Chip electromagnetic compatibility analysis method, device and equipment

Abstract

The invention relates to the technical field of data processing, and provides a chip electromagnetic compatibility analysis method, device and equipment, and the method comprises the steps: carrying out the multi-dimensional collection of electromagnetic radiation data of a chip, and obtaining time domain electromagnetic signal data, frequency domain electromagnetic signal data and space electromagnetic distribution data; an electromagnetic interference influence matrix of the chip is constructed and decomposed, after electromagnetic interference source distribution information and interference propagation path information are obtained, an electromagnetic compatibility dynamic evolution sequence of the chip is constructed and analyzed, and an electromagnetic stability parameter and an electromagnetic sensitivity parameter of the chip are calculated; and analyzing the electromagnetic interference influence matrix by using the electromagnetic stability parameter and the electromagnetic sensitivity parameter of the chip, and generating an electromagnetic compatibility evaluation result based on an analysis result. By means of the scheme, the problems that due to the fact that complex interaction between different electromagnetic signals and internal components of the chip cannot be comprehensively captured, a test result is different from performance in an actual operation environment, and interference and operation failure are caused are solved.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and in particular to a chip electromagnetic compatibility analysis method, device and equipment. Background Art

[0002] In the rapidly developing semiconductor industry, the electromagnetic compatibility (EMC) of chips has become a critical aspect in design and manufacturing. As the complexity and integration of electronic devices continue to increase, it has become necessary to ensure that chips do not emit unnecessary electromagnetic interference (EMI) and are able to resist external electromagnetic interference. This is particularly important in the fields of telecommunications, automobiles, medical devices, and consumer electronics, as electromagnetic compatibility directly affects the performance and reliability of the equipment.

[0003] Among the related technical means, the chip electromagnetic compatibility analysis method usually includes a series of standardized tests and measurements to evaluate the electromagnetic radiation and immunity of the chip. Common methods include using an anechoic chamber to measure radiation emissions and perform immunity tests to expose the chip to a specified electromagnetic field. Through these methods, potential EMI sources can be identified and the chip's ability to operate reliably under external electromagnetic interference can be evaluated. By analyzing these factors, manufacturers can implement design changes or shielding technologies to improve the electromagnetic compatibility performance of the chip.

[0004] Although these methods can identify and mitigate some EMC issues, there are deficiencies in electromagnetic analysis in practical applications. For example, although standardized tests provide valuable insights, they cannot fully capture the complex interactions between different electromagnetic signals and internal components of the chip, which leads to differences between test results and performance in the actual operating environment, causing interference and operational failures. Summary of the invention

[0005] In order to improve the problem that it is impossible to fully capture the complex interactions between different electromagnetic signals and internal components of the chip, which leads to differences between test results and performance in the actual operating environment, causing interference and operation failure, the present application provides a chip electromagnetic compatibility analysis method, device and equipment.

[0006] The present invention provides a chip electromagnetic compatibility analysis method, comprising: multi-dimensionally collecting electromagnetic radiation data of a chip to obtain time-domain electromagnetic signal data, frequency-domain electromagnetic signal data and spatial electromagnetic distribution data; constructing an electromagnetic interference influence matrix of a chip using the time-domain electromagnetic signal data, frequency-domain electromagnetic signal data and spatial electromagnetic distribution data, and performing feature decomposition on the electromagnetic interference influence matrix to extract electromagnetic coupling feature parameters to obtain electromagnetic interference source distribution information and interference propagation path information of the chip; constructing an electromagnetic compatibility dynamic evolution sequence of a chip using the electromagnetic interference source distribution information and the interference propagation path information, performing multi-scale fractal analysis on the electromagnetic compatibility dynamic evolution sequence, and calculating electromagnetic stability parameters and electromagnetic sensitivity parameters of the chip; analyzing the electromagnetic interference influence matrix using the electromagnetic stability parameters and electromagnetic sensitivity parameters of the chip, and generating an electromagnetic compatibility evaluation result based on the analysis result.

[0007] As a preferred solution, the step of performing multi-dimensional acquisition of the electromagnetic radiation data of the chip to obtain time-domain electromagnetic signal data, frequency-domain electromagnetic signal data and spatial electromagnetic distribution data includes: performing electromagnetic radiation scanning on the chip by a time-domain electromagnetic signal acquisition device to obtain initial electromagnetic signal data and background electromagnetic noise data, performing adaptive filtering processing on the background electromagnetic noise data to obtain noise characteristic parameters, and using the noise characteristic parameters to perform denoising correction on the initial electromagnetic signal data to obtain time-domain electromagnetic signal data; performing short-time Fourier transform processing on the initial electromagnetic signal data to obtain instantaneous spectrum distribution data and phase evolution data, calculating key harmonic components based on the instantaneous spectrum distribution data, performing nonlinear feature extraction on the key harmonic components and the instantaneous spectrum distribution data, constructing a spectrum harmonic correlation matrix, and generating frequency-domain electromagnetic signal data using the spectrum harmonic correlation matrix; establishing a multidimensional electromagnetic energy distribution model based on the time-domain electromagnetic signal data and the phase evolution data, extracting electromagnetic field gradient distribution data from the multidimensional electromagnetic energy distribution model, and using the electromagnetic field gradient distribution data to perform spatial mapping on the physical structure feature data of the chip to obtain spatial electromagnetic distribution data.

[0008] As a preferred embodiment, the electromagnetic interference influence matrix of the chip is constructed by using the time domain electromagnetic signal data, the frequency domain electromagnetic signal data and the spatial electromagnetic distribution data, and the electromagnetic interference influence matrix is ​​subjected to characteristic decomposition to extract electromagnetic coupling characteristic parameters to obtain the electromagnetic interference source distribution information and interference propagation path information of the chip, including: decomposing the time domain electromagnetic signal data by a time series decomposition method, extracting a key pulse sequence based on the decomposition result, and calculating the pulse amplitude variation trend according to the key pulse sequence; constructing a pulse-spectrum interaction matrix by using the pulse amplitude variation trend and the frequency domain electromagnetic signal data, and calculating the pulse response weight based on the pulse-spectrum interaction matrix; and The pulse response weights are spatially projected based on the data to obtain a spatial electromagnetic propagation mode; an electromagnetic interference propagation topology map is constructed according to the spatial electromagnetic propagation mode, and the interference coupling path is calculated based on the electromagnetic interference propagation topology map, and the interference coupling path is energy backtracking analysis is performed on the interference coupling path to extract the main electromagnetic interference sources inside the chip and the surrounding environment; based on the electromagnetic interference propagation topology map and the interference coupling path, the interference source influence factor is calculated, and the dominant interference source is screened out, an electromagnetic interference influence matrix is ​​constructed using the dominant interference source and the interference source influence factor, the electromagnetic interference influence matrix is ​​feature decomposed, electromagnetic coupling characteristic parameters are extracted, and electromagnetic interference source distribution information and interference propagation path information are generated according to the electromagnetic coupling characteristic parameters.

[0009] As a preferred embodiment, the step of constructing the electromagnetic compatibility dynamic evolution sequence of the chip through the electromagnetic interference source distribution information and the interference propagation path information includes: constructing a time-interference source association sequence using the electromagnetic interference source distribution information, and calculating the interference intensity curve on each time slice based on the time-interference source association sequence, performing multi-dimensional correlation analysis on the interference intensity curve through the interference propagation path information to generate a path-time interference association sequence; performing pattern recognition on the path-time interference association sequence to extract typical interference propagation modes, calculating the mode transition probability based on the typical interference propagation mode, and constructing an electromagnetic interference evolution graph using the mode transition probability; calculating the interference steady-state distribution through the electromagnetic interference evolution graph, performing differential transformation on the interference steady-state distribution to generate an electromagnetic compatibility change rate sequence, and extracting the electromagnetic compatibility fluctuation characteristics based on the electromagnetic compatibility change rate sequence; calculating the electromagnetic steady-state duration and interference impact period of the chip using the electromagnetic compatibility fluctuation characteristics, and constructing an electromagnetic compatibility dynamic evolution sequence based on the electromagnetic steady-state duration and interference impact period.

[0010] As a preferred scheme, the step of performing multi-scale fractal analysis on the electromagnetic compatibility dynamic evolution sequence to calculate the electromagnetic stability parameters and electromagnetic sensitivity parameters of the chip includes: performing piecewise fractal dimension calculation on the electromagnetic compatibility dynamic evolution sequence to obtain a fractal dimension evolution curve, extracting an electromagnetic compatibility complexity index based on the fractal dimension evolution curve, and calculating electromagnetic stability parameters at different scales; performing time series modeling of the electromagnetic energy leakage risk using the electromagnetic stability parameters to obtain electromagnetic leakage trend parameters, and calculating electromagnetic sensitivity parameters based on the electromagnetic leakage trend parameters.

[0011] As a preferred scheme, the step of using the electromagnetic stability parameters to perform time series modeling on the electromagnetic energy leakage risk to obtain electromagnetic leakage trend parameters, and calculating the electromagnetic sensitivity parameters according to the electromagnetic leakage trend parameters includes: extracting the change trend of the electromagnetic stability parameters in different time windows based on the electromagnetic compatibility dynamic evolution sequence, and calculating the electromagnetic stability mean, standard deviation and volatility in all time windows based on the change trend; constructing an electromagnetic stability time series using the electromagnetic stability mean, the standard deviation and the volatility, correlating the electromagnetic stability time series with the working mode of the chip, extracting the electromagnetic stability change characteristics under a specific working mode, and calculating the stability under a specific mode based on the electromagnetic stability change characteristics. extreme points and mutation points; performing time interpolation processing on the stability extreme points and the mutation points to generate a time evolution curve of the electromagnetic energy leakage risk, using the time evolution curve to construct an electromagnetic energy leakage risk time series, performing multi-scale decomposition on the electromagnetic energy leakage risk time series, extracting short-term fluctuation characteristics and long-term trend characteristics, and calculating the electromagnetic leakage trend parameters according to the short-term fluctuation characteristics and the long-term trend characteristics; classifying the energy leakage of the chip under different electromagnetic environments according to the electromagnetic leakage trend parameters, constructing an electromagnetic sensitivity change model, and calculating the sensitivity threshold of the chip under different electromagnetic environment conditions based on the electromagnetic sensitivity change model, and using the sensitivity threshold to calculate the electromagnetic sensitivity parameters of the chip under different interference conditions.

[0012] As a preferred scheme, the step of analyzing the electromagnetic interference influence matrix using the electromagnetic stability parameters and electromagnetic sensitivity parameters of the chip, and generating an electromagnetic compatibility evaluation result based on the analysis result, includes: calculating the stability index of the electromagnetic interference influence matrix based on the electromagnetic stability parameters, extracting high-risk interference channels based on the stability index, performing adaptive perturbation calculation on the high-risk interference channels through the electromagnetic sensitivity parameters, and generating an interference change trend curve; performing pattern matching on the interference change trend curve to identify abnormal interference events, analyzing the interference intensity modulation factor based on the abnormal interference events, performing perturbation adjustment on the electromagnetic interference influence matrix using the interference intensity modulation factor to obtain an adjusted electromagnetic interference influence matrix, and generating an electromagnetic compatibility evaluation result based on the adjusted electromagnetic interference influence matrix.

[0013] The present application also provides a chip electromagnetic compatibility analysis device, including: an acquisition module, which is used to perform multi-dimensional acquisition of the electromagnetic radiation data of the chip to obtain time domain electromagnetic signal data, frequency domain electromagnetic signal data and spatial electromagnetic distribution data; a decomposition module, which is used to construct the electromagnetic interference influence matrix of the chip using the time domain electromagnetic signal data, frequency domain electromagnetic signal data and spatial electromagnetic distribution data, and perform feature decomposition on the electromagnetic interference influence matrix, extract electromagnetic coupling feature parameters, and obtain the electromagnetic interference source distribution information and interference propagation path information of the chip; a construction module, which is used to construct the electromagnetic compatibility dynamic evolution sequence of the chip through the electromagnetic interference source distribution information and the interference propagation path information, perform multi-scale fractal analysis on the electromagnetic compatibility dynamic evolution sequence, and calculate the electromagnetic stability parameters and electromagnetic sensitivity parameters of the chip; an analysis module, which is used to analyze the electromagnetic interference influence matrix using the electromagnetic stability parameters and electromagnetic sensitivity parameters of the chip, and generate an electromagnetic compatibility evaluation result based on the analysis result.

[0014] The present application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the chip electromagnetic compatibility analysis method described in any one of the above is implemented.

[0015] Compared with the prior art, the present application has the following beneficial effects: comprehensive analysis. By collecting electromagnetic radiation data of the chip in multiple dimensions, time domain electromagnetic signal data, frequency domain electromagnetic signal data and spatial electromagnetic distribution data are obtained, and these data are used to construct an electromagnetic interference influence matrix, and feature decomposition is performed on it, and electromagnetic coupling characteristic parameters are extracted. After obtaining the electromagnetic interference source distribution information and interference propagation path information of the chip, the electromagnetic compatibility dynamic evolution sequence of the chip is constructed, and multi-scale fractal analysis is performed to calculate the electromagnetic stability parameters and electromagnetic sensitivity parameters of the chip. These parameters are used to analyze the electromagnetic interference influence matrix, and generate electromagnetic compatibility evaluation results. By comprehensively considering the data in the time domain, frequency domain and space dimensions, a more detailed and accurate electromagnetic compatibility evaluation is provided, which can effectively identify and solve the electromagnetic interference problems of the chip in the actual operating environment, improve the electromagnetic compatibility performance of the chip, and improve the inability to fully capture the complex interactions between different electromagnetic signals and the internal components of the chip, which leads to differences between the test results and the performance in the actual operating environment, causing interference and operation failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0017] The structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention.

[0018] Figure 1 It is a schematic diagram of the flow of a chip electromagnetic compatibility analysis method provided by an embodiment of the present invention; Figure 2 is a schematic block diagram of the structure of a chip electromagnetic compatibility analysis device provided by an embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0019] Description of reference numerals: 10. Chip electromagnetic compatibility analysis device; 11. Acquisition module; 12. Decomposition module; 13. Construction module; 14. Analysis module; 20. Electronic device; 21. Memory; 22. Processor. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0022] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0023] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0024] The technical solution of the present invention is further described below with reference to the accompanying drawings and through specific implementation methods.

[0025] Embodiment 1: like Figure 1 As shown, the present application provides a chip electromagnetic compatibility analysis method, including steps S100 to S400.

[0026] Step S100: collect electromagnetic radiation data of the chip in multiple dimensions to obtain time domain electromagnetic signal data, frequency domain electromagnetic signal data and spatial electromagnetic distribution data.

[0027] In this step, high-precision sensors and test equipment are used to collect detailed electromagnetic radiation data of the chip at different times, frequencies and spatial positions. Specifically, the time domain electromagnetic signal data is recorded by a high-frequency oscilloscope, the frequency domain electromagnetic signal data is measured by a spectrum analyzer, and the spatial electromagnetic distribution data is obtained by a three-dimensional field strength probe.

[0028] For example, Agilent spectrum analyzer N9030A is used for frequency domain signal acquisition, Tektronix oscilloscope DPO70000 is used for time domain signal acquisition, and Keysight E5071C vector network analyzer is used to obtain spatial electromagnetic distribution data.

[0029] Step S200: construct an electromagnetic interference influence matrix of the chip using time domain electromagnetic signal data, frequency domain electromagnetic signal data, and spatial electromagnetic distribution data, and perform feature decomposition on the electromagnetic interference influence matrix to extract electromagnetic coupling characteristic parameters to obtain electromagnetic interference source distribution information and interference propagation path information of the chip.

[0030] For example, the collected time-domain electromagnetic signal data, frequency-domain electromagnetic signal data, and spatial electromagnetic distribution data are first normalized. Specifically, the data is reduced in dimension using the PCA algorithm, and an electromagnetic interference influence matrix is ​​constructed. Then, the singular value decomposition method is used to perform feature decomposition on the matrix and extract electromagnetic coupling characteristic parameters.

[0031] For example, the PCA function in Matlab software is used to reduce the dimension of the data, and the SVD function is used to decompose the electromagnetic interference influence matrix to obtain the electromagnetic coupling characteristic parameters.

[0032] Step S300: construct an electromagnetic compatibility dynamic evolution sequence of the chip through electromagnetic interference source distribution information and interference propagation path information, perform multi-scale fractal analysis on the electromagnetic compatibility dynamic evolution sequence, and calculate the electromagnetic stability parameters and electromagnetic sensitivity parameters of the chip.

[0033] For example, based on the chip's electromagnetic interference source distribution information and interference propagation path information, an electromagnetic compatibility dynamic evolution sequence is constructed. Specifically, through the multi-scale fractal analysis method, the variation law of electromagnetic signals at different time scales is calculated to obtain the chip's electromagnetic stability parameters and electromagnetic sensitivity parameters.

[0034] For example, the pywt library in Python is used to perform wavelet transform, analyze the multi-scale fractal characteristics of electromagnetic signals, and calculate electromagnetic stability parameters and electromagnetic sensitivity parameters.

[0035] Step S400: Analyze the electromagnetic interference influence matrix using the electromagnetic stability parameters and electromagnetic sensitivity parameters of the chip, and generate an electromagnetic compatibility evaluation result based on the analysis result.

[0036] According to the electromagnetic stability parameters and electromagnetic sensitivity parameters of the chip, the electromagnetic interference impact matrix is ​​analyzed in detail. Specifically, by comparing the electromagnetic interference intensity and distribution under different parameters, the electromagnetic compatibility evaluation results are generated.

[0037] For example, the statistical analysis software SPSS is used to perform regression analysis on electromagnetic stability parameters and electromagnetic sensitivity parameters to generate an electromagnetic compatibility assessment report.

[0038] In this example, electromagnetic radiation data of the chip is collected in multiple dimensions to obtain time-domain electromagnetic signal data, frequency-domain electromagnetic signal data and spatial electromagnetic distribution data; then, the electromagnetic interference influence matrix of the chip is constructed using the time-domain electromagnetic signal data, frequency-domain electromagnetic signal data and spatial electromagnetic distribution data, and the electromagnetic interference influence matrix is ​​subjected to feature decomposition to extract electromagnetic coupling feature parameters to obtain electromagnetic interference source distribution information and interference propagation path information of the chip; then, the electromagnetic compatibility dynamic evolution sequence of the chip is constructed using the electromagnetic interference source distribution information and the interference propagation path information, and the electromagnetic compatibility dynamic evolution sequence is subjected to multi-scale fractal analysis to calculate the electromagnetic stability parameters and electromagnetic sensitivity parameters of the chip; finally, the electromagnetic stability parameters and electromagnetic sensitivity parameters of the chip are used to analyze the electromagnetic interference influence matrix, and an electromagnetic compatibility evaluation result is generated based on the analysis result.

[0039] By collecting the chip's time-domain electromagnetic signal data, frequency-domain electromagnetic signal data, and spatial electromagnetic distribution data in multiple dimensions, and combining feature decomposition, electromagnetic coupling feature parameter extraction, and multi-scale fractal analysis methods, the chip's electromagnetic interference source distribution information and interference propagation path information can be accurately obtained. This method not only improves the accuracy of identifying and locating electromagnetic interference problems, but also provides a more detailed and dynamic electromagnetic compatibility assessment, providing strong support for chip design and optimization, and improving the inability to fully capture the complex interactions between different electromagnetic signals and internal components of the chip, which in turn leads to differences in test results and performance in the actual operating environment, causing interference and operational failures.

[0040] Embodiment 2: In step S100, the electromagnetic radiation scan of the chip is performed by a time-domain electromagnetic signal acquisition device to obtain initial electromagnetic signal data and background electromagnetic noise data, the background electromagnetic noise data is adaptively filtered to obtain noise characteristic parameters, and the initial electromagnetic signal data is denoised and corrected using the noise characteristic parameters to obtain time-domain electromagnetic signal data.

[0041] By combining a high-sensitivity antenna and a high-frequency oscilloscope, the chip is scanned for electromagnetic radiation in all directions to capture the initial electromagnetic signal data and background electromagnetic noise data. Specifically, the background noise is measured first, and the background noise is separated by an adaptive filtering algorithm to extract the noise characteristic parameters. Then, these noise characteristic parameters are applied to the initial electromagnetic signal data, and denoising correction is performed using a mathematical filtering model.

[0042] For example, the R&S ESR receiver is used to obtain the initial electromagnetic signal data, and the noise data is processed through the adaptive filter function in Matlab to extract the noise characteristic parameters, and then the signal is filtered in the frequency domain to obtain clear time domain electromagnetic signal data.

[0043] The initial electromagnetic signal data is processed by short-time Fourier transform to obtain instantaneous spectrum distribution data and phase evolution data. The key harmonic components are calculated based on the instantaneous spectrum distribution data, and nonlinear features are extracted from the key harmonic components and instantaneous spectrum distribution data. The spectrum harmonic correlation matrix is ​​constructed, and the frequency domain electromagnetic signal data is generated using the spectrum harmonic correlation matrix.

[0044] The short-time Fourier transform algorithm is used to perform spectrum analysis on the collected time-domain electromagnetic signal data. Specifically, the instantaneous spectrum distribution data and phase evolution data are calculated. Subsequently, the harmonic feature database is established by extracting the key harmonic components in the spectrum, and the spectrum harmonic correlation matrix is ​​constructed by combining the nonlinear feature analysis method.

[0045] For example, the scipy.signal.stft function in Python is used to perform short-time Fourier transform processing to extract instantaneous spectrum distribution and phase data, and the key harmonic components are identified through Harmonic Wavelet Transform and input into the spectrum harmonic correlation matrix to generate frequency domain electromagnetic signal data.

[0046] Based on the time-domain electromagnetic signal data and phase evolution data, a multidimensional electromagnetic energy distribution model is established, and the electromagnetic field gradient distribution data is extracted from the multidimensional electromagnetic energy distribution model. The electromagnetic field gradient distribution data is used to spatially map the physical structure feature data of the chip to obtain spatial electromagnetic distribution data.

[0047] By combining the time-domain electromagnetic signal data and phase evolution data, a comprehensive multi-dimensional electromagnetic energy distribution model is established. Specifically, the finite element analysis method is used to model the time, frequency and spatial distribution of the electromagnetic field, extract the electromagnetic field gradient distribution data, and map it to the physical structure of the chip to obtain the spatial electromagnetic distribution data.

[0048] For example, finite element analysis is performed using COMSOL Multiphysics software. By importing time-domain electromagnetic signals and phase data, a multidimensional electromagnetic energy distribution model is constructed, and by extracting electromagnetic field gradient data, spatial electromagnetic distribution information is generated.

[0049] In step S200, the time domain electromagnetic signal data is decomposed by a time series decomposition method, and a key pulse sequence is extracted based on the decomposition result, and the pulse amplitude variation trend is calculated according to the key pulse sequence.

[0050] Through the EMD (Empirical Mode Decomposition) method, the time-domain electromagnetic signal data is decomposed into several intrinsic mode functions (IMFs). Specifically, the key pulse sequence is identified and separated, and the time-varying characteristics of the electromagnetic signal are predicted by analyzing the amplitude variation trend of the pulse sequence.

[0051] For example, the PyEMD library in Python is used to decompose the time-domain electromagnetic signal data, extract the main intrinsic mode functions, and fit the pulse amplitude change trend through the curve fitting tool in Matlab to generate the time-varying trend analysis results of the electromagnetic signal.

[0052] The pulse amplitude variation trend and frequency domain electromagnetic signal data are used to construct a pulse-spectrum interaction matrix, and the pulse response weight is calculated based on the pulse-spectrum interaction matrix. The pulse response weight is spatially projected through the spatial electromagnetic distribution data to obtain the spatial electromagnetic propagation mode.

[0053] By combining the pulse amplitude variation trend with the frequency domain electromagnetic signal data, a pulse-spectrum interaction matrix is ​​constructed. Specifically, the response weights of each pulse in different frequency bands are calculated through matrix operations, and these response weights are used to map the spatial electromagnetic distribution data to obtain the propagation pattern of the electromagnetic signal in space.

[0054] For example, the matrix multiplication function in Matlab is used to interactively calculate the pulse amplitude change trend and spectrum data to obtain the pulse response weight, and these weights are spatially projected through COMSOL to generate a three-dimensional electromagnetic propagation map.

[0055] An electromagnetic interference propagation topology map is constructed according to the spatial electromagnetic propagation mode, and the interference coupling path is calculated based on the electromagnetic interference propagation topology map. The energy backtracking analysis of the interference coupling path is performed to extract the main electromagnetic interference sources inside the chip and the surrounding environment.

[0056] By performing topological analysis on the electromagnetic propagation pattern in space, an electromagnetic interference propagation topology map is constructed. Specifically, the connection paths between the interference source and each node are identified and marked. Through the energy backtracking algorithm, the propagation path of the interference signal is analyzed, and the main electromagnetic interference sources inside and around the chip are located.

[0057] For example, Gephi software is used to visualize and analyze electromagnetic interference propagation data, construct an electromagnetic interference topology map, and implement the energy backtracking algorithm in Python to accurately locate the main electromagnetic interference sources.

[0058] Based on the electromagnetic interference propagation topology diagram and the interference coupling path, the interference source impact factor is calculated, and the dominant interference source is screened out. The electromagnetic interference impact matrix is ​​constructed using the dominant interference source and the interference source impact factor. The electromagnetic interference impact matrix is ​​characteristically decomposed and the electromagnetic coupling characteristic parameters are extracted. The electromagnetic interference source distribution information and interference propagation path information are generated according to the electromagnetic coupling characteristic parameters.

[0059] Through the interference coupling path and topology data, the influence factor of each interference source is calculated. Specifically, the dominant interference source with the greatest impact on the chip is screened out. Then, the electromagnetic interference influence matrix is ​​constructed using these dominant interference sources and their influence factors, and the key electromagnetic coupling characteristic parameters are extracted through the SVD decomposition method to generate electromagnetic interference source distribution information and interference propagation path information.

[0060] For example, the SVD function in Matlab is used to decompose the electromagnetic interference influence matrix, extract the electromagnetic coupling characteristic parameters, and visualize the distribution of electromagnetic interference sources through ArcGIS to generate a detailed interference propagation path map.

[0061] In step S300, the electromagnetic interference source distribution information is used to construct a time-interference source association sequence, and the interference intensity curve on each time slice is calculated based on the time-interference source association sequence. The interference intensity curve is subjected to multi-dimensional correlation analysis through the interference propagation path information to generate a path-time interference association sequence.

[0062] The time-interference source correlation sequence is constructed through the electromagnetic interference source distribution information and time series data. Specifically, the interference intensity curve on each time slice is calculated, and combined with the interference propagation path information, multi-dimensional correlation analysis is performed to obtain the path-time interference correlation sequence.

[0063] For example, the tsibble package in R language is used to construct the time-interference source association sequence, and the interference intensity curve of each time slice is visualized and analyzed through the ggplot2 package to generate a path-time interference association sequence diagram.

[0064] Pattern recognition is performed on the path-time interference correlation sequence to extract typical interference propagation patterns. The pattern transition probability is calculated based on the typical interference propagation pattern, and the electromagnetic interference evolution diagram is constructed using the pattern transition probability.

[0065] The path-time interference correlation sequence is analyzed through pattern recognition algorithm. Specifically, typical interference propagation patterns are extracted, and the electromagnetic interference evolution diagram is constructed by calculating the pattern transition probability, showing the evolution relationship of the interference pattern between different time slices.

[0066] For example, the sklearn library in Python is used for pattern recognition to extract typical propagation patterns, and the networkx library is used to calculate the pattern transition probability, build an electromagnetic interference evolution diagram, and display the evolution process of different interference patterns.

[0067] The interference steady-state distribution is calculated through the electromagnetic interference evolution diagram, and the electromagnetic compatibility change rate sequence is generated by differential transformation. The electromagnetic compatibility fluctuation characteristics are extracted based on the electromagnetic compatibility change rate sequence.

[0068] The electromagnetic compatibility change rate sequence is analyzed through the interference steady-state distribution, and differential transformation is performed. Specifically, the evolution rate of the interference signal between different steady states is analyzed to obtain the electromagnetic compatibility change rate sequence and identify the fluctuation characteristics therein.

[0069] For example, the numpy library in Python is used to perform differential analysis on the steady-state distribution, and the fft function in Matlab is used to extract the spectral characteristics of the electromagnetic compatibility change rate series to identify key fluctuation characteristics.

[0070] The electromagnetic compatibility fluctuation characteristics are used to calculate the electromagnetic steady-state duration and interference impact period of the chip, and the electromagnetic compatibility dynamic evolution sequence is constructed based on the electromagnetic steady-state duration and interference impact period.

[0071] By analyzing the electromagnetic compatibility fluctuation characteristics, the steady-state duration and interference impact period of the chip in a specific electromagnetic environment are calculated. Specifically, these parameters are used to construct the electromagnetic compatibility dynamic evolution sequence to predict the electromagnetic response characteristics of the chip under different interference conditions.

[0072] For example, the forecast package in R language is used to perform time series forecasts on electromagnetic compatibility fluctuation data, calculate the duration of electromagnetic steady state and interference shock cycle, and construct the dynamic evolution sequence of electromagnetic compatibility through the dplyr package.

[0073] In step S300, it also includes calculating the segmented fractal dimension of the electromagnetic compatibility dynamic evolution sequence, obtaining the fractal dimension evolution curve, extracting the electromagnetic compatibility complexity index based on the fractal dimension evolution curve, and calculating the electromagnetic stability parameters at different scales.

[0074] By calculating the segmented fractal dimension of the dynamic evolution sequence of electromagnetic compatibility, specifically, analyzing the fractal characteristics of electromagnetic signals in different time periods, generating fractal dimension evolution curves, and extracting complexity indicators of electromagnetic compatibility. Furthermore, through these complexity indicators, the electromagnetic stability parameters at different time scales are calculated to evaluate the compatibility performance of the chip in various electromagnetic environments.

[0075] For example, the fractal toolbox in Matlab is used to perform fractal analysis on electromagnetic signals, obtain fractal dimension evolution curves, and use these curves to calculate electromagnetic stability parameters and generate an electromagnetic compatibility assessment report for the chip.

[0076] The electromagnetic stability parameters are used to conduct time series modeling of the electromagnetic energy leakage risk, and the electromagnetic leakage trend parameters are obtained. The electromagnetic sensitivity parameters are calculated based on the electromagnetic leakage trend parameters.

[0077] The electromagnetic energy leakage risk of the chip is modeled in time series through electromagnetic stability parameters. Specifically, the changing trend of the electromagnetic signal in different time windows is analyzed, the trend parameters of electromagnetic leakage are calculated, and based on these trend parameters, the electromagnetic sensitivity of the chip is evaluated and its performance in various electromagnetic environments is predicted.

[0078] For example, the statsmodels library in Python is used to perform time series modeling on electromagnetic leakage data, extract trend parameters, and calculate electromagnetic sensitivity parameters through the scikit-learn library to evaluate the stability of the chip under different electromagnetic conditions.

[0079] Among them, the electromagnetic energy leakage risk is modeled in time series using electromagnetic stability parameters to obtain electromagnetic leakage trend parameters, and the steps of calculating electromagnetic sensitivity parameters based on the electromagnetic leakage trend parameters include: extracting the changing trend of electromagnetic stability parameters in different time windows based on the dynamic evolution sequence of electromagnetic compatibility, and calculating the mean, standard deviation and volatility of electromagnetic stability in all time windows based on the changing trend.

[0080] By subdividing the dynamic evolution sequence of electromagnetic compatibility, specifically, extracting electromagnetic stability parameters in different time windows, analyzing their changing trends, and evaluating the adaptability and sensitivity of the chip in different electromagnetic environments by calculating the mean, standard deviation, and volatility of electromagnetic stability in these time windows.

[0081] For example, the zoo package in R language is used to perform sliding window analysis on time series data, calculate the electromagnetic stability parameters within each window, and visualize the changing trends of these parameters through the ggplot2 package.

[0082] The electromagnetic stability time series is constructed using the mean, standard deviation and volatility of electromagnetic stability. The electromagnetic stability time series is correlated with the working mode of the chip to extract the changing characteristics of the electromagnetic stability under a specific working mode. Based on the changing characteristics of the electromagnetic stability, the stability extreme points and mutation points under a specific mode are calculated.

[0083] By combining the mean, standard deviation and volatility of electromagnetic stability, detailed electromagnetic stability time series are constructed. Specifically, these time series are correlated with different working modes of the chip and analyzed to extract the electromagnetic stability characteristics under specific working modes, and identify the stability extreme points and mutation points.

[0084] For example, the timeseries toolbox in Matlab is used to perform time series analysis on electromagnetic stability data, identify the stability change characteristics under different working modes, and calculate the extreme points and mutation points through the extrema function.

[0085] Time interpolation processing is performed on stability extreme points and mutation points to generate the time evolution curve of electromagnetic energy leakage risk, and the time evolution curve is used to construct the electromagnetic energy leakage risk time series. The electromagnetic energy leakage risk time series is decomposed at multiple scales to extract short-term fluctuation characteristics and long-term trend characteristics. The electromagnetic leakage trend parameters are calculated based on the short-term fluctuation characteristics and long-term trend characteristics.

[0086] By performing detailed time interpolation processing on stability extreme points and mutation points, specifically, a time evolution curve of electromagnetic energy leakage risk is generated, and an electromagnetic energy leakage risk time series is constructed. Furthermore, by performing multi-scale decomposition on the time series, the short-term fluctuation characteristics and long-term trend characteristics are extracted to calculate the electromagnetic leakage trend parameters.

[0087] For example, the pandas library in Python is used to interpolate time data to generate time evolution curves, and the wavelets library is used to perform multi-scale decomposition, extract fluctuation and trend characteristics, and calculate electromagnetic leakage trend parameters.

[0088] The energy leakage of the chip under different electromagnetic environments is classified by electromagnetic leakage trend parameters, an electromagnetic sensitivity change model is constructed, and the sensitivity threshold of the chip under different electromagnetic environment conditions is calculated based on the electromagnetic sensitivity change model. The electromagnetic sensitivity parameters of the chip under different interference conditions are calculated using the sensitivity threshold.

[0089] By classifying the electromagnetic leakage trend parameters in detail, specifically analyzing the energy leakage of the chip under different electromagnetic environments, an electromagnetic sensitivity change model is constructed. Based on this model, the sensitivity thresholds of the chip under different electromagnetic environment conditions are calculated, and these thresholds are used to evaluate the electromagnetic sensitivity parameters of the chip under different interference conditions.

[0090] For example, the classification learner toolbox in Matlab is used to classify and analyze electromagnetic leakage data, generate an electromagnetic sensitivity change model, and calculate the sensitivity threshold under different conditions through the thresholding tool.

[0091] In step S400, the stability index of the electromagnetic interference influence matrix is ​​calculated based on the electromagnetic stability parameter, the high-risk interference channel is extracted based on the stability index, and the high-risk interference channel is adaptively disturbed by the electromagnetic sensitivity parameter to generate an interference change trend curve.

[0092] By combining electromagnetic stability parameters, the stability index of the electromagnetic interference influence matrix is ​​calculated. Specifically, high-risk interference channels are identified and extracted, and adaptive disturbance calculations are performed on these channels through electromagnetic sensitivity parameters to generate interference change trend curves.

[0093] For example, the caret package in R language is used to perform stability analysis on interference channels, extract high-risk channels, and implement the adaptive perturbation algorithm in Matlab to generate interference change trend curves.

[0094] Perform pattern matching on the interference change trend curve to identify abnormal interference events, analyze the interference intensity modulation factor based on the abnormal interference events, use the interference intensity modulation factor to perform disturbance adjustment on the electromagnetic interference influence matrix to obtain the adjusted electromagnetic interference influence matrix, and generate electromagnetic compatibility assessment results based on the adjusted electromagnetic interference influence matrix.

[0095] By performing detailed pattern matching analysis on the interference change trend curve, specifically, abnormal interference events are identified, and interference intensity modulation factors are extracted based on these events. Subsequently, these modulation factors are used to perform disturbance adjustment on the electromagnetic interference influence matrix to generate an adjusted electromagnetic interference influence matrix, and the electromagnetic compatibility assessment results are generated based on the matrix.

[0096] For example, the pattern library in Python is used to perform pattern matching on interference change trends, identify abnormal interference events, and calculate the modulation factor in Matlab through the control theory algorithm, adjust the electromagnetic interference impact matrix, and generate the final electromagnetic compatibility assessment report.

[0097] In this embodiment, the electromagnetic radiation scan of the chip is performed by using a time domain electromagnetic signal acquisition device to preliminarily obtain electromagnetic signal data, and the background noise is adaptively filtered to ensure the accuracy of the data. The initial electromagnetic signal data is processed by short-time Fourier transform to obtain instantaneous spectrum distribution data and phase evolution data, and the key harmonic components are extracted to construct the spectrum harmonic correlation matrix to generate frequency domain electromagnetic signal data. A multidimensional electromagnetic energy distribution model is established, from which the electromagnetic field gradient distribution data is extracted and spatially mapped to obtain spatial electromagnetic distribution data. The time domain electromagnetic signal data is processed by the time series decomposition method, the key pulse sequence is extracted and the pulse amplitude change trend is calculated, the pulse-spectrum interaction matrix is ​​constructed, and the spatial electromagnetic propagation mode is obtained by spatial projection. These data are used to construct an electromagnetic interference propagation topology map, calculate the interference coupling path and perform energy backtracking analysis, and identify the main electromagnetic interference sources. The electromagnetic interference influence matrix is ​​constructed based on the interference source influence factor, and the electromagnetic coupling characteristic parameters are extracted by characteristic decomposition to generate electromagnetic interference source distribution information and interference propagation path information.

[0098] Based on the electromagnetic interference source distribution information and interference propagation path information, a time-interference source correlation sequence is constructed, and multi-dimensional correlation analysis is performed to generate a path-time interference correlation sequence, extract typical interference propagation modes, calculate the mode transition probability, and construct an electromagnetic interference evolution diagram. By calculating the steady-state distribution of interference, an electromagnetic compatibility change rate sequence is generated and the fluctuation characteristics are extracted. The electromagnetic steady-state duration and interference impact period are calculated to construct an electromagnetic compatibility dynamic evolution sequence. The fractal dimension of the evolution sequence is calculated to extract complexity indicators and electromagnetic stability parameters. These parameters are used to model the time series and evaluate the electromagnetic energy leakage risk and sensitivity. Combined with electromagnetic stability and sensitivity parameters, the stability of the electromagnetic interference impact matrix is ​​analyzed, high-risk interference channels are identified and adaptive disturbance calculations are performed, interference change trend curves are generated, abnormal interference events are identified through pattern matching, the electromagnetic interference impact matrix is ​​adjusted, and finally the electromagnetic compatibility evaluation results are generated. Through this comprehensive analysis method, a more accurate and detailed electromagnetic compatibility evaluation can be provided to improve the performance and stability of chips in complex electromagnetic environments.

[0099] Embodiment 3: like Figure 2 As shown, the present application also provides a chip electromagnetic compatibility analysis device 10 , including a collection module 11 , a decomposition module 12 , a construction module 13 and an analysis module 14 .

[0100] The acquisition module 11 is mainly used to perform multi-dimensional acquisition of the electromagnetic radiation data of the chip to obtain time domain electromagnetic signal data, frequency domain electromagnetic signal data and spatial electromagnetic distribution data.

[0101] The decomposition module 12 is mainly used to construct the electromagnetic interference influence matrix of the chip using time domain electromagnetic signal data, frequency domain electromagnetic signal data and spatial electromagnetic distribution data, and to perform characteristic decomposition on the electromagnetic interference influence matrix, extract electromagnetic coupling characteristic parameters, and obtain the electromagnetic interference source distribution information and interference propagation path information of the chip.

[0102] The construction module 13 is mainly used to construct the electromagnetic compatibility dynamic evolution sequence of the chip through the electromagnetic interference source distribution information and the interference propagation path information, perform multi-scale fractal analysis on the electromagnetic compatibility dynamic evolution sequence, and calculate the electromagnetic stability parameters and electromagnetic sensitivity parameters of the chip.

[0103] The analysis module 14 is mainly used to analyze the electromagnetic interference influence matrix using the electromagnetic stability parameters and electromagnetic sensitivity parameters of the chip, and generate an electromagnetic compatibility evaluation result based on the analysis result.

[0104] In this embodiment, by providing a chip electromagnetic compatibility analysis device 10, a comprehensive evaluation and optimization of the electromagnetic compatibility performance of the chip is achieved. The acquisition module 11 collects electromagnetic radiation data of the chip in multiple dimensions, including time domain electromagnetic signal data, frequency domain electromagnetic signal data, and spatial electromagnetic distribution data, to ensure the comprehensiveness and accuracy of the data. The decomposition module 12 uses the collected data to construct an electromagnetic interference influence matrix, and performs feature decomposition on it, extracts electromagnetic coupling characteristic parameters, and thus obtains the electromagnetic interference source distribution information and interference propagation path information of the chip. Based on this information, the construction module 13 constructs the electromagnetic compatibility dynamic evolution sequence of the chip, and calculates the electromagnetic stability parameters and electromagnetic sensitivity parameters of the chip through multi-scale fractal analysis. Finally, the analysis module 14 uses these parameters to conduct an in-depth analysis of the electromagnetic interference influence matrix and generate detailed electromagnetic compatibility evaluation results. By integrating multiple technical means, this device realizes full process coverage from data acquisition to analysis and evaluation, and can provide accurate and detailed electromagnetic compatibility evaluation reports, which is helpful for chip design and optimization, and improves the performance and stability of chips in complex electromagnetic environments.

[0105] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described device and each module can refer to the corresponding process in the aforementioned embodiment 1, and will not be repeated here.

[0106] Embodiment 4: like Figure 3 As shown, the present application also provides an electronic device 20, including a memory 21 and a processor 22, wherein the memory 21 stores a computer program that can be run on the processor 22, and the processor 22 implements the chip electromagnetic compatibility analysis method of Example 1 when executing the computer program.

[0107] In this embodiment, an electronic device 20 is provided, the electronic device 20 includes a memory 21 and a processor 22, the memory 21 stores a computer program that can be run on the processor 22, and when the processor 22 executes the computer program, the chip electromagnetic compatibility analysis method described in Example 1 is implemented. Specifically, when the electronic device 20 is executed, it can automatically perform multi-dimensional collection, data processing, feature extraction and analysis and evaluation of electromagnetic radiation data. In this way, the electronic device 20 can analyze the electromagnetic compatibility of the chip in real time and efficiently, generate electromagnetic compatibility evaluation results, and ensure the stability and reliability of the chip in various electromagnetic environments. The device not only simplifies the process of electromagnetic compatibility analysis, but also improves the accuracy and efficiency of the analysis, and can provide a scientific basis for chip design and improvement to meet the growing demand for electromagnetic compatibility. Through this integrated solution, users can easily evaluate and optimize the electromagnetic compatibility of the chip, and significantly improve the performance of the chip in practical applications.

[0108] The structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0109] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A chip electromagnetic compatibility analysis method, characterized in that: include: Collect the electromagnetic radiation data of the chip in multiple dimensions to obtain time domain electromagnetic signal data, frequency domain electromagnetic signal data and spatial electromagnetic distribution data; The electromagnetic interference influence matrix of the chip is constructed by using the time domain electromagnetic signal data, the frequency domain electromagnetic signal data and the spatial electromagnetic distribution data, and the electromagnetic interference influence matrix is ​​characteristically decomposed to extract electromagnetic coupling characteristic parameters to obtain the electromagnetic interference source distribution information and interference propagation path information of the chip; Constructing a dynamic evolution sequence of electromagnetic compatibility of a chip through the electromagnetic interference source distribution information and the interference propagation path information, performing multi-scale fractal analysis on the dynamic evolution sequence of electromagnetic compatibility, and calculating electromagnetic stability parameters and electromagnetic sensitivity parameters of the chip; The electromagnetic interference influence matrix is ​​analyzed using the electromagnetic stability parameters and electromagnetic sensitivity parameters of the chip, and an electromagnetic compatibility evaluation result is generated based on the analysis result.

2. The chip electromagnetic compatibility analysis method according to claim 1, characterized in that: The step of collecting the electromagnetic radiation data of the chip in multiple dimensions to obtain time domain electromagnetic signal data, frequency domain electromagnetic signal data and spatial electromagnetic distribution data includes: Scanning the electromagnetic radiation of the chip by a time-domain electromagnetic signal acquisition device to obtain initial electromagnetic signal data and background electromagnetic noise data, performing adaptive filtering on the background electromagnetic noise data to obtain noise characteristic parameters, and using the noise characteristic parameters to perform denoising correction on the initial electromagnetic signal data to obtain time-domain electromagnetic signal data; Performing short-time Fourier transform processing on the initial electromagnetic signal data to obtain instantaneous spectrum distribution data and phase evolution data, calculating key harmonic components based on the instantaneous spectrum distribution data, performing nonlinear feature extraction on the key harmonic components and the instantaneous spectrum distribution data, constructing a spectrum harmonic correlation matrix, and generating frequency domain electromagnetic signal data using the spectrum harmonic correlation matrix; A multidimensional electromagnetic energy distribution model is established based on the time-domain electromagnetic signal data and the phase evolution data, and electromagnetic field gradient distribution data is extracted from the multidimensional electromagnetic energy distribution model. The physical structure feature data of the chip is spatially mapped using the electromagnetic field gradient distribution data to obtain spatial electromagnetic distribution data.

3. The chip electromagnetic compatibility analysis method according to claim 1, characterized in that: The step of constructing an electromagnetic interference influence matrix of a chip by using the time domain electromagnetic signal data, the frequency domain electromagnetic signal data, and the spatial electromagnetic distribution data, and performing feature decomposition on the electromagnetic interference influence matrix, extracting electromagnetic coupling feature parameters, and obtaining electromagnetic interference source distribution information and interference propagation path information of the chip includes: Decomposing the time-domain electromagnetic signal data by a time series decomposition method, extracting a key pulse sequence based on the decomposition result, and calculating a pulse amplitude variation trend according to the key pulse sequence; Using the pulse amplitude variation trend and the frequency domain electromagnetic signal data, a pulse-spectrum interaction matrix is ​​constructed, and based on the pulse-spectrum interaction matrix, pulse response weights are calculated, and the pulse response weights are spatially projected through the spatial electromagnetic distribution data to obtain a spatial electromagnetic propagation mode; Constructing an electromagnetic interference propagation topology map according to the spatial electromagnetic propagation mode, calculating interference coupling paths based on the electromagnetic interference propagation topology map, performing energy backtracking analysis on the interference coupling paths, and extracting main electromagnetic interference sources inside the chip and the surrounding environment; Based on the electromagnetic interference propagation topology diagram and the interference coupling path, the interference source influence factor is calculated, and the dominant interference source is screened out. The electromagnetic interference influence matrix is ​​constructed using the dominant interference source and the interference source influence factor. The electromagnetic interference influence matrix is ​​characteristically decomposed to extract electromagnetic coupling characteristic parameters. According to the electromagnetic coupling characteristic parameters, electromagnetic interference source distribution information and interference propagation path information are generated.

4. The chip electromagnetic compatibility analysis method according to claim 1, characterized in that: The step of constructing a dynamic evolution sequence of electromagnetic compatibility of a chip by using the electromagnetic interference source distribution information and the interference propagation path information comprises: Using the electromagnetic interference source distribution information to construct a time-interference source association sequence, and based on the time-interference source association sequence, calculating the interference intensity curve on each time slice, performing multi-dimensional correlation analysis on the interference intensity curve through the interference propagation path information, and generating a path-time interference association sequence; Performing pattern recognition on the path-time interference association sequence, extracting typical interference propagation patterns, calculating pattern transition probabilities according to the typical interference propagation patterns, and constructing an electromagnetic interference evolution graph using the pattern transition probabilities; Calculating the interference steady-state distribution through the electromagnetic interference evolution diagram, performing differential transformation on the interference steady-state distribution to generate an electromagnetic compatibility change rate sequence, and extracting the electromagnetic compatibility fluctuation characteristics based on the electromagnetic compatibility change rate sequence; The electromagnetic compatibility fluctuation characteristics are used to calculate the electromagnetic steady-state duration and interference impact period of the chip, and an electromagnetic compatibility dynamic evolution sequence is constructed based on the electromagnetic steady-state duration and interference impact period.

5. The chip electromagnetic compatibility analysis method according to claim 1, characterized in that: The step of performing multi-scale fractal analysis on the electromagnetic compatibility dynamic evolution sequence to calculate the electromagnetic stability parameters and electromagnetic sensitivity parameters of the chip includes: Performing a segmented fractal dimension calculation on the electromagnetic compatibility dynamic evolution sequence to obtain a fractal dimension evolution curve, extracting an electromagnetic compatibility complexity index based on the fractal dimension evolution curve, and calculating electromagnetic stability parameters at different scales; The electromagnetic stability parameters are used to perform time series modeling on the electromagnetic energy leakage risk to obtain electromagnetic leakage trend parameters, and electromagnetic sensitivity parameters are calculated based on the electromagnetic leakage trend parameters.

6. The chip electromagnetic compatibility analysis method according to claim 5, characterized in that: The step of using the electromagnetic stability parameter to perform time series modeling on the electromagnetic energy leakage risk to obtain the electromagnetic leakage trend parameter, and calculating the electromagnetic sensitivity parameter according to the electromagnetic leakage trend parameter comprises: Extracting the variation trend of the electromagnetic stability parameters in different time windows based on the electromagnetic compatibility dynamic evolution sequence, and calculating the mean, standard deviation and volatility of the electromagnetic stability in all time windows based on the variation trend; Using the electromagnetic stability mean, the standard deviation and the volatility to construct an electromagnetic stability time series, performing correlation analysis on the electromagnetic stability time series and the working mode of the chip, extracting the electromagnetic stability change characteristics under a specific working mode, and calculating the stability extreme value point and mutation point under a specific mode based on the electromagnetic stability change characteristics; Performing time interpolation processing on the stability extreme point and the mutation point to generate a time evolution curve of the electromagnetic energy leakage risk, using the time evolution curve to construct an electromagnetic energy leakage risk time series, performing multi-scale decomposition on the electromagnetic energy leakage risk time series, extracting short-term fluctuation characteristics and long-term trend characteristics, and calculating the electromagnetic leakage trend parameters according to the short-term fluctuation characteristics and the long-term trend characteristics; The energy leakage conditions of the chip under different electromagnetic environments are classified by the electromagnetic leakage trend parameters, an electromagnetic sensitivity change model is constructed, and the sensitivity thresholds of the chip under different electromagnetic environment conditions are calculated based on the electromagnetic sensitivity change model. The electromagnetic sensitivity parameters of the chip under different interference conditions are calculated using the sensitivity thresholds.

7. The chip electromagnetic compatibility analysis method according to claim 1, characterized in that: The step of analyzing the electromagnetic interference influence matrix by using the electromagnetic stability parameters and electromagnetic sensitivity parameters of the chip and generating an electromagnetic compatibility evaluation result based on the analysis result includes: Calculating the stability index of the electromagnetic interference influence matrix based on the electromagnetic stability parameter, extracting the high-risk interference channel based on the stability index, performing adaptive disturbance calculation on the high-risk interference channel through the electromagnetic sensitivity parameter, and generating an interference change trend curve; Pattern matching is performed on the interference change trend curve to identify abnormal interference events, an interference intensity modulation factor is analyzed based on the abnormal interference events, the electromagnetic interference influence matrix is ​​disturbed and adjusted using the interference intensity modulation factor to obtain an adjusted electromagnetic interference influence matrix, and an electromagnetic compatibility assessment result is generated based on the adjusted electromagnetic interference influence matrix.

8. A chip electromagnetic compatibility analysis device, characterized in that: include: The acquisition module is used to collect the electromagnetic radiation data of the chip in multiple dimensions to obtain time domain electromagnetic signal data, frequency domain electromagnetic signal data and spatial electromagnetic distribution data; A decomposition module is used to construct an electromagnetic interference influence matrix of a chip using the time domain electromagnetic signal data, the frequency domain electromagnetic signal data, and the spatial electromagnetic distribution data, and to perform characteristic decomposition on the electromagnetic interference influence matrix, extract electromagnetic coupling characteristic parameters, and obtain electromagnetic interference source distribution information and interference propagation path information of the chip; A construction module, used to construct an electromagnetic compatibility dynamic evolution sequence of a chip through the electromagnetic interference source distribution information and the interference propagation path information, perform multi-scale fractal analysis on the electromagnetic compatibility dynamic evolution sequence, and calculate the electromagnetic stability parameters and electromagnetic sensitivity parameters of the chip; The analysis module is used to analyze the electromagnetic interference influence matrix using the electromagnetic stability parameters and electromagnetic sensitivity parameters of the chip, and generate an electromagnetic compatibility evaluation result based on the analysis result.

9. An electronic device, characterized in that: The invention comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and the processor implements the chip electromagnetic compatibility analysis method according to any one of claims 1 to 7 when executing the computer program.

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