Optimization design method of double-layer PIN structure of high-speed data connector

Abstract

The present invention relates to the technical field of high-speed connectors, and discloses a method for optimizing the design of a double-layer PIN pin structure of a high-speed data connector. The method utilizes a three-dimensional scanning device to obtain the initial structural parameters of the PIN pin, reconstructs a three-dimensional model through a reverse engineering algorithm, and extracts key geometric features. Secondly, the time domain reflection method is used to simulate the signal transmission path, collect signal integrity data, and identify potential signal distortion areas. At the same time, the near-field scanning technology and the moment method are combined to calculate the electromagnetic coupling degree between adjacent PIN pins. Then, a multi-objective optimization model is constructed based on the above-mentioned characteristic parameters, and a multi-objective genetic algorithm is used to solve and obtain a structural parameter adjustment scheme that meets the requirements of signal integrity and electromagnetic compatibility. The plug-in stress distribution is further evaluated through mechanical strength simulation to ensure structural reliability. Finally, the optimal structural parameters are determined by combining the optimization results and simulation data.

Description

Technical Field

[0001] The present invention relates to the technical field of high-speed connectors, in particular to a method for optimizing the design of a double-layer PIN needle structure of a high-speed data connector. Background Art

[0002] With the rapid development of next-generation information technologies such as 5G communications, cloud computing, and big data centers, data transmission rates continue to climb, placing stringent demands on the performance of high-speed data connectors. As a core component of high-speed data connectors, the structural design of double-layer PIN pins directly impacts signal transmission quality and device operational stability. Existing technologies often rely on empirical experience or optimization of a single performance metric, making them difficult to adapt to the complex demands of high-frequency, high-speed transmission scenarios.

[0003] In traditional designs, initial PIN pin structural parameters are often obtained through manual measurement or simplified modeling. This results in limited geometric feature extraction accuracy, leading to a lack of a reliable foundation for subsequent optimization. Signal transmission performance evaluation often focuses on static parameter testing, failing to capture signal distortion patterns during transmission through dynamic path simulation, making it difficult to accurately identify key areas of signal attenuation and reflection. In electromagnetic compatibility design, calculations of electromagnetic coupling between adjacent PIN pins are often based on theoretical approximations, without incorporating the electromagnetic field distribution characteristics of the actual structure. This can easily lead to biased interference assessments, which in turn affects overall transmission stability.

[0004] Existing optimization methods often disentangle signal integrity, electromagnetic compatibility, and mechanical strength. Some solutions only adjust geometric dimensions, ignoring the impact of electromagnetic interference on signals. Other solutions, while taking electromagnetic factors into account, fail to incorporate stress distribution analysis during the plug-in and unplug process, resulting in optimized structures being susceptible to problems such as poor contact due to mechanical fatigue in actual use. Furthermore, optimization models often employ a single-objective solution strategy. When faced with multiple requirements such as suppressing signal distortion, reducing electromagnetic coupling, and improving mechanical strength, it is difficult to form a balanced solution, making the performance bottleneck of double-layer PINs at high transmission rates even more prominent.

[0005] In practical applications, high-speed data connectors often operate in complex electromagnetic environments and must withstand frequent plugging and unplugging. Traditional double-layer PIN designs are prone to signal transmission delays, increased bit error rates, and structural deformation, hindering the performance of the entire data transmission system. As data rates reach 100Gbps and beyond, traditional design methods are no longer able to meet the low-latency, high-reliability requirements of practical applications, necessitating urgent technological innovation. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for optimizing the design of a double-layer PIN needle structure of a high-speed data connector to solve the problems raised in the above background technology.

[0007] To achieve the above objectives, the present invention provides a method for optimizing the design of a double-layer PIN structure of a high-speed data connector, the method comprising:

[0008] The initial structural parameters of the double-layer PIN were obtained using a 3D scanning device, and a reverse engineering algorithm was used to reconstruct the 3D model to extract key geometric features.

[0009] Simulate the signal transmission path of double-layer PIN pins, collect signal integrity data based on time domain reflectometry, and identify potential signal distortion areas;

[0010] The electromagnetic field distribution information of the double-layer PIN needles is obtained by near-field scanning technology, and the electromagnetic coupling degree between adjacent PIN needles is calculated by combining the moment method.

[0011] Construct a multi-objective optimization model based on key geometric features, potential signal distortion areas, and electromagnetic coupling;

[0012] The multi-objective optimization model is solved by a multi-objective genetic algorithm to generate a structural parameter adjustment scheme that meets the requirements of signal integrity and electromagnetic compatibility;

[0013] Conduct mechanical strength simulation on the structural parameter adjustment scheme to evaluate whether the stress distribution during the plugging and unplugging process is within a safe range;

[0014] According to the mechanical strength simulation results and the solution results of the multi-objective optimization model, the final optimization parameters of the double-layer PIN needle structure are determined.

[0015] Preferably, the initial structural parameters of the double-layer PIN needle are obtained by a three-dimensional scanning device, and a reverse engineering algorithm is used to reconstruct the three-dimensional model to extract key geometric features, specifically including:

[0016] Laser scanning equipment is used to collect all-round data of double-layer PIN needles, obtaining point cloud data including needle diameter, spacing and arrangement;

[0017] Preprocess the initial point cloud data using a point cloud denoising algorithm to remove noise points and redundant data;

[0018] The Poisson surface reconstruction algorithm is used to reconstruct the 3D model of the denoised point cloud data to generate a solid model of the double-layer PIN needle;

[0019] The needle length, cross-sectional shape and pin spacing are extracted from the solid model as key geometric features.

[0020] Preferably, a signal transmission path simulation is performed on the double-layer PIN needle, and signal integrity data is collected based on time domain reflectometry to identify potential signal distortion areas, specifically including:

[0021] Build a signal transmission path model of a double-layer PIN needle in 3D electromagnetic simulation software and set the excitation signal and load parameters;

[0022] A step signal is injected into the signal transmission path model using time domain reflectometry to collect data on reflection coefficients and transmission coefficients changing with time.

[0023] Perform Fourier transform on the reflection coefficient and transmission coefficient data to convert them into insertion loss and return loss parameters in the frequency domain;

[0024] The insertion loss and return loss parameters are compared with preset thresholds, and the physical areas corresponding to the frequency bands exceeding the threshold range are marked as potential signal distortion areas.

[0025] Preferably, the electromagnetic field distribution information of the double-layer PIN pins is obtained by near-field scanning technology, and the electromagnetic coupling degree between adjacent PIN pins is calculated by combining the moment method, which specifically includes:

[0026] The near-field probe is used to scan the electromagnetic field strength of the double-layer PIN needle in the working state to generate a spatial electromagnetic field distribution map;

[0027] Extract the amplitude and phase information of electric field intensity and magnetic field intensity based on the electromagnetic field distribution map;

[0028] The moment method is used to construct the electromagnetic coupling model between adjacent PIN pins, and the electric field strength and magnetic field strength parameters are substituted into the model;

[0029] The mutual inductance and mutual capacitance between adjacent PINs are obtained by solving the electromagnetic coupling model, and the electromagnetic coupling degree is calculated based on the mutual inductance and mutual capacitance.

[0030] Preferably, a multi-objective optimization model is constructed based on key geometric features, potential signal distortion areas, and electromagnetic coupling, specifically including:

[0031] The needle diameter, spacing and arrangement of key geometric features are used as optimization variables, and the value range of each variable is set;

[0032] The optimization goals are to minimize the area of ​​potential signal distortion and maximize the reduction rate of electromagnetic coupling;

[0033] Set constraints on insertion loss, return loss, and crosstalk according to high-speed data transmission protocol requirements;

[0034] The response surface methodology is used to construct the mathematical relationship between the optimization objectives and the optimization variables to form a multi-objective optimization model.

[0035] Preferably, a multi-objective genetic algorithm is used to solve the multi-objective optimization model to generate a structural parameter adjustment scheme that meets the signal integrity and electromagnetic compatibility requirements, specifically including:

[0036] Initialize the population, use the value combination of key geometric features as population individuals, set the population size and evolutionary generations;

[0037] Calculate the optimization objective function value corresponding to each individual and use the non-dominated sorting genetic algorithm to evaluate the fitness of the individual;

[0038] Generate the next generation of population through selection, crossover and mutation operations, and retain individuals in the Pareto optimal solution set;

[0039] When the evolutionary generation reaches the preset value, the solution that meets the constraints is selected from the final Pareto optimal solution set as the structural parameter adjustment plan.

[0040] Preferably, a mechanical strength simulation is performed on the structural parameter adjustment scheme to evaluate whether the stress distribution during the plugging and unplugging process is within a safe range, specifically including:

[0041] Import the structural parameter adjustment plan into the finite element analysis software to establish the mechanical model of the double-layer PIN needle;

[0042] Set material property parameters, including elastic modulus, Poisson's ratio, and yield strength;

[0043] Simulate the axial and radial forces on the needle during insertion and removal, and apply corresponding boundary conditions;

[0044] Solve the mechanical model to obtain the stress cloud map, extract the maximum stress value and compare it with the yield strength of the material to determine whether the stress distribution is within a safe range.

[0045] Preferably, the final double-layer PIN needle structure optimization parameters are determined based on the mechanical strength simulation results and the solution results of the multi-objective optimization model, specifically including:

[0046] When the maximum stress value in the mechanical strength simulation result is less than the material yield strength and the solution of the multi-objective optimization model meets the signal integrity and electromagnetic compatibility requirements, the current structural parameter adjustment scheme is determined as the final optimization parameter; otherwise, the value range of the optimization variable is readjusted and the multi-objective optimization model solution step is returned.

[0047] Preferably, after constructing the multi-objective optimization model, the following step is further included: using a grey correlation analysis method to assign weights to the optimization objectives and determine the priority ranking of the signal integrity index and the electromagnetic compatibility index.

[0048] Preferably, the grey correlation analysis method is used to assign weights to the optimization objectives and determine the priority ranking of the signal integrity index and the electromagnetic compatibility index, specifically including:

[0049] Collect signal integrity data and electromagnetic compatibility test results from historical design cases and establish a sample database;

[0050] The correlation coefficient between each optimization objective and the design effect is solved by the grey correlation calculation method;

[0051] The signal integrity index and electromagnetic compatibility index are sorted based on the size of the correlation coefficient to generate a priority weight vector.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] This high-speed data connector double-layer pin structure optimization design method uses 3D scanning equipment to obtain initial structural parameters and reconstructs a 3D model using a reverse engineering algorithm. This method extracts key geometric features directly from the physical structure, avoiding the errors introduced by traditional manual measurement or simplified modeling, and enabling subsequent optimization processes to rely on accurate structural data. This precise capture of geometric features allows designers to more clearly understand the relationship between pin structure and performance, providing a practical foundation for subsequent multi-dimensional optimization.

[0054] Simulating the signal transmission path of double-layer PINs and collecting data using time-domain reflectometry dynamically tracks signal changes during transmission and precisely locates areas where distortion may occur. This in-depth analysis of signal transmission details allows optimization to move beyond empirical adjustments and target specific sources of distortion, resulting in more targeted improvements to signal integrity.

[0055] Using near-field scanning technology to obtain electromagnetic field distribution information, combined with the method of moments to calculate the electromagnetic coupling between adjacent PIN pins, can truly reflect the electromagnetic environment of the PIN pins under actual operating conditions. Compared with traditional approximate calculations based on theoretical formulas, this method combines the actual structure layout with the electromagnetic field distribution characteristics, making the assessment of electromagnetic coupling closer to reality and providing a more reliable reference for reducing interference between adjacent PIN pins.

[0056] A multi-objective optimization model based on key geometric features, potential signal distortion areas, and electromagnetic coupling breaks the limitations of traditional design, which prioritizes a single performance metric. This model integrates multiple core factors affecting the performance of double-layer PINs into a unified framework, enabling the optimization process to simultaneously address multiple requirements, such as signal transmission quality and electromagnetic environment compatibility. This avoids focusing on one performance aspect at the expense of others, resulting in a more comprehensive design approach.

[0057] Solving multi-objective optimization models using a multi-objective genetic algorithm allows for the exploration of a wide range of possible structural adjustment options within a complex parameter space. This approach, with its global search capabilities, can generate multiple solutions that meet basic performance requirements, providing designers with diverse options to meet the performance needs of different application scenarios.

[0058] Mechanical strength simulation of the structural parameter adjustment scheme and analysis of stress distribution during the plugging and unplugging process allow the mechanical performance of the structure to be incorporated into the final evaluation system. This consideration of the physical state under actual usage scenarios avoids problems such as deformation and fracture of the optimized structure due to insufficient mechanical strength during frequent plugging and unplugging, ensuring that the design solution has reliable mechanical stability while meeting signal and electromagnetic performance requirements.

[0059] The final structural optimization parameters were determined by combining mechanical strength simulation results with the results of a multi-objective optimization model, achieving a synergistic improvement in signal integrity, electromagnetic compatibility, and mechanical strength. This holistic design approach ensures that the double-layer PIN pins maintain stable signal transmission and reduce electromagnetic interference in high-frequency and high-speed transmission environments, while also maintaining structural integrity during long-term use, adapting to the multi-dimensional performance requirements of connectors in high-speed data transmission scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a working principle diagram of the high-speed data connector double-layer PIN needle structure optimization design method of the present invention;

[0061] Figure 2 Flowchart for obtaining initial structural parameters and extracting key geometric features;

[0062] Figure 3 Flowchart for obtaining electromagnetic field distribution information and calculating electromagnetic coupling degree;

[0063] Figure 4 Flowchart for weight assignment of grey relational analysis method. DETAILED DESCRIPTION

[0064] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0065] See also Figure 1 The present invention provides a method for optimizing the design of a double-layer PIN structure of a high-speed data connector, the method comprising:

[0066] Step 1: Obtain the initial structural parameters of the double-layer PIN using a 3D scanner. A reverse engineering algorithm is then used to reconstruct a 3D model and extract key geometric features. A comprehensive 3D scan of the double-layer PIN is performed, generating point cloud data containing structural details. This data is then processed using a reverse engineering algorithm to construct a 3D model, from which key geometric features that significantly impact structural performance are extracted.

[0067] Step 2: Simulate the signal transmission path for the double-layer PINs, collect signal integrity data using time-domain reflectometry, and identify areas of potential signal distortion. Build a signal transmission path model in a simulation environment, inject signals into the model using time-domain reflectometry, collect reflection and transmission data, and analyze this data to identify areas of potential signal distortion.

[0068] Step 3: Use near-field scanning technology to obtain the electromagnetic field distribution information of the double-layer PIN pins, and use the moment method to calculate the electromagnetic coupling degree between adjacent PIN pins. Use a near-field scanning device to scan the double-layer PIN pins in the working state to obtain the distribution of the electromagnetic field in space. Then, use the moment method to calculate the electromagnetic coupling degree between adjacent PIN pins.

[0069] Step 4: Construct a multi-objective optimization model based on key geometric features, potential signal distortion areas, and electromagnetic coupling. Using the key geometric features extracted in Step 1, the potential signal distortion areas identified in Step 2, and the electromagnetic coupling calculated in Step 3 as foundational data, a mathematical model is developed that simultaneously considers multiple optimization objectives.

[0070] Step 5: Solve the multi-objective optimization model using a multi-objective genetic algorithm to generate a series of structural parameter adjustment solutions that meet the requirements of signal integrity and electromagnetic compatibility.

[0071] Step 6: Perform mechanical strength simulation on the structural parameter adjustment scheme to evaluate whether the stress distribution during the plugging and unplugging process is within a safe range. Apply the obtained structural parameter adjustment scheme to the mechanical strength simulation to simulate the stress conditions of the double-layer PIN during the plugging and unplugging process, analyze the stress distribution, and determine whether it is within a safe range.

[0072] Step 7: Determine the final double-layer PIN structural optimization parameters based on the mechanical strength simulation results and the multi-objective optimization model solution. By combining the mechanical strength simulation results with the multi-objective optimization model solution, the structural parameter adjustment options are screened and determined to obtain the final optimization parameters.

[0073] Example 1: See Figure 2 In the process of obtaining the initial structural parameters of the double-layer PIN needle through a 3D scanning device and reconstructing the 3D model using a reverse engineering algorithm to extract key geometric features, the following operations are performed: a laser scanning device is selected to perform all-round data collection on the double-layer PIN needle. The laser beam emitted by the laser transmitter of the laser scanning device covers the outer surface of the double-layer PIN needle at a specific angle, including all areas such as the top, side, bottom and pin connection of the needle body. After the laser beam contacts the surface of the PIN needle, it is reflected. The reflected signal is captured by the receiver of the device. By calculating the time difference or phase difference between laser emission and reception, the spatial coordinates of each reflection point are determined, and finally point cloud data containing the needle body diameter, the spacing between each needle body, and the overall arrangement are formed. These point cloud data fully record the geometric morphological information of the double-layer PIN needle in the form of massive three-dimensional coordinate points.

[0074] The initially collected point cloud data may contain noise points due to factors such as ambient light interference, device vibration, or reflective surfaces on the pins. It may also contain redundant data collected repeatedly. This data can affect the accuracy of subsequent model reconstruction, so it requires preprocessing using a point cloud denoising algorithm. This algorithm analyzes features such as the distance between adjacent points and the angle between normal vectors to identify and remove noise points that do not conform to the distribution pattern of the surrounding point sets. It also merges duplicate point sets to remove redundant data, resulting in purified point cloud data.

[0075] The Poisson surface reconstruction algorithm is used to reconstruct a 3D model from the denoised point cloud data. This algorithm treats the point cloud data as sampling points in 3D space and constructs an implicit function to fit the surface formed by these points. The zero level set of the implicit function is defined as the reconstructed surface. The algorithm solves the Poisson equation to determine the coefficients of the implicit function, ensuring that the reconstructed surface closely matches the geometry described by the point cloud data. Ultimately, a solid model of the double-layer PIN pin is generated. The solid model is presented as a 3D mesh, containing all the surface details and structural features of the PIN pin.

[0076] Key geometric features were extracted from the generated solid model. Using 3D model analysis tools, the needle length (the axial distance from the tip of the needle to the end of the pin) was measured and recorded. Cross-sectional shapes, including circular, square, or other irregular shapes, were observed and determined. Pin spacing (the shortest distance between adjacent pins in the same plane) was measured. These features are directly linked to the physical structural properties of the double-layer pins and serve as fundamental geometric parameters for subsequent structural optimization.

[0077] When simulating the signal transmission path of a double-layer PIN, collecting signal integrity data based on time-domain reflectometry, and identifying potential signal distortion areas, the following steps are performed: A signal transmission path model of the double-layer PIN is constructed in 3D electromagnetic simulation software. Based on the previously reconstructed solid model, the geometric structure of the double-layer PIN is restored in the software, including the spatial position and relative posture of each pin body, as well as the interface structure with external connection components. The excitation signal parameters, including the amplitude, frequency, and rise time, are set to align with the input signal characteristics of the actual double-layer PIN. Load parameters are set to simulate the impedance characteristics of the external circuit to which the PIN is connected, ensuring that the model reflects the actual signal transmission environment.

[0078] A step signal is injected into the signal transmission path model using time-domain reflectometry. The step signal enters the signal transmission path at the model's input and propagates along the needle. During propagation, reflections are generated when the signal encounters impedance discontinuities, such as changes in needle diameter, pin connections, or interfaces between different media. The simulation software's signal acquisition module synchronously records the reflection and transmission coefficients over time. The reflection coefficient reflects the amplitude ratio of the reflected signal to the incident signal, while the transmission coefficient reflects the amplitude ratio of the signal passing through the impedance discontinuity to the incident signal.

[0079] Perform a Fourier transform on the collected time-domain data of the reflection and transmission coefficients. The Fourier transform decomposes the time-domain signal into a superposition of different frequency components, thereby converting the reflection and transmission coefficients into frequency-domain parameters. Insertion loss describes the amplitude attenuation of the signal due to various losses during transmission, while return loss describes the strength of the reflected signal. Frequency-domain transformation allows for clear visualization of signal loss characteristics at different frequencies.

[0080] The resulting insertion loss and return loss parameters are compared against preset thresholds. These thresholds are determined based on high-speed data transmission standards and actual application requirements, covering all frequency bands within the operating frequency range of the double-layer PIN. For frequency bands where insertion loss or return loss exceeds the thresholds, the physical areas corresponding to these frequency bands, where signal transmission experiences unacceptable loss or reflection, are identified and marked as potential signal distortion areas. The geometry or material properties of these areas may impede signal transmission and require adjustment during subsequent optimization.

[0081] Example 2: See Figure 3The process of acquiring electromagnetic field distribution information from a double-layer PIN using near-field scanning technology and calculating the electromagnetic coupling between adjacent PINs using the method of moments involves the following steps: A near-field probe is used to scan the electromagnetic field intensity of the operating double-layer PIN. The near-field probe utilizes highly sensitive sensing elements, capable of capturing electromagnetic field variations within a very close proximity to the PIN surface. During the scanning process, the probe moves along a pre-set path around the double-layer PIN, covering the sides, top, and areas with dense pins. The step size is set based on the desired spatial resolution. The probe converts the sensed electromagnetic field intensity into an electrical signal and transmits it to a data acquisition device. After analog-to-digital conversion, raw data is generated, containing spatial coordinates and corresponding electromagnetic field intensities. This data is processed to form a spatial electromagnetic field distribution map. The color or grayscale variations in different regions of the map visually demonstrate differences in electromagnetic field strength, clearly showing the distribution range and intensity gradient of the magnetic field around the PIN.

[0082] Based on the generated electromagnetic field distribution map, the amplitude and phase information of the electric and magnetic field strengths are extracted. Using a map analysis tool, the electric and magnetic field strength values ​​corresponding to each spatial coordinate point are read. These values ​​constitute the amplitude information, reflecting the strength of the electromagnetic field at that location. Simultaneously, the phase values ​​of the electromagnetic field at different locations are recorded. This phase information reflects the synchronization of the electromagnetic field's vibration state. Points with the same phase at the same time form an isophase surface, demonstrating the fluctuation characteristics of the electromagnetic field. Together, the amplitude and phase data of the electric and magnetic fields constitute a complete parameter set describing the electromagnetic field's characteristics.

[0083] The method of moments is used to construct an electromagnetic coupling model between adjacent PIN pins. First, two adjacent PIN pins and the surrounding space are divided into multiple discrete units, each of which is considered an electric dipole or magnetic dipole. Based on electromagnetic theory, interaction equations between these units are established, which include parameters such as the unit's current density, charge density, and spatial distance. The extracted electric and magnetic field strength parameters are substituted into the model and converted into excitation source parameters for each unit. By solving the matrix system composed of these equations, the mutual inductance and mutual capacitance coefficients between adjacent PIN pins are obtained. The mutual inductance coefficient reflects the magnitude of the electromotive force induced on the other PIN pin by the current change on one PIN pin, while the mutual capacitance coefficient reflects the strength of the capacitive effect caused by the charge accumulation between the two PIN pins. Based on the mutual inductance and mutual capacitance coefficients, the electromagnetic coupling degree is calculated using a formula. This numerical value quantifies the degree of mutual interference between adjacent PIN pins through the electromagnetic field.

[0084] In constructing a multi-objective optimization model based on key geometric features, potential signal distortion areas, and electromagnetic coupling, the following steps were performed: The optimization variables were determined to be the pin diameter, spacing, and arrangement, among the key geometric features. The pin diameter range was determined based on the material properties and installation space of the pins, taking into account the material's conductivity and machinability. The spacing range was determined to balance signal transmission requirements and structural compactness, avoiding excessive coupling due to close proximity or increased overall volume due to distance. The arrangement options ranged from parallel to staggered, depending on the spatial constraints of the double-layer structure.

[0085] The optimization objectives were to minimize the area of ​​the potential signal distortion region and maximize the electromagnetic coupling reduction rate. The area of ​​the potential signal distortion region was determined by measuring the surface area of ​​the marked region in the 3D model. A smaller area indicates a lower likelihood of signal distortion during transmission. The electromagnetic coupling reduction rate was calculated by taking the ratio of the difference in electromagnetic coupling before and after optimization to the initial coupling. A larger ratio indicates a more significant reduction in interference between adjacent pins after optimization.

[0086] According to the requirements of the high-speed data transmission protocol, set constraints for insertion loss, return loss, and crosstalk. The insertion loss constraint range is determined by the frequency range specified in the protocol, and the maximum allowable attenuation value varies at different frequencies. The return loss constraint range ensures that the intensity of the reflected signal does not exceed the specified threshold, preventing the reflected signal from interfering with the incident signal. The crosstalk constraint range limits the signal coupling strength between adjacent pins to prevent data errors caused by signal crosstalk.

[0087] The response surface methodology was used to construct the mathematical relationship between the optimization objective and the optimization variables. By designing an experimental plan, several groups of sample points were selected within the range of the optimization variables, and the optimization objective value corresponding to each sample point was calculated. Using regression analysis, a polynomial function was fitted with the optimization variables as independent variables and the optimization objective as dependent variables. The coefficients of the function were determined using the least squares method, allowing the fitted function to closely approximate the actual target variation trend. These functions were combined to form a multi-objective optimization model containing multiple objective functions and constraints. This model mathematically describes the relationship between structural parameters and performance indicators.

[0088] Example 3: In the process of solving the multi-objective optimization model through a multi-objective genetic algorithm to generate a structural parameter adjustment scheme that meets the requirements of signal integrity and electromagnetic compatibility, the operation is as follows: when initializing the population, the value combination of the key geometric features is used as the individual in the population. Each individual is composed of specific parameters of the needle body diameter, spacing and arrangement, and these parameters are randomly generated within a preset value range. The population size is set according to the complexity of the double-layer PIN needle structure, usually containing tens to hundreds of individuals, each of which corresponds to a possible structural parameter scheme. The evolutionary generation is determined according to the optimization accuracy requirements, and it is necessary to ensure that the algorithm has a sufficient number of iterations to explore the solution space.

[0089] The optimization objective function value corresponding to each individual is calculated. The structural parameter solution represented by each individual is substituted into the multi-objective optimization model to calculate the area of ​​the potential signal distortion region and the electromagnetic coupling reduction rate. A non-dominated sorting genetic algorithm is used to evaluate the fitness of the individuals. By comparing the optimization objective values ​​of the individuals, the non-domination level of each individual is determined. Individuals with lower non-domination levels have higher fitness. During the non-dominated sorting process, an individual dominates the latter if it is not inferior to another individual in all optimization objectives and has at least one objective superior.

[0090] The next generation of the population is generated through selection, crossover, and mutation. The selection process uses a roulette wheel selection algorithm, where the probability of selection is determined based on the fitness of the individual. Individuals with higher fitness values ​​have a greater probability of selection, ensuring the transmission of superior genes. The crossover process randomly selects a crossover point for the selected parent individuals and swaps the gene segments following the crossover point to generate new offspring individuals. For example, for the needle diameter and spacing parameters, a certain position in the numerical sequence is selected as the crossover point, and the parameter values ​​of the two parent individuals after that position are swapped. The mutation process randomly perturbs some genes within an individual. For example, within the range of needle diameter, a new value is randomly generated to replace the original value, or the parameters of the permutation method are modified to increase population diversity and prevent the algorithm from falling into local optimal solutions. In each evolutionary generation, individuals in the Pareto optimal solution set are retained; these individuals constitute the optimal solution set for the current iteration. When the number of evolutionary generations reaches a preset value, a solution that satisfies the insertion loss, return loss, and crosstalk constraints is selected from the final Pareto optimal solution set and selected as the structural parameter adjustment solution.

[0091] In the process of mechanical strength simulation of the structural parameter adjustment plan and evaluation of whether the stress distribution during the plugging and unplugging process is within a safe range, the operation is as follows: the structural parameter adjustment plan is imported into the finite element analysis software. The software automatically generates a three-dimensional mesh model of the double-layer PIN needle based on the input parameters such as the needle body diameter, length, and spacing, and discretizes it into a large number of units and nodes. Each unit has a clear geometric shape and node coordinates, which together constitute a calculation model for mechanical analysis.

[0092] Set material property parameters, including elastic modulus, Poisson's ratio, and yield strength. The elastic modulus reflects the proportional relationship between stress and strain during the elastic deformation stage of the material. The Poisson's ratio reflects the ratio of lateral strain to longitudinal strain when the material is subjected to stress. The yield strength is the stress at which the material begins to plastically deform. These parameters are determined by the type of metal used for the double-layer PIN, such as copper alloy or aluminum alloy.

[0093] Simulate the axial and radial forces acting on the needle during insertion and removal. The axial force is the force acting along the needle's axis during insertion and removal, and its magnitude is determined by the connector's design insertion and removal force standard. The radial force is the force acting perpendicular to the axis due to guide deviation or structural interference during insertion and removal, and is determined based on the possible deviation range in actual use scenarios. Apply appropriate boundary conditions to the finite element model, fix the portion of the needle connected to the connector base, constrain its displacement degrees of freedom, and apply the axial and radial forces to the needle's tip or contact surface.

[0094] Solving the mechanical model yields a stress cloud map. Different colors in the cloud map represent different stress values. The color distribution allows for intuitive observation of the stress magnitude in various parts of the needle. The maximum stress value in the stress cloud map is extracted, representing the maximum load borne by the needle during insertion and removal. This maximum stress value is compared with the yield strength of the material. If the maximum stress value is less than the yield strength, it indicates that the needle only undergoes elastic deformation during insertion and removal, and returns to its original shape after unloading, and the stress distribution is within a safe range. If the maximum stress value is greater than or equal to the yield strength, it indicates that the needle may have undergone plastic deformation or damage, and the stress distribution is outside the safe range.

[0095] In the above process, the stress calculation formula involved is:

[0096]

[0097] in, Show stress, Indicates the force on the needle body, Represents the cross-sectional area of ​​the needle body.

[0098] Example 4: In determining the final optimized parameters for the double-layer PIN structure based on the mechanical strength simulation results and the solution of the multi-objective optimization model, the following steps were performed: A comprehensive analysis of the mechanical strength simulation results was performed, and the stress distribution data output from the simulation was extracted. The darkest region in the stress contour map was specifically identified, as it corresponds to the location where the pin body experiences the greatest stress during insertion and removal. The maximum stress value at this location was recorded. Furthermore, the technical manual for the material used in the double-layer PIN was consulted to obtain the material's yield strength. The maximum stress value was then directly compared to the yield strength value to clarify the numerical relationship between the two.

[0099] Based on the results of the multi-objective optimization model, each performance indicator is individually checked to ensure it meets the preset requirements. The area measurement results of the potential signal distortion area are reviewed to confirm whether they are within the reasonable range of the minimization target. The calculated value of the electromagnetic coupling reduction rate is checked to determine whether it meets the expected standard for the maximization target. In addition, the insertion loss, return loss, and crosstalk parameters are verified to ensure that they are within the threshold range specified by the high-speed data transmission protocol. Each parameter must be tested at all test points within the operating frequency range to ensure that signal integrity and electromagnetic compatibility requirements are met across the entire frequency band.

[0100] When the maximum stress in the mechanical strength simulation results is less than the material yield strength, and the multi-objective optimization model's solution meets signal integrity and electromagnetic compatibility requirements, the current structural parameter adjustment scheme is determined as the final optimization parameters. The structural parameters at this point include details such as the needle diameter, the spacing between adjacent needles, the arrangement of the upper and lower needle layers, the needle length, and the cross-sectional shape. These parameters together form the optimal solution that balances mechanical and electrical performance.

[0101] If the maximum stress value in the mechanical strength simulation results is greater than or equal to the material yield strength, the double-layer PIN, given the current structural parameters, is at risk of plastic deformation or fracture during insertion and removal, and the optimization variable range needs to be readjusted. For example, if the pin diameter is too small, causing stress concentration, the upper limit of the pin diameter can be increased, increasing the pin's cross-sectional area to disperse the stress. If the pin spacing is unreasonable, resulting in excessive localized stress, the spacing range can be adjusted to optimize the force transmission path.

[0102] If the solution of the multi-objective optimization model does not meet the signal integrity or electromagnetic compatibility requirements, it is also necessary to re-set the value range of the optimization variables. If the area of ​​potential signal distortion exceeds expectations, the range of the pin diameter can be narrowed or the optional arrangement types can be adjusted to reduce impedance discontinuities in the signal transmission path. If the electromagnetic coupling is too high, the lower limit of the pin spacing can be increased to reduce the electromagnetic interaction between adjacent pins.

[0103] After readjusting the ranges of the optimization variables, the algorithm returns to the multi-objective optimization model solution step and repeats the previous process, performing population initialization, fitness evaluation, crossover mutation, and other operations to generate a new structural parameter adjustment scheme. Mechanical strength simulation and performance index verification are then performed on this new scheme. This iterative cycle continues until structural parameters that simultaneously meet mechanical strength, signal integrity, and electromagnetic compatibility requirements are obtained, which serve as the final optimized parameters for the double-layer PIN structure. Throughout this process, each adjustment of the value range is combined with the previous simulation and solution results to narrow or expand the variable boundaries in a targeted manner, gradually approaching the optimal solution.

[0104] Example 5: See Figure 4 ,After building the multi-objective optimization model, the gray ,relational analysis method is used to assign weights to the optimization ,objectives and determine the priority ranking of the signal integrity ,index and electromagnetic compatibility index. The operation is as follows:

[0105] Collect signal integrity data and electromagnetic compatibility test results from historical design cases and establish a sample database. Historical design cases cover double-layer PIN needle structure solutions with different specifications and different application scenarios. Each case contains complete design parameters and corresponding performance test data. Signal integrity data includes parameters such as insertion loss, return loss, reflection coefficient, transmission coefficient, and the area of ​​potential signal distortion areas at different frequencies for each case; electromagnetic compatibility test results include electromagnetic coupling, mutual inductance, mutual capacitance, and crosstalk values ​​between adjacent PIN needles. At the same time, each case must also record the final design effect evaluation results, such as whether it meets actual usage requirements and operational stability in specific equipment. These data are organized and entered into the database in a unified format to ensure data integrity and consistency. Each data entry clearly corresponds to the design solution and test conditions.

[0106] The correlation coefficient between each optimization objective and the design effect is calculated using the grey correlation method. First, a reference sequence and a comparison sequence are determined. The reference sequence is the signal integrity and electromagnetic compatibility parameters corresponding to the case with the best design effect in the historical design cases, while the comparison sequence is the corresponding parameters of other design cases. All sequences are dimensionless to eliminate the influence of dimension and order of magnitude differences between the parameters. Initialization or averaging methods are typically used to convert the parameter values ​​into relative proportional values. The absolute difference between the comparison sequence and the reference sequence is calculated to determine the degree of difference between each parameter in different cases. The resolution coefficient is determined based on the absolute difference. The resolution coefficient ranges from 0 to 1 and is used to adjust the sensitivity of the correlation coefficient. Based on the absolute difference and the resolution coefficient, the correlation coefficient is calculated between each comparison sequence and the reference sequence. The size of the correlation coefficient reflects the closeness of the parameters in that case to the optimal design parameters. A larger correlation coefficient indicates a closer correlation between the optimization objective and the optimal design effect in that case.

[0107] Signal integrity and electromagnetic compatibility (EMC) indicators are ranked based on their correlation coefficients to generate a priority weight vector. The correlation coefficients of signal integrity indicators, such as insertion loss, return loss, and potential signal distortion area, are statistically analyzed and averaged to obtain the comprehensive correlation coefficient of the signal integrity indicator. Similarly, the correlation coefficients of EMC indicators, such as electromagnetic coupling and crosstalk, are processed to obtain the comprehensive correlation coefficient of the EMC indicator. The two comprehensive correlation coefficients are compared, and the indicator with the larger correlation coefficient is given a higher priority in the optimization process. Based on the priority ranking results, corresponding weights are assigned, with the sum of the weights being 1, giving the indicator with a higher priority a larger weight ratio. For example, if the comprehensive correlation coefficient of the signal integrity indicator is greater than that of the EMC indicator, the signal integrity indicator is given a higher weight than the EMC indicator. The generated priority weight vector serves as an important parameter in solving the multi-objective optimization model. When calculating individual fitness and performing Pareto sorting, the function values ​​of different optimization objectives are weighted according to the weights, ensuring that the optimization results better align with the emphasis placed on each performance indicator in actual design requirements.

[0108] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0109] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A high-speed data connector double-layer PIN pin structure optimization design method, characterized in that: The steps include: The initial structural parameters of the double-layer PIN were obtained using a 3D scanning device, and a reverse engineering algorithm was used to reconstruct the 3D model to extract key geometric features. Simulate the signal transmission path of double-layer PIN pins, collect signal integrity data based on time domain reflectometry, and identify potential signal distortion areas; The electromagnetic field distribution information of the double-layer PIN needles is obtained by near-field scanning technology, and the electromagnetic coupling degree between adjacent PIN needles is calculated by combining the moment method. Construct a multi-objective optimization model based on key geometric features, potential signal distortion areas, and electromagnetic coupling; The multi-objective optimization model is solved by a multi-objective genetic algorithm to generate a structural parameter adjustment scheme that meets the requirements of signal integrity and electromagnetic compatibility; Conduct mechanical strength simulation on the structural parameter adjustment scheme to evaluate whether the stress distribution during the plugging and unplugging process is within a safe range; According to the mechanical strength simulation results and the solution results of the multi-objective optimization model, the final optimization parameters of the double-layer PIN needle structure are determined.

2. The high-speed data connector double-layer PIN needle structure optimization design method according to claim 1, characterized in that: The initial structural parameters of the double-layer PIN were obtained using a 3D scanning device, and a reverse engineering algorithm was used to reconstruct the 3D model to extract key geometric features, including: Laser scanning equipment is used to collect all-round data of double-layer PIN needles, obtaining point cloud data including needle diameter, spacing and arrangement; Preprocess the initial point cloud data using a point cloud denoising algorithm to remove noise points and redundant data; The Poisson surface reconstruction algorithm is used to reconstruct the 3D model of the denoised point cloud data to generate a solid model of the double-layer PIN needle; The needle length, cross-sectional shape and pin spacing are extracted from the solid model as key geometric features.

3. The high-speed data connector double-layer PIN needle structure optimization design method according to claim 2, characterized in that: Simulate the signal transmission path of a double-layer PIN, collect signal integrity data based on time domain reflectometry, and identify potential signal distortion areas, including: Build a signal transmission path model of a double-layer PIN needle in 3D electromagnetic simulation software and set the excitation signal and load parameters; A step signal is injected into the signal transmission path model using time domain reflectometry to collect data on reflection coefficients and transmission coefficients changing with time. Perform Fourier transform on the reflection coefficient and transmission coefficient data to convert them into insertion loss and return loss parameters in the frequency domain; The insertion loss and return loss parameters are compared with preset thresholds, and the physical areas corresponding to the frequency bands exceeding the threshold range are marked as potential signal distortion areas.

4. The high-speed data connector double-layer PIN needle structure optimization design method according to claim 3, characterized in that: The electromagnetic field distribution information of the double-layer PIN pins is obtained through near-field scanning technology, and the electromagnetic coupling between adjacent PIN pins is calculated using the moment method. Specifically, the following steps are performed: The near-field probe is used to scan the electromagnetic field strength of the double-layer PIN needle in the working state to generate a spatial electromagnetic field distribution map; Extract the amplitude and phase information of electric field intensity and magnetic field intensity based on the electromagnetic field distribution map; The moment method is used to construct the electromagnetic coupling model between adjacent PIN pins, and the electric field strength and magnetic field strength parameters are substituted into the model; The mutual inductance and mutual capacitance between adjacent PINs are obtained by solving the electromagnetic coupling model, and the electromagnetic coupling degree is calculated based on the mutual inductance and mutual capacitance.

5. The high-speed data connector double-layer PIN needle structure optimization design method according to claim 4, characterized in that: A multi-objective optimization model is constructed based on key geometric features, potential signal distortion areas, and electromagnetic coupling. Specifically, it includes: The needle diameter, spacing and arrangement of key geometric features are used as optimization variables, and the value range of each variable is set; The optimization goals are to minimize the area of ​​potential signal distortion and maximize the reduction rate of electromagnetic coupling; Set constraints on insertion loss, return loss, and crosstalk according to high-speed data transmission protocol requirements; The response surface methodology is used to construct the mathematical relationship between the optimization objectives and the optimization variables to form a multi-objective optimization model.

6. The method for optimizing the design of a double-layer PIN structure of a high-speed data connector according to claim 5, characterized in that: The multi-objective genetic algorithm is used to solve the multi-objective optimization model and generate a structural parameter adjustment plan that meets the signal integrity and electromagnetic compatibility requirements, including: Initialize the population, use the value combination of key geometric features as population individuals, set the population size and evolutionary generations; Calculate the optimization objective function value corresponding to each individual and use the non-dominated sorting genetic algorithm to evaluate the fitness of the individual; Generate the next generation of population through selection, crossover and mutation operations, and retain individuals in the Pareto optimal solution set; When the evolutionary generation reaches the preset value, the solution that meets the constraints is selected from the final Pareto optimal solution set as the structural parameter adjustment plan.

7. The method for optimizing the design of a double-layer PIN structure of a high-speed data connector according to claim 6, characterized in that: Perform mechanical strength simulation on the structural parameter adjustment scheme to evaluate whether the stress distribution during the plugging and unplugging process is within a safe range, including: Import the structural parameter adjustment plan into the finite element analysis software to establish the mechanical model of the double-layer PIN needle; Set material property parameters, including elastic modulus, Poisson's ratio, and yield strength; Simulate the axial and radial forces on the needle during insertion and removal, and apply corresponding boundary conditions; Solve the mechanical model to obtain the stress cloud map, extract the maximum stress value and compare it with the yield strength of the material to determine whether the stress distribution is within a safe range.

8. The method for optimizing the design of a double-layer PIN structure of a high-speed data connector according to claim 7, characterized in that: Based on the mechanical strength simulation results and the solution of the multi-objective optimization model, the final optimization parameters of the double-layer PIN needle structure are determined, including: When the maximum stress value in the mechanical strength simulation result is less than the material yield strength and the solution of the multi-objective optimization model meets the signal integrity and electromagnetic compatibility requirements, the current structural parameter adjustment scheme is determined as the final optimization parameter; otherwise, the value range of the optimization variable is readjusted and the multi-objective optimization model solution step is returned.

9. The method for optimizing the design of a double-layer PIN structure of a high-speed data connector according to claim 1, characterized in that: After constructing the multi-objective optimization model, the following steps are also included: using the grey correlation analysis method to assign weights to the optimization objectives and determine the priority ranking of signal integrity indicators and electromagnetic compatibility indicators.

10. The high-speed data connector double-layer PIN needle structure optimization design method according to claim 9, characterized in that: The grey correlation analysis method is used to assign weights to the optimization objectives and determine the priority of signal integrity indicators and electromagnetic compatibility indicators, including: Collect signal integrity data and electromagnetic compatibility test results from historical design cases and establish a sample database; The correlation coefficient between each optimization objective and the design effect is solved by the grey correlation calculation method; The signal integrity index and electromagnetic compatibility index are sorted based on the size of the correlation coefficient to generate a priority weight vector.

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