PCB signal integrity analysis method, apparatus and device, and storage medium

Abstract

The invention provides a PCB signal integrity analysis method, device and equipment and a storage medium, and the method comprises the steps: building a finite element model of a PCB according to the design parameters of the PCB, the finite element model comprising a cavity structure, a top gummosis shape and a structural region; acquiring electromagnetic field data of the finite element model, wherein the electromagnetic field data comprises electromagnetic field intensity distribution and characteristic impedance; calculating a first energy loss diagram according to the electromagnetic field intensity distribution and the structural region, calculating and generating a second energy loss diagram according to a finite element model and theoretical minimum loss, and determining an energy optimization diagram according to the first energy loss diagram and the second energy loss diagram; constructing a target function according to the energy optimization diagram and a preset performance index, and obtaining preset first mapping data, second mapping data and third mapping data; and determining a first optimization parameter of the cavity structure and a second optimization parameter of the top gummosis shape according to the target function, the first mapping data, the second mapping data and the third mapping data.

Description

Technical Field

[0001] The present application relates to the technical field of PCB signal analysis, and in particular to a PCB signal integrity analysis method, apparatus, device, and storage medium. Background Art

[0002] During the PCB design process, traditional methods for ensuring high-frequency signal integrity rely heavily on the engineer's personal experience to optimize parameters (such as cavity structure and top-flow adhesive shape). This involves repeated simulations and manual adjustments to suppress signal reflections, crosstalk, and loss. Faced with increasingly complex high-frequency and high-speed designs, manual experience struggles to fully and accurately capture the complex relationship between electromagnetic field characteristics (such as intensity distribution and impedance variation) and energy loss. While existing simulation tools can provide analytical data, they lack automated processes to directly and efficiently translate electromagnetic field information into specific structural optimization parameters. This results in unclear optimization objectives and reliance on subjective judgment. Summary of the Invention

[0003] The present application provides a PCB signal integrity analysis method, apparatus, device, and storage medium for automatically identifying PCB design parameters that affect signals during the PCB design process, thereby improving optimization speed and reducing reliance on manual experience.

[0004] In a first aspect, an embodiment of the present application provides a PCB signal integrity analysis method, the method comprising:

[0005] Acquiring design parameters of a PCB, and establishing a finite element model of the PCB according to the design parameters, wherein the finite element model includes: a cavity structure, a top glue flow shape, and a structural area; Inputting a test signal into the finite element model to obtain electromagnetic field data of the finite element model, wherein the electromagnetic field data includes: electromagnetic field intensity distribution and characteristic impedance; Calculating a first energy loss map based on the electromagnetic field intensity distribution and the structural area, generating a second energy loss map based on the finite element model and theoretical minimum loss calculation, and determining an energy optimization map based on the first energy loss map and the second energy loss map; Constructing an objective function according to the energy optimization graph and preset performance indicators, and obtaining preset first mapping data, second mapping data, and third mapping data; A first optimization parameter of the cavity structure and a second optimization parameter of the top flow shape are determined according to the objective function, the first mapping data, the second mapping data, and the third mapping data.

[0006] In a second aspect, an embodiment of the present application provides a PCB signal integrity analysis device, the PCB signal integrity analysis device comprising: A model building module is used to obtain design parameters of the PCB and establish a finite element model of the PCB according to the design parameters, wherein the finite element model includes: a cavity structure, a top glue flow shape, and a structural area; An electromagnetic analysis module, configured to input a test signal into the finite element model to obtain electromagnetic field data of the finite element model, wherein the electromagnetic field data includes: electromagnetic field intensity distribution and characteristic impedance; an energy optimization module, configured to calculate a first energy loss map based on the electromagnetic field intensity distribution and the structural area, generate a second energy loss map based on the finite element model and theoretical minimum loss calculation, and determine an energy optimization map based on the first energy loss map and the second energy loss map; A function construction module, configured to construct an objective function according to the energy optimization graph and preset performance indicators, and obtain preset first mapping data, second mapping data, and third mapping data; A parameter generation module is used to determine a first optimization parameter of the cavity structure and a second optimization parameter of the top flow glue shape according to the objective function, the first mapping data, the second mapping data and the third mapping data.

[0007] 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 PCB signal integrity analysis method as described in any one of the embodiments of the present application when executing the computer program.

[0008] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor enables the processor to implement a PCB signal integrity analysis method as described in any one of the embodiments of the present application.

[0009] An embodiment of the present application provides a PCB signal integrity analysis method, the method comprising: obtaining design parameters of the PCB, establishing a finite element model of the PCB based on the design parameters, the finite element model comprising: a cavity structure, a top flow glue shape, and a structural area; inputting a test signal into the finite element model to obtain electromagnetic field data of the finite element model, the electromagnetic field data comprising: an electromagnetic field intensity distribution and a characteristic impedance; calculating a first energy loss graph based on the electromagnetic field intensity distribution and the structural area, generating a second energy loss graph based on the finite element model and a theoretical minimum loss calculation, and determining an energy optimization graph based on the first energy loss graph and the second energy loss graph; constructing an objective function based on the energy optimization graph and preset performance indicators, obtaining preset first mapping data, second mapping data, and third mapping data; and determining a first optimization parameter of the cavity structure and a second optimization parameter of the top flow glue shape based on the objective function, the first mapping data, the second mapping data, and the third mapping data. In the above method, by establishing a finite element model including the cavity structure, the top flow glue shape and the structural area, and combining the input test signal to obtain the electromagnetic field intensity distribution and characteristic impedance data, the first energy loss graph and the second energy loss graph with the theoretical minimum loss are automatically generated to form an energy optimization graph. Based on the graph and the preset performance indicators, an objective function is constructed, and the first, second and third mapping data are associated to determine the key optimization parameters of the cavity structure and the top flow glue shape. This realizes the quantitative analysis of PCB signal integrity problems and automatic parameter optimization, significantly improves the design efficiency and optimization accuracy, and reduces the design risks caused by subjective experience differences. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] 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.

[0011] Figure 1 A schematic flow chart of a PCB signal integrity analysis method provided in an embodiment of the present application; Figure 2 A schematic block diagram of a PCB signal integrity analysis device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0012] 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.

[0013] 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.

[0014] 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.

[0015] 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.

[0016] See also Figure 1 , Figure 1 FIG. 1 is a schematic flow chart of a PCB signal integrity analysis method provided by an embodiment of the present application. Figure 1 As shown, the specific steps of the PCB signal integrity analysis method include: S101-S105.

[0017] S101. Obtain design parameters of the PCB, and establish a finite element model of the PCB according to the design parameters. The finite element model includes: a cavity structure, a top glue flow shape, and a structural area.

[0018] For example, PCB design parameters encompass the physical properties and geometric structure of the PCB board, including key parameters such as the substrate's dielectric constant, loss tangent, copper foil thickness, and stackup structure. The finite element model established based on these design parameters is a three-dimensional mathematical model that accurately describes the PCB's electromagnetic properties. The cavity structure serves as the primary channel for signal transmission, and its geometric dimensions directly impact signal transmission performance. The top flow pattern is a unique structure formed during the PCB manufacturing process. The flow pattern can be flat, arched, or irregular. This variation in shape can lead to local variations in the dielectric constant, which in turn affects signal transmission continuity. The structural region is the main component of the PCB and includes the substrate, conductor, and transition regions. When establishing the finite element model, each of these three parameters must be assigned corresponding material properties and boundary conditions to ensure that the model accurately reflects the PCB's physical properties. The model's meshing must also be optimized based on the characteristics of each region, with a denser mesh used in areas with drastic electromagnetic field variations to ensure simulation accuracy.

[0019] S102 , inputting a test signal into the finite element model to obtain electromagnetic field data of the finite element model, where the electromagnetic field data includes: electromagnetic field intensity distribution and characteristic impedance.

[0020] For example, commonly used test signals include step signals, pulse signals, sine wave signals, and actual high-speed digital signal patterns. After inputting the test signals into the finite element model, the electromagnetic field distribution throughout the entire PCB structure can be obtained by solving Maxwell's equations. Electromagnetic field data is a four-dimensional dataset containing spatial and temporal information. The electromagnetic field intensity distribution describes the magnitude and direction of the electric and magnetic fields at various locations on the PCB. This distribution not only reflects the propagation path of signal energy but also reveals possible electromagnetic interference and coupling. Characteristic impedance is a key parameter of transmission lines, characterizing the degree to which the transmission line impedes signal transmission. In cavity transmission line structures, characteristic impedance varies with factors such as cavity size and top-fill adhesive shape. Electromagnetic field simulation can determine the distribution of characteristic impedance along the transmission line and identify impedance discontinuities. These discontinuities often occur in transition regions or where the top-fill adhesive has irregular shapes, leading to signal reflections and increased transmission losses. The acquisition of electromagnetic field data must take into account frequency dependence, as the electromagnetic field distribution can vary significantly at different frequencies. High-frequency signals propagate more easily on the surface of a conductor, resulting in greater conductor loss. Losses in the dielectric also increase with increasing frequency. By analyzing electromagnetic field data at different frequencies, you can comprehensively evaluate the signal transmission performance of a PCB across a wide frequency band.

[0021] S103 , calculating a first energy loss graph according to the electromagnetic field intensity distribution, generating a second energy loss graph according to the finite element model and the theoretical minimum loss calculation, and determining an energy optimization graph according to the first energy loss graph and the second energy loss graph.

[0022] For example, the first energy loss graph is calculated based on the actual electromagnetic field intensity distribution and reflects the actual signal transmission losses in the PCB. Energy loss primarily consists of three components: conductor loss, dielectric loss, and radiation loss. Conductor loss results from Joule heating generated by signal current flowing in the conductor and is related to the conductor's conductivity and surface current density. Dielectric loss is energy dissipated due to polarization hysteresis in the dielectric material and is related to the dielectric's loss tangent and electric field strength. Radiation loss is the loss caused by the radiation of signal energy into space in the form of electromagnetic waves. These three losses are spatially superimposed and integrated along the transmission direction to obtain the cumulative distribution of losses. The second energy loss graph represents the theoretical minimum loss state and is a reference benchmark calculated under ideal conditions. The theoretical minimum loss assumes that the transmission line is in perfect air, the conductor has ideal conductivity, and there are no discontinuities. While this ideal state cannot be fully achieved in practice, it provides a clear target for optimization. The energy optimization graph is generated by comparing the first and second energy loss graphs. It intuitively shows which areas have significant losses deviating from the ideal state and require targeted optimization. When generating the energy optimization map, we use color gradation mapping to convert the loss differences into different colors, making key optimization areas clear at a glance. This visual representation helps designers quickly identify problem areas and take targeted optimization measures.

[0023] S104: Construct an objective function according to the energy optimization graph and preset performance indicators, and obtain preset first mapping data, second mapping data, and third mapping data.

[0024] Exemplarily, the energy optimization map provides spatially distributed optimization requirement information, and converting these information into mathematical expressions is a key step in constructing the objective function. The construction of the objective function needs to consider multiple performance indicators, including insertion loss, return loss, characteristic impedance deviation, group delay variation, etc. These performance indicators reflect different aspects of signal integrity, and the weight coefficients of each indicator need to be determined according to the specific application scenario. The preset performance indicators serve as the constraint conditions for optimization, ensuring that the optimization results meet the requirements of practical applications. The first mapping data establishes a quantitative relationship between the cavity structure height and the loss optimization amount. This mapping relationship is obtained through a large amount of simulation data fitting. Research shows that the increase of cavity height can significantly reduce signal transmission loss, but at the same time, it will also lead to the increase of characteristic impedance and the decrease of mechanical strength. The second mapping data describes the influence law of cavity structure width on characteristic impedance. The change of cavity width mainly affects the capacitance and inductance parameters of the transmission line, and then changes the characteristic impedance value. Reasonable width design needs to find a balance point between impedance matching and manufacturing feasibility. The third mapping data focuses on the relationship between the top flow glue shape and the characteristic impedance. The irregularity of the flow glue shape will cause local impedance changes, resulting in signal reflection. By establishing a mapping relationship between the shape parameters and the impedance changes, the manufacturing process can be improved. The three kinds of mapping data together constitute the knowledge base of the optimization algorithm, so that the optimization process can be based on existing experience and rules, avoiding blind parameter adjustment.

[0025] In some embodiments, the first mapping data is a mapping relationship between a height of the cavity structure and a loss optimization amount, the second mapping data is a mapping relationship between a width of the cavity structure and a characteristic impedance, and the third mapping data is a mapping relationship between a top flow glue shape and the characteristic impedance.

[0026] For example, in the first mapping data, as the cavity height increases from a small value, the loss optimization amount increases rapidly. This is because a taller cavity can accommodate more electromagnetic field energy propagating in the low-loss air medium. The loss optimization amount is defined as the difference between the actual loss and the theoretical minimum loss. Increasing the cavity height directly reduces the proportion of signal propagation in the high-loss substrate. The second mapping data includes the effect of the cavity structure width on the characteristic impedance. Changes in the cavity structure width will change the capacitance and inductance parameters of the transmission line. The mapping relationship between characteristic impedance and cavity width is affected by multiple factors, including conductor width, cavity height, and the dielectric constant of the surrounding medium. In practical applications, it is necessary to adjust the cavity width to achieve a specific characteristic impedance value to meet impedance matching requirements. In the third mapping data, the mapping relationship between top flow shape and characteristic impedance shows that a smooth arched flow shape produces the smallest impedance change, while an irregular flow shape can cause large fluctuations in local impedance. These three mapping data together form the knowledge foundation of the optimization algorithm and have a complex interplay. For example, while increasing the cavity height can reduce losses, it also affects the characteristic impedance, requiring compensation by adjusting the cavity width. Optimizing the top flow shape must consider not only its impact on the characteristic impedance but also the controllability of the manufacturing process and cost factors. These mapping data, obtained through extensive simulation and experimental data fitting, can provide quantitative guidance for design optimization.

[0027] S105 , determining a first optimization parameter of the cavity structure and a second optimization parameter of the top flow shape according to the objective function, the first mapping data, the second mapping data, and the third mapping data.

[0028] Exemplarily, a preset optimization algorithm searches for an optimal solution in the multidimensional parameter space corresponding to the first and second optimization parameters. The first optimization parameters of the cavity structure primarily include the optimal values ​​of the cavity height and width. These parameters directly determine the signal transmission loss level and impedance characteristics. When determining the first optimization parameters, it is important to fully utilize the parameter sensitivity information provided by the first and second mapping data. For example, when the objective function indicates a need to reduce transmission loss, the cavity height can be appropriately increased based on the first mapping data; when characteristic impedance needs to be adjusted, the cavity width can be adjusted based on the second mapping data. The second optimization parameters for the top glue shape involve controlling the glue geometry, such as the arch radius of curvature and the uniformity of glue thickness. The third mapping data provides guidance for optimizing the glue shape. The interaction between parameters must be considered during the optimization process; changes in one parameter may affect other performance indicators. Through iterative optimization, the global optimal solution is gradually approached. The optimization results include not only the specific parameter values, but also the expected performance improvement and implementation recommendations. These optimization parameters will directly guide PCB design modifications and manufacturing process adjustments, significantly improving signal integrity.

[0029] An embodiment of the present application provides a PCB signal integrity analysis method, the method comprising: obtaining design parameters of the PCB, establishing a finite element model of the PCB based on the design parameters, the finite element model comprising: a cavity structure, a top flow glue shape, and a structural area; inputting a test signal into the finite element model to obtain electromagnetic field data of the finite element model, the electromagnetic field data comprising: an electromagnetic field intensity distribution and a characteristic impedance; calculating a first energy loss graph based on the electromagnetic field intensity distribution and the structural area, generating a second energy loss graph based on the finite element model and a theoretical minimum loss calculation, and determining an energy optimization graph based on the first energy loss graph and the second energy loss graph; constructing an objective function based on the energy optimization graph and preset performance indicators, obtaining preset first mapping data, second mapping data, and third mapping data; and determining a first optimization parameter of the cavity structure and a second optimization parameter of the top flow glue shape based on the objective function, the first mapping data, the second mapping data, and the third mapping data. In the above method, by establishing a finite element model including the cavity structure, the top flow glue shape and the structural area, and combining the input test signal to obtain the electromagnetic field intensity distribution and characteristic impedance data, the first energy loss graph and the second energy loss graph with the theoretical minimum loss are automatically generated to form an energy optimization graph. Based on the graph and the preset performance indicators, an objective function is constructed, and the first, second and third mapping data are associated to determine the key optimization parameters of the cavity structure and the top flow glue shape. This realizes the quantitative analysis of PCB signal integrity problems and automatic parameter optimization, significantly improves the design efficiency and optimization accuracy, and reduces the design risks caused by subjective experience differences.

[0030] 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.

[0031] In some embodiments, a test signal is input to a finite element model to obtain electromagnetic field data of the finite element model, including: setting boundary conditions and port excitation conditions for the finite element model, the boundary conditions including absorption boundary conditions and symmetric boundary conditions, and the port excitation conditions including input port impedance and output port impedance; according to the boundary conditions and port excitation conditions, the test signal is applied to the input port of the finite element model, and the transient electromagnetic field response in the cavity structure is calculated by an electromagnetic field simulation algorithm; according to the transient electromagnetic field response, the three-dimensional electric field distribution and the three-dimensional magnetic field distribution are extracted within a preset frequency range, and the Poynting vector distribution is calculated according to the three-dimensional electric field distribution and the three-dimensional magnetic field distribution; according to the three-dimensional electric field distribution, the three-dimensional magnetic field distribution and the Poynting vector distribution, the spatial distribution and propagation constant of the characteristic impedance are calculated, and the three-dimensional electric field distribution, the three-dimensional magnetic field distribution, the spatial distribution and the propagation constant of the characteristic impedance are integrated into electromagnetic field data.

[0032] Exemplary boundary conditions include: absorbing boundary conditions, symmetric boundary conditions, and port excitation conditions. Absorbing boundary conditions simulate the propagation characteristics of electromagnetic waves in infinite space, avoiding spurious reflections at the boundaries of the computational domain. These boundary conditions are typically implemented using perfectly matched layer (PML) technology, effectively absorbing outward-propagating electromagnetic waves. Symmetric boundary conditions leverage the geometric symmetry of the PCB structure by setting electric or magnetic walls to reduce the size of the computational domain, significantly improving simulation efficiency. Port excitation conditions define the input and output characteristics of the signal. The input and output port impedances must match the impedance characteristics of the actual circuit and are typically set to a standard impedance value of 50 ohms. After the test signal is injected into the finite element model through the input port, the electromagnetic field simulation algorithm begins calculating the electromagnetic field distribution within the cavity structure. The transient electromagnetic field response reflects the signal propagation process in the time domain, including the rising and falling edges of the signal, as well as the steady-state response characteristics. This transient response contains rich frequency domain information, and the response characteristics of different frequency components can be obtained through Fourier transform.

[0033] Extracting the three-dimensional electric and magnetic field distributions within a preset frequency range is a key step in analyzing signal transmission characteristics. The three-dimensional electric field distribution E(x, y, z, f) describes the magnitude and direction of the electric field intensity at each point in space. The electric field distribution exhibits significant inhomogeneity, particularly at the cavity edges and in the top glue flow region. The three-dimensional magnetic field distribution H(x, y, z, f) is coupled with the electric field distribution to jointly determine the transmission characteristics of electromagnetic energy. The Poynting vector distribution S = E × H represents the direction and density of electromagnetic energy flow. Analyzing the Poynting vector provides a visual understanding of the flow path of signal energy within the transmission line. The spatial distribution of the characteristic impedance reflects the impedance characteristics at different locations along the transmission line. Significant variations in the characteristic impedance occur at cavity transition regions and in irregular regions of top glue flow. These variations are the primary locations for signal reflections. The propagation constant γ = α + jβ, where α is the attenuation constant and β is the phase constant, describing the signal amplitude attenuation and phase variation, respectively. Integrating these parameters into a complete electromagnetic field dataset provides comprehensive foundational information for subsequent loss analysis and optimization.

[0034] In some embodiments, the structural area includes: a substrate area and a conductor area, and a first energy loss map is calculated according to the electromagnetic field intensity distribution, including: calculating the dielectric loss distribution according to the electric field distribution in the electromagnetic field intensity distribution and the dielectric loss tangent of the substrate area, calculating the conductor loss distribution according to the surface current density in the electromagnetic field intensity distribution and the conductivity of the conductor area, and calculating the radiation loss distribution according to the far-field component of the electromagnetic field intensity distribution; superimposing the dielectric loss distribution, the conductor loss distribution and the radiation loss distribution according to the spatial position to obtain the total loss spatial distribution, and integrating the total loss spatial distribution along the transmission direction to obtain the loss accumulation curve; dividing the cavity structure into multiple loss calculation units according to a preset grid division rule, and calculating the average loss value and loss gradient value of each loss calculation unit according to the loss accumulation curve and the total loss spatial distribution; according to a preset loss-color scale mapping table, converting the average loss value of each loss calculation unit into a corresponding color scale value, arranging the color scale values ​​according to the spatial position of the loss calculation unit, and generating a first energy loss map.

[0035] For example, the substrate region is the main body of a PCB, and its dielectric properties determine the basic signal transmission loss level. The conductor region includes metal structures such as signal lines and reference planes. The conductivity and surface roughness of these conductors affect the magnitude of conductor loss. The transition region is the interface between the cavity structure and the substrate region. This region is prone to impedance discontinuities due to abrupt dielectric changes, and can also be a parameter of interest when more precise signal analysis is required. Calculating the dielectric loss distribution requires a comprehensive consideration of the spatial variation of the electric field distribution and the material properties of the substrate region. Electric fields induce polarization in dielectrics, and the dielectric loss tangent characterizes the material's ability to dissipate energy under an alternating electric field. In high-frequency signal transmission, dielectric loss increases significantly with increasing frequency, a significant factor limiting high-speed PCB performance. The calculation of conductor loss distribution is based on the distribution characteristics of surface current density. The skin effect of high-frequency signals causes current to flow primarily within the thin layer on the conductor surface. The conductivity of the conductor region determines the power loss per unit volume. Surface roughness increases the actual current path length, further increasing conductor loss. The radiation loss distribution is obtained by analyzing the far-field component of the electromagnetic field intensity distribution. When there is discontinuity in the transmission line structure, part of the signal energy will be radiated into space in the form of electromagnetic waves, resulting in energy loss.

[0036] The total spatial loss distribution is a comprehensive representation of the three loss mechanisms. It is obtained by superimposing the dielectric loss distribution, conductor loss distribution, and radiation loss distribution at the same spatial coordinates. Integration along the transmission direction transforms the three-dimensional loss distribution into a one-dimensional loss accumulation curve, which clearly shows the signal power attenuation along the transmission path. The meshing rules must balance computational accuracy and efficiency. A finer mesh is used in areas with drastic loss variations, while a coarser mesh can be used in areas with uniform loss distribution. The loss calculation unit, the smallest unit of analysis, records both the average loss value and the loss gradient for each unit. The average loss value reflects the overall loss level of the unit, while the loss gradient characterizes the spatial rate of loss variation. High-gradient regions typically correspond to structural discontinuities or material abrupt changes. A loss-to-color scale mapping table establishes a correspondence between numerical values ​​and visual representations. Through appropriate color scale design, the loss distribution can be more intuitive. The spatial arrangement of color scale values ​​forms a complete first-energy loss map. This visual representation not only facilitates the identification of high-loss areas but also reveals the spatial patterns and regularities of the loss distribution.

[0037] In some embodiments, a second energy loss map is generated based on a finite element model and a theoretical minimum loss calculation, including: calculating the minimum dielectric loss under ideal transmission conditions based on the geometric parameters of the cavity structure and the dielectric constant of the air medium, calculating the minimum conductor loss under ideal transmission conditions based on the ideal conductivity and ideal current distribution of the conductor area, superimposing the minimum dielectric loss and the minimum conductor loss to obtain a theoretical minimum loss distribution; calculating the theoretical minimum loss distribution in segments along the transmission direction based on the transmission line length of the finite element model, assigning a corresponding theoretical loss value to each segment, and setting a theoretical loss transition interval based on the interface position between the cavity structure and the substrate area; mapping the theoretical minimum loss distribution to the corresponding loss calculation unit according to a preset grid division rule, converting the theoretical loss value of each loss calculation unit into a color scale value according to a loss-color scale mapping table, and generating a second energy loss map.

[0038] For example, in an ideal model, the signal is assumed to be transmitted under optimal conditions. The geometric parameters of the cavity structure include the cavity's height, width, length, and cross-sectional shape; these parameters collectively determine the electromagnetic properties of the transmission line. The dielectric constant of air is close to 1, and the dielectric loss tangent is close to 0, making it an ideal transmission medium. When calculating minimum dielectric loss, it is necessary to consider the ideal electric field distribution within the cavity—that is, a uniform electric field distribution with no localized concentration. Ideal conductivity is typically taken as the theoretical conductivity of copper at room temperature, while ideal current distribution refers to a uniform current distribution across the conductor's cross-section, free of skin and proximity effects. Minimum conductor loss is calculated based on this ideal current distribution and represents the lowest achievable loss level for a given conductor material and geometry. The superposition of minimum dielectric loss and minimum conductor loss requires considering the spatial distribution characteristics of the two loss mechanisms. Dielectric loss primarily occurs in areas of high electric field intensity, while conductor loss is concentrated on the conductor surface, where current density is high. The theoretical minimum loss distribution exhibits a gradual accumulation along the transmission direction, providing a reference benchmark for optimizing actual losses. Transmission line length is a significant factor influencing total loss. The theoretical minimum loss distribution requires segmented calculation based on the actual transmission line length. The goal of segmented calculation is to accurately describe the loss distribution characteristics at different locations. The length of each segment can be adjusted based on the severity of loss variations. In areas where loss variations are gradual, longer segments can be used; in areas of structural variation, shorter segments are needed to capture rapid loss variations. The assignment of theoretical loss values ​​requires consideration of the average loss rate within each segment, which is the loss per unit length calculated under ideal transmission conditions. The interface between the cavity structure and the substrate region is a unique location where dielectric properties undergo abrupt changes, resulting in some reflection and mode conversion even under ideal conditions. The theoretical loss transition interval must account for this dielectric abrupt change. Within this transition interval, the loss value gradually transitions from low loss in the cavity region to higher loss in the substrate region. Meshing rules ensure that the theoretical loss distribution is accurately mapped onto discrete computational elements. Each loss computation element is assigned a theoretical loss value, representing the expected loss level under ideal conditions. The loss-to-color mapping table converts numerical loss information into intuitive color information. The spatial distribution of theoretical loss patterns can be clearly seen through the changes in color values. The second energy loss graph, serving as a reference for ideal conditions, contrasts with the first energy loss graph, revealing the gap between the actual design and the ideal state.

[0039] In some embodiments, determining the energy optimization map according to the first energy loss map and the second energy loss map comprises: aligning the first energy loss map and the second energy loss map according to the same coordinate system, determining the position mapping relationship between each loss calculation unit in the first energy loss map and the corresponding loss calculation unit in the second energy loss map; according to the position mapping relationship, calculating the difference value between the actual loss value of each loss calculation unit in the first energy loss map and the theoretical loss value in the second energy loss map, to obtain a loss difference value matrix; according to the loss difference value matrix, identifying a high loss area with a loss difference value greater than a preset threshold, and according to the spatial distribution characteristics of the high loss area, merging adjacent high loss areas into a loss optimization block; performing weight distribution on each loss optimization block to generate an optimization weight matrix; according to the loss difference value matrix and the optimization weight matrix, calculating the optimization target value of each loss calculation unit; according to a preset optimization value-color step mapping table, converting the optimization target value of each loss calculation unit into an optimization color step value, and arranging the optimization color step value according to the spatial position to generate the energy optimization map.

[0040] Before accurately comparing the two energy loss maps, the two energy loss maps need to be aligned in the coordinate system. Although the first energy loss map and the second energy loss map describe the same PCB structure, due to the differences in generation process and data source, there may be differences in coordinate origin, scale or direction definition. The alignment process needs to identify feature points or feature areas in the two maps, such as the starting position of the cavity structure and the port position of the transmission line, and establish a coordinate transformation relationship through these reference points. The determination of the position mapping relationship involves spatial interpolation and grid matching technology. When the grid division of the two maps is not completely consistent, a bilinear interpolation or nearest neighbor interpolation method needs to be used to obtain the loss value of the corresponding position. Each loss calculation unit has its corresponding position in the two maps, and this one-to-one correspondence ensures the accuracy of the subsequent difference calculation. The actual loss value reflects the real loss level under the current design state, while the theoretical loss value represents the lowest loss that can be achieved under ideal conditions, and the difference between the two directly reflects the potential space for optimization.

[0041] The loss difference matrix is a data structure with the same dimensions as the original loss map, and each element in the matrix represents the loss optimization potential of the corresponding position. The selection of the preset threshold needs to be determined according to the specific application requirements and optimization goals, and is usually set to a certain multiple of the average loss difference to distinguish between areas that need to be optimized and areas that have good performance. The identification of high-loss areas not only considers the loss difference of a single computing unit, but also analyzes its spatial distribution characteristics. Isolated high-loss points may be data noise or local defects, while continuously distributed high-loss areas reflect systematic problems in structural design. The merging process of adjacent high-loss areas uses region growing or clustering algorithms to group spatially adjacent and loss-similar units into the same loss optimization block. The formation of the loss optimization block allows the subsequent optimization strategy to be tailored to the characteristics of different areas, avoiding the limitations of a uniform optimization scheme for the entire structure. The weight allocation mechanism considers two key factors: the loss difference mean reflects the urgency of optimization for the block, and the area size represents the impact of the block on the overall performance. The weight values in the optimization weight matrix are normalized to ensure that the sum of all weights is 1, which allows for a reasonable allocation of optimization resources among multiple optimization blocks.

[0042] The calculation of the optimization target value combines the loss difference and weight information, and the optimization target value of each loss calculation unit is equal to the product of its loss difference and the weight of the block it belongs to. This calculation method ensures that units with high loss difference and located in important areas have higher optimization priority. The design of the optimization value-color scale mapping table needs to consider the color perception characteristics of the human eye, and usually adopts a gradual change scheme from cool to warm, so that the change of optimization demand from low to high can be intuitively reflected through color change. The spatial arrangement of the optimization color scale value preserves the original geometric position relationship, so that the energy optimization map not only contains quantitative information of optimization demand, but also retains the characteristics of spatial distribution. The generated energy optimization map becomes an important basis for guiding subsequent parameter optimization, and designers can quickly identify areas that need to be improved and evaluate the effects of different optimization schemes through this map.

[0043] In some embodiments, a first optimization parameter of the cavity structure and a second optimization parameter of the top flow glue shape are determined according to the objective function, the first mapping data, the second mapping data and the third mapping data, including: establishing an optimization variable set according to the objective function, the optimization variable set including: cavity height variable, cavity width variable and top flow glue shape variable, and setting an initial value and a variation range for each variable; establishing a functional relationship between the cavity height variable and the loss optimization amount according to the first mapping data, establishing a functional relationship between the cavity width variable and the characteristic impedance according to the second mapping data, and establishing a functional relationship between the top flow glue shape variable and the characteristic impedance according to the third mapping data; substituting the functional relationship into the objective function, constructing a comprehensive optimization model including the optimization variable set, and setting constraints; iteratively solving the comprehensive optimization model, and in each iteration, updating the optimization variable set according to the gradient information rule or the heuristic rule; when the convergence condition is met, extracting the corresponding target cavity height value and target cavity width value from the optimization variable set as the first optimization parameter, and extracting the target top flow glue shape value as the second optimization parameter.

[0044] For example, the initial value of the cavity height variable is usually determined based on empirical design rules, and the range of variation is limited by the manufacturing process capabilities and mechanical strength requirements. The cavity height cannot be increased indefinitely. A cavity that is too high will reduce the mechanical strength of the PCB, increase the risk of warping, and also increase the manufacturing cost. The setting of the cavity width variable needs to take into account the signal line width and impedance matching requirements. The initial value is usually 3-5 times the signal line width to ensure that the electromagnetic field can be fully expanded. The top glue shape variable is a more complex parameter, which may include multiple sub-parameters such as glue height, curvature radius, and shape regularity. The initial value setting of these variables has an important influence on the optimization convergence speed. Reasonable initial values ​​can reduce the number of iterations and improve optimization efficiency. The setting of the variation range should not only consider physical feasibility, but also reserve a certain safety margin to avoid the optimization results being in the process limit state.

[0045] Establishing a functional relationship is a key step in converting discrete mapping data into a continuous mathematical model. The functional relationship between the cavity height variable and the loss optimization value often exhibits nonlinear characteristics. When the cavity height is small, the loss optimization value increases rapidly with increasing height; once the height reaches a certain value, the optimization effect tends to saturate. This nonlinear relationship can be expressed using polynomial fitting, exponential functions, or piecewise functions. The functional relationship between the cavity width variable and the characteristic impedance is primarily based on transmission line theory, but requires correction using secondary mapping data to account for edge effects and higher-order modes in the actual structure. The functional relationship between the top flow shape variable and the characteristic impedance is the most complex, as the irregularity of the flow shape is difficult to describe with a simple mathematical expression. A parametric description method is often used to simplify the complex shape characteristics into a number of key parameters, and then establish the relationship between these parameters and the impedance change. The comprehensive optimization model integrates these functional relationships into the objective function, forming a multivariable, nonlinear optimization problem. Constraints are set to ensure that the optimization results are within the engineering achievable range, including the tolerance range of the characteristic impedance, manufacturing tolerances, and cost constraints.

[0046] During the iterative solution process, each iteration involves three main steps: objective function evaluation, search direction determination, and variable update. Calculating the objective function requires substituting the current optimization variable values ​​into the comprehensive optimization model. This process may involve complex numerical calculations, including loss prediction and impedance calculation. The gradient information rule utilizes the partial derivatives of the objective function with respect to each optimization variable to determine the direction of steepest descent. This method is most efficient when the objective function is smooth and differentiable. Heuristic rules, such as crossover and mutation in genetic algorithms and swarm collaboration in particle swarm optimization, do not rely on gradient information and instead employ search strategies designed based on problem characteristics. These methods are more suitable for non-smooth and multi-peak optimization problems. When updating the optimization variable values, step size control must be considered. Excessively large step sizes can lead to optimization divergence, while excessively small step sizes can slow convergence. Convergence criteria are typically determined by multiple criteria, including the rate of change of the objective function value, the magnitude of the change in the optimization variables, and the limit on the number of iterations. When convergence criteria are met, the current optimization variable values ​​constitute the optimization result. The target cavity height and target cavity width constitute the first optimization parameters, which together determine the basic geometric characteristics of the cavity structure. The target top flow shape value is used as the second optimization parameter to guide the improvement direction of the manufacturing process.

[0047] See also Figure 2 , Figure 2 1 is a schematic block diagram of a PCB signal integrity analysis device provided in an embodiment of the present application, wherein the PCB signal integrity analysis device 200 is used to perform the aforementioned PCB signal integrity analysis method. The PCB signal integrity analysis device 200 can be configured in a server.

[0048] 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.

[0049] like Figure 2 As shown, the PCB signal integrity analysis device 200 includes: a model construction module 201, an electromagnetic analysis module 202, an energy optimization module 203, a function construction module 204 and a parameter generation module 205.

[0050] The model building module 201 is used to obtain the design parameters of the PCB and establish a finite element model of the PCB according to the design parameters. The finite element model includes: a cavity structure, a top glue flow shape, and a structural area; The electromagnetic analysis module 202 is used to input the test signal into the finite element model to obtain the electromagnetic field data of the finite element model, the electromagnetic field data including: electromagnetic field intensity distribution and characteristic impedance; An energy optimization module 203 is configured to calculate a first energy loss map based on the electromagnetic field intensity distribution and the structural area, generate a second energy loss map based on the finite element model and the theoretical minimum loss calculation, and determine an energy optimization map based on the first energy loss map and the second energy loss map; A function construction module 204 is configured to construct an objective function according to the energy optimization graph and preset performance indicators, and obtain preset first mapping data, second mapping data, and third mapping data; The parameter generation module 205 is used to determine a first optimization parameter of the cavity structure and a second optimization parameter of the top glue flow shape according to the objective function, the first mapping data, the second mapping data and the third mapping data.

[0051] 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 a PCB signal integrity analysis method such as any one of the embodiments of the present application when executing the computer program.

[0052] 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 a PCB signal integrity analysis method as described in any one of the embodiments of the present application.

[0053] 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 PCB signal integrity analysis method, characterized in that: The method comprises: Acquiring design parameters of a PCB, and establishing a finite element model of the PCB according to the design parameters, wherein the finite element model includes: a cavity structure, a top glue flow shape, and a structural area; Inputting a test signal into the finite element model to obtain electromagnetic field data of the finite element model, wherein the electromagnetic field data includes: electromagnetic field intensity distribution and characteristic impedance; Calculating a first energy loss map based on the electromagnetic field intensity distribution and the structural area, generating a second energy loss map based on the finite element model and theoretical minimum loss calculation, and determining an energy optimization map based on the first energy loss map and the second energy loss map; Constructing an objective function according to the energy optimization graph and preset performance indicators, and obtaining preset first mapping data, second mapping data, and third mapping data; A first optimization parameter of the cavity structure and a second optimization parameter of the top flow shape are determined according to the objective function, the first mapping data, the second mapping data, and the third mapping data.

2. The PCB signal integrity analysis method according to claim 1, wherein: The inputting a test signal to the finite element model to obtain electromagnetic field data of the finite element model includes: Setting boundary conditions and port excitation conditions for the finite element model, wherein the boundary conditions include an absorbing boundary condition and a symmetric boundary condition, and the port excitation conditions include an input port impedance and an output port impedance; Applying the test signal to the input port of the finite element model according to the boundary conditions and the port excitation conditions, and calculating the transient electromagnetic field response in the cavity structure using an electromagnetic field simulation algorithm; extracting a three-dimensional electric field distribution and a three-dimensional magnetic field distribution within a preset frequency range according to the transient electromagnetic field response, and calculating a Poynting vector distribution according to the three-dimensional electric field distribution and the three-dimensional magnetic field distribution; According to the three-dimensional electric field distribution, the three-dimensional magnetic field distribution and the Poynting vector distribution, the spatial distribution and the propagation constant of the characteristic impedance are calculated, and the three-dimensional electric field distribution, the three-dimensional magnetic field distribution, the spatial distribution of the characteristic impedance and the propagation constant of the characteristic impedance are integrated into electromagnetic field data.

3. The PCB signal integrity analysis method according to claim 1, wherein: The structural region includes: a substrate region and a conductor region, and the calculating of a first energy loss graph according to the electromagnetic field intensity distribution includes: Calculating dielectric loss distribution based on the electric field distribution in the electromagnetic field intensity distribution and the dielectric loss tangent of the substrate region, calculating conductor loss distribution based on the surface current density in the electromagnetic field intensity distribution and the conductivity of the conductor region, and calculating radiation loss distribution based on the far-field component of the electromagnetic field intensity distribution; The dielectric loss distribution, the conductor loss distribution, and the radiation loss distribution are superimposed according to spatial positions to obtain a total loss spatial distribution, and the total loss spatial distribution is integrated along the transmission direction to obtain a loss accumulation curve; Divide the cavity structure into a plurality of loss calculation units according to a preset grid division rule, and calculate the average loss value and loss gradient value of each loss calculation unit according to the loss accumulation curve and the total loss spatial distribution; According to a preset loss-color scale mapping table, the average loss value of each loss calculation unit is converted into a corresponding color scale value, and the color scale values ​​are arranged according to the spatial position of the loss calculation unit to generate a first energy loss map.

4. The PCB signal integrity analysis method according to claim 3, wherein: The generating of a second energy loss graph according to the finite element model and the theoretical minimum loss calculation includes: Calculating the minimum dielectric loss under ideal transmission conditions based on the geometric parameters of the cavity structure and the dielectric constant of the air medium; calculating the minimum conductor loss under ideal transmission conditions based on the ideal conductivity and ideal current distribution of the conductor area; and superimposing the minimum dielectric loss and the minimum conductor loss to obtain a theoretical minimum loss distribution; According to the transmission line length of the finite element model, the theoretical minimum loss distribution is calculated in segments along the transmission direction, a corresponding theoretical loss value is assigned to each segment, and a theoretical loss transition interval is set according to the interface position between the cavity structure and the substrate area; According to the preset grid division rules, the theoretical minimum loss distribution is mapped to the corresponding loss calculation unit, and according to the loss-color scale mapping table, the theoretical loss value of each loss calculation unit is converted into a color scale value to generate a second energy loss map.

5. The PCB signal integrity analysis method according to claim 4, wherein: The determining of an energy optimization graph according to the first energy loss graph and the second energy loss graph includes: Aligning the first energy loss graph and the second energy loss graph according to the same coordinate system, and determining a position mapping relationship between each loss calculation unit in the first energy loss graph and the corresponding loss calculation unit in the second energy loss graph; Calculating, according to the position mapping relationship, the difference between the actual loss value of each loss calculation unit in the first energy loss map and the theoretical loss value in the second energy loss map to obtain a loss difference matrix; According to the loss difference matrix, identifying high-loss areas where the loss difference is greater than a preset threshold, and merging adjacent high-loss areas into loss-optimized blocks based on spatial distribution characteristics of the high-loss areas; Assigning weights to each of the loss optimization blocks to generate an optimization weight matrix; Calculating the optimization target value of each loss calculation unit according to the loss difference matrix and the optimization weight matrix; According to a preset optimization value-color scale mapping table, the optimization target value of each loss calculation unit is converted into an optimization color scale value, and the optimization color scale values ​​are arranged according to spatial positions to generate an energy optimization map.

6. The PCB signal integrity analysis method according to claim 1, wherein: The determining, according to the objective function, the first mapping data, the second mapping data, and the third mapping data, a first optimization parameter of the cavity structure and a second optimization parameter of the top flow glue shape comprises: Establishing an optimization variable set according to the objective function, the optimization variable set including: a cavity height variable, a cavity width variable, and a top glue flow shape variable, and setting an initial value and a variation range for each variable; Establishing a functional relationship between the cavity height variable and the loss optimization amount based on the first mapping data, establishing a functional relationship between the cavity width variable and the characteristic impedance based on the second mapping data, and establishing a functional relationship between the top flow shape variable and the characteristic impedance based on the third mapping data; Substituting the functional relationship into the objective function, constructing a comprehensive optimization model including the set of optimization variables, and setting constraint conditions; Iteratively solving the comprehensive optimization model, and in each iteration, updating the optimization variable set according to a gradient information rule or a heuristic rule; When the convergence condition is met, the corresponding target cavity height value and target cavity width value are extracted from the optimization variable set as the first optimization parameter, and the target top flow shape value is extracted as the second optimization parameter.

7. The PCB signal integrity analysis method according to claim 1, wherein: The first mapping data is the mapping relationship between the height of the cavity structure and the loss optimization amount, the second mapping data is the mapping relationship between the width of the cavity structure and the characteristic impedance, and the third mapping data is the mapping relationship between the top flow glue shape and the characteristic impedance.

8. A PCB signal integrity analysis device, characterized in that: The PCB signal integrity analysis device is used to perform the PCB signal integrity analysis method according to any one of claims 1 to 7, and the PCB signal integrity analysis device includes: A model building module is used to obtain design parameters of the PCB and establish a finite element model of the PCB according to the design parameters, wherein the finite element model includes: a cavity structure, a top glue flow shape, and a structural area; An electromagnetic analysis module, configured to input a test signal into the finite element model to obtain electromagnetic field data of the finite element model, wherein the electromagnetic field data includes: electromagnetic field intensity distribution and characteristic impedance; an energy optimization module, configured to calculate a first energy loss map based on the electromagnetic field intensity distribution and the structural area, generate a second energy loss map based on the finite element model and theoretical minimum loss calculation, and determine an energy optimization map based on the first energy loss map and the second energy loss map; A function construction module, configured to construct an objective function according to the energy optimization graph and preset performance indicators, and obtain preset first mapping data, second mapping data, and third mapping data; A parameter generation module is used to determine a first optimization parameter of the cavity structure and a second optimization parameter of the top flow glue shape according to the objective function, the first mapping data, the second mapping data and the third mapping data.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor; The memory is used to store computer programs; The processor is configured to execute the computer program and implement the PCB signal integrity analysis method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the PCB signal integrity analysis method according to any one of claims 1 to 7.

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