Parameter adjusting method for electromagnetic simulation
By automating the analysis of time-domain reflectometer curve data, dividing performance stages and establishing correlation models, the problems of cumbersome manual operation and difficult parameter optimization in traditional electromagnetic simulation are solved, and efficient and accurate optimization of high-frequency circuit design is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- LINKTEL TECH CO LTD
- Filing Date
- 2026-01-09
- Publication Date
- 2026-05-05
AI Technical Summary
Traditional electromagnetic simulation processes involve cumbersome manual operations, difficult parameter optimization, a lack of systematic strategies, subjective result analysis, long iteration cycles, and an inability to quickly achieve closed-loop optimization.
By analyzing time-domain reflectometer curve data, performance stages are divided, performance indicators are extracted, correlation models are established, and electromagnetic simulation parameters are automatically adjusted to achieve fully automated optimization.
It enables objective, accurate, and automatic simulation result analysis and parameter adjustment, improving the efficiency and accuracy of high-frequency circuit design and overcoming the reliance on human experience.
Smart Images
Figure CN121980779A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of electronic design automation technology, and more specifically to a parameter adjustment method for electromagnetic simulation. Background Technology
[0002] With the increasing frequency of circuit design, signal integrity simulation and optimization have become increasingly important. HFSS (High-Frequency Structure Simulator) is widely used as an industry-standard simulation software. In signal integrity analysis, the analysis and optimization of TDR (Time Domain Reflectometry) curves are crucial. Traditional simulation processes suffer from the following problems: cumbersome manual operation, requiring users to manually set parameters, run simulations, and analyze results, leading to low efficiency; difficulty in parameter optimization, lacking systematic parameter adjustment strategies and over-reliance on engineer experience; subjective result analysis, with model correspondence and index judgment in TDR curve analysis relying on manual judgment; long iteration cycles; and the need for re-simulation after each parameter adjustment to confirm the next optimization direction, hindering rapid closed-loop optimization. Summary of the Invention
[0003] In view of the above problems, this disclosure provides a parameter adjustment method for electromagnetic simulation that includes fully automatic parameter scanning, intelligent TDR curve analysis, multi-stage index extraction, and parameter optimization.
[0004] This disclosure provides a method for adjusting parameters in electromagnetic simulation, comprising: analyzing curve data from a time-domain reflectometer to divide the data into multiple performance stages; extracting performance indicators from each performance stage and establishing a correlation model between the performance indicators; wherein the performance indicators include at least impedance; and adjusting the electromagnetic simulation parameters according to the correlation model and the mapping relationship between the performance indicators and the electromagnetic simulation parameters.
[0005] According to an embodiment of this disclosure, the analysis of the curve data of the time-domain reflectometer is used to divide the data into multiple performance stages, including: calculating the second derivative of the curve data, identifying a stable region where the absolute value of the second derivative is lower than a preset threshold, and determining the start and end times of the multiple performance stages based on the boundaries of the stable region.
[0006] According to embodiments of this disclosure, the step of extracting performance indicators from each performance stage and establishing a correlation model between performance indicators includes: determining a combination of performance stages that are correlated based on the correspondence between the performance stages and the physical structure of the electromagnetic simulation circuit; establishing a correlation model between performance indicators for each combination of performance stages; the correlation model is capable of outputting a correlated performance indicator of another performance stage that fluctuates according to the benchmark performance indicator based on the benchmark performance indicator of at least one performance stage.
[0007] According to embodiments of this disclosure, establishing a correlation model between performance indicators for each performance stage combination includes constructing a correlation model sample set based on historical curve data from electromagnetic simulation; the correlation model sample set includes benchmark performance indicator samples and correlated performance indicator samples; and training the correlation model using the correlation model sample set.
[0008] According to embodiments of this disclosure, adjusting the electromagnetic simulation parameters based on the correlation model and the mapping relationship between the performance index and the electromagnetic simulation parameters includes: adjusting the benchmark electromagnetic simulation parameters of a first performance stage based on the benchmark performance index so that the benchmark performance index is within a first preset range; adjusting the correlated electromagnetic simulation parameters of a second performance stage based on the correlation model and the adjusted benchmark electromagnetic simulation parameters so that the correlated performance index is within a second preset range; and determining the parameter adjustment result based on the curve data of the time-domain reflectometer after adjusting the electromagnetic simulation parameters.
[0009] According to an embodiment of this disclosure, determining the parameter adjustment result based on the time-domain reflectometer curve data after adjusting the electromagnetic simulation parameters includes: performing electromagnetic simulation to obtain the curve data based on the adjusted reference electromagnetic simulation parameters and the associated electromagnetic simulation parameters; determining whether the performance index of each performance stage is within a preset range; if yes, then completing the adjustment of the electromagnetic simulation parameters; if no, then re-executing the step of analyzing the time-domain reflectometer curve data to divide it into multiple performance stages.
[0010] According to embodiments of this disclosure, the step of adjusting the electromagnetic simulation parameters based on the association model and the mapping relationship between the performance indicators and the electromagnetic simulation parameters includes: identifying a set of electromagnetic simulation parameters that affect the performance indicators of the corresponding performance stage based on the physical structure of the electromagnetic simulation circuit; and establishing a mapping relationship between the set of electromagnetic simulation parameters and the performance indicators for each performance stage using polynomial regression.
[0011] The second aspect of this disclosure provides a parameter adjustment system for electromagnetic simulation, which can be used to implement the above-mentioned parameter adjustment method for electromagnetic simulation, including: a stage division module for analyzing the curve data of a time-domain reflectometer and dividing it into multiple performance stages; an index extraction module for extracting performance indices from each performance stage and establishing a correlation model between the performance indices; the performance indices include at least impedance; and a relationship modeling module for adjusting the electromagnetic simulation parameters according to the correlation model and the mapping relationship between the performance indices and the electromagnetic simulation parameters.
[0012] A third aspect of this disclosure provides an electronic device comprising: one or more processors; and a memory for storing one or more programs, wherein, when the one or more programs are executed by the one or more processors, the one or more processors perform the parameter adjustment method of the electromagnetic simulation described above.
[0013] A fourth aspect of this disclosure also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the parameter adjustment method of the electromagnetic simulation described above.
[0014] According to the parameter adjustment method for electromagnetic simulation provided in this disclosure, the stable region is identified and the performance stages are divided by calculating the second derivative of the TDR curve data. Based on the performance indicators of the divided performance stages and the mapping relationship between the performance indicators and the electromagnetic simulation parameters, the parameters are automatically adjusted. This avoids the manual analysis in traditional HFSS control, realizes objective, accurate and automatic simulation result analysis and parameter adjustment, and achieves full-process automation from simulation to optimization. This significantly improves the efficiency and accuracy of high-frequency circuit design and overcomes the excessive reliance on human experience. Attached Figure Description
[0015] Figure 1 A flowchart illustrating a parameter adjustment method for electromagnetic simulation according to an embodiment of the present disclosure is shown schematically.
[0016] Figure 2 A schematic diagram illustrating the overall system architecture according to an embodiment of the present disclosure is shown.
[0017] Figure 3 A schematic diagram illustrating the stage division of a TDR curve according to an embodiment of the present disclosure is shown.
[0018] Figure 4 A flowchart illustrating parameter optimization according to an embodiment of the present disclosure is shown schematically.
[0019] Figure 5 A schematic diagram of an association model according to an embodiment of the present disclosure is shown.
[0020] Figure 6 A simulation diagram according to an embodiment of the present disclosure is shown schematically;
[0021] Figure 7 A block diagram of an electronic device suitable for implementing a parameter adjustment method for electromagnetic simulation according to an embodiment of the present disclosure is shown schematically. Detailed Implementation
[0022] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.
[0023] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.
[0024] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.
[0025] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).
[0026] Figure 1 A flowchart illustrating a parameter adjustment method for electromagnetic simulation according to an embodiment of the present disclosure is shown, such as... Figure 1 As shown, embodiments of this disclosure provide a method for adjusting electromagnetic simulation parameters, including: analyzing curve data from a time-domain reflectometer to divide the data into multiple performance stages; extracting performance indicators from each performance stage and establishing a correlation model between the performance indicators; the performance indicators include at least impedance; and adjusting the electromagnetic simulation parameters according to the correlation model and the mapping relationship between the performance indicators and the electromagnetic simulation parameters.
[0027] Before analyzing the time-domain reflectometer (TDR) curve data and dividing it into multiple performance stages, system configuration and initialization are also included. Users need to set the following parameters in the configuration area: HFSS project path, design name, simulation settings; name of the parameter to be optimized, initial value, unit, and boundary; target index value; key parameter mapping relationship, number of simulations, output folder path, etc. The process includes generating parameter combinations (single parameter variation and random combination of two parameters); calling the HFSS control class to set parameters and running the simulation; and exporting the TDR results to a CSV file.
[0028] Figure 2 A schematic diagram illustrating the overall system architecture according to an embodiment of the present disclosure is shown, such as... Figure 2 As shown, the HFSS control module interacts with the HFSS software via a COM interface to automatically set parameters, run simulations, and export TDR results. The TDR analysis module automatically divides the TDR curve into stages by calculating the second derivative of the TDR curve and identifying its stationary region, extracting key indicators (such as impedance values and extreme points) for each stage, achieving automatic division and indicator extraction of each performance stage, establishing an inter-stage correlation model, and predicting extreme points under ideal conditions. The parameter optimization module uses multinomial regression to establish a mapping relationship between design parameters and stage performance indicators, and based on key parameter mapping and staged optimization strategies, it precisely adjusts key parameters sequentially, ultimately combining a global optimization algorithm to achieve multi-objective parameter tuning.
[0029] The specific process is as follows: During initialization, the system receives parameter configurations, performance target settings, and boundary constraints from the user configuration layer. The core control layer controls the HFSS module to perform the first simulation based on the input from the user configuration layer. The TDR analysis module, based on the obtained TDR curve data, performs stage division, extracts indicators, and models the relationships between performance stages. The core control layer parameter optimization module optimizes the parameters based on the results of the TDR analysis module, and then controls the HFSS control module to run the simulation again based on the optimized parameters. Finally, an optimization report and final parameter recommendations and performance predictions are output. The optimization report includes the unified stage division time point, regression model fitting accuracy, and optimized parameter values and predicted indicators.
[0030] It should be noted that during the process, the user configuration layer, HFSS control module, TDR analysis module, and parameter optimization module will all store data in the data storage layer to obtain the simulation boundary library, parameter database, and index database.
[0031] Through embodiments of this disclosure, a set of design parameter values are automatically set into electromagnetic simulation software. The electromagnetic simulation software is run, and time-domain reflectometry (TDR) result data is exported. Based on the TDR result data, a parameter optimization process is automatically executed. Based on the mapping relationship between the extracted performance indicators and the design parameters, the values of the design parameters are adjusted to bring the performance indicators closer to the target values. Therefore, a system and method are provided that can automatically complete HFSS simulation parameter setting, simulation execution, TDR result analysis, indicator extraction, and parameter optimization. From parameter setting to result analysis, no manual intervention is required throughout the entire process, thereby improving simulation efficiency and design accuracy. This method is applicable to parameter optimization design in high-frequency circuits, microwave components, and signal integrity analysis.
[0032] Based on the above embodiments, the curve data of the time domain reflectometer is analyzed to divide the performance into multiple stages, including: calculating the second derivative of the curve data and identifying a stable region where the absolute value of the second derivative is lower than a preset threshold; and determining the start and end times of multiple performance stages based on the boundaries of the stable region.
[0033] Figure 3 A schematic diagram illustrating the stage division of a TDR curve according to an embodiment of the present disclosure is shown, such as... Figure 3 As shown, all CSV files are loaded and parameter values are parsed; a unified stage division method is used to determine the time boundaries of each stage. The stable region of the second derivative (i.e., the region where the absolute value is lower than the preset threshold) can effectively identify the smooth segment of the TDR curve, which corresponds to the stable impedance region in the physical structure.
[0034] Through the embodiments of this disclosure, by analyzing the second derivative of the TDR result data, multiple performance stages of the TDR curve are automatically divided. Based on the time boundary of the stationary region of the second derivative, the start and end times of the multiple performance stages can be determined, thereby realizing the division of the performance stages of the corresponding simulation circuit.
[0035] Based on the above embodiments, performance indicators are extracted from each performance stage, and a correlation model between performance indicators is established, including: determining the combination of related performance stages according to the correspondence between performance stages and the physical structure of the electromagnetic simulation circuit; establishing a correlation model between performance indicators for each combination of performance stages; the correlation model can output the correlation performance indicator of another performance stage that fluctuates according to the benchmark performance indicator based on the benchmark performance indicator of at least one performance stage.
[0036] like Figure 3 As shown, the indicators for each stage are extracted: Stage 2 and 4: impedance value at the center point; Stage 3: impedance value at the extreme point or the predicted indicator point; establish a linear regression prediction model for the indicator points of Stage 2, 4 and Stage 3 to predict the location of the Stage 3 indicator under ideal conditions.
[0037] Through the embodiments of this disclosure, by establishing an association model based on the correspondence between performance stages and the physical structure of the electromagnetic simulation circuit, the technical problem of difficulty in automatically optimizing the coupling of indicators between stages is solved, the association of indicators between stages is realized, and the optimization efficiency is improved.
[0038] Figure 4 A flowchart illustrating parameter optimization according to an embodiment of the present disclosure is shown schematically. The following steps are described in detail. Figure 4 The process shown includes training the association model, phased optimization, and iterative optimization based on the results.
[0039] (1) Training the correlation model: For each performance stage combination, establish a correlation model between performance indicators, including constructing a correlation model sample set based on historical curve data of electromagnetic simulation; the correlation model sample set includes benchmark performance indicator samples and correlation performance indicator samples; and use the correlation model sample set to train the correlation model.
[0040] Figure 5 The schematic diagram illustrates the correlation model according to an embodiment of the present disclosure. After determining that stage 2 and stage 4 affect stage 3 based on the circuit, a multiple linear regression model is trained based on the impedance values of stage 2 and 4 in historical data and the time points of the extreme points of stage 3. Finally, a correlation model is obtained that can output the ideal time point of the index of stage 3 based on the ideal impedance values of stage 2 and stage 4.
[0041] Through the embodiments of this disclosure, a correlation model is trained using historical curve data, thereby accelerating the computational efficiency in the parameter optimization process.
[0042] (2) Phased optimization: Based on the correlation model and the mapping relationship between performance indicators and electromagnetic simulation parameters, adjust the electromagnetic simulation parameters, including: based on the benchmark performance indicators, adjust the benchmark electromagnetic simulation parameters of the first performance stage so that the benchmark performance indicators are within the first preset range; based on the correlation model and the adjusted benchmark electromagnetic simulation parameters, adjust the correlation electromagnetic simulation parameters of the second performance stage so that the correlation performance indicators are within the second preset range; and determine the parameter adjustment result based on the curve data of the time domain reflectometer after adjusting the electromagnetic simulation parameters.
[0043] In this embodiment, based on the correlation model and the adjusted baseline electromagnetic simulation parameters, the correlation electromagnetic simulation parameters for the second performance stage are adjusted so that the correlation performance indicators are within a second preset range. This is followed by a global optimization step: fine-tuning all design parameters to bring all performance indicators close to their respective target values within preset tolerances. This process of first adjusting dispersedly and then fine-tuning globally improves convergence efficiency.
[0044] Through the embodiments of this disclosure, orderly priority optimization is achieved by adjusting the benchmark and associated electromagnetic simulation parameters in stages, preventing the invalidation of preliminary work caused by optimizing the associated electromagnetic simulation parameters first and then optimizing the benchmark electromagnetic simulation parameters.
[0045] (3) Iterative optimization based on results: Based on the curve data of the time domain reflectometer after adjusting the electromagnetic simulation parameters, determine the parameter adjustment results, including: performing electromagnetic simulation to obtain curve data based on the adjusted baseline electromagnetic simulation parameters and associated electromagnetic simulation parameters; determining whether the performance indicators of each performance stage are within the preset range; if yes, then complete the adjustment of electromagnetic simulation parameters; if no, then re-execute the steps of analyzing the curve data of the time domain reflectometer and dividing it into multiple performance stages.
[0046] Through the embodiments of this disclosure, by automatically determining the parameter adjustment results and iteratively retrying, closed-loop automatic iteration is achieved without relying on manual verification, which shortens the optimization cycle and improves reliability.
[0047] Based on the above embodiments, the electromagnetic simulation parameters are adjusted according to the correlation model and the mapping relationship between performance indicators and electromagnetic simulation parameters. This includes: identifying the set of electromagnetic simulation parameters that affect the performance indicators of the corresponding performance stage based on the physical structure of the electromagnetic simulation circuit; and establishing the mapping relationship between the set of electromagnetic simulation parameters and performance indicators for each performance stage using polynomial regression.
[0048] like Figure 3 As shown, with Figure 3 For example, multinomial regression is used to fit the relationship between parameters and indicators. The first subset of key parameters affecting stage 2 is identified and prioritized for optimization. The second subset of key parameters affecting stage 4 is identified and optimized after optimizing the first subset. The third subset of key parameters affecting stage 3 is identified and optimized after optimizing the second subset. Finally, all parameters are fine-tuned globally; the optimized parameter combinations and predicted indicators are output.
[0049] It should be noted that since stages 2 and 4 affect the performance indicators of stage 3, and stages 2 and 4 are relatively stable and less affected by other stages, a correlation prediction model is established between the actual TDR values of stages 2 and 4 and the time points corresponding to the extreme values in the fixed region of stage 3; the time points of the indicators of stage 3 under ideal conditions are accurately predicted, and stages 2 and 4 are optimized first, followed by stage 3.
[0050] Through the embodiments of this disclosure, key parameters for each stage are defined to achieve precise control; parameter boundary constraints and adjustment amount analysis are supported.
[0051] Figure 6 The illustration shows a simulation diagram according to an embodiment of the present disclosure, taking a differential via design as an example, such as... Figure 6 To facilitate demonstration of the optimization effect, the target values for stages 2, 3, and 4 were set to 99.5Ω, 105Ω, and 100.5Ω, respectively. After 16 simulations, the system successfully optimized the impedance values for stages 2, 3, and 4 from the initial values of 96.735Ω, 102.264Ω, and 94.963Ω to 99.536Ω, 104.981Ω, and 100.487Ω, respectively, close to the target values, and all deviations were within the tolerance range. Based on the HFSS simulation results of the optimized parameters, as shown... Figure 6(c) The impedance values at each stage are all within the allowable deviation range, meeting the design requirements. It can also be seen that the stage correlation prediction model accurately predicted the index points for stage 3. The entire optimization process lasted approximately 50 minutes, including simulation time.
[0052] Based on the above-described parameter adjustment method for electromagnetic simulation, this disclosure also provides a parameter adjustment system for electromagnetic simulation, which can be used to implement the above-described parameter adjustment method for electromagnetic simulation, including: a stage division module, used to analyze the curve data of the time-domain reflectometer and divide it into multiple performance stages; an index extraction module, used to extract performance indices from each performance stage and establish a correlation model between performance indices; the performance indices include at least impedance; and a relationship modeling module, used to adjust the electromagnetic simulation parameters according to the correlation model and the mapping relationship between performance indices and electromagnetic simulation parameters.
[0053] Figure 7 A block diagram of an electronic device suitable for implementing a parameter adjustment method for electromagnetic simulation according to an embodiment of the present invention is shown schematically.
[0054] like Figure 7 As shown, an electronic device 700 according to an embodiment of the present invention includes a processor 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage portion 708 into a random access memory (RAM) 703. The processor 701 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 701 may also include onboard memory for caching purposes. The processor 701 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.
[0055] RAM 703 stores various programs and data required for the operation of electronic device 700. Processor 701, ROM 702, and RAM 703 are interconnected via bus 704. Processor 701 executes various operations of the method flow according to embodiments of the present invention by executing programs in ROM 702 and / or RAM 703. It should be noted that the programs may also be stored in one or more memories other than ROM 702 and RAM 703. Processor 701 may also execute various operations of the method flow according to embodiments of the present invention by executing programs stored in said one or more memories.
[0056] According to an embodiment of the present invention, the electronic device 700 may further include an input / output (I / O) interface 705, which is also connected to a bus 704. The electronic device 700 may also include one or more of the following components connected to the I / O interface 705: an input section 706 including a keyboard, mouse, etc.; an output section 707 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card, modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. A removable medium 711, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 710 as needed so that computer programs read from it can be installed into the storage section 708 as needed.
[0057] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the present invention.
[0058] According to embodiments of the present invention, a computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of the present invention, a computer-readable storage medium may include ROM 702 and / or RAM 703 and / or one or more memories other than ROM 702 and RAM 703 described above.
[0059] Embodiments of the present invention also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the methods provided in the embodiments of the present invention.
[0060] When the computer program is executed by the processor 701, it performs the functions defined in the system / apparatus of this invention. According to embodiments of the invention, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.
[0061] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 709, and / or installed from a removable medium 711. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.
[0062] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 709, and / or installed from the removable medium 711. When the computer program is executed by the processor 701, it performs the functions defined in the system of this embodiment of the invention. According to embodiments of the invention, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.
[0063] According to embodiments of the present invention, program code for executing the computer programs provided in the embodiments of the present invention can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages include, but are not limited to, languages such as Java, C++, Python, "C", or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).
[0064] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
[0065] Those skilled in the art will understand that the features described in the various embodiments and / or claims of the present invention can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, the features described in the various embodiments and / or claims of the present invention can be combined or combined in various ways without departing from the spirit and teachings of the present invention. All such combinations and / or combinations fall within the scope of the present invention.
[0066] The embodiments of the present invention have been described above. However, these embodiments are merely illustrative and not intended to limit the scope of the invention. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of the invention is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of the invention, and all such substitutions and modifications should fall within the scope of the invention.
Claims
1. A parameter adjustment method for electromagnetic simulation, characterized in that, include: By analyzing the curve data from the time-domain reflectometer, multiple performance stages were identified. Extract performance metrics from each performance stage and establish a correlation model between performance metrics; The performance indicators include at least impedance; The electromagnetic simulation parameters are adjusted based on the correlation model and the mapping relationship between the performance indicators and the electromagnetic simulation parameters.
2. The method according to claim 1, wherein, The analysis of the time-domain reflectometer curve data yielded multiple performance stages, including: Calculate the second derivative of the curve data and identify the stable region where the absolute value of the second derivative is lower than a preset threshold; Based on the boundaries of the stable region, the start and end times of multiple performance phases are determined.
3. The method according to claim 1, wherein, The step of extracting performance metrics from each performance stage and establishing a correlation model between performance metrics includes: Based on the correspondence between the performance stages and the physical structure of the electromagnetic simulation circuit, determine the associated combinations of performance stages; For each performance stage combination, a correlation model is established between performance indicators; the correlation model can output a correlation performance indicator for another performance stage that fluctuates according to the baseline performance indicator based on the baseline performance indicator of at least one performance stage.
4. The method according to claim 3, wherein, For each performance stage combination, a correlation model is established between performance metrics, including... Based on historical curve data from electromagnetic simulation, a correlation model sample set is constructed; the correlation model sample set includes benchmark performance index samples and correlation performance index samples. The association model is trained using the aforementioned association model sample set.
5. The method according to claim 3, wherein, The step of adjusting the electromagnetic simulation parameters based on the correlation model and the mapping relationship between the performance indicators and the electromagnetic simulation parameters includes: Based on the aforementioned benchmark performance index, adjust the benchmark electromagnetic simulation parameters of the first performance stage so that the benchmark performance index is within a first preset range. Based on the correlation model and the adjusted baseline electromagnetic simulation parameters, the correlation electromagnetic simulation parameters for the second performance stage are adjusted so that the correlation performance index is within the second preset range. The result of the parameter adjustment is determined based on the curve data of the time-domain reflectometer after adjusting the electromagnetic simulation parameters.
6. The method according to claim 5, wherein, The step of determining the parameter adjustment result based on the curve data of the time-domain reflectometer after adjusting the electromagnetic simulation parameters includes: Based on the adjusted baseline electromagnetic simulation parameters and the associated electromagnetic simulation parameters, electromagnetic simulation is performed to obtain the curve data; Determine whether the performance metrics for each performance stage are within the preset range; If so, then complete the adjustment of the electromagnetic simulation parameters; If not, the steps of analyzing the time-domain reflectometer curve data and dividing it into multiple performance stages are repeated.
7. The method according to claim 1, wherein, The step of adjusting the electromagnetic simulation parameters based on the correlation model and the mapping relationship between the performance indicators and the electromagnetic simulation parameters includes: Based on the physical structure of the electromagnetic simulation circuit, identify the set of electromagnetic simulation parameters that affect the performance indicators of the corresponding performance stage; Using polynomial regression, a mapping relationship between the electromagnetic simulation parameter set and performance indicators for each performance stage is established.
8. A parameter adjustment system for electromagnetic simulation, characterized in that, Capable of implementing the method according to any one of claims 1 to 7, comprising: The phase division module is used to analyze the curve data of the time domain reflectometer and divide it into multiple performance phases; The indicator extraction module is used to extract performance indicators from each of the performance stages and establish a correlation model between the performance indicators; the performance indicators include at least impedance. The relationship modeling module is used to adjust the electromagnetic simulation parameters based on the association model and the mapping relationship between the performance indicators and the electromagnetic simulation parameters.
9. An electronic device, comprising: One or more processors; Storage device for storing one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors perform the method according to any one of claims 1 to 7.
10. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the method according to any one of claims 1 to 7.