Circuit board electroplating control method and related equipment
By combining a target dynamic prediction model with an adjustable anode, real-time dynamic compensation and parameter optimization of current density distribution during circuit board electroplating are achieved, solving the problems of uneven plating thickness and reliance on experience for parameter adjustment, thereby improving the electroplating quality and production efficiency of circuit boards.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-25
- Publication Date
- 2026-03-10
AI Technical Summary
Existing circuit board electroplating processes suffer from problems such as uneven plating thickness, complex plating parameters making it difficult to find the optimal parameter combination, and reliance on experience for parameter adjustment, leading to poor product quality stability.
A target dynamic prediction model is used to predict the three-dimensional distribution of current density in real time. High-precision, dynamic, and adaptive compensation based on real-time operating conditions and electroplating solution state is achieved through an adjustable anode. A hybrid model combining self-attention mechanism and convolutional long short-term memory network is used for dynamic prediction of current density distribution and parameter optimization.
It significantly improves the uniformity of current density distribution and the accuracy of control, thereby improving the electroplating quality and efficiency of circuit boards and ensuring the uniformity of plating thickness.
Smart Images
Figure CN121629488A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present disclosure relates to the technical field of industrial internet, and in particular, to a circuit board electroplating control method, a circuit board electroplating control device, an electronic device, a computer readable storage medium, and a computer program product. BACKGROUND
[0002] A circuit board is a core component of an electronic device, and is widely used in the fields of communication, computer, automotive electronics, etc., and the market demand is increasing year by year. The electroplating process directly determines the conductivity, corrosion resistance and service life of the circuit board, and is an indispensable key link in the manufacturing process.
[0003] Although the circuit board electroplating process has been developed for many years and the industry has become mature, there are still problems in the actual production process, such as uneven plating layer thickness, complex electroplating parameters making it difficult to find the optimal parameter combination, and poor product quality stability due to the dependence on experience for parameter adjustment.
[0004] It should be noted that the information disclosed in the above background section is only used to strengthen the understanding of the background of the present disclosure, and therefore can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY
[0005] The present disclosure provides a circuit board electroplating control method and related devices, which at least partially overcome the problems of uneven plating layer thickness and poor quality stability caused by existing circuit board electroplating processes.
[0006] Other characteristics and advantages of the present disclosure will become apparent from the following detailed description, or will be learned by practice of the present disclosure.
[0007] According to one aspect of the present disclosure, a circuit board electroplating control method is provided, comprising: obtaining process parameter values of a circuit board at different times within a preset time period, the process parameter values including working condition parameter values and current density distribution data, the circuit board including a plurality of controllable regions, each of the controllable regions being provided with an adjustable anode; inputting the process parameter values of the circuit board at different times within the preset time period into a target dynamic prediction model, and outputting current density prediction values of different controllable regions of the circuit board at a target time; if the current density prediction value of one controllable region at the target time does not meet a preset condition, determining a target parameter of an adjustable anode corresponding to the one controllable region; and controlling the adjustable anode corresponding to the one controllable region to adjust to the target parameter, so as to complete the electroplating process of the circuit board.
[0008] In one embodiment of the present disclosure, the preset condition comprises a first current density value; the target parameter comprises a first parameter value, the first parameter value comprising at least one of a first distance value and a first current value; wherein, if the current density prediction value of one controllable region at the target time does not satisfy the preset condition, determining the target parameter of the adjustable anode corresponding to the one controllable region comprises: if the current density prediction value of one controllable region at the target time is greater than the first current density value, determining the target parameter as the first parameter value; the control of adjusting the adjustable anode corresponding to the one controllable region to the target parameter comprises at least one of: controlling the distance between the adjustable anode and the circuit board to increase from an initial distance value to the first distance value; controlling the current value flowing through the adjustable anode to decrease from an initial current value to the first current value.
[0009] In one embodiment of the present disclosure, the preset condition further comprises a second current density value, the second current density value being less than the first current density value; the target parameter comprises a second parameter value, the second parameter value comprising at least one of a second distance value and a second current value; wherein, if the current density prediction value of one controllable region at the target time does not satisfy the preset condition, determining the target parameter of the adjustable anode corresponding to the one controllable region comprises: if the current density prediction value of one controllable region at the target time is less than the second current density value, determining the target parameter as the second parameter value; the control of adjusting the adjustable anode corresponding to the one controllable region to the target parameter comprises at least one of: controlling the distance between the adjustable anode and the circuit board to decrease from an initial distance value to the second distance value; controlling the current value flowing through the adjustable anode to increase from an initial current value to the second current value.
[0010] In one embodiment of the present disclosure, the working condition parameter value comprises at least one of: a geometric parameter of the circuit board; a real-time parameter of the electroplating solution; a state of the electroplating equipment.
[0011] In one embodiment of the present disclosure, the method further comprises: obtaining historical process parameter values of the target circuit board at different time instants in a historical period and corresponding current density prediction values of different controllable regions at target historical time instants; training an initial dynamic prediction model based on the historical process parameter values to obtain the current density prediction values of different controllable regions at the target historical time instants; if the error between the current density prediction value of at least one controllable region at the target historical time instant and the current density real value is greater than a preset error threshold, adjusting the model parameters of the initial dynamic prediction model; if the errors between the current density prediction values of each controllable region at the target historical time instant and the current density real value are less than or equal to the preset error threshold, determining the initial dynamic prediction model as the target dynamic prediction model.
[0012] In one embodiment of the present disclosure, the target dynamic prediction model comprises an artificial intelligence model based on a spatio-temporal attention mechanism, and the artificial intelligence model comprises at least one of a hybrid model combining a self-attention mechanism with a convolutional long short-term memory network and a spatio-temporal attention network.
[0013] According to another aspect of the present disclosure, there is also provided a circuit board electroplating control device, comprising: a data acquisition module configured to acquire process parameter values of a circuit board at different time instants within a preset time period, the process parameter values comprising working condition parameter values and current density distribution data, the circuit board comprising a plurality of controllable regions, each of the controllable regions being provided with an adjustable anode; a current prediction module configured to input the process parameter values of the circuit board at different time instants within the preset time period into a target dynamic prediction model, and output current density prediction values of different controllable regions of the circuit board at a target time instant; a parameter determination module configured to determine a target parameter of the adjustable anode corresponding to a controllable region if the current density prediction value of the controllable region at the target time instant does not satisfy a preset condition; and a process control module configured to control the adjustable anode corresponding to the controllable region to adjust to the target parameter, so as to complete an electroplating process of the circuit board.
[0014] According to another aspect of the present disclosure, there is provided an electronic device, comprising: a processor; and a memory configured to store executable instructions of the processor; wherein the processor is configured to execute the above-mentioned circuit board electroplating control method via execution of the executable instructions.
[0015] According to another aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program, the computer program being executed by a processor to implement the above-mentioned circuit board electroplating control method.
[0016] According to another aspect of the present disclosure, there is provided a computer program product comprising executable instructions stored in a computer readable storage medium, the executable instructions being read by a processor of an electronic device from the computer readable storage medium, the processor executing the executable instructions to cause the electronic device to perform the above-mentioned circuit board electroplating control method.
[0017] In the embodiment of the present disclosure, process parameter values of the circuit board at different time points in a preset time period are acquired, the process parameter values include working condition parameter values and current density distribution data, the circuit board includes a plurality of controllable regions, each controllable region is provided with an adjustable anode; the process parameter values of the circuit board at different time points in the preset time period are input into a target dynamic prediction model, and current density prediction values of different controllable regions of the circuit board at a target time point are output; if the current density prediction value of one controllable region at the target time point does not meet a preset condition, a target parameter of the adjustable anode corresponding to the one controllable region is determined; the adjustable anode corresponding to the one controllable region is controlled to adjust to the target parameter, so as to complete the electroplating process of the circuit board. In the process of preparing the circuit board, the present disclosure realizes real-time prediction of the three-dimensional distribution of the current density through the target dynamic prediction model, and based on the prediction result, realizes high-precision, dynamic and self-adaptive compensation based on the real-time running condition and the electroplating solution state through the adjustable anode, realizes collaborative control, significantly improves the uniformity of the current density distribution, improves the accuracy and replicability of the regulation and control, and comprehensively improves the electroplating quality and efficiency of the circuit board, and the thickness uniformity of the circuit board is better.
[0018] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF DRAWINGS
[0019] The drawings incorporated into the specification and forming part of the specification, show embodiments consistent with the present disclosure, and together with the specification, serve to explain the principles of the present disclosure. It is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative labor.
[0020] Figure 1A A schematic diagram of a circuit board electroplating principle in an embodiment of the present disclosure is shown.
[0021] Figure 1B A schematic diagram of an exemplary system architecture of a circuit board electroplating control method in an embodiment of the present disclosure is shown.
[0022] Figure 2 A flowchart of a circuit board electroplating control method in an embodiment of the present disclosure is shown.
[0023] Figure 3 A flowchart of another circuit board electroplating control method in an embodiment of the present disclosure is shown.
[0024] Figure 4 A flowchart of another circuit board electroplating control method in an embodiment of the present disclosure is shown.
[0025] Figure 5 A flowchart of a training method of a target dynamic prediction model in an embodiment of the present disclosure is shown.
[0026] Figure 6 Fig. 1 shows a structural schematic diagram of a circuit board electroplating control device according to an embodiment of the present disclosure.
[0027] Figure 7 Fig. 2 shows a structural block diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION
[0028] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations can be implemented in any
[0029] Moreover, the drawings are not necessarily to scale. Like reference numerals can be used to denote like parts throughout the specification. Some of the blocks in the drawings can be functional building blocks, and can not necessarily correspond to physical or logical independent entities. These functional building blocks can be implemented in software, or in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.
[0030] The electroplating process of the circuit board, also known as the printed circuit board (PCB) or the printed wiring board, can include direct current electroplating, pulse electroplating and chemical plating. Among them, the direct current electroplating deposits metal ions by continuous direct current, which is simple to operate but the current distribution is uneven; the pulse electroplating is powered by pulse current, which can refine the crystal grains, but the pulse frequency, duty cycle and other parameters need to be repeatedly adjusted manually; the chemical plating is to deposit metal by chemical reaction, which has good uniformity of the plating layer, but the electroplating solution has poor stability and high cost.
[0031] Figure 1A Fig. 1 shows a schematic diagram of the principle of direct current electroplating of the circuit board provided by the embodiment of the present disclosure. As shown in Fig. 1, the electroplating equipment includes a plating tank 10, an electroplating solution 20, a plurality of anodes 40, a circuit board 30, a direct current power supply, a heating pipe 50, a stirring device 60, etc. During electroplating of the circuit board, the circuit board 30 and the plurality of anodes 40 are vertically immersed in the electroplating solution 20, the circuit board 30 is electrically connected to the negative electrode of the direct current power supply, and the plurality of anodes 40 is electrically connected to the positive electrode of the direct current power supply. The plurality of anodes 40 can be uniformly arranged on both sides of the circuit board 30. When a direct current voltage is applied to the anodes 40 and the circuit board 30, the anodes 40 ionize the copper ions Cu 2+ Under the action of the electric field, the copper ions Cu2+ Moving towards the cathode (i.e. the circuit board 30) and finally adhering to the surface of the circuit board 30 until the thickness of the plating layer (i.e. Cu) reaches the specified thickness of the product.
[0032] In addition, a heating pipe 50 can be inserted into the electroplating solution 20 to improve the ionization efficiency of copper ions, or a stirring device 60 can be added to the electroplating solution 20 to improve the uniformity of the distribution of copper ions in the electroplating solution 20.
[0033] Although the electroplating of circuit boards has been developed for many years and the industry has gradually matured, there are still many technical problems to be solved in the actual production process: (1) Uneven thickness of product plating layer: due to the three-phase coupling of the electroplating solution flow field-electric field-chemical field, the current density distribution is uneven, resulting in uneven thickness of the plating layer at different positions of the product, usually thick at the edges and thin in the middle. Due to the variety of product types, it is difficult to adjust uniformly. Uneven thickness of the plating layer will lead to unstable quality of the circuit board. In order to ensure that the thickness of all positions reaches the lower limit, the plating layer in some areas will be too thick, causing waste of electroplating materials; (2) Complex electroplating parameters: electroplating parameters include current, voltage, temperature, electroplating solution concentration and other key parameters. The coupling relationship between parameters is strong, there is a dynamic change and a nonlinear relationship, it is difficult to find the optimal parameter combination, and it is easy to cause defects such as uneven plating layer thickness and pinholes; (3) Parameter adjustment depends on experience: usually, electroplating parameters are adjusted manually, relying on engineers' experience, which has a long personnel training period and is difficult. It is difficult to process massive data, unable to calculate the optimal solution of parameters in real time, with a large number of product types, it is difficult to set a unified adjustment scheme. Manual adjustment has a long cycle, a lagging response, a low production efficiency, and poor quality stability, and a high rework rate.
[0034] In related technologies, Response Surface Methodology (RSM), PLC combined with Proportional Integral Derivative (PID) control method, and SVM / XGBoost model prediction method can be used to improve the uniformity of the circuit board. However, the RSM response surface method cannot handle nonlinear coupling; the PLC combined with the PID control method only maintains a steady state and has no prediction ability; the feature engineering of the Support Vector Machine (SVM) / eXtreme Gradient Boosting (XGBoost) model prediction method relies on expert knowledge and requires a large amount of high-quality labeled data. That is, related technologies cannot real-time analyze the multi-physical field coupling effect of the electroplating solution flow field-electric field-chemical field, and cannot handle the model drift problem caused by the drift of the electroplating solution components over time.
[0035] To at least partially solve the above technical problems, the present disclosure fuses data of an electroplating liquid flow field-electric field-chemical field based on a multi-modal target dynamic prediction model, realizes dynamic prediction of current density distribution under different working conditions, and intelligent optimization of various electroplating parameters, so that the current density is uniformly distributed, and the electroplating quality and efficiency of the circuit board are improved.
[0036] Figure 1B A schematic diagram of an exemplary system architecture of a circuit board electroplating control method or a circuit board electroplating control device that can be applied to the embodiments of the present disclosure is shown.
[0037] As shown in FIG. 1, the system architecture can include a cloud server 100, an edge node 200, and a controller 300. Among them, the controller 300 is arranged in the production line of the circuit board, and one controller 300 is arranged in each electroplating production line. The controller 300 can directly interface with the sensors of the data perception layer and the controlled devices, realize localized collection and real-time control of device data, the sensors can include sensors for collecting current, temperature, electroplating liquid concentration, etc., and the controlled devices can be adjustable anodes.
[0038] The edge node 200 is used to deploy the core inference module of the target dynamic prediction model, such as current density prediction and parameter optimization inference, and uses the computing power (such as GPU acceleration card) of the edge node 200 to realize millisecond-level response. Based on the preprocessed sensor data, product data (shape, size, etc.), and locally cached historical process data, the edge node 200 outputs an optimized process parameter instruction set (such as anode adjustment parameters) in real time, directly driving the controller 300 to perform dynamic adjustment. The target dynamic prediction model can simultaneously serve multiple edge servers of the edge node 200, thereby fully utilizing computing resources and reducing costs. It should be noted that the edge node 200 only retains lightweight models and key data required for real-time inference.
[0039] The cloud server 100 is used to process non-real-time tasks, such as model training iteration, full-quantity historical data storage, process knowledge update, etc.
[0040] The edge node 200 and the cloud server 100 can synchronize data through an industrial bus or a wireless communication network (such as a 5G network, a 6G network, etc.), to ensure that the target dynamic prediction model deployed in the edge node 200 can be continuously optimized based on the global data updated by the cloud server 100.
[0041] The edge server and the cloud server 100 can be servers that provide various services, such as a background management server that provides support for devices operated by users using terminal equipment. The background management server can analyze and process received requests and other data, and feed back the processing results.
[0042] Optionally, the edge server can be a standalone physical server, or a server cluster or distributed system composed of multiple physical servers, and the cloud server 100 can be a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and basic cloud computing services such as big data and artificial intelligence platforms.
[0043] Those skilled in the art can know that, Figure 1B The number of cloud servers 100, edge nodes 200, and controllers 300 in the above embodiment is only illustrative, and any number of cloud servers 100, edge nodes 200, and controllers 300 can be provided according to actual needs. The present disclosure does not limit this.
[0044] Based on this, the scheme provided by the embodiment of the present disclosure acquires process parameter values of the circuit board at different times within a preset time period, the process parameter values include working condition parameter values and current density distribution data, the circuit board includes a plurality of controllable regions, and each controllable region is provided with an adjustable anode; the process parameter values of the circuit board at different times within the preset time period are input into a target dynamic prediction model, and current density prediction values of different controllable regions of the circuit board at a target time are output; if the current density prediction value of one controllable region at the target time does not meet a preset condition, a target parameter of the adjustable anode corresponding to the one controllable region is determined; and the adjustable anode corresponding to the one controllable region is controlled to adjust to the target parameter, so as to complete the electroplating process of the circuit board. In the process of preparing the circuit board, the present disclosure predicts the three-dimensional distribution of the current density in real time through the target dynamic prediction model, and based on the prediction result, high-precision, dynamic, and self-adaptive compensation based on real-time operating conditions and electroplating liquid states is realized through the adjustable anode, collaborative control is realized, the uniformity of the current density distribution is significantly improved, the accuracy and replicability of the regulation and control are improved, and the electroplating quality and efficiency of the circuit board are comprehensively improved, and the thickness uniformity of the circuit board is better.
[0045] In the embodiment of the present disclosure, a circuit board electroplating control method is provided, which can be executed by any system with computing processing capability. The process corresponding to the method can be executed by an edge node.
[0046] Figure 2 A flowchart of a circuit board electroplating control method in the embodiment of the present disclosure is shown as follows. Figure 2 As shown in the flowchart, the circuit board electroplating control method provided in the embodiment of the present disclosure includes the following steps: S202, acquire process parameter values of the circuit board at different times within a preset time period, the process parameter values include working condition parameter values and current density distribution data, the circuit board includes a plurality of controllable regions, and each controllable region is provided with an adjustable anode.
[0047] In an embodiment, the circuit board can be a circuit board without deposited plating, or a circuit board in the electroplating process, i.e., a circuit board with partially deposited plating but the thickness of the plating does not meet the product requirements.
[0048] The preset time period can be a time period from the start time of the electroplating process to the current time, or a part of the time period in the electroplating process. The preset time period is at least part of the time period in which the plating has been completed in the electroplating process.
[0049] In an embodiment, the circuit board can be divided into a plurality of controllable regions along the width direction of the circuit board, and each controllable region is provided with an adjustable anode. The adjustable anode is connected to a micro-displacement driving device (precision: ±0.1 mm) and a current output regulator, so that the distance between the adjustable anode and the circuit board and the local current output can be independently adjusted. For example, the circuit board is divided into n x m independent controllable regions, and n and m are positive integers, such as 6 x 6 partitioning.
[0050] The above process parameter values include working condition parameter values and current density distribution data. The working condition parameter values represent the values of controllable parameters in the electroplating process, and the current density distribution data represents the current density values in different controllable regions of the circuit board.
[0051] In an embodiment, the working condition parameter values include at least one of the following: geometric parameters of the circuit board; real-time parameters of the electroplating solution; and state of the electroplating equipment.
[0052] The geometric parameters of the circuit board are the geometric parameters designed for the circuit board, which can include line width, hole depth-diameter ratio, pattern distribution, etc.
[0053] The real-time parameters of the electroplating solution can include concentration, temperature, flow rate, etc.
[0054] The state of the electroplating equipment includes the initial position of the adjustable anode, the output stability of the power supply, the concentration and temperature of the electroplating solution, etc. The initial position of the adjustable anode refers to the initial distance between the adjustable anode and the circuit board.
[0055] In an embodiment, after obtaining the process parameter values, the process parameter values can be preprocessed. The preprocessing can include, but is not limited to, data cleaning, data normalization, etc.
[0056] The preprocessed process parameter values can be sorted based on different time points and stored in an array to obtain the input data of the target dynamic prediction model.
[0057] S204, input the process parameter values of the circuit board at different time points in the preset time period into the target dynamic prediction model, and output the current density prediction values of the circuit board in different controllable regions at the target time point.
[0058] The target dynamic prediction model is a trained dynamic prediction model, which can couple the static geometric features (e.g., parameters of spatial dimensions) and dynamic process parameters (parameters of time dimensions), and output the predicted current density values of the circuit board in different controllable regions at the target time as a three-dimensional distribution prediction result of position-time-current density.
[0059] The target time can be the current time or a time after the current time by a preset time length. For example, the target dynamic prediction model can predict the trend of the current density of the edge controllable region of the circuit board after 5 minutes.
[0060] The target dynamic prediction model includes an artificial intelligence model based on a spatio-temporal attention mechanism, and the artificial intelligence model includes at least one of a hybrid model combining a self-attention mechanism (Self-Attention) and a convolutional long short-term memory network (ConvLSTM), and a spatio-temporal attention network.
[0061] For the hybrid model combining the self-attention mechanism and the convolutional long short-term memory network, the convolution kernel of the ConvLSTM can process two-dimensional spatial data (such as position-current density distribution), and the LSTM unit can model the long-term dependence of the time series to realize joint learning of spatio-temporal features; the self-attention mechanism automatically identifies the regions that have a greater impact on the current density prediction value by calculating the similarity of different positions in the feature map, thereby improving the ability of the target dynamic prediction model to capture complex spatio-temporal patterns.
[0062] For the spatio-temporal attention network, the spatial correlation between different electroplating liquid flow fields can be analyzed through a spatio-temporal attention module, and the time pattern of the current density distribution at different times within a preset time period can be captured through a time attention module, and finally the two are fused to predict the current density.
[0063] S206, if the current density prediction value of one controllable region at the target time does not meet the preset condition, the target parameter of the adjustable anode corresponding to the one controllable region is determined.
[0064] The preset condition is used to ensure the uniformity of the current density, and the preset condition can be determined according to the actual process requirements and can be pre-configured in the edge server.
[0065] When the current density prediction values of multiple controllable regions at the target time all meet the preset condition, it indicates that the distribution of the current density prediction values of the multiple controllable regions is uniform, and accordingly, the thickness of the deposited plating layer in different controllable regions is also uniform.
[0066] When the current density prediction value of at least one controllable region at the target moment does not satisfy the preset condition, it indicates that the distribution of the current density prediction value of the at least one controllable region is uneven, and accordingly, the thickness of the plated layer deposited in the at least one controllable region is also uneven.
[0067] The target parameter of the adjustable anode can include at least one of a distance between the adjustable anode and the circuit board, and a current value flowing through the adjustable anode.
[0068] When the distance between the adjustable anode and the circuit board decreases, the electric field strength between the adjustable anode and the circuit board increases, the current density in the controllable region increases, and the plated layer thickness grows faster, which is suitable for a scenario with thinner plated layer thickness; when the distance between the adjustable anode and the circuit board increases, the electric field strength between the adjustable anode and the circuit board decreases, the current density in the controllable region decreases, and the plated layer thickness grows slower, which is suitable for a scenario with thicker plated layer thickness.
[0069] When the current value flowing through the adjustable anode increases, the electric field strength between the adjustable anode and the circuit board increases, the current density in the controllable region increases, and the plated layer thickness grows faster, which is suitable for a scenario with thinner plated layer thickness; when the current value flowing through the adjustable anode decreases, the electric field strength between the adjustable anode and the circuit board decreases, the current density in the controllable region decreases, and the plated layer thickness grows slower, which is suitable for a scenario with thicker plated layer thickness.
[0070] S208, control the adjustable anode corresponding to one controllable region to adjust to the target parameter, so as to complete the electroplating process of the circuit board.
[0071] In one embodiment, the controller can be used to control the adjustable anode to adjust to the target parameter.
[0072] It should be noted that the target dynamic prediction model can output the current density prediction value every 0.5 seconds, and the controller synchronously adjusts the adjustable anode, forming a millisecond-level closed loop of prediction-decision-execution-feedback, and the adjustment response time is less than or equal to 2000 ms. Through prediction, it can intervene in advance and avoid uneven current density distribution from the root, such as pinholes and plated layer thickness deviation.
[0073] In the embodiment of the present disclosure, process parameter values of the circuit board at different time points in a preset time period are acquired, the process parameter values include working condition parameter values and current density distribution data, the circuit board includes a plurality of controllable regions, each controllable region is provided with an adjustable anode; the process parameter values of the circuit board at different time points in the preset time period are input into a target dynamic prediction model, and current density prediction values of different controllable regions of the circuit board at a target time point are output; if the current density prediction value of one controllable region at the target time point does not meet a preset condition, a target parameter of the adjustable anode corresponding to the one controllable region is determined; the adjustable anode corresponding to the one controllable region is controlled to adjust to the target parameter, so as to complete the electroplating process of the circuit board. In the process of preparing the circuit board, the present disclosure realizes real-time prediction of the three-dimensional distribution of the current density through the target dynamic prediction model, and based on the prediction result, realizes high-precision, dynamic and self-adaptive compensation based on the real-time running condition and the electroplating solution state through the adjustable anode, realizes collaborative control, significantly improves the uniformity of the current density distribution, improves the accuracy and replicability of the regulation and control, and comprehensively improves the electroplating quality and efficiency of the circuit board, and the thickness uniformity of the circuit board is better.
[0074] Figure 3 A flowchart of another electroplating control method of a circuit board in an embodiment of the present disclosure is shown. As shown in Figure 3 The preset condition includes a first current density value; the target parameter includes a first parameter value, and the first parameter value includes at least one of a first distance value and a first current value. If the current density prediction value of one controllable region at the target time point does not meet the preset condition, the target parameter of the adjustable anode corresponding to the one controllable region is determined, including: S2062, if the current density prediction value of one controllable region at the target time point is greater than the first current density value, the target parameter is determined as the first parameter value. The control of the adjustable anode adjusting to the target parameter includes at least one of the following: S2082, the distance between the adjustable anode and the circuit board is increased from an initial distance value to a first distance value. The current value flowing through the adjustable anode is reduced from an initial current value to a first current value.
[0075] In an embodiment, the first current density value can be a first preset multiple of the current density standard value, and the first preset multiple can be 1.05-1.2, for example, the first current density value is 1.1 times the current density standard value.
[0076] The first parameter value is a parameter value capable of causing the current density to change in a direction of decrease by controlling the adjustable anode. The first parameter value includes a first distance value representing a distance between the adjustable anode and the circuit board, and a first current value representing a current value flowing through the adjustable anode.
[0077] The initial distance value and the initial current value can be set in the electroplating equipment state.
[0078] In one embodiment, when the current density prediction value of one controllable region at the target time is greater than the first current density value, it indicates that the plating layer of the one controllable region is deposited too fast, and the deposition speed needs to be reduced. At this time, the distance between the adjustable anode and the circuit board can be increased from the initial distance value to the first distance value, and / or the current value flowing through the adjustable anode can be reduced from the initial current value to the first current value, so as to reduce the current density and improve the uniformity of the plating layer thickness.
[0079] It should be noted that after the adjustable anode corresponding to one controllable region is adjusted to the first parameter value, the current process parameter value can be input to the target dynamic prediction model to predict the current density prediction value. At this time, the initial distance value is updated to the first distance value, and the initial current value is updated to the first current value.
[0080] In one embodiment, the distance between the adjustable anode and the circuit board can also be controlled in a step-by-step manner to gradually increase from the initial distance value to the first distance value, and the current value flowing through the adjustable anode can be controlled to gradually decrease from the initial current value to the first current value. The step size of each adjustment can be determined according to actual conditions, so as to ensure the plating layer quality and performance of the circuit board.
[0081] For example, when the current density prediction value at the corner of the circuit board at the target time exceeds the first current density value, the adjustable anode is controlled to retreat 2 mm away from the circuit board. In a specific implementation, the control can be performed in steps of 1 mm, and the position adjustment of the adjustable anode is completed through 2 times.
[0082] Figure 4 A flow chart of another circuit board electroplating control method in the embodiments of the present disclosure is shown. As shown in Figure 4 In one embodiment, the preset condition further includes a second current density value, and the second current density value is less than the first current density value. The target parameter includes a second parameter value, and the second parameter value includes at least one of a second distance value and a second current value.
[0083] If the current density prediction value of one controllable region at the target time does not satisfy the preset condition, the target parameter of the adjustable anode corresponding to one controllable region is determined, including: S2064, if the current density prediction value of one controllable region at the target time is less than the second current density value, determining that the target parameter is the second parameter value; The S208 described above controls the adjustable anode corresponding to one controllable region to adjust to the target parameter, including at least one of the following: S2084, controlling the distance between the adjustable anode and the circuit board to decrease from the initial distance value to the second distance value; controlling the current value flowing through the adjustable anode to increase from the initial current value to the second current value.
[0084] In one embodiment, the second current density value can be a second preset multiple of the current density standard value, and the second preset multiple can be 0.85-0.95, for example, the second current density value is 0.9 times of the current density standard value.
[0085] The second parameter value is a parameter value that can make the current density change in the direction of increasing by controlling the adjustable anode. The second parameter value includes the second distance value and / or the second current value. The initial distance value and the initial current value can be set in the electroplating equipment state.
[0086] In one embodiment, when the current density prediction value of one controllable region at the target time is less than the second current density value, it indicates that the deposition of the plating layer of the one controllable region is too slow, and the deposition speed needs to be improved. At this time, the distance between the adjustable anode and the circuit board can be controlled to decrease from the initial distance value to the second distance value; and / or the current value flowing through the adjustable anode can be controlled to increase from the initial current value to the second current value, so as to improve the current density and thus improve the uniformity of the plating layer thickness.
[0087] It should be noted that after the adjustable anode corresponding to one controllable region is controlled to adjust to the second parameter value, the current process parameter value can be input to the target dynamic prediction model to predict the current density prediction value. At this time, the initial distance value is updated to the second distance value, and the initial current value is updated to the second current value.
[0088] In one embodiment, the distance between the adjustable anode and the circuit board can also be controlled in a step-by-step manner to gradually decrease from the initial distance value to the second distance value, and the current value flowing through the adjustable anode can be controlled to gradually increase from the initial current value to the second current value. The step length of each adjustment can be determined according to actual conditions, so as to ensure the plating layer quality and performance of the circuit board.
[0089] Figure 5 A flow chart of a training method of a target dynamic prediction model in an embodiment of the present disclosure is shown. As shown in Figure 5 In one embodiment, the method further includes a training process of the target dynamic prediction model, specifically including: S502, obtain historical process parameter values of the target circuit board at different time points in a historical period and corresponding current density prediction values of different controllable regions at a target historical time point; S504, train the initial dynamic prediction model based on the historical process parameter values to obtain the current density prediction values of the different controllable regions at the target historical time point; S506, if the error between the current density prediction value of at least one controllable region at the target historical time point and the current density true value is greater than a preset error threshold, adjust the model parameters of the initial dynamic prediction model; S508, if the errors between the current density prediction values of each controllable region at the target historical time point and the current density true value are less than or equal to the preset error threshold, determine the initial dynamic prediction model as the target dynamic prediction model.
[0090] In one embodiment, the target circuit board is a product with the same working condition as the circuit board in the foregoing embodiments, and is used for model training.
[0091] The historical process parameter values at different time points in the historical period and the corresponding current density prediction values of different controllable regions at the target historical time point are data actually tested by the target circuit board, and are used for model training.
[0092] The initial dynamic prediction model is an initialized artificial intelligence model based on a spatio-temporal attention mechanism, i.e., a model whose model parameters have not been optimized.
[0093] The current density true value can be distributed current sensor data collected through a data perception layer, such as a 16-point matrix sensor embedded in a plating tank.
[0094] The error between the current density prediction value of each controllable region at the target historical time point and the current density true value is the ratio between the absolute value of the difference between the two and the current density true value. The preset error threshold can be determined according to actual needs, for example, the preset error threshold is 2%, 3%, etc.
[0095] When the error between the current density prediction value of at least one controllable region at the target historical time point and the current density true value is greater than the preset error threshold, the model parameters of the initial dynamic prediction model are adjusted (for example, the weight coefficient of the controllable region with high current density is adjusted), and iterative training is performed until the error between the current density prediction value of each controllable region at the target historical time point and the current density true value is less than or equal to the preset error threshold.
[0096] When the error between the current density prediction value of each controllable region at the target historical time point and the current density true value is less than or equal to the preset error threshold, the initial dynamic prediction model is determined as the target dynamic prediction model, so that the target dynamic prediction model meeting the accuracy is trained.
[0097] In the embodiment of the present disclosure, the distributed current sensor data collected in combination with the data perception layer is dynamically corrected by the deviation feedback of the current density prediction value and the current density measured value, so that the error of the target dynamic prediction model is controlled within the preset error range, and the prediction accuracy of the target dynamic prediction model is improved.
[0098] In the present disclosure, by fusing the target dynamic prediction model of multiple working condition data and historical current distribution, the current density three-dimensional distribution can be predicted in real time, and based on the current density prediction value, the position and current output of the multi-partition adjustable anode are controlled, so that the current density distribution uniformity error is less than or equal to 5%.
[0099] The target dynamic prediction model of the present disclosure is a hybrid model combining self-attention mechanism (Self-Attention) and convolutional long short-term memory network (ConvLSTM), a spatiotemporal attention network and other artificial intelligence models based on attention mechanism. On the one hand, it can perform spatial reasoning on the current density field of the anode-cathode, and on the other hand, it can dynamically compensate for the plating solution concentration drift based on the time sequence self-attention mechanism, so as to realize online decoupling of the three-phase coupling field.
[0100] Based on the target dynamic prediction model, high-precision, dynamic and adaptive compensation based on real-time running state and plating solution state is realized through the adjustable anode, which significantly improves the uniformity of the plated layer. According to different process flow stages, precise control and adjustment of process parameters such as current, voltage, temperature and plating solution concentration are automatically realized, which does not depend on manual experience and improves the accuracy and replicability of the control.
[0101] Based on the same inventive concept, the present disclosure also provides a circuit board electroplating control device, as described in the following embodiments. Since the principle of solving the problem of the system embodiment is similar to that of the above-mentioned method embodiment, the implementation of the system embodiment can be referred to the implementation of the above-mentioned method embodiment, and the repeated parts will not be described here.
[0102] Figure 6 The structure schematic diagram of a circuit board electroplating control device in an embodiment of the present disclosure is shown, as shown in Figure 6 The circuit board electroplating control device includes a data acquisition module 610, a current prediction module 620, a parameter determination module 630 and a process control module 640.
[0103] The data acquisition module 610 is configured to acquire process parameter values of the circuit board at different times within a preset time period, the process parameter values including working condition parameter values and current density distribution data, the circuit board including a plurality of controllable regions, each controllable region being provided with an adjustable anode. The current prediction module 620 is configured to input the process parameter values of the circuit board at different time points in a preset time period into a target dynamic prediction model, and output current density prediction values of different controllable regions of the circuit board at a target time point. The parameter determination module 630 is configured to determine a target parameter of an adjustable anode corresponding to one controllable region if the current density prediction value of the one controllable region at the target time point does not satisfy a preset condition. The process control module 640 is configured to control the adjustable anode corresponding to the one controllable region to adjust to the target parameter, so as to complete the electroplating process of the circuit board.
[0104] In one embodiment, the preset condition includes a first current density value; the target parameter includes a first parameter value, the first parameter value including at least one of a first distance value and a first current value; the parameter determination module 630 is configured to determine the target parameter as the first parameter value if the current density prediction value of the one controllable region at the target time point is greater than the first current density value; and the process control module 640 is configured to control the distance between the adjustable anode and the circuit board to increase from an initial distance value to the first distance value; and / or control the current value flowing through the adjustable anode to decrease from an initial current value to the first current value.
[0105] In one embodiment, the preset condition further includes a second current density value, the second current density value being less than the first current density value; the target parameter includes a second parameter value, the second parameter value including at least one of a second distance value and a second current value; the parameter determination module 630 is configured to determine the target parameter as the second parameter value if the current density prediction value of the one controllable region at the target time point is less than the second current density value; and the process control module 640 is configured to control the distance between the adjustable anode and the circuit board to decrease from the initial distance value to the second distance value; and / or control the current value flowing through the adjustable anode to increase from the initial current value to the second current value.
[0106] In one embodiment, the process parameter value includes at least one of the following: a geometric parameter of the circuit board; a real-time parameter of the electroplating solution; a state of the electroplating equipment.
[0107] In one embodiment, the device further comprises a model training module not shown in the drawings, the model training module is configured to obtain historical process parameter values of the target circuit board at different time points in a historical period and corresponding current density prediction values of different controllable regions at the target historical time point; train the initial dynamic prediction model based on the historical process parameter values to obtain the current density prediction values of different controllable regions at the target historical time point; if the error between the current density prediction value of at least one controllable region at the target historical time point and the real value is greater than a preset error threshold, adjust the model parameters of the initial dynamic prediction model; if the errors between the current density prediction values of each controllable region at the target historical time point and the real values are less than or equal to the preset error threshold, determine the initial dynamic prediction model as the target dynamic prediction model.
[0108] It should be noted that the target dynamic prediction model comprises an artificial intelligence model based on a space-time attention mechanism, and the artificial intelligence model comprises at least one of a hybrid model combining a self-attention mechanism and a convolutional long short-term memory network and a space-time attention network.
[0109] In the embodiments of the present disclosure, process parameter values of the circuit board at different time points in a preset period are obtained, the process parameter values comprise working condition parameter values and current density distribution data, the circuit board comprises a plurality of controllable regions, and each controllable region is provided with an adjustable anode; the process parameter values of the circuit board at different time points in the preset period are input into the target dynamic prediction model, and current density prediction values of different controllable regions of the circuit board at a target time point are output; if the current density prediction value of one controllable region at the target time point does not meet a preset condition, a target parameter of the adjustable anode corresponding to the one controllable region is determined; and the adjustable anode corresponding to the one controllable region is controlled to be adjusted to the target parameter, so as to complete the electroplating process of the circuit board. In the process of preparing the circuit board, the present disclosure realizes real-time prediction of the three-dimensional distribution of the current density through the target dynamic prediction model, and based on the prediction result, realizes high-precision, dynamic and self-adaptive compensation based on real-time operating conditions and electroplating liquid states through the adjustable anode, realizes collaborative control, significantly improves the uniformity of the current density distribution, improves the accuracy and replicability of the regulation and control, and comprehensively improves the electroplating quality and efficiency of the circuit board, and the thickness uniformity of the circuit board is better.
[0110] Those skilled in the art can understand that each aspect of the present application can be implemented as a system, a method or a program product. Therefore, each aspect of the present application can be specifically implemented as a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which can be collectively referred to as "circuitry", "module" or "system" here.
[0111] The electronic device 700 according to this embodiment of the present application will be described below with reference to Figure 7 .Figure 7 The electronic device 700 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.
[0112] like Figure 7 As shown, the electronic device 700 is manifested in the form of a general-purpose computing device. The components of the electronic device 700 may include, but are not limited to: at least one processing unit 710, at least one storage unit 720, and a bus 730 connecting different system components (including storage unit 720 and processing unit 710).
[0113] The storage unit stores program code that can be executed by the processing unit 710, causing the processing unit 710 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 710 can perform actions such as... Figure 2 The process parameter values of the circuit board at different times within a preset time period are obtained. The process parameter values include operating condition parameter values and current density distribution data. The circuit board includes multiple controllable regions, and each controllable region is equipped with an adjustable anode. The process parameter values of the circuit board at different times within the preset time period are input into the target dynamic prediction model, and the predicted current density values of the circuit board in different controllable regions at the target time are output. If the predicted current density value of a controllable region at the target time does not meet the preset conditions, the target parameter of the adjustable anode corresponding to the controllable region is determined. The adjustable anode corresponding to the controllable region is controlled to adjust to the target parameter to complete the electroplating process of the circuit board.
[0114] Storage unit 720 may include readable media in the form of volatile storage units, such as random access memory (RAM) 7201 and / or cache memory 7202, and may further include read-only memory (ROM) 7203.
[0115] The storage unit 720 may also include a program / utility 7204 having a set (at least one) program module 7205, such program module 7205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.
[0116] Bus 730 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.
[0117] The electronic device 700 can also communicate with one or more external devices 740 such as a keyboard or pointing device, a Bluetooth device, or a Figure 7 network adapter 760 which enables the electronic device 700 to communicate with one or more networks, such as a local area network (LAN), a wide area network (WAN), and / or the Internet, as
[0118] Those skilled in the art will readily observe that the example embodiments described herein can be implemented in a software application, firmware application, circuit, and / or hardware. As such, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash disk, or mobile hard disk) or a network, and includes a number of instructions to enable a computing device (such as a personal computer, a server, a terminal device, or a network device) to execute the methods according to the embodiments of the present disclosure.
[0119] In the example embodiments of the present disclosure, a computer readable storage medium is also provided, which stores a program product capable of implementing the above-mentioned method. In some possible embodiments, various aspects of the present disclosure can also be implemented in the form of a program product, which includes program codes for causing a terminal device to execute the steps described in the above-mentioned “example method” section according to various example embodiments of the present disclosure when the program product is run on the terminal device.
[0120] A program product for implementing the above-mentioned method according to the embodiments of the present disclosure is described, which can be in the form of a portable compact disc read-only memory (CD-ROM) and includes program codes, and can be run on a terminal device such as a personal computer. However, the program product of the present disclosure is not limited to this, and in this document, the readable storage medium can be any tangible medium containing or storing a program, which can be used by or in conjunction with an instruction execution system, device, or apparatus.
[0121] The program product can take any combination of one or more computer-readable media. The computer-readable media can be a computer-readable storage medium or a computer-readable signal medium. The computer-readable storage medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include the following: an electrical connection having one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
[0122] The computer-readable signal medium can include a computer-readable storage medium that is configured to store and deliver a computer-readable program code. The computer-readable program code can be propagated as a computer-readable signal medium.
[0123] The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination of the foregoing.
[0124] The program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++, etc., and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server. In the latter scenario, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing device, such as through the Internet using an Internet Service Provider. The present application can be implemented as a computer program product, which can include a computer-readable medium having stored computer program code.
[0125] It should be noted that, although several modules or units of the device for action execution are mentioned in the foregoing detailed description, such a division is not mandatory. Indeed, according to an embodiment of the present disclosure, features and functionalities of two or more modules or units described above can be embodied in one module or unit. Conversely, features and functionalities of one module or unit described above can be further divided into a plurality of modules or units.
[0126] Moreover, although individual steps of the methods in the present disclosure are described in a particular order in the drawings, this is not required or implied as to the order of the steps, nor is it required that all of the steps be performed to achieve the desired result. Additionally or alternatively, certain steps can be omitted, multiple steps can be combined into one step, one step can be broken into multiple steps, etc.
[0127] Those skilled in the art will readily understand that the example embodiments described herein can be implemented by software and / or by hardware coupled with software, as described above. Thus, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash disk, a mobile hard disk, or the like) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the methods according to the embodiments of the present disclosure.
[0128] Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure following the general principles thereof and including such departures from the present disclosure that come within known use or custom in the art. It is intended that the specification and examples be considered exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.
Claims
1. A method of controlling plating of a circuit board, characterized by, The method comprises the following steps: acquiring process parameter values of a circuit board at different time points in a preset time period, the process parameter values comprising working condition parameter values and current density distribution data, the circuit board comprising a plurality of controllable regions, each controllable region being provided with an adjustable anode; inputting the process parameter values of the circuit board at different time points in the preset time period into a target dynamic prediction model to output current density prediction values of different controllable regions of the circuit board at a target time point; if a current density prediction value of one controllable region at the target time point does not meet a preset condition, determining a target parameter of an adjustable anode corresponding to the one controllable region; controlling the adjustable anode corresponding to the one controllable region to adjust to the target parameter to complete an electroplating process of the circuit board.
2. The circuit board plating control method according to claim 1, characterized by, The preset condition comprises a first current density value, and the target parameter comprises a first parameter value, the first parameter value comprising at least one of a first distance value and a first current value. If the current density prediction value of the one controllable region at the target time point is greater than the first current density value, the target parameter is determined as the first parameter value. The control of the adjustable anode to adjust to the target parameter comprises at least one of the following: controlling a distance between the adjustable anode and the circuit board to increase from an initial distance value to the first distance value; controlling a current value flowing through the adjustable anode to decrease from an initial current value to the first current value. The preset condition further comprises a second current density value, the second current density value being smaller than the first current density value, and the target parameter comprises a second parameter value, the second parameter value comprising at least one of a second distance value and a second current value.
3. The circuit board plating control method according to claim 2, characterized by, If the current density prediction value of the one controllable region at the target time point is smaller than the second current density value, the target parameter is determined as the second parameter value. The control of the adjustable anode to adjust to the target parameter comprises at least one of the following: controlling the distance between the adjustable anode and the circuit board to decrease from an initial distance value to the second distance value; controlling the current value flowing through the adjustable anode to increase from an initial current value to the second current value. The working condition parameter values comprise at least one of the following: a geometric parameter of the circuit board; 4. The circuit board plating control method according to claim 1, characterized by, a real-time parameter of an electroplating solution; a state of an electroplating device. The method further comprises the following steps: acquiring historical process parameter values of a target circuit board at different time points in a historical time period and corresponding current density prediction values of different controllable regions at target historical time points; 5. The plating control method of claim 1, wherein training an initial dynamic prediction model based on the historical process parameter values to obtain the current density prediction values of the different controllable regions at the target historical time points; if an error between the current density prediction value of the at least one controllable region at the target historical time and the current density true value is greater than a preset error threshold, adjusting a model parameter of the initial dynamic prediction model; if the error between the current density prediction value of each controllable region at the target historical time and the current density true value is less than or equal to the preset error threshold, determining the initial dynamic prediction model as the target dynamic prediction model.
6. The circuit board plating control method according to any one of claims 1 to 5, characterized by, The target dynamic prediction model comprises an artificial intelligence model based on a space-time attention mechanism, and the artificial intelligence model comprises at least one of a hybrid model combining a self-attention mechanism and a convolutional long short-term memory network and a space-time attention network.
7. A circuit board plating control device characterized by comprising: The method comprises: The data acquisition module is configured to acquire process parameter values of the circuit board at different times within a preset time period, wherein the process parameter values comprise working condition parameter values and current density distribution data, and the circuit board comprises a plurality of controllable regions, and each controllable region is provided with an adjustable anode. The current prediction module is configured to input the process parameter values of the circuit board at different times within a preset time period into a target dynamic prediction model, and output current density prediction values of different controllable regions of the circuit board at a target time. The parameter determination module is configured to determine a target parameter of the adjustable anode corresponding to the one controllable region if the current density prediction value of the one controllable region at the target time does not meet a preset condition. The process control module is configured to control the adjustable anode corresponding to the one controllable region to be adjusted to the target parameter, so as to complete the electroplating process of the circuit board.
8. An electronic device, comprising: The method comprises: A processor and a memory for storing executable instructions of the processor, wherein the processor is configured to execute the executable instructions to perform the circuit board electroplating control method according to any one of claims 1-6. The computer program is executed by the processor to implement the circuit board electroplating control method according to any one of claims 1-6.
9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program or instructions are executed by the processor to implement the circuit board electroplating control method according to any one of claims 1-6.
10. A computer program product, characterised in that,