A high-reliability underfill encapsulation intelligent molding control method and system

Abstract

The application provides a high-reliability underfill encapsulation intelligent forming control method and system, relates to the technical field of package manufacturing, and converts the electric field intensity into a target filler concentration distribution through finite element simulation of electric field distribution of the package under working voltage. The electric field is used to drive the directional migration of the filler in the photocuring resin, so that the dielectric constant of the corresponding area is enhanced, the deviation of the filler distribution caused by flow channel fluctuation and viscosity change in the dispensing filling process is avoided, the breakdown risk is reduced, and meanwhile, a high-frequency alternating current signal is embedded in the filling process to realize real-time inversion of the filler concentration of each node. Through dynamic fine adjustment of the driving voltage by minimizing the loss function, the deviation is corrected, errors caused by filler batch differences are avoided, and the filler distribution meets the requirements.

Description

Technical Field

[0001] This invention relates to the field of packaging manufacturing technology, specifically to a highly reliable intelligent molding control method and system for bottom-fill packaging. Background Technology

[0002] Encapsulation fillers are key components in the encapsulation field. They are uniformly dispersed in polymer matrices such as epoxy resin and silicone to form high-performance composite insulating mixtures. Common fillers include silica, alumina, aluminum nitride, and boron nitride. Fillers can optimize the performance of the package: by precisely adjusting the filler loading, the coefficient of thermal expansion of the composite material can be effectively reduced, making it more compatible with the silicon chip and circuit board, thereby significantly alleviating mechanical stress caused by thermal mismatch and preventing cracking or delamination of the device during service. Specific fillers can significantly improve the thermal conductivity of the encapsulation layer, assisting high-power devices in rapid heat dissipation, and also enhance the moisture resistance and electrical insulation properties of the package.

[0003] Generally, inorganic fillers are often used as bottom fillers during filling. They are injected into the gap between the chip and the substrate through capillary flow or pressure-assisted filling. The filler particles are randomly and uniformly distributed in the resin matrix, and their distribution is passively determined by the laws of fluid mechanics. Due to flow channel fluctuations and viscosity changes, the filler distribution may deviate, which increases the risk of breakdown.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a highly reliable intelligent molding control method and system for bottom filling and encapsulation, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A highly reliable intelligent molding control method for underfill packaging, comprising the following steps:

[0008] S1. Based on the package design model, the package filling region is constructed in the finite element simulation software. After applying the rated working voltage, the target relative permittivity distribution is extracted and converted into the target filler concentration distribution.

[0009] S2. After mixing the filler with the photocurable resin, obtain the interfacial electrokinetic potential of the mixture, fit the viscosity parameters using a viscosity model, and measure the viscosity.

[0010] S3. During dispensing, the mixed product flows out and the driving electric field is turned on simultaneously. During the pause interval of the driving electric field, an AC signal is injected into the sensing electrode to obtain the real-time filler concentration.

[0011] S4. Compare the real-time packing concentration with the target packing concentration distribution to obtain the root mean square deviation across the entire domain. Based on the threshold, determine the output driving electric field voltage adjustment amount and execute it.

[0012] S5. Turn on the ultraviolet light source to irradiate and cure the filled area.

[0013] Furthermore, the target relative permittivity distribution is extracted after applying the rated operating voltage, based on the following formula:

[0014]

[0015] in, Let m be the target relative permittivity at node m in the filled region. The dielectric constant of the resin matrix is ​​. The maximum dielectric constant of the filler is Let m be the electric field strength at node m in the filled region. The maximum electric field strength in the entire region. The gradient exponent is used to balance the gradient steepness. Based on the relationship between the dielectric constant and the component ratio, the target relative dielectric constant distribution gradient is converted into the target filler concentration distribution corresponding to each node. .

[0016] Furthermore, the formula used to measure viscosity is:

[0017]

[0018] in, For apparent viscosity, The local shear rate during the filling process, zero shear viscosity. Infinite shear viscosity These are inherent parameters of the mixture, obtained through experimental measurements. Viscosity parameters include the characteristic time constant. Power Law Index All were obtained through fitting.

[0019] Furthermore, the interfacial electrodynamic potential Interfacial electrokinetic potential was obtained by measuring with a Zeta potential analyzer. The effective voltage reflecting the surface charge layer of particles in the mixture is the interfacial electrokinetic potential. and apparent viscosity Send to the control terminal.

[0020] Furthermore, the particles in the mixture migrate directionally along the driving electric field lines under the influence of the driving electric field, and the transient electrophoretic velocity is denoted as . The formula used is:

[0021]

[0022] in, The velocity of the particles relative to the fluid. The vacuum permittivity, The dielectric constant of the resin is... To drive the electric field strength;

[0023] During the 10ms interval when the driving electric field is paused, an AC signal is injected into the sensing electrode to retrieve the real-time filler concentration at the current moment. The formula used is as follows:

[0024]

[0025] in, This represents the real-time filler concentration at the m-th node in the filled region. The imaginary unit, The angular frequency of the AC signal. The complex impedance of the filled region, The electrode geometric factors are obtained through conformal mapping. The vacuum permittivity, The calculated value of the dielectric constant is the real part of the dielectric constant extracted from the complex impedance. This is a mixed-law function.

[0026] Furthermore, during the dispensing process, the complex impedance of the filling area is acquired in real time using sensing electrodes arranged on the substrate.

[0027] Furthermore, the real-time filler concentration of the mixed product is compared with the target filler concentration designed for the package to obtain the global root mean square deviation (RMSD). Based on a threshold, the adjustment amount of the output driving electric field voltage is determined. The formula used is as follows:

[0028]

[0029] in, For loss function, For the predicted concentration in the next cycle, This is a regularization parameter to prevent the voltage from adjusting too quickly. To determine the voltage adjustment of the i-th electrode in the driving electric field, the voltage adjustment of the i-th electrode is obtained by minimizing the loss function. N represents the total number of electrode voltage paths, m is used to index the discrete nodes in the filled region, and M represents the total number of discrete nodes in the filled region.

[0030] Furthermore, based on real-time filler concentration and transient electrophoresis rate Obtain the predicted concentration for the next period The formula used is:

[0031]

[0032] m is used to index discrete nodes in the filling region. The real-time filler concentration at discrete node m in the filled region.

[0033] This invention discloses a high-reliability underfill packaging intelligent molding control system for executing a high-reliability underfill packaging intelligent molding control method, comprising:

[0034] The target filler concentration analysis module, based on the package design model, constructs the package filling region in finite element simulation software, extracts the target relative permittivity distribution after applying the rated working voltage, and converts it into the target filler concentration distribution;

[0035] The viscosity analysis module obtains the interfacial electrokinetic potential of the mixture after mixing the filler and the photocurable resin, and measures the viscosity by fitting the viscosity parameters through a viscosity model.

[0036] The real-time analysis module works by injecting an AC signal into the sensing electrode during dispensing, as the mixed product flows out and the driving electric field is activated simultaneously. During the pause interval of the driving electric field, an AC signal is injected into the sensing electrode to obtain the real-time filler concentration.

[0037] The correction module compares the real-time packing concentration with the target packing concentration distribution to obtain the root mean square deviation across the entire domain. Based on the threshold, it determines and executes the adjustment amount of the driving electric field voltage.

[0038] The curing module activates the ultraviolet light source to irradiate and cure the filled area.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] This invention uses finite element simulation to model the electric field distribution of the package under working voltage, converting the electric field intensity into the target filler concentration distribution. The electric field drives the filler to migrate directionally in the photocurable resin, thereby enhancing the dielectric constant of the corresponding region. This avoids filler distribution deviations caused by flow channel fluctuations and viscosity changes during dispensing, reducing the risk of breakdown. Simultaneously, a high-frequency AC signal is embedded during the filling process to invert the filler concentration at each node in real time. By minimizing the loss function, the driving voltage is dynamically fine-tuned to correct deviations and avoid errors caused by batch differences in fillers, ensuring that the filler distribution meets the requirements. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the overall method flow of the present invention;

[0042] Figure 2 This is a block diagram of the overall system of the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0044] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0045] Example:

[0046] Please see Figure 1 The present invention provides a technical solution:

[0047] A highly reliable intelligent molding control method for underfill packaging, comprising the following steps:

[0048] Step 1: Based on the package design model, construct the package filling region in the finite element simulation software, apply the rated working voltage, extract the target relative permittivity distribution and convert it into the target filler concentration distribution;

[0049] When the package is working, the internal electric field distribution is often non-uniform, and the concentrated electric field is often the failure point. If the dielectric constant of these places is insufficient, it is easy to break down. The electric field intensity under working conditions is restored by finite element simulation, and based on the charge balance and dielectric enhancement theory, it is converted into the target relative dielectric constant.

[0050] The formula used to extract the target relative permittivity distribution after applying the rated operating voltage is:

[0051]

[0052] in, The target relative permittivity at node m in the filled region reflects the ideal permittivity that the package should achieve at node m, as simulated, to resist the electric field stress caused by the internal electric field. The dielectric constant of the resin matrix is ​​. The maximum dielectric constant of the filler is Let m be the electric field strength at node m in the filled region. The maximum electric field strength in the entire region. To normalize the electric field strength, a higher electric field stress requires a correspondingly higher dielectric constant. The gradient exponent reflects the steepness of the transition of the dielectric constant from low-stress to high-stress regions. It is a power-law weight for adjusting the spatial distribution morphology of the filler and also reflects the sensitivity of the target dielectric distribution to changes in electric field stress. It is set based on the sharpness of the electric field distribution and the properties of the filler itself. The target relative dielectric constant is positively correlated with the nodal electric field strength; the greater the simulated electric field strength, the greater the target relative dielectric constant, and the greater the corresponding target filler concentration and maximum dielectric constant of the filler. The higher the relative permittivity, the greater the adjustable space for the filler concentration distribution. The magnitude of the target relative permittivity determines the required filler density. Based on the effective medium theory, the gradient of the target relative permittivity distribution is converted into the target filler concentration distribution corresponding to each node. Target filler concentration distribution Design the filler concentration for the target encapsulation.

[0053] Step 2: After mixing the filler with the photocurable resin, obtain the interfacial electrokinetic potential of the mixture, fit the viscosity parameters using a viscosity model, and measure the viscosity.

[0054] The filler is a modified particle. Ordinary filler particles are modified through surface chemical treatment. The modified particles will dissociate or adsorb ions in the photocurable resin, thereby forming an electric double layer at the interface. When an external electric field is applied to the mixture, the Coulomb force acts on the charged modified particles. Because a stable charge layer is formed on the surface of the modified particles, the particles can overcome resistance in the viscous resin and achieve directional movement.

[0055] The interfacial electrokinetic potential and apparent viscosity were obtained through experimental measurements. The interfacial electrokinetic potential reflects the stress sensitivity of the filler in the driving electric field, and the apparent viscosity reflects the flow resistance of the mixture during filling. The formula used to measure the viscosity is as follows:

[0056]

[0057] in, For apparent viscosity, The local shear rate during the filling process, zero shear viscosity. Infinite shear viscosity These are inherent parameters of the mixture. Zero-shear viscosity reflects the viscosity of the mixture at rest, while infinite-shear viscosity reflects the viscosity limit of the mixture at extremely high flow rates. These parameters are obtained through experimental measurements and include the characteristic time constant. Power Law Index All data were obtained through fitting. The shear rate of the mixture was scanned using a rotational rheometer, and the apparent viscosity corresponding to the shear rate of the mixture was measured. Based on a nonlinear least squares regression algorithm, the characteristic time constant was fitted. Power Law Index A larger characteristic time constant indicates that the fluid begins to thin at extremely low flow rates. The power-law exponent reflects the intensity of shear thinning; a smaller exponent indicates that the viscosity decreases faster with increasing shear rate. The local shear rate is obtained based on the state of the mixture during filling. During filling, as the filling pressure increases, the local shear rate increases, the flow resistance decreases, and thus the apparent viscosity decreases.

[0058] Interfacial electrodynamic potential Interfacial electrokinetic potential was obtained by measuring with a Zeta potential analyzer. The effective voltage reflecting the surface charge layer of particles in the mixture is the interfacial electrokinetic potential. and apparent viscosity Send to the control terminal.

[0059] When fillers are dispersed in a mixture, a double-layer structure is formed due to surface charge attraction. The Zeta potential analyzer, based on the laser Doppler effect, measures the velocity of the fillers under a known weak electric field, thereby obtaining the effective voltage on the shear surface, i.e., the interfacial kinetic potential. The magnitude of the interfacial kinetic potential affects the velocity of the fillers under the same electric field strength; a larger value indicates stronger velocities.

[0060] Step 3: During dispensing, the mixed product flows out and the driving electric field is turned on simultaneously. During the pause interval of the driving electric field, an AC signal is injected into the sensing electrode to obtain the real-time filler concentration.

[0061] The filler is induced to generate directional electric displacement by a driving electric field. High-frequency AC sensing is performed using the driving gap to invert the real-time filler concentration at each node of the filled region. Since the filler and resin matrix have different dielectric constants, the overall complex impedance of the mixture changes with the filler concentration. Geometric factors are extracted through conformal mapping to restore the macroscopic impedance signal to the real part of the microscopic dielectric constant, thus mapping it to a concentration value. Referring to step 2, the resin matrix of this invention is a photocurable resin.

[0062] The particles in the mixture migrate directionally along the driving electric field lines under the influence of the driving electric field, and the transient electrophoretic velocity is denoted as . The formula used is:

[0063]

[0064] in, This represents the migration velocity of particles relative to the fluid, reflecting how quickly the filler moves along the electric field lines within the resin matrix. The vacuum permittivity, The dielectric constant of the resin matrix is ​​. To drive the electric field strength;

[0065] During the 10ms interval when the driving electric field is paused, an AC signal is injected into the sensing electrode to retrieve the real-time filler concentration at the current moment. The formula used is as follows:

[0066]

[0067] in, This represents the real-time filler concentration at the m-th node of the filled region, i.e., the modified particle concentration. It reflects the volume percentage of the filler in the resin matrix at node m of the filled region at the current moment. The imaginary unit, The angular frequency of the AC signal. To determine the complex impedance of the filling region, when an AC signal is input to the sensing electrodes, the sensing circuit acquires in real time the current response signal flowing through the mixed products between the sensing electrodes and the voltage signal across the electrodes. It measures the amplitude ratio and phase difference between the voltage and current to obtain the complex impedance. When the filler concentration changes, the capacitance and conductivity characteristics shift, leading to a change in the complex impedance between the sensing electrodes. As the electrode geometry factor, the electric field lines of the sensing electrodes on the substrate are non-uniform arcs. By using conformal mapping, the complex physical plane is mapped to the plane of a parallel-plate capacitor, thereby obtaining the electrode geometry factor and correcting the influence of electrode shape on the measurement results. The vacuum permittivity, The calculated value of the dielectric constant is the real part of the dielectric constant extracted from the complex impedance. This indicates taking the real part. Since the dielectric constant of the filler is much higher than that of the resin matrix, the higher the filler concentration, The larger the value, the smaller the capacitive reactance and the smaller the complex impedance. The mixing law function is determined based on the effective medium theory and is calculated using either the Maxwell-Garnett model or the Bruggeman model, depending on the packing concentration.

[0068] Step 4: Compare the real-time packing concentration with the target packing concentration distribution to obtain the root mean square deviation across the entire domain. Based on the threshold, determine the output driving electric field voltage adjustment amount and execute it.

[0069] In actual filling processes, changes in the flow channel and fluctuations in resin viscosity can cause filler migration to deviate from expectations. The continuity equation is used to predict future states, which is then transformed into a loss function optimization problem.

[0070] The real-time filler concentration of the mixed product is compared with the target filler concentration of the package design to obtain the global root mean square deviation (RMSD). Based on a threshold judgment, when the global RMSD exceeds a set threshold, the driving electric field voltage adjustment is output. The threshold setting is selected and set based on historical data and experience. The larger the threshold, the larger the allowable range of the global RMSD. The loss function formula is as follows:

[0071]

[0072] in, For loss function, For the predicted concentration in the next cycle, This is a regularization parameter, set empirically based on response requirements, to prevent the voltage from adjusting too quickly. To determine the voltage adjustment of the i-th electrode in the driving electric field, the voltage adjustment of the i-th electrode is obtained by minimizing the loss function. N represents the total number of electrode voltage paths, m is used to index discrete nodes in the filling region, and M is the total number of discrete nodes in the filling region. By minimizing the loss function, the packing distribution is made closest to the target while ensuring that the voltage adjustment is minimized. The squared difference between the predicted value and the target value is the main source of the loss function. The larger the difference, the less ideal the current distribution is, leading to an increase in the function and triggering adjustment. When the packing in the target region is insufficient, the electric field is strengthened, i.e., the voltage is positively adjusted, to obtain more particles. When the packing is about to exceed the limit, the electric field is weakened, i.e., the voltage is negatively adjusted, to suppress particle aggregation.

[0073] Based on real-time filler concentration and transient electrophoresis rate Obtain the predicted concentration for the next period The formula used is:

[0074]

[0075] m is used to index discrete nodes in the filling region. This represents the real-time filler concentration at discrete node m in the filled region. To predict the current physical motion state, After a certain period of time, the packing concentration at node m, As a basis for prediction, transient electrophoresis velocity The speed at which particles transfer is determined and controlled by the driving voltage. The divergence term reflects the inflow and outflow effects of the packing particles. In the direction of packing source, the greater the transient electrophoresis velocity, the more packing is brought into node m, and the higher the predicted concentration. If the packing accelerates away from node m, the predicted concentration decreases.

[0076] Step 5: Turn on the ultraviolet light source to irradiate and cure the filled area.

[0077] By turning on the ultraviolet light source, a chemical reaction is initiated in the photocurable resin within the filling area, causing the mixed product to transform into a solid and completing the curing process.

[0078] See Figure 2 This invention discloses a high-reliability underfill packaging intelligent molding control system for executing a high-reliability underfill packaging intelligent molding control method, comprising:

[0079] The target filler concentration analysis module, based on the package design model, constructs the package filling region in finite element simulation software, extracts the target relative permittivity distribution after applying the rated working voltage, and converts it into the target filler concentration distribution;

[0080] The viscosity analysis module obtains the interfacial electrokinetic potential of the mixture after mixing the filler and the photocurable resin, and measures the viscosity by fitting the viscosity parameters through a viscosity model.

[0081] The real-time analysis module works by injecting an AC signal into the sensing electrode during dispensing, as the mixed product flows out and the driving electric field is activated simultaneously. During the pause interval of the driving electric field, an AC signal is injected into the sensing electrode to obtain the real-time filler concentration.

[0082] The correction module compares the real-time packing concentration with the target packing concentration distribution to obtain the root mean square deviation across the entire domain. Based on the threshold, it determines and executes the adjustment amount of the driving electric field voltage.

[0083] The curing module activates the ultraviolet light source to irradiate and cure the filled area.

[0084] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0085] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0086] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0087] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A highly reliable intelligent molding control method for bottom filling and encapsulation, characterized in that, The specific steps include: S1. Based on the package design model, the package filling region is constructed in the finite element simulation software. After applying the rated working voltage, the target relative permittivity distribution is extracted and converted into the target filler concentration distribution. S2. After mixing the filler with the photocurable resin, obtain the interfacial electrokinetic potential of the mixture, fit the viscosity parameters using a viscosity model, and measure the viscosity. S3. During dispensing, the mixed product flows out and the driving electric field is turned on simultaneously. During the pause interval of the driving electric field, an AC signal is injected into the sensing electrode to obtain the real-time filler concentration. S4. Compare the real-time packing concentration with the target packing concentration distribution to obtain the root mean square deviation across the entire domain. Based on the threshold, determine the output driving electric field voltage adjustment amount and execute it. S5. Turn on the ultraviolet light source to irradiate and cure the filled area.

2. The high-reliability bottom-fill encapsulation intelligent molding control method according to claim 1, characterized in that: The formula used to extract the target relative permittivity distribution after applying the rated operating voltage is: in, Let m be the target relative permittivity at node m in the filled region. The dielectric constant of the resin matrix is ​​. The maximum dielectric constant of the filler is Let m be the electric field intensity at node m in the filled region. The maximum electric field strength in the entire region. The gradient exponent is used to balance the gradient steepness. Based on the relationship between the dielectric constant and the component ratio, the target relative dielectric constant distribution gradient is converted into the target filler concentration distribution corresponding to each node. .

3. The high-reliability bottom-fill encapsulation intelligent molding control method according to claim 2, characterized in that: The formula used to measure viscosity is: in, For apparent viscosity, The local shear rate during the filling process, zero shear viscosity. Infinite shear viscosity These are inherent parameters of the mixture, obtained through experimental measurements. Viscosity parameters include the characteristic time constant. Power Law Index All were obtained through fitting.

4. The high-reliability bottom-fill encapsulation intelligent molding control method according to claim 3, characterized in that: Interfacial electrodynamic potential Interfacial electrokinetic potential was obtained by measuring with a Zeta potential analyzer. The effective voltage reflecting the surface charge layer of particles in the mixture is the interfacial electrokinetic potential. and apparent viscosity Send to the control terminal.

5. The high-reliability bottom-fill encapsulation intelligent molding control method according to claim 4, characterized in that: The particles in the mixture migrate directionally along the driving electric field lines under the influence of the driving electric field, and the transient electrophoretic velocity is denoted as . During the 10ms interval when the driving electric field pauses, an AC signal is injected into the sensing electrode to retrieve the real-time filler concentration. The formula used is as follows: in, This represents the real-time filler concentration at the m-th node in the filled region. The imaginary unit, The angular frequency of the AC signal. The complex impedance of the filled region, For electrode geometric factors, The vacuum permittivity, This is the calculated value of the dielectric constant. Used to extract the real part. This is a mixed-law function.

6. The high-reliability bottom-fill encapsulation intelligent molding control method according to claim 5, characterized in that: During the dispensing process, the complex impedance of the filling area is acquired in real time using sensing electrodes arranged on the substrate.

7. The high-reliability bottom-fill encapsulation intelligent molding control method according to claim 6, characterized in that: The real-time filler concentration of the mixed product is compared with the target filler concentration designed for the package to obtain the global root mean square deviation (RMSD). The adjustment amount of the output drive electric field voltage is determined based on a threshold, using the following formula: in, For loss function, For the predicted concentration in the next cycle, This is a regularization parameter to prevent the voltage from adjusting too quickly. To determine the voltage adjustment of the i-th electrode in the driving electric field, the voltage adjustment of the i-th electrode is obtained by minimizing the loss function. N represents the total number of electrode voltage paths, m is used to index the discrete nodes in the filled region, and M represents the total number of discrete nodes in the filled region.

8. The high-reliability bottom-fill encapsulation intelligent molding control method according to claim 7, characterized in that: Based on real-time filler concentration and transient electrophoresis rate Obtain the predicted concentration for the next period The formula used is: m is used to index discrete nodes in the filling region. The real-time filler concentration at discrete node m in the filled region.

9. A high-reliability underfill packaging intelligent molding control system, used to execute the high-reliability underfill packaging intelligent molding control method according to claim 1, characterized in that, include: The target filler concentration analysis module, based on the package design model, constructs the package filling region in finite element simulation software, extracts the target relative permittivity distribution after applying the rated working voltage, and converts it into the target filler concentration distribution; The viscosity analysis module obtains the interfacial electrokinetic potential of the mixture after mixing the filler and the photocurable resin, and measures the viscosity by fitting the viscosity parameters through a viscosity model. The real-time analysis module works by injecting an AC signal into the sensing electrode during dispensing, as the mixed product flows out and the driving electric field is activated simultaneously. During the pause interval of the driving electric field, an AC signal is injected into the sensing electrode to obtain the real-time filler concentration. The correction module compares the real-time packing concentration with the target packing concentration distribution to obtain the root mean square deviation across the entire domain. Based on the threshold, it determines and executes the adjustment amount of the driving electric field voltage. The curing module activates the ultraviolet light source to irradiate and cure the filled area.

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