Double-layer circuit board electroplating layer precision electroplating control system and method

Abstract

The application relates to the technical field of integrated circuit manufacturing, in particular to a double-layer circuit board electroplating layer precision electroplating control system and method, which comprises the following steps: a multi-physical field coupling simulation model is established to simulate the internal current density and ion concentration distribution of a via; a mapping relationship between the current density and the side wall electroplating thickness is constructed based on the simulation result to perform electroplating thickness prediction; a time-adjustable pulse current control strategy is designed through thickness prediction feedback, the pulse amplitude, duty ratio and on-off period are dynamically adjusted to guide the current distribution optimization; the state of the electroplating process is perceived by combining the real-time monitoring and analysis of the multi-source parameters of the electrode potential, conductivity and fluid velocity; and the pulse waveform and timing are closed-loop compensated based on the prediction model and real-time parameters. The method can significantly improve the uniformity and controllability of the via side wall electroplating layer thickness.

Description

Technical Field

[0001] This application relates to the field of integrated circuit manufacturing technology, specifically to a control system and method for precise electroplating of double-layer circuit board electroplating layers. Background Technology

[0002] With the continuous improvement of the integration of electronic components, double-layer printed circuit boards (PCBs) are widely used in high-density, miniaturized electronic products. To achieve electrical interconnection between different layers, a large number of tiny vias are usually processed on the PCB, and a uniform metal plating layer is deposited on the inner sidewalls of the vias to ensure conductivity. Especially for tiny vias with a diameter of less than 0.3 mm, the thickness of the sidewall plating layer directly affects the reliability of the circuit board and the product life.

[0003] Existing PCB via plating processes generally face the following technical challenges: On the one hand, the high aspect ratio of the via structure makes it difficult for metal ions in the electrolyte to reach the bottom of the via evenly, easily leading to concentration polarization; on the other hand, the current often concentrates at the via opening during plating, resulting in extremely uneven current density distribution, causing the plating layer thickness on the sidewalls and even at the bottom of the via to be significantly thinner. This uneven thickness not only reduces electrical performance but also easily leads to quality defects such as cold solder joints and open circuits. High-end electronic products have particularly stringent requirements for plating uniformity.

[0004] Traditional PCB electroplating processes often use constant current or simple pulse current control. Parameter adjustment mainly relies on operational experience, making it difficult to perceive and accurately adjust the current, electric field, and ion concentration distribution inside the vias in real time. This cannot effectively solve the problems of thin and uneven plating thickness on the sidewalls of high aspect ratio vias.

[0005] In view of this, this application proposes a control system and method for precise electroplating of double-layer circuit board electroplating layers. Summary of the Invention

[0006] To achieve the above objectives, this application provides a control system and method for precise electroplating of double-layer circuit board electroplating layers, the specific technical solution of which is as follows:

[0007] A method for precise electroplating control of the electroplating layer on a double-layer circuit board includes:

[0008] A multiphysics field coupling simulation model was established, which includes the kinetics of cathode, anode, electrolyte, electrode reaction, and ion migration and diffusion mechanisms, to simulate the current density and ion concentration distribution inside the vias of a double-layer circuit board.

[0009] Based on the current density distribution results output by the coupled simulation model, a mapping relationship model between current density and sidewall plating thickness is constructed as a thickness prediction model to predict the plating thickness of the via sidewall.

[0010] A time-adjustable pulse current control method based on thickness prediction results is constructed. By dynamically adjusting the pulse current amplitude, duty cycle and on / off period, the current distribution is guided to shift towards the via sidewall.

[0011] Based on the characteristic parameters of electrode potential, electrolyte conductivity and fluid velocity, a real-time acquisition and analysis method for multi-source parameter fusion is constructed to obtain key state information in the electroplating process;

[0012] Based on the electroplating thickness prediction model and real-time electroplating parameters, the pulse current waveform and timing are dynamically adjusted to perform closed-loop compensation adjustment of the electroplating layer thickness on the via sidewall of the double-layer circuit board.

[0013] Preferably, within the via structure of the double-layer circuit board, considering the spatial layout of the cathode, anode, and electrolyte, the electrode reaction kinetics, and the ion migration and diffusion mechanism, the potential, current density, ion concentration, and fluid flow are coupled and modeled using the finite element method to reflect the multi-field interaction relationship inside the via.

[0014] Preferably, based on the spatial distribution data obtained from the multiphysics simulation model, combined with the current density distribution in the axial and radial directions of the via, a numerical interpolation method is used to predict the thickness of the sidewall electroplating layer at different spatial locations.

[0015] Preferably, in the pulse current control method, the electroplating process is divided into multiple stages, and the on / off frequency, amplitude, and on / off time of the pulse current are adjusted in stages to meet the requirements of current distribution and electroplating quality at different stages.

[0016] Preferably, during the pulse current control process, based on the thickness prediction model results, the amplitude and timing of the applied pulse current are dynamically adjusted according to the thickness deviation at different parts of the via, thereby achieving adaptive adjustment for different depth regions of the via.

[0017] Preferably, the current characteristics during the on and off periods of the pulse current are adjusted, a low-amplitude reverse current is applied during the current off period, and the off period is extended to promote the flow of electrolyte in the via.

[0018] Preferably, multiple types of sensors are deployed in the electroplating tank to collect state parameters in real time, such as cathode surface electrode potential, anode potential, electrolyte conductivity, and flow rate at the inlet and outlet of the via. The state parameters during the electroplating process are then jointly analyzed using a data fusion algorithm.

[0019] Preferably, feature extraction is performed on the collected multi-source data to identify abnormal states such as abnormal potential distribution, conductivity fluctuation and uneven flow velocity, and graded alarms and prompts are given for different levels of abnormal conditions in combination with the set multi-level threshold system.

[0020] Preferably, the thickness prediction model is periodically invoked, and the amplitude, duty cycle and waveform parameters of the pulse current are jointly adjusted based on the deviation between the real-time monitoring data and the target thickness. A correction factor is introduced based on the real-time state parameters to dynamically adjust the electroplating process.

[0021] A precision electroplating control system for a double-layer circuit board, used to implement the precision electroplating control method for the double-layer circuit board, includes: a multi-physics field coupling simulation module, an electroplating thickness prediction module, a pulse current control module, an electroplating process data acquisition module, and a dynamic adjustment module.

[0022] The multiphysics coupling simulation module is used to establish a multiphysics coupling simulation model that includes cathode, anode, electrolyte, electrode reaction kinetics and ion migration and diffusion mechanisms, and is used to simulate the current density and ion concentration distribution inside the vias of a double-layer circuit board.

[0023] The electroplating thickness prediction module constructs a mapping relationship model between current density and sidewall electroplating thickness based on the current density distribution results output by the coupled simulation model, and uses this model as a thickness prediction model to predict the electroplating thickness of the via sidewall.

[0024] The pulse current control module constructs a time-adjustable pulse current control method based on thickness prediction results. By dynamically adjusting the pulse current amplitude, duty cycle, and on / off cycle, it guides the current distribution to shift towards the via sidewall.

[0025] The electroplating process data acquisition module, based on the characteristic parameters of electrode potential, electrolyte conductivity and fluid velocity, constructs a real-time acquisition and analysis method that integrates multiple parameters to obtain key state information during the electroplating process;

[0026] The dynamic adjustment module, based on the electroplating thickness prediction model and real-time electroplating parameters, dynamically adjusts the pulse current waveform and timing to perform closed-loop compensation adjustment of the electroplating layer thickness on the via sidewall of the double-layer circuit board.

[0027] The beneficial effects of this application are: by establishing a multi-physics coupling simulation model, this application can comprehensively and accurately simulate the current density and ion concentration distribution inside the vias of a double-layer circuit board, and reveal the influence relationship between different physical fields.

[0028] This application constructs a mapping model between current density and sidewall plating thickness to achieve quantitative prediction of the plating thickness on the sidewall of vias. This enables early identification of weak plating areas and improves the uniformity and consistency of plating thickness distribution.

[0029] This application utilizes an adjustable pulse current control method based on thickness prediction results to dynamically adjust pulse current parameters and actively guide the current distribution to shift towards the via sidewall, effectively avoiding the problem of uneven thickness in traditional electroplating and improving the density and reliability of the electroplated layer.

[0030] This application achieves real-time perception and early warning of abnormalities in the electroplating process by acquiring key state information such as electrode potential, electrolyte conductivity, and fluid velocity during the electroplating process, thereby ensuring the stability and safety of the electroplating process.

[0031] This application achieves dynamic adjustment of pulse current waveform and timing through a closed-loop compensation control mechanism based on predictive models and real-time parameters, which can promptly correct deviations in the plating thickness of via sidewalls and improve product consistency and yield. Attached Figure Description

[0032] Figure 1 Flowchart of the precise electroplating control method for the double-layer circuit board electroplating layer provided in this application;

[0033] Figure 2 The multiphysics simulation modeling flowchart provided for this application;

[0034] Figure 3 Flowchart for constructing the thickness prediction model provided in this application;

[0035] Figure 4 The flowchart of the pulse current control strategy provided in this application;

[0036] Figure 5 The flowchart for multi-parameter real-time monitoring and analysis provided in this application;

[0037] Figure 6 The closed-loop compensation control flowchart provided in this application;

[0038] Figure 7 The structural diagram of the precision electroplating control system for the double-layer circuit board electroplating layer provided in this application. Detailed Implementation

[0039] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the specific embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0040] Many specific details are set forth in the following description in order to provide a full understanding of this application. However, this application may also be implemented in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0041] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of this application. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that excludes other embodiments.

[0042] Example 1

[0043] Reference Figures 1 to 6 This is the first embodiment of the present application, which provides a method for precise electroplating control of the electroplating layer of a double-layer circuit board.

[0044] Step 1: Establish a multiphysics coupled simulation model including cathode, anode, electrolyte, electrode reaction kinetics, and ion migration and diffusion mechanisms to simulate the current density and ion concentration distribution inside the vias of a double-layer circuit board; see [link / reference] Figure 2 This is a flowchart for multiphysics simulation modeling in this step.

[0045] Establish the electric field control equations and use the Laplace equation to describe the potential distribution in the electrolyte: in, Here, σ is the gradient operator, φ is the electrolyte conductivity, and φ is the potential.

[0046] On the cathode surface of the via wall in a double-layer circuit board, the current density is determined by the Butler-Volmer equation: Where j is the current density, j0 is the exchange current density, and α a and ɑ c These are the anode and cathode transfer coefficients, n, respectively. e Here, η is the electron transfer number, F is the Faraday constant, η is the overpotential, R is the gas constant, and T is the temperature. The constructed electric field model can accurately describe the complex current distribution characteristics inside the via, providing a foundation for subsequent electroplating thickness prediction.

[0047] An ion transport model was constructed, and the Nernst-Planck equation was used to describe the migration-diffusion behavior of copper ions in the electrolyte: Where c is the copper ion concentration, y is time, D is the diffusion coefficient, and z is the ion charge number. This is the fluid velocity vector.

[0048] At the cathode surface, the ion consumption rate is related to the current density, and an ion transport model is constructed. in It represents the concentration gradient in the normal direction; the ion transport model can accurately calculate the ion concentration distribution inside the vias of double-layer circuit boards, especially the concentration depletion phenomenon at the bottom of high aspect ratio vias, which helps to identify weak areas in the electroplating of double-layer circuit boards.

[0049] The flow field governing equations are established, and the Navier-Stokes equations are used to describe the flow characteristics of the electrolyte: Where ρ is the electrolyte density, p is the pressure, and μ is the dynamic viscosity; in accordance with the continuity equation Ensure mass conservation. Set a velocity boundary condition u at the via entrance of the double-layer circuit board. in This enables forced convection to enhance mass transfer. The flow field control equation can simulate the circulating flow of electrolyte in the via, promoting the electrolyte to enter the depth of the via and mitigating the concentration polarization effect.

[0050] The electric field, concentration field, and flow field are coupled through exchange current density: Where j 0,ref For reference exchange current density, c ref The reference concentration is γ, and the concentration dependence coefficient is γ. The relationship between conductivity and concentration is: σ = σ0 + k σ c, where σ0 is the fundamental conductivity, k σ It is a conductivity-concentration coefficient; through the coupling mechanism, it can truly reflect the interaction of various physical fields in the electroplating process and improve the simulation accuracy.

[0051] Set the model boundary conditions and solution parameters. For example, set a constant potential φ on the anode surface. anode =0V, initial cathode potential φ cathode = -0.3V, initial copper ion concentration of electrolyte c0 = 0.8mol / L, temperature constant T = 298K; the coupled equation system is discretized using the finite element method, with time step set to Δt = 0.01s, and the spatial mesh is locally refined in the via region.

[0052] When using the PARDISO direct solver to solve linear equation systems, a convergence criterion is set during the iterative solution process to ensure the accuracy and stability of the numerical solution. To determine if the equation system has been successfully solved, the relative error or residual of the solution during iteration is typically used as the convergence criterion. When the relative error of the solution obtained from two consecutive iterations is lower than a preset threshold (e.g., 10), the convergence criterion is established. -6 If the equation system converges or the residuals of the equations are below the standard, the numerical solution can be considered to have met the required accuracy, and the solution process can be terminated.

[0053] This step, by establishing the aforementioned multiphysics coupling simulation model, can comprehensively simulate the electroplating process inside the vias of a double-layer circuit board, accurately predicting the current density and ion concentration distribution at different locations. The constructed multiphysics coupling simulation model considers the combined effects of electrode reaction kinetics, ion transport mechanisms, and fluid dynamics, providing a reliable theoretical basis for subsequent electroplating layer thickness prediction and process parameter optimization, and improving the controllability of the electroplating process.

[0054] Step 2: Based on the current density distribution results output by the coupled simulation model, construct a mapping relationship model between current density and sidewall plating thickness, which serves as a thickness prediction model to predict the plating thickness of the via sidewall; see [link / reference]. Figure 3 A flowchart for building the thickness prediction model for this step.

[0055] A basic model of the relationship between current density and electroplating rate is established based on Faraday's law. According to Faraday's law, the rate of increase in electroplating layer thickness is directly proportional to the local current density. Where v d M represents the rate of increase in electroplated layer thickness. w Let j be the molar mass of copper. l Let n be the local current density, ε be the current efficiency, and n be the local current density. e ρ is the electron transfer number, F is the Faraday constant, and ρ is the electron transfer number. m Given the density of copper, a quantitative relationship between current density and electroplating rate was established using Faraday's law.

[0056] A model for calculating the electroplating layer thickness considering the time accumulation effect is constructed, and the cumulative thickness is obtained by integrating the electroplating rate over time.

[0057]

[0058] Where h(x,y,t) is the electroplating thickness at time t at coordinates (x,y), and τ is the integration variable; to improve computational efficiency, the trapezoidal integration method is used for numerical solution: Where h i Let Δt be the thickness at the i-th time step. i v is the time step. d,i Let be the electroplating rate at the i-th time step; the time integral model can accurately track the dynamic growth process of the electroplated layer thickness.

[0059] A correction coefficient model related to the position of the via sidewall is established. Due to mass transfer limitations and edge effects inside the via, a position-related correction coefficient is introduced:

[0060]

[0061] Where α(z) is the correction coefficient at depth z, β1 is the mass transfer limitation coefficient, typically taken as 0.3, and L c β2 is the characteristic length, β2 is the edge effect coefficient, which is usually taken as 0.1, and H is the via depth.

[0062] The corrected electroplating thickness is: h c (x,y,z,t)=α(z)·h(x,y,t); The correction coefficient model can reflect the differences in electroplating characteristics at different depths of the via, thus improving the prediction accuracy.

[0063] A thickness uniformity evaluation model based on radial distribution is constructed, and the thickness distribution function of the via sidewall is defined: Where h w (z,t) represents the average sidewall thickness at depth z, r is the via radius, and θ is the circumferential angle. The thickness uniformity index is defined as: in This represents the average thickness.

[0064] The thickness uniformity evaluation model based on radial distribution can quantitatively characterize the uniformity of electroplated layer thickness and provide an objective function for process optimization.

[0065] A fast algorithm for predicting electroplated layer thickness is established, and a piecewise linear interpolation method is used to accelerate the calculation: the simulated current density distribution data is stored in a spatial grid, and for any query point (x... q ,y q ,z q The local current density is obtained through trilinear interpolation: Where w abk For interpolation weights, j abk The current density of the grid nodes is used; a lookup table is established to store the thickness distribution data under typical operating conditions, so as to achieve millisecond-level thickness prediction response.

[0066] This step, by constructing a mapping model between the aforementioned current density and the sidewall plating thickness, achieves accurate prediction from the simulated current density distribution to the actual plating thickness. This step comprehensively considers Faraday's law, the time accumulation effect, spatial location correction, and thickness uniformity evaluation, providing a reliable predictive basis for subsequent pulse current control and closed-loop regulation.

[0067] Step 3: Construct a time-adjustable pulse current control method based on thickness prediction results. By dynamically adjusting the pulse current amplitude, duty cycle, and on / off period, the current distribution is guided to shift towards the via sidewall; see [link / reference]. Figure 4 This is a flowchart of the pulse current control strategy for this step.

[0068] A three-stage pulse current control architecture is designed, dividing the entire electroplating process into a nucleation control stage, a thickness growth stage, and a densification stage.

[0069] During the nucleation control stage, high-frequency short pulses are used to promote the uniform formation of crystal nuclei on the sidewalls of the vias. For example, the current pulse frequency is set to 800-1000Hz, the duty cycle is controlled within the range of 20%-30%, and the duration is 15% of the total electroplating time. By frequently switching the current on and off during the nucleation control stage, the electric field distribution inside the via is made more uniform, avoiding the problem of current concentration at the via opening in traditional DC electroplating.

[0070] During the thickness growth stage, for example, the current pulse frequency is reduced to 200–400 Hz, and the duty cycle is increased to 50%–70%. This stage accounts for 60% of the total electroplating time. The thickness growth stage, through pulse control at a moderate frequency, ensures both the electroplating rate and provides sufficient time for ion diffusion.

[0071] During the densification stage, a low-frequency, long-pulse mode is employed, for example, with the frequency reduced to 50–100 Hz and the duty cycle increased to 80%–90%, to fill the weak areas left by the previous electroplating. This three-stage control architecture can balance electroplating efficiency and uniformity, effectively improving the electroplating quality of the via sidewalls.

[0072] A segmented pulse current waveform parameterization model is constructed to realize pulse current control at different stages of the electroplating process; the pulse current waveform function is defined as follows:

[0073]

[0074] Where I p (t) represents the pulse current at time t, I base Based on the base current, A k Let t be the amplitude of the k-th pulse segment, rect(·) be a rectangular window function, and t be the amplitude of the k-th pulse segment. k Let T be the start time of the k-th segment. k Let N be the number of segments, sgn(·) be the sign function, and the duty cycle be defined as: Where T on,k Let k be the conduction time of the kth segment. The segmented pulse current waveform parameterization model can flexibly adjust the current pulse characteristics and achieve precise control of the current distribution.

[0075] An adaptive pulse amplitude adjustment mechanism based on thickness prediction feedback is established. Based on the obtained electroplated layer thickness prediction results, the thickness deviation distribution at different depths of the via is calculated in real time. When insufficient thickness is detected at the bottom of the via, the pulse current amplitude is automatically increased. The amplification calculation formula is: ΔI = K a ·(h target -h pred ), where K a h is the adjustment coefficient. target For the target thickness, h pred To predict the thickness.

[0076] To prevent overcompensation, the upper limit of amplitude adjustment is set to 150% of the reference current. When the thickness at the top of the via is too thick, the electroplating rate is suppressed by reducing the pulse amplitude of the corresponding time period in that area. The adjustment mechanism based on predictive feedback can achieve dynamic optimization of the electroplating process and significantly improve the uniformity of the electroplating layer thickness distribution.

[0077] A duty cycle gradient modulation technique is constructed, and a gradient variation strategy for the duty cycle is designed based on the difference in mass transfer difficulty along the depth direction of the via. Within a single pulse period, the duty cycle is designed as a time-varying function. Where D0 is the basic duty cycle, ΔD is the modulation depth, and ω is the modulation angular frequency. This is the initial phase.

[0078] By periodically changing the duty cycle, a dynamic distribution of current density is generated during pulse conduction, which causes the electroplating reaction to alternate at different positions on the via sidewall; during pulse rest, the concentration gradient is used to drive ions to diffuse into the consumption area, achieving spontaneous concentration homogenization.

[0079] A smart rest period control strategy is designed. During the rest period of the pulsed current, instead of directly cutting off the current, a weak reverse current is applied to activate the surface. For example, the reverse current density is controlled within the range of 5% to 10% of the forward current, and the duration is the first 1 / 3 of the rest period. The weak reverse current can dissolve loose deposits on the surface, improving the density of the subsequent electroplated layer. During the latter 2 / 3 of the rest period, the current is completely cut off, allowing the electrolyte to be fully renewed. Especially in high aspect ratio vias, appropriately extending the rest period ensures that fresh electrolyte reaches the bottom of the via, mitigating concentration polarization. By optimizing the rest period control strategy, not only can the quality of the electroplated layer be improved, but also the retention of bubbles in the vias can be reduced, avoiding pinhole defects during the electroplating process.

[0080] This step achieves precise control and dynamic optimization of the electroplating process by designing a time-adjustable pulse current control method based on the electroplating thickness prediction results. Through a three-stage control architecture, adaptive amplitude adjustment, duty cycle gradient modulation, intelligent pause control, and multi-parameter collaborative optimization, the current distribution is effectively guided to shift towards the via sidewall, which solves the problem of uneven thickness distribution in high aspect ratio vias in traditional electroplating methods.

[0081] Step 4: Based on the characteristic parameters of electrode potential, electrolyte conductivity, and fluid velocity, a real-time acquisition and analysis method integrating multiple parameters is constructed to obtain key state information during the electroplating process; see [link / reference]. Figure 5 This is a flowchart of the multi-parameter real-time monitoring and analysis process for this step.

[0082] A multi-sensor collaborative data acquisition mechanism is constructed, deploying various types of sensors at key locations in the electroplating tank to achieve comprehensive data monitoring. A miniature reference electrode array is installed on the cathode surface; for example, Ag / AgCl reference electrodes are arranged in a 5×5 matrix, covering a monitoring area of ​​100mm×100mm. A platinum auxiliary electrode is installed at the corresponding position on the anode to measure the local potential distribution. A conductivity sensor is installed in the main electrolyte channel, using a four-electrode method for measurement. Miniature hot-wire anemometers are installed at the inlet and outlet of the vias to measure the electrolyte flow rate. Through the collaborative operation of multiple sensors, real-time changes in the electric field, concentration field, and flow field during the electroplating process are acquired.

[0083] A spatiotemporal fusion algorithm for multi-source data is established to comprehensively analyze sensor data of different types and locations; the fusion state vector is defined as: X(t)=[φ1(t),...,φ 25 (t),σ e (t),v in (t),v out (t)] T , where φ i (t) represents the potential value of the i-th electrode, σ e (t) represents the measured conductivity value, v in (t) and v out (t) represent the inlet and outlet flow velocities, respectively. State estimation is performed using Kalman filtering: X k|k =X k|k-1 +K k (Y k -HX k|k-1 ), where X k|k Let K be the state estimate at time k. k Y is the Kalman gain matrix. k Let H be the observation vector and H be the observation matrix. Data fusion can reduce the impact of measurement noise and improve the accuracy and robustness of state estimation.

[0084] Real-time state recognition is performed based on feature extraction, and key feature parameters are extracted from fused data for electroplating state judgment; the key features include spatial gradient features of potential distribution, conductivity fluctuation index and flow velocity distribution uniformity.

[0085] Calculate the spatial gradient characteristics of the potential distribution: in For the average potential distribution, G φ This refers to the gradient magnitude. Set the gradient magnitude threshold G. th When G φ Greater than the gradient magnitude threshold G th This indicates the presence of localized current concentration. A conductivity fluctuation index is defined as follows: Where V σ Let σ be the volatility. max σ min and σ mean These represent the maximum, minimum, and average values ​​within the time window; a volatility threshold V is set. th When volatility V σ Greater than the volatility threshold V th This indicates that the electrolyte composition may have changed significantly. Calculate the uniformity of the flow rate distribution: Set the flow velocity distribution uniformity threshold F th When F u Less than the flow velocity distribution uniformity threshold F th When the flow field distribution is uneven, it indicates that adjustments are needed. The calculated characteristic parameters can intuitively reflect the critical states of the electroplating process, providing a basis for control decisions.

[0086] An anomaly detection and early warning mechanism is established to monitor abnormal situations during the electroplating process in real time and issue timely alarms. A multi-level early warning threshold system is set up, for example, classifying abnormal situations into three levels: minor deviation, significant anomaly, and serious fault. When the electrode potential at five consecutive sampling points deviates from the set value by more than ±50mV, a minor deviation warning is triggered, and the system automatically fine-tunes the control parameters. When the conductivity drops by more than 20% within 10 seconds, it is judged as a significant anomaly, possibly indicating that the additive is being consumed too quickly, requiring the replenishment of electrolyte. When any electrode potential mutation exceeding ±200mV or the flow rate drops below 0.1m / s is detected, a serious fault alarm is immediately triggered, the electroplating process is suspended, and manual inspection is performed.

[0087] This step, by constructing the aforementioned multi-source parameter fusion real-time acquisition and analysis method, achieves comprehensive acquisition and intelligent analysis of key state information in the electroplating process. Through multi-sensor collaborative monitoring, high-speed data acquisition, spatiotemporal fusion algorithms, feature extraction and recognition, and anomaly detection and early warning technologies, it can accurately grasp the real-time status of the electroplating process, promptly detect and handle abnormal situations, provide reliable information support for subsequent closed-loop control, and significantly improve the stability and controllability of the double-layer circuit board electroplating process.

[0088] Step 5: Based on the electroplating thickness prediction model and real-time electroplating parameters, dynamically adjust the pulse current waveform and timing to perform closed-loop compensation adjustment of the electroplating layer thickness on the via sidewalls of the double-layer circuit board; see [link / reference] Figure 5 This is the closed-loop compensation control flowchart for this step.

[0089] A closed-loop feedback control mechanism based on thickness prediction deviation is established. The through-hole sidewall is divided into 8 monitoring points along the axial direction. The thickness prediction model is called at intervals of Δt to obtain the current thickness distribution; the thickness deviation is calculated as: δ(k)=H obj -Hcur (v), where δ(v) is the thickness deviation vector at the v-th control time, H obj For the target thickness, H cur (v) represents the current predicted thickness. When the deviation exceeds... The compensation control is activated in time to achieve real-time monitoring and timely adjustment of the electroplating process.

[0090] A dynamic adjustment algorithm for pulse parameters is constructed. The pulse current amplitude is adjusted based on the thickness deviation distribution. Where I adj (u) represents the adjusted current, I0 is the reference current, Γ is the adjustment gain, and δ u Let δ be the deviation at the u-th monitoring point. th This is for characteristic deviation. Simultaneously adjust the duty cycle: Where u is a parameter, D base ξ is the base duty cycle, δ is the adjustment range, and ξ is the duty cycle. max To achieve maximum deviation; differential compensation for different depth regions is achieved through dynamic adjustment.

[0091] Establish a corrective control strategy based on real-time parameters, and introduce a correction factor by combining the acquired real-time monitoring data: Where σ real and σ nom σ represents the measured and nominal conductivity, respectively. tol For tolerance, v meas and v ref These are the measured and reference flow velocities, respectively, with Ψ being the correction coefficient; the corrected control output is: I out =I adj ·Ω.

[0092] This step uses a closed-loop compensation adjustment method to precisely control the thickness of the electroplating layer on the sidewall of the via in the double-layer circuit board. Through the organic combination of real-time monitoring, dynamic adjustment and parameter correction, the consistency of electroplating quality can be significantly improved.

[0093] Example 2

[0094] Reference Figure 7 This is the second embodiment of the present application, which provides a precision electroplating control system for a double-layer circuit board electroplating layer.

[0095] The system includes: a multiphysics coupling simulation module, an electroplating thickness prediction module, a pulse current control module, an electroplating process data acquisition module, and a dynamic adjustment module.

[0096] The multiphysics coupling simulation module is used to establish a multiphysics coupling simulation model that includes cathode, anode, electrolyte, electrode reaction kinetics and ion migration and diffusion mechanisms, and is used to simulate the current density and ion concentration distribution inside the vias of a double-layer circuit board.

[0097] The electroplating thickness prediction module constructs a mapping relationship model between current density and sidewall electroplating thickness based on the current density distribution results output by the coupled simulation model, and uses this model as a thickness prediction model to predict the electroplating thickness of the via sidewall.

[0098] The pulse current control module constructs a time-adjustable pulse current control method based on thickness prediction results. By dynamically adjusting the pulse current amplitude, duty cycle and on / off period, it guides the current distribution to shift towards the via sidewall.

[0099] The electroplating process data acquisition module, based on the characteristic parameters of electrode potential, electrolyte conductivity and fluid velocity, constructs a real-time acquisition and analysis method that integrates multiple parameters to obtain key state information during the electroplating process.

[0100] The dynamic adjustment module, based on the electroplating thickness prediction model and real-time electroplating parameters, dynamically adjusts the pulse current waveform and timing to perform closed-loop compensation adjustment of the electroplating layer thickness on the via sidewall of the double-layer circuit board.

[0101] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings or direct couplings or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0102] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments under the guidance of this application without departing from the spirit and scope of protection of the claims. All of these variations are within the protection scope of this application.

Claims

1. A method for precise electroplating control of double-layer circuit board electroplating layers, characterized in that, include: A multiphysics field coupled simulation model was established, which includes the kinetics of cathode, anode, electrolyte, electrode reaction, and ion migration and diffusion mechanisms, to simulate the current density and ion concentration distribution inside the vias of a double-layer circuit board. Based on the current density distribution results output by the coupled simulation model, a mapping relationship model between current density and sidewall plating thickness is constructed as a thickness prediction model to predict the plating thickness of the via sidewall. A time-adjustable pulse current control method based on thickness prediction results is constructed. By dynamically adjusting the pulse current amplitude, duty cycle and on / off period, the current distribution is guided to shift towards the via sidewall. Based on the characteristic parameters of electrode potential, electrolyte conductivity and fluid velocity, a real-time acquisition and analysis method for multi-source parameter fusion is constructed to obtain key state information in the electroplating process; Based on the electroplating thickness prediction model and real-time electroplating parameters, the pulse current waveform and timing are dynamically adjusted to perform closed-loop compensation adjustment of the electroplating layer thickness on the via sidewall of the double-layer circuit board.

2. The method for precise electroplating control of the double-layer circuit board electroplating layer according to claim 1, characterized in that, Within the via structure of a double-layer circuit board, considering the spatial layout of the cathode, anode, and electrolyte, as well as the electrode reaction kinetics and ion migration and diffusion mechanisms, the potential, current density, ion concentration, and fluid flow are coupled and modeled using the finite element method to reflect the multi-field interaction relationships inside the via.

3. The method for precise electroplating control of the double-layer circuit board electroplating layer according to claim 2, characterized in that, Based on the spatial distribution data obtained from the multiphysics simulation model, and combined with the current density distribution in the axial and radial directions of the via, a numerical interpolation method is used to predict the thickness of the sidewall electroplating layer at different spatial locations.

4. The method for precise electroplating control of the double-layer circuit board electroplating layer according to claim 3, characterized in that, In the pulse current control method, the electroplating process is divided into multiple stages, and the on / off frequency, amplitude, and on / off time of the pulse current are adjusted in stages to meet the requirements of current distribution and electroplating quality at different stages.

5. The method for precise electroplating control of the double-layer circuit board electroplating layer according to claim 4, characterized in that, During the pulse current control process, based on the thickness prediction model results, the amplitude and timing of the applied pulse current are dynamically adjusted according to the thickness deviation at different parts of the via, so as to adaptively adjust the via depth regions.

6. The method for precise electroplating control of the double-layer circuit board electroplating layer according to claim 5, characterized in that, The current characteristics during the turn-on and turn-off of the pulse current are adjusted. A low-amplitude reverse current is applied during the current turn-off period, and the turn-off time is extended to promote the flow of electrolyte in the via.

7. The method for precise electroplating control of the double-layer circuit board electroplating layer according to claim 6, characterized in that, Multiple types of sensors are deployed in the electroplating tank to collect real-time state parameters such as cathode surface electrode potential, anode potential, electrolyte conductivity, and flow velocity at the inlet and outlet of the vias. The state parameters during the electroplating process are then jointly analyzed using a data fusion algorithm.

8. The method for precise electroplating control of the double-layer circuit board electroplating layer according to claim 7, characterized in that, Feature extraction is performed on the collected multi-source data to identify abnormal states such as abnormal potential distribution, conductivity fluctuation and uneven flow velocity. Combined with the set multi-level threshold system, different levels of abnormal conditions are graded and alarmed and prompted.

9. The method for precise electroplating control of the double-layer circuit board electroplating layer according to claim 8, characterized in that, The thickness prediction model is periodically invoked, and the amplitude, duty cycle, and waveform parameters of the pulse current are jointly adjusted based on the deviation between the real-time monitoring data and the target thickness. A correction factor is introduced based on the real-time state parameters to dynamically adjust the electroplating process.

10. A precision electroplating control system for a double-layer circuit board, used to implement the precision electroplating control method for a double-layer circuit board as described in any one of claims 1 to 9, characterized in that, include: The system includes a multiphysics coupling simulation module, an electroplating thickness prediction module, a pulse current control module, an electroplating process data acquisition module, and a dynamic adjustment module. The multiphysics coupling simulation module is used to establish a multiphysics coupling simulation model that includes cathode, anode, electrolyte, electrode reaction kinetics and ion migration and diffusion mechanisms, and is used to simulate the current density and ion concentration distribution inside the vias of a double-layer circuit board. The electroplating thickness prediction module constructs a mapping relationship model between current density and sidewall electroplating thickness based on the current density distribution results output by the coupled simulation model, and uses this model as a thickness prediction model to predict the electroplating thickness of the via sidewall. The pulse current control module constructs a time-adjustable pulse current control method based on thickness prediction results. By dynamically adjusting the pulse current amplitude, duty cycle, and on / off cycle, it guides the current distribution to shift towards the via sidewall. The electroplating process data acquisition module, based on the characteristic parameters of electrode potential, electrolyte conductivity and fluid velocity, constructs a real-time acquisition and analysis method that integrates multiple parameters to obtain key state information during the electroplating process; The dynamic adjustment module, based on the electroplating thickness prediction model and real-time electroplating parameters, dynamically adjusts the pulse current waveform and timing to perform closed-loop compensation adjustment of the electroplating layer thickness on the via sidewall of the double-layer circuit board.

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