2D-band and G-band ratio analysis chart of the Raman spectrum of a five-layer graphene film grown on a glass substrate.

Figure 20 is a 2D-band and G-band ratio analysis chart of different parts of the Raman spectrum of a five-layer graphene film grown on a glass substrate.

Figure 21 is a graph showing the breakthrough spectrum analysis of a three-layer graphene film grown on a ceria/germanium substrate.

Figure 22 is a graph of a smaller scale range of penetration spectrum analysis of a three-layer graphene film grown on a ceria/ruthenium substrate.

Figure 23 is a graph showing the reflection spectrum analysis of a three-layer graphene film grown on a ceria/ruthenium substrate.

Figure 24 is a graph showing the penetration spectrum analysis of a five-layer graphene film grown on a glass substrate.

Figure 25 is a graph showing the reflection spectrum analysis of a five-layer graphene film grown on a glass substrate.

In order to make the technical features and the practical functions of the present invention can be understood in detail, and can be implemented according to the contents of the specification, the embodiments of the present invention will be described in detail below with reference to the drawings.

(1) Image analysis system applied to the detection of graphene film layers

The image analysis system of the present invention is applied to the image analysis system for the detection of the number of layers of the graphene film. Referring to the embodiment shown in FIG. 1 , the invention comprises an observation module 10 , an image capturing module 20 and a multi-spectral color image reproduction module 30 . .

The observation module 10 has a structure for fixing a graphene film sample 40 and projecting a light source for the graphene film sample 40 to optically observe the graphene film sample 40. Specifically, the observation module includes a stage unit. 11 and a lighting unit 12. Preferably, the viewing module 10 further includes an image magnifying unit 13 for enlarging the image of the graphene film sample 40 from the stage unit 11, that is, the image magnifying unit 13 has a fixing for the stage unit 11. The graphene film sample 40 provides a structure for magnifying the image.

The stage unit 11 is used to carry the thin graphene to be detected Film sample 40. The illumination unit 12 provides a graphene film sample 40 on which the illumination source is projected onto the stage unit 11. The image magnifying unit 13 is disposed on the stage unit 11 to further enlarge the image of the graphene film sample 40 on the stage unit 11 for identification.

The viewing module 10 can further include a filter unit 14. The filter unit 14 is located on a light source projection path of the illumination unit 12, and the illumination source provided by the illumination unit 12 is filtered to generate a filter light source of a desired wavelength band to provide graphite on the stage unit 11. The olefin film sample 40 is a filter including red, green, blue, cyan, magenta, and yellow, and can be used in combination; and the lighting unit 12 can also be used without the filter unit 14 Light elements that can be switched in different color systems to provide different wavelengths of light.

As shown in FIG. 2, the illumination unit 12 of the observation module 10 can not only reflect the graphene film sample 40 grown on an opaque substrate such as a ceria/iridium substrate in a reflective manner as shown in FIG. 1 above. The stage unit 10 projects the illumination source in a reflective manner, and can also cooperate with the stage unit 11 having a light transmitting structure when observing the graphene film sample 40 grown on a transparent substrate such as a glass substrate, so that the illumination unit 12 The illumination source is projected toward the image capturing module 20 by penetrating the graphene film sample 40 placed on the stage unit 11. Preferably, the illumination unit 12 is switchable between a state in which the illumination source is projected in a reflective manner as shown in FIG. 1 and a state in which the illumination source is projected in a penetrating manner as shown in FIG. 2. In other words, the illumination unit 12 has a structure in which the illumination unit is projected in a reflective manner to the stage unit 10, a structure in which the illumination unit is projected in a penetrating manner to the stage unit 10, or a configuration in which the above two configurations can be switched.

The image capturing module 20 has a structure for optically observing the graphene film sample 40. Specifically, the image capturing module 20 is located at an output path of the viewing module 10 (ie, the graphene film sample) 40 is optically observable) and includes a photosensitive coupling (CCD) unit 22, a lens unit 21, and a capture unit 23.

The photosensitive coupling unit 22 is formed by an array of a plurality of rectangular photosensitive elements in a row and a column, and is recorded as a pixel of an electronic image by a photosensitive element of a horizontal dimension and a vertical dimension. The photosensitive coupling unit 22 senses an image of the graphene film sample 40 from the stage unit 11 amplified by the image magnifying unit 13.

The lens unit 21 is disposed on the photosensitive coupling unit 22, and focuses an image of the graphene film sample 40 amplified by the image amplifying unit 13 and supplies the image to the photosensitive coupling unit 22 to obtain a clear graphene film. A magnified image of sample 40. More preferably, the lens unit 21 can focus the transmitted light source from the illumination unit 12; the capture unit 23 is coupled to the photosensitive coupling unit 22 to capture an enlarged image of the focused graphene film sample 40. Moreover, the capturing unit 23 can be a camera or a spectrum analyzer; more preferably, the spectrum analyzer can be a spectrometer of the model QE65000 produced by Konica Minolta Co., Ltd. model CS1000A or Ocean Optics. Also known as "spectrophotometer" or "spectrometer").

The multi-spectral color image reproduction module 30 is operatively coupled to the image capture module to provide graphene film layer detection data. Specifically, the multi-spectral color image reproduction module 30 captures an enlarged image of the focused graphene film sample 40 from the capture unit 23, After the spectrum analysis step 31, the color gain step 32 and the color image reproduction step 33 are performed in conjunction with the discriminant classification of the image to reprocess and present the enlarged image of the graphene film sample 40, and provide the user with an intuitive and fast image accordingly. Graphene film layer number detection.

(2) Graphene film layer number detection method

The method for detecting the number of layers of the graphene film of the present invention will be described below.

The foregoing detection method comprises a process for establishing a graphene film layer spectrum database, which firstly constructs a low-layer graphene film spectrum database, and reconstructs an image by multi-spectral color according to the low-layer graphene film spectrum database. The process of detecting the number of layers of the graphene film by detecting the number of layers of the graphene film and performing a multi-spectral color reproduction image.

1. The process of establishing the graphene film layer spectrum database includes:

(1) A spectrum analysis step for performing spectrum analysis on a low number of graphene films grown in different layers of different substrates to provide a spectrum analysis result:

(1-a) A graphene film having a low number of layers grown on different substrates. For example, a low-layer graphene film is grown on a different substrate such as a cerium oxide/germanium substrate or a glass substrate.

(1-b) Obtaining an image of the low-layer graphene film. For example, an image of the low-layer graphene film is imaged by an image capturing device such as a microscope or a camera.

(1-c) The number of layers of the low-layer graphene film was confirmed. For example, the number of layers of the low-layer graphene film is confirmed by Raman spectrum analysis, penetration spectrum analysis, or atomic force microscopy.

(1-d) Using multi-spectral imaging technology to obtain the penetration spectrum of different substrates and different number of graphene films for the image of the low-layer graphene film Provide spectrum analysis results.

(2) A spindle component analysis step for performing spindle component analysis on the aforementioned spectrum analysis results to provide a discriminant:

(2-a) Perform spindle component analysis on the breakthrough spectra of graphene films of different substrates and different layers to obtain spindle component analysis results.

(2-b) Based on the results of the aforementioned spindle component analysis, a discriminant of different substrates and different number of graphene films is established. For example, when the first main component is y0 and the second main component is y1, the graphene thin films having different numbers of layers grown on the glass substrate have the discriminant formula shown in Table 1 below:

(3) Database establishment step: based on the spectrum analysis result of the multi-spectral image technology and the discriminant of the spindle component analysis, a database is established to provide data on the correspondence between the number of layers of the graphene film and the discriminant.

2. The graphene film layer number detection process of the multi-spectral color reproduction image includes:

(1) Step of capturing image: Obtain an image of the graphene film with a low number of layers to be tested. For example, an image of the low-layer graphene film is imaged by an image capturing device such as a microscope or a camera.

(2) Spectrum analysis step: The multi-spectral image technique is used to obtain the penetration spectrum of the graphene film to be tested with respect to the image of the low-layer graphene film to be tested.

(3) Steps of classifying the number of layers of graphene film: by using the above-mentioned database for different substrate and different number of graphene film discriminants, the penetration spectrum of the graphene film to be tested is classified to obtain a discriminating result.

(4) Color gain step: confirm the analog spectrum of the number of layers of the graphene film classified based on the above discrimination result.

(5) Color image reproduction step: color image reproduction is performed by the multi-spectral image technology according to the analog spectrum corresponding to the discrimination result.

(6) Graphene film layer number detecting step: based on the image reproduced by the color image, the intuitive and rapid graphene film layer number detection is performed.

Preferably, the detection method is implemented in the multi-spectral color image reproduction module 30, and the multi-spectral color image reproduction module 30 captures the focused graphene film sample 40 from the capture unit 23. Enlarging the image to perform the step of capturing the image, performing a spectrum analysis step 31 on the image, and performing a graphene film layer number sorting step with the data library to present a classification result, and then performing a color gain step according to the classification result. 32, to further implement the color image reproduction step 33, whereby the multi-spectral color image reproduction module 30 can reprocess the focused graphene film sample 40 captured by the capture unit 23 to magnify the image to simulate The spectrum is reconstructed to provide a user-color-reproduced image for the graphene film layer number detection step.

Taking the five-layer graphene film grown on a glass substrate as an example, if the low-layer graphene film layer is analyzed by Raman spectrum technology, it takes a long time to perform intensive analysis work, so it cannot be fast and intuitive. The number of layers of the real graphene film is determined. As shown in FIG. 3 and FIG. 4, the analysis results obtained by the Raman spectrum technique analysis technique have a considerable degree of blurring, so it is difficult to clearly obtain the result of the graphene layer number detection.

In contrast, the technical means of the invention of the present invention can quickly discriminate the penetration spectrum of the graphene film to be tested and perform color image reproduction. Therefore, as shown in FIG. 5 and FIG. 6, the invention can confirm the number of layers of the graphene film in a short time, and display the energy. Effectively improve the shortcomings of the existing technology, and provide a layer layer graphene film with excellent efficiency.

The embodiments of the present invention are further described below in conjunction with the embodiments:

[Example 1]

This example relates to the preparation of a low layer graphene film.

In this embodiment, a copper foil is used as a catalyst to grow a large-area single-layer graphene film, and methane (CH4) is used as a carbon source, and a graphene film is grown at a low pressure, and then a polymer cover film (PMMA) is used. The graphene film is transferred to a different substrate such as a ceria/iridium substrate or a glass substrate. Since the preparation of the graphene film is a prior art, the details thereof will not be described.

In this embodiment, as shown in FIG. 7, a three-layer graphene film is grown on the ceria/germanium substrate, and a zero layer is formed on the ceria/germanium substrate (ie, the substrate is not a graphene film). Covered, one, two and three layers are marked as "0L", "1L", "2L" and "3L" respectively. As shown in FIG. 8, in this embodiment, a five-layer graphene film is grown on a glass substrate, which is extremely difficult to directly observe and discriminate the number of graphene film layers by an optical microscope. As shown in FIG. 9, in the present embodiment, the growth is based on a glass base. The structure of the five-layer graphene film on the plate exhibits a form in which the number of layers increases from both sides toward the middle. For the purpose of analysis, the glass substrate is divided as shown in FIG. 10, so that different regions can be selected for analysis by the partition as shown in FIG.

[Example 2]

This embodiment relates to a conversion matrix (ie, "relationship matrix") between a spectrum analyzer and an image capturing device in a low-layer graphene film multi-spectral reproduction technique. The image pickup device used in this embodiment is a photocoupler camera of an optical microscope.

In this embodiment, the multi-spectral image system and the color reproduction technology are used, and the model QE65000 spectrum analyzer produced by Ocean Optics is used to set the spectrum in the visible light band (wavelength: 380 nm to 780 nm) in the environment of the optical microscope. The measurement of the penetration spectrum of the 24 color blocks listed in the Macbeth ColorChecker color table is measured.

After obtaining the penetration spectrum, the multi-spectrum technique is used to build the module with the measured penetration spectrum, and as shown in FIG. 12, the color difference between the analog color block and the image capturing device color block can be calculated, and then simulated. The spectrum of the image of the imager is taken to find the relationship between the spectrum of the spectrum analyzer and the image of the image capturing device, and the difference in the penetration spectrum of the graphene films of different layers is analyzed.

In this embodiment, the calculation flow shown in FIG. 13 is used to find the conversion matrix between the penetration spectrum and the image of the image capturing device to obtain the penetration spectrum of each pixel in each image.

For ease of analysis, in the present embodiment, these penetration spectra are organized into a matrix of 401*24. Each column of the 401*24 matrix is The intensity value corresponding to the wavelength, and each row represents the number of patches.

After the calculation process shown in Figure 14, the simulated spectrum can be obtained, and the six sets of eigenvalues (6*24) corresponding to the six sets of eigenvectors (6*401) are obtained by the feature system and principal component analysis. The following mathematical formula 1: [Math 1] [ α ] ^T = [ D ] ^T pinv [ E ]

Where pinv is a pseudo inverse matrix. These color patches are simultaneously acquired by the image capture device in an optical microscope environment and output JPEG image data in the format of sRGB. By computer calculation, the R, G, and B values (0~255) in the color patches of each image data can be obtained, and then converted to a smaller scale (0~1) to obtain R _srgb , G _{srgb .} , B _srgb . The aforementioned RGB values will be converted to the three-magnitude X , Y , Z of the CIE specification by the following mathematical formulas 2 to 4.

among them:

Since the standard white in the sRGB space is the reference white under the D65 source, it is different from the reference white of the reflected spectrum measured by the spectrum analyzer under the halogen light source. Therefore, the RGB value needs to be corrected by the color adaptation conversion. In order to accurately estimate the spectral value of the patch, the imaging device must be corrected.

Similarly, the reflectance spectrum measured by the spectrum analyzer is also converted into the three-magnitude X , Y , Z of the CIE specification by the following mathematical formulas 5 to 8. Where S ( λ ) is the spectrum of the source of the halogen lamp, and R ( λ ) is the reflection spectrum of each patch, and ( λ ), ( λ ), and ( λ ) is a color matching function.

Of which: [Math 8]

After the color adaptation is converted, the RGB value of the camera is converted into a new XYZ value by the calculation of the aforementioned Mathematical Formulas 2 to 4, and is set as a matrix [ A ]. Through the third-order polynomial regression of RGB, the conversion relationship between the spectrum analyzer and the camera can be obtained. The following mathematical formula 9 is a matrix of third-order polynomial regression: [Math 9] [ C ]=[ A ] pinv [ B ]

Where: [Math 10] [ B ]=[1, R , G , B , RG , GB , BR , R ² , G ² , B ² , RGB , R ³ , G ³ , B ³ , RG ² , RB ² , GR ² , GB ² , BR ² , BG ² ] ^T

Among them, "R", "G", and "B" are RGB values obtained by the image capturing device corresponding to the respective color patches. After the color block is corrected by RGB and converted to CIE three-time excitation value XYZ is set to [ β ], the conversion matrix [ M ] of the spectrum analyzer and the image capturing device can be obtained by the following mathematical expression 11: [Math 11] [ M ]= [ α ] pinv [ β ]

[Example 3]

This embodiment relates to color reproduction in an analog spectrum.

The pixels of the image acquired by the image capturing device can be respectively obtained by multiplying RGB to obtain a linear regression matrix [ C ] and calculating the corresponding XYZ values by the calculation of Mathematical Formulas 2 to 4. The analog spectrum of each color block (bands from 380 nm to 780 nm) can be obtained by the following mathematical formula 12:

By means of the technical means of the invention, the spectrum of the halogen lamp is measured by the spectrometer, divided by the spectrum of the source of the original image capturing scene and multiplied by the spectrum of the new replacement source, that is, the color can be reproduced in the replacement light source. Thus, the color can be reproduced by different light sources.

In order to confirm the feasibility of color reproduction, the present embodiment evaluates the error between the actual spectrum and the analog spectrum by the color difference formula. The calculation process of the color difference is as follows:

(1) Converting the three-magnitude XYZ measured by the two instruments into the chromaticity coordinate values (L*, a*, b*) in the CIE 1976 space, where:

(b) Calculate the Euclid distance of two points in the CIE 1976 chromaticity coordinate (ie, the color difference between two points):

The color difference values of the respective color patches of the aforementioned 24 color patches are as shown in FIG. The average color difference of the 24 color patches is 4.21. The results of this embodiment show that the technical means of the present invention can achieve the effect of color reproduction, thereby being applied to the color representation of an image.

[Embodiment 4]

In this embodiment, the main component points are calculated by the spindle component analysis, and the spectrum of the three-layer graphene film grown on the ceria/germanium substrate and the spectrum of the five-layer graphene film grown on the glass substrate are respectively divided. class.

As shown in Figure 15 and Figure 16. The principal component points can simplify the high-dimensional data into lower-dimensional data and project it into the feature vector space for data analysis. The formula for the principal component points is as shown in the following Mathematical Formula 18:

Where x _{1 i} , x _{2 i} ... x _pi are the corresponding spectral intensity values at the first, second ... p-th wavelength. , ... The average spectral intensity value at the pth wavelength of the first, second, .... a _{j 1} , a _{j 2} ... a _jp are the coefficients of the eigenvectors after the spectrum takes the covariation matrix.

In terms of the spindle component analysis method, the first principal component presents the most information in the original data and can be regarded as a comprehensive index. The second principal component and the third principal component also present partial information of the original data, which can be used to classify each group. In order to understand the distribution of the data, a principal component analysis is performed for the individual group data, and the range of the group is represented by an ellipse. The equation for the ellipse is shown in the following equation 19:

Where a ₁ , b ₁ , a ₂ , b ₂ are the eigenvector coefficients of the inverse co-mutation matrix of the group, the physical meaning of which is the rotation of the coordinate axis; c ₁ and c ₂ are the average values of the group data values, due to the group In the principal component analysis, all the point data are translated, so the center of the ellipse must be moved back to the original space; d ₁ and d ₂ are the eigenvalues of the covariation matrix, and the physical meaning represents the long axis and one half of the short axis of the ellipse.

[Embodiment 5]

This embodiment demonstrates the effect of the present invention by analysis by a Raman spectrometer.

The Raman effect can be used to study the molecular structure, molecular vibrational energy levels and rotational energy levels, determine the various functional or chemical bond positions in the molecule, and quantitatively analyze complex hybrid molecules. The Raman scattering system causes the energy exchange between the incident photons and the dielectric molecules due to the vibration or rotation of the dielectric molecules themselves, so that the frequency of the scattered light after the reflection changes.

This embodiment uses a micro-Raman spectrometer (Invia 1000 system manufactured by Renishaw Co., Ltd.), which focuses the laser light through an optical microscope on the test piece, and transmits the scattered light through the same microscope into the spectrum analyzer to be analyzed. spectrum.

In this embodiment, the Raman signal is measured by a 40-fold objective lens by using the aforementioned Raman spectrometer with a 633 nm red laser with a power of 8.6 mW.

As described in reference 22, the Raman shift of the graphene film is mainly exhibited by a G-band located at 1582 cm ^-1 and a 2D-band seated at 2676 cm ^-1 . 17 is a Raman spectrum analysis chart of a three-layer graphene film grown on a ceria/germanium substrate, and FIG. 18 is a Raman spectrum analysis chart of a five-layer graphene film grown on a glass substrate. It can be seen that the graphene films of different layers exhibit different Raman shifts, the signal intensity of G-band increases with the number of layers, and the signal intensity of 2D-band shifts with the increase of the number of layers. .

The three-layer graphene film grown on the ceria/ruthenium substrate verified in this example has been shown in FIG. By Raman spectrum analysis as shown in Fig. 17, it can be verified that the three-layer graphene film layer number detection result of the present invention is indeed consistent with the results of the existing Raman spectrum analysis.

Further, for the five-layer graphene film grown on the glass substrate as shown in FIGS. 8 to 11, the layer number detection result is performed by the technique of the present invention, and the 2D-band and G are also analyzed with the Raman spectrum shown in FIGS. 18 and 19. The ratio of -band is consistent, and as shown in Fig. 20, the Raman spectrum also shows the results of the layer detection of the five layers of graphene film grown on the glass substrate. Compared with the foregoing Figures 3 and 4 and Figures 5 and 6, it can be further confirmed that the technique of the present invention can be quickly and intuitively detected, providing an effect of the invention superior to the existing Raman spectrum analysis technique.

[Embodiment 6]

This embodiment demonstrates the effects of the present invention by analyzing the spectrum analyzer.

Ultraviolet-visible spectroscopy (UV-Vis, also known as ultraviolet-visible molecular absorption spectroscopy), which uses a continuous spectrum of ultraviolet-visible region electromagnetic waves as a light source to study the relative intensity of light absorption of matter molecules. The method.

Qualitative analysis can be performed by molecular ultraviolet-visible molecular absorption spectroscopy and quantitative analysis can be performed according to Lambert-Beer law. When the light When the wavelength is reduced to a certain value, the solvent will strongly absorb it, that is, "end absorption", so that the sample test is within the transparent limit of the "end absorption".

As shown in FIG. 21 and FIG. 24, when a penetrating spectrum analyzer is used to prove the number of graphene film layers, the graphene films of different layers on different substrates exhibit different penetration spectra. 21 and 22 show the results of the penetration spectrum analysis of the three-layer graphene film grown on the ceria/germanium substrate, and the three-layer graphene film grown on the ceria/germanium substrate according to the present invention. The test results are consistent. According to the results of the reflection spectrum analysis of FIG. 23, it is also consistent with the detection result of the present invention for the three-layer graphene film grown on the ceria/germanium substrate.

As shown in FIG. 24, the results of the penetration spectrum analysis of the five-layer graphene film grown on the glass substrate are consistent with the detection results of the five-layer graphene film grown on the glass substrate of the present invention; The results of the reflection spectrum analysis shown in Fig. 25 are also consistent with the detection results of the five-layer graphene film grown on the glass substrate of the present invention.

In summary, the technique of the present invention rapidly detects the number of graphene film layers by optical microscopy by using multi-spectral technology and spindle component analysis method, and the detection result is verified by existing techniques, and is indeed related to, for example, a specific band. The verification result of increasing the number of layers and increasing the reflectivity is consistent, and it is consistent with the Raman spectrum analysis results. Therefore, the present invention can accurately and quickly detect the number of graphene thin film layers in a low cost and high efficiency by using an image pickup device such as a microscope and a photosensitive coupling element.

【references】

The following references are cited as part of the description of the invention that constitutes the invention:

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10‧‧‧Observation module

11‧‧‧stage unit

12‧‧‧Lighting unit

13‧‧‧Image magnification unit

14‧‧‧ Filter unit

20‧‧‧Image capture module

21‧‧‧ lens unit

22‧‧‧Photosensitive coupling unit

23‧‧‧Capture unit

30‧‧‧Multi-spectral color image reproduction module

31‧‧‧ Spectrum analysis steps

32‧‧‧Color gain step

33‧‧‧Color image reproduction steps

40‧‧‧ Graphene film samples

Claims (7)

An image analysis system for graphene film layer number detection comprises an observation module, an image capture module and a multi-spectral color image reproduction module; the observation module has a sample for fixing a graphene film and a structure for projecting a light source of the graphene film sample to optically observe the graphene film sample; the image capturing module has a structure for optically observing the graphene film sample; the multi-spectral color image reproducing module The photographic module is operatively coupled to the imaging module to provide graphene film layer detection data. The image analysis system of claim 1, wherein the observation module comprises a stage unit and a lighting unit, the structure of the lighting unit is selected from the group consisting of: a) reflecting the illumination source in a reflective manner for the stage unit; And b) switching the structure of the illumination source for the stage unit in a penetrating manner, and c) switching between a structure for projecting the illumination source for the stage unit in a reflective manner and a structure for penetrating the illumination source for the stage unit A group of structures. The image analysis system of claim 2, wherein the observation module further comprises an image magnifying unit having a structure for providing an enlarged image of the graphene film sample fixed by the stage unit. The image analysis system of claim 3, wherein the image capturing module comprises a photosensitive coupling unit, a lens unit and a capture unit; The photosensitive coupling unit senses the enlarged image provided by the image magnifying unit; the lens unit is disposed on the photosensitive coupling unit and focuses the enlarged image and provides a focused image of the photosensitive coupling unit; the capturing unit The photosensitive coupling unit is connected to capture the focused enlarged image. The image analysis system of claim 4, wherein: the multi-spectral color image reproduction module a) extracts the focused enlarged image from the capturing unit, and b) performs spectral analysis on the focused enlarged image. , c) based on a multi-spectral imaging technique and a spindle component analysis to provide a database of graphene film number and discriminant correspondence data, the graphene film layer number classification is performed on the focused magnified image to present a classification As a result, d) color gain is performed according to the classification result, e) and color image reproduction is performed to f) provide a color reproduction image. A method for detecting the number of layers of graphene film includes a process for establishing a graphene film layer spectrum database and a graphene layer number detecting process for a multi-spectral color reproduction image; the foregoing graphene thin layer layer spectrum database establishing process includes : performing spectral analysis on a low-layer graphene film grown on different layers of different substrates to provide a spectrum analysis step of spectrum analysis results, performing spindle component analysis on the aforementioned spectrum analysis results to provide a discriminant spindle component analysis step, And a database establishing step of establishing a database based on the foregoing spectrum analysis result and the discriminant to provide a correspondence between the graphene film layer number and the discriminant; The step of detecting the graphene film layer number of the multi-spectral color reproduction image comprises: obtaining a image capturing image of a low-layer graphene film image to be tested, and obtaining a graphene film to be tested on the image of the low-layer graphene film to be tested a spectrum analysis step of the transmissive spectrum, a step of classifying the graphene film layer number based on the spectrum of the graphene film to be tested to obtain a discriminating result based on the foregoing database, and confirming the classified graphene film layer analog spectrum based on the discriminating result The color gain step, the color image reproduction step of color image reproduction according to the analog spectrum corresponding to the determination result, and the graphene film layer number detection step of detecting the graphene film layer number based on the image reproduced by the color image. A graphene layer layer detection process includes: obtaining a image capturing image of a low-layer graphene film to be tested; and obtaining a graphene film penetration spectrum of the graphene film to be tested The spectrum analysis step is based on a database based on a multi-spectral image technique and a spindle component analysis to provide graphene film number and discriminant correspondence data for the spectrum of the graphene film to be tested to obtain a discriminating result. a step of classifying the number of layers of the graphene film; determining a color gain step of the analog spectrum of the graphene film layer based on the discrimination result; and performing a color image reproduction step of color image reproduction according to the analog spectrum corresponding to the discrimination result; The image of the color image reproduction is subjected to a graphene film layer number detecting step of detecting the number of layers of the graphene film.