SI simulation optimization method and device for PCB

Abstract

The invention provides an SI simulation optimization method and device for a PCB, and the method comprises the steps: selecting a simulation boundary according to the via hole type, the board thickness and the signal frequency of the PCB, and carrying out the one-time simulation according to the simulation boundary, and obtaining an S parameter; gradually expanding the simulation boundary by adopting a progressive method, carrying out secondary simulation, and determining an optimal boundary range when the S parameter change of two adjacent simulations is smaller than a preset convergence threshold value; electromagnetic field distribution parameters are calculated in the optimal boundary range, and an area with concentrated electromagnetic energy in the electromagnetic field distribution parameters is set as an area to be improved; a passive device is arranged in the to-be-improved area, the position and parameters of the passive device are iteratively optimized, and device layout parameters are obtained; and optimizing and adjusting the PCB based on the device layout parameters.

Description

Technical Field

[0001] The present application relates to the field of PCB simulation technology, and in particular to a method and device for SI simulation optimization of PCB. Background Art

[0002] In high-speed PCB design, vias are key structures connecting different signal layers, and signal integrity is a crucial design metric. Traditional PCB design methods typically employ fixed simulation boundary settings and a single via parameter optimization strategy, making it difficult to accurately assess the electromagnetic field distribution characteristics around the vias, leading to problems such as signal reflection, crosstalk, and loss. Existing simulation methods lack adaptability in setting boundary conditions, making simulation accuracy difficult to guarantee. Furthermore, existing methods typically only consider geometric parameter adjustments when optimizing vias, ignoring the impact of surrounding device layout on signal integrity. This makes it impossible to achieve coordinated optimization of the via structure and its surrounding environment. Summary of the Invention

[0003] The present application provides a method and apparatus for SI simulation optimization of a PCB, which is used to optimize the signal integrity of vias of the PCB and improve the simulation effect.

[0004] In a first aspect, an embodiment of the present application provides a SI simulation optimization method for a PCB, the method comprising: Selecting a simulation boundary based on the PCB via type, board thickness, and signal frequency, and performing a simulation based on the simulation boundary to obtain S parameters; The simulation boundary is gradually expanded using a progressive method and a secondary simulation is performed, and when the S parameter change between two adjacent simulations is less than a preset convergence threshold, an optimal boundary range is determined; Calculating electromagnetic field distribution parameters within the optimal boundary range, and setting areas where electromagnetic energy is concentrated in the electromagnetic field distribution parameters as areas to be improved; Arranging passive devices in the area to be improved and iteratively optimizing positions and parameters of the passive devices to obtain device layout parameters; The PCB is optimized and adjusted based on the device layout parameters.

[0005] In a second aspect, an embodiment of the present application provides a SI simulation and optimization device for a PCB, the device comprising: A first simulation module is configured to select a simulation boundary based on the PCB via type, board thickness, and signal frequency, and perform a simulation based on the simulation boundary to obtain S parameters; A second simulation module is configured to gradually expand the simulation boundary using a progressive method and perform a secondary simulation, and determine an optimal boundary range when the S parameter change between two adjacent simulations is less than a preset convergence threshold; A region marking module, configured to calculate electromagnetic field distribution parameters within the optimal boundary range, and set regions where electromagnetic energy is concentrated in the electromagnetic field distribution parameters as regions to be improved; a layout optimization module, configured to arrange passive devices in the area to be improved and iteratively optimize the positions and parameters of the passive devices to obtain device layout parameters; A solution generation module is used to optimize and adjust the PCB based on the device layout parameters.

[0006] In a third aspect, an embodiment of the present application provides an electronic device, the electronic device including a memory and a processor; The memory is used to store computer programs; The processor is used to execute the computer program and implement the SI simulation optimization method for PCB as described in any one of the embodiments of the present application when executing the computer program.

[0007] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the processor implements the SI simulation optimization method for PCB as described in any one of the embodiments of the present application.

[0008] An embodiment of the present application provides a SI simulation optimization method for a PCB, the method comprising: selecting a simulation boundary based on the PCB via type, board thickness, and signal frequency, performing a simulation based on the simulation boundary to obtain S parameters; gradually expanding the simulation boundary using a progressive method and performing a secondary simulation, and determining an optimal boundary range when the change in S parameters between two adjacent simulations is less than a preset convergence threshold; calculating electromagnetic field distribution parameters within the optimal boundary range, and setting an area with concentrated electromagnetic energy in the electromagnetic field distribution parameters as an area to be improved; arranging passive components in the area to be improved and iteratively optimizing the position and parameters of the passive components to obtain device layout parameters; and optimizing and adjusting the PCB based on the device layout parameters. In the above method, the simulation boundary is determined according to the PCB via type, board thickness and signal frequency, and basic simulation is performed to obtain S parameters. Then, the optimal simulation range is automatically determined by the progressive boundary expansion method combined with S parameter convergence judgment. Then, the electromagnetic field distribution is accurately calculated within the optimized boundary range, and the areas to be improved where electromagnetic energy is concentrated are automatically identified. Passive components are intelligently arranged in these areas to be improved, and the optimal device positions and parameters are determined through iterative optimization. The via geometric parameters and surrounding structure dimensions are collaboratively optimized based on the device layout parameters, significantly improving the simulation accuracy, optimization efficiency and signal integrity of the PCB via design. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic flow chart of a SI simulation optimization method for PCB provided in an embodiment of the present application; Figure 2 A schematic block diagram of an SI simulation and optimization device for PCB provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] 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 them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0012] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0013] It should also be understood that the terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0014] It should be further understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0015] See also Figure 1 , Figure 1 FIG. 1 is a schematic flow chart of a SI simulation optimization method for PCB provided in an embodiment of the present application. Figure 1 As shown, the specific steps of the SI simulation optimization method for PCB include: S101-S105.

[0016] S101. Select a simulation boundary according to the via type, board thickness, and signal frequency of the PCB, and perform a simulation according to the simulation boundary to obtain S parameters.

[0017] Exemplarily, the via geometry information in the PCB design file is read and the via type is automatically identified as a through hole, a blind hole, or a buried hole. At the same time, the board thickness data is extracted from the PCB stacking information and the operating frequency range is obtained from the signal definition file. The boundary coefficient is determined according to the via type, wherein the basic boundary coefficient corresponding to the through hole is greater than the boundary coefficients of the blind hole and the buried hole. The boundary correction factor is calculated according to the board thickness. When the board thickness is small, the correction factor is less than the standard value to reduce the amount of calculation. When the board thickness is large, the correction factor is greater than the standard value to ensure the simulation accuracy. At the same time, the frequency correction coefficient is calculated according to the signal frequency. The coefficient is small at low frequencies and large at high frequencies. The basic boundary coefficient, the boundary correction factor, and the frequency correction coefficient are multiplied to obtain the initial simulation boundary distance. A three-dimensional electromagnetic simulation model is established around the via and the radiation absorption boundary condition is set. The finite element solver is called to perform electromagnetic field calculations. The reflection parameters and transmission parameters are extracted to form an S parameter matrix. The initial S parameters are stored in the memory as the reference data for subsequent convergence judgment.

[0018] S102, gradually expanding the simulation boundary using a progressive method and performing a secondary simulation, and determining the optimal boundary range when the S parameter change between two adjacent simulations is less than a preset convergence threshold.

[0019] Exemplarily, based on the initial S parameters obtained in the previous step, a boundary optimization loop is entered, the boundary extension step is set to a fixed ratio of the initial boundary distance, the current simulation boundary is extended outward by a step distance and the expanded three-dimensional electromagnetic simulation model is reconstructed, the boundary condition settings are updated and the finite element solver is called to perform a secondary simulation calculation to obtain a new S parameter matrix, the newly obtained S parameters are compared and analyzed with the S parameters of the previous iteration, the amplitude difference and phase difference of the reflection parameters and the transmission parameters are calculated respectively, and it is judged whether the amplitude difference is less than a preset amplitude threshold and whether the phase difference is less than a preset phase threshold. When both conditions are met at the same time, the simulation result is considered to have converged and the current boundary range is marked as the optimal boundary range and the loop is exited. If the convergence condition is not met, the next boundary extension and simulation calculation are continued, and the boundary extension distance and the corresponding S parameter convergence index of each iteration are recorded.

[0020] S103 , calculating electromagnetic field distribution parameters within the optimal boundary range, and setting the area where electromagnetic energy is concentrated in the electromagnetic field distribution parameters as the area to be improved.

[0021] For example, based on the optimal boundary range determined in the previous step, a more refined three-dimensional grid division is established within the range to ensure that the grid density meets the accuracy requirements of electromagnetic field calculations. The electric field intensity vector and magnetic field intensity vector are calculated for each grid node, and the electromagnetic energy density of each grid point is calculated according to the electromagnetic field theory formula. The calculation process includes the sum of the electric field energy density and the magnetic field energy density, statistical analysis of the electromagnetic energy density of all grid points, and calculation of the average energy density value as a benchmark. The energy density judgment threshold is set to a preset multiple of the average value, and all grid points are traversed to find points where the electromagnetic energy density exceeds the threshold. These high energy density points are subjected to spatial cluster analysis to identify continuously distributed high energy areas. The spatial position characteristics of these high energy areas are further analyzed and areas within a preset range from the via center are screened out. These areas are defined as areas to be improved, and each area to be improved is numbered and marked, and its three-dimensional coordinate information, energy density distribution characteristics, and spatial size parameters are recorded.

[0022] S104 , arranging passive components in the area to be improved and iteratively optimizing the positions and parameters of the passive components to obtain component layout parameters.

[0023] Exemplarily, the area to be improved is read, and the electromagnetic field frequency characteristics of each area to be improved are analyzed, and the main interference frequency range of the area is determined through spectrum analysis. When the main interference frequency is low, a capacitor is selected as a passive component, when the frequency is in the medium range, an inductor is selected, and when the frequency is high, a magnetic bead or ferrite bead is selected. The selected passive component is initially placed at the geometric center of each area to be improved and the initial parameter value of the component is set. A position optimization loop is entered, and a coordinate grid search method is used to move the device position within the area to be improved, each movement being a preset minimum step distance. Signal integrity indicators including insertion loss, return loss, and crosstalk level are calculated for each candidate position. Parameter optimization is performed for each device position, and the capacitance value, inductance value, or magnetic bead impedance value is scanned within the preset parameter range to find the parameter combination that optimizes the signal integrity indicator. When a position and parameter combination is found that improves the insertion loss or return loss by more than a preset performance improvement threshold, the position coordinates and device parameters are saved as the optimal device layout parameters, and the same optimization process is continued for the next area to be improved.

[0024] S105. Optimize and adjust the PCB based on device layout parameters.

[0025] Exemplarily, the device layout parameters generated in the previous step are loaded and the spatial position and size information of the devices already arranged in the PCB are analyzed. The keep-out area occupied by the devices is calculated. The adjustable range of the via geometric parameters is determined based on these constraints to ensure that the parameter adjustment process does not cause physical conflicts with the already arranged passive devices. A hierarchical optimization strategy is used to optimize the core geometric parameters of the via, including the via drill diameter and the anti-pad diameter. The parameter scanning step size is set within the adjustable range. A fast electromagnetic simulation is performed on each parameter combination to calculate the corresponding S parameters and signal integrity indicators. The parameter combination with the best performance indicators is selected as the optimized via geometric parameters. Based on the optimized core geometric parameters, the dimensions of the surrounding structures are further adjusted, including parameters such as the isolation slot width, the ground via spacing, and the copper foil shape. A parameter scanning method is used to find the optimal structural dimension parameters within the constraint range. The optimized via geometric parameters and structural dimension parameter combination are finally verified. The comprehensive performance indicators are calculated and compared with the initial design for analysis. An optimized design solution is automatically generated, including a parameter optimization process record, a performance improvement data comparison table, and a three-dimensional structure visualization graphic. At the same time, a design file that meets the format requirements of the PCB design software is output for direct use by engineers.

[0026] In order to more clearly introduce the technical solution of the present application, the technical solution of the present application will be introduced through specific embodiments below. It should be noted that the specific embodiments are used to expand the technical solution of the present application, but are not intended to limit the present application.

[0027] In some embodiments, a simulation boundary is selected based on the via type, board thickness, and signal frequency of the PCB, including: determining a basic boundary coefficient based on the via type, wherein the basic boundary coefficient of the through hole is greater than the basic boundary coefficient of the blind hole and the basic boundary coefficient of the buried hole; calculating a boundary correction factor based on the PCB board thickness, wherein the boundary correction factor is less than a standard value when the board thickness is small, and the boundary correction factor is greater than the standard value when the board thickness is large; calculating a frequency correction coefficient based on the signal frequency, wherein the frequency correction coefficient is less than the standard value when the frequency is low, and the frequency correction coefficient is greater than the standard value when the frequency is high; and multiplying the basic boundary coefficient, the boundary correction factor, and the frequency correction coefficient to obtain a simulation boundary distance.

[0028] For example, by parsing the via structure information in the PCB design file to automatically identify the via type, for a through-hole structure that penetrates the entire PCB stack, the corresponding basic boundary coefficient is extracted from the built-in boundary coefficient lookup table, and the coefficient value is set to a relatively high value to consider the electromagnetic field impact of the through-hole on the entire board thickness range, for a blind via structure that only connects part of the layers, a medium basic boundary coefficient is assigned, and for a buried via structure that is completely between the inner layers, a relatively small basic boundary coefficient is set because its electromagnetic impact range is relatively limited. Read the total PCB board thickness value from the PCB stack definition file, calculate the boundary correction factor based on the board thickness, and set the boundary correction factor to a value less than the standard value when the board thickness is below the preset thin board threshold to reduce unnecessary computational overhead. When the board thickness exceeds the preset thick board threshold, the boundary correction factor is set to a value greater than the standard value to ensure that the simulation includes sufficient electromagnetic field decay distance. Extract the target signal frequency information from the signal definition configuration, and set the frequency correction coefficient to a value less than the standard value when the frequency is below the preset low frequency threshold because the electromagnetic field of a low frequency signal decays faster. When the frequency is higher than the preset high frequency threshold, set the frequency correction coefficient to a value greater than the standard value to consider the far-field radiation effects of high frequency signals. Multiply the obtained basic boundary coefficient, boundary correction factor, and frequency correction coefficient to obtain the initial simulation boundary distance as the spatial range reference for establishing the electromagnetic simulation model. This simulation boundary distance will be the starting point for subsequent gradual boundary optimization.

[0029] In some embodiments, the simulation boundary is gradually expanded and a secondary simulation is performed using a gradual method, and when the S parameters of two adjacent simulations change by less than a preset convergence threshold, the optimal boundary range is determined, including: setting the boundary expansion step to a proportion of the preset initial boundary distance, and increasing the simulation boundary by one expansion step each time; performing electromagnetic simulation on the expanded simulation boundary, and extracting the reflection parameters and transmission parameters as the current S parameters; calculating the amplitude difference and phase difference of the current S parameters and the previous S parameters; when the amplitude difference and phase difference are both less than the respective preset thresholds, it is determined that the simulation result converges; the boundary range at the time of convergence is determined as the optimal boundary range, and the number of boundary expansions during the convergence process is recorded.

[0030] Exemplarily, the boundary extension step is set to a fixed proportional value of the distance based on the initial simulation boundary distance. This proportional setting method ensures that the extension step matches the initial boundary scale, and enters an iterative boundary optimization loop. In each loop, the current simulation boundary is radially extended outward by a distance of the extension step, and the outer boundary coordinates of the simulation space are redefined. The three-dimensional electromagnetic field finite element solver is called to perform a complete electromagnetic field calculation on the expanded simulation model. The solver calculates the scattering characteristics of the via structure at a specified frequency, extracts reflection parameters and transmission parameters from the simulation results, and combines these parameters to form the S parameter matrix of the current iteration. The currently obtained S parameters are numerically compared and analyzed with the S parameters stored in the previous iteration, respectively. The amplitude difference and phase difference of the reflection parameters and the amplitude difference and phase difference of the transmission parameters are calculated to determine whether the amplitude difference of the reflection parameters is less than the preset amplitude convergence threshold. At the same time, it is determined whether the phase difference of the reflection parameters is less than the preset phase convergence threshold. The same convergence judgment process is performed on the transmission parameters. When the amplitude difference and phase difference of all parameters meet their respective convergence conditions, the simulation results are considered to have reached a converged state. The current boundary range is marked as the optimal boundary range and the iterative loop is terminated. The number of boundary extensions performed during the entire convergence process and the S-parameter convergence index corresponding to each extension are recorded to determine the boundary convergence characteristics of the specific via structure and provide an empirical reference for the boundary setting of subsequent similar structures.

[0031] In some embodiments, electromagnetic field distribution parameters are calculated within the optimal boundary range, and areas where electromagnetic energy is concentrated in the electromagnetic field distribution parameters are set as areas to be improved. A three-dimensional grid is established within the optimal boundary range, and the electric field strength and magnetic field strength of each grid point are calculated; the electromagnetic energy density of each grid point is calculated based on the electric field strength and magnetic field strength; the electromagnetic energy density threshold is set to a preset multiple of the average energy density, and continuous areas where the electromagnetic energy density exceeds the threshold are marked as high-energy areas; the spatial distribution characteristics of the high-energy areas are analyzed, and high-energy areas that are less than a preset range from the via center are set as areas to be improved.

[0032] Exemplarily, a high-density three-dimensional grid division is generated within the corresponding spatial range based on the optimal boundary range. The grid density setting needs to meet the accuracy requirements of the electromagnetic field numerical calculation, especially in the area near the via structure, where a finer grid distribution is required. The electric field intensity vector component and the magnetic field intensity vector component in the three-dimensional space are calculated for each grid node position. These field strength calculations are based on the finite element numerical solution of Maxwell's equations. According to the physical definition formula of the electromagnetic field energy density, the electric field intensity and magnetic field intensity of each grid point are used to numerically calculate the corresponding electromagnetic energy density. The calculation includes the summation process of the electric field energy density and the magnetic field energy density. The electromagnetic energy density of all grid points in the entire simulation area is statistically analyzed, and the spatial average energy density value is calculated as the benchmark parameter of the energy distribution. The electromagnetic energy density threshold is set to a preset multiple of the average energy density. The selection of this multiple needs to balance the recognition sensitivity and noise suppression effect. All grid points are traversed to identify points where the electromagnetic energy density exceeds the threshold. These high energy density points are clustered in three-dimensional space, and the high energy point groups continuously distributed in space are marked as high energy areas. The geometric features of each identified high energy area are analyzed, and its spatial center position, volume size and shape characteristics are calculated. The spatial distance between the center position of each high energy area and the geometric center of the via is calculated. The high energy areas with a distance less than the preset range threshold are screened out and set as areas to be improved. A unique area number is assigned to each area to be improved, and its three-dimensional coordinate boundary, energy density statistical characteristics and relative position information with the via are recorded.

[0033] In some embodiments, passive devices are arranged in the area to be improved and the positions and parameters of the passive devices are iteratively optimized to obtain device layout parameters, including: selecting the type of passive device according to the frequency characteristics of the area to be improved, selecting a capacitor when the frequency is low, selecting an inductor when the frequency is in a medium range, and selecting a ferrite bead when the frequency is high; initially placing the selected passive device at the center position of each area to be improved, and setting the initial parameter values ​​of the device; using a coordinate scanning method to move the device position within the area to be improved, with a preset step size each time, and calculating the signal integrity index of each position; for each device position, using a parameter scanning method to optimize the device parameter value, and scanning within a preset parameter range; when the insertion loss improvement or return loss improvement in the signal integrity index exceeds a preset threshold, determining the corresponding position and the corresponding parameters as the device layout parameters.

[0034] Exemplarily, based on the characteristics of the area to be improved, the frequency domain characteristics of the electromagnetic field distribution in each area to be improved are analyzed, and the dominant interference frequency components of the area are extracted by fast Fourier transform. When the analysis results show that the frequency characteristics are mainly concentrated in the low frequency band, capacitors are selected as passive devices to provide a low-impedance high-frequency bypass channel. When the frequency characteristics show that the main energy distribution is in the mid-frequency band, inductors are selected as passive devices to provide RF choking effects. When the frequency characteristics show that the high-frequency components are dominant, magnetic beads are selected as passive devices to provide broadband impedance characteristics. The selected passive devices are initially placed at the geometric center coordinate position of each area to be improved. The corresponding initial parameter values ​​are set according to the device type, including the capacitance value of the capacitor, the inductance value of the inductor, or the impedance characteristic parameters of the magnetic beads. The position optimization loop is started, and the grid coordinate scanning method is used to systematically move the device position within the three-dimensional space of the area to be improved. The step size of each position movement is set to a preset minimum moving distance to ensure the meticulousness of the search. Fast Fourier transform is performed on each candidate device position. Electromagnetic simulation calculations are performed to extract the corresponding signal integrity indicators, including signal insertion loss, return loss, crosstalk level, and signal eye diagram quality parameters. A parameter optimization cycle is performed at each position point, and a systematic scan is performed within the preset device parameter range. The capacitor is scanned within the preset capacitance value range, the inductor is scanned within the preset inductance value range, and the ferrite bead is scanned within the preset impedance value range. The signal integrity indicators corresponding to each parameter combination are calculated. When a certain position and parameter combination is found to make the insertion loss improvement degree or return loss improvement degree exceed the preset performance improvement threshold, the spatial coordinate position and the corresponding device parameter value are saved as the optimal device layout parameter of the area to be improved. The same device selection, position optimization, and parameter optimization process are repeated for all identified areas to be improved to construct a complete device layout parameter containing the optimization results of all areas.

[0035] In some embodiments, the geometric parameters of the via and the dimensions of the surrounding structures are optimized based on the device layout parameters to obtain an optimized design scheme, including: determining the adjustable range of the geometric parameters based on the device layout parameters to generate parameter constraints; optimizing the core geometric parameters of the via based on the parameter constraints to obtain optimized via geometric parameters; adjusting the dimensions of the surrounding structures based on the optimized via geometric parameters to obtain structural dimension parameters; simulating and verifying the optimized via geometric parameters and structural dimension parameters to obtain a performance evaluation score; and determining the optimized layout parameters of the PCB based on the optimized via geometric parameters and the structural dimension parameters only when the performance optimization score is greater than a preset scoring threshold.

[0036] Exemplarily, the spatial occupancy of passive devices arranged in various areas to be improved is analyzed based on device layout parameters, including the physical size, installation footprint, and safety spacing requirements of the devices. The prohibited areas and constrained areas around these devices are calculated. The adjustable range of the via geometric parameters is determined based on the spatial constraint analysis. Parameter constraints containing minimum and maximum limits are generated to ensure that the geometric parameter adjustments do not cause physical or electrical conflicts with the already arranged devices. Optimization calculations are performed on the core geometric parameters of the via within the range of the parameter constraints, including key dimensions such as the via drilling diameter, anti-pad diameter, and via wall thickness. A parameter scanning method is used to set an appropriate step size within the constraint range for a systematic search. Electromagnetic simulation calculations are performed on the corresponding impedance characteristics and S-parameter performance of each set of core geometric parameter combinations. The parameter combination that optimizes the overall electrical performance is selected as the optimized via geometric parameters. Based on the determined optimized via geometric parameters, the surrounding structure optimization stage is entered to adjust the structural dimensions related to the via. Parameters, including the isolation groove width around the via, the spacing distribution of the ground vias, the copper foil shape and the routing layout, which are key structural parameters affecting signal integrity. In the process of adjusting the structural parameters, it is necessary to consider the space constraints of the arranged devices and the comprehensive requirements of signal integrity and electromagnetic compatibility. Simulation verification is performed on each set of structural parameter configurations to obtain the corresponding structural size parameters. The optimized via geometry parameters and structural size parameters are combined to form a complete optimized configuration. Comprehensive electromagnetic simulation verification is performed on this configuration. The comprehensive performance evaluation scores of the optimized design, such as S parameters, impedance characteristics, signal eye diagrams, crosstalk levels and electromagnetic radiation characteristics, are calculated. The performance evaluation scores are compared and analyzed in detail with the preset scoring thresholds of the initial design to quantify the improvement degree of each indicator. The optimized layout parameters are automatically generated, including a complete optimization process record, a comparison table of key parameters before and after, statistics on the improvement degree of performance indicators, and a three-dimensional structure visualization display. The optimized layout parameters meet the format requirements of the PCB design software and can be converted into design files for direct use by engineers.

[0037] See also Figure 2 , Figure 2 1 is a schematic block diagram of an SI simulation and optimization device for a PCB provided in an embodiment of the present application. The SI simulation and optimization device 200 for a PCB is used to execute the aforementioned SI simulation and optimization method for a PCB. The SI simulation and optimization device 200 for a PCB can be configured in a server.

[0038] Among them, the server can be an independent server, a server cluster, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0039] like Figure 2 As shown, the SI simulation and optimization device 200 for PCB includes: a first simulation module 201, a second simulation module 202, an area marking module 203, a layout optimization module 204, a solution generation module and a solution generation module 205.

[0040] The first simulation module 201 is used to select a simulation boundary according to the via type, board thickness and signal frequency of the PCB, and perform a simulation according to the simulation boundary to obtain S parameters.

[0041] The second simulation module 202 is configured to gradually expand the simulation boundary using a progressive method and perform a secondary simulation, and determine the optimal boundary range when the S parameter change between two adjacent simulations is less than a preset convergence threshold.

[0042] The region marking module 203 is used to calculate the electromagnetic field distribution parameters within the optimal boundary range, and set the region where the electromagnetic energy is concentrated in the electromagnetic field distribution parameters as the region to be improved.

[0043] The layout optimization module 204 is used to arrange passive devices in the area to be improved and iteratively optimize the positions and parameters of the passive devices to obtain device layout parameters.

[0044] The solution generation module 205 is used to optimize the geometric parameters of the via and the dimensions of the surrounding structures based on the device layout parameters to obtain an optimized design solution.

[0045] An embodiment of the present application provides an electronic device, which includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and implement the SI simulation optimization method for PCB as any one of the embodiments of the present application when executing the computer program.

[0046] An embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the processor implements any one of the SI simulation optimization methods for PCBs according to the embodiments of the present application.

[0047] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and such modifications or substitutions should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A SI simulation optimization method for PCB, characterized in that: The method comprises: Selecting a simulation boundary based on the PCB via type, board thickness, and signal frequency, and performing a simulation based on the simulation boundary to obtain S parameters; The simulation boundary is gradually expanded using a progressive method and a secondary simulation is performed, and when the S parameter change between two adjacent simulations is less than a preset convergence threshold, an optimal boundary range is determined; Calculating electromagnetic field distribution parameters within the optimal boundary range, and setting areas where electromagnetic energy is concentrated in the electromagnetic field distribution parameters as areas to be improved; Arranging passive devices in the area to be improved and iteratively optimizing positions and parameters of the passive devices to obtain device layout parameters; The PCB is optimized and adjusted based on the device layout parameters.

2. The SI simulation optimization method for PCB according to claim 1, wherein: The simulation boundary is selected based on the PCB via type, board thickness, and signal frequency, including: Determine a basic boundary coefficient according to the via type, wherein the basic boundary coefficient of the through hole is greater than the basic boundary coefficient of the blind hole and the basic boundary coefficient of the buried hole; Calculating a boundary correction factor according to the PCB board thickness, wherein the boundary correction factor is less than a standard value when the board thickness is small, and the boundary correction factor is greater than the standard value when the board thickness is large; Calculating a frequency correction coefficient according to the signal frequency, wherein when the frequency is low, the frequency correction coefficient is less than a standard value, and when the frequency is high, the frequency correction coefficient is greater than the standard value; The basic boundary coefficient, the boundary correction factor and the frequency correction coefficient are multiplied to obtain a simulation boundary distance.

3. The SI simulation optimization method for PCB according to claim 1, wherein: The step of gradually expanding the simulation boundary and performing a secondary simulation by a progressive method, and determining the optimal boundary range when the S parameter change between two adjacent simulations is less than a preset convergence threshold, includes: Setting the boundary extension step length to a ratio of the preset initial boundary distance, and increasing the simulation boundary outward by one of the extension steps each time; Perform electromagnetic simulation on the expanded simulation boundary and extract reflection parameters and transmission parameters as current S parameters; Calculating the amplitude difference and phase difference between the current S parameter and the previous S parameter; When the amplitude difference and the phase difference are both smaller than respective preset thresholds, determining that the simulation result has converged; The boundary range at the time of convergence is determined as the optimal boundary range, and the number of boundary expansions during the convergence process is recorded.

4. The SI simulation optimization method for PCB according to claim 1, wherein: The calculating of electromagnetic field distribution parameters within the optimal boundary range and setting the area where electromagnetic energy is concentrated in the electromagnetic field distribution parameters as the area to be improved includes: Establishing a three-dimensional grid within the optimal boundary range and calculating the electric field intensity and magnetic field intensity of each grid point; Calculating the electromagnetic energy density of each grid point according to the electric field strength and the magnetic field strength; Setting an electromagnetic energy density threshold as a preset multiple of the average energy density, and marking a continuous area where the electromagnetic energy density exceeds the threshold as a high-energy area; The spatial distribution characteristics of the high-energy region are analyzed, and the high-energy region whose distance from the center of the via hole is less than a preset range is set as the region to be improved.

5. The SI simulation optimization method for PCB according to claim 1, wherein: The step of arranging passive devices in the area to be improved and iteratively optimizing the positions and parameters of the passive devices to obtain device layout parameters includes: Select the type of passive component according to the frequency characteristics of the area to be improved, select a capacitor when the frequency is low, select an inductor when the frequency is in a medium range, and select a magnetic bead when the frequency is high; Initially placing the selected passive components at the center of each area to be improved, and setting initial parameter values ​​of the components; Using a coordinate scanning method to move the device position within the area to be improved, with a preset step length each time, and calculating the signal integrity index of each position; For each device position, a parameter scanning method is used to optimize the device parameter value, and the scanning is performed within a preset parameter range; When the insertion loss improvement or the return loss improvement in the signal integrity indicator exceeds a preset threshold, the corresponding position and the corresponding parameter are determined to be device layout parameters.

6. The SI simulation optimization method for PCB according to claim 1, wherein: The optimizing and adjusting the PCB based on the device layout parameters includes: Determining an adjustable range of geometric parameters according to the device layout parameters and generating parameter constraints; Optimizing the core geometric parameters of the via based on the parameter constraints to obtain optimized geometric parameters of the via; Adjusting the surrounding structure dimensions according to the optimized via geometric parameters to obtain structure dimension parameters; Performing simulation verification on the optimized via geometric parameters and the structural size parameters to obtain a performance evaluation score; Only when the performance optimization score is greater than a preset scoring threshold, the optimized layout parameters of the PCB are determined according to the optimized via geometric parameters and the structural size parameters.

7. A SI simulation and optimization device for PCB, characterized in that: The SI simulation and optimization device for PCB is used to execute the SI simulation and optimization method for PCB according to any one of claims 1 to 6, and the SI simulation and optimization device for PCB includes: A first simulation module is configured to select a simulation boundary based on the PCB via type, board thickness, and signal frequency, and perform a simulation based on the simulation boundary to obtain S parameters; A second simulation module is configured to gradually expand the simulation boundary using a progressive method and perform a secondary simulation, and determine an optimal boundary range when the S parameter change between two adjacent simulations is less than a preset convergence threshold; A region marking module, configured to calculate electromagnetic field distribution parameters within the optimal boundary range, and set regions where electromagnetic energy is concentrated in the electromagnetic field distribution parameters as regions to be improved; a layout optimization module, configured to arrange passive devices in the area to be improved and iteratively optimize the positions and parameters of the passive devices to obtain device layout parameters; A solution generation module is used to optimize the geometric parameters of the via and the dimensions of the surrounding structures based on the device layout parameters to obtain an optimized design solution.

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