Accurate electroplating control system and method for electroplated layer of double-layer circuit board

Abstract

The invention relates to the technical field of integrated circuit manufacturing, in particular to a precise electroplating control system and method for an electroplated layer of a double-layer circuit board, and the method comprises the steps: building a multi-physical field coupling simulation model, and simulating the internal current density and ion concentration distribution of a via hole; constructing a mapping relation between the current density and the side wall electroplating thickness based on a simulation result, and predicting the electroplating thickness; through thickness prediction feedback, a time sequence adjustable pulse current control strategy is designed, the pulse amplitude, the duty ratio and the on-off period are dynamically adjusted, and current distribution optimization is guided; the state of the electroplating process is sensed in combination with real-time monitoring and analysis of multi-source parameters of electrode potential, conductivity and fluid velocity; and based on the prediction model and the real-time parameters, adjusting the pulse waveform and the time sequence through closed-loop compensation. According to the method, the uniformity and controllability of the thickness of the electroplated layer on the side wall of the via hole can be remarkably improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of integrated circuit manufacturing, in particular to a double-layer circuit board electroplating layer precise electroplating control system and method. BACKGROUND

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

[0003] The existing PCB via electroplating process generally faces the following technical problems: on the one hand, the high aspect ratio of the via structure makes it difficult for metal ions in the electrolyte to uniformly reach the bottom of the hole, and concentration polarization phenomenon is easily formed; on the other hand, the current is often concentrated at the opening of the via during electroplating, resulting in a very uneven current density distribution, which makes the thickness of the side wall and even the bottom of the hole significantly thinner. This thickness unevenness problem not only reduces the electrical performance, but also easily causes quality defects such as virtual welding and open circuit, and high-end electronic products have particularly strict requirements on electroplating uniformity.

[0004] Traditional PCB electroplating processes mostly use constant current or simple pulse current control, and parameter adjustment mainly relies on operating experience, making it difficult to real-time perceive and accurately adjust the current, electric field and ion concentration distribution inside the via, and unable to effectively solve the problems of thin and uneven distribution of via side wall electroplating thickness.

[0005] In view of this, the application provides a double-layer circuit board electroplating layer precise electroplating control system and method. SUMMARY

[0006] To achieve the above-mentioned purpose, the application provides a double-layer circuit board electroplating layer precise electroplating control system and method, and the specific technical solutions are as follows:

[0007] The double-layer circuit board electroplating layer precise electroplating control method comprises:

[0008] A multi-physics field coupling simulation model containing a cathode, an anode, an electrolyte, electrode reaction kinetics and ion migration and diffusion mechanism is established, which is used to simulate the current density and ion concentration distribution inside the double-layer circuit board via;

[0009] Based on the current density distribution result output by the coupling simulation model, a mapping relationship model between the current density and the side wall electroplating thickness is constructed, which is used as a thickness prediction model to predict the electroplating layer thickness of the via side wall;

[0010] A time-adjustable pulse current control method based on the thickness prediction results is constructed, which guides the current distribution to shift to the via sidewall by dynamically adjusting the pulse current amplitude, duty cycle and on-off period;

[0011] Based on the characteristic parameters of electrode potential, electrolyte conductivity and fluid velocity, a real-time acquisition and analysis method of 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 on the thickness of the double-layer circuit board via sidewall electroplated layer.

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

[0014] Preferably, based on the spatial distribution data obtained from the multi-physical field simulation model, combined with the current density distribution in the axial and radial directions of the via, a numerical interpolation method is used to realize the prediction of the thickness of the sidewall electroplated layer at different spatial positions.

[0015] Preferably, in the pulse current control method, according to the electroplating process, the on-off frequency, amplitude and on-off time of the pulse current are parameterized and adjusted in stages to meet the requirements of current distribution and electroplating quality at different stages.

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

[0017] Preferably, the current characteristics during the conduction and turn-off of the pulse current are adjusted, a low-amplitude reverse current is applied during the turn-off of the current, and the turn-off time is prolonged to promote the flow of electrolyte in the via.

[0018] Preferably, multiple types of sensors are arranged in the electroplating tank to real-time collect the state parameters of the cathode surface electrode potential, anode potential, electrolyte conductivity, via inlet and outlet flow rate, and the state parameters in the electroplating process are jointly analyzed through a data fusion algorithm.

[0019] Preferably, the collected multi-source data are subjected to feature extraction to identify abnormal states such as potential distribution abnormality, conductivity fluctuation and flow rate unevenness, and a multi-level threshold system is set to grade the abnormal conditions and issue graded alarm and prompt.

[0020] Preferably, the thickness prediction model is called regularly to jointly adjust the amplitude, duty cycle and waveform parameters of the pulse current according to the deviation between the real-time monitoring data and the target thickness, introduce a correction factor according to the real-time state parameters, and dynamically adjust the electroplating process.

[0021] The double-layer circuit board electroplating layer precision electroplating control system is used for realizing the double-layer circuit board electroplating layer precision electroplating control method and comprises a multi-physical 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 multi-physical field coupling simulation module is used for establishing a multi-physical field coupling simulation model comprising a cathode, an anode, an electrolyte, electrode reaction kinetics and ion migration and diffusion mechanism, and is used for simulating the current density and ion concentration distribution inside the double-layer circuit board via hole.

[0023] The electroplating thickness prediction module is based on the current density distribution result output by the coupling simulation model, constructs a mapping relationship model between the current density and the side wall electroplating thickness, takes the model as a thickness prediction model, and predicts the electroplating layer thickness of the via hole side wall.

[0024] The pulse current control module constructs a time-adjustable pulse current control method based on the thickness prediction result, dynamically adjusts the pulse current amplitude, duty cycle and on-off period, and guides the current distribution to shift to the via hole side wall.

[0025] The electroplating process data acquisition module is based on the characteristic parameters of the electrode potential, electrolyte conductivity and fluid velocity, constructs a real-time acquisition and analysis method of multi-source parameter fusion, and obtains the key state information in the electroplating process.

[0026] The dynamic adjustment module dynamically adjusts the pulse current waveform and time sequence based on the electroplating thickness prediction model and real-time electroplating parameters, and performs closed-loop compensation adjustment on the via hole side wall electroplating layer thickness of the double-layer circuit board.

[0027] The application has the beneficial effects that the application can comprehensively and accurately simulate the current density and ion concentration distribution inside the double-layer circuit board via hole by establishing a multi-physical field coupling simulation model, and reveals the influence relationship between different physical fields.

[0028] The application constructs a mapping relationship model of the current density and the side wall electroplating thickness, realizes quantitative prediction of the via hole side wall electroplating layer thickness, can identify the electroplating weak area in advance, and improves the uniformity and consistency of the electroplating layer thickness distribution.

[0029] The application can dynamically adjust the pulse current parameters, actively guide the current distribution to the via sidewall, effectively avoid the problem of uneven thickness in traditional electroplating, and improve the density and reliability of the electroplated layer through the adjustable pulse current control method based on the thickness prediction result.

[0030] The application realizes real-time perception and abnormal early warning of the electroplating process state by real-time acquisition of key state information such as electrode potential, electrolyte conductivity and fluid velocity in the electroplating process, and guarantees the stability and safety of the electroplating process.

[0031] The application realizes dynamic adjustment of the pulse current waveform and timing through the closed-loop compensation control mechanism based on the prediction model and real-time parameters, can timely correct the deviation of the via sidewall electroplating thickness, and improves the consistency and yield of the product. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 The application provides a double-layer circuit board electroplated layer precise electroplating control method flowchart;

[0033] Figure 2 The application provides a multi-physical field simulation modeling flowchart;

[0034] Figure 3 The application provides a thickness prediction model construction flowchart;

[0035] Figure 4 The application provides a pulse current control strategy flowchart;

[0036] Figure 5 The application provides a multi-parameter real-time monitoring and analysis flowchart;

[0037] Figure 6 The application provides a closed-loop compensation control flowchart;

[0038] Figure 7 The application provides a double-layer circuit board electroplated layer precise electroplating control system structure diagram. DETAILED DESCRIPTION

[0039] In order to make the above-mentioned purposes, features and advantages of the application more obvious and easy to understand, the specific embodiments of the application will be described in detail below with reference to the accompanying drawings.

[0040] In the following description, many specific details are set forth in order to provide a thorough understanding of the application, but the application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the application, therefore the application is not limited by the specific embodiments disclosed below.

[0041] Second, the "one embodiment" or "an embodiment" referred to herein can include a particular feature, structure, or characteristic. The various embodiments are not mutually exclusive, but a single embodiment can be selected from a plurality of mutually exclusive or alternative embodiments.

[0042] Embodiment 1

[0043] Reference Figures 1 to 6 For the first embodiment of the present application, a precise electroplating control method for electroplating layers of double-layer circuit boards is provided.

[0044] Step 1: Establish a multi-physics field coupling simulation model including cathode, anode, electrolyte, electrode reaction kinetics and ion migration and diffusion mechanism, which is used to simulate the distribution of current density and ion concentration inside the via of double-layer circuit board; see Figure 2 The flow chart of multi-physics field simulation modeling for this step.

[0045] The electric field control equation is established, and Laplace equation is used to describe the potential distribution in the electrolyte: Wherein, is the gradient operator, σ is the conductivity of the electrolyte, and φ is the electric potential.

[0046] At the cathode surface of the via inner wall of the 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, ɑ a and ɑ c are the anode and cathode transfer coefficients, n e is the number of electron transfer, 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 basis for subsequent electroplating thickness prediction.

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

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

[0049] The flow field control equation is established, and the Navier-Stokes equation is used to describe the flow characteristics of the electrolyte: Wherein p is the density of the electrolyte, p is the pressure, and m is the dynamic viscosity; in combination with the continuity equation Ensure mass conservation. Set the velocity boundary condition u in at the inlet of the double-layer circuit board via to realize forced convection to enhance mass transfer, and the flow field control equation can simulate the circulating flow of the electrolyte in the via, promote the electrolyte to enter the deep part of the via, and alleviate the concentration polarization effect.

[0050] The electric field, concentration field and flow field are coupled through the exchange current density: Wherein j 0,ref is the reference exchange current density, c ref is the reference concentration, and g is the concentration-dependent coefficient. The relationship between conductivity and concentration is: s = s0 + k σ c, wherein s0 is the basic conductivity, and k σ is the conductivity-concentration coefficient; through the coupling mechanism, the mutual influence of various physical fields in the electroplating process can be truly reflected, and the simulation accuracy is improved.

[0051] Set the model boundary conditions and solve the parameters. For example, set the constant potential on the anode surface as f anode = 0V, the initial potential of the cathode as f cathode =-0.3V, the initial copper ion concentration of the electrolyte as c0=0.8mol / L, and the temperature as constant T=298K; the coupled equation set is discretized by using the finite element method, the time step is set as At=0.01s, and the spatial grid is locally refined in the via area.

[0052] The PARDISO direct solver is used to solve the linear equation set. In order to ensure the accuracy and stability of the numerical solution, the convergence criterion is set in the iterative solution process. In order to determine whether the equation set has been successfully solved, the relative error or residual of the solution in the iteration process is usually used as the convergence criterion; when the relative error of the solutions obtained by adjacent two iterations is lower than the preset threshold value (such as 10 -6 ), or the equation residual is lower than the standard, it is considered that the equation set has converged, the numerical solution meets the required accuracy, and the solution process can be terminated.

[0053] This step can comprehensively simulate the electroplating process inside the double-layer circuit board via by establishing the above-mentioned multi-physical field coupling simulation model, and accurately predict the current density and ion concentration distribution at different positions. The constructed multi-physical field coupling simulation model considers the comprehensive influence of electrode reaction kinetics, ion transport mechanism and fluid dynamics, provides a reliable theoretical basis for subsequent electroplated layer thickness prediction and process parameter optimization, and can improve the controllability of the electroplating process.

[0054] Step 2: Based on the current density distribution results output by the coupled simulation model, a mapping relationship model between current density and side wall plating thickness is constructed as a thickness prediction model to predict the plating layer thickness of the via side wall; see Figure 3 A flowchart is constructed for the thickness prediction model of this step.

[0055] A basic relationship model between current density and plating rate is established based on Faraday's law. According to Faraday's law, the plating layer thickness growth rate is proportional to the local current density: Where v d is the plating layer thickness growth rate, M w is the molar mass of copper, j l is the local current density, ε is the current efficiency, n e is the number of electron transfers, F is the Faraday constant, and ρ m is the density of copper. The quantitative relationship between current density and plating rate is established through Faraday's law.

[0056] A plating layer thickness calculation model considering time accumulation effect is constructed, and the plating rate is time-integrated to obtain the cumulative thickness:

[0057]

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

[0059] A position-dependent correction coefficient model is established for the via side wall. Due to mass transfer limitation and edge effect inside the via, a position-dependent correction coefficient is introduced:

[0060]

[0061] Where α(z) is the correction coefficient at depth z, β1 is the mass transfer limitation coefficient, generally taking a value of 0.3, L c is the characteristic length, β2 is the edge effect coefficient, generally taking a value of 0.1, and H is the via depth.

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

[0063] A radial distribution-based thickness uniformity evaluation model is constructed, and a via sidewall thickness distribution function is defined: where h w (z, t) is 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: where is the average thickness.

[0064] The radial distribution-based thickness uniformity evaluation model can quantitatively characterize the uniformity of the electroplated layer thickness, providing a target function for process optimization.

[0065] A fast prediction algorithm for 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 according to the spatial grid, and for any query point (x q ,y q ,z q ), the local current density is obtained by trilinear interpolation: where w abk is the interpolation weight, and j abk is the grid node current density; a lookup table is established to store the thickness distribution data under typical working conditions, achieving a millisecond-level thickness prediction response.

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

[0067] Step 3: Construct a time-adjustable pulse current control method based on the thickness prediction results, and guide the current distribution to shift towards the via sidewall by dynamically adjusting the pulse current amplitude, duty cycle, and on-off period; refer to Figure 4 is the pulse current control strategy flowchart 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] In the nucleation control stage, high-frequency short pulses are used to promote the uniform formation of crystal nuclei on the via sidewall. For example, the current pulse frequency is set to 800-1000 Hz, the duty cycle is controlled within 20%-30%, and the duration is 15% of the total electroplating time. In the nucleation control stage, frequent current on-off makes the via internal electric field distribution more uniform, avoiding the problem of current concentration at the orifice in traditional direct current electroplating.

[0070] In the thickness growth stage, the pulse frequency is reduced to 200-400 Hz and the duty cycle is increased to 50-70%, which accounts for 60% of the total plating time. The thickness growth stage is controlled by medium-frequency pulses, which ensures the plating rate and provides sufficient time for ion diffusion.

[0071] In the densification stage, a low-frequency long-pulse mode is used, with the frequency reduced to 50-100 Hz and the duty cycle increased to 80-90%, which is used to fill the weak areas of the previous plating. This three-stage control architecture can balance plating efficiency and uniformity, effectively improving the plating quality of via sidewalls.

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

[0073]

[0074] where I p (t) is the pulse current at time t, I base is the base current, A k is the kth pulse amplitude, rect(·) is the rectangular window function, t k is the kth starting time, T k is the kth period, N is the number of segments, and sgn(·) is the sign function; the duty cycle is defined as: where T on,k is the kth on-time. The segmented pulse current waveform parameterization model can flexibly adjust the pulse current characteristics and achieve precise control of current distribution.

[0075] An adaptive pulse amplitude adjustment mechanism based on thickness prediction feedback is established. Based on the obtained thickness prediction results of the plated layer, the thickness deviation distribution at different depths of the via is calculated in real time; when the thickness at the bottom of the via is detected to be insufficient, the pulse current amplitude is automatically increased, and the increase formula is: ΔI = K a ·(h target -h pred ), where K a is the adjustment coefficient, h target is the target thickness, and h pred is the predicted thickness.

[0076] To prevent overcompensation, the upper limit of the amplitude adjustment is set to 150% of the reference current; when the thickness at the top of the via is too thick, the plating rate is suppressed by reducing the pulse amplitude in the corresponding period of this area; the adjustment mechanism based on prediction feedback can realize dynamic optimization of the plating process, significantly improving the uniformity of the plated layer thickness distribution.

[0077] A duty cycle gradient modulation technique is constructed, and a gradient change strategy of duty cycle is designed according to the difference in mass transfer difficulty in the depth direction of the via; wherein D0 is a basic duty cycle, ΔD is a modulation depth, and ω is a modulation angular frequency, is an initial phase.

[0078] Through the periodic change of the duty cycle, a dynamic distribution of current density is generated during the pulse conduction, which promotes the alternating of the electroplating reaction at different positions of the via sidewall; during the pulse rest period, the ions are driven to diffuse to the consumption area by the concentration gradient, and spontaneous concentration homogenization is achieved.

[0079] An intelligent rest period control strategy is designed, and during the rest period of the pulse current, a weak reverse current is applied for surface activation instead of directly turning off the current; for example, the reverse current density is controlled within 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 the loose deposits on the surface and improve the compactness of the subsequent electroplated layer. During the last 2 / 3 of the rest period, the current is completely turned off to allow the electrolyte to be fully refreshed. Especially in high aspect ratio vias, appropriately extending the rest period can ensure that fresh electrolyte reaches the bottom of the hole and alleviate the concentration polarization phenomenon. By optimizing the control strategy of the rest period, not only the quality of the electroplated layer can be improved, but also the retention of bubbles in the via can be reduced, and pinhole defects can be avoided during the electroplating process.

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

[0081] Step 4: Based on the characteristic parameters of electrode potential, electrolyte conductivity, and fluid velocity, a multi-source parameter fusion real-time acquisition and analysis method is constructed to obtain key state information in the electroplating process; referring to Figure 5 is the multi-parameter real-time monitoring and analysis flowchart of this step.

[0082] A multi-sensor collaborative data acquisition mechanism is constructed, and various types of sensors are arranged at key positions of the electroplating tank to realize omnidirectional data monitoring. A micro reference electrode array is installed on the cathode surface, and Ag / AgCl reference electrodes are used as an example, arranged in a 5x5 matrix, covering a monitoring area of 100mmx100mm. A platinum auxiliary electrode is installed at the corresponding position of the anode for measuring local potential distribution. A conductivity sensor is installed in the main flow channel of the electrolyte, and a four-electrode method is used for measurement. Micro hot-wire anemometers are installed at the inlet and outlet of the via hole to measure the flow rate of the electrolyte. Through the collaborative work of multiple sensors, real-time change information of the electric field, concentration field and flow field during the electroplating process is obtained.

[0083] A multi-source data space-time fusion algorithm is established to comprehensively analyze sensor data of different types and different positions. A fusion state vector is defined: X(t)=[φ1(t),...,φ 25 (t),σ e (t),v in (t),v out (t)] T , where φ i (t) is the potential value of the i-th electrode, σ e (t) is the conductivity measurement value, v in (t) and v out (t) are the inlet and outlet flow rates, respectively. Kalman filtering is used for state estimation: X k|k =X k|k-1 +K k (Y k -HX k|k-1 ), where X k|k is the state estimation value at the k-th time, K k is the Kalman gain matrix, Y k is the observation vector, and H is the observation matrix. Through data fusion, the influence of measurement noise can be reduced, and the accuracy and robustness of state estimation can be improved.

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

[0085] The spatial gradient feature of the potential distribution is calculated as follows: where φ is the average potential distribution, and G φ is the gradient amplitude. A gradient amplitude threshold G th is set, and when G φ is greater than the gradient amplitude threshold G th , it indicates that there is a local current concentration phenomenon. The conductivity fluctuation index is defined as: where V σ is the fluctuation rate, σ max , σ min and σ mean are the maximum, minimum and average values within the time window respectively; set the fluctuation rate threshold V th , when the fluctuation rate V σ is greater than the fluctuation rate threshold V th , prompt that the electrolyte composition may have significant changes. Calculate the flow rate distribution uniformity: Set the flow rate distribution uniformity threshold F th , when F u is less than the flow rate distribution uniformity threshold F th , it indicates that the flow field distribution is uneven and needs to be adjusted. The calculated characteristic parameters can intuitively reflect the key state of the electroplating process and provide a basis for control decisions.

[0086] Establish an abnormality detection and early warning mechanism to monitor abnormal situations in real time and alarm in time; set a multi-level early warning threshold system, for example, divide the abnormal situations into three levels of slight deviation, significant abnormality and serious failure; when the electrode potential of 5 consecutive sampling points deviates from the set value by more than ±50mV, trigger the slight deviation warning, and the system automatically adjusts the control parameters. When the conductivity drops by more than 20% within 10 seconds, it is determined to be a significant abnormality, which may be due to the rapid consumption of additives, and the electrolyte needs to be supplemented. When any electrode potential mutation is detected to be more than ±200mV or the flow rate drops to below 0.1m / s, immediately trigger the serious failure alarm, suspend the electroplating process and perform manual inspection.

[0087] This step realizes the comprehensive acquisition and intelligent analysis of key state information of the electroplating process by constructing the above-mentioned real-time acquisition and analysis method of multi-source parameter fusion. Through multi-sensor collaborative monitoring, high-speed data acquisition, space-time fusion algorithm, feature extraction identification and abnormality detection and early warning technical means, the real-time state of the electroplating process can be accurately mastered, abnormal situations can be discovered and handled in time, reliable information support can be provided for subsequent closed-loop control, and the stability and controllability of the double-layer circuit board electroplating process can be significantly improved.

[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 on the thickness of the via sidewall electroplated layer of the double-layer circuit board; refer to Figure 5 for the closed-loop compensation control flowchart of this step.

[0089] Establish a closed-loop feedback control mechanism based on thickness prediction deviation. Divide the via sidewall into 8 monitoring points along the axial direction, and call the thickness prediction model every Δt time length to obtain the current thickness distribution; calculate the thickness deviation: δ(k) = H obj -Hcur (v), where δ(v) is the thickness deviation vector at the vth control moment, H obj is the target thickness, H cur (v) is the current predicted thickness. When the deviation exceeds the compensation control is started, realizing 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 according to the thickness deviation distribution: where I adj (u) is the adjusted current, I0 is the reference current, Γ is the adjustment gain, δ u is the deviation at the uth monitoring point, δ th is the characteristic deviation. The duty cycle is also adjusted: where u is the parameter, D base is the reference duty cycle, ξ is the adjustment range, δ max is the maximum deviation; through dynamic adjustment, differentiated compensation for different depth regions is realized.

[0091] A correction control strategy based on real-time parameters is established, combining the obtained real-time monitoring data, and introducing a correction factor: where σ real and σ nom are the measured and nominal conductivities, respectively, σ tol is the tolerance, v meas and v ref are the measured and reference flow rates, respectively, and Ψ is the correction coefficient; the corrected control output is: out I adj = I out · Ω.

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

[0093] Embodiment 2

[0094] Referring to Figure 7 , this is the second embodiment of the present application, which provides a precise electroplating control system for the electroplated layer of a double-layer circuit board.

[0095] The system comprises a multi-physical 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.

[0096] The multi-physical field coupling simulation module is used to establish a multi-physical field coupling simulation model containing a cathode, an anode, an electrolyte, electrode reaction kinetics, and ion migration and diffusion mechanisms, for simulating the distribution of current density and ion concentration inside the via of a double-layer circuit board.

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

[0098] The pulse current control module constructs a timing adjustable pulse current control method based on the thickness prediction result, and guides the current distribution to shift to the via sidewall by dynamically adjusting the pulse current amplitude, duty ratio and on-off period.

[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 of multi-source parameter fusion to obtain key state information in the electroplating process.

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

[0101] In the embodiments provided by the present application, it should be understood that the disclosed device and method can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There can be another division during actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between the units can be indirect couplings or communication connections through some communication interfaces, devices or units, and can be electrical, mechanical or other forms.

[0102] The embodiments of the present application are described above with reference to the drawings, but the present application is not limited to the specific implementation described above, and the specific implementation described above is only illustrative, not restrictive. A person of ordinary skill in the art can make changes, modifications, replacements and variations to the above-described embodiments without departing from the scope of the present application and the scope of protection of the claims, and these are all within the scope of protection of the present application.

Claims

1. A method for controlling the precise electroplating of a double-layer circuit board, characterized in that: include: A multi-physics coupling simulation model including cathode, anode, electrolyte, electrode reaction kinetics, and ion migration and diffusion mechanisms was established 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 electroplating thickness is constructed as a thickness prediction model to predict the electroplating 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 cycle, the current distribution is guided to shift toward the via sidewall. Based on the characteristic parameters of electrode potential, electrolyte conductivity and fluid velocity, a real-time acquisition and analysis method of multi-source parameter fusion is constructed to obtain key status information of 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 on the electroplating layer thickness on the side walls of the via holes in the double-layer circuit board.

2. The method for precise electroplating control of the electroplating layer of a double-layer circuit board according to claim 1, characterized in that: In the via structure of a double-layer circuit board, the spatial layout of the cathode, anode, and electrolyte, the electrode reaction kinetics, and the ion migration and diffusion mechanism are considered. The finite element method is used to couple the potential, current density, ion concentration, and fluid flow to model the multi-field interaction relationship inside the via.

3. The method for controlling the precise electroplating of the electroplating layer of a double-layer circuit board according to claim 2, characterized in that: Based on the spatial distribution data obtained by the multi-physics field simulation model and combined with the axial and radial current density distributions of the via, the numerical interpolation method is used to predict the thickness of the sidewall electroplating layer at different spatial positions.

4. The method for controlling the precise electroplating of the electroplating layer of a double-layer circuit board 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 parameterized and adjusted in stages to meet the requirements of current distribution and electroplating quality in different stages.

5. The method for controlling the precise electroplating of the electroplating layer of a double-layer circuit board according to claim 4, characterized in that: During the pulse current control process, based on the results of the thickness prediction model, the amplitude and timing of the applied pulse current are dynamically adjusted according to the thickness deviation of different parts of the via, and adaptive adjustment is performed on different depth areas of the via.

6. The method for controlling the precise electroplating of the electroplating layer of a double-layer circuit board according to claim 5, characterized in that: 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 time is extended to promote the flow of electrolyte in the via.

7. The method for controlling the precise electroplating of the electroplating layer of a double-layer circuit board according to claim 6, characterized in that: Multiple types of sensors are deployed in the electroplating tank to collect the state parameters of the cathode surface electrode potential, anode potential, electrolyte conductivity, and through-hole inlet and outlet flow rates in real time, and the state parameters of the electroplating process are jointly analyzed through data fusion algorithms.

8. The method for controlling the precise electroplating of the electroplating layer of a double-layer circuit board according to claim 7, characterized in that: Feature extraction is performed on the collected multi-source data to identify abnormal potential distribution, conductivity fluctuations and uneven flow rate. In combination with the set multi-level threshold system, graded alarms and prompts are given for different levels of abnormal conditions.

9. The method for controlling the precise electroplating of the electroplating layer of a double-layer circuit board according to claim 8, characterized in that: The thickness prediction model is called regularly, and the amplitude, duty cycle and waveform parameters of the pulse current are jointly adjusted according to the deviation between the real-time monitoring data and the target thickness. The correction factor is introduced according to the real-time status parameters to dynamically adjust the electroplating process.

10. A precise electroplating control system for a double-layer circuit board electroplating layer, which is used to implement the precise electroplating control method for a double-layer circuit board electroplating layer according to any one of claims 1 to 9, characterized in that: include: Multi-physics field coupling simulation module, electroplating thickness prediction module, pulse current control module, electroplating process data acquisition module and dynamic adjustment module; The multi-physics coupling simulation module is used to establish a multi-physics coupling simulation model including cathode, anode, electrolyte, electrode reaction kinetics and ion migration and diffusion mechanism, and is used to simulate the current density and ion concentration distribution inside the via hole of the 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 it as a thickness prediction model to predict the electroplating thickness of the via sidewall; The pulse current control module constructs a timing-adjustable pulse current control method based on the thickness prediction result, and guides the current distribution to shift toward the side wall of the via by dynamically adjusting the pulse current amplitude, duty cycle and on-off cycle; The electroplating process data acquisition module constructs a real-time acquisition and analysis method based on the characteristic parameters of electrode potential, electrolyte conductivity and fluid velocity to obtain key status information of the electroplating process; The dynamic adjustment module dynamically adjusts the pulse current waveform and timing based on the electroplating thickness prediction model and real-time electroplating parameters, and performs closed-loop compensation adjustment on the electroplating layer thickness on the side wall of the double-layer circuit board through hole.

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