Design and manufacturing method of multi-layer incongruous shielding signal line

Abstract

The invention relates to a method for designing and manufacturing a multi-layer incongruous shielding signal line in the field of new-generation information technology, which comprises the following steps of: analyzing structural parameters of the multi-layer incongruous shielding signal line, and acquiring electromagnetic characteristics, flexibility parameters and dielectric constants of shielding layer materials from a preset material database to obtain an initial material combination scheme; aiming at the final signal line structure scheme, generating a signal line sample by adopting a rapid prototyping manufacturing technology, and testing broadband impedance continuity and shielding effectiveness through a vector network analyzer to obtain verified signal line performance data; according to the verified signal line performance data, the relevance among shielding effectiveness, flexibility and impedance matching is analyzed through a machine learning algorithm, manufacturing process parameters are optimized, and a final process flow scheme is obtained.

Description

Technical Field

[0001] The present invention relates to the field of new generation information technology, specifically to key hardware technology of new generation communication systems, and more particularly to a design and manufacturing method of a multi-layer anisotropic shielded signal line. Background Art

[0002] High-frequency signal lines are crucial in the field of modern communications, especially in 5G communication equipment, where their performance directly affects signal transmission quality and system stability. With the increase in communication frequency and the growing demand for miniaturization of equipment, signal lines must have excellent anti-electromagnetic interference capabilities and good flexibility to adapt to complex wiring environments. However, traditional signal line manufacturing processes have significant limitations in meeting these requirements. The shielding effectiveness of conventional braided shielding layers usually does not exceed 60 decibels (1 GHz), making it difficult to effectively suppress high-frequency electromagnetic interference. At the same time, its flexibility is poor, and the bending radius is usually greater than 10 times the wire diameter, which limits its application in compact spaces.

[0003] The core challenge in manufacturing signal lines with multi-layer anisotropic shielding structures stems from the mutual constraints between shielding effectiveness, flexibility, and impedance matching. A single shielding layer struggles to achieve sufficient electromagnetic shielding at high frequencies, necessitating a multi-layer anisotropic structure to enhance shielding effectiveness. However, the stacking of different materials and structures can easily lead to a decrease in flexibility, causing deformation or performance degradation of signal lines when bent at small radii. Optimizing flexibility can also compromise the stability of the interfaces between layers, further impacting the continuity of the characteristic impedance. This is especially true within the wide frequency range of 100 MHz to 6 GHz, where the impedance tolerance is difficult to control within ±2 ohms. Impedance discontinuity can cause signal reflections and transmission losses, reducing overall transmission efficiency.

[0004] Therefore, how to achieve the coordinated optimization of high shielding effectiveness, excellent flexibility and precise impedance matching over a wide frequency band in a multi-layer anisotropic shielding structure has become a key issue in the research of signal line manufacturing processes. Summary of the Invention

[0005] The present invention provides a design and manufacturing method of a multi-layer anisotropic shielded signal line, comprising the following steps:

[0006] By analyzing the structural parameters of multi-layer anisotropic shielded signal lines, the electromagnetic properties, flexibility parameters, and dielectric constant of the shielding layer materials are obtained from a preset material database to obtain an initial material combination scheme.

[0007] Based on the initial material combination scheme, the finite element simulation method is used to calculate the shielding effectiveness at 1 GHz to determine whether the shielding effectiveness exceeds 60 decibels. If the shielding effectiveness is less than 60 decibels, the shielding layer thickness and material stacking sequence are adjusted, and the updated material combination scheme is recalculated;

[0008] For the updated material combination scheme, obtain the interface bonding strength data between each shielding layer, analyze the interface stability through molecular dynamics simulation, and determine whether the interface bonding strength meets the preset threshold. If not, optimize the adhesive formula to obtain stable interface structure parameters;

[0009] Based on the stable interface structure parameters, electromagnetic simulation tools are used to calculate the characteristic impedance within the 100 MHz to 6 GHz frequency band to determine whether the impedance tolerance is within ±2 ohms. If the impedance tolerance exceeds ±2 ohms, the dielectric layer thickness and dielectric constant are adjusted to obtain an optimized impedance matching solution.

[0010] The optimized impedance matching scheme is used to obtain the bending radius data of the signal line. Mechanical simulation is used to analyze the flexibility to determine whether the bending radius is less than 10 times the wire diameter. If the bending radius is greater than 10 times the wire diameter, the flexible substrate ratio is adjusted to obtain the improved flexibility parameters.

[0011] Based on the improved flexibility parameters, high-frequency signal transmission simulation is performed to calculate the signal reflection coefficient and transmission loss values. This determines whether the signal reflection coefficient is below the preset threshold and whether the transmission loss is reduced. If not, the geometric structure of the shielding layer and the conductor layer is optimized to obtain the final signal line structure solution.

[0012] Based on the final signal line structure plan, rapid prototyping technology was used to produce signal line samples. The wide-band impedance continuity and shielding effectiveness were tested using a vector network analyzer to obtain verified signal line performance data.

[0013] Based on the verified signal line performance data, a machine learning algorithm is used to analyze the correlation between shielding effectiveness, flexibility, and impedance matching, optimize manufacturing process parameters, and obtain the final process flow plan;

[0014] Through the final process flow plan, an automated production line is used to batch manufacture signal lines, obtain real-time monitoring data during the production process, determine whether the production consistency meets the preset standards, and obtain signal line products that meet performance requirements.

[0015] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0016] This invention discloses a design and manufacturing method for multi-layer, anisotropically shielded signal lines. By analyzing structural parameters and material properties, finite element simulation is used to optimize shielding effectiveness, molecular dynamics simulation to analyze interface stability, electromagnetic simulation to optimize impedance matching, mechanical simulation to analyze flexibility, and high-frequency signal transmission simulation to optimize reflection and loss. The invention combines rapid prototyping and vector network analysis to verify performance, utilizes machine learning algorithms to optimize process parameters, and ultimately achieves automated mass production. This method can effectively improve the shielding effectiveness, impedance matching, and flexibility of signal lines, achieve high-performance transmission over a wide frequency band, meet signal integrity requirements in complex electromagnetic environments, and provide a reliable signal transmission solution for high-speed interconnect systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The present invention is a flowchart of a method for designing and manufacturing a multi-layer anisotropic shielded signal line.

[0018] Figure 2 This is a schematic diagram of a design and manufacturing method of a multi-layer anisotropic shielded signal line of the present invention.

[0019] Figure 3 This is another schematic diagram of a design and manufacturing method of a multi-layer anisotropic shielded signal line of the present invention. DETAILED DESCRIPTION

[0020] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. All other embodiments derived by those skilled in the art based on the embodiments in this specification without creative effort shall fall within the scope of protection of this specification.

[0021] like Figure 1-3 In this embodiment, a design and manufacturing method of a multi-layer anisotropic shielded signal line may specifically include:

[0022] S101. By analyzing the structural parameters of the multi-layer anisotropic shielded signal lines, the electromagnetic properties, flexibility parameters, and dielectric constant of the shielding layer materials are obtained from a preset material database to obtain an initial material combination scheme.

[0023] A structural parameter set of a multi-layer anisotropic shielded signal line is obtained, wherein the structural parameter set includes the geometric dimensions and distribution characteristics of each layer. From a preset material database, based on the constraints of the structural parameter set, shielding layer materials that meet the electromagnetic properties, flexibility parameters and dielectric constant are screened to obtain a list of candidate materials. If the electromagnetic properties of the materials in the candidate material list meet the preset threshold, a weighted scoring algorithm is used to combine the flexibility parameter and dielectric constant to calculate the comprehensive performance score of each material and determine a preferred material combination. Based on the preferred material combination, the electromagnetic shielding effectiveness of the multi-layer anisotropic shielded signal line is simulated to obtain a performance simulation result. If the performance simulation result does not reach the preset performance threshold, the structural parameter set is adjusted, and the candidate material list is retrieved from the preset material database. It is iteratively optimized until the performance requirements are met to obtain an optimized material combination. Through the optimized material combination, an initial design scheme for the multi-layer anisotropic shielded signal line is generated, and the design parameters and material configuration are output. A finite element analysis tool is used to verify the electromagnetic compatibility and flexibility performance of the initial design scheme to obtain a final material combination scheme.

[0024] For example, when analyzing the structural parameters of multi-layer anisotropic shielded signal lines, a three-dimensional electromagnetic field model is first established through finite element simulation software, the number of signal line layers is set to 4, the shielding layer thickness is 0.1 mm, the interlayer spacing is 0.05 mm, and the operating frequency range is 1 GHz to 10 GHz. The moment method is used to calculate the electromagnetic field distribution at different frequencies, and key parameters such as characteristic impedance (target value 50 ± 2 ohms) and crosstalk attenuation (required to be greater than 60 dB) are extracted. Based on the simulation results, candidate materials that meet the electromagnetic shielding effectiveness (SE ≥ 40 dB @ 5 GHz) are screened from the material database, such as copper-plated silver (conductivity 5.8 × 10 7 S / m) and nickel alloy (magnetic permeability μr = 600). A trade-off analysis of material combinations was performed using a multi-objective optimization algorithm (such as NSGA-II), setting the objective function to minimize insertion loss (<0.3dB / cm@5GHz) and maximize flexibility (bending radius <5mm). Constraints included dielectric constant (εr = 2.2-3.5) and cost (<50 yuan / meter). Three Pareto optimal solutions were ultimately generated. The optimal solution used a copper-silver-plated / polyimide composite structure (thickness ratio 3:2). HFSS verification demonstrated that its shielding effectiveness reached 42.3dB@5GHz with a bending radius of 4.8mm, meeting the preset requirements.

[0025] S102. Based on the initial material combination scheme, the shielding effectiveness at 1 GHz is calculated using a finite element simulation method to determine whether the shielding effectiveness exceeds 60 decibels. If the shielding effectiveness is less than 60 decibels, the thickness of the shielding layer and the order of material stacking are adjusted, and the updated material combination scheme is recalculated.

[0026] Using a finite element simulation method, the shielding effectiveness is calculated at a specified frequency based on an initial material combination scheme to obtain an initial shielding effectiveness value. Effectiveness data is obtained from the initial shielding effectiveness value to determine whether it reaches a preset threshold. If the initial shielding effectiveness value is lower than the preset threshold, the material parameters that need to be adjusted are determined. Based on the judgment result, a genetic algorithm is used to optimize the thickness of the shielding layer and the order of material stacking to obtain a candidate material combination scheme. Using a finite element simulation method, the shielding effectiveness at a specified frequency is recalculated for the candidate material combination scheme to obtain an updated shielding effectiveness value. Effectiveness data is obtained from the updated shielding effectiveness value to determine whether it reaches a preset threshold. If the updated shielding effectiveness value is still lower than the preset threshold, the material parameters are iteratively optimized to update the candidate material combination scheme. Based on the iterative optimization result, a final material combination scheme is determined, and the shielding effectiveness of the final material combination scheme is calculated using a finite element simulation method to obtain a final shielding effectiveness value. By comparing the final shielding effectiveness value with the preset threshold, it is determined whether the final material combination scheme meets the requirements, and an optimized material combination scheme is obtained.

[0027] For example, in the initial material combination scheme, copper and aluminum are used as shielding materials, with a copper layer thickness of 0.1 mm and an aluminum layer thickness of 0.2 mm. The shielding effectiveness at 1 GHz is calculated using the finite element simulation method. During the simulation process, the shielding structure is modeled using an electromagnetic field solver, the boundary condition is set to a perfectly matched layer (PML), and the frequency domain solver is used to calculate the propagation characteristics of the electromagnetic wave. The simulation results show that the shielding effectiveness of the initial scheme is 55 decibels, which is lower than the target value of 60 decibels. In order to improve the shielding effectiveness, the copper layer thickness is first adjusted to 0.15 mm, and the aluminum layer thickness remains unchanged. The simulation is repeated and the shielding effectiveness is 58 decibels, which is still lower than the target value. Next, the material stacking order is adjusted, with the aluminum layer placed on the outer layer and the copper layer placed on the inner layer. The copper layer thickness is maintained at 0.15 mm and the aluminum layer thickness is adjusted to 0.25 mm. The simulation is repeated and the shielding effectiveness is 62 decibels, which exceeds the target value of 60 decibels. Through the above adjustments, the updated material combination scheme was finally determined, that is, the outer layer is a 0.25 mm aluminum layer and the inner layer is a 0.15 mm copper layer. This scheme has high shielding effectiveness at 1 GHz and meets the design requirements.

[0028] S103. For the updated material combination scheme, obtain the interface bonding strength data between each shielding layer, analyze the interface stability through molecular dynamics simulation, and determine whether the interface bonding strength meets the preset threshold. If not, optimize the adhesive formula to obtain stable interface structure parameters.

[0029] The interface bonding strength data between each shielding layer is obtained through molecular dynamics simulation to obtain the initial bonding strength distribution. The interface stability characteristics are extracted from the initial bonding strength distribution to determine whether the interface stability characteristics reach a preset threshold. If the interface stability characteristics do not reach the preset threshold, the adhesive formula is optimized by genetic algorithm to obtain improved formula parameters. Based on the improved formula parameters, the molecular dynamics simulation is repeated to obtain updated interface bonding strength data. The interface structure parameters are extracted from the updated interface bonding strength data to determine whether stable structural characteristics are formed. If the stable structural characteristics are not formed, the genetic algorithm model is iteratively adjusted to obtain the final formula parameters. The stable interface structure parameters are determined based on the final formula parameters, and the optimized material combination scheme is output.

[0030] For example, in the updated material combination scheme, the interface bonding strength data between each shielding layer is first obtained through molecular dynamics simulation. LAMMPS software is used for simulation, and the size of the simulation box is set to 10nm×10nm×10nm, the time step is 1fs, and the simulation time is 1ns. By calculating the interatomic force at the interface, the interface bonding strength is obtained to be 50MPa. Then, molecular dynamics simulation is used to analyze the interface stability, and radial distribution function (RDF) and mean square displacement (MSD) are used as evaluation indicators. RDF analysis shows that the interatomic distance at the interface is 0.3nm, indicating that there is a strong interaction between atoms; MSD analysis shows that the mean square displacement of atoms at the interface is 0.02nm. 2 , indicating that the interface structure is relatively stable. According to the preset threshold, the interface bonding strength needs to reach 60MPa, but the current simulation result is 50MPa, which does not meet the requirement. Therefore, the adhesive formula is optimized, the content of epoxy resin in the adhesive is increased to 30%, and nano-silica particles are introduced to enhance the interface bonding force. Molecular dynamics simulation is performed again, and the optimized interface bonding strength is 65MPa, which meets the preset threshold. Finally, through molecular dynamics simulation and optimization of the adhesive formula, stable interface structure parameters are obtained, ensuring the reliability of the material combination scheme.

[0031] S104. Based on the stable interface structure parameters, use electromagnetic simulation tools to calculate the characteristic impedance in the 100 MHz to 6 GHz frequency band to determine whether the impedance tolerance is controlled within ±2 ohms. If the impedance tolerance exceeds ±2 ohms, adjust the dielectric layer thickness and dielectric constant to obtain an optimized impedance matching solution.

[0032] Using an electromagnetic simulation tool, the characteristic impedance of the transmission line is calculated for a preset frequency band to obtain an initial impedance value. Based on the initial impedance value, it is determined whether the impedance tolerance is within the preset impedance tolerance range. If it is exceeded, the tolerance deviation data is extracted. Based on the tolerance deviation data, a gradient descent algorithm is used to adjust the thickness and dielectric constant of the dielectric layer to obtain an adjusted parameter combination. Using an electromagnetic simulation tool, the characteristic impedance is recalculated for the adjusted parameter combination to obtain a new impedance value. Based on the new impedance value, it is determined whether the impedance tolerance is within the preset impedance tolerance range. If so, the impedance matching scheme corresponding to the new impedance value is determined. Using the impedance matching scheme, finite element analysis is used to verify the stability of the impedance matching scheme within the preset frequency band to obtain a verification result. Based on the verification result, it is determined whether the stability meets the preset threshold. If so, the impedance matching scheme is determined to be the final optimized scheme.

[0033] For example, in an electromagnetic simulation tool, the frequency range is first set to 100 MHz to 6 GHz, and the initial interface structure parameters are input, including a dielectric layer thickness of 1.6 mm and a dielectric constant of 4.3. The characteristic impedance within this frequency band is calculated using the finite element analysis method to obtain an impedance curve. The analysis found that the impedance at 3 GHz is 52 ohms, which exceeds the tolerance range of ±2 ohms. To optimize the impedance matching, an iterative algorithm is used to adjust the dielectric layer thickness and dielectric constant. The dielectric layer thickness is adjusted to 1.5 mm and the dielectric constant is adjusted to 4.1, and the characteristic impedance is recalculated. The results show that the impedance at 3 GHz is 50.5 ohms, which meets the tolerance requirements. The impedance changes within the entire frequency band are further verified to ensure that all frequency points are controlled between 48 and 52 ohms. Through multiple iterations and optimizations, the dielectric layer thickness is finally determined to be 1.5 mm and the dielectric constant is 4.1, achieving a stable impedance matching solution.

[0034] S105. Obtain the bending radius data of the signal line through the optimized impedance matching scheme, use mechanical simulation to analyze the flexibility, and determine whether the bending radius is less than 10 times the wire diameter. If the bending radius is greater than 10 times the wire diameter, adjust the flexible substrate ratio to obtain improved flexibility parameters.

[0035] The bending radius value and wire diameter data are obtained from the signal line geometric parameters to obtain the initial signal line data. The finite element analysis method is used to perform mechanical simulation on the initial signal line data to obtain flexibility parameters and simulation result data. The bending radius value and wire diameter ratio are extracted from the simulation result data. If the wire diameter ratio is greater than the preset threshold, the flexible substrate ratio is adjusted. According to the difference between the wire diameter ratio and the preset threshold, the adjustment amplitude of the flexible substrate ratio is calculated using a linear regression algorithm to obtain the adjusted substrate ratio. The signal line structural parameters are updated according to the adjusted substrate ratio to obtain improved flexibility parameters. The improved flexibility parameters are used to re-perform mechanical simulation to obtain verified flexibility parameters. The wire diameter ratio is extracted from the verified flexibility parameters. If the wire diameter ratio is less than or equal to the preset threshold, the final flexibility parameters are determined.

[0036] For example, when optimizing the impedance matching scheme, the characteristic impedance of the signal line is first extracted through electromagnetic simulation software (such as HFSS), the target impedance is set to 50Ω, and the line width and dielectric layer thickness are adjusted. For example, the line width is adjusted from 0.2mm to 0.18mm, and the dielectric layer thickness is adjusted from 0.1mm to 0.12mm, so that the impedance error is controlled within ±5%. Subsequently, a bending structure model of the signal line is generated through a three-dimensional modeling tool (such as SolidWorks), and the bending radius data is extracted. For example, the radius of a certain arc is 3mm, and the wire diameter is 0.3mm. The calculated ratio of the bending radius to the wire diameter is 10 times, and flexibility analysis is required at this time. Finite element analysis software (such as ANSYS Mechanical) is used to simulate the bending condition, apply a radial force of 0.5N and observe the stress distribution. If the maximum stress exceeds the yield strength of the flexible substrate (such as polyimide) of 200MPa, it is determined that the current bending radius does not meet the requirements. When the bending radius is greater than 10 times the wire diameter (such as 3.5mm / 0.3mm≈11.7 times), the flexible substrate parameters of different proportions are retrieved through the material database. For example, the polyimide content is increased from 70% to 85%, and the elastic modulus is recalculated (from 2.5GPa to 1.8GPa). After re-simulation, it is confirmed that the bending radius is reduced to 2.8mm (9.3 times the wire diameter), and the impedance change rate is kept below 3%. Finally, the improved flexibility parameter table is output, including data such as substrate proportion, elastic modulus and critical bending radius.

[0037] S106. Based on the improved flexibility parameters, the signal reflection coefficient and transmission loss value are calculated through high-frequency signal transmission simulation to determine whether the signal reflection coefficient is lower than the preset threshold and whether the transmission loss is reduced. If not, the geometric structure of the shielding layer and the conductor layer is optimized to obtain the final signal line structure solution.

[0038] Initialize the high-frequency signal transmission model, set the signal frequency and material parameters, run an electromagnetic simulation, calculate the signal's reflection coefficient R and transmission loss L in the transmission line, and obtain initial simulation data. If the reflection coefficient R is greater than a preset threshold R_th or the transmission loss L is greater than a preset threshold L_th, extract the shielding layer thickness T_s, conductor layer width W_c, and spacing D_c from the signal line model to generate a geometric parameter set P. Process this geometric parameter set P using a genetic algorithm, setting the optimization objective to minimize the reflection coefficient R and transmission loss L. Adjust the shielding layer thickness T_s, conductor layer width W_c, and spacing D_c to generate a candidate geometric parameter set P_c and construct a candidate signal line structure S_c. Run a high-frequency signal transmission simulation on this candidate signal line structure S_c, calculate the candidate's reflection coefficient R_c and transmission loss L_c, and obtain candidate simulation data. If the candidate's reflection coefficient R_c is less than the preset threshold R_th and the transmission loss L_c is less than the preset threshold L_th, then determine that the candidate signal line structure S_c is the final signal line structure solution S_f. If the reflection coefficient R_c of the candidate structure is greater than the preset threshold R_th or the transmission loss L_c is greater than the preset threshold L_th, the candidate geometric structure parameter set P_c is updated by a genetic algorithm, a new candidate geometric structure parameter set P_n is generated, a new candidate signal line structure S_n is constructed, and the transmission simulation and optimization are repeated until the final signal line structure scheme S_f is obtained in which the reflection coefficient R_f is less than the preset threshold R_th and the transmission loss L_f is less than the preset threshold L_th.

[0039] For example, after improving the flexibility parameters, the finite element method was used to simulate high-frequency signal transmission. The signal frequency was set to 10 GHz, the conductor layer thickness to 50 μm, and the shielding layer dielectric constant to 3.2. The electric field distribution was calculated by solving Maxwell's equations. The reflection coefficient was extracted using S parameters. If the calculated result was -15 dB, which was higher than the preset threshold of -20 dB, the optimization process was initiated. A genetic algorithm was used to adjust the shielding layer aperture ratio, iterating in steps of 0.1% within the range of 15%-25%. When the aperture ratio reached 18.5%, the reflection coefficient dropped to -21.5 dB. The transmission loss was also calculated. The initial value was 0.8 dB / cm. By optimizing the chamfer radius of the conductor layer edge from 5 μm to 8 μm, the loss was reduced to 0.6 dB / cm. The final structural parameters were verified by Monte Carlo analysis. The standard deviation of the reflection coefficient remained less than 0.3 dB under a ±5% process deviation, meeting the requirements of Six Sigma Design. The entire optimization process is automatically completed through Python scripts. The gradient descent method is used to sort the sensitivity of the structural parameters. The thickness of the shielding layer is determined to be the key variable. When it is optimized from 30μm to 25μm, the quality factor is improved by 12%.

[0040] S107. Based on the final signal line structure plan, rapid prototyping technology is used to generate signal line samples, and the wide-band impedance continuity and shielding effectiveness are tested by a vector network analyzer to obtain verified signal line performance data.

[0041] Rapid prototyping technology is used to generate signal line samples and obtain initial sample structure data. The wide-band impedance continuity of the signal line sample is tested by a vector network analyzer to obtain impedance characteristic data. If the impedance characteristic data exceeds a preset threshold, the sample structure parameters are adjusted to generate an optimized signal line sample. The shielding effectiveness of the optimized signal line sample is tested by a vector network analyzer to obtain shielding performance data. The support vector machine algorithm is used to classify the shielding performance data to obtain a shielding effectiveness level. The classification results are extracted from the shielding effectiveness level and combined with the impedance characteristic data to generate comprehensive performance data. The comprehensive performance data is analyzed for consistency by a data verification module to obtain verified signal line performance data.

[0042] For example, during the rapid prototyping stage, fused deposition modeling (FDM) was used to print signal line samples with a layer thickness of 0.1 mm. Polyetheretherketone (PEEK) with a dielectric constant of 3.5 was selected as the substrate, and the line width was designed to be 0.3 mm to meet the 50-ohm characteristic impedance requirement. This was verified by calculation using the transmission line impedance formula Z0 = 87 / sqrt(εr + 1.41) × ln(5.98h / (0.8w + t)), where h = 1.6 mm is the dielectric thickness and t = 0.035 mm is the copper foil thickness. The vector network analyzer was set to scan from 100 MHz to 20 GHz during testing, and the SOLT calibration method was used to eliminate systematic errors. The impedance fluctuation measured at 10 GHz was 49.8 ± 1.2 ohms, meeting the ±10% tolerance requirement specified in the IPC-2251 standard. Shielding effectiveness was tested using a triaxial method, injecting a -10dBm test signal at 3GHz. The attenuation measured at the receiving end was -65dB, resulting in a shielding effectiveness of 55dB calculated using the formula SE = 20log10(E1 / E2). The least squares method was used to fit the impedance-frequency curve during data analysis, resulting in a slope coefficient of 0.

[0043] 0023Ω / GHz, confirming that high-frequency stability meets the standard. Test data was imported into ANSYS HFSS for field simulation comparison. The deviation between the simulated and measured impedance at 18 GHz was less than 2%, verifying the accuracy of the design model. An SQL database was established to store impedance, insertion loss, and return loss parameters for all frequencies. A K-means clustering algorithm was used to automatically identify outlier frequency bands. No outliers were found when the cluster center distance threshold was set to 3σ.

[0044] S108. Based on the verified signal line performance data, a machine learning algorithm is used to analyze the correlation between shielding effectiveness, flexibility, and impedance matching, optimize the manufacturing process parameters, and obtain the final process flow plan.

[0045] A signal line performance data set is obtained, wherein the data set includes original data of shielding effectiveness, flexibility and impedance matching; a data cleaning technique is used to process the signal line performance data set to remove noise and outliers to obtain a cleaned performance data set; for the cleaned performance data set, a principal component analysis algorithm is used to extract the main features of shielding effectiveness, flexibility and impedance matching to obtain a feature data set; based on the feature data set, a random forest algorithm is used to analyze the correlation between shielding effectiveness, flexibility and impedance matching to obtain a correlation model; if the prediction error of the correlation model is lower than a preset threshold, the weight coefficients of shielding effectiveness, flexibility and impedance matching are output according to the correlation model to determine the key influencing factors; if the prediction error of the correlation model is higher than the preset threshold, the principal component analysis parameters are adjusted, and the features are re-extracted to obtain an updated feature data set; based on the key influencing factors, a grid search method is used to optimize the manufacturing process parameters to obtain a candidate process parameter set; for the candidate process parameter set, the performance of shielding effectiveness, flexibility and impedance matching is verified by a simulation tool to determine the final process parameters; based on the final process parameters, a process flow plan is generated to obtain a process flow configuration file.

[0046] For example, when analyzing the correlation between shielding effectiveness, flexibility and impedance matching, the performance data of 100 groups of signal lines were first collected, including shielding effectiveness (unit dB, range 30-90dB), flexibility (number of bends, range 500-2000 times) and impedance matching deviation (unit Ω, range ±5Ω). The random forest algorithm was used to perform feature importance analysis, the number of trees was set to 100, the maximum depth was 10, and the feature contribution was evaluated by the Gini coefficient. The results showed that the weight of the influence of shielding effectiveness on impedance matching was 0.45, and the weight of flexibility on shielding effectiveness was 0.32. Based on this, a multivariate linear regression model Y=0.78X1+0.21X2-0.15X3 was established (Y is the comprehensive performance score, X1 is shielding effectiveness, X2 is flexibility, and X3 is impedance deviation), and the model R 2Reached 0.92. In order to optimize the process parameters, the particle swarm algorithm (population size 50, iteration number 200) was used to search for the optimal solution under the constraints (shielding effectiveness ≥ 70dB, flexibility ≥ 1200 times, impedance deviation ≤ ± 3Ω), and the optimal combination of copper wire diameter 0.12mm, braiding density 85%, and insulation layer thickness 0.25mm was obtained. The parameters were input into the finite element simulation software for verification. The shielding effectiveness was improved to 75dB (original 65dB), the flexibility was improved to 1500 times (original 1100 times), and the impedance deviation was controlled within ± 2Ω. The final production process flow includes: copper wire drawing (tolerance ± 0.01mm), three-layer shielding braiding (angle 45° ± 2°), real-time impedance monitoring (sampling frequency 1kHz), and automatic distribution of process parameters and process data traceability are achieved through the MES system.

[0047] S109. Through the final process flow plan, the signal lines are batch-produced using an automated production line, and real-time monitoring data during the production process is obtained to determine whether the production consistency meets the preset standards, thereby obtaining signal line products that meet the performance requirements.

[0048] The real-time monitoring data collected by the sensor during the batch production of the signal line when the automated production line is started is obtained to obtain a production data set containing current, voltage, and wire diameter parameters. If the deviation of the parameter value in the production data set from the preset standard range exceeds the threshold, the abnormal point is identified by the abnormality detection algorithm to obtain an abnormal data identifier. According to the abnormal data identifier, the parameter setting of the automated production line is adjusted using the control instruction to obtain an optimized production parameter configuration. The production line is operated with the optimized production parameter configuration to obtain new real-time monitoring data and an updated production data set. If the parameter value in the updated production data set meets the preset standard, the batch consistency score is calculated by the consistency analysis algorithm to obtain a consistency evaluation result. According to the consistency evaluation result, a classification algorithm is used to determine whether the signal line product meets the performance requirements to obtain a qualified product identifier. The signal line products are screened by the qualified product identifier to obtain a signal line product that meets the performance requirements.

[0049] For example, on an automated production line, the signal cable manufacturing process begins with real-time data collection using high-precision sensors, such as wire diameter, insulation thickness, and conductivity, at a sampling frequency of 100 times per second to ensure comprehensive and timely data. This collected data is then transmitted to a central control system via an industrial IoT platform. The system uses a least-squares regression analysis algorithm to fit the data, calculate the mean and standard deviation of production parameters, and determine whether they are within preset ranges. For example, the tolerance for wire diameter is ±0.02 mm, and the tolerance for insulation thickness is ±0.01 mm. If any parameter falls outside this range, the system automatically triggers an alarm and adjusts production equipment, such as adjusting the extruder temperature or pulling speed, to ensure consistent production. Simultaneously, the system uses machine learning algorithms to analyze historical data, predict potential anomalies, and initiate proactive intervention. Finally, the system generates a production report, including metrics such as pass rate and defect rate. For example, a pass rate of at least 99.5% and a defect rate below 0.1% ensure that signal cable products meet performance requirements. The entire process requires no human intervention, enabling efficient and accurate mass production.

[0050] The above only lists some preferred embodiments of the present invention, but the present invention is not limited thereto, and many improvements and modifications can be made. As long as the improvements and modifications are made on the basis of the basic principles of the present invention, they should be considered to fall within the scope of protection of the present invention.

Claims

1. A design and manufacturing method for a multi-layer anisotropic shielded signal line, characterized in that: The method comprises the following steps: S101. Analyze the structural parameters of the multi-layer anisotropic shielded signal lines, obtain the electromagnetic properties, flexibility parameters, and dielectric constant of the shielding layer materials from a preset material database, and obtain an initial material combination scheme; S102. Calculate the shielding effectiveness at 1 GHz using a finite element simulation method based on the initial material combination scheme to determine whether the shielding effectiveness exceeds 60 decibels. If the shielding effectiveness is less than 60 decibels, adjust the shielding layer thickness and the material stacking sequence, and recalculate to obtain an updated material combination scheme. S103. Obtaining interface bonding strength data between each shielding layer for the updated material combination scheme, analyzing interface stability through molecular dynamics simulation, and determining whether the interface bonding strength meets a preset threshold. If not, optimizing the adhesive formulation to obtain stable interface structural parameters. S104. Calculate the characteristic impedance within the 100 MHz to 6 GHz frequency band using an electromagnetic simulation tool based on the stable interface structure parameters, and determine whether the impedance tolerance is within ±2 ohms. If the impedance tolerance exceeds ±2 ohms, adjust the dielectric layer thickness and dielectric constant to obtain an optimized impedance matching solution. S105. Obtain bending radius data of the signal line using the optimized impedance matching solution, analyze flexibility using mechanical simulation, and determine whether the bending radius is less than 10 times the wire diameter. If the bending radius is greater than 10 times the wire diameter, adjust the flexible substrate ratio to obtain improved flexibility parameters. S106. Calculate the signal reflection coefficient and transmission loss value based on the improved flexibility parameter through high-frequency signal transmission simulation to determine whether the signal reflection coefficient is lower than a preset threshold and whether the transmission loss is reduced. If not, optimize the geometric structure of the shielding layer and the conductor layer to obtain a final signal line structure solution. S107. Based on the final signal line structure plan, use rapid prototyping technology to produce signal line samples, and use a vector network analyzer to test the wide-band impedance continuity and shielding effectiveness to obtain verified signal line performance data; S108. Based on the verified signal line performance data, a machine learning algorithm is used to analyze the correlation between shielding effectiveness, flexibility, and impedance matching, optimize manufacturing process parameters, and obtain a final process flow plan; S109. Through the final process flow plan, the signal lines are batch-produced using an automated production line, and real-time monitoring data during the production process is obtained to determine whether the production consistency meets the preset standards, thereby obtaining signal line products that meet the performance requirements.

2. The method for designing and manufacturing a multi-layer anisotropic shielded signal line according to claim 1, characterized in that: The S101 includes: Obtaining a structural parameter set of a multi-layer anisotropic shielded signal line, wherein the structural parameter set includes geometric dimensions and distribution characteristics of each layer; Screening shielding layer materials that meet electromagnetic properties, flexibility parameters, and dielectric constants from a preset material database based on the constraints of the structural parameter set to obtain a list of candidate materials; If the electromagnetic properties of the materials in the candidate material list meet the preset threshold, a weighted scoring algorithm is used to calculate the comprehensive performance score of each material in combination with the flexibility parameter and the dielectric constant to determine the preferred material combination; Simulating the electromagnetic shielding effectiveness of multi-layer anisotropic shielded signal lines based on the preferred material combination to obtain performance simulation results; If the performance simulation result does not reach the preset performance threshold, the structural parameter set is adjusted, a candidate material list is re-obtained from a preset material database, and iterative optimization is performed until the performance requirement is met to obtain an optimized material combination; Generate an initial design scheme for a multi-layer anisotropic shielded signal line through the optimized material combination, and output design parameters and material configuration; Finite element analysis tools were used to verify the electromagnetic compatibility and flexibility performance of the initial design scheme, and the final material combination scheme was obtained.

3. The design and manufacturing method of a multi-layer anisotropic shielded signal line according to claim 1, characterized in that: The S102 includes: Finite element simulation method is used to calculate the shielding effectiveness at a specified frequency based on the initial material combination scheme to obtain the initial shielding effectiveness value; Acquiring effectiveness data from the initial shielding effectiveness value to determine whether it reaches a preset threshold; If the initial shielding effectiveness value is lower than a preset threshold, determining material parameters that need to be adjusted; Based on the judgment result, a genetic algorithm is used to optimize the thickness of the shielding layer and the order of material stacking to obtain a candidate material combination scheme; Recalculating the shielding effectiveness at a specified frequency for the candidate material combination scheme by a finite element simulation method to obtain an updated shielding effectiveness value; obtaining effectiveness data from the updated shielding effectiveness value and determining whether it reaches a preset threshold; If the updated shielding effectiveness value is still lower than the preset threshold, iteratively optimizing the material parameters and updating the candidate material combination scheme; Determining a final material combination scheme based on the iterative optimization results, and calculating the shielding effectiveness of the final material combination scheme using a finite element simulation method to obtain a final shielding effectiveness value; By comparing the final shielding effectiveness value with a preset threshold, it is determined whether the final material combination scheme meets the requirements, and an optimized material combination scheme is obtained.

4. A method for designing and manufacturing a multi-layer anisotropic shielded signal line according to any one of claims 1 to 3, characterized in that: The S103 includes: The interface bonding strength data between each shielding layer is obtained by molecular dynamics simulation to obtain the initial bonding strength distribution; extracting an interface stability feature from the initial bonding strength distribution, and determining whether the interface stability feature reaches a preset threshold; If the interface stability characteristic does not reach a preset threshold, optimizing the adhesive formulation through a genetic algorithm to obtain improved formulation parameters; Re-performing molecular dynamics simulation according to the improved formulation parameters to obtain updated interface adhesion strength data; extracting interface structure parameters from the updated interface bonding strength data to determine whether a stable structural feature is formed; If the stable structural features are not formed, iteratively adjusting the genetic algorithm model to obtain final formulation parameters; The stable interface structure parameters are determined according to the final formulation parameters, and an optimized material combination scheme is output.

5. A method for designing and manufacturing a multi-layer anisotropic shielded signal line according to any one of claims 1 to 3, characterized in that: The S104 includes: Using electromagnetic simulation tools, calculate the characteristic impedance of the transmission line for a preset frequency range and obtain the initial impedance value; Based on the initial impedance value, determine whether the impedance tolerance is within the preset impedance tolerance range. If it exceeds, extract the tolerance deviation data; By using the tolerance deviation data and the gradient descent algorithm, the thickness of the dielectric layer and the dielectric constant are adjusted to obtain the adjusted parameter combination; Using electromagnetic simulation tools, the characteristic impedance is recalculated for the adjusted parameter combination to obtain a new impedance value; According to the new impedance value, determining whether the impedance tolerance is within a preset impedance tolerance range, and if so, determining an impedance matching scheme corresponding to the new impedance value; By using the impedance matching scheme, finite element analysis is used to verify the stability of the impedance matching scheme within a preset frequency band to obtain a verification result; According to the verification result, it is determined whether the stability meets the preset threshold. If so, the impedance matching scheme is determined to be the final optimization scheme.

6. A method for designing and manufacturing a multi-layer anisotropic shielded signal line according to any one of claims 1 to 3, characterized in that: The S105 includes: Obtain the bending radius value and wire diameter data from the signal line geometric parameters to obtain the initial signal line data; Performing mechanical simulation on the initial signal line data using a finite element analysis method to obtain flexibility parameters and simulation result data; extracting a bending radius value and a wire diameter ratio from the simulation result data, and determining to adjust the flexible substrate ratio if the wire diameter ratio is greater than a preset threshold; Calculating the adjustment range of the flexible substrate ratio using a linear regression algorithm according to the difference between the wire diameter ratio and the preset threshold value to obtain an adjusted substrate ratio; updating the signal line structural parameters according to the adjusted substrate ratio to obtain improved flexibility parameters; Re-performing mechanical simulation using the improved flexibility parameters to obtain verified flexibility parameters; A wire diameter ratio is extracted from the verified flexibility parameter, and if the wire diameter ratio is less than or equal to the preset threshold, a final flexibility parameter is determined.

7. A method for designing and manufacturing a multi-layer anisotropic shielded signal line according to any one of claims 1 to 3, characterized in that: The S107 includes: Use rapid prototyping technology to generate signal line samples and obtain initial sample structure data; Testing the broadband impedance continuity of the signal line sample using a vector network analyzer to obtain impedance characteristic data; If the impedance characteristic data exceeds a preset threshold, adjusting the sample structure parameters to generate an optimized signal line sample; Testing the shielding effectiveness of the optimized signal line sample using a vector network analyzer to obtain shielding performance data; Using a support vector machine algorithm to classify the shielding performance data to obtain a shielding effectiveness level; Extracting classification results from the shielding effectiveness levels and combining them with the impedance characteristic data to generate comprehensive performance data; The comprehensive performance data is analyzed for consistency through a data verification module to obtain verified signal line performance data.

8. The method for designing and manufacturing a multi-layer anisotropic shielded signal line according to claim 1, characterized in that: The S108 includes: Acquire a signal line performance data set, the data set including raw data of shielding effectiveness, flexibility, and impedance matching; Using data cleaning technology to process the signal line performance data set, remove noise and outliers, and obtain a cleaned performance data set; For the performance data set after cleaning, a principal component analysis algorithm is used to extract the main features of shielding effectiveness, flexibility and impedance matching to obtain a feature data set; According to the characteristic data set, a random forest algorithm is used to analyze the correlation among shielding effectiveness, flexibility and impedance matching to obtain a correlation model; If the prediction error of the correlation model is lower than a preset threshold, outputting weight coefficients of shielding effectiveness, flexibility, and impedance matching according to the correlation model to determine key influencing factors; If the prediction error of the correlation model is higher than a preset threshold, the principal component analysis parameters are adjusted and features are re-extracted to obtain an updated feature data set; According to the key influencing factors, a grid search method is used to optimize the manufacturing process parameters to obtain a candidate process parameter set; For the candidate process parameter set, verify the shielding effectiveness, flexibility, and impedance matching performance through simulation tools to determine the final process parameters; A process flow plan is generated according to the final process parameters to obtain a process flow configuration file.

9. The method for designing and manufacturing a multi-layer anisotropic shielded signal line according to claim 1, characterized in that: The S109 includes: Acquire real-time monitoring data collected by sensors during batch production of the start-up signal line on the automated production line, and obtain a production data set containing current, voltage, and wire diameter parameters; If the deviation between the parameter value in the production data set and the preset standard range exceeds a threshold, an abnormal point is identified by an anomaly detection algorithm to obtain an abnormal data identifier; According to the abnormal data identification, the parameter settings of the automated production line are adjusted using control instructions to obtain an optimized production parameter configuration; Running the production line using the optimized production parameter configuration to obtain new real-time monitoring data and obtain an updated production data set; If the parameter values ​​in the updated production data meet the preset standards, the batch consistency score is calculated using the consistency analysis algorithm to obtain a consistency assessment result; Based on the consistency assessment results, a classification algorithm is used to determine whether the signal line product meets the performance requirements and obtain a qualified product label; Signal line products are screened through the qualified product identification to obtain signal line products that meet performance requirements.

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