Special-shaped mold modeling method and micro-nano machining method of coronal bionic adhesion array

Abstract

The invention relates to a special-shaped mold modeling method and a micro-nano machining method for a coronal bionic adhesion array, and the method comprises the steps: carrying out the parametric modeling of a special-shaped micro-nano structure through three-dimensional digital scanning and curvature analysis, and achieving the high-precision molding of the spatial distribution special-shaped micro-nano structure of a mold through a focused ion beam or ultrafast laser or other micro-nano machining technologies; in combination with plasma-assisted multi-step deposition, in-situ growth of graded nano textures such as nano spikes and nanotube arrays in different space areas of the mold is realized, and precise regulation and control of nano structure space distribution and surface energy state are realized; according to the technology, the surface anti-pollution and coining consistency of the mold is effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of micro-nano manufacturing and mold surface engineering, and particularly relates to a modeling method of a crown-shaped biomimetic adhesion array special-shaped mold and a micro-nano processing method. BACKGROUND

[0002] In the current field of micro-nano manufacturing and mold surface engineering, with the significant increase in the demand for batch production of various high-precision functional surfaces and biomimetic structures, the high-consistency forming of limit micro-nano structures and the guarantee of structure surface self-cleaning have become the core challenges of advanced forming processes such as nano-imprinting. In the high-end applications of crown-shaped biomimetic adhesion array and complex spatial special-shaped micro-nano mold, the mainstream nano-imprinting micro-nano processing method, including mold surface physical etching, laser direct writing, plasma deposition and other processes based on metal and ceramic high-strength substrates, has been widely used to prepare high-resolution, ultra-fine micro-nano structure arrays. There is a technical evolution trend in this preparation route, that is, upgrading to multi-level hierarchical, complex spatial distribution and functional integrated micro-nano structures, and coupling with surface energy regulation, foreign matter barrier and super-lyophobicity and other self-cleaning functions, so as to realize high-fidelity replication of micro-nano structures and long-period service stability.

[0003] The representative technical solutions currently disclosed in the industry mainly use single-stage nano-imprinting, simple plasma post-processing or mold surface layer chemical modification (such as fluorination treatment, thin film coating) to improve the anti-pollution ability of nano-imprinted products. These processes can inhibit the embedding of microparticles and surface pollution to a certain extent, and are suitable for nano-imprinting of regular planes or low-curvature single structures, but they still have obvious deficiencies in terms of spatial consistency of micro-nano structures, multi-zone coverage of self-cleaning performance, and self-recovery ability of micro-defects on the surface of high-precision molds with high spatial complexity, variable curvature and large-area arrays. Many known documents reveal that various high-end micro-nano molds are extremely susceptible to perturbations such as fine particles, foreign matter, pollutants, nano residues, high-curvature zone special-shaped stress and local energy state unevenness on the mold surface in batch, automated and continuous nano-imprinting processes, which can cause the nano-imprinted products to have uncontrollable quality, structural damage and high surface defect density, and ultimately cause a significant decline in the consistency and process robustness of the products, thereby restricting the stable mass production of industrial-grade and the application expansion of high-end biomimetic structures.

[0004] Specifically, the existing technology has the following key deficiencies:

[0005] (1) It is difficult to accurately adjust the spatial distribution consistency and local self-cleaning synergy of the multi-region micro-nano structure on the complex spatial special-shaped mold surface, and the cumulative error of the multi-step processing is significant, which is difficult to meet the full-surface high-fidelity consistent transfer;

[0006] (2) Single liquid-repellent coating or physical modification cannot provide long-period batch embossing under sustained self-cleaning and micro-defect tolerance, the mold is easily contaminated, worn or structurally degraded, resulting in a decrease in the consistency of embossed products and high mold maintenance costs;

[0007] (3) Lack of automated, real-time interface state monitoring and process self-adaptive adjustment mechanism, mold micro-defects, and irregular structure self-cleaning ability are difficult to find and compensate in time, especially in continuous production situation, lacking dynamic repair and regional performance regeneration path;

[0008] (4) The existing technology is mostly flat or single hierarchical structure, which is difficult to realize the hierarchical nano-texture collaborative growth and functional layout on complex surfaces with spatial irregularity, sharp curvature change, and variable cross-section, resulting in weak spatial adaptability and anti-disturbance ability of micro-nano structure, which forms a significant bottleneck for high-end biomimetic functional surface applications. SUMMARY

[0009] The present application provides a modeling method for irregular mold of a coroniform biomimetic adhesion array and a micro-nano processing method, which aims to solve the problems of the existing technology mentioned in the background art or one of the problems.

[0010] The modeling method for irregular mold of a coroniform biomimetic adhesion array and the micro-nano processing method provided by the present application specifically include the following steps:

[0011] S1: Obtain the complex spatial irregular structure parameters corresponding to the target coroniform biomimetic adhesion array, and parameterize the modeling of various spatial bending degrees and scaling variable cross-section morphological characteristics to obtain an irregular micro-nano cavity model.

[0012] S2: Based on the irregular micro-nano cavity model, taking a high-strength mold substrate such as metal or ceramic as the bearing object, using focused ion beam etching or ultrafast laser direct writing to implement micro-nano structure pre-processing treatment on different regions of the substrate, generating micro-nano structure prototypes at different spatial positions.

[0013] S3: For the micro-nano structure prototypes obtained from different surface regions, realize the collection of multi-position micro-nano structure distribution characteristics through microscopic morphology analysis and surface roughness determination, and establish an irregular surface micro-nano structure distribution model.

[0014] S4: Based on the irregular surface micro-nano structure distribution model, use multi-step plasma-assisted deposition process (including PECVD) to grow hierarchical nano-spikes, nanotube arrays or nano-flower texture layers in different spatial positions in situ, forming spatially consistent hierarchical confined nano-texture structures.

[0015] S5: Perform surface energy state regulation and super-lyophobic / super-oleophobic performance test on the grown hierarchical confined nano-textured structure, optimize the physical parameters of different regions according to the nano-structure characteristics of different regions, and generate a self-cleaning ability tag mapping table, complete the modeling of the crown-shaped biomimetic adhesion array.

[0016] S6: Before the nanoimprint process, determine whether the surface self-cleaning ability tag meets the high-fidelity imprinting requirements of covering complex spatial special-shaped structure micro-nano mold cavity; if the local self-cleaning ability is insufficient, perform regeneration deposition or strengthening treatment for the region.

[0017] S7: Apply the high-precision mold with hierarchical confined self-cleaning nano-texture to the biomimetic array nanoimprint process, use the surface nano-texture to actively inhibit the embedding of microparticles, foreign matter or pollutants in different spatial regions, and realize passive self-cleaning under continuous process.

[0018] S8: Perform high-resolution determination on the spatial distribution adhesion performance and local structure consistency of the imprinted product, and real-time correct the hierarchical confined nano-texture design parameters based on the distribution data to dynamically adapt to the process robustness requirements of different spatial special-shaped structure regions.

[0019] S9: Use high-temperature, ultraviolet or customized atmosphere treatment process to periodically regenerate and strengthen the hierarchical confined nano-texture, ensure the self-cleaning property and structural integrity in long-period batch nanoimprint process, and prolong the service life of the high-precision mold.

[0020] The crown-shaped biomimetic adhesion array special-shaped mold modeling method and micro-nano processing method provided by the application has the following beneficial effects:

[0021] (1) Greatly improve the spatial distribution consistency and functional controllability of the mold micro-nano structure. The application adopts high-resolution three-dimensional scanning and principal curvature / variable cross-section parameter modeling to finely dataize the micro-nano structure parameters in complex special-shaped space, supports individualized optimization of process path and deposition parameters, realizes high-precision consistent distribution of hierarchical texture in multi-region special-shaped structure, and greatly reduces structural redundancy and local mismatch compared with traditional single-parameter or two-dimensional process, with more than 30% increase in spatial consistency.

[0022] (2) Significantly improve the robustness of the nanoimprint process to micro-defects and pollution and the consistency of the finished product. By in-situ multi-step plasma-assisted partition deposition, the application innovatively constructs a multi-level confined nano-spine, nano-tube / flower and other biomimetic self-cleaning layer on the whole surface of the mold, uses the super-lyophobic / super-oleophobic and foreign matter blocking effect to realize passive active self-cleaning, and significantly inhibits the interference of microparticles and pollutants on the micro-nano structure in the imprinting process. According to actual measurement, the consistency of the nanoimprinted product is significantly improved, and the imprinting defect rate is greatly reduced.

[0023] (3) A dynamic self-cleaning control system for the surface of an embossing mold with spatial adaptive capabilities is formed. Compared with existing methods that rely solely on surface coatings or external cleaning processes, this invention achieves precise spatial distribution and maintenance of self-cleaning capabilities through zonal control of surface energy, optimization of layered superhydrophobic performance, automatic labeling and regeneration enhancement of self-cleaning area capabilities. It can also rapidly regenerate areas with localized performance degradation, effectively extending the mold's service life. The number of embossing batches per unit life of a high-precision mold can be increased by more than 2 times, significantly reducing the frequency of maintenance and replacement.

[0024] (4) Breaking through the technical bottlenecks of limited functionality of traditional single-level nanotextures and low processing yield of complex irregular molds. This method combines parameterized spatial distribution micro-nano models with multi-level plasma processes, enabling hierarchical nanotextures with different curvatures and variable cross-sections to be designed and manufactured in situ "on demand". This overcomes the problem that conventional homogenization processes cannot cover irregular regions, improves the synergistic function and processing yield of micro-nano structures under extreme spatial morphologies, has a wide range of applications, and is suitable for industrial-grade mass production of large-size and complex irregular structures.

[0025] (5) Supports intelligent closed-loop optimization of imprint quality and adaptive process control. The solution integrates high-resolution real-time acquisition, parameter sensitivity analysis and adaptive optimization of multi-source data such as the adhesion performance of imprinted finished structures, spatial distribution consistency and micro / nano structure forming quality, and automatically feeds process parameters back to the front-end hierarchical nanotexture design and deposition process. Compared with traditional manual parameter adjustment or passive post-processing inspection, the process robustness is improved and the finished product qualification rate is significantly improved; it effectively ensures high consistency and precision reliability in the mass production of complex structures. Attached Figure Description

[0026] Appendix Figure 1 This is the main flowchart of the modeling method and micro / nano fabrication method for the irregular mold of the crown-shaped biomimetic adhesive array.

[0027] Appendix Figure 2 This is a sub-flowchart of the modeling method and micro / nano fabrication method for the irregular mold of the crown-shaped biomimetic adhesive array.

[0028] Appendix Figure 3 This is another sub-flowchart of the modeling method and micro / nano fabrication method for the irregular mold of the crown-shaped biomimetic adhesive array.

[0029] Appendix Figure 4 The rolling die is generated using the irregular mold modeling method of the coronal biomimetic adhesion array of this application.

[0030] Appendix Figure 5 This is a magnified micrograph of a rolling die generated using the irregular die modeling method based on the coronal biomimetic adhesion array of this application.

[0031] Appendix Figure 6is a micrograph of another side of a rolling die generated using a shaped die modeling method of the coronal biomimetic adhesion array of the present application

[0032] attached Figure 7 is a coronal biomimetic adhesion array processed using a micro-nano processing method of the coronal biomimetic adhesion array of the present application DETAILED DESCRIPTION

[0033] Embodiments of the present application are described in detail below with reference to the attached drawing figures, wherein the same or like component have the same or similar designations throughout the attaching figures. The embodiments described below are exemplary and are not intended to be limiting of the present application unless otherwise explicitly indicated herein.

[0034] The disclosure given below provides many different embodiments or examples for implementing different structures of the present application. For the purpose of simplification, the components and arrangements of specific examples are described below. Of course, they are only examples and are not intended to limit the present application. In addition, the present application can repeatedly refer to reference numerals and or reference letters in different examples, and such repetition is for the purpose of simplification and clarity, which does not indicate the relationship between the various embodiments and or arrangements discussed.

[0035] As shown in the attached Figure 1 The present application provides a shaped die modeling method and micro-nano processing method of a coronal biomimetic adhesion array, specifically comprising:

[0036] S1: Obtain the complex spatial shaped structure parameters corresponding to the target coronal biomimetic adhesion array, and parameterize the modeling of multiple spatial bending degrees and scaled variable cross-sectional morphological characteristics to obtain a shaped micro-nano cavity model.

[0037] S2: Based on the shaped micro-nano cavity model, taking a high-strength mold substrate such as metal or ceramic as the bearing object, using focused ion beam etching or ultrafast laser direct writing to implement micro-nano structure pre-processing treatment on different regions of the substrate, generating micro-nano structure prototypes at different spatial positions.

[0038] S3: Through microscopic morphology analysis and surface roughness determination, realize the collection of multi-position micro-nano structure distribution characteristics for the micro-nano structure prototypes obtained from different surface regions, and establish a shaped surface micro-nano structure distribution model.

[0039] S4: Based on the shaped surface micro-nano structure distribution model, using a multi-step plasma-assisted deposition process (including PECVD) to in-situ grow a hierarchical nano-spine, nanotube array or nano-flower texture layer at different spatial positions, forming a spatially consistent hierarchical confined nano-texture structure.

[0040] S5: Perform surface energy state regulation and super-lyophobic / super-oleophobic performance test on the grown hierarchical confined nano-textured structure, optimize the partition physical parameters according to the nanostructure characteristics of different regions, and generate a self-cleaning ability tag mapping table to complete the modeling of the crown-shaped biomimetic adhesion array.

[0041] S6: Before the nanoimprint process, determine whether the surface self-cleaning ability tag meets the high-fidelity imprinting requirements of covering the complex spatial special-shaped structure micro-nano mold cavity; if the local self-cleaning ability is insufficient, perform regenerative deposition or strengthening treatment on the region.

[0042] S7: Apply the high-precision mold with hierarchical confined self-cleaning nano-texture to the biomimetic array nanoimprint process, use the surface nano-texture to actively suppress the embedding of micro-particles, foreign matter or pollutants in different spatial regions, and realize passive self-cleaning under continuous process.

[0043] S8: Perform high-resolution determination on the spatial distribution adhesion performance and local structure consistency of the imprinted product, and real-time correct the hierarchical confined nano-texture design parameters based on the distribution data to dynamically adapt to the process robustness requirements of different spatial special-shaped structure regions.

[0044] S9: Use high-temperature, ultraviolet or custom atmosphere treatment process to periodically regenerate and strengthen the hierarchical confined nano-texture, ensure the self-cleaning and structural integrity in long-period batch nanoimprint process, and prolong the service life of the high-precision mold.

[0045] The step S1: Obtain the complex spatial special-shaped structure parameters corresponding to the target crown-shaped biomimetic adhesion array, and parameterize the modeling of various spatial bending degrees and scaled variable cross-section morphological characteristics to obtain a special-shaped micro-nano cavity model. Specifically, it includes:

[0046] S1.1: Perform three-dimensional space surface topography digitization scanning processing on the target process object of the crown-shaped biomimetic adhesion array, collect the original point cloud data and spatial coordinate parameters of the to-be-processed spatial special-shaped structure, and fully capture the complex geometric information of the spatial special-shaped structure.

[0047] The three-dimensional surface of the target process object of the crown-shaped biomimetic adhesion array is the object to be collected, and the spatial special-shaped structure involved has high complexity and multi-dimensional curved surface characteristics.

[0048] The parameterized modeling includes principal curvature analysis, cross-section scaling function and spatial discretization coding, and models the special-shaped cavity as a three-dimensional vector matrix.

[0049] A high-resolution three-dimensional laser scanner or a structured light three-dimensional scanning system (parameters: scanning accuracy 0.5 μm, working distance 20-100 mm) is used to realize high-density non-contact three-dimensional sampling of the surface of a spatially shaped target object, to implement point cloud data collection on the full surface of the target object, and to obtain an original point cloud matrix with a spatial coordinate resolution better than 1 μm, and to generate a dot matrix file containing X, Y, and Z three-axis spatial coordinate parameters.

[0050] The three-dimensional digitizing scanning uses a structured light three-dimensional scanner or a laser scanner, and the point cloud resolution is 0.1-1 μm.

[0051] Further, through automatic pose adjustment and multi-view splicing scanning strategies (parameters: automatic turntable step accuracy 0.1°, multi-view coverage not less than 8 directions), full-coverage sampling of the directly unobservable area of the shaped structure is realized, point cloud missing caused by single-view occlusion, reflection, or uneven lighting is eliminated, and a complete spatial point cloud data surface coverage is formed.

[0052] Further, a point cloud preprocessing algorithm (including density equalization, wavelet denoising, and false point removal) is used for abnormal point identification and interpolation fusion processing, spatial noise suppression and defect correction are performed on the initially collected point cloud data, outlier points and scanning artifacts are removed, and a Kriging spatial interpolation is used to reconstruct the subdivided missing area, to obtain a corrected point cloud dataset with high fidelity and high consistency.

[0053] Further, a point cloud spatial registration algorithm (based on iterative closest point ICP or global registration GSF, etc.) is applied to perform spatial alignment and splicing on the local point cloud obtained from multiple views, to generate a complete original point cloud model under a unified global coordinate system, and to ensure one-to-one correspondence of the spatial objects in subsequent parameter modeling.

[0054] Through the above point cloud collection and preprocessing steps, a three-dimensional surface complex shaped structure physical object is converted into an original point cloud data and its three-dimensional spatial coordinate parameters with highly complete structural parameters and a true reflection of the geometric profile, to realize comprehensive digital modeling of complex geometric information of the shaped structure.

[0055] For example, taking the shaped protrusions at the end of a bionic gecko foot adhesion array as an object, a blue light structured light three-dimensional scanner (scanning range 30 mm x 30 mm, spatial resolution 0.8 μm, collection speed ≥1 M points / s) is used to perform partition collection on the full surface, and a 5-axis turntable is used to assist in the collection at a minimum curved radius of 0.3 mm, to realize omnidirectional point cloud sampling. After collection, a density equalization algorithm is used to adjust the original point density to not less than 2 x 10 5Points, by wavelet threshold noise removal due to surface reflection produced outliers, overall about 2% of abnormal points. For the point cloud hole caused by occlusion, the application based on Kriging interpolation algorithm to complete the reconstruction. Using GSF global registration algorithm to map 12 local point cloud to a unified coordinate system, the final output covering the whole surface, the coordinate error is less than 0.5 μm high-density three-dimensional point cloud model. The data model for subsequent crown biomimetic array complex space special-shaped micro-nano cavity parameterized modeling and three-dimensional structure restoration provides high-fidelity, full-coverage of the spatial geometry basis.

[0056] S1.2: Based on the obtained spatial special-shaped structure original point cloud data, using principal curvature analysis algorithm and surface fitting modeling method, the spatial curvature distribution extraction and surface reconstruction processing of point cloud data are carried out, to form a preliminary three-dimensional surface model containing various spatial bending parameters, and realize the quantitative characterization of special-shaped structure multi-dimensional spatial curvature.

[0057] The input condition is the original point cloud data of the crown biomimetic adhesion array spatial special-shaped structure, including a set of three-dimensional coordinate parameters.

[0058] The principal curvature analysis algorithm (parameters: point cloud density, normal vector calculation window, smoothing coefficient) is used to estimate the normal vector of the original point cloud data, calculate the principal curvature value of the local surface of any point, and obtain the principal curvature distribution of each point.

[0059] Further, by the surface fitting modeling method (parameters: finite element interpolation order or B-spline / Bezier surface smoothing parameters), the global point cloud space is reconstructed based on the principal curvature data to form a preliminary three-dimensional surface topology model.

[0060] Further, using the spatial curvature distribution extraction technology (parameters: principal curvatures K1, K2 and Gaussian curvature K), the reconstructed surface is analyzed by partition, the spatial bending parameter distribution of the surface at different positions is quantified, and the curvature vector set of multi-dimensional spatial region is obtained.

[0061] By multi-dimensional curvature feature parameter integration method, the principal curvature, Gaussian curvature and normal distribution of each sub-section of space are vectorized to realize the quantitative characterization of the multi-dimensional curvature of the complex special-shaped structure surface.

[0062] The following mathematical formulas of principal curvature and Gaussian curvature are used to complete the key parameter derivation:

[0063]

[0064]

[0065] Wherein, K1 and K2 are the principal curvatures of the curved surface obtained by first and second derivatives, K is the Gaussian curvature, E, F, G are the first order fundamental quantities, L, M, N are the second order fundamental quantities, and the related parameters are obtained by local fitting of the curved surface.

[0066] Through the above algorithm chain, the principal curvature distribution data and the fitting curved surface reconstruction result obtained by processing the three-dimensional space original point cloud are converted into a preliminary three-dimensional curved surface model of the spatial special-shaped micro-nano structure, and the standardization and data expression of the special-shaped structure multi-dimensional space curvature are realized.

[0067] Exemplarily, in the parameterization link of the spatial special-shaped structure of the target biomimetic adhesion array, the input is a 1.5 million point fine point cloud file obtained based on a GOMAtos three-dimensional scanner.

[0068] The normal estimation is performed on the full point cloud using a spherical window with a radius of 0.5 mm, the smoothing factor is set to 0.002 in the principal curvature analysis algorithm, the PCA method is used to calculate the principal curvatures K1 and K2, and the principal curvature range of the point cloud is about [0.01, 2.5] mm -1 . The surface fitting modeling selects a cubic B-spline surface, the interpolation order is 3, the maximum residual value is less than 0.1 mm, and the Gaussian curvature distribution range is [0.01, 6.25] mm -2 .

[0069] After the spatial curvature distribution is extracted, 1000 groups of curvature vectors of different spatial sections are obtained, each group including K1, K2 and K, and the output forms a multi-dimensional curvature vector feature matrix of the special-shaped structure.

[0070] Using the above formula, after the point cloud-surface chain derivation, the output preliminary three-dimensional curved surface model and curvature distribution parameters successfully reflect the bending degree of each curved surface area of the coronal biomimetic array, and create a high-fidelity data basis for subsequent high-precision cross-section scaling fitting and standard model derivation.

[0071] S1.3: For the preliminary three-dimensional curved surface model, a cross-section scaling function and a parameter fitting algorithm are used to calculate the scaling rate and perform modeling fitting processing on the variable cross-section regions of each spatial position of the model, so as to obtain high-precision data of the local variable cross-section parameters in the spatial special-shaped structure, and complete the cross-section change representation of the parameterized spatial special-shaped structure.

[0072] S1.4: Perform parameterized feature vector construction processing on the output spatial curvature distribution parameters and variable cross-section parameters, and use a standard geometric parameter coding method to generate a special-shaped micro-nano cavity morphology parameter set, so as to provide a unified parameter input for subsequent CAD modeling and process path generation of the structure model.

[0073] S1.5: Based on the morphological parameter set of the special micro-nano cavity, a parameterized three-dimensional modeling tool is used to realize the construction of the special micro-nano cavity model in space, and a high-precision standard model format file is exported, which provides a structural model basis for subsequent micro-nano structure processing simulation and special array redundancy layout optimization.

[0074] The step S2: based on the special micro-nano cavity model, taking a high-strength mold substrate such as metal or ceramic as a bearing object, using focused ion beam etching or ultrafast laser direct writing to implement micro-nano structure pre-processing treatment on different regions of the substrate to generate micro-nano structure prototypes in different spatial positions. Specifically, it includes:

[0075] S2.1: Discretization processing is performed on the different spatial position parameters of the special micro-nano cavity model to obtain a spatial position coding matrix suitable for the metal or ceramic mold substrate, which provides a spatial position reference for subsequent micro-nano structure processing path planning and regional process parameter setting.

[0076] For the three-dimensional data input of the special micro-nano cavity model, the input conditions include the spatial position parameter set and the corresponding three-dimensional coordinate information output by the parameterized modeling stage.

[0077] The spatial position discretization algorithm (parameters: preset discretization accuracy Δx, Δy, Δz, spatial adaptive encryption factor η) is used to realize the discrete division of the three-dimensional surface space of the special micro-nano cavity model. The continuous complex surface is divided into a finite number of discrete units according to the regular or irregular grid, ensuring the spatial coverage integrity of the special micro-nano structure distribution.

[0078] Further, through the spatial coding method based on the discretized grid points (such as hierarchical octree coding, nested hash mapping coding), a unique position identifier is assigned to each discrete spatial unit, realizing the bidirectional mapping between the three-dimensional space discrete unit and the micro-nano cavity entity structure, and establishing the association index of spatial position and model topography.

[0079] Further, relying on the mapping relationship between spatial position and structure parameters, a parameter screening algorithm (parameters: cavity local curvature threshold κ, variable cross-section rate threshold λ) is used to assign weights to the discrete spatial units in a regional manner, and dense discretization is performed on key micro-nano functional areas such as high curvature and variable cross-section, and sparse discretization is performed on low complexity areas, improving the resolution adaptability of the subsequent micro-nano processing path.

[0080] Further, through the matrix reorganization algorithm, the position coding, structure parameters and weighting coefficients of all spatial discrete units are combined to generate a spatial position coding matrix. Each row of the matrix corresponds to a spatial position unit, and each column is a spatial coordinate, a surface normal, a curvature parameter and a functional area label in turn, realizing the standardized integration of structure characteristics and spatial index.

[0081] Through the above series of space discretization and coding processing methods, the complex spatial structure parameters of the special micro-nano cavity model are converted into a standardized space position coding matrix suitable for metal or ceramic mold substrates, providing accurate spatial position references for subsequent micro-nano structure machining path planning and partition process parameter setting, and realizing precise pre-layout of special structure multi-region micro-nano machining.

[0082] For example, for a set of typical crown-shaped biomimetic adhesion array special molds, the input model is 100 μm × 100 μm × 20 μm three-dimensional space region point cloud data obtained based on laser scanning, the discretization accuracy is set as Δx = 1 μm, Δy = 1 μm, Δz = 0.5 μm, the adaptive encryption factor η = 2 is used for encryption in high-curvature areas, and the space octree coding method is used to assign 12-bit binary space coding to each discrete unit. For regions with surface principal curvature K greater than 0.5 μm -1 and cross-section scaling rate S greater than 10%, high weight (w = 3) is given, and the rest is given weight 1. The final space position coding matrix size is N × M (such as N = 5000 space units, M = 7 parameter items), and each row records three-dimensional coordinates, space coding, curvature, scaling rate, normal vector, and weight label. It has been verified that the space position coding matrix can accurately map the functional units of complex special regions, enable subsequent micro-nano structure machining partition, and improve the spatial distribution consistency and process adaptability of micro-nano arrays.

[0083] S2.2: Based on the space position coding matrix, a CAD / CAM integrated modeling and numerical control process parameter generation strategy is used to generate a set of micro-nano structure machining process instructions for each space section, to realize parallel machining path assignment for mold substrate multi-region special micro-nano structures.

[0084] S2.3: Taking the generated micro-nano structure machining process instruction set as input, selecting focused ion beam etching or ultrafast laser direct writing micro-nano machining process, implementing special micro-nano structure pattern transfer in different spatial regions of metal or ceramic mold substrates, and realizing spatial distribution controllable forming of micro-nano structure prototypes.

[0085] S2.4: Profile measurement and surface roughness analysis are performed on the preliminarily obtained spatially distributed special micro-nano structure surface, and professional detection equipment such as three-dimensional confocal microscope and AFM are used to extract accurate three-dimensional topography data of the spatially distributed special micro-nano structure prototype, providing detection results for subsequent error correction and structure consistency optimization.

[0086] S2.5: Based on the three-dimensional topographic data and the preset special-shaped micro-nano cavity target model, a micro-nano processing or point repair compensation is performed on the micro-nano structure primary type at different spatial positions of the mold base by using error analysis and multi-stage iterative correction algorithm, so that the special-shaped micro-nano structure primary type meeting the design requirements in spatial distribution and geometric parameters is obtained.

[0087] The step S3: for the micro-nano structure primary type obtained from different surface regions, the multi-position micro-nano structure distribution characteristics are collected by micro-topography analysis and surface roughness determination, and a special-shaped surface micro-nano structure distribution model is established. As shown in Figure 2

[0088] S3.1: The spatial position of the target metal or ceramic mold base is graded and calibrated, and the spatial coordinate data of each micro-nano structure primary type is obtained, so that the spatial position of the subsequent micro-topography analysis is accurately mapped.

[0089] S3.2: Based on the spatial coordinate data, the micro-topography of each spatial position of the micro-nano structure primary type is collected by scanning electron microscope (SEM) or atomic force microscope (AFM), and the corresponding micro-nano structure three-dimensional topographic data set is obtained.

[0090] The S3.2 sub-step is the execution unit, and the input object is the data set calibrated by the spatial position grading in S3.1. The data set contains the three-dimensional coordinate parameters and position information of the micro-nano structure primary type at different spatial positions of the special-shaped surface.

[0091] The scanning electron microscope (SEM) micro-topography collection method (parameters: accelerating voltage 0.5-5 kV, resolution <10 nm, working distance 5-15 mm) is used to obtain the two-dimensional high-resolution topographic image of the micro-nano structure primary type surface at each spatial position coordinate point.

[0092] Further, by atomic force microscope (AFM) three-dimensional topographic scanning (parameters: scanning area typically 10 μm× 10 μm or higher, Z-axis resolution better than 1 nm, scanning mode is contact mode or tapping mode), the real three-dimensional terrain height data of the micro-nano structure primary type is obtained, and a three-dimensional dot matrix height matrix is formed.

[0093] Further, by using the two-dimensional high-resolution topographic image and the three-dimensional height matrix obtained by SEM and AFM, the spatial registration and resampling algorithm (parameters: spatial interpolation method, nearest neighbor or bilinear interpolation) is applied in the data fusion module, and the two-dimensional SEM image and the three-dimensional AFM data are one-to-one mapped according to the spatial coordinate label, and a multi-channel high-precision three-dimensional micro-nano structure topographic data set is generated.

[0094] ​Further, by the denoising and surface reconstruction algorithm (method: median filter, pseudo-color mapping, hexahedral interpolation reconstruction, etc.) based on the topography data, the smoothing processing of the micro-nano structure initial three-dimensional topography data is realized, the measurement error and noise interference are eliminated, and the unified three-dimensional topography model for spatial distribution characteristic analysis is output.

[0095] Through the above multi-modal microscopic topography acquisition and data fusion processing, the micro-nano structure initial result under the spatial position calibration is converted into a continuous and high-resolution three-dimensional topography data set, and the expected technical effect of the three-dimensional characterization consistency of the special-shaped surface multi-position micro-nano structure is realized.

[0096] For example, for a special-shaped crown-shaped biomimetic adhesion array mold segment containing high principal curvature (K>0.5 μm -1 ) and large cross-section change, 50 spatial calibration points are set, 10 nm resolution surface topography images are collected by SEM at each point with an acceleration voltage of 5 kV, and AFM is used for 10 μm × 10 μm scanning to obtain a three-dimensional height matrix with a Z-axis resolution of 0.5 nm. In the subsequent registration process, by the spatial interpolation and coordinate mapping algorithm, the two-dimensional SEM image and the AFM three-dimensional height matrix collected at each point are resampled to generate a three-dimensional spatial topography data channel containing 512 × 512 data points. The surface reconstruction is completed by median filter denoising and hexahedral interpolation, the root mean square roughness (Ra) and the maximum height (Rmax) parameters are output at each spatial point, and 50 complete three-dimensional topography data sets are formed. The detection results show that the three-dimensional topography acquisition consistency error in the high curvature interval is less than 2%, which indicates that the acquisition scheme can realize high-fidelity three-dimensional micro-nano structure characterization in special-shaped space, and effectively support the subsequent partition analysis and parameter modeling process.

[0097] S3.3: Apply a statistical topography reconstruction algorithm to the three-dimensional topography data set, and extract and parameterize the key geometric parameters (such as height, spacing, topography diversity, etc.) of the micro-nano structure in different spatial regions to preliminarily form a regional distribution characteristic parameter matrix.

[0098] The multi-space position three-dimensional topography data set obtained by a scanning electron microscope (SEM) or an atomic force microscope (AFM) is used as input.

[0099] A statistical topography reconstruction algorithm based on regional spatial hierarchical calibration (parameters: minimum sample block size l_min, fitting window width w, sampling density ρ) is used to aggregate the three-dimensional point cloud of the micro-nano structure in each spatial region to form a regional division topography data group.

[0100] Further, by using a least square fitting and polynomial surface reconstruction method (parameters: fitting polynomial order n, residual threshold ε), surface reconstruction is performed on the micro-nano structure point cloud of each region to obtain key geometric parameters of the micro-nano structure such as vertex height h, base width b, and periodical spacing d, and to synchronously output topography diversity statistical indicators including structure height-width ratio distribution, dispersion coefficient, and diversity entropy S.

[0101] Further, by using a multi-region batch feature extraction algorithm, parameterized encoding processing is performed on the reconstruction results of each region to construct the height h i , spacing d i , and diversity entropy S i of each region into a feature parameter vector.

[0102] Further, by using a region feature parameter matrix assembly method, the feature parameter vectors of all spatial regions are spliced to generate a region distribution feature parameter matrix P:

[0103]

[0104] wherein h is the micro-nano structure height of the i-th region, is the spacing, is the structure diversity index, is the total number of spatial regions.

[0105] Through the above chain derivation processing, the micro-nano structure geometric and diversity feature parameter matrix under spatial partition calibration is output, and standardized description of key indicators of the micro-nano structure at multiple positions on the special-shaped mold surface is realized.

[0106] For example, for a 100 μm × 100 μm × 20 μm curved mold, in 64 spatial partitions, AFM three-dimensional topography point clouds are collected in each region, and by using a 5-order polynomial surface fitting (n=5, ε=0.02 μm), the micro-nano column average height h i is reconstructed to be between 7.5-8.2 μm, the spacing d i is 2.1-2.3 μm, and the diversity entropy S i is distributed in 0.03-0.08. After assembling the data of each region, a 64-row 3-column feature parameter matrix is output, which verifies that it can support partition control and spatial uniformity optimization in subsequent process design and data analysis, and significantly improves the overall hierarchical nano-texture consistency and process controllability.

[0107] S3.4: Using a surface profiler or AFM to measure the surface roughness of the extracted key regions, performing correlation analysis on the surface roughness parameters and key geometric parameters, and generating a high-resolution roughness distribution map of multiple sites.

[0108] With the extracted key area spatial coordinates and three-dimensional topography data as the input data set, high-resolution surface roughness analysis is performed on the multi-position characteristic area of the initial shape of the special micro-nano structure.

[0109] The surface micro-scan is performed on each key area by using a surface profiler (such as a white light interferometer) or an atomic force microscope (AFM) measurement method (parameters: scanning resolution 0.01 μm, scanning area 50 μm × 50 μm, etc.), and a three-dimensional surface height value matrix of the micro-nano structure surface of the area is obtained.

[0110] Further, the multi-domain surface roughness parameter calculation algorithm (including the arithmetic average roughness Ra, the root mean square roughness Rq, the maximum peak-to-valley height Rz, etc.) is used to realize the standardized extraction of the roughness parameters at different spatial positions, and the specific calculation formula is as follows:

[0111]

[0112]

[0113]

[0114] wherein, is the height value of the i th sampling point, is the average height, is the number of sampling points, is the local maximum height, and is the local minimum height.

[0115] Further, the data matching and correlation analysis algorithm (such as the Pearson correlation coefficient or the Spearman rank correlation method) is used to pair the area roughness parameters with the previously obtained key geometric parameters (such as the micro-column height-diameter ratio, the pitch, and the array distribution density) point by point, to construct a high-dimensional parameter comparison matrix, and to analyze the correlation between them. For the parameter pairs with strong correlation, the statistical relationship is quantitatively described, and a spatial position parameter-roughness response mapping is generated.

[0116] Further, the spatial interpolation algorithm (such as the inverse distance weighted IDW or the Kriging interpolation) is used to perform regional interpolation operation on the roughness parameters of all sampling points, to generate a multi-position high-resolution surface roughness distribution map of the special curved surface, and to realize the spatial continuity modeling of the surface roughness of the special mold.

[0117] The multi-site, high-resolution roughness distribution map output by this step provides basic data for subsequent spatial consistency optimization and partition process parameter adjustment of the special surface micro-nano structure, and realizes the accurate measurement and early warning of the surface quality of the multi-zone micro-nano structure of the complex spatial special mold.

[0118] ​Exemplary, for the 11 feature functional areas of the crown-shaped biomimetic adhesion array mold surface, the AFM scanning resolution is 0.01 μm, the scanning area is 50 μm × 50 μm, and 250,000 height data points are collected in each area. The Ra, Rq, and Rz calculation formulas are applied to each area to obtain the Ra distribution range of 7.2 nm to 15.8 nm, the Rq of 9.1 nm to 19.3 nm, and the Rz distributed between 42 nm and 163 nm. Pearson correlation coefficient analysis is performed on the obtained geometric spacing and height-diameter ratio characteristics of each area and Ra, Rq, respectively, and it is found that the correlation coefficient of the micro-column spacing and Ra reaches 0.82, and the correlation coefficient of the height-diameter ratio and Rq is 0.74, both of which are significantly positively correlated. Based on the Ra parameters of all positions, the IDW spatial interpolation algorithm (power index p = 2) is used to generate the entire irregular surface Ra high-resolution distribution map, which has good continuity and local high roughness area height consistent with the mold surface geometric convergence area. The output high-resolution roughness map is used to guide the process window setting and spatial section accuracy compensation in the subsequent plasma deposition process, effectively improving the consistency and quality control capability of complex mold surface micro-nano structures.

[0119] S3.5: Based on the roughness distribution map and parameter matrix, a multi-dimensional spatial distribution modeling method (such as principal component analysis or cluster analysis) is used to generate a micro-nano structure distribution model for each spatial area of the irregular surface, providing data support for irregular surface micro-nano structure consistency optimization and subsequent hierarchical nano-textured deposition process parameters.

[0120] The step S4: based on the irregular surface micro-nano structure distribution model, a multi-step plasma-assisted deposition process (including PECVD) is used to grow hierarchical nano-spikes, nanotube arrays or nano-flower texture layers in different spatial positions in situ, forming spatially distributed hierarchical confined nano-textured structures. As shown in Figure 3 The specific steps include:

[0121] S4.1: The spatial distribution parameter analysis is applied to the established irregular surface micro-nano structure distribution model to obtain the micro-nano structure distribution parameters at different spatial positions, which are used as the parameterized input objects of the multi-step plasma-assisted deposition process, and the differentiated guidance of the hierarchical confined nano-textured structure design scheme is realized.

[0122] The irregular surface micro-nano structure distribution model and the spatial distribution characteristic parameter matrix output therefrom are used as input data basis.

[0123] The spatial distribution parameter analysis algorithm (parameters: spatial region calibration set N, characteristic parameter matrix P, spatial position recognition accuracy σ) is used to quantitatively disassemble the micro-nano structure height, spacing and diversity index of different spatial areas of the irregular mold, and obtain the spatial position distribution of the micro-nano structure parameter set.

[0124] Further, by the spatial vectorization encoding method (parameters: one-to-one spatial coordinate label set L, parameter normalization scale s), each type of geometric index in the feature parameter matrix P is accurately matched with each physical space position, generating a multi-dimensional space micro-nano structure parameter vector group with global space labels and local feature quantities.

[0125] Further, by using the spatial distribution feature clustering and partitioning algorithm (parameters: spatial connectivity weight λ, feature index similarity threshold θ), the micro-nano structure parameter group is spatially partitioned and aggregated, the functional area distribution and typical area representative parameters are extracted, and the rapid identification of the functional uniformity of each irregular area and the abnormal parameter block is realized.

[0126] Further, combined with the spatial distribution feature vector and the clustering result, through the parameterized input generation module, the target micro-nano structure parameters of each spatial partition are automatically assigned for the multi-step plasma assisted deposition process (including PECVD and multi-atmosphere switching deposition), forming a spatial process parameter mapping table for the differential preparation of hierarchical limited nano-textured structures.

[0127] Through the spatial distribution parameter analysis and parameterized input generation processing mode, the multi-zone micro-nano structure parameter data of irregular surfaces is accurately and efficiently converted into spatially distributed parameters of multi-step plasma deposition processes, fully realizing the differential process guidance and parameter optimization configuration for the growth of complex micro-nano structures on spatial irregular surfaces, and enhancing the spatial distribution consistency and customization degree of subsequent hierarchical nano-textured structures.

[0128] For example, taking a irregular spatial crown-shaped biomimetic array mold with a height variation range of 10-15 μm, a pitch of 2.0-3.0 μm, and a diversity entropy S interval of 0.03-0.09 as an object, through the spatial distribution parameter analysis algorithm, the height, pitch, and S three-dimensional parameters are extracted in 72 spatial calibration zones, and the three-dimensional parameters are matched with the spatial coordinate labels L by using the spatial vectorization encoding, forming 72 groups of [height, pitch, S] spatial parameter vectors. Through the spatial distribution feature clustering algorithm, 8 types of functional partitions are aggregated, among which the first zone has a height mean of 13.2 μm, a pitch of 2.5 μm, and an S value of 0.06; the eighth zone has a height of 11.0 μm, a pitch of 2.1 μm, and an S value of 0.04. The parameters of each partition are mapped to the target input module of the multi-step plasma assisted deposition process to determine the plasma power, precursor atmosphere ratio, and deposition timing settings for each spatial region during the deposition process. The results show that the minimum controllable unit area of the deposition parameters after spatial partitioning is 2 mm 2, the parameter difference degree adapts to the spatial distribution of the complex special-shaped area, realizes the automation of the whole process parameter input and the accurate guidance of spatial differentiation, and provides key data and process guarantee for the spatial consistency and multi-functional collaborative customization of the nano-spike layer and the nano-tube array hierarchical structure in the next step. The 72 groups of parameter mapping tables can be directly connected to the plasma process automation platform, and the test results show that the parameter input error is controlled within 1.5%, the deposition effect of each hierarchical area tends to be consistent, and the consistency and customizability of the hierarchical limited nano-texture on the complex structure surface are significantly improved.

[0129] S4.2: Based on the micro-nano structure distribution parameters, a preset plasma deposition process parameter set is used to sequentially perform a plasma precursor pretreatment step on each spatial area of the metal or ceramic high-strength mold substrate, so as to adjust the surface energy level state of each area and provide uniform surface activity conditions for the in-situ growth of the hierarchical nanostructure.

[0130] With the special-shaped surface micro-nano structure distribution parameter matrix and spatial distribution model as input, a preset plasma deposition process parameter set (the parameter set includes plasma type, action atmosphere type, plasma power P, processing temperature T, pretreatment time t p , reaction chamber pressure P c , etc.) is called, which corresponds to the micro-nano structure distribution characteristics of each spatial area, and the spatial sequential process configuration is performed.

[0131] A multi-zone plasma precursor pretreatment method is used, the plasma power P i and the reaction atmosphere component C i are weighted and set through mask shielding or area sweeping of spatial position separation, the surface micro-area of each spatial area of the complex mold substrate is independently activated, the surface energy level state of the area is effectively adjusted, and the uniformity of the surface activity is improved.

[0132] Further, through the plasma physical / chemical activation reaction mechanism (including inert gas activation, oxygen plasma cleaning or special atmosphere such as Ar / H2 combination), the removal of surface adsorbed molecules and the introduction of high-activity functional groups are promoted, and the following parameters are adjusted: processing temperature T=30~60℃, plasma power P=20~100 W, substrate-electrode distance d=10~30 mm, processing time t p =120~480 s, atmosphere flow rate Q=10~100 sccm, pressure P c =10~100 Pa, to obtain high-density hydrophilic groups such as hydroxyl, carbonyl or amine groups, and to improve the surface free energy. The atmosphere, power and deposition time can be dynamically adjusted based on the partition parameter mapping.

[0133] Further, in combination with the regional surface area integral and surface energy change Δγ, the following formula is used to quantitatively evaluate the regional surface energy level change after plasma action:

[0134]

[0135] wherein, is the surface free energy before treatment, is the surface free energy of the region after treatment. By sequentially determining at multiple points, it is confirmed that all regions Δγ meet the standard, and the spatial variation σ(Δγ)≤5%, ensuring the uniformity of the initial surface energy.

[0136] Further, online surface energy measurement during the process (optionally using the contact angle method, surface potential meter or UPS spectrometer) is used for quantitative feedback, and through real-time verification at multiple points, the plasma pretreatment parameters are automatically adjusted to ensure the spatial distribution consistency of the surface activity and energy level state of different spatial regions.

[0137] Through the above fine partition plasma precursor pretreatment method, the micro-nano structure initial region surface energy state on the complex spatial shape is uniformly activated, improving the spatial uniformity and nucleation consistency of subsequent hierarchical nanostructure in-situ deposition, and achieving the technical effect of improving the spatial consistency and functional balance of nano-texture.

[0138] For example, for a 40 mm × 40 mm × 7 mm shaped crown biomimetic adhesion array mold metal substrate, 32 equally spaced spatial regions are selected on the substrate surface, and a preset plasma pretreatment process is called: using a parallel mask partitioning method, the regional atmosphere is O2 / Ar, the flow rates are 40 sccm and 60 sccm respectively, the chamber pressure P c = 30 Pa, the pretreatment temperature T = 35℃, the regional plasma power P i = 30~55 W (according to the spatial self-adaptive optimization distribution of regional roughness), the processing time t p = 6 min. After plasma treatment, online measurement using regional contact angle measuring instrument found that the contact angle decreased from 82±3° before pretreatment to 27±2°, the surface energy increased from 30.5 mN / m to 62.3 mN / m, the standard deviation σ(Δγ) of the spatial distribution region Δγ = 2.1 mN / m, which meets the standard of spatial variation ≤5%. The uniformity of surface polarity distribution is compensated by in-situ UPS test, the difference between the surface energy values of each spatial region is less than 3%, effectively balancing the surface energy level state of multiple regions, providing a consistent surface activity basis for the spatial consistent nucleation of subsequent hierarchical structures such as nano-spikes and nanotubes. The final output of the surface energy regional distribution data serves as an input condition to support the spatial uniform growth of subsequent plasma-assisted hierarchical nanostructures, effectively improving the spatial functional consistency of hierarchical confined nano-texture.

[0139] S4.3: In-situ growth of nano-spikes base layer in each target space region by first step plasma assisted deposition process (e.g. low pressure PECVD), through precise control of reaction atmosphere, plasma power and timing, to achieve controllable nucleation of nano-spikes spacing, aspect ratio and spatial distribution, forming a spatial position-dependent nanostructure primary template.

[0140] With the metal or ceramic high-strength substrate after spatial distribution parameter analysis and regional surface energy level state homogenization treatment on the surface of the special-shaped mold as the input object, the first step plasma assisted deposition process (such as low pressure PECVD) is performed for each spatial region to achieve controllable spatial distribution growth of the nano-spikes base layer in-situ.

[0141] Using regional process parameter input mapping table (parameter set including plasma type, atmosphere composition, plasma power P, reaction chamber pressure P c , substrate temperature T, deposition time t, reaction gas flow Q, etc.), directional regulation is performed for each spatial position unit to ensure spatial parameter self-adaptation of the nano-spikes growth process.

[0142] Further, through low pressure PECVD plasma deposition method (deposition atmosphere can include SiH4, CH4, Ar and O2, parameter range: atmosphere flow Q = 5~80 sccm, chamber pressure P c = 10~50 Pa, base temperature T = 50~200℃, plasma radio frequency power P = 10~100 W, deposition time t = 2~30 min), controllable nucleation initiation of nano-spikes structure in each spatial region is achieved.

[0143] Further, using spatial nucleation regulation algorithm (with parameters: reactant concentration C, ion density n i , energy flux density J, deposition timing Δt as variables), the spike density ρ s , average spacing d s and aspect ratio A r growth rate of each region are adjusted. The regulation depends on the following formula:

[0144]

[0145] Wherein is the number of initial nucleation per unit area, is the area of the region.

[0146] The spike aspect ratio growth process satisfies:

[0147]

[0148] Wherein is the average height of the spike, is the average spacing, is the material deposition constant, is the ion energy flux density per unit area, is the deposition time.

[0149] Further, an in-situ spatial zoning deposition monitoring system (including high-resolution SEM online imaging and AFM topography probe) is used to continuously collect real-time topography images and three-dimensional height matrix of the growth of nanospikes in each region. Based on the deposition process modeling feedback adjustment algorithm, the deposition power, gas composition and timing are adjusted in real time to dynamically match the spike spacing and height-to-diameter ratio preset values of the target region.

[0150] Further, a spatial nucleation distribution uniformity statistical analysis method is used to calculate the spike number distribution variance per unit area, average spacing standard deviation and height-to-diameter ratio dispersion coefficient in each region, forming the nanospike primary template consistency evaluation index related to spatial position.

[0151] Through the above plasma-assisted in-situ multi-zone synchronous deposition process, the pre-processed surface in each spatial region is converted in-situ into a nanospike base layer with controllable distribution, realizing a spatially uniformly distributed nanoscale structure primary template on a complex surface, and providing a microstructure basis for the subsequent coordinated distribution and functional integration of multi-level hierarchical structures.

[0152] For example, for a crown-shaped biomimetic array mold metal substrate with a size of 22 mm × 22 mm × 4 mm, the region distribution is 36 spatial zones, and each zone independently inputs a set of plasma deposition parameters: the deposition atmosphere is SiH4 / Ar (24 sccm / 36 sccm, respectively), the cavity pressure P c = 20 Pa, the radio frequency power P = 25 W, the substrate temperature T = 110℃, and the deposition time t = 8 min. The nucleation concentration C in the region is optimized zone by zone from the spatial characteristic parameters, and the spike spacing design value d s = 120 ± 7 nm is implemented during the deposition process. According to the above height-to-diameter ratio growth formula, the final spike height-to-diameter ratio A r in the region is distributed in the interval 8.8~10.2, the spike number per unit area ρ s = 6.5~7.2 × 10 8 ·cm⁻², the spatial zoning height-to-diameter ratio dispersion coefficient CV = 5.5%, and the average spacing standard deviation σ(d s )= 4.3 nm. The high-resolution SEM and AFM real-time topography output shows that the nanospike layer is coherent between regions, and there is no significant mismatch in spatial transition, fully verifying that this step can realize the in-situ construction of a spatially consistent nanospike base layer on a complex spatially shaped mold, providing key microstructure support for the subsequent secondary nanotexture superposition and overall hierarchical structure layout.

[0153] S4.4: Based on the formed nano-spike base layer, a secondary texture structure of nanotube array or nanoflower is grown in the specified area by a second step of plasma-assisted deposition (e.g. PECVD of unknown polymer precursor), and a multi-step continuous atmosphere switching technique is used to realize spatial hierarchical assembly of multi-level distributed nano-texture, so as to optimize the horizontal and vertical synergistic superposition effect of the hierarchical confined structure.

[0154] S4.5: The spatial distribution consistency of the completed hierarchical confined nano-texture structure grown in situ is detected, including high-resolution SEM imaging analysis and surface roughness three-dimensional reconstruction, so as to extract the spatial consistency distribution parameters of the hierarchical nanostructure under the special-shaped space, and provide the morphology basis for subsequent nanoimprint consistency.

[0155] The step S5: The surface energy state of the grown hierarchical confined nano-texture structure is regulated and the super-lyophobic / super-oleophobic performance is tested, the partition physical parameters are optimized and a self-cleaning ability label mapping table is generated according to the nanostructure characteristics of different regions, and the special-shaped mold modeling of the crown-shaped biomimetic adhesion array is completed. Specifically, it includes:

[0156] S5.1: The scanning electron microscope (SEM) image and atomic force microscope (AFM) surface three-dimensional morphology data of the micro-nano texture layer of the hierarchical confined nano-texture structure in each spatial position region are collected, so as to obtain the nano-texture structure parameters and surface roughness data, and provide basic input for the regulation of the surface energy attribute of the region.

[0157] S5.2: Based on the obtained nano-texture structure parameters and surface roughness data, the surface energy (including surface free energy, surface polarity component, etc.) of different spatial positions is tested by ultraviolet photoelectron spectroscopy (UPS) and contact angle analysis, the spatial heterogeneity of the surface physical and chemical properties is revealed by the surface energy distribution model, and the regional surface energy distribution data is formed.

[0158] The multi-dimensional characterization of the regional surface energy is carried out for different spatial position regions with the nano-texture structure parameters and surface roughness data of the hierarchical confined nano-texture structure as input objects.

[0159] The ultraviolet photoelectron spectroscopy (UPS) analysis method (parameters: sampling point coordinate set L, ultraviolet light source energy hν, electron collector working voltage V e , energy spectrum resolution R s ) is used to realize the determination of the surface electron energy level of the nano-texture surface in each spatial position region. Further, the work function , the valence band top energy level and the surface electron density distribution of each spatial region surface are extracted by UPS spectrum analysis. The surface work function is determined by the following formula:

[0160]

[0161] wherein, is the UV light source energy, is the secondary electron cut-off energy, is the Fermi level. The surface work function distribution of each spatial sampling area is extracted.

[0162] Further, through the contact angle analysis test method (parameters: spatial sampling area identifier L, liquid type (water / oil / standard solution), test volume V, static contact angle s , dynamic rolling angle r ), the surface free energy and polarity component quantitative measurement of the multi-space area surface are realized. The Young equation is used to inverse the relationship between the contact angle and the surface free energy, and the following formula is obtained:

[0163]

[0164] wherein, is the solid-gas surface free energy, is the solid-liquid interface free energy, is the liquid surface tension, is the measured contact angle.

[0165] Further, through the Owens-Wendt-Rabel-Kaelble (OWRK) model, the surface free energy is decomposed into the polar component and the dispersive component , and the following formula is used to jointly fit the multi-liquid test data:

[0166]

[0167] The , parameters of each spatial area are obtained by point-by-point measurement.

[0168] Further, the regional surface energy distribution modeling algorithm (parameters: spatial coordinate label L, UPS energy spectrum parameter set, contact angle data set, surface free energy component distribution) is used to fuse and analyze the surface energy level extracted by the UPS and the surface polarity component measured by the contact angle, and generate a multi-dimensional parameter vector set of the surface physical and chemical properties indexed by the spatial area.

[0169] Through multi-region data aggregation and principal component spatial distribution analysis, a surface energy spatial heterogeneity distribution model is constructed, realizing the spatial regionalization and parameterization characterization of the surface energy physical and chemical properties of the complex spatial heteromorphic nano-textured structure. Finally, the regional surface energy distribution data are output, providing basic physical parameters and performance indicators for subsequent precise surface energy state regulation and self-cleaning partition optimization.

[0170] Exemplarily, on a 40 mm x 40 mm x 5 mm shaped crown biomimetic adhesion array mold, 18 different curvature spatial calibration zones are selected, and for each zone, the UPS instrument is used to sample the light source energy of 21.2 eV and the energy spectrum resolution of 0.05 eV to obtain the regional surface work function The distribution interval is 4.68~5.12 eV. For each zone, deionized water and diiodomethane are dropped respectively, and the static contact angle θ s is tested. The contact angle of water is 41°~62°, and the contact angle of diiodomethane is 31°~45°. Through the OWRK model, the regional dispersion component and the polarity component are inverted, and the distribution is 21.3~30.1 mN / m, The distribution is 8.0~19.7 mN / m. By fusing the UPS and OWRK results, the surface energy parameter distribution matrix of the 18 zones is established for downstream surface energy state customization regulation. The spatial principal component analysis outputs two principal factors C1 (dispersion distribution factor) and C2 (polarity distribution factor), which explain the cumulative variance ratio of 93%. The spatial heterogeneity coefficient Cv of the test machine surface layer region attribute is 0.14. The performance verification shows that after the subsequent partition optimization, the regional self-cleaning performance is obviously improved. The regional surface energy distribution data provides strong data support for the quantitative optimization and subsequent self-cleaning parameter adjustment technical links.

[0171] S5.3: Using the regional surface energy distribution data, adopting surface energy state regulation processes such as plasma treatment and chemical modification (such as fluorination treatment), the surface polarity and surface free energy parameters of the nano-texture at each spatial position are adjusted, the precise regulation of the partition surface energy is realized, and the surface energy regulation region model is obtained.

[0172] With the obtained regional surface energy distribution data and the corresponding spatial position nano-texture layer as the input object, the spatial partition identification and the surface energy parameter matrix are used to establish the regional mapping, and the functional distribution of each spatial partition nano-texture structure is realized.

[0173] Using the plasma treatment method (parameters: plasma type O2, Ar or CF4, treatment power P = 20~100 W, reaction pressure P c = 10~100 Pa, action time t p = 60~600 s, treatment temperature T = 25~80°C), the nano-textured surface of different spatial regions is subjected to spatial partition plasma functionalization treatment according to the regional surface energy distribution model, and the surface group introduction and surface polarity adjustment are realized.

[0174] Further, by chemical modification process (parameters: chemical modifier type such as fluorinated reagent CF3(CF2)7CH2CH2SiCl3, solution concentration c = 0.01 ~ 0.2 mol / L, reaction temperature T r = 20 ~ 60 °C, processing time t r = 5 ~ 60 min), the nano-textured surface after plasma treatment is subjected to zoned chemical modification, introducing low surface energy groups with special functions (such as -CF3, -CH3 or other fluorinated organic groups), realizing further regulation of the surface free energy of each spatial position nano-textured layer.

[0175] Further, based on the online detection of surface polarity and surface free energy of spatial regions (optional ultraviolet photoelectron spectroscopy UPS, dynamic contact angle measurement), the surface energy parameter data of each region after processing are retrieved, and a regional parameter feedback adjustment algorithm is used to real-time correct the plasma and chemical modification process parameters, and the spatial blocks with surface energy deviation are compensated, improving the surface energy regulation accuracy under spatial distribution.

[0176] Further, a zoned modeling method (such as principal component analysis PCA or spatial clustering regression K-means) is used to model the spatial correlation of the regulated regional surface polarity components and surface free energy data, generate a zoned surface energy regulation region model, and realize systematic and spatial continuity description of the physical parameters of the nano-textured surface region.

[0177] Through the above chain multi-level surface energy regulation processing method, the pre-position surface energy distribution data is converted into the surface energy parameter matrix and spatial distribution model after zoned accurate regulation, realizing accurate and uniform regulation of the surface energy of each zoned nano-textured layer of complex spatial special-shaped structures, and providing a high consistency physical basis for subsequent super-lyophobic performance testing and self-cleaning ability label grading.

[0178] Exemplarily, on a piece of surface size of 25 mm x 25 mm of a special-shaped crown-shaped biomimetic mold, a surface energy distribution matrix of 16 spatial partitions has been obtained. For the 6 low-energy areas with a regional surface energy lower than 55 mN / m, O2 plasma treatment is selected, with a power of 40 W, a pressure of 30 Pa, a treatment time of 300 s, and a temperature of 30°C, and all 16 areas are treated. Secondly, for the areas with a surface energy lower than 60 mN / m after surface energy regulation, CF3(CF2)7CH2CH2SiCl3 solution (concentration 0.05 mol / L, reaction time 20 min, temperature 25°C) is used for further fluorination modification. After treatment, the average surface energy of the 16 areas is 63.5 mN / m, and the standard deviation of each area is 1.2 mN / m. Through contact angle and UPS detection, the spatial fluctuation of surface polarity and surface free energy is controlled within 2%. Using K-means algorithm clustering, a surface energy regulation area model with better spatial distribution consistency is obtained, providing accurate physical parameter basis for subsequent super-lyophobic performance evaluation and label allocation, and significantly improving the spatial consistency and anti-micro-pollution ability of the self-cleaning nano-textured layer in the subsequent imprinting process.

[0179] S5.4: Taking the nano-texture with completed surface energy regulation as the object, the super-lyophobic / super-oleophobic performance test is implemented according to the ISO standard, including static and dynamic liquid (oil / water) contact angle, roll angle and surface energy measurement, to obtain the super-lyophobic performance data set of each partition surface, and to correlate and map with the regional surface energy regulation model.

[0180] S5.5: Based on the regional super-lyophobic performance data set and the surface energy regulation area model, through data clustering and principal component analysis, the physical parameter settings of each area are optimized to form a parameter configuration scheme with better spatial distribution consistency, and a self-cleaning ability evaluation index system is defined.

[0181] S5.6: Using the physical parameter configuration scheme with better spatial distribution consistency, according to the partition self-cleaning ability evaluation index, the nano-texture structure at different spatial positions is labeled and a self-cleaning ability label is generated, realizing one-to-one mapping between the self-cleaning ability label and the spatial area, physical parameter and performance test result, forming a mapping table to provide data support for subsequent imprinting process parameter selection and mold maintenance.

[0182] The step S6: Before the nano-imprint process, it is judged whether the surface self-cleaning ability label meets the high-fidelity imprinting demand of covering complex spatial special-shaped structure micro-nano cavity; if the local self-cleaning ability is insufficient, regeneration deposition or strengthening treatment is performed for the area. Specifically, it includes:

[0183] S6.1: Take the self-cleaning ability tag mapping table optimized by the partitioned physical parameters as input, and for each spatial region of the hierarchical confined self-cleaning nano-texture structure, apply the self-cleaning performance judgment algorithm to compare the self-cleaning tags of different spatially shaped micro-nano cavity surfaces one by one, so as to obtain the candidate high-fidelity embossing regions and potential self-cleaning performance deficiency regions.

[0184] S6.2: Based on the judgment result of the self-cleaning performance deficiency region, combined with the shaped micro-nano cavity model and the surface micro-nano structure distribution model, the local defect recognition algorithm is used to calculate the specific measurement of the hierarchical confined nano-texture structure parameter deviation and the self-cleaning ability deficiency of the region, and output the defect parameter list with spatial position information.

[0185] Take the spatial self-cleaning performance deficiency region judgment result obtained by S6.1, the shaped micro-nano cavity model, and the surface micro-nano structure distribution model as input, and for each self-cleaning performance deficiency region in the spatially distributed shaped structure, use the local defect recognition algorithm (parameters: spatial coordinate tag, target region self-cleaning tag, hierarchical confined nano-texture design parameter set) to realize the point-by-point comparison of the actual parameters of the hierarchical confined nano-texture structure and the target design parameters of the target region.

[0186] Further, by using the spatial parameter deviation quantification algorithm (parameters: spatial partition identifier, actual texture parameter vector, target parameter vector, performance criterion function), the deviation distribution between the actual value and the design target of the hierarchical confined nano-texture structure parameters of each self-cleaning performance deficiency region is calculated. For each spatial point , the parameter deviation amount is defined as follows:

[0187]

[0188] Wherein, is the current nano-texture parameter vector of the spatial partition, is the design target parameter vector of the region, is the parameter deviation amplitude.

[0189] Further, according to the spatial self-cleaning performance evaluation function (such as super-lyophobic / super-oleophobic contact angle threshold, surface energy difference index, foreign matter blocking efficiency, etc.), the regional parameter deviation and the regional self-cleaning ability are functionally mapped to generate the self-cleaning ability deficiency measurement of each region:

[0190]

[0191] Wherein, is the specific measurement of the low self-cleaning performance of the region, For the functional relationship of multi-variable parameters and performance evaluation, regression or principal component analysis can be used to obtain.

[0192] Further, by using spatial information fusion and labeling algorithm, the spatial coordinates, structural parameter deviation , self-cleaning ability evaluation of each self-cleaning performance insufficient area are composed into a spatial defect parameter tuple , and organized into a defect parameter list carrying positioning markers, parameter deviation and self-cleaning performance insufficient indicators.

[0193] Through the above local defect identification and parameter deviation quantization algorithm, the spatial position, parameter deviation and performance evaluation result of the self-cleaning performance insufficient area are efficiently integrated, and the partition defect parameter list with complete spatial position information and defect indicators is output, realizing targeted and accurate defect identification and subsequent countermeasure input.

[0194] For example, for a 40 mm x 40 mm irregular shaped bionic crown-shaped adhesion array mold, the hierarchical confined nano-texture structure parameters of 18 typical spatial partitions are measured by scanning electron microscope and AFM, and the target design parameters (nanopin height-diameter ratio 2.5±0.1, pitch 180±20 nm, superhydrophobic contact angle ≥153°) are determined. In actual detection, the parameters of regions L7, L12 and L15 are: L7 height-diameter ratio 2.27, pitch 195 nm, contact angle 151.8°; L12 height-diameter ratio 2.09, pitch 182 nm, contact angle 147.1°; L15 height-diameter ratio 2.70, pitch 210 nm, contact angle 148.9°. By using

[0195]

[0196] The parameter deviation of L12 is 0.41, combined with the self-cleaning ability threshold C≥0.90, the self-cleaning ability evaluation D of L12 is 0.82, which does not meet the subsequent high-fidelity imprinting requirements. The defect parameter list is summarized as: {(L7, 0.23, 0.89), (L12, 0.41, 0.82), (L15, 0.22, 0.87)}. The output result generates a defect parameter marker queue positioned on the surface of the mold through the matching software, providing accurate input and spatial guidance for subsequent S6.3 local regenerative deposition regulation and self-cleaning performance enhancement processing. The quantitative identification and parameterization expression of self-cleaning performance insufficient area are realized, which effectively guarantees the mold imprinting consistency, process robustness and high-fidelity manufacturing ability.

[0197] S6.3: For each spatial region in the defect parameter list, according to its hierarchical limit nanotexture current parameter, call in-situ multi-step plasma assisted deposition process or physical strengthening processing algorithm, execute local regenerative deposition or surface energy state strengthening processing operation, to restore or enhance the self-cleaning ability of the hierarchical limit self-cleaning nanotexture structure of the target region, and obtain the strengthened hierarchical limit self-cleaning nanotexture structure parameter.

[0198] S6.4: For the spatial region after regenerative deposition or strengthening processing, use the self-cleaning performance determination algorithm again for iterative detection to verify whether the surface hierarchical limit self-cleaning nanotexture structure meets the high-fidelity imprinting requirements. If the detection result shows that the self-cleaning label still does not meet the requirements, repeat S6.3 until all the shaped structure micro-nano cavities achieve full-area self-cleaning ability that meets the requirements, and obtain the final high-precision mold surface self-cleaning label distribution map that meets the imprinting requirements.

[0199] S6.5: The final strengthened and verified high-precision mold hierarchical limit self-cleaning nanotexture structure and its surface self-cleaning ability label distribution map are taken as output, providing basic data for spatial region matching, process parameter setting and dynamic regulation in the continuous production process of subsequent biomimetic array nano-imprinting process, realizing seamless connection of mold self-cleaning performance and high-fidelity imprinting process.

[0200] The step S7: The high-precision mold with hierarchical limit self-cleaning nanotexture is applied to the biomimetic array nano-imprinting process, which actively suppresses the embedding of micro-particles, foreign matter or pollutants in different spatial regions, and realizes passive self-cleaning under continuous process. Specifically, it includes:

[0201] S7.1: Performance confirmation test is performed on the hierarchical limit self-cleaning nanotexture high-precision mold. The input condition is a high-resolution electron microscope analyzer for imprinting process. The micro-nano surface energy state characterization and pollutant adsorption test method is adopted to quantitatively evaluate the foreign matter blocking ability and super-lyophobic / super-oleophobic effect of the nanotexture structure surface. The nanotexture self-cleaning ability parameter matrix is output, which provides basic data for subsequent imprinting process adaptability determination.

[0202] S7.2: Based on the hierarchical limit self-cleaning nanotexture high-precision mold with confirmed performance, combined with the process input conditions of continuous nano-imprinting equipment, mold loading and pretreatment are implemented. Automatic clamping, surface electrostatic dust removal and atmosphere cleaning process are adopted to output the initial nanotexture mold cavity structure without foreign matter embedding, and realize the starting condition of high cleanliness imprinting.

[0203] S7.3: The pre-processed hierarchical confined self-cleaning nano-textured high-precision mold is inputted with multiple batches of polymer raw materials in the continuous nanoimprint process. Through the high-precision template imprinting control algorithm, the micro-nano array structure transfer process is implemented, and the foreign matter embedding situation of the imprint interface is monitored in real time. The mold surface nano-texture foreign matter inhibition efficiency is used as the key parameter, and the high-fidelity replicated biomimetic array structure polymer product is outputted.

[0204] The hierarchical confined self-cleaning nano-textured high-precision mold after pre-processing in S7.2 is taken as the execution object, and the foreign matter-free initial state and complete self-cleaning ability label distribution of the surface nano-texture region are processed.

[0205] The continuous nanoimprint process multi-batch polymer raw material automatic feeding module is used. According to the differences in self-cleaning ability label and special-shaped structure parameters of each spatial region, the partition raw material preheating and rheological parameter self-adaptive adjustment are implemented to realize accurate control of polymer fluidity and filling efficiency.

[0206] The high-precision template imprinting control algorithm (parameter settings including imprinting temperature, pressure, pressure holding time, imprinting speed, release rate, and each spatial position attitude compensation) is used to realize the process parameter partition self-adaptive setting of the nanoimprint equipment, and drive the high-precision mold and polymer raw materials to complete the spatial transfer and molding of special-shaped micro-nano structure in a high-clean environment.

[0207] Further, through the real-time imprint interface foreign matter embedding monitoring system (high-resolution optical imaging, real-time electron beam scanning or laser particle counting analysis combined), the mold-polymer interface of each spatial region is monitored throughout the period, the spatial distribution data of foreign matter, microparticles and imprint residues are collected, and the process parameters (such as local pressure, temperature, imprinting rate, etc.) in the imprinting process are dynamically adjusted according to the above data to inhibit the further embedding and residue of foreign matter.

[0208] For example, in the continuous nanoimprint production line, the hierarchical confined self-cleaning nano-textured high-precision stainless steel mold (such as Figure 4 ) after electrostatic dust removal and atmosphere cleaning pre-processing is loaded on the equipment workbench, and Figure 5 and Figure 6Microscopic morphologies of the special-shaped structures on the front and back surfaces of the mold were shown, respectively. A three-section partition temperature control strategy was adopted, and the mold temperature was set to 110°C (low curvature area), 125°C (high curvature area), and 115°C (transition area); the imprinting pressure was adjusted to 10 MPa, 13 MPa, and 11 MPa, respectively. A polyether ether ketone (PEEK) polymer film with a thickness of 150 μm was continuously supplied, and after preheating for 2 minutes, it entered the high-precision imprinting environment, and the pressure was maintained for 12 seconds, and the demolding rate was 2 mm / s. The real-time monitoring system used high-resolution optical imaging and laser particle counting method to measure the initial density of foreign matter in the corresponding area before imprinting The initial density of foreign matter in the corresponding area before imprinting was 12 particles / mm², and the density of foreign matter particles detected per unit area after imprinting was 1 particle / mm², and the foreign matter suppression efficiency was 91.7%, and the suppression efficiency was 91.7%; the suppression efficiency in the low curvature area and the transition area was higher than 95%. The imprinting structure uniformity of the final output PEEK biomimetic array polymer product in each spatial segment was higher than 98%, the adhesion performance distribution standard deviation was better than 0.8 mN, and the SEM imaging (as shown in the accompanying drawings) confirmed that the structure was complete and defect-free, and the high-fidelity imprinting replication and self-cleaning process robustness of the present process were verified. Figure 7 S7.4: Based on the chemical and physical action mechanisms of the nano-texture on the surface of the mold during the high-precision imprinting process, the imprinting byproduct particle analysis algorithm was used to detect the distribution of micro-pollution accumulation and foreign matter residues in each spatial area, and the residual distribution of the local self-cleaning ability of the mold after each imprinting cycle was output, which was used to assist in the continuous monitoring process robustness.

[0209] S7.5: Using the high-resolution consistency of the finished product structure and the local adhesion performance test after imprinting as input, the structure-performance quantitative correlation algorithm was used to close-loop verify the self-cleaning efficiency of the hierarchical limited nano-texture structure in different spatial areas, and the spatial mapping self-cleaning performance feedback result was output, which provided input parameters for the subsequent mold surface structure regeneration reinforcement and process optimization.

[0210] The step S8: The spatial distribution adhesion performance and local structure consistency of the imprinting finished product were determined at high resolution, and the hierarchical limited nano-texture design parameters were corrected in real time based on the distributed data, so as to dynamically adapt to the process robustness requirements of different spatial special-shaped structure areas. Specifically, it includes:

[0211] S8.1: High-resolution adhesion force test was performed on the spatial distribution area of the imprinting finished product to obtain the adhesion performance distribution data of the imprinting finished product in different spatial areas.

[0212]

[0213] ​​S8.2: Based on the adhesion performance distribution data, use high-precision scanning electron microscopy and three-dimensional profilometry to quantitatively determine the local structure consistency of the embossed product, and output the local structure consistency data distribution graph.

[0214] S8.3: Use the spatial distribution adhesion performance distribution data and local structure consistency data to construct an embossing quality distribution model for complex spatial special-shaped structure regions, realizing the systematic integration of multi-source process data for spatial special-shaped structure regions.

[0215] The spatial distribution adhesion performance data and the local structure consistency data of the embossed product are synchronized and standardized through a data interface module, forming a multi-source data set and loading it into a data analysis engine.

[0216] Using a region layering method based on spatial grid division (parameters: spatial coordinate system, region division granularity), the embossed product surface is divided into multiple special-shaped regions or functional sub-blocks, and the adhesion performance distribution data and structure consistency data in each region are collected and aligned to realize the standardized integration of data in the spatial domain.

[0217] Through principal component analysis (PCA) and spatial statistical modeling methods (parameters: characteristic covariance matrix, contribution rate threshold), the adhesion performance feature vector and structure consistency index in each region are reduced and integrated to extract the principal component parameter group describing the embossing quality.

[0218] Further, through multivariate regression analysis method (parameters: structure consistency principal component, adhesion performance principal component), a linear or nonlinear correlation model between embossing quality and key physical parameters in spatial special-shaped structure regions is established, the contribution of each principal component to the change of embossing quality is quantitatively identified, and an embossing quality evaluation index is generated.

[0219] Using spatial interpolation algorithms (such as Kriging interpolation, parameters: spatial semivariogram function), high-resolution reconstruction and interpolation simulation of embossing quality data are performed on incomplete sampling data regions to complete the data missing points in the spatial special-shaped structure distribution, and a continuous embossing quality distribution parameter field is obtained.

[0220] Based on the above parameter field, using spatial thermal distribution visualization methods, the adhesion performance, local structure consistency and comprehensive embossing quality index are multi-dimensionally mapped to generate an embossing quality distribution model for complex spatial special-shaped structure regions, realizing the systematic integration of adhesion performance and structure consistency multi-source process data.

[0221] Through the embossing quality distribution model, the spatial multi-element data of the previous step are converted into process quality evaluation indexes for complex special-shaped regions, realizing real-time monitoring and partition optimization setting of the embossing quality of complex spatial special-shaped structures.

[0222] For an exemplary embossed product with a multi-curvature special-shaped micro-nano structure area, the spatial division granularity is set to 500 pm x 500 pm, and the entire product surface is divided into 100 functional cells. 30 times of adhesion performance test results and 5 pm resolution structure consistency data under a scanning electron microscope are collected in each cell, respectively, and after standardization, the analysis is loaded. The PCA algorithm is used to reduce the adhesion performance characteristics from 30 dimensions to 3 principal components, and the structure consistency characteristics from 50 dimensions to 5 principal components, forming 8 principal component data for each region. Through multivariate regression model analysis, it is found that the contribution degrees of principal component 1 and principal component 3 to the local embossing quality are 0.41 and 0.34, respectively. The missing area data is reconstructed by Kriging interpolation, and the mean residual error after interpolation is less than 0.015. Through spatial thermal force mapping, the embossing quality distribution map of the whole surface is obtained, which can accurately identify the cells with consistency deviation and adhesion performance degradation at the edge. The model provides a high-confidence decision basis for subsequent targeted parameter correction and adaptive process optimization, and finally realizes the embossed product qualified rate to 98.6%, and the edge consistency index is improved by more than 20%.

[0223] S8.4: Apply a multi-dimensional parameter sensitivity analysis algorithm to the embossing quality distribution model to identify key correlation parameters between hierarchical limited nano-texture design parameters and spatial distribution adhesion performance and structure consistency, and form a parameter optimization indication vector.

[0224] S8.5: Based on the parameter optimization indication vector, automatically correct the design parameters of the current hierarchical limited nano-texture in real time, form a spatial special-shaped structure area personalized self-cleaning nano-texture parameter set, and realize adaptive adjustment of the hierarchical limited nano-texture structure.

[0225] S8.6: Feedback the updated spatial special-shaped structure area personalized self-cleaning nano-texture parameter set to the front-end hierarchical limited nano-texture process module for guiding the dynamic iteration of the subsequent nano-texture construction process, and completing the closed-loop control of process robustness and embossing consistency.

[0226] The step S9: Periodically regenerate and strengthen the hierarchical limited nano-texture by using high-temperature, ultraviolet or customized atmosphere treatment process, to ensure the self-cleaning property and structure integrity in long-period batch nano-embossing process, and prolong the service life of high-precision mold. Specifically includes:

[0227] S9.1: On-line monitoring of hierarchical limited nano-texture surface parameters, using high-resolution surface energy state detection technology to periodically collect surface self-cleaning ability, structure integrity and nano-texture roughness and other parameters, to obtain hierarchical limited nano-texture state data as the input basis for subsequent regeneration processing.

[0228] S9.2: Based on hierarchical confinement nano-texture state data, using adaptive regeneration strategy algorithm, determine the self-cleaning performance decline or structural integrity degradation area of hierarchical confinement nano-texture, generate the surface partition task list that needs to be strengthened as the input object of directional processing.

[0229] S9.3: For the surface partition task list that needs to be strengthened, select the corresponding processing parameters, execute high-temperature processing, ultraviolet irradiation or special atmosphere process (such as inert atmosphere, plasma atmosphere) periodic action, use thermal activation, photorepair or atmosphere catalysis mechanism, carry out local or overall regeneration strengthening treatment on hierarchical confinement nano-texture, output the hierarchical confinement nano-texture structure after regeneration strengthening.

[0230] S9.4: For the hierarchical confinement nano-texture structure after regeneration strengthening, use super-liquid repellency / super-oil repellency performance test and micro-nano structure integrity detection to obtain the restored self-cleaning ability parameters and structure retention rate data, evaluate the regeneration treatment effect, and generate regeneration treatment feedback data.

[0231] S9.5: Use the regeneration treatment feedback data to dynamically correct the high-temperature, ultraviolet or atmosphere strengthening process parameter settings, and through the process traceability management module, realize the closed-loop intelligent optimization of the periodic regeneration process of hierarchical confinement nano-texture, and finally output the hierarchical confinement nano-texture with long-term stability, high self-cleaning and high structural integrity, which provides continuous and efficient mold surface function guarantee for batch nanoimprinting.

[0232] For those skilled in the art, other various corresponding changes and transformations can be made according to the above described technical solutions and concepts, and all of these changes and transformations should belong to the protection scope of the claims of the present application.

[0233] Unless otherwise defined, the technical terms or scientific terms used herein should be understood as the usual meaning understood by a person with ordinary skills in the art to which the present application belongs. The "first", "second", "third" and similar words used in the patent application specification and claims of the present application do not represent any order, quantity or importance, but are used to distinguish different components. Similarly, "one" or "a" and similar words do not represent a quantity limitation, but represent the existence of at least one. "Including" or "containing" and similar words mean that the elements or objects appearing before "including" or "containing" cover the elements or objects listed after "including" or "containing" and their equivalents, and do not exclude other elements or objects. The plurality of the embodiments of the present application refers to two or more. A and / or B means that there are three cases: A; B; and A and B.

[0234] The above description is only exemplary embodiments of the present application, and the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present application, and these modifications or replacements should be encompassed in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for modeling a special mold of a coronal biomimetic adhesion array, specifically comprising: S1: obtaining complex spatial special structure parameters corresponding to a target coronal biomimetic adhesion array, and parameterizing modeling of a plurality of spatial bending degrees and scaled variable cross-sectional morphological features to obtain a special micro-nano cavity model; S2: based on the special micro-nano cavity model, taking a metal or ceramic high-strength mold base as a bearing object, performing micro-nano structure pre-processing on different regions of the base to generate micro-nano structure prototypes at different spatial positions; S3: collecting multi-position micro-nano structure distribution characteristics of the micro-nano structure prototypes, and establishing a special surface micro-nano structure distribution model; S4: based on the special surface micro-nano structure distribution model, using a multi-step plasma-assisted deposition process to in-situ grow a hierarchical nano-spine, nano-tube array or nano-flower texture layer at different spatial positions, forming a spatially consistent hierarchical confined nano-texture structure; S5: performing surface energy state regulation and super-lyophobicity performance testing on the grown hierarchical confined nano-texture structure, optimizing partition physical parameters according to nano-structure characteristics of different regions and generating a self-cleaning ability tag mapping table to complete the special mold modeling of the coronal biomimetic adhesion array.

2. The method of modeling a contoured mold for a corona-inspired adhesive array of claim 1, wherein, The parameterized modeling in step S1 includes principal curvature analysis, cross-sectional scaling function and spatial discretization coding, and the special cavity is modeled as a three-dimensional vector matrix.

3. The method of modeling a contoured mold for a biomimetic crown adhesion array of claim 1, wherein, The high-density non-contact three-dimensional sampling of the spatial special target surface is realized by using a high-resolution three-dimensional laser scanner or a structured light three-dimensional scanning system, and point cloud data is collected on the full surface of the target object.

4. The method of modeling a contoured mold for a corona-inspired adhesive array of claim 1, wherein, The step S3 specifically comprises: spatial position grading calibration of the special curved surface region of the target metal or ceramic mold base is performed to obtain spatial coordinate data of each micro-nano structure prototype; based on the spatial coordinate data, scanning electron microscope or atomic force microscope microscopic morphology collection is performed on the micro-nano structure prototypes at each spatial position to obtain corresponding micro-nano structure three-dimensional morphology data sets; statistical morphology reconstruction algorithm is applied to the three-dimensional morphology data sets to extract and parameterize model key geometric parameters of the micro-nano structures in different spatial regions, and a regional distribution characteristic parameter matrix is initially formed; surface roughness of the extracted key regions is determined by using a surface profilometer or an AFM, and correlation analysis of the surface roughness parameters and the key geometric parameters is performed to generate a multi-site high-resolution roughness distribution map; based on the roughness distribution map and the parameter matrix, a multi-dimensional spatial distribution modeling method is used to generate a micro-nano structure distribution model for each spatial region of the special curved surface.

5. The method of modeling a contoured mold for a corona-inspired adhesive array of claim 1, wherein, The step S4 specifically comprises: spatial distribution parameter analysis is performed on the established special surface micro-nano structure distribution model to obtain micro-nano structure distribution parameters at different spatial positions; based on the micro-nano structure distribution parameters, a preset plasma deposition process parameter set is used to sequentially perform a plasma precursor pretreatment step on each spatial region of the metal or ceramic high-strength mold base; The first step of the plasma-assisted deposition process is used to grow a nano-spike base layer in situ in each target space region; and the second step of the plasma-assisted deposition process is used to grow a nano-tube array or a nano-flower secondary texture structure in a specified region based on the nano-spike base layer. The spatial distribution and morphology consistency of the in-situ grown hierarchical confined nano-texture structure is detected to extract the spatial consistency distribution parameters of the hierarchical nano-structure in the abnormal space, and to provide the morphology basis for subsequent nano-imprinting consistency.

6. The method of modeling a contoured mold for a corona-inspired adhesive array of claim 3, wherein: The three-dimensional digital scanning is performed by using a structured light three-dimensional scanner or a laser scanner.

7. The method of modeling a contoured mold for a coroniform biomimetic adhesion array of claim 1, wherein: The multi-step plasma-assisted deposition process is configured with a regional partition plasma power of 20-100 W, a processing temperature of 30-60 DEG C, and dynamic adjustment of the atmosphere, power and deposition time based on the partition parameters.

8. A micro-nano processing method of a coroniform biomimetic adhesion array, which uses a mold formed by the mold modeling method of the coroniform biomimetic adhesion array according to any one of claims 1-8 to perform micro-nano processing of the coroniform biomimetic adhesion array, comprising: Before the nano-imprinting process, it is judged whether the surface self-cleaning ability label meets the high-fidelity imprinting demand of covering the micro-nano mold cavity of the complex spatial abnormal structure. If the local self-cleaning ability is insufficient, then the area is subjected to regenerative deposition or reinforcement treatment. The mold with hierarchical confined self-cleaning nano-texture is applied to the nano-imprinting process of the biomimetic array, and the surface nano-texture is used to actively suppress the embedding of micro-particles, foreign matters or pollutants in different spatial regions. The spatial distribution adhesion performance and local structure consistency of the imprinted product are determined at high resolution, and the design parameters of the hierarchical confined nano-texture are corrected in real time based on the distribution data.

9. The method of micro- and nano-fabrication of a coroniform biomimetic adhesion array according to claim 9, wherein, It also includes: Periodic regeneration and reinforcement of the hierarchical confined nano-texture by using high-temperature, ultraviolet or customized atmosphere treatment process.

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