Graphene film evaluation parameter screening method, evaluation method and related device

Abstract

The invention discloses a graphene film evaluation parameter screening method, a graphene film evaluation method and a related device.Nonlinear sample characteristic evaluation parameters are subjected to nonlinear treatment, a parameter matrix for analysis is obtained, primary dimension reduction is conducted on the parameter matrix for analysis through a principal component analysis method, and a graphene film evaluation parameter is obtained; the method comprises the following steps: acquiring an evaluation parameter matrix corresponding to a principal component, screening the evaluation parameter matrix by adopting a consistency test method, namely performing secondary dimensionality reduction to obtain a final evaluation parameter of the graphene film, and the final evaluation parameter can be directly associated to the whole life cycle of the graphene film and can reflect most information of actual parameters and ensure the generality requirement of the industry. Therefore, the graphene film can be evaluated in a point-to-surface manner, the data calculation amount required by graphene evaluation is reduced, the graphene film evaluation efficiency is improved, and decision support is provided for industrial application and production process improvement of the graphene film.

Description

Technical Field

[0001] The invention relates to a graphene film evaluation parameter screening method, an evaluation method and related devices, and belongs to the field of graphene film evaluation. Background Art

[0002] In industrial metrology, metrologists focus on the measurement procedures throughout the entire industry chain and the entire life cycle. Different graphene film samples have various characteristic parameters and common parameters, that is, there are many evaluation parameters under the same characteristic, and each evaluation parameter corresponds to a complex measurement procedure and metrology standard.

[0003] At present, when evaluating graphene films, the evaluation parameters are often determined based on the subjectivity of the metrologists or the customized needs of the customers, covering hundreds of parameters such as geometric parameters, mechanical properties, electrical properties, thermal properties, chemical properties, and optical properties. In addition, due to the different numbers of samples, there can be tens of thousands of sets of measurement data, which brings a large amount of data calculation to industrial metrologists to analyze and evaluate graphene films. Summary of the invention

[0004] The present invention provides a graphene film evaluation parameter screening method, an evaluation method and related devices, which solve the problems disclosed in the background technology.

[0005] According to one aspect of the present disclosure, a method for screening graphene film evaluation parameters is provided, comprising:

[0006] Obtaining evaluation parameters of each characteristic of the sample, and constructing an original parameter matrix according to the evaluation parameters of all samples; wherein the sample is a graphene film sample; and different evaluation parameters are selected for the same characteristic of several samples;

[0007] If the elements in the original parameter matrix are nonlinear elements, the elements in the original parameter matrix are processed nonlinearly to obtain the parameter matrix for analysis;

[0008] According to the analysis parameter matrix, the principal component analysis method is used to obtain the first m principal components in the ranking; where m is the full life cycle of the graphene film sample in industrial metrology;

[0009] The evaluation parameters in the m principal components are screened according to the characteristic threshold, and the evaluation parameter matrix corresponding to the m principal components is constructed;

[0010] If the dimensionality reduction rate of the number of elements in all evaluation parameter matrices compared to the number of elements in the original parameter matrix exceeds the dimensionality reduction threshold, the consistency test method is used to screen the elements in all evaluation parameter matrices to obtain the final graphene film evaluation parameters.

[0011] Furthermore, the nonlinear processing performed on the elements in the original data matrix is ​​to perform a square root transformation on the elements in the original data matrix.

[0012] Furthermore, if the elements in the original parameter matrix are linear elements, the original parameter matrix is ​​used as the parameter matrix for analysis.

[0013] Furthermore, the consistency test method is used to screen the elements in all evaluation parameter matrices, including:

[0014] According to the level of the evaluation parameter matrix where the element S is located, the number of samples corresponding to the element S in the original parameter matrix, and the eigenvalue corresponding to the element S in the principal component, the consistency test index value of the element S is calculated;

[0015] If the consistency test index value is not greater than the test threshold, it is determined that the element S passes the consistency test and the element S is retained; wherein the test threshold is a threshold calculated based on the eigenvalue corresponding to the element S in the principal component.

[0016] Furthermore, the consistency test index value of element S is calculated using the formula:

[0017] ;

[0018] Where W is the consistency test index value of element S, n is the number of samples corresponding to element S in the original parameter matrix, R i is the level of the preset i-th evaluation parameter matrix, λ is the eigenvalue corresponding to the element S in the principal component, The sorting number corresponding to the principal component; the test threshold is λ-1.

[0019] According to another aspect of the present disclosure, a graphene film evaluation parameter screening device is provided, comprising:

[0020] The original parameter matrix construction module obtains the evaluation parameters of each characteristic of the sample, and constructs the original parameter matrix according to the evaluation parameters of all samples; wherein the sample is a graphene film sample; and different evaluation parameters are selected for the same characteristic of several samples;

[0021] A nonlinear processing module performs nonlinear processing on the elements in the original parameter matrix if the elements in the original parameter matrix are nonlinear elements to obtain a parameter matrix for analysis;

[0022] The principal component analysis module uses the principal component analysis method to obtain the first m principal components according to the analysis parameter matrix; where m is the full life cycle of the graphene film sample in industrial metrology;

[0023] An evaluation parameter matrix construction module selects the evaluation parameters in the m principal components according to the characteristic threshold value and constructs the evaluation parameter matrix corresponding to the m principal components;

[0024] The consistency check module uses a consistency check method to screen the elements in all evaluation parameter matrices to obtain the final graphene film evaluation parameters if the dimensionality reduction rate of the number of elements in all evaluation parameter matrices compared to the number of elements in the original parameter matrix exceeds a dimensionality reduction threshold.

[0025] According to another aspect of the present disclosure, there is provided a graphene film evaluation method, comprising:

[0026] Obtaining the value of the graphene film evaluation parameter; wherein the evaluation parameter is obtained by pre-screening, and the screening method adopts the above-mentioned graphene film evaluation parameter screening method;

[0027] The graphene film evaluation is performed according to the values ​​of the preselected evaluation parameters.

[0028] According to another aspect of the present disclosure, there is provided a graphene film evaluation device, comprising:

[0029] A parameter value acquisition module is used to acquire the value of the graphene film evaluation parameter; wherein the evaluation parameter is obtained by pre-screening, and the screening method adopts the above-mentioned graphene film evaluation parameter screening method;

[0030] The evaluation module evaluates the graphene film according to the values ​​of preselected evaluation parameters.

[0031] According to another aspect of the present disclosure, a computer-readable storage medium is provided, which stores one or more programs, and the one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform a graphene film evaluation parameter screening method or a graphene film evaluation method.

[0032] According to another aspect of the present disclosure, a computer device is provided, comprising one or more processors and one or more memories, wherein one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing a graphene film evaluation parameter screening method or a graphene film evaluation method.

[0033] The beneficial effects achieved by the present invention are as follows: the present invention performs nonlinear processing on nonlinear sample characteristic evaluation parameters to obtain an analysis parameter matrix, adopts a principal component analysis method to perform a dimensionality reduction on the analysis parameter matrix to obtain an evaluation parameter matrix corresponding to the principal component, adopts a consistency test method to screen the evaluation parameter matrix, i.e., a secondary dimensionality reduction, to obtain final graphene film evaluation parameters, and the final evaluation parameters can be directly related to the entire life cycle of the graphene film, can reflect most of the information of the actual parameters and ensure the common needs of the industry, so that the graphene film can be evaluated from a point to a whole, reducing the amount of data calculation required for graphene evaluation, improving the efficiency of graphene film evaluation, and providing decision support for the industrial application of graphene film and the improvement of production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A schematic diagram of the process of screening the graphene membrane evaluation parameters;

[0035] Figure 2 A block diagram of a device for screening parameters for graphene film evaluation;

[0036] Figure 3 is a schematic diagram of the flow chart of the graphene film evaluation method;

[0037] Figure 4 Block diagram of the graphene film evaluation device. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. It is obvious that the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present disclosure and its application or use. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0039] Unless specifically stated otherwise, the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure.

[0040] At the same time, it should be understood that the techniques, methods and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but in appropriate cases, the techniques, methods and equipment should be considered as part of the specification.

[0041] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0042] The principal component analysis method is the most widely used data dimensionality reduction method. Its idea is to map N-dimensional features to K dimensions. K dimensions are new orthogonal features, also known as principal components, which are reconstructed on the basis of the original N-dimensional features. Principal component analysis is to find a set of orthogonal coordinate axes in the original space in sequence. The selection of new coordinate axes is closely related to the data itself. Among them, the first new coordinate axis is the direction with the largest variance in the original data, the second new coordinate axis is the direction with the largest variance in the plane orthogonal to the first coordinate axis, and the third axis is the plane with the largest variance in the plane orthogonal to the first and second axes. N coordinate axes can be obtained by analogy. Most of the variance is contained in the first K coordinate axes, and the variance of the latter coordinate axes is almost zero. Therefore, only the first K coordinate axes containing most of the variance can be retained, and the remaining coordinate axes can be ignored. In fact, this is equivalent to retaining only the dimensional features containing most of the variance and ignoring the feature dimensions with almost zero variance, thus achieving data dimensionality reduction. At present, principal component analysis methods are widely used in many fields, mainly including data analysis, image processing, economic and financial analysis, social science research, medical research, etc.

[0043] The embodiment of the present application provides a method for screening graphene film evaluation parameters based on the principal component analysis method technology, which aims to obtain evaluation parameters that can evaluate graphene films from point to surface by reducing the dimension of the key parameters of the graphene film (i.e., parameters representing industrial characteristics and commonalities). The method for screening graphene film evaluation parameters can be performed by a screening device, which can be a terminal device or a server. Among them, the terminal device can include but is not limited to mobile phones, computers, smart wearable devices, smart vehicle-mounted devices, etc., which are not limited by the embodiment of the present application; the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, big data and artificial intelligence platforms, etc., which are not limited by the embodiment of the present application. Optionally, the method for screening graphene film evaluation parameters can also be collaboratively performed by multiple electronic devices with computing power. For the sake of convenience, the subsequent embodiments are described as being performed by a screening device.

[0044] See also Figure 1 , Figure 1 1 is a flow chart of a method for screening graphene film evaluation parameters provided in an embodiment of the present application. The method for screening graphene film evaluation parameters can be performed by a screening device. The method for screening graphene film evaluation parameters can at least include the following steps:

[0045] Step 1, obtaining the evaluation parameters of each characteristic of the sample, and constructing an original parameter matrix according to the evaluation parameters of all samples; wherein the sample is a graphene film sample; and different evaluation parameters are selected for the same characteristic of several samples.

[0046] It should be noted that the characteristics of the graphene film sample may at least include powder morphology-related characteristics, slurry morphology-related characteristics, conductive application-related characteristics and thermal management-related characteristics. There are multiple evaluation parameters under each characteristic. For example, the evaluation parameters under the powder morphology-related characteristics include size, specific surface area, monolayer rate, etc., the evaluation parameters under the slurry morphology-related characteristics include solid content, water content, slurry fineness, etc., the evaluation parameters under the conductive application-related characteristics include powder conductivity, electrode resistivity, volume resistivity, etc., and the evaluation parameters under the thermal management-related characteristics include thickness, specific heat capacity, thermal conductivity, etc.

[0047] In view of the situation of a large number of evaluation parameters, key parameters, i.e. parameters representing industrial characteristics and commonalities, can be selected from a large number of parameters. Due to different preparation processes, they will be applied in different fields. Therefore, for graphene films in the same field, different evaluation parameters can be selected for the same characteristics of some different samples. Of course, it is preferable to select different evaluation parameters for the same characteristics of all different samples, and try to cover as many evaluation parameters as possible to achieve the best evaluation and application effects while improving the screening efficiency.

[0048] Taking 10 samples as an example, the evaluation parameters selected for each sample can be seen in Table 1, which can form an original parameter matrix with 10 rows and 4 columns.

[0049] Table 1 Original evaluation parameters

[0050]

[0051] Step 2: If the elements in the original parameter matrix are nonlinear elements, nonlinear processing is performed on the elements in the original parameter matrix to obtain a parameter matrix for analysis.

[0052] It should be noted that the traditional principal component analysis method is a parallelized linear data dimensionality reduction method. It not only performs the first layer of data dimensionality reduction, but also evaluates the index through the variance contribution rate after sorting the eigenvalues ​​and special vectors. This method is only limited to the case where the variance contribution rate of the first principal component is high. Therefore, using the contribution rate index as a weight for comprehensive evaluation is subjective and is not suitable for actual industrial applications in the selection of industrial parameters.

[0053] The elements in the original parameter matrix can be nonlinear or linear, which is related to the specific selected evaluation parameters. For example, if the units and measurement characteristics of the selected evaluation parameters are similar, then the elements in the original parameter matrix are linear. For this kind of matrix, the principal component analysis method can be directly used for dimensionality reduction. That is, if the elements in the original parameter matrix are linear elements, the original parameter matrix is ​​used as the parameter matrix for analysis and principal component analysis is performed.

[0054] In order to achieve the best evaluation, the units and measurement characteristics of the selected evaluation parameters are often anisotropic, that is, the elements in the original parameter matrix are nonlinear. Such a matrix cannot be adapted to the principal component analysis method, and the elements need to be processed nonlinearly. Nonlinear processing can reduce element deviation, clarify the relationship between elements and parameters, and stabilize the sample variance. After nonlinear processing, multiple characteristics of multiple samples can be linearly combined, that is, the sum of the squares of the linear combination coefficients is 1.

[0055] From the scatter distribution of all parameters of industrial characteristic parameters drawn by industrial metrologists, it can be seen that the distribution is non-normal and slightly deviates from the distribution. Its variance is close to the mean value. Therefore, in some embodiments, the elements in the original data matrix can be square root transformed. The formula can be expressed as , thus obtaining the parameter matrix for analysis; among them, represents the evaluation parameter corresponding to the jth characteristic of the i-th sample, x ij Represents nonlinear processing .

[0056] Step 3, according to the analysis parameter matrix, use the principal component analysis method to obtain the first m principal components in the ranking; wherein m is the full life cycle of the graphene film sample in industrial metrology.

[0057] Principal component analysis is a dimensionality reduction process for high-dimensional spatial data. The so-called dimensionality reduction here means studying a few linear combinations of the original data, and the comprehensive indicators composed of these linear combinations will retain as much information as possible on the variation of the original indicators. These comprehensive indicators are called principal components. Same as factor analysis, they can also be represented by F.

[0058] Assuming q samples, each sample has p characteristics, the original parameter matrix after nonlinear processing can be expressed as:

[0059] ;

[0060] The relationship between the principal component F and the variable can be expressed as:

[0061] ;

[0062] Since it has been processed nonlinearly, the coefficient a ls satisfy ; Where l = 1, 2, ..., p, s = 1, 2, ..., p;

[0063] So the linear combination can be simplified to ; Among them, F s X1~X p The linear combination of X s Represents the variable consisting of the elements in the sth column of X.

[0064] The full life cycle of graphene film samples in industrial metrology is generally 3, so the top 3 principal components are obtained.

[0065] Step 4: Screen the evaluation parameters in the m principal components according to the characteristic threshold value, and construct an evaluation parameter matrix corresponding to the m principal components.

[0066] It should be noted that not all evaluation parameters in the top 3 principal components can be used. The evaluation parameters in the top 3 principal components can be screened according to the characteristic threshold. For example, all evaluation parameters with eigenvalues ​​less than the characteristic threshold are eliminated, and then the evaluation parameter matrix is ​​further constructed; wherein the characteristic threshold can be set to 1.

[0067] Taking the original parameter matrix in Table 1 as an example, after principal component analysis, the parameters in the evaluation parameter matrix obtained can be seen in Table 2.

[0068] Table 2 Evaluation parameters after dimensionality reduction

[0069]

[0070] In the table, the source quality control matrix, production control matrix and product evaluation matrix represent three principal components. After principal component analysis, the source quality control matrix is ​​composed of size, carbon-oxygen ratio, dispersibility, elastic modulus and water content, the production control matrix is ​​composed of solid content, slurry fineness, pH value, viscosity and specific heat capacity, and the product evaluation matrix is ​​composed of the first coulomb efficiency, specific capacity, rate performance, resistivity and carrier mobility.

[0071] Step 5: If the dimensionality reduction rate of the number of elements in all evaluation parameter matrices compared to the number of elements in the original parameter matrix exceeds the dimensionality reduction threshold, the elements in all evaluation parameter matrices are screened using a consistency check method to obtain the final graphene film evaluation parameters.

[0072] It should be noted that, as can be seen from Table 2, there are still many parameters after one dimensionality reduction (i.e., principal component analysis dimensionality reduction). In order to further reduce the dimensionality, when the dimensionality reduction rate of the first dimensionality reduction exceeds the dimensionality reduction threshold, a second dimensionality reduction is performed. When the dimensionality reduction rate exceeds the dimensionality reduction threshold, the original parameter matrix is ​​reconstructed and a dimensionality reduction is performed. Among them, the dimensionality reduction threshold is generally set to 80%.

[0073] It should be noted that the first-order dimensionality reduction is based on the principal component analysis derived from mathematical statistics, while the empirical formula based on industrial application includes the identification of data quality and accuracy, different production processes, and support decisions in different application scenarios. Therefore, it is necessary to use a consistency test method to further screen the evaluation parameters.

[0074] Taking the element S in the evaluation parameter matrix as an example, the screening process can be as follows:

[0075] 1) Calculate the consistency test index value of element S according to the level of the evaluation parameter matrix where element S is located, the number of samples corresponding to element S in the original parameter matrix, and the eigenvalue corresponding to element S in the principal component.

[0076] It should be noted that the levels are set based on experience. The level of the source quality control matrix can be set to 1, the level of the production control matrix to 2, and the level of the product evaluation matrix to 3.

[0077] The calculation formula of the consistency test index value of element S can be expressed as:

[0078] ;

[0079] Where W is the consistency test index value of element S, n is the number of samples corresponding to element S in the original parameter matrix, R i is the level of the preset i-th evaluation parameter matrix, λ is the eigenvalue corresponding to the element S in the principal component, The sort number corresponding to the principal component.

[0080] 2) If the consistency test index value is not greater than the test threshold, it is determined that the consistency test of element S passes and element S is retained; wherein the test threshold is the threshold calculated based on the eigenvalue corresponding to element S in the principal component, and the test threshold is generally set to λ-1.

[0081] For example, the number of samples corresponding to the resistivity in the product evaluation matrix is ​​6. , eigenvalue 12.7, it can be calculated that W=1.354, and λ-1=11.7, satisfying W≤λ-1, that is, the resistivity is retained.

[0082] The number of samples corresponding to the pH value in the production control matrix is ​​5. , eigenvalue 8.6, it can be calculated that W=6.55, while λ-1=7.6, which does not satisfy W≤λ-1, that is, PH value is eliminated.

[0083] The remaining evaluation parameters after screening the evaluation parameters using the consistency test method can be seen in Table 3.

[0084] Table 3 Evaluation parameters after screening

[0085]

[0086] In Table 3, × indicates elimination, and √ indicates retention. It can be seen from Table 3 that the final evaluation parameters of the graphene film are elastic modulus, resistivity and carrier mobility.

[0087] The above method performs nonlinear processing on the nonlinear sample characteristic evaluation parameters to obtain an analysis parameter matrix, uses the principal component analysis method to perform a dimensionality reduction on the analysis parameter matrix to obtain an evaluation parameter matrix corresponding to the principal components, and uses a consistency test method to screen the evaluation parameter matrix, that is, a secondary dimensionality reduction, to obtain the final graphene film evaluation parameters. The final evaluation parameters can be directly related to the entire life cycle of the graphene film, can reflect most of the information of the actual parameters and ensure the common needs of the industry, so that the graphene film can be evaluated from a point-to-surface perspective, reducing the amount of data calculation required for graphene evaluation, improving the efficiency of graphene film evaluation, and providing decision support for the industrial application of graphene films and production process improvements.

[0088] See also Figure 2 , Figure 2 is a block diagram of a graphene film evaluation parameter screening device provided in an embodiment of the present application, Figure 2 The embodiment is a virtual device that can be loaded and executed by a computer device, and the computer device can include the above-mentioned screening device, Figure 2 The device may include an original parameter matrix construction module, a nonlinear processing module, a principal component analysis module, an evaluation parameter matrix construction module and a consistency check module, which, when used to execute the above-mentioned graphene film evaluation parameter screening method, can:

[0089] The original parameter matrix construction module is used to obtain the evaluation parameters of each characteristic of the sample, and construct the original parameter matrix according to the evaluation parameters of all samples; wherein the samples are graphene film samples; and different evaluation parameters are selected for the same characteristic of several samples.

[0090] The nonlinear processing module is used to perform nonlinear processing on the elements in the original parameter matrix if the elements in the original parameter matrix are nonlinear elements, so as to obtain the parameter matrix for analysis.

[0091] The principal component analysis module is used to obtain the top m principal components according to the analysis parameter matrix by using the principal component analysis method; wherein m is the full life cycle of the graphene film sample in industrial metrology.

[0092] The evaluation parameter matrix construction module screens the evaluation parameters in the m principal components according to the characteristic threshold value and constructs the evaluation parameter matrix corresponding to the m principal components.

[0093] The consistency check module is used to screen the elements in all evaluation parameter matrices using a consistency check method to obtain the final graphene film evaluation parameters if the dimensionality reduction rate of the number of elements in all evaluation parameter matrices compared to the number of elements in the original parameter matrix exceeds a dimensionality reduction threshold.

[0094] Similar to the above method, this device performs nonlinear processing on the nonlinear sample characteristic evaluation parameters to obtain an analysis parameter matrix, uses the principal component analysis method to perform a dimensionality reduction on the analysis parameter matrix to obtain an evaluation parameter matrix corresponding to the principal components, and uses a consistency test method to screen the evaluation parameter matrix, that is, a secondary dimensionality reduction, to obtain the final graphene film evaluation parameters. The final evaluation parameters can be directly related to the entire life cycle of the graphene film, can reflect most of the information of the actual parameters and ensure the common needs of the industry, so that the graphene film can be evaluated from a point-to-surface perspective, reducing the amount of data calculation required for graphene evaluation, improving the efficiency of graphene film evaluation, and providing decision support for the industrial application of graphene films and production process improvements.

[0095] See also Figure 3 , Figure 3 1 is a flow chart of a graphene film evaluation method provided in an embodiment of the present application. The graphene film evaluation method can be performed by an evaluation device. The graphene film evaluation method can at least include the following steps:

[0096] Step S1, obtaining the value of the graphene film evaluation parameter; wherein the evaluation parameter is pre-screened and obtained, and the screening method adopts the above-mentioned graphene film evaluation parameter screening method.

[0097] It should be noted that the screened evaluation parameters are elastic modulus, resistivity and carrier mobility; among them, the elastic modulus of the graphene film can be measured by a nanoindenter, the resistivity of the graphene film can be measured by a four-probe resistivity tester, and the carrier mobility of the graphene film can be measured by a Hall effect tester.

[0098] Step S2, evaluating the graphene film according to the values ​​of the preselected evaluation parameters.

[0099] It should be noted that, taking the elastic modulus, resistivity and carrier mobility selected above as an example, the elastic modulus is the ratio of stress to strain of the material in the elastic deformation stage, which reflects the ability of graphene to resist elastic deformation. For graphene, a high elastic modulus means that it has good mechanical strength and rigidity. Generally, high-quality graphene has a higher elastic modulus. If the elastic modulus of a single-layer graphene is ≥200GPa, it indicates that the graphene sample is of good quality; resistivity is the inherent resistance characteristic of graphene, which indicates the resistance of the material per unit length and unit cross-sectional area. It is an important indicator for measuring the electrical conductivity of graphene. Low resistivity means that graphene has good conductivity, which is crucial for applications such as electronic devices and sensors. High-quality graphene has a lower resistivity, and the resistivity of a single-layer graphene is ≤10 -4Ω·m, it indicates that the graphene sample has good electrical conductivity; carrier mobility refers to the speed at which carriers (such as electrons or holes) move in graphene under the action of a unit electric field, which reflects the transmission efficiency of electrons or holes in graphene. High mobility means that graphene has excellent electron transmission performance. The carrier mobility of single-layer graphene is ≥10 5 cm 2 / V·s indicates that the graphene sample has excellent electron transport performance.

[0100] In the above method, the screened evaluation parameters are used to evaluate the graphene film, which greatly reduces the number of tests required for the graphene film evaluation and improves the efficiency of the graphene film evaluation.

[0101] See also Figure 4 , Figure 4 is a block diagram of a graphene film evaluation device provided in an embodiment of the present application, Figure 4 The embodiment is a virtual device that can be loaded and executed by a computer device, and the computer device may include the above-mentioned evaluation device, Figure 4 The device may include a parameter value acquisition module and an evaluation module, which, when used to execute the above-mentioned graphene film evaluation method, may:

[0102] The parameter value acquisition module is used to obtain the value of the graphene film evaluation parameter; wherein the evaluation parameter is pre-screened and obtained, and the screening method adopts the above-mentioned graphene film evaluation parameter screening method.

[0103] The evaluation module is used to evaluate the graphene film according to the values ​​of pre-selected evaluation parameters.

[0104] In the above device, the screened evaluation parameters are used to evaluate the graphene film, which greatly reduces the number of tests required for the graphene film evaluation and improves the efficiency of the graphene film evaluation.

[0105] The present disclosure also relates to a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, cause the computing device to execute a graphene film evaluation parameter screening method or a graphene film evaluation method.

[0106] Based on the same technical solution, the present disclosure also relates to a computer device, comprising one or more processors and one or more memories, one or more programs stored in the one or more memories and configured to be executed by the one or more processors, and the one or more programs include instructions for executing a graphene film evaluation parameter screening method or a graphene film evaluation method.

[0107] It should be understood by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0108] The present invention is described with reference to flowcharts and / or block diagrams of methods, apparatuses, and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as a combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A structure that specifies functions in one or more boxes.

[0109] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0110] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0111] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A method for screening graphene film evaluation parameters, characterized in that: include: Obtaining evaluation parameters of each characteristic of the sample, and constructing an original parameter matrix according to the evaluation parameters of all samples; wherein the sample is a graphene film sample; and different evaluation parameters are selected for the same characteristic of several samples; If the elements in the original parameter matrix are nonlinear elements, the elements in the original parameter matrix are processed nonlinearly to obtain the parameter matrix for analysis; According to the analysis parameter matrix, the principal component analysis method is used to obtain the first m principal components in the ranking; where m is the full life cycle of the graphene film sample in industrial metrology; The evaluation parameters in the m principal components are screened according to the characteristic threshold, and the evaluation parameter matrix corresponding to the m principal components is constructed; If the dimensionality reduction rate of the number of elements in all evaluation parameter matrices compared to the number of elements in the original parameter matrix exceeds the dimensionality reduction threshold, the consistency test method is used to screen the elements in all evaluation parameter matrices to obtain the final graphene film evaluation parameters.

2. The method according to claim 1, characterized in that The nonlinear processing of the elements in the original data matrix is ​​to perform a square root transformation on the elements in the original data matrix.

3. The method according to claim 1, characterized in that If the elements in the original parameter matrix are linear elements, the original parameter matrix is ​​used as the parameter matrix for analysis.

4. The method according to claim 1, characterized in that: The consistency test method is used to screen the elements in all evaluation parameter matrices, including: According to the level of the evaluation parameter matrix where the element S is located, the number of samples corresponding to the element S in the original parameter matrix, and the eigenvalue corresponding to the element S in the principal component, the consistency test index value of the element S is calculated; If the consistency test index value is not greater than the test threshold, it is determined that the element S passes the consistency test and the element S is retained; wherein the test threshold is a threshold calculated based on the eigenvalue corresponding to the element S in the principal component.

5. The method according to claim 4, characterized in that Calculate the consistency test index value of element S, the formula is: ； Where W is the consistency test index value of element S, n is the number of samples corresponding to element S in the original parameter matrix, R i is the level of the preset i-th evaluation parameter matrix, λ is the eigenvalue corresponding to the element S in the principal component, is the sorting number corresponding to the principal component; The test threshold is λ-1.

6. A graphene film evaluation parameter screening device, characterized in that: include: The original parameter matrix construction module obtains the evaluation parameters of each characteristic of the sample, and constructs the original parameter matrix according to the evaluation parameters of all samples; wherein the sample is a graphene film sample; and different evaluation parameters are selected for the same characteristic of several samples; A nonlinear processing module performs nonlinear processing on the elements in the original parameter matrix if the elements in the original parameter matrix are nonlinear elements to obtain a parameter matrix for analysis; The principal component analysis module uses the principal component analysis method to obtain the first m principal components according to the analysis parameter matrix; where m is the full life cycle of the graphene film sample in industrial metrology; An evaluation parameter matrix construction module selects the evaluation parameters in the m principal components according to the characteristic threshold value and constructs the evaluation parameter matrix corresponding to the m principal components; The consistency check module uses a consistency check method to screen the elements in all evaluation parameter matrices to obtain the final graphene film evaluation parameters if the dimensionality reduction rate of the number of elements in all evaluation parameter matrices compared to the number of elements in the original parameter matrix exceeds a dimensionality reduction threshold.

7. A graphene film evaluation method, characterized in that: include: Obtaining the value of the graphene film evaluation parameter; wherein the evaluation parameter is pre-screened and obtained by the screening method using the method described in any one of claims 1 to 5; The graphene film evaluation is performed according to the values ​​of the preselected evaluation parameters.

8. A graphene film evaluation device, characterized in that: include: A parameter value acquisition module, which acquires the value of the graphene film evaluation parameter; wherein the evaluation parameter is obtained by pre-screening, and the screening method adopts the method described in any one of claims 1 to 5; The evaluation module evaluates the graphene film according to the values ​​of preselected evaluation parameters.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores one or more programs, and the one or more programs include instructions. When the instructions are executed by a computing device, the computing device executes any one of the methods described in claims 1 to 5 and 7.

10. A computer device, characterized in that include: One or more processors and one or more memories, one or more programs are stored in the one or more memories and are configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the methods described in claims 1 to 5 and 7.

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