Differentiated and Precise Current Distribution Control Method in the Process of Multi-Workpiece Batch Electroplating

Abstract

The present invention discloses a differential precise current distribution control method in the process of batch electroplating of multiple workpieces, including: acquiring and preprocessing multi-workpiece characteristic data; analyzing and grouping the differential characteristics of the workpieces; modeling and analyzing the electric field distribution of the electrolytic cell; dynamically monitoring and predicting the growth of the coating; generating a differential current distribution strategy. The present invention uses a multi-frequency differential resistance spectrum acquisition system to realize real-time monitoring of coating growth, applies an improved independent component analysis algorithm with fused spatial constraints to decouple the resistance spectra of workpiece groups, and realizes precise micro-region current modulation through an adaptive waveform library in the electric field distortion region, solving the problems of difficult dynamic characteristic monitoring of coatings and insufficient differential control of workpieces with similar characteristics in multi-workpiece electroplating, and improving the consistency of coating quality.

Description

Technical Field

[0001] The present invention belongs to the field of electroplating processes and their control, and in particular, to a method for differential and precise current distribution control in the process of batch electroplating of multiple workpieces. Background Art

[0002] As an important branch of surface treatment technology, the electroplating process has a wide range of applications in the fields of electronics, aviation, automotive, etc. In a batch production environment, it is an inevitable choice to process multiple workpieces with different shapes, sizes or materials simultaneously to improve production efficiency, but this also places higher requirements on the precise control of the electroplating process. Achieving differential and precise current distribution control in the process of batch electroplating of multiple workpieces can not only improve the consistency of coating quality, but also reduce material consumption, energy loss, and improve production efficiency, which has important economic and technical value.

[0003] Current multi-workpiece electroplating control technologies are mainly divided into three categories: batch electroplating methods based on pre-classification of workpieces, electric field regulation methods based on auxiliary shielding devices, and current control methods based on pulse parameter adjustment. The method based on pre-classification of workpieces realizes differential control between batches by pre-measuring the geometric features of workpieces and dividing similar workpieces into the same batch for electroplating; the method based on auxiliary shielding devices adjusts the local electric field distribution by arranging auxiliary shielding objects around the workpieces to achieve protection of high current density areas; the method based on pulse parameter adjustment controls the current distribution of different types of workpieces in the time dimension by changing parameters such as pulse frequency and duty cycle. In addition, some researchers have tried to identify the shape of workpieces through simple computer vision technology and set basic electroplating parameters in combination with empirical models, but lack the ability to accurately perceive and control the microscopic differences between and within workpieces.

[0004] However, the existing technologies have obvious deficiencies in the monitoring of the dynamic characteristics of coating growth and the differential control of groups of workpieces with similar characteristics. First, in terms of the dynamic monitoring of coating growth, the existing methods mainly rely on the initial workpiece characteristics for open-loop control or simple timed feedback adjustment, lacking real-time monitoring and accurate modeling of the changes in surface geometric characteristics during the coating growth process. Especially in the multi-workpiece co-bath environment, the electric fields generated by each workpiece interfere with each other, resulting in complex changes in the electric field distribution. The existing resistance spectrum monitoring technology cannot effectively distinguish this interference, thus unable to accurately capture the dynamic characteristics of the coating growth of each workpiece. Second, in terms of the differential control of workpiece groups, the existing technologies mostly adopt rough grouping methods based on specification classification, lacking the ability to identify and handle the subtle differences within the same specification workpiece group. When there are workpieces with similar shapes but different detailed structures in a batch of workpieces, these local differences will cause subtle changes in the electric field distribution, and traditional control methods are difficult to achieve precise adjustment of electroplating parameters for these subtle differences, resulting in fluctuations in the coating quality of workpieces in the same batch. In addition, in terms of the micro-region current control of complex-shaped workpieces, the existing pulse modulation methods lack consideration of the interaction between electric fields in each micro-region and cannot solve the problem of mutual interference of micro-region current parameters, restricting the further improvement of electroplating quality. Summary of the Invention

[0005] The object of the invention is to provide a differential precise current distribution control method in the process of multi-workpiece batch electroplating, in order to solve at least one technical problem existing in the existing technologies.

[0006] Technical solution: The differential precise current distribution control method in the process of multi-workpiece batch electroplating includes:

[0007] Obtain the characteristic data of multi-workpieces and perform preprocessing to obtain a workpiece data set;

[0008] Based on the workpiece data set, analyze the differential characteristics of workpieces and group them to obtain an optimized workpiece grouping and calculate the within-group differential matrix;

[0009] Based on the optimized workpiece grouping and the workpiece data set, conduct modeling and analysis of the electric field distribution in the electrolytic cell to obtain the actual current density distribution in the micro-region and the workpiece electric field interference matrix;

[0010] Based on the workpiece electric field interference matrix and the within-group differential matrix, conduct dynamic monitoring and prediction of coating growth to obtain the coating growth characteristics of the group;

[0011] Read the coating growth characteristics of the group, and combine the actual current density distribution in the micro-region and the optimized workpiece grouping to generate a differential current distribution control strategy.

[0012] Beneficial effects: The present invention solves the problems of difficult monitoring of the dynamic characteristics of the coating in multi-workpiece electroplating and insufficient differential control of workpieces with similar characteristics, realizes the precise identification and control of the tiny differences within the group of workpieces with similar characteristics, and at the same time realizes the precise current regulation of the complex micro-regions on the workpiece surface, improving the uniformity and quality consistency of the coating. Description of the Drawings

[0013] Figure 1 It is a flowchart of the steps of a differential precise current distribution control method in the process of multi-workpiece batch electroplating provided by an embodiment of the present application.

[0014] Figure 2 It is a flowchart of the steps of analyzing the differential characteristics of workpieces and grouping them provided by an embodiment of the present application.

[0015] Figure 3 It is a flowchart of the steps of constructing a multi-level workpiece similarity metric provided by an embodiment of the present application.

[0016] Figure 4 It is a flowchart of the steps of grouping workpieces and selecting representative workpieces provided by an embodiment of the present application. Detailed Embodiments

[0017] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0018] It should be particularly noted that, for clearly showing the step flow of the present application, numbers are marked for each step in the specification. These numbers are only for the convenience of description and do not limit the execution order of the steps. In actual operation, according to the technical requirements of the specific implementation scenario, the steps can be executed in an order different from that shown in the specification, and in some cases, parallel processing between steps can also be achieved.

[0019] As Figure 1 shown, the differential precise current distribution control method in the process of multi-workpiece batch electroplating includes the following steps:

[0020] S1. Obtain the characteristic data of the multi-workpieces and perform preprocessing to obtain a workpiece data set;

[0021] Specifically, the characteristic data of multiple workpieces include the three-dimensional point cloud data of the workpieces, the surface conductivity distribution data of each part of the workpieces, and the material composition data of the workpiece surfaces. Among them, the three-dimensional point cloud data describes the detailed information of the surface shape and spatial structure of the workpieces; the surface conductivity distribution data reflects the current conduction ability of different parts of the workpiece surfaces; the material composition data records the chemical components of the workpiece surface materials.

[0022] S2. Based on the workpiece dataset, analyze the differential characteristics of the workpieces and group them to obtain the optimized workpiece grouping and calculate the within-group differential matrix;

[0023] Specifically, use the existing dataset to extract the unique attributes of each workpiece, which may affect the electroplating effect. For example, the surface of one workpiece is relatively rough, while that of another is smooth, and the current distribution during electroplating will be different. According to the analysis results, divide the workpieces into different groups. The characteristics of the workpieces in each group are as similar as possible, which can ensure that the electroplating strategy for each group is more accurate. For example, divide the workpieces with smooth surfaces into one group and those with rough surfaces into another group. For each group, further analyze the subtle differences between the workpieces within the group and describe these differences with a mathematical matrix, which helps to further fine-tune the electroplating parameters and minimize the electroplating effect differences between the workpieces within the group.

[0024] S3. Based on the optimized workpiece grouping and the workpiece dataset, conduct modeling and analysis of the electrolytic cell electric field distribution to obtain the actual current density distribution in the micro-region and the workpiece electric field interference matrix;

[0025] Specifically, establish a virtual electrolytic cell model, which can simulate how the current flows in the electrolytic cell and how these currents are distributed to the surfaces of different workpieces. Through modeling, calculate the actual current density borne by each small region (micro-region) on the workpiece surface. This is like analyzing how much current each piece of the workpiece surface "absorbs", reflecting the accuracy of the current distribution during the electroplating process. The electric fields between the workpieces will affect each other. For example, one workpiece may "block" part of the current path of another workpiece. The electric field interference matrix is used to describe this complex relationship of electric field interaction between the workpieces.

[0026] S4. Based on the workpiece electric field interference matrix and the within-group differential matrix, conduct dynamic monitoring and prediction of the coating growth to obtain the coating growth characteristics of the group;

[0027] Specifically, use real-time data monitoring technology to observe the actual growth of the coating in each region of the workpiece surface. For example, the coating may grow too fast or too slow in some regions, and this dynamic information can help to discover problems. Combine the monitoring data and the previous analysis results to predict the coating growth pattern and overall characteristics of each group of workpieces, such as which region of the coating is most likely to be completed early or requires more current.

[0028] S5. Read the growth characteristics of the group plating layer, and combine the actual current density distribution in the micro-region and optimize the workpiece grouping to generate a differentiated current distribution control strategy.

[0029] Specifically, construct a differentiated current distribution plan to ensure that the current distribution during electroplating can meet the specific requirements of different workpieces and regions.

[0030] In this embodiment, by accurately analyzing the differential characteristics of each workpiece and the characteristics of the electric field distribution, a differentiated current distribution strategy is generated, making the electroplated layer more uniform, reducing defects, and improving the quality and performance of the plating layer. The intelligent grouping of workpieces and the current distribution control strategy can reduce the electroplating time, lower energy consumption, and improve production efficiency. Due to the use of dynamic monitoring and prediction technology, the current distribution strategy can be adjusted in real time to adapt to the complex shapes and materials of different workpieces, realizing personalized electroplating. By reducing material waste during electroplating, lowering power consumption, and improving the efficiency of batch production, the production cost can be effectively saved. Based on comprehensive data analysis and modeling, it can provide scientific guidance for the electroplating process and reduce the dependence on traditional experimental and empirical methods.

[0031] According to one aspect of the present application, the steps of performing preprocessing include:

[0032] S11. Collect multi-modal workpiece feature data: Use a three-dimensional laser scanner to perform high-precision scanning on the geometric features of the workpiece to obtain the three-dimensional point cloud data of the workpiece; use an array of conductivity sensors to measure the surface conductivity distribution data of each part of the workpiece; detect the material composition data on the surface of the workpiece through an X-ray fluorescence analyzer.

[0033] S12. Extract the microscopic surface morphology features of the workpiece: Apply a local curvature analysis algorithm to the three-dimensional point cloud data to calculate the curvature distribution features of the workpiece surface; perform multi-scale analysis on the workpiece surface based on wavelet transform to extract the surface roughness features of the workpiece; use the region growing method to identify the microscopic concave and convex features on the workpiece surface, including sharp corners, holes, and grooves, etc.

[0034] S13. Map and fuse the key parameters of electroplating: Establish a mapping relationship between the material composition data and the electrochemical activity to generate an electrochemical activity distribution map of the workpiece; combine the surface conductivity distribution data and the curvature distribution features to construct a current absorption tendency index of the workpiece; fuse the surface roughness features and the microscopic concave and convex features to generate an electric field perturbation sensitivity map of the workpiece.

[0035] S14. Standardize the feature data and detect anomalies: Perform unified dimension processing on all feature data to convert it into a standardized workpiece feature vector; apply the Local Outlier Factor (LOF) algorithm to identify the feature anomaly regions; perform adaptive sampling enhancement on the feature anomaly regions to improve the data accuracy and reliability of the anomaly regions, and generate a workpiece dataset including normal and anomaly region markings.

[0036] As Figure 2 shown, according to one aspect of the present application, the steps of analyzing the differential features of workpieces and grouping them include:

[0037] S21. Read the workpiece dataset, perform dimensionality reduction and extract key features to obtain a dimensionality-reduced feature representation and feature importance weights; based on the dimensionality-reduced feature representation and feature importance weights, construct a multi-level workpiece similarity metric to obtain a comprehensive similarity;

[0038] S22. Based on the comprehensive similarity, group the workpieces and select representative workpieces to obtain an optimized workpiece grouping and representative workpieces;

[0039] S23. Based on the optimized workpiece grouping and representative workpieces, perform intra-group differential quantification and analysis to obtain an intra-group differential matrix and intra-group differential principal components.

[0040] As Figure 3 shown, according to one aspect of the present application, the steps of constructing a multi-level workpiece similarity metric to obtain a comprehensive similarity include:

[0041] Extract data from the dimensionality-reduced feature representation and feature importance weights, and use a multi-level similarity metric function for the electroplating process to calculate the macro shape similarity, micro-structure similarity, and material property similarity;

[0042] Based on the macro shape similarity, micro-structure similarity, and material property similarity, calculate the similarity scores between workpieces to obtain a workpiece similarity matrix;

[0043] According to the distribution of each level of features in the workpiece similarity matrix, combined with the electroplating perturbation sensitivity weighting mechanism, dynamically adjust the level weight coefficients;

[0044] Combine the workpiece similarity matrix, level weight coefficients, and feature importance weights to calculate the comprehensive similarity between workpieces.

[0045] As Figure 4 shown, according to one aspect of the present application, the steps of grouping the workpieces and selecting representative workpieces to obtain an optimized workpiece grouping and representative workpieces include:

[0046] [[ID=�8]]Based on the comprehensive similarity, preliminarily group the workpieces through a spectral clustering algorithm to obtain an initial workpiece grouping;

[0047] Adopt a grouping evaluation index based on the electroplating process sensitivity to evaluate the rationality of the initial workpiece grouping to obtain a grouping evaluation result;

[0048] Adopt an adaptive grouping adjustment algorithm to optimize the grouping boundary according to the grouping evaluation result to obtain an optimized workpiece grouping;

[0049] For each optimized workpiece group, perform centrality analysis and use the workpiece with the highest centrality as the representative workpiece.

[0050] Specifically, perform non-linear feature dimensionality reduction and key feature extraction. Apply the t-SNE (t-Distributed Stochastic Neighbor Embedding) algorithm to the workpiece dataset to project high-dimensional features into a low-dimensional space and obtain the dimensionality-reduced feature representation; adopt an improved adaptive feature importance evaluation algorithm to identify the key electroplating features affecting electroplating quality from the dimensionality-reduced feature representation; based on feature sensitivity analysis, calculate the influence degree of each feature on the electroplating result and generate the feature importance weights.

[0051] Construct a multi-level workpiece similarity metric. Design a multi-level similarity metric function for the electroplating process, including macroscopic shape similarity, microscopic structure similarity, and material property similarity; calculate the similarity scores of each pair of workpieces at each level to generate a workpiece similarity matrix; apply the information entropy weight method to dynamically adjust the level weight coefficients according to the distribution of features at each level; combine the feature importance weights and the level weight coefficients to calculate the comprehensive similarity between workpieces.

[0052] Select adaptive workpiece grouping and representative workpieces. Based on the comprehensive similarity, apply the spectral clustering algorithm to initially group the workpieces to obtain the initial workpiece groups; design a grouping evaluation index based on the electroplating process sensitivity to evaluate the rationality of the initial workpiece groups; adopt an adaptive grouping adjustment algorithm to optimize the grouping boundaries according to the evaluation results and generate optimized workpiece groups; for each group, select a representative workpiece that can represent the characteristics of the group based on centrality analysis.

[0053] Perform intra-group difference quantification and analysis. For the workpieces within each optimized workpiece group, calculate the feature deviation between them and the representative workpiece to generate an intra-group difference matrix; apply principal component analysis (PCA) to identify the main patterns of intra-group differences and extract the intra-group difference principal components; based on the intra-group difference principal components, construct a difference distribution model for the workpieces within the group to provide a basis for subsequent differential current distribution.

[0054] In another embodiment of the present application, obtaining the hierarchical weight coefficients further includes: applying an improved information entropy weight method and introducing an electroplating perturbation sensitivity weighting mechanism, where high-curvature and high-current density fluctuation regions obtain enhanced weights, and the hierarchical weight coefficients are dynamically adjusted.

[0055] Specifically, in an actual electroplating production line, when applying the improved information entropy weight method to 50 connector workpieces with different shapes, an electroplating perturbation sensitivity weighting mechanism is introduced. The specific implementation process is as follows: First, calculate the similarity between workpiece i and j at three levels of macroscopic shape, microscopic structure, and material properties: Macroscopic shape similarity: SS(i, j) = exp(-‖S_i - S_j‖2 / 0.2 2 ); Microstructural similarity: MS(i, j) = exp(-‖M_i - M_j‖ 2 / 0.15 2 ); Material property similarity: CS(i, j) = exp(-‖C_i - C_j‖ 2 / 0.25 2 ). Then, construct the workpiece similarity matrix SM with a size of 50×50, where each element represents the similarity score between two workpieces at each level. Next, apply the improved information entropy weight method and introduce the electroplating perturbation sensitivity weighting mechanism. The traditional information entropy weight calculation formula is: w_k = (1 - E_k) / ∑(1 - E_j); where E_k and E_j are different information entropies. The improved calculation formula is: w_k = (1 - E_k) × D_k / ∑[(1 - E_j) × D_j], where D_k and D_j are electroplating perturbation sensitivity factors. For high curvature regions (average curvature > 0.5mm -1 ) and high current density fluctuation regions (fluctuation > 15%), set enhanced weights: D_k = 1.0 + 0.5×(H_curve + H_current), where H_curve and H_current are binary indicators of curvature and current density fluctuation (present = 1, absent = 0). Through this improvement, when processing "U"-shaped small connectors, the weight of the microstructural level increases from 0.25 to 0.42, making the grouping focus more on the key factors of coating quality, and ultimately the uniformity index of workpiece grouping increases by 18%.

[0056] According to one aspect of the present application, the steps of modeling and analyzing the electric field distribution in the electrolytic cell to obtain the actual current density distribution in the micro-region and the workpiece electric field interference matrix include:

[0057] S31. Obtain the geometric structure of the electrolytic cell and the electrode arrangement, and combine the optimized workpiece grouping and the workpiece dataset to model the reference electric field of the multi-workpiece electrolytic cell, obtaining the complete model of the multi-workpiece electrolytic cell and the ideal reference electric field distribution;

[0058] S32. Based on the complete model of the multi-workpiece electrolytic cell and the ideal reference electric field distribution, perform electric field interference mapping and quantification between workpieces to obtain the workpiece electric field interference matrix and the mutual interference intensity coefficient;

[0059] S33. Based on the workpiece electric field interference matrix and the mutual interference intensity coefficient, construct a dynamic electric field distribution prediction model;

[0060] S34. Combine the workpiece feature vectors in the workpiece dataset to perform micro-region electric field characteristic analysis and current density mapping to obtain the actual current density distribution in the micro-region.

[0061] According to one aspect of the present application, the steps of mapping and quantifying the electric field interference between workpieces to obtain the workpiece electric field interference matrix and the mutual interference intensity coefficient include:

[0062] Based on the complete model of the multi-workpiece electrolytic cell, simulate the electric field distribution when a single workpiece exists, and generate a series of single-workpiece electric field perturbation maps;

[0063] Calculate the difference between the single-workpiece electric field perturbation map and the ideal reference electric field distribution, and construct the workpiece electric field interference matrix;

[0064] Combine the preset electric field interference intensity quantization index and the workpiece electric field interference matrix to calculate the mutual interference intensity coefficient between workpieces;

[0065] Based on the mutual interference intensity coefficient, identify the main propagation paths of the electric field interference, and construct an interference propagation network model.

[0066] Specifically, perform reference electric field modeling for the multi-workpiece electrolytic cell. Based on the geometric structure and electrode arrangement of the electrolytic cell, construct the basic geometric model of the electrolytic cell; combine the conductivity distribution and temperature distribution of the electrolyte to establish the electrolyte conductivity field; integrate the workpiece geometric feature data, place the workpieces in the basic geometric model of the electrolytic cell to generate the complete model of the multi-workpiece electrolytic cell; apply the finite element analysis method to calculate the ideal reference electric field distribution without workpiece interference.

[0067] Perform mapping and quantification of the electric field interference between workpieces. Based on the complete model of the multi-workpiece electrolytic cell, simulate the electric field distribution when a single workpiece exists, and generate a series of single-workpiece electric field perturbation maps; construct the workpiece electric field interference matrix by superimposing the differences between each single-workpiece electric field perturbation map and the ideal reference electric field distribution; design an electric field interference intensity quantization index to calculate the mutual interference intensity coefficient between workpieces; identify the main propagation paths of the electric field interference and construct an interference propagation network model.

[0068] Construct a dynamic electric field distribution prediction model. Based on the workpiece electric field interference matrix and the mutual interference intensity coefficient, construct a static electric field distribution model considering the interaction of multiple workpieces; integrate the principles of fluid dynamics to simulate the influence of the flow of the electroplating solution on the electric field distribution and generate a flow field influence factor; combine the static electric field distribution model and the flow field influence factor to construct a dynamic electric field distribution prediction model; apply the Monte Carlo method to simulate the influence of small changes in the workpiece position on the electric field distribution and generate an uncertainty interval of the electric field distribution.

[0069] Perform micro-region electric field characteristic analysis and current density mapping. Based on the dynamic electric field distribution prediction model, calculate the initial electric field intensity at each point on the workpiece surface; combine the microscopic topography data in the workpiece feature vector to identify the electric field hot spots with abnormal electric field distribution; apply the improved Butler-Volmer equation to establish the mapping relationship between the micro-region electric field intensity and the theoretical current density; consider the cathode polarization effect to correct the theoretical current density and obtain a more accurate micro-region actual current density distribution.

[0070] In another embodiment of the present application, a modified Laplace equation considering the non-linear effect of ion migration is used to calculate the difference between the electric field perturbation map of a single workpiece and the ideal reference electric field distribution, and a workpiece electric field interference matrix is constructed; an electric field interference intensity quantification index is designed, a spatial gradient weighting factor is introduced, and the mutual interference intensity coefficient between workpieces is calculated based on the workpiece electric field interference matrix.

[0071] Specifically, during the construction of the electroplating tank model, a modified Laplace equation considering the non-linear effect of ion migration is applied for electric field interference mapping between workpieces. The specific implementation process is as follows: The traditional Laplace equation is: ▽·(σ▽φ)= 0; the modified equation considers the non-linear effect of ion migration: ▽·(σ▽φ) + ▽·(σμ|▽φ|▽φ) =0, where μ is the ion migration coefficient, and the non-linear effect is significant in the high current density region (>4A / dm 2 )). Where σ is the conductivity of the workpiece material; ▽ is the vector differential operator; φ is the electric potential.

[0072] During the implementation process, finite element mesh division is performed on the electroplating tank. Fine meshes with a size of 0.5mm are used for the meshes close to the workpiece surface, and meshes with a size of 2 - 5mm are used for other regions. For the "L"-shaped connector with sharp corners, after separately simulating its electric field distribution, the difference from the ideal reference electric field is calculated, and a workpiece electric field interference matrix FEDM is constructed: FEDM(i, x, y, z) = SEDF(i, x, y, z) - REF(x, y, z); where (x, y, z) are the spatial coordinate points, SEDF represents the electric field interference distribution function, and REF is the ideal reference electric field function; when quantifying the electric field interference intensity, a spatial gradient weighting factor G(x, y, z) is introduced: G(x, y, z) =1 + λ×|▽φ(x, y, z)| 2, λ is set to 0.2 to increase the interference weight in the high-gradient region. Calculate the mutual interference intensity coefficient between workpieces i and j: MIFC(i, j) = ∫∫∫ |FEDM(i, x, y, z) × FEDM(j, x, y, z)| × G(x, y, z) dxdydz. For two adjacent "U"-shaped connectors, the interference coefficient calculated by the traditional method is 0.167, and the improved method obtains 0.231, which more accurately reflects the strong interference in the high current density region, thus making more effective compensation in the subsequent current distribution and improving the final plating thickness uniformity by 15%.

[0073] According to one aspect of the present application, the steps of performing dynamic monitoring and prediction of coating growth to obtain the growth characteristics of the group coating include:

[0074] S41. Obtain differential resistance spectrum data through a multi-frequency differential resistance spectrum acquisition system;

[0075] S42. Based on the differential resistance spectrum data, obtain coating growth state data through the mapping relationship between the resistance spectrum characteristics and the coating growth state;

[0076] S43. Combine the workpiece electric field interference matrix, differential resistance spectrum data, and intra-group difference matrix to perform decoupling and interference elimination of the workpiece group resistance spectrum to obtain the growth characteristics of the group coating;

[0077] S44. Based on the coating growth state data and the growth characteristics of the group coating, perform simulation using the coating growth dynamic prediction model to obtain a prediction uncertainty assessment.

[0078] Specifically, perform the mapping between the resistance spectrum characteristics and the coating growth state. Extract the frequency domain characteristics of the differential resistance spectrum data to identify the key frequency characteristics representing the coating growth state; based on the principle of electrochemical impedance spectroscopy (EIS), construct an equivalent circuit model to correlate the resistance spectrum characteristics with the physical parameters of the electroplating process; apply a non-linear regression algorithm to fit the parameters of the equivalent circuit model to generate electroplating process parameters; establish the mapping relationship between the electroplating process parameters and indicators such as the coating growth rate and uniformity to generate coating growth state data.

[0079] Construct a coating growth dynamic prediction model. Integrate the coating growth state data and the growth characteristics of the group coating to construct an initial coating growth history database; apply a long short-term memory network (LSTM) to train a coating growth time series prediction model based on the coating growth history database; combine the workpiece feature vector and the actual current density distribution in the micro-region to develop a micro-region coating growth predictor considering micro-region characteristics; design a dynamic confidence interval estimation method to provide a prediction uncertainty assessment for the prediction results to guide the adjustment of subsequent control strategies.

[0080] According to one aspect of the present application, a multi-frequency differential resistance spectrum acquisition system includes:

[0081] An alternating current impedance measurement circuit with frequency scanning ability, which superimposes a weak alternating current signal without disturbing the normal electroplating process;

[0082] A reference electrode array arranged in the electrolytic cell to form a multi-point sampling network;

[0083] A partition modulation strategy module that develops a partition modulation strategy for the electrolytic cell, and realizes the acquisition of the partition resistance spectrum of different regions of the electrolytic cell by controlling the alternating current impedance measurement circuit and the reference electrode array;

[0084] A timing differential sampling module that performs differential processing on the resistance spectrum data at adjacent time points to generate differential resistance spectrum data.

[0085] According to one aspect of the present application, the steps of decoupling the resistance spectrum of a workpiece group and eliminating interference to obtain the growth characteristics of the group plating layer include:

[0086] Based on the workpiece electric field interference matrix, construct a resistance spectrum interference model in a multi-workpiece environment;

[0087] Combined with the resistance spectrum interference model, use the independent component analysis algorithm (ICA) to separate the independent resistance spectrum components of each workpiece group from the differential resistance spectrum data;

[0088] Use a method based on Kalman filtering to process the independent resistance spectrum components, eliminate the resistance spectrum fluctuations caused by non-plating growth factors, and obtain purified resistance spectrum data;

[0089] Combined with the intra-group difference matrix, further refine the purified resistance spectrum data to obtain the growth characteristics of the group plating layer.

[0090] Specifically, the mixed resistance spectrum signal is combined with the resistance spectrum interference model (ESIM), and the ICA algorithm is applied for signal separation, which is specifically as follows: Construct a priori information matrix: Use ESIM to construct the a priori mixing matrix \(A_{prior}\), where each element \(A_{prior}(i, j)\) represents the theoretical influence intensity of workpiece \(j\) on measurement point \(i\), and \(A_{prior}(i, j)=\alpha\times\exp(-d(i, j) / \lambda)\times FEDM_{norm}(i, j)\), where \(d(i, j)\) is the distance from measurement point \(i\) to workpiece \(j\), \(\lambda\) is the attenuation coefficient (set to 15 mm), \(FEDM_{norm}(i, j)\) is the normalized electric field interference intensity, and \(\alpha\) is the proportionality coefficient. The traditional ICA algorithm usually uses a randomly initialized mixing matrix, and here \(A_{prior}\) is used as the initial estimate. Set the mixing matrix \(A_{initial}\) of ICA as \(A_{initial}=A_{prior}+\varepsilon\), where \(\varepsilon\) is a small random perturbation (to prevent local optimum). Expand the standard ICA objective function and add a penalty term for the a priori knowledge of ESIM: \(J(W)=J_{standard}(W)+\beta\times||W\cdot A_{prior}-I||_F\), where \(W\) is the separation matrix, \(J_{standard}\) is the standard ICA objective function, \(||\cdot||_F\) is the Frobenius norm, and \(\beta\) is the penalty weight (set to 0.3). In each ICA iteration, both the statistical independence of the signal and the consistency with the theoretical model are considered; an alternating optimization strategy is used: first update the separation matrix \(W\) based on statistical independence, and then fine-tune the separation matrix \(W\) based on the ESIM constraint; the gradient update formula is: \(W_{new}=W_{old}-\eta\times[\nabla J_{standard}(W)+\beta\times\nabla(||W\cdot A_{prior}-I||_F)]\), where \(\eta\) is the learning rate and \(W_{old}\) is the separation matrix before update. As the iteration progresses, the value of \(\beta\) is dynamically adjusted according to the convergence situation. In the initial stage, \(\beta\) is larger (0.5) to emphasize a priori knowledge; in the later stage, \(\beta\) is reduced (down to 0.1) to enhance the adaptability to actual data.

[0091] In another embodiment of the present application, in the partition modulation strategy module, an electrolytic cell partition modulation strategy is developed, and the spatial phase encoding technology is introduced. By injecting phase-orthogonal excitation signals in different regions, spatial separation of multi-workpiece interference sources is achieved, and partition resistance spectrum acquisition of different regions of the electrolytic cell is realized based on the alternating current impedance measurement circuit and the reference electrode array. In the time series differential sampling module, combined with the spatial phase decoding algorithm, the resistance spectrum data at adjacent time points are differentially processed to generate differential resistance spectrum data.

[0092] Specifically, when implementing the spatial phase encoding differential resistance spectroscopy technology on an actual electroplating production line, the specific operations are as follows: Design an AC impedance measurement circuit with a frequency scanning range of 100 Hz - 100 kHz, and the signal amplitude is 10 mV, which is much lower than the normal electroplating voltage (2 - 3 V). Arrange 16 platinum reference electrodes in the electrolytic cell to form a 4×4 grid sampling array, and each electrode maintains a distance of 10 mm from the nearest workpiece. The traditional partition acquisition method injects and acquires signals sequentially for 9 regions (3×3 grid), but this method cannot distinguish the contributions of different workpieces in a multi-workpiece environment. Introduce the spatial phase encoding technology to inject excitation signals with the same frequency but different phases into 9 regions simultaneously: s_i(t) = A_i × sin(ωt + θ_i), where A_i is the signal amplitude factor of region i; ω is the angular frequency of the signal; t is time; θ_i is the phase of region i, and an orthogonal design is adopted: θ_i = 2π×(i - 1) / 9 to ensure a phase interval of 40°. At the signal acquisition end, use the spatial phase decoding algorithm: r_i = ∑ R(t)·sin(ωt + θ_i) dt; where r_i is the result signal after spatial phase decoding of region i; R(t) is the mixed received signal, and through the orthogonal correlation with the specific phase of each region, the signals of each region are separated. The traditional time-series differential sampling is: DERS(f, t) = ERS(f, t) - ERS(f, t - Δt); after improvement, phase decoding is added: DERS(f, t, i)= ERS_i(f, t) - ERS_i(f, t - Δt), where ERS_i represents the decoded signal of region i, and Δt is the time difference. After applying this technology, when testing the simultaneous electroplating of 7 workpieces, the signal interference suppression rate is increased from the original 12 dB to 26 dB, and the accuracy of resistance spectrum feature extraction is increased by 32%, providing a more reliable data basis for subsequent coating growth monitoring.

[0093] In another embodiment of the present application, an improved independent component analysis algorithm integrating spatial constraints is applied. By introducing the prior knowledge of the spatial position of the workpiece and the constraint of the electroplating solution flow pattern, the independent resistance spectrum components of each workpiece group are separated from the differential resistance spectrum data. Design a signal processing method based on adaptive Kalman filtering. By adjusting the process noise covariance matrix in real time, the low-frequency noise caused by the electroplating solution flow is selectively suppressed, and the independent resistance spectrum components are processed to eliminate the resistance spectrum fluctuations caused by non-coating growth factors, obtaining purified resistance spectrum data.

[0094] Specifically, when processing the resistance spectrum signals during the simultaneous electroplating of 50 workpieces, the specific process of implementing the improved independent component analysis algorithm with fused spatial constraints and adaptive Kalman filtering is as follows: First, based on the workpiece electric field disturbance matrix FEDM, a resistance spectrum interference model ESIM is constructed: ESIM(i, j) = ∑ FEDM(i, x, y, z) × FEDM(j, x, y, z)dxdydz; The separation process of the traditional ICA algorithm is: S = W × X, where S is the separated signal source matrix, X is the mixed observation signal matrix, and W is the separation matrix. The improved ICA algorithm incorporates the workpiece spatial position constraint: F(W) = J(W) + λ × ∑∑ C(i, j) × |W_i · W_j|, where J(W) is the original ICA objective function, C(i, j) is the spatial proximity of workpieces i and j, and λ = 0.3 is the balance parameter. The workpiece spatial position constraint term penalizes the solutions that violate the spatial distribution law, making the separation result more physically meaningful.

[0095] In addition, the electroplating solution flow pattern constraint is also incorporated. Through hydrodynamic simulation, the electroplating solution flow velocity field v(x, y, z) is obtained, the signal propagation delay matrix T(i, j) is calculated, and the ICA solution is corrected in terms of time sequence. In the signal processing stage, the traditional Kalman filter uses a fixed process noise covariance matrix Q. The improved adaptive Kalman filter dynamically adjusts Q by real-time estimating the low-frequency noise characteristics caused by the electroplating solution flow: Q(t) = Q_base + β × A_flow(t) × A_flow(t) T , where A_flow(t) is the flow noise direction vector, β = 0.4 is the weight coefficient, T is the transpose, and Q_base is the basic process noise covariance matrix. Finally, in combination with the intra-group difference matrix IDM, the group plating growth feature GDGF is calculated: GDGF(g) = ∑ [w_i × PERS(i) × (1 + γ × IDM(i, RW))], where w_i is the weight of workpiece i within the group, γ = 0.2 is the difference adjustment coefficient, IDM(i, RW) is the difference between workpiece i and the representative workpiece RW, and PERS(i) is the purified resistance spectrum data. Applying this technology to process an electroplating batch containing 7 groups of workpieces, the signal source separation accuracy rate is increased from 72% to 91%, the low-frequency flow noise suppression rate is increased by 18 dB, and the plating growth monitoring accuracy is increased by 25%.

[0096] According to one aspect of the present application, the steps of generating a differentiated current distribution control strategy include:

[0097] S51. Construct a multi-objective electroplating quality evaluation index, including a quality-parameter mapping relationship;

[0098] S52. Based on the optimized workpiece grouping and the growth characteristics of group coatings, combined with the quality-parameter mapping relationship, construct an optimized strategy for group-level current parameters to obtain anti-interference group current parameters;

[0099] S53. Based on the actual current density distribution in micro-regions, formulate a differential current modulation scheme for micro-regions to obtain a workpiece micro-region current modulation scheme;

[0100] S54. Integrate the anti-interference group current parameters and the workpiece micro-region current modulation scheme, and integrate a multi-level current distribution control strategy to obtain a complete current distribution control strategy.

[0101] Specifically, construct a multi-objective electroplating quality evaluation index. Define an electroplating quality index system including coating thickness uniformity, surface roughness, bonding strength, etc.; based on the electroplating process requirements, assign index weights to each quality index; combined with the workpiece data set, set differential target quality parameters for different workpiece groups; construct an association model between electroplating quality and current parameters to form a quality-parameter mapping relationship.

[0102] Construct an optimized strategy for group-level current parameters. Based on the optimized workpiece grouping and the growth characteristics of group coatings, determine the basic group current parameters for each workpiece group; apply a multi-objective optimization algorithm to optimize the group current parameters according to the quality-parameter mapping relationship and the target quality parameters; consider the mutual interference intensity coefficient, and perform interference compensation on the optimized parameters to obtain anti-interference group current parameters; combined with the prediction uncertainty assessment, design a parameter fault tolerance interval for each workpiece group to improve the robustness of the control strategy.

[0103] Construct a differential current modulation scheme for micro-regions within the workpiece. Based on the actual current density distribution in micro-regions and the output of the micro-region coating growth predictor, identify the micro-region regulation targets that need differential treatment; design a dedicated pulse waveform template library for different types of micro-regions, including various waveforms such as square waves, trapezoidal waves, and exponential waves; select the most suitable waveform template for each micro-region regulation target, and optimize the waveform parameters according to the micro-region characteristics to generate a micro-region pulse parameter set; ensure the coordination between the micro-region pulse parameters to avoid mutual interference and form a complete workpiece micro-region current modulation scheme.

[0104] Integrate a multi-level current distribution control strategy. Integrate the anti-interference group current parameters and the workpiece micro-region current modulation scheme to form a preliminary multi-level current distribution strategy; apply electroplating process simulation technology to verify the effectiveness of the multi-level current distribution strategy and optimize it according to the simulation results; design a parameter adjustment strategy for different stages of the electroplating process to form a complete timing control strategy; formulate alternative solutions for abnormal situations to enhance the adaptability of the control strategy, and finally generate a complete current distribution control strategy.

[0105] According to one aspect of the present application, formulating a micro-region differential current modulation scheme, the steps for obtaining the workpiece micro-region current modulation scheme include:

[0106] Based on the actual current density distribution in the micro-region and the output of the micro-region coating growth predictor, identify the micro-region regulation targets that require differential treatment;

[0107] Design an adaptive waveform library for the electric field distortion region, including special waveforms such as variable amplitude exponential waves for high curvature sharp corners, feedback modulation trapezoidal waves for deep holes, and double-peak composite waves for slits;

[0108] Select the most suitable waveform template for each micro-region regulation target, and dynamically optimize the waveform parameters through the electric field gradient response algorithm to generate a micro-region pulse parameter set;

[0109] Adopt waveform interference pre-compensation technology to predict and eliminate the waveform crosstalk effect between adjacent micro-regions, ensure the coordination between the pulse parameters of each micro-region, and form a complete workpiece micro-region current modulation scheme.

[0110] Specifically, during the electroplating process of precision connectors, the specific process of implementing differential current modulation on the complex micro-regions of the workpiece surface is as follows: First, identify the micro-region regulation targets that require special treatment. Divide the surface of the "U"-shaped connector into 30 micro-regions. When the predicted local coating growth rate deviation exceeds ±15%, mark it as a regulation target. In the example, 8 key micro-regions are identified, including 3 high curvature sharp corner regions, 2 deep hole regions, and 3 slit regions. For the electric field distortion region, design an adaptive waveform library, including three types of special waveforms:

[0111] Variable amplitude exponential wave (for high curvature sharp corners): i(t) = i_base × [1 + α × exp(-t / τ1)] × rect(t / T, DC), where α is the variable amplitude coefficient (range 0.2 - 0.8), τ1 is the time constant (1 - 10 ms), rect is the rectangular wave function, DC is the duty cycle, i_base is the reference value of the current, and T is the period of the waveform.

[0112] Feedback modulation trapezoidal wave (for deep holes): i(t) = i_base × [1 + β × (1 - exp(-t / τ2))] × trap(t / T, RT, FT), where β is the modulation coefficient (0.1 - 0.5), τ2 is the deep hole charging time constant (5 - 20 ms), trap is the trapezoidal wave function, and RT and FT are the rising and falling time ratios.

[0113] Bimodal composite wave (for slit): i(t) = i_base × [rect(t / T, DC1) + γ × rect((t - Δt) / T, DC2)], where γ is the amplitude ratio of the secondary peak (0.3 - 0.7), Δt is the delay between peaks (T / 4 - T / 2), and DC1 and DC2 are the duty cycles of the two rectangular waves.

[0114] Waveform parameter optimization uses the electric field gradient response algorithm: Params = Params_init + η × ▽E_local × (E_target / E_local - 1), where ▽E_local is the local electric field gradient, η is the learning rate (0.05 - 0.2), E_target is the target electric field strength, Params_init is the initial parameter value in the waveform optimization algorithm, and E_local is the local electric field strength.

[0115] For adjacent micro-regions, the waveform interference pre-compensation technique is applied: i_adj(t) = i_orig(t) - ∑ κ_j × i_j(t - δ_j), where κ_j is the interference coefficient of adjacent micro-region j (usually <0.15), δ_j is the interference delay time (0 - 2 ms), i_orig(t) is the original uncorrected modulated current signal, and i_j is the modulated current signal of adjacent micro-region j. This embodiment improves the plating quality of the complex micro-regions of the "U"-shaped connector: the thickness uniformity of the sharp corner region is increased by 29%, the deep hole coverage rate is increased by 35%, the bonding strength of the slit region is increased by 22%, and the plating transition between different micro-regions is smoother, and the surface quality score is increased from 72 points to 91 points.

[0116] This embodiment is applied to an electroplating production line for manufacturing precision electronic connectors, which requires gold plating of copper alloy connectors with different shapes and sizes simultaneously. The production batch contains 50 workpieces, including three basic shapes: long strip, "L"-shaped, and "U"-shaped. The workpieces of each shape have differences in size and fine structure. The specific implementation steps are as follows:

[0117] Step 1: Acquisition and preprocessing of workpiece feature data.

[0118] Fifty workpieces were scanned using a KEYENCE VK-X1000 3D laser scanner with the resolution set to 0.5 μm. The original 3D point cloud data obtained from the scan was denoted as P_raw. The point cloud of each workpiece contained approximately 1 million points, and each point included spatial coordinate (x, y, z) information. Meanwhile, 20 measurement points were evenly selected on the surface of each workpiece using a Fischer SIGMASCOPE SMP350 conductivity measuring instrument to obtain the surface conductivity distribution data C_raw. The measurement conditions were: frequency 2 MHz, probe diameter 2 mm. A Bruker M4 TORNADO X-ray fluorescence analyzer was used to detect the material composition on the surface of the workpieces. Five representative points were selected for measurement on each workpiece to obtain the material composition data M_raw.

[0119] Apply the local curvature analysis algorithm to the 3D point cloud data P_raw to calculate the curvature distribution characteristics K of the workpiece surface. For each point p_i in the point cloud, select the point set N_i within its neighborhood radius r = 0.8 mm, and use the least squares method to fit the local quadratic surface: z = ax 2 + by 2 + cxy + dx + ey + f, where a, b, c, d, e, f are fitting parameters. Calculate the principal curvatures κ_1 and κ_2 through these parameters: κ_1 = a + b + sqrt((a - b) 2 + c 2 ) ; κ_2 = a + b - sqrt((a - b) 2 + c 2 ), and obtain the mean curvature K_i = (κ_1 + κ_2) / 2 and Gaussian curvature G_i = κ_1 × κ_2 of the point p_i. Conduct statistics on the curvature data of all points to generate the curvature distribution characteristics K of the workpiece, including the mean curvature distribution map and Gaussian curvature distribution map.

[0120] Apply the Daubechies wavelet transform to perform a 4-level decomposition on the workpiece surface to extract the surface roughness characteristics R. After meshing the point cloud data, perform wavelet decomposition along the x and y directions to extract the surface detail information at different scales, and generate multi-scale roughness indices, including Ra (arithmetic mean roughness), Rq (root mean square roughness), and Rz (ten-point mean roughness). The region growing method is used to identify the microscopic concave and convex characteristics F on the workpiece surface. The algorithm starts from the curvature extreme points and performs region growing based on the curvature similarity criterion (the curvature difference between adjacent points < 5%) to identify feature regions such as sharp corners, holes, and grooves, and record their positions, areas, and depth / height information.

[0121] Based on the material composition data M_raw, establish the mapping relationship with the electrochemical activity. For the gold plating process, mainly investigate the copper content (Cu%), zinc content (Zn%) and impurity element content, and calculate the electrochemical activity index EA: EA = 0.85×Cu% - 0.2×Zn% - 1.5×Impurity%, where Impurity% is the total content of impurity elements. Map the EA value to the workpiece surface to generate the electrochemical activity distribution map. Combine the surface conductivity distribution data C_raw and the curvature distribution feature K to construct the current absorption tendency index CAI of the workpiece: CAI = w1×C_norm + w2×K_norm, where C_norm is the normalized conductivity value, K_norm is the absolute value of the normalized average curvature, and w1 = 0.7 and w2 = 0.3 are the weight coefficients (determined based on experience). Integrate the surface roughness feature R and the micro-convexity and concavity feature F to generate the electric field perturbation sensitivity map EDS of the workpiece: EDS = α×R_norm + β×F_intensity + γ×F_density, where R_norm is the normalized roughness value, F_intensity represents the intensity (depth / height) of the convexity and concavity feature, F_density represents the density of the convexity and concavity features per unit area, and α = 0.4, β = 0.35 and γ = 0.25 are the weight coefficients.

[0122] Perform standardization processing on all feature data, using the min-max normalization method: X_norm = (X - X_min) / (X_max - X_min), where X represents the original feature value, and X_min and X_max are the minimum and maximum values of this feature respectively. The standardized feature vector FV includes: geometric dimension parameters, curvature distribution feature K, surface roughness feature R, micro-convexity and concavity feature F, electrochemical activity index EA, current absorption tendency index CAI, and electric field perturbation sensitivity EDS. Apply the improved Local Outlier Factor (LOF) algorithm to identify the feature anomaly regions. Compared with the traditional LOF algorithm, this method introduces feature weights and assigns higher weights to the features that have a greater impact on the electroplating quality. For each sample point p, calculate the average distance ratio_p of its k nearest neighbors (k = 5), and the average distance ratio_N(p) of the k nearest neighbor points themselves. The LOF value is calculated as: LOF(p) = ratio_N(p) / ratio_p; when the LOF value is greater than 1.8, mark this region as a feature anomaly region. Perform adaptive sampling enhancement on the feature anomaly regions, increase the sampling density in the anomaly regions, and improve the data accuracy. Generate the final workpiece dataset FD.

[0123] Step 2: Analyze and group the differential features of the workpieces.

[0124] Apply the t-SNE algorithm to the workpiece dataset FD for non-linear dimensionality reduction. Different from traditional t-SNE, this method uses an adaptive perception parameter σ, which is dynamically adjusted according to local density: σ_i = σ_base × (1 + 0.5×log(ρ_i / ρ_avg)), where σ_base = 30 is the base parameter value, ρ_i is the local density around point i, and ρ_avg is the average density. The initial dimension is 27 dimensions (including all features), and a 3D representation is obtained after dimensionality reduction, generating a dimensionality-reduced feature representation RF. An improved adaptive feature importance evaluation algorithm is used to identify key electroplating features from the dimensionality-reduced feature representation RF. Combining permutation importance and partial dependence analysis, calculate the contribution degree of each feature to the electroplating quality. The feature importance is calculated as: FI(j) = w1×PI(j) + w2×PD(j), where PI(j) is the permutation importance score of feature j, PD(j) is the partial dependence score of feature j, and w1 = 0.6 and w2 = 0.4 are weight coefficients. According to the calculation results, the top 5 most important features are: Current Absorption Tendency Index (CAI), Microscopic Concavo-Convex Feature Density, Electrochemical Activity Index (EA), Surface Mean Curvature, and Gaussian Curvature Extreme Value Distribution. Assign importance weights FIW to these features, and the weight values are proportional to their importance scores.

[0125] Design a multi-level similarity metric function for the electroplating process, including macro shape similarity, micro-structure similarity, and material property similarity, and integrate the dimensionality-reduced feature representation RF and the feature importance weight FIW. The calculation method of macro shape similarity SS: SS(i, j) = exp(-‖S_i - S_j‖ 2 / σ_s 2 ), where S_i and S_j represent the macro shape features of workpiece i and j respectively, which are extracted from the dimensionality-reduced feature representation RF and weighted according to the shape-related weights in the feature importance weight FIW. σ_s is the kernel width parameter (set to 0.2).

[0126] The calculation method of micro-structure similarity MS: MS(i, j) = exp(-‖M_i - M_j‖ 2 / σ_m 2 ), where M_i and M_j represent the micro-structure features of workpiece i and j (including surface roughness and microscopic concavo-convex features), which are also extracted from RF and weighted according to the relevant weights in FIW. σ_m is the kernel width parameter (set to 0.15).

[0127] The calculation method of material property similarity MTS: MTS(i, j) = exp(-‖C_i - C_j‖ 2 / σ_c 2), where \(C_i\) and \(C_j\) represent the material properties of workpieces \(i\) and \(j\) (including the electrochemical activity index and current absorption tendency), which are extracted from the RF and weighted according to the relevant weights in the FIW. \(\sigma_c\) is the kernel width parameter (set to 0.25).

[0128] Calculate the above three types of similarity scores for each pair of workpieces to generate a workpiece similarity matrix SM: SM(i, j) = [SS(i, j), MS(i, j), MTS(i, j)]; Apply the information entropy weight method to dynamically adjust the hierarchical weight coefficient LW according to the distribution of each hierarchical feature. For the similarity metric \(k\), calculate its information entropy: \(E_k = -\sum p_{ki}×\ln(p_{ki}) / \ln(n)\), where \(p_{ki}\) is the normalized value of feature \(k\) on the workpiece pair \((i, j)\), and \(n\) is the total number of workpiece pairs. The hierarchical weight is calculated as: \(LW_k = (1 - E_k) / \sum(1 - E_j)\). Combine the feature importance weight FIW and the hierarchical weight coefficient LW to calculate the comprehensive similarity CS between workpieces \(i\) and \(j\): CS(i, j) = \(LW_1×SS(i, j)+LW_2×MS(i, j)+LW_3×MTS(i, j)\). Through the above calculations, a comprehensive similarity matrix CS is generated for all possible pairings of 50 workpieces, and this matrix will be used as the basis for subsequent workpiece grouping.

[0129] Based on the comprehensive similarity CS, apply the spectral clustering algorithm to preliminarily group the 50 workpieces. Different from traditional spectral clustering, this method uses an adaptive similarity threshold to automatically determine the optimal threshold by analyzing the similarity distribution. Construct a similarity matrix W, calculate the Laplacian matrix \(L = D - W\), where \(D\) is the degree matrix (the diagonal elements are the sums of the corresponding rows). Solve the generalized eigenvalue problem \(Lv = \lambda Dv\), select the eigenvectors corresponding to the first \(k\) smallest non-zero eigenvalues to form the eigenmatrix V. Perform K-means clustering on the rows of the eigenmatrix V to obtain the initial workpiece grouping IG.

[0130] Design grouping evaluation indicators based on the sensitivity of the electroplating process, including intra-group similarity, inter-group difference, and electroplating parameter sensitivity. The comprehensive score is: Score = 0.4×IntraSim + 0.3×InterDiff + 0.3×ParamSens; Use the adaptive grouping adjustment algorithm to optimize the grouping boundary through simulated annealing to generate the optimized workpiece grouping OG. Finally, the 50 workpieces are divided into 7 groups, namely long and large (8), long and medium (7), long and small (6), "L"-shaped large (9), "L"-shaped small (7), "U"-shaped large (6), and "U"-shaped small (7).

[0131] For each group, representative workpieces are selected based on centrality analysis. Calculate the centrality index of workpiece i: Centrality(i) = ∑ CS(i, j) / |G|; where |G| is the number of workpieces in the group. The workpiece with the highest centrality is selected as the representative workpiece RW of the group.

[0132] For the workpieces within each optimized workpiece group, calculate the feature deviation from the representative workpiece to generate the intra-group difference matrix IDM: IDM(i) = [FV(i) - FV(RW)] / FV(RW) × 100%; where FV(i) is the feature vector of workpiece i and FV(RW) is the feature vector of the representative workpiece. Apply principal component analysis (PCA) to identify the main patterns of intra-group differences and extract the intra-group difference principal components IDP. Retain the principal components that explain 95% of the variance, usually 3 - 5. Based on the intra-group difference principal components IDP, construct the intra-group workpiece difference distribution model IDM, and use the multivariate Gaussian distribution to describe the distribution of the features of the workpieces within the group.

[0133] Step 3: Modeling and analysis of the electric field distribution in the electrolytic cell.

[0134] Based on the geometric structure and electrode arrangement of the electrolytic cell, construct the basic geometric model BGM of the electrolytic cell. The electroplating cell used is a rectangular cell with dimensions of 1200mm × 800mm × 600mm, and the anode is a titanium iridium oxide-coated titanium plate vertically placed on both sides with dimensions of 1000mm × 500mm. Measure the conductivity distribution of the electrolyte (potassium gold cyanide solution) at different positions, with the temperature set at 65 ± 2°C and the pH value at 10.5 ± 0.2. Construct the electrolyte conductivity field ECF, and generate the conductivity distribution model of the entire electroplating cell through cubic spline interpolation. Integrate the workpiece geometric feature data, place the workpieces in the basic geometric model of the electrolytic cell to generate the complete model FCM of the multi-workpiece electrolytic cell. The workpieces are placed on a dedicated workpiece rack in 7 groups, and the workpiece spacing is not less than 30mm. Apply the finite element analysis method, use tetrahedral meshing for the electrolytic cell model, with the mesh size set to 0.5mm near the workpiece surface and 2 - 5mm in other areas. Solve the Laplace equation: ▽·(σ▽φ) = 0, where σ is the conductivity and φ is the electric potential. The boundary conditions are set as the anode electric potential of 1V and the cathode (workpiece) electric potential of 0V. Calculate the ideal reference electric field distribution REF without workpiece interference.

[0135] Based on the complete model of the multi-workpiece electrolytic cell FCM, the electric field distribution when a single workpiece exists is simulated in sequence to generate a series of single-workpiece electric field disturbance maps SEDF. During the simulation, other conditions are kept unchanged, and only one workpiece is placed. Calculate the difference between each single-workpiece electric field disturbance map and the ideal reference electric field distribution, and construct the workpiece electric field interference matrix FEDM: FEDM(i, x, y, z) = SEDF(i, x, y, z) - REF(x, y, z), where (x, y, z) represents the spatial coordinate points.

[0136] Design an electric field interference intensity quantification index and calculate the mutual interference intensity coefficient MIFC between workpieces. For workpieces i and j, the interference coefficient is calculated as: MIFC(i, j) = ∫∫∫ |FEDM(i, x, y, z) × FEDM(j, x, y, z)| dxdydz, and the integration range is the intersection of the influence regions of the two workpieces. Identify the main propagation paths of electric field interference and construct the interference propagation network model IPNM. Construct a directed graph according to the MIFC value, with the nodes being workpieces, the weight of the edge being the interference coefficient, and the direction pointing from the interference source to the interfered workpiece.

[0137] Based on the workpiece electric field interference matrix FEDM and the mutual interference intensity coefficient MIFC, construct a static electric field distribution model SEFM considering the interaction of multiple workpieces. Adopt a modified linear superposition method: SEFM(x, y, z) = REF(x, y, z) + ∑ αi × FEDM(i, x, y, z), where αi is the influence coefficient of workpiece i, which is determined by iterative optimization with an initial value set to 1.0. Use the computational fluid dynamics method to simulate the influence of the electroplating solution flowing in a cycle at a flow rate of 200 L / min on the electric field distribution, and generate the flow field influence factor FIF: FIF(x, y, z) = 1 + β × v(x, y, z) / v_max × cos(θ), where v(x, y, z) is the flow velocity at point (x, y, z), v_max is the maximum flow velocity, θ is the angle between the flow direction and the electric field direction, and β = 0.15 is the influence coefficient. Combine the static electric field distribution model SEFM and the flow field influence factor FIF to construct a dynamic electric field distribution prediction model DEFM: DEFM(x, y, z, t) = SEFM(x, y, z) × FIF(x, y, z, t), where t represents the time variable. Apply the Monte Carlo method to perform 1000 simulations by randomly perturbing the workpiece positions (within the range of ±2 mm), calculate the standard deviation of the electric field intensity at each point, and generate the electric field distribution uncertainty interval EFUI.

[0138] Based on the dynamic electric field distribution prediction model (DEFM), calculate the initial electric field strength (IEF) at each point on the workpiece surface. Combining the microscopic topography data in the workpiece feature vector, identify the electric field hot spot region (EHR) where the electric field distribution is abnormal. When the local electric field strength exceeds 1.5 times the average value or is lower than 0.6 times the average value, mark this region as the hot spot region. Apply the improved Butler-Volmer equation to establish the mapping relationship between the micro-region electric field strength and the theoretical current density. The improvement lies in considering the local electric field distortion effect, and the equation is: i = i0 × [exp(αaFη / RT) - exp(-αcFη / RT)] × f(E_local / E_avg), where i is the current density, i0 is the exchange current density, αa and αc are the anodic and cathodic transfer coefficients (both 0.5), F is the Faraday constant, η is the overpotential, R is the gas constant, T is the absolute temperature, and f(E_local / E_avg) is the local electric field correction function, expressed as: f(x) = x γ , where γ is the electric field sensitivity coefficient, experimentally determined to be 1.25. Considering the cathodic polarization effect, correct the theoretical current density to obtain a more accurate actual current density distribution in the micro-region (ACDD): ACDD = i_theory × [1 - δ×exp(-t / τ)], where t is the electroplating time, τ is the polarization time constant (about 30 seconds), and δ is the polarization coefficient (about 0.2).

[0139] Step Four: Dynamic Monitoring and Prediction of Coating Growth.

[0140] Design an alternating current impedance measurement circuit with frequency scanning ability, with a frequency range of 100 Hz - 100 kHz, divided into 10 logarithmically equally spaced points, and a signal amplitude of 10 mV (far lower than the normal electroplating voltage and does not interfere with the normal electroplating process). Arrange 16 platinum reference electrodes in the electrolytic cell to form a 4×4 grid sampling array, and keep a distance of 10 mm between each electrode and the nearest workpiece. Develop an electrolytic cell partition modulation strategy, divide the electrolytic cell into 9 regions (3×3 grid), and independently control signal injection and acquisition for each region. Design a time series differential sampling scheme with a sampling interval of 5 seconds, perform differential processing on the resistance spectrum data at adjacent time points to generate differential resistance spectrum data (DERS): DERS(f, t) = ERS(f, t) - ERS(f, t - Δt), where ERS(f, t) represents the resistance spectrum data at frequency f at time t, and Δt = 5 seconds.

[0141] Extract the frequency-domain features of the differential resistance spectroscopy data DERS. Use wavelet transform to decompose the signal and extract the energy distribution in different frequency bands as the key frequency features KFF. Based on the principle of electrochemical impedance spectroscopy (EIS), construct an equivalent circuit model ECM. For the gold plating process, adopt the Randles equivalent circuit, including Rs: solution resistance; Rct: charge transfer resistance; Cdl: double-layer capacitance; Zw: Warburg impedance (representing the diffusion process). The model expression is: Z(ω) = Rs + Rct / (1 + jωCdlRct) + σw / sqrt(jω), where Z(ω) is the total impedance, ω is the angular frequency, j is the imaginary unit, and σw is the Warburg coefficient. Apply the nonlinear regression algorithm to fit the parameters of the equivalent circuit model and generate the electroplating process parameters EPP. The specific fitting method uses the Levenberg-Marquardt algorithm, and the objective function is: F = ∑|Z_meas(ωi) - Z_model(ωi)| 2 / |Z_meas(ωi)| 2 , where Z_meas is the measured value and Z_model is the model predicted value. Establish the mapping relationship between the electroplating process parameters and the coating growth state, and focus on the change trends of Rct (charge transfer resistance) and Cdl (double-layer capacitance): a decrease in Rct indicates an increase in electroplating activity and an accelerated coating growth rate; an increase in Cdl indicates an increase in the effective surface area and a more uniform coating growth. According to these relationships, calculate the coating growth rate index DGR and the uniformity index DGU: DGR = k1 / Rct; DGU = k2×Cdl / Cdl_0, where k1 and k2 are proportionality coefficients and Cdl_0 is the initial double-layer capacitance value.

[0142] Based on the workpiece electric field disturbance matrix FEDM, a resistance spectrum interference model ESIM in a multi-workpiece environment is constructed. The independent component analysis (ICA) algorithm is applied to separate the independent resistance spectrum components IESC of each workpiece group from the mixed resistance spectrum signals. Compared with the traditional ICA, in this embodiment, spatial prior information is introduced, and the physical position relationship of the workpieces is used to assist signal separation: S = W×X, where S is the separated signal source matrix, X is the mixed observation signal matrix, and W is the separation matrix (determined by maximizing non-Gaussianity). A signal processing method based on Kalman filtering is designed to eliminate the resistance spectrum fluctuations caused by non-plating growth factors. The state equation and the observation equation are set as: x(t+1) = Ax(t) + w(t); z(t) = Hx(t) + v(t), where x represents the state vector (including the true resistance spectrum parameters), z is the observation vector, A is the state transition matrix, H is the observation matrix, and w and v are the system noise and the observation noise respectively. Through the Kalman filtering update and prediction steps, the purified resistance spectrum data PERS is obtained. Combining the intra-group difference matrix IDM, the purified resistance spectrum data is further refined to obtain more accurate group plating growth features GDGF: GDGF(g) = ∑ w_i × PERS(i), where g represents the workpiece group, and w_i is the weight of the workpiece i within the group (related to the similarity with the representative workpiece).

[0143] Integrate the plating growth status data and the group plating growth characteristics to construct an initial plating growth history database DGDB, which contains the following information: timestamp, workpiece group identifier, resistance spectrum parameters (Rs, Rct, Cdl, Zw), plating growth rate index (DGR), uniformity index (DGU), and electroplating current parameters. Apply the long short-term memory network (LSTM) to train the plating growth time series prediction model TPML based on the plating growth history database. The network structure includes: Input layer: 8 neurons (corresponding to the input feature dimension); LSTM layer 1: 64 neurons, dropout rate 0.2; LSTM layer 2: 32 neurons, dropout rate 0.2; Fully connected layer: 16 neurons, activation function ReLU; Output layer: 2 neurons (corresponding to DGR and DGU). The training parameters are: batch size: 32; learning rate: 0.001, using the Adam optimizer; sequence length: 10 time steps (50 seconds). Combine the workpiece feature vector and the actual current density distribution in the micro-region to develop a micro-region plating growth predictor RDGP that considers micro-region characteristics. Divide the workpiece surface into multiple micro-regions, and adjust the prediction parameters according to the characteristics of each micro-region: DGR_local = DGR × f_shape × f_material × f_current, where f_shape, f_material, and f_current are the shape factor, material factor, and current factor, respectively, calculated according to the local characteristics. Design a dynamic confidence interval estimation method to provide a prediction uncertainty evaluation PUE for the prediction results. Use the Monte Carlo Dropout technique to keep the dropout activation during the inference stage, perform 50 forward propagations, calculate the mean and standard deviation of the prediction results, and obtain a 95% confidence interval.

[0144] Step 5: Generate a differentiated current distribution strategy.

[0145] Define an electroplating quality index system QIS including plating thickness uniformity, surface roughness, adhesion strength, etc. The specific indicators include: TU: plating thickness uniformity, deviation < ±5%; SR: surface roughness, Ra < 0.2μm; AH: adhesion (adhesion strength), > 15N / mm 2 ; BS: surface brightness, reflectivity > 90%; PD: plating defect density, < 2 per cm 2Based on the requirements of the electroplating process, index weights IW are assigned to various quality indicators: TU: 0.35; SR: 0.25; AH: 0.20; BS: 0.10; PD: 0.10. Combining with the workpiece dataset, different target quality parameters TQP are set for different workpiece groups. For example, for the "U"-shaped small workpiece group in the precision connection part, a higher thickness uniformity requirement (deviation < ±3%) is set. An association model between electroplating quality and current parameters is constructed to form a quality-parameter mapping relationship (QPM). For each quality indicator, a functional relationship with key electroplating parameters is established: TU = f1(CD, PF, DC, CT); SR = f2(CD, PF, DC); AH = f3(CD, CT, TE); BS = f4(CD, PF, DC, TE); PD = f5(CD, PF, TEMP), where CD represents current density, PF represents pulse frequency, DC represents duty cycle, CT represents electroplating time, TE represents electrolyte stirring intensity, and TEMP represents electroplating solution temperature. These functional relationships are established through historical data analysis and electroplating experiments and are described using a multiple regression model.

[0146] Based on the optimized workpiece grouping OG and the group plating growth characteristics GDGF, the basic group current parameters GCP are determined for each workpiece group. The initial parameter settings are as follows: large long strip: current density 2.8 A / dm 2 , pulse frequency 80 Hz, duty cycle 65%; medium long strip: current density 3.0 A / dm 2 , pulse frequency 85 Hz, duty cycle 70%; small long strip: current density 3.2 A / dm 2 , pulse frequency 90 Hz, duty cycle 75%; large "L"-shaped: current density 2.6 A / dm 2 , pulse frequency 75 Hz, duty cycle 60%; small "L"-shaped: current density 2.9 A / dm 2 , pulse frequency 80 Hz, duty cycle 70%; large "U"-shaped: current density 2.5 A / dm 2 , pulse frequency 70 Hz, duty cycle 55%; small "U"-shaped: current density 2.7 A / dm 2 , pulse frequency 75 Hz, duty cycle 65%,

[0147] Apply a multi-objective optimization algorithm to optimize the group current parameters according to the quality-parameter mapping relationship QPM and the target quality parameters TQP. The weighted sum method is used to transform the multi-objective problem into a single-objective problem: F(x) = ∑ IW_i × [TQP_i - f_i(x)] 2 / TQP_i 2, where \(x\) represents the current parameter vector, \(IW_i\) represents the weight of index \(i\), \(TQP_i\) represents the target value of index \(i\), and \(f_i\) represents the prediction function of index \(i\). The particle swarm optimization (PSO) algorithm is used to solve for the optimal parameters. After 100 iterations with 50 particles, the optimized group current parameters are obtained. Considering the mutual interference strength coefficient MIFC, interference compensation is performed on the optimized parameters. The compensation formula is: \(CD_{adj}=CD_{opt}\times(1 + \sum MIFC(g,j)\times\lambda_j)\), where \(CD_{adj}\) is the adjusted current density, \(CD_{opt}\) is the optimized current density, \(MIFC(g,j)\) is the interference coefficient of group \(g\) affected by group \(j\), and \(\lambda_j\) is the compensation coefficient (usually from -0.05 to 0.05). After interference compensation, the interference-resistant group current parameters RIGCP are obtained. Combining the prediction uncertainty evaluation PUE, parameter tolerance intervals are designed for each workpiece group: \(CD_{range}=CD_{adj}\pm k\times\sigma_{CD}\); \(PF_{range}=PF_{adj}\pm k\times\sigma_{PF}\); \(DC_{range}=DC_{adj}\pm k\times\sigma_{DC}\); where \(k\) is the confidence level parameter (set to 1.96 for a 95% confidence interval), and \(\sigma\) represents the standard deviation of the corresponding parameter.

[0148] Based on the output of the actual current density distribution in the micro-region ACDD and the micro-region coating growth predictor RDGP, identify the micro-region control target MACT that requires differential treatment. When the predicted local coating growth rate deviation exceeds ±15% or the uniformity index deviation exceeds ±10%, mark this micro-region as the target area to be controlled. Design a dedicated pulse waveform template library PWTL for different types of micro-regions. The templates include: standard square wave: suitable for flat regions with a constant duty cycle; trapezoidal wave: suitable for transition regions with slow rising and falling edges; exponential wave: suitable for deep holes and grooves with exponential rising / falling characteristics; composite wave: suitable for complex-shaped regions, composed of basic waveforms.

[0149] Select the most suitable waveform template for each micro-region regulation target and optimize the waveform parameters according to the micro-region characteristics. The selection strategy is based on a decision tree model, considering factors such as the shape characteristics, curvature, depth-to-width ratio, etc. of the micro-region. For example: For high-curvature sharp corners: Select an exponential wave with a short rise time and a long fall time; For deep holes: Select an exponential wave with a long rise time and a short fall time; For grooves: Select a trapezoidal wave with moderate rise / fall times. The waveform parameter optimization is based on the micro-region current density requirements, adjusting the pulse frequency (PF), duty cycle (DC), rise time (RT), and fall time (FT). Generate a micro-region pulse parameter set MRPP. Ensure the coordination between the pulse parameters of each micro-region to avoid mutual interference. Check the parameter differences between adjacent micro-regions. When the frequency difference > 20 Hz or the duty cycle difference > 15%, introduce a smooth transition zone and establish a parameter gradient. Form a complete workpiece micro-region current modulation scheme MCMS.

[0150] Integrate the anti-interference group current parameters RIGCP and the workpiece micro-region current modulation scheme MCMS to form a preliminary multi-level current distribution strategy MLCDS. This strategy consists of two levels: Group level: Set the basic current parameters for each workpiece group; Micro-region level: On the basis of the group parameters, perform differential modulation on specific micro-regions. Apply electroplating process simulation technology to verify the effectiveness of the multi-level current distribution strategy. Use the finite element method to simulate the electroplating process and predict the coating thickness distribution and quality indicators. According to the simulation results, adjust the parameters to optimize the electroplating effect. Design a parameter adjustment strategy for different stages of the electroplating process to form a complete timing control strategy TSCS. Divide the electroplating process into three stages: Initial stage (0 - 30 seconds): Use a lower current density (80% of the base value) to establish the initial coating; Main growth stage (30 seconds - 90% of the total time): Apply the complete multi-level control strategy; Trimming stage (the last 10% of the time): Reduce the current density (70% of the base value) and increase the pulse frequency (120% of the base value) to improve the surface finish. Develop alternative solutions for abnormal situations to enhance the adaptability of the control strategy. Design response strategies for common abnormal situations: Abnormal resistance spectrum fluctuation: Temporarily switch to the safe parameter mode and reduce the current density by 20%; Abnormal temperature: Adjust the pulse duty cycle to reduce heat generation; Workpiece position deviation: Recalculate the electric field distribution and update the current parameters. Finally, generate a complete current distribution control strategy CCDCS, including: The basic current parameters of 7 workpiece groups; The micro-region differential modulation scheme for the workpieces within each group; The timing control strategy for the entire electroplating process; The response strategy for abnormal situations.

[0151] To verify the effectiveness of this embodiment, three typical workpieces were selected for comparative tests: a complex "U"-shaped small connector (high-precision requirement), a long-strip large connector (conventional requirement), and an "L"-shaped large connector (mixed requirement). The electroplating was carried out using the method of this embodiment, the traditional constant current method, and the simple grouping method respectively, with the electroplating time being 15 minutes and the gold thickness target being 1.5 μm. The results showed that the thickness deviation of this embodiment was ±3.2%, that of the traditional method was ±12.7%, and that of the simple grouping method was ±7.5%. The surface roughness Ra value of this embodiment was 0.15 μm, that of the traditional method was 0.28 μm, and that of the simple grouping method was 0.21 μm. The bonding strength of this embodiment was 17.8 N / mm 2 , that of the traditional method was 14.2 N / mm 2 , and that of the simple grouping method was 15.6 N / mm 2 . The surface brightness (reflectivity) of this embodiment was 93%, that of the traditional method was 85%, and that of the simple grouping method was 89%. The plating defect density of this embodiment was 0.8 pieces / cm 2 , that of the traditional method was 3.5 pieces / cm 2 , and that of the simple grouping method was 1.9 pieces / cm 2 . Considering all indicators, this embodiment is superior to the traditional method and the simple grouping method in all aspects, especially in terms of thickness uniformity and defect control of workpieces with complex shapes, with the most significant effects.

[0152] This embodiment effectively solves the signal interference problem during co-bath electroplating of multiple workpieces through time series difference and spatial partition sampling, and realizes high-precision real-time monitoring of the coating growth process. Based on the multi-level similarity measurement and spectral clustering improvement algorithm, it realizes the precise grouping of workpieces with similar features and the quantitative characterization of intra-group differences. By designing differentiated pulse waveforms for the complex micro-regions on the workpiece surface, it solves the problem of inconsistent coating quality in micro-regions in traditional electroplating technology. Integrating group-level parameter control and micro-level pulse modulation, it realizes all-round differentiated current control from macro to micro.

[0153] The present invention solves the problem of differential control for groups of workpieces with similar features. A multi-level similarity metric function for the electroplating process is constructed to calculate the similarity of workpieces in terms of macroscopic shape, microscopic structure, and material properties. A weighted mechanism for electroplating perturbation sensitivity is introduced, enabling enhanced weights to be obtained in regions with high curvature and high current density fluctuations, thereby enabling the identification of minor differences within a group of workpieces of the same specification. Based on the comprehensive similarity, a spectral clustering algorithm and an adaptive grouping adjustment algorithm are applied to accurately group the workpieces, and representative workpieces are selected through centrality analysis. For the workpieces within each optimized workpiece group, the feature deviation from the representative workpiece is calculated, and a within-group difference distribution model is constructed to provide a basis for subsequent differential current parameter adjustment, thus achieving the accurate identification and control of minor differences within groups of workpieces with similar features. The problem of mutual interference of micro-region current parameters for workpieces with complex shapes is solved. An adaptive waveform library for the electric field distortion region is constructed, including special waveforms such as variable-amplitude exponential waves, feedback modulation trapezoidal waves, and double-peak composite waves, respectively for the electroplating requirements of special micro-regions such as high-curvature sharp corners, deep holes, and narrow slits. Through the electric field gradient response algorithm to dynamically optimize the waveform parameters, the system can select the most suitable waveform template for each micro-region control target and accurately adjust the parameters. More importantly, a waveform interference pre-compensation technology is introduced, which can predict and eliminate the waveform crosstalk effect between adjacent micro-regions, ensuring the coordination between the pulse parameters of each micro-region, effectively solving the problem of mutual interference of micro-region current parameters, achieving precise current control for complex micro-regions on the workpiece surface, and improving the uniformity and quality consistency of the coating. The problem of mutual interference of electric fields in the electroplating environment of multiple workpieces is solved. A modified Laplace equation considering the non-linear effect of ion migration is used to accurately simulate and calculate the difference between the electric field perturbation of a single workpiece and the ideal reference electric field, and a workpiece electric field interference matrix is constructed. By introducing a spatial gradient weighting factor, the system can accurately quantify the mutual interference intensity between workpieces, especially effectively compensating for the strong interference in high current density regions. Based on the mutual interference intensity coefficient, an interference propagation network model is constructed to clearly identify the main propagation paths of electric field interference, and when generating a differential current distribution strategy, the optimized current parameters are adjusted through an interference compensation mechanism, effectively eliminating the adverse effects of mutual interference of electric fields between workpieces, and enabling each workpiece to obtain a more uniform and consistent coating quality during the batch electroplating process.

[0154] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all fall within the protection scope of the present invention.

Claims

1. A differential precise current distribution control method in the multi-workpiece batch electroplating process, characterized in that Including: Obtain the characteristic data of multiple workpieces and perform preprocessing to obtain a workpiece dataset; Based on the workpiece dataset, analyze the differential characteristics of the workpieces and group them to obtain an optimized workpiece grouping and calculate the within-group differential matrix; Based on the optimized workpiece grouping and the workpiece dataset, conduct electrolytic cell electric field distribution modeling and analysis to obtain the actual current density distribution in the micro-region and the workpiece electric field interference matrix; Based on the workpiece electric field interference matrix and the within-group differential matrix, conduct dynamic monitoring and prediction of coating growth to obtain the coating growth characteristics of the group; Read the coating growth characteristics of the group, and combine the actual current density distribution in the micro-region and the optimized workpiece grouping to generate a differential current distribution control strategy.

2. The method according to claim 1, wherein Obtain the optimized workpiece grouping and calculate the within-group differential matrix, including: Read the workpiece dataset, perform dimensionality reduction and extract key features to obtain a dimensionality-reduced feature representation and feature importance weights, and construct a multi-level workpiece similarity metric based on this to obtain a comprehensive similarity; Based on the comprehensive similarity, group the workpieces and select representative workpieces to obtain an optimized workpiece grouping and representative workpieces, and conduct within-group differential quantification and analysis based on them to obtain the within-group differential matrix.

3. The method according to claim 2, wherein Calculate the comprehensive similarity, including: Extract data from the dimensionality-reduced feature representation and feature importance weights, and use a multi-level similarity metric function for the electroplating process to calculate the macroscopic shape similarity, microscopic structure similarity, and material property similarity; calculate the similarity scores between workpieces based on this to obtain a workpiece similarity matrix; According to the distribution of each level of features in the workpiece similarity matrix, combined with the electroplating perturbation sensitivity weighting mechanism, dynamically adjust the level weight coefficients; Combine the workpiece similarity matrix, level weight coefficients, and feature importance weights to calculate the comprehensive similarity between workpieces.

4. The method according to claim 2, wherein Obtain the optimized workpiece grouping and representative workpieces, including: Based on the comprehensive similarity, preliminarily group the workpieces through a spectral clustering algorithm to obtain an initial workpiece grouping; Adopt a grouping evaluation index based on electroplating process sensitivity to evaluate the rationality of the initial workpiece grouping to obtain a grouping evaluation result; Adopt an adaptive grouping adjustment algorithm to optimize the grouping boundary according to the grouping evaluation result to obtain an optimized workpiece grouping; For each optimized workpiece grouping, conduct centrality analysis and use the workpiece with the highest centrality as the representative workpiece.

5. The method according to claim 1, wherein Conduct electrolytic cell electric field distribution modeling and analysis to obtain the actual current density distribution in the micro-region and the workpiece electric field interference matrix, including: Obtain the geometric structure and electrode arrangement of the electrolytic cell, combine the optimized workpiece grouping and the workpiece dataset, model the reference electric field of the multi-workpiece electrolytic cell to obtain a complete model of the multi-workpiece electrolytic cell and the ideal reference electric field distribution; and conduct electric field interference mapping and quantification between workpieces based on this to obtain the workpiece electric field interference matrix and the mutual interference intensity coefficient; Based on the workpiece electric field interference matrix and the mutual interference intensity coefficient, construct a dynamic electric field distribution prediction model; combine the workpiece feature vectors in the workpiece dataset to conduct micro-region electric field characteristic analysis and current density mapping to obtain the actual current density distribution in the micro-region.

6. The method according to claim 5, wherein Conduct electric field interference mapping and quantification between workpieces to obtain the workpiece electric field interference matrix and the mutual interference intensity coefficient, including: Based on the complete model of the multi-workpiece electrolytic cell, simulate the electric field distribution when a single workpiece exists, and generate an electric field perturbation map of the single workpiece; Calculate the difference between the electric field perturbation map of the single workpiece and the ideal reference electric field distribution, and construct an electric field interference matrix of the workpiece; Combine the preset electric field interference intensity quantization index and the electric field interference matrix of the workpiece to calculate the mutual interference intensity coefficient between workpieces; Based on the mutual interference intensity coefficient, identify the main propagation paths of electric field interference and construct an interference propagation network model.

7. The method according to claim 1, characterized in that, Conduct dynamic monitoring and prediction of coating growth to obtain the growth characteristics of the group coating, including: Obtain differential resistance spectrum data through a multi-frequency differential resistance spectrum acquisition system; Based on the differential resistance spectrum data, through the mapping relationship between the resistance spectrum characteristics and the coating growth state, obtain the coating growth state data; Combine the electric field interference matrix of the workpiece, the differential resistance spectrum data and the intra-group difference matrix to decouple and eliminate the interference of the resistance spectrum of the workpiece group, and obtain the growth characteristics of the group coating; Based on the coating growth state data and the growth characteristics of the group coating, use the coating growth dynamic prediction model for simulation to obtain the prediction uncertainty assessment.

8. The method according to claim 7, wherein The multi-frequency differential resistance spectrum acquisition system includes: An alternating current impedance measurement circuit with frequency scanning ability, which superimposes a weak alternating current signal without disturbing the normal electroplating process; A reference electrode array arranged in the electrolytic cell to form a multi-point sampling network; A partition modulation strategy module that realizes the acquisition of the partition resistance spectrum of different regions of the electrolytic cell by controlling the alternating current impedance measurement circuit and the reference electrode array; A time-sequence differential sampling module that performs differential processing on the resistance spectrum data at adjacent time points to generate differential resistance spectrum data.

9. The method according to claim 7, wherein The steps of decoupling and eliminating the interference of the resistance spectrum of the workpiece group to obtain the growth characteristics of the group coating include: Based on the electric field interference matrix of the workpiece, construct a resistance spectrum interference model in a multi-workpiece environment; Combine the resistance spectrum interference model and use ICA to separate the independent resistance spectrum components of each workpiece group from the differential resistance spectrum data; Use a method based on Kalman filtering to process the independent resistance spectrum components to eliminate the resistance spectrum fluctuations caused by non-coating growth factors and obtain purified resistance spectrum data; Combine the intra-group difference matrix to refine the purified resistance spectrum data and obtain the growth characteristics of the group coating.

10. The method according to claim 1, characterized in that, The steps of generating a differentiated current distribution control strategy include: Construct a multi-objective electroplating quality evaluation index, including the quality-parameter mapping relationship; Based on the optimized workpiece grouping and the growth characteristics of the group coating, combine the quality-parameter mapping relationship to construct a group-level current parameter optimization strategy to obtain anti-interference group current parameters; Based on the actual current density distribution in the micro-region, formulate a micro-region differentiated current modulation scheme to obtain the workpiece micro-region current modulation scheme; Integrate the anti-interference group current parameters and the workpiece micro-region current modulation scheme, and integrate a multi-level current distribution control strategy to obtain a complete current distribution control strategy.

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