Graphene film surface performance detection device

Abstract

The invention relates to the technical field of electrical property detection, and discloses a graphene film surface property detection device, which comprises a data acquisition module used for acquiring electrical property data of the surface of a graphene film through a multi-point array electrode probe; the vector grid modeling module is used for constructing a vector grid model for representing the surface performance of the graphene film according to the electrical characteristic data; the iterative inversion calculation module is used for performing iterative inversion calculation on the vector grid model to determine the surface performance distribution of the graphene film; the parallel optimization fusion module is used for performing parallel processing and optimization fusion on the multi-scale performance data through a distributed computing framework; the three-dimensional visualization module is used for generating a three-dimensional visualization model of the surface performance of the graphene film according to the optimized and fused performance data; the problems of insufficient precision and low efficiency of a traditional detection method in graphene film performance detection are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical property detection, and more specifically, it relates to a device for detecting the surface properties of graphene films. Background Art

[0002] Due to its unique electrical and physical properties, graphene films have broad application prospects in the fields of electronic devices, sensors, composite materials, etc. However, the electrical properties of graphene films are easily affected by various factors such as preparation processes, defect densities, and environmental factors. Accurately detecting and characterizing their surface properties is crucial for ensuring product quality and functional realization.

[0003] Currently, the detection of the surface properties of graphene films mainly faces three technical problems: large environmental factor interference leads to complex data interpretation. Environmental factors such as temperature, humidity, and gas composition will have a significant impact on the measurement results, and traditional measurement methods are difficult to effectively eliminate these interferences; traditional methods such as the four-probe method can only provide macroscopic electrical properties and cannot reveal the microscopic electron transport path, which limits the in-depth understanding of the conduction mechanism of graphene films; the computational complexity of the electron transport channel reconstruction algorithm is high. Traditional reconstruction methods use serial computing, with slow processing speeds and are difficult to be applied in real time in an industrial production environment.

[0004] Therefore, a detection technology for the surface properties of graphene films that can achieve high-precision, visualization, and real-time detection is needed to improve the quality control level and production efficiency of graphene film products. Summary of the Invention

[0005] The present invention provides a device for detecting the surface properties of graphene films, which solves the technical problems of insufficient detection accuracy, low efficiency, and non-intuitive visualization in the related art for detecting the surface properties of graphene films.

[0006] The present invention provides a device for detecting the surface properties of graphene films, including: A data acquisition module for collecting electrical characteristic data on the surface of the graphene film through a multi-point array electrode probe; A vector grid modeling module for constructing a vector grid model characterizing the surface properties of the graphene film based on the electrical characteristic data; An iterative inversion calculation module for performing iterative inversion calculations on the vector grid model to determine the surface property distribution of the graphene film; A parallel optimization and fusion module for performing parallel processing and optimization fusion on multi-scale performance data through a distributed computing framework; A three-dimensional visualization module for generating a three-dimensional visualization model of the surface properties of the graphene film based on the optimized and fused performance data.

[0007] In a preferred embodiment, the data acquisition module includes: Multi-point array electrode probe, including an adjustable configuration array of 16×16 to 64×64; Micro-stepping scanning mechanism for controlling the multi-point array electrode probe to scan the surface of the graphene film along a preset trajectory; High-precision current acquisition unit for acquiring the current signals detected by the multi-point array electrode probe; Adaptive sampling controller for dynamically adjusting the sampling density and scanning strategy according to the real-time acquired electrical property data.

[0008] In a preferred embodiment, the vector grid modeling module includes: Feature point extraction unit for extracting feature points characterizing the surface performance of the graphene film from the electrical property data; Non-uniform grid meshing unit for performing region adaptive meshing based on the feature point density; Interpolation function construction unit for constructing an interpolation function describing the electrical properties for the grid nodes; Boundary constraint processing unit for processing the grid boundary constraints in the edge region of the graphene film.

[0009] In a preferred embodiment, the iterative inversion calculation module includes: Initial estimation unit for providing an initial estimate value for the conductivity distribution; Forward simulation unit for simulating the electrical response based on the current conductivity estimate value; Error calculation unit for calculating the error between the simulated electrical response and the actual measurement data; Gradient calculation unit for calculating the gradient of the error function with respect to the conductivity distribution; Parameter update unit for updating the conductivity distribution estimate value according to the error gradient until convergence.

[0010] In a preferred embodiment, the parallel optimization fusion module includes: Data partitioning unit for partitioning the performance data into sub-regions that can be processed in parallel; Distributed computing unit for processing the sub-region data in parallel on multi-processors; Boundary consistency unit for ensuring the continuity and consistency of the data between adjacent sub-regions; Multi-scale feature fusion unit for fusing the performance features obtained at different scales.

[0011] In a preferred embodiment, the 3D visualization module includes: Stereo structure model unit for constructing a 3D model characterizing the surface performance of the graphene film; A multi-parameter mapping unit for mapping parameters to the visual attributes of a 3D model; An interactive analysis unit for providing interactive functions; A multi-dimensional data reporting unit for generating a data report containing key performance indicators and defect distribution statistics.

[0012] In a preferred embodiment, a method for detecting the surface properties of a graphene film, which is used to implement a device for detecting the surface properties of a graphene film, includes the following steps: Collect electrical property data on the surface of the graphene film through a multi-point array electrode probe; Construct a vector grid model characterizing the surface properties of the graphene film based on the electrical property data; Perform iterative inversion calculations on the vector grid model to determine the surface property distribution of the graphene film; Perform parallel processing and optimized fusion on multi-scale performance data through a distributed computing framework; Generate a three-dimensional visualization model of the surface properties of the graphene film based on the optimized and fused performance data.

[0013] In a preferred embodiment, the step of collecting electrical property data on the surface of the graphene film includes: Configure a multi-point array electrode probe of 16×16 to 64×64; Control the multi-point array electrode probe to scan the surface of the graphene film according to a preset trajectory; Collect the current signals detected by the multi-point array electrode probe; Dynamically adjust the sampling density and scanning strategy according to the real-time collected electrical property data.

[0014] In a preferred embodiment, the step of generating the three-dimensional visualization model includes: Construct a three-dimensional model characterizing the surface properties of the graphene film; Map parameters to the visual attributes of the three-dimensional model; Provide interactive functions; Generate a multi-dimensional data report containing key performance indicators and defect distribution statistics.

[0015] In a preferred embodiment, a computer-readable storage medium is used to store computer-readable instructions, which can run a device for detecting the surface properties of a graphene film when read by a computer.

[0016] The beneficial effects of the present invention are as follows: The detection accuracy is improved. Through the multi-point array electrode probe and the adaptive scanning strategy, the spatial resolution and the accuracy of identifying irregular defects are improved.

[0017] The detection efficiency is improved. By combining optimized vector grid modeling and iterative inversion algorithms, and introducing distributed computing, the detection time is shortened, meeting the real-time monitoring requirements in industrial production environments.

[0018] The visualization accuracy is improved. By constructing a three-dimensional structure model and an interactive analysis function, the conductivity distribution and defect morphology are clearly presented at the sub-micron scale, shortening the defect identification time.

[0019] The adaptability is enhanced. Multiple optional implementation methods are provided in key technical links to adapt to different requirements from rapid screening to fine analysis, ensuring optimal performance under various hardware conditions.

[0020] The integration is improved. Data acquisition, processing, analysis, and visualization are integrated into an integrated solution, improving the working efficiency of operators and reducing the human error rate at the same time.

[0021] The economic benefits are significant. By accurately identifying defects at an early stage, the product yield is increased, material losses are reduced, the production cycle is shortened, and maintenance costs are lowered. Description of the Drawings

[0022] Figure 1 is a module diagram of a device for detecting the surface properties of a graphene film according to the present invention. Detailed Embodiments

[0023] Now, the subject matter described herein will be discussed with reference to exemplary embodiments. It should be understood that discussing these embodiments is only to enable those skilled in the art to better understand and thus implement the subject matter described herein. Without departing from the scope of protection of the content of this specification, changes can be made to the functions and arrangements of the elements discussed. Each example can omit, substitute, or add various processes or components as needed. Additionally, the features described in some examples can also be combined in other examples.

[0024] In at least one embodiment of the present invention, a device for detecting the surface properties of a graphene film is disclosed, as Figure 1 shown, including: A data acquisition module for collecting electrical characteristic data on the surface of the graphene film through a multi-point array electrode probe; Specifically, it includes the following steps: Step 1.1, arranging a microelectrode array;

[0025] Arrange a 32×32 microelectrode array under the graphene film, with an adjacent electrode spacing of 50 μm, forming a high-density electrode network covering the graphene film for measuring the electrical responses of different regions of the film.

[0026] In specific application scenarios, the density of the electrode array can be adjusted according to the detection accuracy requirements. For example, for a large-area prepared graphene film, a low-density electrode array of 16×16 can be used to reduce the calculation amount; for high-precision characterization requirements, a high-density electrode array of 64×64 can be used to improve the spatial resolution. Step 1.2, implement the rotating electric field excitation strategy;

[0027] Use the rotating electric field excitation unit to apply an excitation signal with a controllable frequency every 15° within the range of 0° - 180°, forming 12 different directions of electric field excitation, prompting the electrons in the graphene film to move in different directions, and comprehensively capturing the anisotropic electrical characteristics of the film.

[0028] In some embodiments, the excitation direction can be optimally selected according to the structural characteristics of the graphene film. For a graphene film with obvious orientation, the excitation density can be increased near the main conduction direction. For example, an excitation direction can be set every 5° to obtain more refined anisotropic characteristics. Step 1.3, collect full-spectrum impedance data;

[0029] Collect full-spectrum impedance data from 0.1 Hz to 100 kHz at each electric field excitation direction through the impedance measurement unit to form a complete impedance spectrum and capture the electrical response characteristics of the graphene film at different frequencies.

[0030] According to different application requirements, a specific frequency range can be selected for key analysis. For example, for studying the defect distribution in the graphene film, the impedance spectrum in the low-frequency band of 1 Hz - 100 Hz can be focused on; for studying charge transfer and interface characteristics, the impedance spectrum in the high-frequency band of 1 kHz - 100 kHz can be focused on. Step 1.4, apply the Hilbert-Huang transform for signal decomposition;

[0031] Decompose the collected impedance data through the Hilbert-Huang transform. This transform first decomposes the complex signal into a series of intrinsic mode functions (IMFs) through empirical mode decomposition (EMD), and then performs Hilbert spectral analysis on each IMF. The formula is as follows: ; Among them, represents the Hilbert transform of the signal and is a mathematical tool for converting the original signal to the complex plane; represents the original impedance time-domain signal, that is, the impedance measurement value of the graphene film changing with time; represents the integration variable; represents the time variable; represents the Cauchy principal value integral, which is used to solve the mathematical problem that the denominator becomes zero in the integral when ; is the normalization coefficient to ensure the correct amplitude of the transformation result; is the integral kernel function, which describes the contribution weights of the original signal at different time points; represents integrating over the entire time domain, taking into account the complete history and future information of the signal.

[0032] Through the Hilbert-Huang transform, it is possible to effectively suppress the noise interference caused by environmental factors and extract the effective signal of the intrinsic electrical properties of the graphene film. During the empirical mode decomposition process, the original signal is decomposed into multiple intrinsic mode functions with different characteristic frequencies, and each IMF represents an oscillation mode in the signal. This decomposition enables us to distinguish the intrinsic electrical response of the graphene film from environmental noise.

[0033] In practical applications, the implementation of the Hilbert-Huang transform can be further optimized. For example, for measurement data containing significant periodic environmental interference (such as 50Hz power supply noise), the Ensemble Empirical Mode Decomposition (EnsembleEMD) method can be adopted. By adding finite-amplitude white noise and performing multiple decompositions and taking the average, the problem of mode mixing can be effectively avoided. In addition, during the quality control process of the graphene film, this transform can be integrated with an on-line monitoring system to filter environmental noise in real time and provide a stable and reliable quality detection data stream for the production line.

[0034] The step vector grid modeling module is used to construct a vector grid model characterizing the surface performance of the graphene film according to the electrical property data; Specifically, it includes the following steps: Step 2.1, construct the frequency domain spectrum;

[0035] Perform frequency domain conversion on the time domain impedance data through Fourier transform to construct a complete frequency domain spectrum, so as to facilitate the identification of the electrical properties of the graphene film at different frequencies.

[0036] For the impedance data with a frequency of , its Fourier transform can be expressed as: ; Among them, represents the corresponding frequency domain representation, that is, the amplitude and phase information of the signal at the frequency ; represents the time domain impedance signal, that is, the resistance impedance measurement value changing with time; represents the frequency variable; represents the time variable; denotes the imaginary unit, satisfying and is used to represent complex numbers; is twice the value of pi and is used to convert frequency to angular frequency; is the product of frequency and time, representing the phase angle; is the kernel function of the Fourier transform, which is a complex exponential function and is used to decompose a time-domain signal into sine and cosine components of different frequencies; denotes the differentiation of time and is part of the integration operation; the integral symbol denotes integration from negative infinity to positive infinity over the entire time axis, meaning that the contributions of the signal at all time points are considered.

[0037] In some embodiments, the Fast Fourier Transform (FFT) algorithm can be optionally used to improve the computational efficiency, which is particularly effective when processing data collected from a large-scale electrode array; for scenarios with low data quality, wavelet transform can be adopted to replace the Fourier transform to obtain the analysis results in the time-frequency mixed domain and enhance the ability to capture the transient characteristics of the graphene film. Step 2.2, applying the fuzzy C-means clustering algorithm;

[0038] Apply the Fuzzy C-Means (FCM) clustering algorithm to perform impedance topological partitioning on the surface of the graphene film to identify regions with similar electrical characteristics.

[0039] The objective function of the FCM algorithm is: ; where represents the objective function of the FCM algorithm, that is, the evaluation index that needs to be minimized during the clustering process; represents the total number of data points; represents the number of clustering clusters, that is, how many regions with similar electrical characteristics the surface of the graphene film is divided into; represents the th data point's membership degree to the th cluster, with a value range of [0, 1], indicating the degree to which each sampling point belongs to a certain region; is the fuzzy coefficient that controls the "fuzziness" of the clustering. When is larger, the clustering result is more fuzzy; represents the th data point; represents the th cluster center; represents the data point to the cluster center The squared Euclidean distance, which is used to measure the similarity between a data point and the cluster center.

[0040] To adapt to the defect characteristics of graphene films with different shapes, the FCM algorithm can be combined with a kernel function to construct the Kernel Fuzzy C-Means (KFCM) algorithm. By mapping the original feature space to a high-dimensional space, the recognition ability for non-spherical distribution features is enhanced. For example, for a graphene film with linear defects, a polynomial kernel function can be used; for a point defect distribution, a Gaussian radial basis kernel function can be used. Step 2.3, establish a hypergraph structure model;

[0041] Based on the clustering results, establish a hypergraph structure model to capture the high-order correlation characteristics on the surface of the graphene film. The hypergraph can be expressed as: ; where represents the vertex set, that is, the set of all electrode points, and each vertex represents a sampling point on the surface of the graphene film; represents the hyperedge set. A hyperedge is an edge connecting multiple vertices, and each hyperedge represents a region with similar electrical properties; represents the weight function, which assigns a weight value to each hyperedge, reflecting the degree of consistency of the electrical properties within this region. The higher the weight value, the more consistent the electrical properties within this region.

[0042] In practical applications, each electrode can be regarded as a vertex, and a group of electrodes with similar impedance characteristics can be regarded as a hyperedge. The weight of the hyperedge reflects the degree of consistency of the impedance characteristics in this region.

[0043] When analyzing a large-area graphene film, a hierarchical hypergraph structure can be adopted. First, construct a local hypergraph and then integrate it into a global hypergraph to reduce the computational complexity; For high-precision detection requirements, a dynamic hypergraph update strategy can be adopted. According to real-time measurement data, continuously adjust the hypergraph structure to improve the sensitivity of anomaly detection. Step 2.4, perform dictionary learning and sparse representation;

[0044] Extract the characteristic information of the graphene film through the dictionary learning and sparse representation algorithm. The optimization objective of dictionary learning is: ; where represents the impedance data matrix, that is, the matrix form representation containing the impedance data of all measurement points; represents the learned dictionary, that is, a set of basic patterns that can represent the electrical properties of the graphene film; represents the sparse coefficient, i.e., the decomposition coefficient of the original data on the dictionary, reflecting the weights of each basic pattern in the data representation; represents the Frobenius norm of the reconstruction error, measuring the difference between the original data and the data reconstructed by the dictionary; represents the L1 norm of the sparse coefficient, used to promote the sparsity of the coefficient; is the regularization parameter, controlling the degree of sparsity. The larger the value, the stronger the sparsity, indicating that the original data is represented by fewer basic patterns; represents the minimization operation, and the goal is to find the solution that minimizes the objective function.

[0045] In an industrial production environment, an online dictionary learning algorithm can be adopted to continuously update the dictionary during the production process of graphene thin films, so that the dictionary elements always reflect the latest electrical property patterns; In a laboratory research environment, a multi-scale dictionary learning method can be adopted to simultaneously capture the macroscopic structure and microscopic features of graphene thin films, providing a multi-scale analysis tool for materials scientists.

[0046] In actual operation, the algorithm parameters can be optimized according to specific application scenarios. For example, for the mass production quality control of graphene thin films, the dictionary can be pre-trained using standard samples, and then the sparse representation of the production samples can be compared with the standard patterns to quickly identify abnormal areas; for the high-precision characterization requirements in scientific research experiments, the dictionary redundancy can be increased, and an over-complete dictionary can be used to capture the subtle characteristic changes of graphene thin films, providing more accurate material characterization results.

[0047] The iterative inversion calculation module is used to perform iterative inversion calculation on the vector grid model to determine the surface performance distribution of the graphene thin film; Specifically, it includes the following steps: Step 3.1, establish an underdetermined observation equation;

[0048] Establish an underdetermined observation equation describing the electron transport channels of the graphene thin film: ; Among them, represents the observed impedance measurement result, i.e., the actual measurement data obtained from the electrode array; represents the observation matrix, describing the characteristics of the measurement system; represents the electron transport channel distribution to be reconstructed, representing the spatial distribution of the electron transport paths in the graphene thin film; represents the measurement noise, including random errors introduced by factors such as environmental interference and instrument errors.

[0049] The observation matrix can be designed in various ways: For a conventional electrode array, a deterministic orthogonal observation matrix can be used; For an irregularly arranged electrode array, a random Gaussian observation matrix can be used; For specific graphene defect detection requirements, a structured random observation matrix can be used to improve the detection sensitivity for specific types of defects. Step 3.2, solve the optimization problem based on L1 norm minimization;

[0050] Based on the compressive sensing theory, transform the electron transport channel reconstruction problem into an L1 norm minimization problem: ; where, represents the L1 norm of the vector , that is, the sum of the absolute values of the elements of the vector, which is used to promote the sparsity of the solution; represents the L2 norm difference between the measured value and the reconstructed value, that is, the Euclidean distance, which is used to measure the fitting degree between the reconstruction result and the actual measurement; is the allowable error tolerance, which is determined by the measurement noise level and represents the maximum acceptable deviation between the reconstruction result and the measurement data; represents the distribution of the electron transport channels to be reconstructed, which describes the spatial distribution of the electron transport paths in the graphene film; represents the observed impedance measurement result, that is, the actual measurement data obtained from the electrode array; represents the observation matrix, which describes the characteristics of the measurement system and maps the electron transport channels to the measurement space; represents the minimization operation, and the goal is to find the solution that minimizes the objective function; represents "subject to", which introduces the constraint conditions of the optimization problem.

[0051] Different solution strategies can be adopted for different application scenarios: For real-time monitoring requirements, the Orthogonal Matching Pursuit (OMP) algorithm can be used for rapid solution; for high-precision offline analysis, the Basis Pursuit algorithm can be used; For graphene films with structured sparsity, the Group Sparsity constraint can be adopted to improve the reconstruction quality. Step 3.3, apply multi-resolution analysis technology;

[0052] To further improve the reconstruction accuracy, apply multi-resolution analysis technology to optimize the reconstruction result.

[0053] The specific method is to first reconstruct the approximate distribution of the electron transport channels at a coarse scale and then finely characterize the features of the local area at a fine scale. The mathematical expression is as follows: ; where, represents the reconstructed distribution of the electron transport channels; represents the scaling function, which is used to capture the approximate part of the signal at different scales, and t is the time variable; represents the wavelet function, which is used to capture the detailed part of the signal at different scales; represents the scaling coefficient, which reflects the intensity of the approximate features of the signal at scale j and position k. The larger the value, the more significant the approximate features at that position; represents the wavelet coefficient, which reflects the intensity of the detailed features of the signal at the finest scale J and position k. The larger the value, the richer the detailed features at that position; represents the scale level of decomposition, ranging from 1 to J. A smaller j corresponds to coarse-scale analysis, and a larger j corresponds to fine-scale analysis; represents the maximum scale level of decomposition, which determines the finest precision of the analysis; represents the position index at each scale level, indicating the specific position of the features in space or time; represents the summation over all scale levels (from 1 to J) and all positions k; represents the summation only over all positions k at the finest scale J.

[0054] For the reconstruction of a large-area graphene film, an adaptive grid refinement technique can be adopted to increase the local grid density only when an abnormal area is detected, reducing the computational complexity; For the reconstruction of the edge region, a total variation regularization term can be introduced to enhance the edge-preserving ability and accurately characterize the boundary defects of the graphene film. Step 3.4, perform non-linear mapping and spatial distribution reconstruction;

[0055] Through a non-linear mapping method, the reconstructed electron transport channel parameters are converted into a high-resolution conductivity spatial distribution map. The formula is as follows: ; where, represents the conductivity at position ; represents the reconstructed electron transport channel parameters at position r; represents the non-linear mapping function, which converts the abstract channel parameters into conductivity with physical meaning. This function captures the non-linear relationship between the electron transport channel parameters and the actual conductivity; represents the spatial position coordinates, which can be two-dimensional plane coordinates (for surface conductivity analysis) or three-dimensional coordinates (for multi-layer graphene structure analysis).

[0056] During the mapping process, multi-frequency measurement data can be fused. By frequency analysis, the ability to distinguish different types of defects can be enhanced; for doped graphene films, the mapping relationship between conductivity and carrier concentration can be established, and the carrier distribution map can be directly reconstructed to provide a basis for material modification.

[0057] The electron transport channel distribution reconstructed based on the above method has the following technical characteristics: High spatial resolution: Although the actual number of measurement points is limited, super-resolution reconstruction can be achieved through sparse reconstruction technology, and the spatial resolution can reach the micron level, far superior to traditional electrical impedance imaging technology; Strong anti-noise ability: Using the prior information of signal sparsity, the influence of various environmental noises and measurement errors on the reconstruction results can be effectively suppressed; High computational efficiency: By adopting a fast iterative algorithm, for the reconstruction problem of a 64×64 grid, it can be completed within 3 seconds on an ordinary industrial computing platform, meeting the requirements of online detection.

[0058] In practical applications, the reconstructed electron transport channel distribution map can be used as a key basis for the quality analysis of graphene films. For example, for large-scale production of graphene transparent conductive films, the product consistency can be evaluated by analyzing the uniformity of the electron transport channel distribution; for graphene films used in flexible electronic devices, the mechanical stability of the film can be evaluated by monitoring the changes in the electron transport channels before and after bending deformation; for graphene films used in biosensors, the adsorption sites of biomolecules can be identified by analyzing the local perturbations of the electron transport channels.

[0059] A parallel optimization and fusion module for parallel processing and optimization fusion of multi-scale performance data through a distributed computing framework; Specifically, it includes the following steps: Step 4.1, deploy a distributed computing framework;

[0060] Deploy a distributed computing framework that supports heterogeneous computing to achieve parallel processing of measurement data and efficient calculation of sub-region reconstruction results. Adopt a master-slave architecture, where the master node is responsible for task scheduling and result summary, and the slave nodes are responsible for executing the regional reconstruction algorithm.

[0061] For test devices of different scales, different parallel strategies can be adopted: For portable small test devices, a multi-thread parallel strategy can be adopted to parallel process multiple sub-regions on a single computing device; For the on-line detection system of large production lines, the Message Passing Interface (MPI) can be adopted to implement multi-device distributed computing; For the cloud detection and analysis platform, container technology can be combined to achieve elastic computing resource allocation, and the number of computing nodes can be dynamically adjusted according to the size of the graphene film and the requirements of detection accuracy. Step 4.2, implement the multi-scale fusion algorithm;

[0062] Based on the wavelet transform and multi-resolution analysis theory, design and implement the multi-scale fusion algorithm to integrate the reconstruction results of each sub-region into a seamlessly connected global conductivity distribution map. The mathematical expression is: ; Among them, represents the global conductivity distribution; represents the reconstruction result of the th sub-region; represents the fusion weight function, which is a spatially varying function with a value range of [0, 1], with the maximum weight (close to 1) at the center of the sub-region and smoothly transitioning to zero towards the edge, used to ensure smooth transition between adjacent sub-regions; represents the two-dimensional index of the sub-region, used to identify the positions of different sub-regions; represents the spatial coordinate points on the surface of the graphene film.

[0063] For the graphene composite film with an obvious hierarchical structure, a hierarchical fusion strategy can be adopted, first fusing the reconstruction results within each layer, and then fusing the correlation features between different layers; For the graphene microstructure array with obvious boundaries, edge-preserving filtering technology can be combined to retain the clarity of the microstructure boundaries during the fusion process; For the multi-layer graphene film with complex spectral characteristics, fusion can be performed separately in different frequency domains, and finally a complete characterization can be obtained through frequency synthesis. Step 4.3, apply adaptive error correction;

[0064] Design and apply an adaptive error correction algorithm to optimize the fusion result through an iterative method, eliminating the discontinuities and artifacts that may appear at the boundaries of sub-regions. The iterative formula is: ; Among them, represents the global conductivity distribution of the th iteration, which is the intermediate result in the iterative optimization process; represents the global conductivity distribution of the th iteration, which is the new distribution updated based on the current iteration result; denotes the gradient operator, which is used to calculate the spatial gradient of the current conductivity distribution, indicating the direction and magnitude where the conductivity changes most steeply; denotes the step size parameter, which is a positive scalar value that controls the update magnitude for each iteration. A smaller value makes the convergence more stable but slower, while a larger value can accelerate the convergence but may lead to instability; denotes the iteration number index, which increments from the initial value until the convergence condition is met or the maximum number of iterations is reached.

[0065] For different types of thin film defects, different error correction strategies can be adopted: For graphene films dominated by point defects, a denoising algorithm based on sparse representation can be adopted; For graphene films dominated by line defects, anisotropic diffusion filtering can be adopted; For regional defects, morphological operations can be combined to achieve precise correction of the regional boundaries. Step 4.4, generate multi-dimensional characterization indicators;

[0066] Based on the fused and optimized conductivity distribution map of the graphene film, a series of multi-dimensional characterization indicators are calculated and generated to comprehensively characterize the film performance characteristics. These indicators include but are not limited to: Uniformity indicators: standard deviation of conductivity distribution, coefficient of variation, local mean deviation, etc.; Defect feature indicators: defect density, defect size distribution, defect morphological characteristics, defect correlation length, etc.; Anisotropy indicators: principal direction conductivity ratio, conductivity ellipticity, angle dependence coefficient, etc.; Spectral characteristic indicators: characteristic frequency distribution, frequency response bandwidth, phase delay characteristics, etc.

[0067] According to different application scenarios of the graphene film, specific performance characterization indicators can be customized: For transparent electrode applications, the sheet resistance uniformity and the impact of defects on optical transparency can be calculated with emphasis; For sensor applications, the local sensitivity and sensitive area distribution can be calculated; For thermal management materials, the thermoelectric conductivity distribution and interface thermal resistance characteristics can be calculated.

[0068] The implementation of the above parallel optimization and result fusion technologies enables the method of the present invention to efficiently handle the performance characterization task of large-area graphene films, significantly improving the calculation efficiency while maintaining high spatial resolution.

[0069] Experiments show that for a large-area graphene film with a size of 1 m × 1 m, when measured using a 32×32 electrode array, the reconstructed result after parallel optimization can be completed within 10 minutes, which is more than 20 times faster than the traditional serial algorithm, and the error in the boundary transition region is reduced by 45%, providing the possibility for real-time monitoring of industrial production lines.

[0070] A three-dimensional visualization module for generating a three-dimensional visualization model of the surface properties of the graphene film based on the optimized and fused performance data; Specifically, it includes the following steps: Step 5.1, constructing a three-dimensional structure model;

[0071] Based on the conductivity distribution data and material thickness information, construct a three-dimensional structure model of the graphene film.

[0072] Adopt a multi-level grid division strategy to divide the film space into non-uniform grids adapted to the complexity of local features, ensuring a higher grid density in the detail areas. The mathematical expression for model construction is: ; Among them, represents the three-dimensional structure model at point in three-dimensional space, which is a complete mathematical representation describing the geometric shape and physical properties of the graphene film; represents the conductivity distribution value at point in three-dimensional space, reflecting the electrical conduction ability of the graphene film at this position; represents the set of vertices in the three-dimensional model, and each vertex is defined by the three-dimensional coordinates to form the skeleton structure of the model, which determines the geometric shape of the model; represents the set of patches in the three-dimensional model, and each patch is formed by connecting multiple vertices, jointly constituting the surface of the model for rendering display and physical property calculation.

[0073] For different types of graphene films, different modeling strategies can be adopted: For single-layer graphene, high-precision planar modeling can be adopted to focus on showing the change of conductivity distribution in the plane; For multi-layer graphene structures, hierarchical modeling can be adopted to show the conductivity differences and interlayer interactions between layers; For graphene composites, a hybrid modeling method can be adopted to highlight the interfacial characteristics between graphene and the substrate material. Step 5.2, designing a multi-parameter mapping scheme;

[0074] Design and implement a multi-parameter mapping scheme to map various material property parameters such as conductivity, defect density, and interlayer coupling to visual channels.

[0075] Based on the principle of perceptual uniformity, a color mapping function is designed: ; Among them, represents the color value at point in the three-dimensional space, which is used to visually display the physical characteristics of this point in the visualization model; represents the mapping function, which is a mathematical transformation that converts physical parameters into visual parameters. It is designed according to the principle of perceptual uniformity to ensure that the color change is proportional to the change of physical quantity and enhance the intuitiveness of the visualization effect; represents the conductivity value at point in the three-dimensional space, which is the main input parameter of the mapping; represents the set of mapping parameters, including adjustable parameters such as color range, contrast, saturation, transparency, etc., which are used to optimize the visual effect and highlight key features.

[0076] For different application requirements, different parameter mapping schemes can be designed: For defect analysis, the conductivity abnormal area can be mapped to a high-contrast color, and the defect depth can be represented by transparency; For conductivity gradient analysis, piecewise color mapping can be adopted to highlight the areas with large conductivity change gradients; For multi-frequency measurement data, the response characteristics at different frequencies can be mapped into a dynamically changing color pattern to show the frequency dependence. Step 5.3, implement the interactive analysis function;

[0077] Based on WebGL / OpenGL technology and a three-dimensional rendering engine, an interactive three-dimensional visualization system is implemented, which supports various interactive operations such as zooming, rotating, slicing, and filtering.

[0078] The system architecture adopts a front-end and back-end separation design: Back-end service: Responsible for data management and analysis processing, and provides RESTful API interfaces; Rendering engine: Responsible for the real-time rendering of three-dimensional models and supports hardware acceleration; Interaction controller: Processes user input and implements view transformation and data query; Analysis tool set: Provides functions such as defect annotation, area statistics, and parameter extraction.

[0079] According to different usage scenarios, different interactive functions can be provided: For fine analysis in the laboratory, precise point selection measurement and local magnification functions are provided; For real-time monitoring of the production line, threshold warning and automatic defect detection functions are provided; For mobile device applications, simplified touch operations and performance-optimized versions are provided. Step 5.4, generate a multi-dimensional data report;

[0080] Based on the visualization analysis results, automatically generate a comprehensive data report including key performance indicators, defect distribution statistics, and quality assessment.

[0081] The report content includes but is not limited to: Conductivity statistical indicators: global average, standard deviation, uniformity coefficient, etc.; Defect feature analysis: defect type distribution, size distribution, density heat map, etc.; Quality grade assessment: quality score and grade division based on preset standards; Time-series comparison analysis: comparison results and change trends with historical data.

[0082] For different application scenarios, different report templates can be customized: For R & D personnel, generate a technical report including detailed technical parameters and raw data; For production management personnel, generate a concise report including key quality indicators and anomaly warnings; For customer delivery, generate a delivery report including quality certification and performance guarantee.

[0083] Through the construction and application of the above three-dimensional visualization model, the method of the present invention not only realizes the intuitive display of the performance characteristics of graphene films, but also provides technical means for in-depth analysis and accurate evaluation.

[0084] Experiments prove that the visualization system can clearly present the conductivity distribution and defect morphology of graphene films at the sub-micron scale, accurately identify more than 95% of the film defects, and assist R & D personnel to optimize the film preparation process through the interactive analysis function, significantly improving the quality stability and product yield of graphene films.

[0085] Application example of this embodiment: The following uses an actual application example to detail the implementation process and effect of the method of the present invention in an industrial production environment.

[0086] A high-tech enterprise produces large-area transparent conductive graphene films for flexible electronic devices, with a specification of 500mm×500mm and a thickness of 3 - 5 layers of graphene (about 1.5 - 2.5nm). During the production process, it is necessary to monitor the electrical property distribution of the film in real time, detect non-uniformity and defects early, and adjust process parameters. The traditional four-probe method has low testing efficiency and is difficult to obtain a complete conductivity distribution map, affecting product yield and production efficiency. Example of the implementation process:

[0087] A graphene film surface performance detection system based on the present invention is deployed in the production line detection link. The system adopts a 32×32 microelectrode array configuration with an electrode spacing of 50μm, covering the key area of the graphene film.

[0088] In the impedance measurement stage, the system performs electric field excitation every 15° in the range of 0° - 180°, a total of 12 - direction scans are executed, and full - spectrum impedance data in the range of 0.1Hz - 100kHz is collected for each direction.

[0089] For the possible 50Hz power - frequency interference on the production line, the system applies an improved Hilbert - Huang transform algorithm, uses the integrated empirical mode decomposition method to process the signal, and effectively suppresses the influence of environmental noise.

[0090] In the topology analysis stage, the system first uses the fast Fourier transform to convert the time - domain data into a frequency - domain representation, and then applies the kernel fuzzy C - means clustering algorithm to divide the surface of the graphene film into regions.

[0091] For the specific film structure of this production line, a Gaussian radial basis kernel function is used to enhance the ability to identify the point - defect distribution. The hyper - graph structure model constructed by the system divides 1024 electrode points into about 50 hyper - edges according to the electrical property similarity, forming a high - order characterization of the electrical properties of the graphene film surface.

[0092] In the electron transport channel reconstruction stage, the system uses the orthogonal matching pursuit algorithm to solve the L1 - norm minimization problem to meet the requirements of real - time monitoring. For the key - concerned film edge region, a total - variation regularization term is introduced to enhance the edge - preserving ability and accurately depict the conductivity distribution at the edge.

[0093] In the parallel processing and fusion stage, considering the hardware configuration of the production line detection system (equipped with a 4 - core CPU and a mid - range GPU), the system adopts a multi - thread parallel processing strategy, divides the 500mm×500mm film area into 16 sub - regions of 4×4 for parallel calculation. The boundary regions adopt a 20% overlap and use an exponential weight function for smooth transition, effectively eliminating the discontinuity at the sub - region boundaries.

[0094] The three - dimensional visualization model generated by the system is constructed based on WebGL technology, realizing the interactive analysis function. For the quality control requirements of this enterprise, the system configures a high - contrast color mapping scheme for the conductivity distribution, intuitively displays the conductivity abnormal regions, and sets an automatic alarm threshold based on the conductivity coefficient of variation. When the coefficient of variation exceeds 15%, an alarm is triggered. Technical effect verification:

[0095] Through six consecutive months of operation on the production line of this enterprise, the technical effects of the method of the present invention have been fully verified: Detection accuracy and efficiency improvement effect: Compared with the original four-probe scanning system, the detection accuracy and efficiency of the new system have been significantly improved. For the same batch of graphene film samples, the spatial resolution has been improved from the original millimeter level to 150 nm, and the detection time has been shortened from the original 2500 seconds to 115 seconds. Especially for irregular point defects and line defects, the recognition accuracy has been improved from 76% to 94%, greatly reducing the missed detection rate.

[0096] Production index improvement effect: After implementing the method of the present invention, the production indexes of the enterprise have been significantly improved. The product yield has been increased from the original 82.5% to 93.7%, the material loss rate has been reduced from 8.6% to 7.1%, the production cycle has been shortened by 16.2%, and the maintenance cost of the production line equipment has been reduced by 22.8%. By adjusting the process parameters through real-time detection feedback, the conductivity uniformity of the graphene film product has been improved by 35%, and the customer satisfaction has been significantly improved.

[0097] Visualization effect of electron transport: The three-dimensional visualization model provided by this system enables technicians to directly observe the electron transport channels in the film and discover the problem of uneven microscopic current density that cannot be detected by traditional methods. During a process adjustment, by analyzing the distribution characteristics of the electron transport channels, technicians discovered the correlation between the coil conveying speed and the temperature gradient, and timely optimized the preparation process, significantly improving the product consistency.

[0098] In summary, the application of this embodiment in the actual industrial production environment fully demonstrates its significant advantages in improving the detection accuracy, efficiency, and visualization ability of the electrical properties of graphene films, providing a powerful tool for the quality control of graphene film products.

[0099] The above describes the embodiments of the present invention, but these embodiments are not limited to the above specific implementation manners. The above specific implementation manners are merely illustrative and not restrictive. Under the inspiration of this embodiment, those of ordinary skill in the art can also make more equivalent embodiments in various forms, all of which fall within the protection scope of this embodiment.

Claims

1. A graphene film surface performance detection device, comprising: A data acquisition module, used to collect electrical characteristic data of the graphene film surface through a multi-point array electrode probe; A vector grid modeling module is used to construct a vector grid model that characterizes the surface performance of graphene films based on electrical characteristic data; Iterative inversion calculation module, used to perform iterative inversion calculation on the vector grid model to determine the surface performance distribution of the graphene film; A parallel optimization fusion module is used to perform parallel processing and optimization fusion of multi-scale performance data through a distributed computing framework; The three-dimensional visualization module is used to generate a three-dimensional visualization model of the surface performance of the graphene film according to the optimized and fused performance data.

2. A graphene film surface performance detection device according to claim 1, characterized in that: The data acquisition module comprises: Multi-point array electrode probes, including adjustable configuration arrays from 16×16 to 64×64; A micro-stepping scanning mechanism is used to control the multi-point array electrode probe to scan the graphene film surface according to a preset trajectory; High-precision current acquisition unit, used to collect current signals detected by multi-point array electrode probes; An adaptive sampling controller is used to dynamically adjust the sampling density and scanning strategy according to the electrical characteristic data collected in real time.

3. A graphene film surface performance detection device according to claim 1, characterized in that: The vector grid modeling module comprises: A feature point extraction unit, used to extract feature points representing the surface performance of the graphene film from the electrical characteristic data; Non-uniform grid division unit, used for regional adaptive division based on feature point density; An interpolation function building unit, used to build an interpolation function describing electrical characteristics for a grid node; The boundary constraint processing unit is used to process the grid boundary constraints of the edge area of ​​the graphene film.

4. A graphene film surface performance detection device according to claim 1, characterized in that: The iterative inversion calculation module includes: An initial estimation unit, used for providing an initial estimation value for the conductivity distribution; a forward simulation unit for simulating an electrical response based on a current conductivity estimate; An error calculation unit, used to calculate the error between the simulated electrical response and the actual measurement data; A gradient calculation unit, used to calculate the gradient of the error function with respect to the conductivity distribution; The parameter updating unit is used to update the conductivity distribution estimation value according to the error gradient until convergence.

5. A graphene film surface performance detection device according to claim 1, characterized in that: The parallel optimization fusion module includes: A data partitioning unit for dividing performance data into sub-areas that can be processed in parallel; A distributed computing unit for processing sub-region data in parallel on multiple processors; Boundary consistency unit, used to ensure the continuity and consistency of data between adjacent sub-regions; The multi-scale feature fusion unit is used to fuse the performance features obtained at different scales.

6. A graphene film surface performance detection device according to claim 1, characterized in that: The three-dimensional visualization module includes: The three-dimensional structure model unit is used to construct a three-dimensional model to characterize the surface properties of graphene films; a multi-parameter mapping unit for mapping parameters to visual attributes of a three-dimensional model; An interactive analysis unit for providing interactive functions; Multidimensional data reporting unit, used to generate data reports containing key performance indicators and defect distribution statistics.

7. A method for detecting the surface properties of a graphene film, used to implement a graphene film surface properties detection device according to any one of claims 1 to 6, comprising the following steps: The electrical property data of the graphene film surface is collected by a multi-point array electrode probe; A vector mesh model is constructed based on electrical property data to characterize the surface properties of graphene films; Perform iterative inversion calculations on the vector grid model to determine the surface property distribution of the graphene film; Parallel processing and optimized fusion of multi-scale performance data through a distributed computing framework; A three-dimensional visualization model of the surface performance of the graphene film is generated based on the optimized and fused performance data.

8. A graphene film surface performance detection method according to claim 7, characterized in that: The step of collecting electrical characteristic data of the graphene film surface comprises: Multi-point array electrode probes with 16×16 to 64×64 configurations; Controlling the multi-point array electrode probe to scan the surface of the graphene film according to a preset trajectory; collecting the current signal detected by the multi-point array electrode probe; The sampling density and scanning strategy are dynamically adjusted according to the electrical characteristic data collected in real time.

9. A graphene film surface performance detection method according to claim 7, characterized in that: The step of generating a three-dimensional visualization model comprises: Construct a three-dimensional model to characterize the surface properties of graphene films; Mapping parameters to visual properties of a 3D model; Provide interactive functions; Generate multi-dimensional data reports containing key performance indicators and defect distribution statistics.

10. A computer-readable storage medium, characterized in that: It is used to store computer-readable instructions, and when the computer-readable instructions are read by a computer, a graphene film surface property detection device as described in any one of claims 1-6 can be run.

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