A method for simulating stress loss of an electronic package curing agent

Abstract

The present application relates to the technical field of computer simulation, and particularly relates to a kind of loss stress simulation analysis method of electronic packaging curing agent.The method comprises the following steps: obtaining the physical and chemical parameters corresponding to electronic packaging curing agent and mechanical property data, to generate packaging curing agent basic performance data set;Based on the packaging curing agent basic performance data set, generate curing agent microstructure evolution data and stress source identification and packaging stress field distribution mapping, obtain the initial distribution data of packaging structure stress field;According to the initial distribution data of packaging structure stress field, stress evolution simulation under temperature cycle load is carried out, and the loss factor and stress relaxation coefficient corresponding to electronic packaging curing agent are calculated, to obtain curing agent loss stress characteristic parameter;Obtain humidity aging factor and carry out influence dynamic evaluation, while generating curing agent loss stress life evaluation report.The present application can realize the accurate simulation process of curing agent loss and stress distribution in electronic packaging process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer simulation, and particularly relates to a loss stress simulation analysis method of electronic packaging curing agent. BACKGROUND

[0002] In the electronic packaging process, the curing agent is an important component of epoxy resin. As a high-efficiency epoxy resin curing agent, methyl tetrahydrophthalic anhydride (MTHPA) is widely used in electronic packaging, pouring, high-voltage electrical appliances, and glass steel oil pipelines in oil fields, and plays a crucial role. At present, the existing loss stress simulation analysis method of electronic packaging material is a theoretical modeling method. Although it can make preliminary stress prediction, there are still certain limitations in the simulation accuracy under complex multi-factor environment, thereby reducing the loss stress prediction efficiency of electronic packaging curing agent. SUMMARY

[0003] Therefore, it is necessary to provide a loss stress simulation analysis method of electronic packaging curing agent to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, a loss stress simulation analysis method of electronic packaging curing agent comprises the following steps:

[0005] Step S1: Obtain the physical and chemical parameters and mechanical property data corresponding to the electronic packaging curing agent to generate a packaging curing agent basic performance data set; based on the packaging curing agent basic performance data set, a curing agent molecular dynamics model is constructed to simulate the microstructure change in the curing process and generate curing agent microstructure evolution data;

[0006] Step S2: Stress source identification is performed on the curing agent microstructure evolution data to generate curing agent internal stress source distribution data; electronic packaging structure parameters are obtained, and the curing agent internal stress source distribution data is mapped based on the electronic packaging structure parameters to obtain packaging structure stress field initial distribution data;

[0007] Step S3: According to the packaging structure stress field initial distribution data, stress evolution simulation under temperature cycle load is performed to generate packaging structure temperature-dependent stress evolution data; based on the packaging structure temperature-dependent stress evolution data, the loss factor and stress relaxation coefficient corresponding to the electronic packaging curing agent are calculated to obtain the curing agent loss stress characteristic parameters; a humidity aging factor is obtained, and the influence dynamic evaluation is performed based on the humidity aging factor combined with the curing agent loss stress characteristic parameters to obtain the corresponding curing agent loss stress evaluation data under the comprehensive environment;

[0008] Step S4: Obtain the cumulative effect of the loss stress of the electronic packaging curing agent based on the corresponding loss stress evaluation data of the curing agent under the comprehensive environment, and generate a curing agent loss stress life evaluation report based on the cumulative effect of the loss stress of the electronic packaging curing agent.

[0009] Further, step S1 includes the following steps:

[0010] Step S11: Perform physical and chemical parameter testing on the electronic packaging curing agent using a differential scanning calorimeter, a dynamic mechanical analyzer, and a thermal mechanical analyzer to collect data on the curing reaction exothermic curve, the storage modulus, the loss modulus as a function of temperature, and the volume shrinkage rate and thermal expansion coefficient during the curing process, and to monitor and obtain the corresponding mechanical property data of the electronic packaging curing agent, generating multi-dimensional raw data of the basic performance of the curing agent;

[0011] Step S12: Perform outlier rejection and data smoothing processing on the raw data of the basic performance of the curing agent, and screen out key performance parameters that significantly affect the curing stress through correlation analysis, generating a pre-processed curing agent basic performance data set;

[0012] Step S13: Based on a molecular simulation software, construct an initial molecular model corresponding to the electronic packaging curing agent, set the molecular chain length, functional group type, and initial density parameters, and define the cross-linking reaction rules and energy minimization conditions to generate a molecular dynamics model of the curing agent;

[0013] Step S14: Run molecular dynamics simulation based on the molecular dynamics model of the curing agent to record the molecular chain entanglement density, cross-linking point distribution, and free volume change during the curing process, generating microstructure evolution data of the curing agent, including molecular arrangement characteristics and microdefect distribution at different curing stages.

[0014] Further, step S2 includes the following steps:

[0015] Step S21: Perform stress source positioning and identification on the microstructure evolution data of the curing agent to calculate the distribution difference of intermolecular interaction forces, and based on the distribution difference, identify the micro stress concentration areas formed by the cross-linking density gradient and component segregation, generating micro stress source positioning data;

[0016] Step S22: Perform stress source distribution statistics on the microstructure evolution data of the curing agent based on the micro stress source positioning data to generate internal stress source distribution data of the curing agent;

[0017] Step S23: Perform distribution scale conversion on the internal stress source distribution data of the curing agent based on the principle of continuum mechanics to establish a mapping relationship between the micro stress and the macro stress, generating stress scale conversion parameters of the curing agent;

[0018] Step S24: Obtain a three-dimensional geometric model corresponding to the electronic packaging structure, and perform structural parameter analysis on the electronic packaging curing agent based on the three-dimensional geometric model to analyze the base material type, packaging size, lead frame layout, and curing agent filling area, and generate electronic packaging structure parameters;

[0019] Step S25: Apply the curing agent stress scale conversion parameter to the electronic packaging structure parameter, and map the corresponding micro stress source distribution to the corresponding position of the macro electronic packaging structure, while combining the interface bonding performance parameter between the electronic packaging curing agent and the base material, to generate packaging structure stress field initial distribution data.

[0020] Further, step S22 includes the following steps:

[0021] Based on the micro stress source positioning data, the spatial coordinates, stress amplitude, and stress type of the micro stress concentration area are obtained, including tensile stress and shear stress;

[0022] Based on the curing agent microstructure evolution data, the molecular crosslinking density distribution, component concentration gradient, and micro defect position information of the electronic packaging curing agent at different curing stages are extracted to generate curing agent structure feature distribution data set;

[0023] The micro stress source positioning data and the curing agent structure feature distribution data set are stress source space matched to establish the corresponding relationship between the stress source and the microstructure feature through coordinate alignment, and identify the structure factors corresponding to the micro stress concentration area, including low correlation density area and component enrichment area, to obtain the curing agent matched stress source distribution data;

[0024] According to different curing stages, the curing agent matched stress source distribution data is grouped and counted to calculate the number ratio, average amplitude, and spatial distribution density of different types of stress sources in each curing stage, and generate curing agent stress source stage distribution statistical data;

[0025] Based on the curing agent stress source stage distribution statistical data, the spatial distribution diagram of the stress source in the micro electronic packaging structure is drawn, and the curing agent stress source stage distribution statistical data and the spatial distribution diagram are integrated to generate the curing agent internal stress source distribution data containing stress source type, distribution law, formation reason, and distribution evolution trend.

[0026] Further, step S24 includes the following steps:

[0027] Step S241: Obtain the three-dimensional geometric model corresponding to the electronic packaging structure through three-dimensional scanning or CAD drawing import, and simplify the three-dimensional geometric model to retain key structural features, including chip edges, wire bonding points, and curing agent filling boundaries, and ignore subtle structures that do not affect stress distribution, to generate an electronic packaging three-dimensional simplified model;

[0028] Step S242: Material property assignment is performed on the electronic package three-dimensional simplified model to determine the area division of the substrate type, lead frame and curing agent, and the material identification and spatial coordinate range of each area are recorded to generate material partition data;

[0029] Step S243: Based on the material partition data, the size parameters in the electronic package structure are extracted, including the chip thickness, curing agent layer thickness, lead diameter and overall package size, to generate a structure size parameter set;

[0030] Step S244: By integrating the material partition data and the structure size parameter set, the electronic package structure parameters including the substrate type, package size, lead frame layout and curing agent filling area are generated.

[0031] Further, step S244 includes the following steps:

[0032] Topological relationship analysis is performed on the material partition data to determine the contact interface between the electronic package curing agent and the substrate and lead frame, and the spatial position and contact area corresponding to the contact interface are recorded to generate interface contact relationship data;

[0033] Each parameter in the structure size parameter set is associated with the corresponding feature point of the electronic package three-dimensional simplified model, and the corresponding relationship between the size parameters and the model topological structure is established to generate a parameterized model association table;

[0034] Based on the parameterized model association table, pre-processing is performed on the electronic package three-dimensional simplified model before mesh division to generate mesh division preparation data;

[0035] Based on the mesh division preparation data and in combination with the material partition data, structure parameter refinement evaluation is performed on the electronic package curing agent to generate electronic package structure parameters including the substrate type, package size, lead frame layout and curing agent filling area.

[0036] Further, step S3 includes the following steps:

[0037] Step S31: By setting temperature cycle load parameters, including cycle temperature range, heating and cooling rate, holding time and cycle number, a temperature load sequence is generated;

[0038] Step S32: The temperature load sequence is applied to the corresponding finite element model constructed by the initial distribution data of the package structure stress field, and the thermal expansion coefficient mismatch condition of the electronic package curing agent and the substrate is defined, and the interface contact behavior is set, including the friction coefficient, the bonding strength, the thermal-structure coupling simulation is performed to generate the corresponding thermal-structure coupling simulation data of the package structure under temperature cycle load;

[0039] Step S33: Based on the corresponding thermal-structure coupling simulation data of the packaging structure under temperature cycle load, the stress peak, stress variation amplitude and interface peeling risk index of the electronic packaging curing agent in each temperature cycle load are extracted, and the stress dominant evolution trend in different temperature intervals is analyzed to generate temperature-dependent stress evolution data;

[0040] Step S34: Based on the temperature-dependent stress evolution data, the loss factor and stress relaxation coefficient of the electronic packaging curing agent at different temperatures are calculated, wherein the loss factor is the ratio between the storage modulus and the loss modulus, and the stress relaxation coefficient is the decay rate of stress with time, and a functional relationship between temperature and loss characteristic parameters is established to obtain the curing agent loss stress characteristic parameters;

[0041] Step S35: Obtain the humidity aging factor, and based on the humidity aging factor, the influence of dynamic evaluation is carried out combined with the curing agent loss stress characteristic parameters to obtain the corresponding curing agent loss stress evaluation data under the comprehensive environment.

[0042] Further, step S35 includes the following steps:

[0043] Step S351: Obtain the humidity aging factor, and based on the humidity aging factor, design a humidity aging test to place the packaging sample in an environment chamber with different humidity levels, set a constant temperature condition, and periodically collect the weight change, elastic modulus attenuation and interface adhesion strength change data of the electronic packaging curing agent to generate humidity aging performance original data;

[0044] Step S352: Time series analysis is performed on the humidity aging performance original data to identify the curing agent moisture saturation time, performance decay inflection point, and a three-dimensional relationship model of humidity-time-performance decay is established based on the curing agent moisture saturation time and performance decay inflection point, and a humidity aging characteristic curve is generated according to the three-dimensional relationship model;

[0045] Step S353: Based on the humidity aging characteristic curve, the performance decay coefficient under different humidity conditions is calculated, and its correlation analysis with the curing agent loss stress characteristic parameters is performed to determine the influence weight of humidity on the loss factor and stress relaxation coefficient, and a humidity influence correction coefficient is generated;

[0046] Step S354: The humidity influence correction coefficient is embedded into the temperature-dependent stress evolution data for loss stress influence evaluation to obtain the corresponding curing agent loss stress evaluation data under the comprehensive consideration of temperature and humidity environment.

[0047] Further, step S353 includes the following steps:

[0048] The humidity aging characteristic curve is segmented according to the humidity level, and the performance decay rate of each segment is calculated to generate the performance decay coefficient under different humidity conditions;

[0049] Perform humidity sensitivity analysis on the curing agent loss stress characteristic parameters to determine the influence degree of single humidity change on the curing agent loss stress characteristic parameters by the control variable method, and generate parameter sensitivity coefficients;

[0050] Based on the parameter sensitivity coefficients and the performance attenuation coefficients under different humidity conditions, the corresponding correction weights under different humidity conditions are calculated by using the analytic hierarchy process to generate a humidity correction weight matrix;

[0051] Multiply the humidity correction weight matrix and the curing agent loss stress characteristic parameters to obtain the correction coefficients under different humidity conditions, and integrate to generate humidity influence correction coefficients.

[0052] Further, step S4 includes the following steps:

[0053] Based on the corresponding curing agent loss stress evaluation data under the comprehensive environment, the service environment parameter sequence of the electronic packaging structure is set, including the temperature cycle period, the humidity fluctuation range and the duration, and an environmental loss load spectrum of the electronic packaging structure is generated;

[0054] Based on the environmental loss load spectrum of the electronic packaging structure, loss stress cumulative evaluation is performed on the curing agent loss stress evaluation data, so as to simulate the stress amplitude change, stress cycle number and loss accumulation process of the electronic packaging curing agent under the action of the cyclic load based on the environmental loss load spectrum of the electronic packaging structure, and the cumulative law of the loss stress with time is obtained, and the loss stress cumulative effect of the electronic packaging curing agent is generated;

[0055] The stress accumulation parameters of the loss stress cumulative effect of the electronic packaging curing agent are statistically analyzed to quantitatively extract the maximum cumulative stress value, the stress accumulation rate and the stress attenuation inflection point, and the loss stress cumulative parameter set of the electronic packaging curing agent is generated;

[0056] The material fatigue limit coefficient of the electronic packaging curing agent is obtained, and the curing failure life evaluation analysis is performed based on the material fatigue limit coefficient and the loss stress cumulative parameter set, so as to evaluate and analyze the loss stress accumulation amount and the corresponding service time when the electronic packaging curing agent reaches the failure state, and generate the curing agent loss stress life evaluation parameter;

[0057] Based on the curing agent loss stress life evaluation parameter, a curing agent loss stress life evaluation report including the loss stress accumulation curve, the life prediction result and the failure risk analysis is generated.

[0058] The beneficial effects of the present application are:

[0059] The method for simulating and analyzing the loss stress of electronic packaging curing agents proposed in this invention, compared with existing technologies, offers the following advantages: By acquiring the physicochemical parameters and mechanical property data of the electronic packaging curing agent, it provides fundamental data support for subsequent analysis and simulation. The physicochemical parameters of the curing agent can influence its behavior in actual packaging. The mechanical property data determines the response characteristics of the curing agent under stress. By constructing a molecular dynamics model of the curing agent based on these data, the microstructure evolution during the curing process can be simulated, helping to predict changes in molecular structure during curing and providing a deeper understanding of the reaction mechanism and performance change patterns of the curing agent under different temperatures, humidity, and stress environments. Secondly, stress source identification is performed using the microstructure evolution data of the curing agent. This process can identify the distribution of stress sources within the curing agent, providing support for analyzing the stability and mechanical properties of the curing agent. After acquiring the electronic packaging structural parameters, the stress source distribution mapping can accurately describe the stress transmission process within the packaging structure caused by changes in the microstructure of the curing agent. Then, by simulating the initial distribution data of the stress field of the packaging structure, the stress evolution process under temperature cyclic loading is studied, the impact of temperature changes on the packaging structure is quantified, and temperature-dependent stress evolution data is generated. This data can not only predict the stress changes of the curing agent under different temperature conditions, but also help evaluate its performance in actual working environments. The loss factor can reveal the degree of energy loss of the material under dynamic load, while the stress relaxation coefficient reflects the characteristic of the material's stress gradually weakening during long-term use. Combined with the humidity aging factor, these parameters can be used to comprehensively evaluate the dynamic stress changes of the curing agent, which can help predict its service life and reliability under complex environmental conditions. By comprehensively evaluating the curing agent loss stress under various environmental conditions, the cumulative effect of loss stress caused by environmental changes during long-term use of electronic packaging curing agents is obtained. Based on this effect, a loss stress life assessment report of the curing agent is further generated, which can help designers predict the simulation accuracy of the packaging curing agent under various environmental conditions, improve the simulation accuracy in complex environments with multiple factors, and thus ensure the efficiency of loss stress prediction for electronic packaging curing agents. Attached Figure Description

[0060] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0061] Figure 1 This is a schematic diagram of the steps in the loss stress simulation analysis method for the electronic packaging curing agent of the present invention;

[0062] Figure 2 for Figure 1 A detailed flowchart of step S1;

[0063] Figure 3 To Figure 1 Detailed step flow diagram of step S2. DETAILED DESCRIPTION

[0064] The technical method of the present application will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0065] In addition, the accompanying drawings are only schematic illustrations of the present application and are not necessarily drawn to scale. Identical reference numerals in the drawings represent identical or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities, which do not necessarily have to correspond to physically or logically independent entities. The functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0066] It should be understood that although the terms "first", "second" and the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of the exemplary embodiments, a first element can be referred to as a second element, and similarly a second element can be referred to as a first element. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0067] To achieve the above-mentioned purpose, please refer to Figures 1 to 3 The present application provides a method for simulating and analyzing the loss stress of an electronic packaging curing agent. In the embodiments of the present application, please refer to Figure 1 The method for simulating and analyzing the loss stress of an electronic packaging curing agent includes the following steps:

[0068] Step S1: Obtain the physical and chemical parameters and mechanical property data of the electronic packaging curing agent to generate a basic performance data set of the packaging curing agent; and construct a corresponding curing agent molecular dynamics model based on the basic performance data set of the packaging curing agent to simulate the microstructure changes in the curing process and generate curing agent microstructure evolution data.

[0069] In the embodiment of the present application, the corresponding electronic packaging curing agent (such as methyl tetrahydrophthalic anhydride (MTHPA)) is tested by using a differential scanning calorimeter (heating rate 5℃ / min, temperature range 25-200℃), and the curing reaction exothermic peak temperature is 120℃, the total exothermic quantity is 380J / g; the dynamic mechanical analyzer (frequency 1Hz, -50 to 150℃) is used to measure the storage modulus at 25℃ of 2.1GPa and the loss modulus of 0.126GPa; the thermal mechanical analyzer (2℃ / min, 25-180℃) is used to obtain the volume shrinkage of 2.48% and the glass thermal expansion coefficient of 30×10 -6 / ℃. The universal testing machine (tensile rate 5mm / min) is used to measure the tensile strength of 45MPa and the bending strength of 70MPa. These data are integrated into the packaging curing agent basic performance data set, including temperature-exothermic rate, modulus, volume change and mechanical parameters. Based on the data set, a molecular dynamics model is constructed, the molecular chain length is set to 10 repeating units (molecular weight 1661.8), the functional groups are anhydride groups and methyl groups, and the initial density is 1.210g / cm³. The crosslinking reaction rules are defined: the anhydride group and the hydroxyl group undergo ring-opening reaction at 65-160℃, the activation energy is 60kJ / mol, and the energy minimization uses COMPASS force field (maximum force <0.001kcal / (mol·Å)). The curing process is simulated for 10ns, the crosslinking density is recorded as 0.02 / ų at the initial stage (65℃), 0.05 / ų at the middle stage (120℃), and 0.06 / ų at the later stage (160℃), and the curing agent microstructure evolution data including molecular entanglement density and crosslinking point distribution are generated.

[0070] Step S2: stress source identification is performed on the curing agent microstructure evolution data to generate curing agent internal stress source distribution data; the electronic packaging structure parameters are obtained, and the curing agent internal stress source distribution data is mapped based on the electronic packaging structure parameters to obtain the initial distribution data of the packaging structure stress field;

[0071] In the embodiment of the present application, the intermolecular force difference is calculated by stress source identification of the curing agent microstructure evolution data: the intermolecular force difference of 3-4 nm area in the early stage of reaction is 80 MPa, which is determined as a tensile stress concentration area (coordinates (3.2, 4.5, 5.1) nm, etc., amplitude 80-120 MPa); the shear force difference of anhydride group concentration 5.2×10 22 per m 3 area is 40 MPa, which is a shear stress concentration area (coordinates (1.5, 6.2, 3.8) nm, etc., amplitude 40-60 MPa). Statistics show that there are 32 tensile stress sources and 18 shear stress sources, and the curing agent internal stress source distribution data is generated. The electronic packaging structure parameters are obtained: chip 5.0×5.0×0.5mm, epoxy resin substrate 10.0×10.0×0.5mm (Young's modulus 3.2GPa), copper lead frame 8 (diameter 0.1mm, length 2.0mm), and curing agent filling area 10.0×10.0×2.5mm. Based on these parameters, the micro stress source is mapped to the macro structure: micro (3.2, 4.5, 5.1) nm corresponds to macro (0.32, 0.45, 0.51) mm, and the stress value is calculated according to the conversion coefficient 0.04 (95MPa→3.8MPa). Combined with the interface performance parameters (shear strength 25MPa, peeling strength 5MPa), the interface stress is corrected, and the initial distribution data of the stress field of the packaging structure is generated, including three-dimensional coordinates (accurate to 0.01mm) and stress components (σ x,σ y, etc., retaining two decimal places).

[0072] Step S3: stress evolution simulation under temperature cycle load according to the initial distribution data of the stress field of the packaging structure to generate temperature-dependent stress evolution data of the packaging structure; based on the temperature-dependent stress evolution data of the packaging structure, the loss factor and stress relaxation coefficient of the electronic packaging curing agent are calculated to obtain the curing agent loss stress characteristic parameters; the humidity aging factor is obtained, and the influence dynamic evaluation is carried out based on the humidity aging factor combined with the curing agent loss stress characteristic parameters to obtain the corresponding curing agent loss stress evaluation data under the comprehensive environment;

[0073] In the embodiment of the present application, the temperature cycle load simulation is carried out according to the initial distribution data of the stress field of the packaging structure (initial stress 50MPa), and the cycle parameters are set: -40℃ to 125℃, temperature rising rate 5℃ / min, high and low temperature each for 10min, and cycle 50 times. The thermal expansion coefficient mismatch is defined: curing agent 60×10 -6 / ℃, substrate 50×10 -6 / ℃, lead frame 17×10 -6 / ℃, interface friction coefficient 0.3-0.4, bonding strength 25-30 MPa. The thermal-structural coupling simulation shows that the stress peak value is 120 MPa at 125℃ in the 50th cycle, and the temperature-dependent stress evolution data is generated. The loss characteristic parameters are calculated: loss factor 0.05 (-40℃, 0.15 GPa / 3.0 GPa), stress relaxation coefficient 0.0005 / h; loss factor 0.06 (25℃, 0.126 GPa / 2.1 GPa), coefficient 0.001 / h; loss factor 0.12 (125℃, 0.12 GPa / 1.0 GPa), coefficient 0.003 / h, and a functional relationship (loss factor = 0.0005 x temperature + 0.07) is established. The humidity aging factor (20%-95%RH) is obtained, and the parameters are evaluated: loss stress 174.4 MPa at 25℃ / 20%RH, 218.2 MPa at 45℃ / 60%RH, and 668.9 MPa at 85℃ / 95%RH, and the curing agent loss stress evaluation data under comprehensive environment is generated.

[0074] Step S4: obtaining the loss stress cumulative effect of the electronic packaging curing agent based on the corresponding curing agent loss stress evaluation data under the comprehensive environment, and generating a curing agent loss stress life evaluation report based on the loss stress cumulative effect of the electronic packaging curing agent.

[0075] In the embodiment of the present application, the environmental loss load spectrum is generated based on the loss stress evaluation data under the comprehensive environment: within 3 years (1095 days), the temperature is cycled 28 times per day (-40℃→125℃, 86min / time), and the humidity is 20%, 40%, 60%, and 85%RH according to the season, and 95%RH for 15 days in the plum rain season. The rain flow counting method is used to evaluate the cumulative effect: 77.4 MPa per day (6966 MPa in total for 90 days) at 20%RH stage, 133.96 MPa per day (12190.36 MPa in total for 91 days) at 40%RH stage, 186 MPa per day (16926 MPa in total for 91 days) at 60%RH stage, 218.2 MPa per day (14619.4 MPa in total for 67 days) at 85%RH stage, and 421.6 MPa per day (10033.5 MPa in total for 15 days) at 95%RH stage, with a total cumulative value of 102735.26 MPa in 3 years. The maximum cumulative value is 102735.26 MPa, the rate at 95%RH stage is 421.6 MPa / day (increasing by 93%), and the inflection point is at the 340th day (entering 95%RH). Combined with the material fatigue limit coefficient of 150000 MPa, the failure life is evaluated to be 1603 days (4.4 years). The generated life evaluation report contains the cumulative curve (annotating the rate of each stage and the inflection point), the life prediction result (4.4 years), and the risk analysis (95%RH is an extremely high risk), the data is kept to two decimal places, the curve fitting error is <5%, and the quantitative basis is provided for maintenance.

[0076] Further, as an embodiment of the present application, referring to Figure 2 as shown in FIG. 1, Figure 1 The detailed step flow diagram of step S1 in the embodiment is shown in FIG. 2, and step S1 in the embodiment includes the following steps:

[0077] Step S11: Physical and chemical parameter testing of the electronic packaging curing agent is performed by using a differential scanning calorimeter, a dynamic mechanical analyzer and a thermal mechanical analyzer to collect data of the curing reaction exothermic curve, the storage modulus, the loss modulus with temperature variation, the volume shrinkage rate and the thermal expansion coefficient in the curing process, and to monitor and obtain the corresponding mechanical property data of the electronic packaging curing agent, thereby generating multi-dimensional original data of the basic performance of the curing agent;

[0078] In the embodiment of the present application, methyltetrahydrophthalic anhydride (MTHPA) is tested by using a differential scanning calorimeter (heating rate 5℃ / min, temperature range 25-200℃, nitrogen atmosphere flow rate 50ml / min), and the curing reaction exothermic curve is recorded, with a peak temperature of 120℃, a total exothermic quantity of 380J / g, an exothermic starting temperature of 65℃ and an ending temperature of 160℃. The dynamic mechanical analyzer (tension mode, frequency 1Hz, heating rate 3℃ / min, temperature range -50 to 150℃) measures the storage modulus of 2.1GPa and the loss modulus of 0.3GPa at 25℃, and the glass transition temperature of 85℃, wherein the storage modulus decreases to 1.8GPa and the loss modulus increases to 0.4GPa at 60℃. The thermal mechanical analyzer (compression mode, load 0.5N, heating rate 2℃ / min, temperature range 25-180℃) monitors the volume shrinkage rate in the curing process: the initial volume at 25℃ is 1.21cm³ / g, and the volume after the curing is completed (at 160℃) is 1.18cm³ / g, with a total shrinkage rate of 2.48%; the thermal expansion coefficient in the glass state (25-60℃) is 30×10 -6 / ℃, and the thermal expansion coefficient in the rubber state (90-120℃) is 120×10 -6 / ℃. The mechanical property testing is performed by using a universal testing machine (tension rate 5mm / min, sample size 100×10×4mm), and the tensile strength is measured to be 45MPa, the elongation at break is 3.5%, the bending strength is 70MPa, and the bending modulus is 2.5GPa. All the tests are repeated for 3 times, and the average value is taken to generate multi-dimensional original data of the basic performance of the curing agent including temperature-exothermic rate, temperature-modulus, temperature-volume variation and mechanical parameters.

[0079] Step S12: The original data of the basic performance of the curing agent is subjected to outlier rejection and data smoothing processing, and the key performance parameters significantly affecting the curing stress are screened out by correlation analysis, thereby generating a pre-processed data set of the basic performance of the curing agent;

[0080] In the embodiment of the application, by removing outliers from the original data of the curing agent basic performance, adopting the 3 sigma criterion (sigma is the standard deviation), removing the 49.5 MPa data point (only 1) deviating from the mean value of 45 MPa by more than ±3 sigma (sigma = 2.1 MPa) in the tensile strength test. The data smoothing processing adopts the moving average method (window size 5 data points), smoothes the storage modulus curve of dynamic mechanical analysis, eliminates the instantaneous fluctuation near 60 DEG C (from 1.8 GPa to 1.75 GPa and then rises again), and makes the curve change continuously. The correlation degree of each parameter with the curing stress is calculated by Pearson correlation analysis: volume shrinkage (0.92), storage modulus (0.88), thermal expansion coefficient (0.85), heat release rate peak (0.72), elongation at break (0.65), and the parameters with correlation degree > 0.8 are selected as the key performance parameters, including volume shrinkage (2.48%), 25 DEG C storage modulus (2.1 GPa), glassy thermal expansion coefficient (30 x 10 -6 / ℃), and rubbery thermal expansion coefficient (120 x 10 -6 / ℃). These parameters are classified and arranged according to the temperature interval (25-60 DEG C, 60-85 DEG C, 85-160 DEG C) to generate the pretreated curing agent basic performance data set, ensuring that the data accuracy is retained to two decimal places (shrinkage) or integer (modulus).

[0081] Step S13: based on the molecular simulation software, an initial molecular model corresponding to the electronic packaging curing agent is constructed, and the molecular chain length, functional group type and initial density parameters are set, and the cross-linking reaction rule and energy minimization condition are defined to generate a molecular dynamics model of the curing agent;

[0082] In the embodiment of the application, by constructing the MTHPA initial molecular model based on the molecular simulation software, the molecular structure is built according to the C9H10O3 molecular formula, which contains 3,4-methyl tetrahydrophthalic anhydride isomer structure (methyl substitution position on benzene ring is 3 and 4, and the proportion of two structures is 1:1). The molecular chain length is set to 10 repeating units (molecular weight 1661.8), the functional group is anhydride group (-C(O)OC(O)-) and methyl group (-CH3), and the initial density is 1.210 g / cm3 (consistent with the measured value at 20 DEG C). The cross-linking reaction rule is defined: the anhydride group reacts with the hydroxyl group (-OH) of the epoxy resin to form an ester bond (-C(O)O-), the reaction activation energy is 60 kJ / mol, and the reaction temperature range is 65-160 DEG C (matching the initial-termination temperature of the heat release curve). The energy minimization condition is set as follows: the molecular mechanics optimization adopts COMPASS force field, the convergence standard is maximum force <0.001 kcal / (mol·Å), maximum displacement <0.001 Å, and maximum energy change <1 x 10 -6kcal / mol. The initial configuration was optimized using a simulated annealing algorithm (temperature decreased from 300K to 100K in 10K increments) to ensure no significant overlap in molecular arrangement, generating a molecular dynamics model of the curing agent containing 200 molecules with a model size of 10×10×10nm³ and periodic boundary conditions.

[0083] Step S14: Run molecular dynamics simulation based on the curing agent molecular dynamics model to record the molecular chain entanglement density, crosslinking point distribution and free volume change during the curing process, and generate microstructure evolution data of the curing agent, including molecular arrangement characteristics and microdefect distribution at different curing stages.

[0084] In this embodiment of the invention, molecular dynamics simulation was performed based on a molecular dynamics model of the curing agent, using an NVT ensemble (isothermal 300K, time step 1fs), with a total simulation duration of 10ns. The microstructure evolution of the curing process (divided into three stages according to the exothermic curve: 65-90℃ for the initial reaction, 90-120℃ for the middle reaction, and 120-160℃ for the later reaction) was recorded: In the initial reaction (65℃), the molecular chain entanglement density was 0.02 chains / ų, the number of crosslinking points was 10 (accounting for 10% of the theoretical maximum), and the free volume fraction was 15% (the proportion of free volume to total volume); in the middle reaction (120℃), the entanglement density increased to 0.05 chains / ų, the number of crosslinking points was 50 (50%), and the free volume fraction decreased to 8%; in the later reaction (160℃), the entanglement density stabilized at 0.06 chains / ų, the number of crosslinking points was 95 (95%), and the free volume fraction was 5%. Simultaneously, molecular arrangement characteristics were observed: initially, molecules exhibited a disordered distribution; in the middle stage, locally ordered regions formed (parallel arrangement of benzene rings); and in the later stage, a three-dimensional cross-linked network (cross-linking point spacing 5-8 Å) was formed. Microscopic defects mainly consisted of unreacted anhydride groups (5%) and voids (diameter < 1 nm), distributed between the nodes of the cross-linked network. These data were organized according to reaction stages (early, middle, and late stages) to generate microstructure evolution data for the curing agent, including entanglement density (retaining two decimal places), number of cross-linking points (integer), free volume fraction (integer percentage), and defect distribution coordinates, fully reflecting the molecular-level changes during the curing process.

[0085] Furthermore, as an embodiment of the present invention, reference is made to... Figure 3 As shown, Figure 1 A detailed flowchart of step S2 is shown below. In this embodiment, step S2 includes the following steps:

[0086] Step S21: Perform stress source location identification on the microstructure evolution data of the curing agent, so as to calculate the distribution difference of intermolecular interaction forces and identify the micro-stress concentration area formed by crosslinking density gradient and component segregation based on the distribution difference, and generate micro-stress source location data.

[0087] In the embodiment of the present application, by stress source positioning identification of the curing agent microstructure evolution data (65℃ in the early stage of reaction, 120℃ in the middle stage, and 160℃ in the late stage), the distribution difference of intermolecular interaction force (Van der Waals force and covalent bond force) is obtained by using molecular mechanics calculation method. In the simulation space of 10×10×10 nm³, the stress gradient of each molecular unit is calculated: in the early stage of reaction, the intermolecular interaction force difference of the region with crosslinking density of 0.02 / ų (in the coordinate range of 3-4 nm) reaches 80 MPa (40 MPa higher than the average level), which is determined as the stress concentration area; the intermolecular shear force difference of the component segregation area (anhydride group concentration of 5.2×10²² / m³) is 40 MPa, forming a shear stress concentration. The stress distribution nephogram is drawn by three-dimensional interpolation method, and the tensile stress concentration area 12 (coordinates such as (3.2, 4.5, 5.1) nm, etc., amplitude 80-120 MPa) and the shear stress concentration area 8 (coordinates such as (1.5, 6.2, 3.8) nm, etc., amplitude 40-60 MPa) are identified. The spatial coordinates of all stress sources are accurate to 0.1 nm, and the stress amplitude is retained to the integer place. The generated micro stress source positioning data contains the type (tensile / shear), coordinate and amplitude of each stress concentration area, and clearly shows the origin position and intensity of the micro stress.

[0088] Step S22: stress source distribution statistics of the curing agent microstructure evolution data based on the micro stress source positioning data, to generate the curing agent internal stress source distribution data;

[0089] In the embodiment of the application, by performing stress source distribution statistics on the curing agent microstructure evolution data based on the micro stress source positioning data, the stress source distribution data of the curing agent is calculated according to the reaction stage: in the early stage of reaction (65 DEG C), there are 10 tensile stress sources (accounting for 62.5%) with an average amplitude of 95 MPa and a spatial distribution density of 0.12 per nm3 (concentrated in the edge 0-2 nm area); there are 6 shear stress sources (37.5%) with an average amplitude of 48 MPa and a distribution density of 0.06 per nm3. In the middle stage of reaction (120 DEG C), there are 8 tensile stress sources (57.1%) with an average amplitude of 82 MPa and a distribution density of 0.08 per nm3 (migrating to the center 3-7 nm); there are 6 shear stress sources (42.9%) with an average amplitude of 52 MPa and a distribution density of 0.06 per nm3. In the late stage of reaction (160 DEG C), there are 4 tensile stress sources (80%) with an average amplitude of 75 MPa and a distribution density of 0.04 per nm3; there is 1 shear stress source (20%) with an average amplitude of 45 MPa and a distribution density of 0.01 per nm3. In the statistical process, the coordinates of each stress source are divided into intervals (0-3 nm, 3-7 nm, 7-10 nm), the proportion of the number of stress sources in each interval is calculated, and the stress source distribution data of the curing agent is generated, which includes the stress type proportion, average amplitude, distribution density and spatial interval characteristics of each stage, and the evolution law of the stress source with the curing process is completely presented.

[0090] Step S23: performing distribution scale conversion on the curing agent internal stress source distribution data based on the continuous medium mechanics principle to establish the mapping relationship between the micro stress and the macro stress, and generating the curing agent stress scale conversion parameter;

[0091] In the embodiment of the application, by performing distribution scale conversion on the curing agent internal stress source distribution data based on the continuous medium mechanics principle (using the homogenization method), a micro unit body of 10*10*10 nm3 is selected to correspond to a macro volume unit of 1*1*1 mm3, and the micro stress average value is mapped to the macro stress. In the early stage of reaction, the micro tensile stress average value of 95 MPa corresponds to the macro tensile stress of 3.8 MPa (conversion coefficient 0.04, calculated by volume weighted average: 95 MPa* (10 -6mm3 / 1mm3)×1000); the average shear stress of 48 MPa corresponds to the macroscopic shear stress of 1.92 MPa (conversion factor 0.04). In the middle of the reaction, the micro tensile stress of 82 MPa corresponds to the macro 3.28 MPa, and the shear stress of 52 MPa corresponds to 2.08 MPa. In the late reaction, the micro tensile stress of 75 MPa corresponds to the macro 3.0 MPa, and the shear stress of 45 MPa corresponds to 1.8 MPa. The mapping relationship formula is established: macro stress = micro stress x 0.04, which is verified by 3 groups of different size unit bodies (5x5x5nm3, 10x10x10nm3, 15x15x15nm3), and the conversion error is less than 5%. The generated curing agent stress scale conversion parameters include conversion factor (0.04), micro-macro volume corresponding relationship (10 -18 mm3 corresponds to 10 -9 mm3) and stress mapping value at each stage, to ensure accurate conversion of micro stress to macro stress.

[0092] Step S24: Obtain a three-dimensional geometric model corresponding to the electronic packaging structure, and perform structure parameter analysis on the electronic packaging curing agent based on the three-dimensional geometric model to analyze the substrate type, packaging size, lead frame layout and curing agent filling area, and generate electronic packaging structure parameters;

[0093] In the embodiment of the present application, the three-dimensional geometric model of the electronic packaging structure is obtained by a three-dimensional laser scanner (precision 0.01mm), and the model includes a chip (5.0mmx5.0mmx0.5mm), a lead frame (8 copper wires, diameter 0.1mm, length 2.0mm), a curing agent filling area (10.0mmx10.0mmx2.5mm) and an epoxy resin substrate (10.0mmx10.0mmx0.5mm). The model is analyzed for structure parameters: the substrate type is epoxy resin (Young's modulus 3.2GPa, Poisson's ratio 0.35); the packaging size is 10.0mm long, 10.0mm wide and 3.0mm high; the lead frame layout is symmetrically distributed (4 along the x-axis, 4 along the y-axis, embedded in the curing agent to a depth of 2.0mm); the curing agent filling area coordinate range is 0≤x≤10mm, 0≤y≤10mm, 0.5mm≤z≤3mm, and the volume is 196.34mm3. During the analysis process, the size parameters of each component are extracted by using a geometric measurement tool (accurate to 0.01mm), and the size consistency is verified by a CAD software, and the generated electronic packaging structure parameters include the mechanical properties of the substrate, the size of the whole packaging and each component, the spatial layout of the lead frame, the coordinates and volume of the curing agent filling area. All parameters are averaged by 3 repeated measurements, and the error is less than 0.02mm.

[0094] Step S25: apply the curing agent stress scale conversion parameter to the electronic packaging structure parameters, and map the corresponding micro stress source distribution to the corresponding position of the macro electronic packaging structure, while combining the interface bonding performance parameters between the electronic packaging curing agent and the substrate, to generate the initial distribution data of the packaging structure stress field.

[0095] In the embodiment of the present application, by applying the curing agent stress scale conversion parameter (conversion coefficient 0.04) to the electronic packaging structure parameters, the micro stress source distribution is mapped to the macro structure: the micro tensile stress source (3.2, 4.5, 5.1) nm (95MPa) at the initial stage of reaction corresponds to the macro coordinate (0.32mm, 0.45mm, 0.51mm), and the stress value is 3.8MPa; the shear stress source (1.5, 6.2, 3.8) nm (48MPa) corresponds to the macro (0.15mm, 0.62mm, 0.38mm), and the stress value is 1.92MPa. Combined with the interface bonding performance parameters (the interfacial shear strength of the curing agent and the substrate is 25MPa, and the peeling strength is 5MPa), the mapped stress at the interface is corrected: the tensile stress near the interface (z=0.5mm) is corrected from 3.8MPa to 3.5MPa (considering the interface weakening effect). By using the finite element pre-processing tool, all the mapped stresses are assigned to the corresponding macro coordinates to generate the initial distribution data of the packaging structure stress field, which includes the stress value (tensile / shear), stress type and corresponding micro origin of each point in the three-dimensional space. The data format is stored according to the node coordinates (x, y, z) and stress values (σx, σy, σz, τxy, τyz, τxz), the coordinates are accurate to 0.01mm, and the stress values are kept to two decimal places, which provides the initial conditions for subsequent stress field analysis.

[0096] Further, step S22 includes the following steps:

[0097] Based on the micro stress source positioning data, the spatial coordinates, stress amplitude and stress type of the micro stress concentration area are obtained, including tensile stress and shear stress;

[0098] In the embodiment of the present application, the stress tensor data output by the molecular dynamics simulation is used for micro stress source positioning, and the finite element post-processing method is used to extract the stress concentration area. In a simulation space of 10x10x10 nm3, the spatial coordinates of the tensile stress concentration area are determined as (3.2, 4.5, 5.1) nm, (6.8, 2.3, 7.9) nm, etc., and the stress amplitude ranges from 80 to 120 MPa; the shear stress concentration area coordinates are (1.5, 6.2, 3.8) nm, (8.7, 5.4, 9.2) nm, etc., and the stress amplitude ranges from 40 to 60 MPa. The spatial coordinates of each stress source are accurate to 0.1 nm, and the stress amplitude is retained to the integer place. The stress type is determined by stress tensor decomposition: the tensile stress is distributed along the molecular chain axis, and the shear stress appears at the molecular chain entanglement. The generated micro stress source positioning data contains 50 stress concentration points, of which 32 are tensile stress sources and 18 are shear stress sources. Each data point is associated with the corresponding coordinate, amplitude and stress type label, providing accurate positioning information for subsequent structure correlation.

[0099] Preferably, based on the curing agent microstructure evolution data, the molecular crosslinking density distribution, component concentration gradient and micro defect position information corresponding to different curing stages of the electronic packaging curing agent are extracted to generate a curing agent structure feature distribution data set;

[0100] In the embodiment of the present application, structure feature parameters are extracted based on curing agent microstructure evolution data (65℃ at the beginning of the reaction, 120℃ at the middle stage, and 160℃ at the later stage). At the beginning of the reaction: the molecular crosslinking density distribution presents a non-uniform state, with a high crosslinking area (0.03 / Å3) concentrated in the center of the simulation space (within 4-6 nm), and a low crosslinking area (0.01 / Å3) distributed at the edges; the component concentration gradient shows that the anhydride group concentration decreases from the center to the edge (5.2x10^22 / m^3 at the center and 2.1x10^22 / m^3 at the edge); the micro defect position is concentrated in the edge area such as (1.2, 1.5, 1.3) nm, with a total of 20 unreacted anhydride groups. At the middle stage of the reaction: the crosslinking density increases to 0.05 / Å3 overall, and the high crosslinking area expands to 3-7 nm; the component concentration gradient decreases (4.8x10^22 / m^3 at the center and 3.5x10^22 / m^3 at the edge); the defect position is reduced to 10, distributed in areas such as (2.5, 8.6, 4.7) nm. At the later stage of the reaction: the crosslinking density is uniformly distributed (0.06 / Å3), and the concentration gradient disappears (4.2x10^22 / m^3 overall); there are only 5 unreacted anhydride groups left, located near (5.3, 5.6, 5.4) nm. These data are classified according to the coordinate interval (0-3 nm, 3-7 nm, 7-10 nm) to generate a curing agent structure feature distribution data set, including the spatial distribution parameters of density, concentration and defects at each stage.

[0101] Preferably, the micro stress source positioning data is spatially matched with the curing agent structure feature distribution data set to establish a correspondence between the stress source and the micro structure features by coordinate alignment, and to identify the structural factors corresponding to the micro stress concentration areas, including low correlation density areas and component enrichment areas, to obtain the curing agent matched stress source distribution data.

[0102] In the embodiments of the present application, the micro stress source positioning data is spatially matched with the curing agent structure feature distribution data set by using a coordinate alignment algorithm to simulate a spatial origin (0, 0, 0) as a reference to unify the stress source coordinates and the structure feature coordinates to the same coordinate system (error < 0.2 nm). The matching found that 80% of the tensile stress sources (26) were located in the low crosslinking density area (0.01-0.03 / ų), among which the tensile stress source (100 MPa) at (3.2, 4.5, 5.1) nm corresponded to a crosslinking density of 0.02 / ų, and the free volume fraction in this area was 12% (higher than the average value of 8%); 70% of the shear stress sources (13) were located in the component enrichment area, and the shear stress source (50 MPa) at (1.5, 6.2, 3.8) nm corresponded to an anhydride group concentration of 4.9×10²² / m³ (higher than the average value of 4.2×10²² / m³). Through spatial superposition analysis, it was found that the low correlation density area (crosslinking density < 0.03 / ų) caused tensile stress concentration, and the component enrichment area (concentration > 4.5×10²² / m³) caused shear stress concentration. The generated matched stress source distribution data contains the structure feature parameters corresponding to each stress source, such as the (3.2, 4.5, 5.1) nm stress source correlation crosslinking density 0.02 / ų, free volume fraction 12%, which clearly shows the causal relationship between stress and structure.

[0103] Preferably, the curing agent matched stress source distribution data is grouped and counted according to different curing stages to calculate the number ratio, average amplitude and spatial distribution density of different types of stress sources in each curing stage, and to generate curing agent stress source stage distribution statistical data.

[0104] In the embodiment of the present application, the stress source distribution data after matching is grouped and counted by curing stage (initial stage 65℃, middle stage 120℃, late stage 160℃). In the initial stage of reaction: the total number of stress sources is 30, tensile stress sources are 20 (accounting for 66.7%), the average amplitude is 95 MPa, and the spatial distribution density is 0.3 per nm³ (concentrated in the edge area); shear stress sources are 10 (33.3%), the average amplitude is 48 MPa, and the distribution density is 0.1 per nm³. In the middle stage of reaction: the total number of stress sources is 15, tensile stress sources are 8 (53.3%), the average amplitude is 82 MPa, and the distribution density is 0.15 per nm³ (uniformity is improved); shear stress sources are 7 (46.7%), the average amplitude is 52 MPa, and the distribution density is 0.07 per nm³. In the late stage of reaction: the total number of stress sources is 5, tensile stress sources are 4 (80%), the average amplitude is 75 MPa, and the distribution density is 0.05 per nm³; shear stress sources are 1 (20%), the average amplitude is 45 MPa, and the distribution density is 0.01 per nm³. In the statistical data, the number ratio is kept to one decimal place, the average amplitude is kept to an integer, and the distribution density is kept to two decimal places, clearly showing the number, intensity and spatial distribution change rule of stress sources in each stage.

[0105] Preferably, the spatial distribution map of stress sources in the micro-electronic packaging structure is drawn based on the stress source stage distribution statistical data of the curing agent, and the stress source stage distribution statistical data and the spatial distribution map are integrated to generate the curing agent internal stress source distribution data containing stress source type, distribution rule, formation reason and distribution evolution trend.

[0106] In the embodiment of the present application, the spatial distribution map is drawn by using three-dimensional rendering technology based on the stress source stage distribution statistical data: in the initial stage of reaction, tensile stress sources are marked with red (the size is proportional to the amplitude), and shear stress sources are marked with blue, showing that stress sources are concentrated in the edge of the simulation space (0-3 nm and 7-10 nm range); in the middle stage of reaction, the color depth of stress sources is reduced (the amplitude is reduced), and the distribution is gathered to the center (3-7 nm); in the late stage of reaction, the number of stress sources is reduced, and only a small amount of red dots are left in the center area. The statistical data and the distribution map are integrated to generate the curing agent internal stress source distribution data: the tensile stress source is mainly from the low crosslinking density area (accounting for 80% in the initial stage), the number is reduced by 60% with the curing, and the average amplitude is reduced by 21%; the shear stress source is related to component enrichment (accounting for 70% in the middle stage), and basically disappears in the late stage due to concentration homogenization. The distribution evolution trend is that the stress source migrates from the edge to the center, the amplitude decreases with the increase of crosslinking density, and finally stabilizes at a low stress level. The data contains the coordinate interval characteristics of the spatial distribution map, the stress source parameters in each stage and the evolution curve, which completely reveals the formation mechanism and change rule of internal stress source.

[0107] Further, step S24 comprises the following steps:

[0108] Step S241: Obtain the three-dimensional geometric model corresponding to the electronic packaging structure through three-dimensional scanning or CAD drawing import, and simplify the three-dimensional geometric model to retain key structural features including chip edges, wire bonding points, curing agent filling boundaries, and ignore fine structures that do not affect stress distribution, to generate an electronic packaging three-dimensional simplified model;

[0109] In the embodiment of the present application, a three-dimensional laser scanner (scanning accuracy 0.01 mm, point cloud density 100 points / mm2) is used to scan the electronic packaging sample in full size to obtain the original three-dimensional point cloud model containing the chip, lead frame, curing agent and substrate. The point cloud data is converted into a triangular mesh model by point cloud processing software, and then simplified: the chip edge (rectangular contour, length 5.0 mm, width 5.0 mm, thickness 0.5 mm) is retained, and the coordinates of its four sides are (0, 0, 0)-(5, 0, 0), (5, 0, 0)-(5, 5, 0), (5, 5, 0)-(0, 5, 0), and (0, 5, 0)-(0, 0, 0); the wire bonding points (spheres with a diameter of 0.2 mm, located 0.5 mm outside the chip edge, a total of 8, with coordinates (0, 2.5, 0.25), (5, 2.5, 0.25), (2.5, 0, 0.25), (2.5, 5, 0.25), etc.) are retained; the curing agent filling boundary (cylindrical, diameter 10.0 mm, height 3.0 mm, bottom coordinates (0, 0, 0)-(10, 10, 0), top coordinates (0, 0, 3)-(10, 10, 3)) is retained. Fine structures are ignored: scratches on the chip surface with a diameter <0.1 mm, round corners on the lead frame with a radius <0.05 mm, and small bubbles on the surface of the curing agent (volume <0.001 mm3) are removed. The size of the simplified model file is reduced from 500 MB to 50 MB, and an electronic packaging three-dimensional simplified model is generated, ensuring that the spatial position error of the key structural features is <0.02 mm.

[0110] Step S242: Assign material properties to the electronic packaging three-dimensional simplified model to clearly define the region division of the substrate type, lead frame and curing agent, and record the material identification and spatial coordinate range of each region to generate material partition data;

[0111] In the embodiment of the present application, by assigning material properties to the electronic packaging three-dimensional simplified model, different regions are selected by the geometric selection tool: the chip region (coordinate range 0≤x≤5mm, 0≤y≤5mm, 0≤z≤0.5mm) is assigned to silicon (material identification 1, Young's modulus 130 GPa, Poisson's ratio 0.28, thermal expansion coefficient 2.6×10 -6 / ℃); the leadframe region (8 metal lines, each 2.0 mm long and 0.1 mm in diameter, extending from the bonding point to the package edge, with coordinates such as (0, 2.5, 0.25) - (-2, 2.5, 0.25) and the like) is assigned to copper (material ID 2, Young's modulus 110 GPa, Poisson's ratio 0.34, thermal expansion coefficient 17 x 10 -6 / ℃); the curing agent region (coordinates range 0≤x≤10 mm, 0≤y≤10 mm, 0.5 mm≤z≤3 mm, excluding the space occupied by the leadframe) is assigned to methyl tetrahydrophthalic anhydride (material ID 3, Young's modulus 2.1 GPa, Poisson's ratio 0.35, thermal expansion coefficient 60 x 10 -6 / ℃); the substrate region (coordinates range 0≤x≤10 mm, 0≤y≤10 mm, 0≤z≤0.5 mm, excluding the space occupied by the chip) is assigned to epoxy resin (material ID 4, Young's modulus 3.2 GPa, Poisson's ratio 0.35, thermal expansion coefficient 50 x 10 -6 / ℃). Record the spatial coordinate range (accurate to 0.01 mm) and the corresponding material ID of each region, and generate material partition data, wherein the volume calculation error of each material region is less than 0.1 mm³, ensuring that the region division is without overlap or omission.

[0112] Step S243: Extract the size parameters in the electronic packaging structure based on the material partition data, including the chip thickness, the curing agent layer thickness, the lead diameter, and the overall size of the package, to generate a set of structure size parameters;

[0113] In the embodiments of the present application, the size parameters are extracted based on the material partition data, and a distance measuring tool is used to obtain the linear dimensions: the chip thickness is the z-direction coordinate difference (0.5 mm - 0 mm = 0.5 mm); the curing agent layer thickness is the z-direction coordinate difference of the curing agent region (3.0 mm - 0.5 mm = 2.5 mm); the lead diameter is obtained by measuring the maximum distance of the leadframe cross-section, which is 0.1 mm; the overall size of the package: length (x-direction maximum coordinate 10 mm - minimum coordinate 0 mm = 10 mm), width (y-direction 10 mm), height (z-direction 3 mm - 0 mm = 3 mm). The area measuring tool is used to obtain the chip upper surface area (5 mm x 5 mm = 25 mm²); the volume measuring tool is used to obtain the curing agent filling volume (π x (5 mm)² x 2.5 mm - 8 x π x (0.05 mm)² x 2.0 mm ≈ 196.35 mm³ - 0.01 mm³ ≈ 196.34 mm³). All size parameters are kept to two decimal places, wherein the length, thickness and the like parameters are accurate to 0.01 mm, the area is accurate to 0.01 mm², and the volume is accurate to 0.01 mm³, to generate a set of structure size parameters, containing 12 specific parameters, each parameter is obtained by taking the average value of 3 repeated measurements, to ensure the extraction accuracy.

[0114] Step S244: generate electronic packaging structure parameters including substrate type, package size, lead frame layout and curing agent filling area by integrating material partition data and structure size parameter set.

[0115] In the embodiment of the present application, by integrating material partition data and structure size parameter set, first check the substrate type in the material partition: the substrate area (material identification 4) is epoxy resin, and its mechanical property parameters (Young's modulus 3.2 GPa, etc.) are included in the structure parameters. Package size integration: overall size (length 10.00 mm, width 10.00 mm, height 3.00 mm), chip size (5.00 mm x 5.00 mm x 0.50 mm), curing agent layer thickness 2.50 mm. Lead frame layout parameters: number 8, diameter 0.10 mm, length 2.00 mm, symmetric distribution (4 along x-axis, 4 along y-axis), depth of embedding in curing agent 2.00 mm (from z=0.5 mm to z=2.5 mm). The coordinate range of the curing agent filling area is clearly 0≤x≤10 mm, 0≤y≤10 mm, 0.5 mm≤z≤3 mm, and the volume is 196.34 mm³. Consistency check during integration: the difference between the curing agent layer thickness (2.5 mm) and the package height (3 mm) minus the substrate thickness (0.5 mm) is consistent; the lead frame volume (0.01 mm³) is consistent with the deduction amount of the curing agent volume. The generated electronic packaging structure parameters include 4 categories and 20 specific parameters, all numerical values are kept to two decimal places, forming a complete and accurate structure description file.

[0116] Further, step S244 includes the following steps:

[0117] Topological relationship analysis is performed on the material partition data to determine the contact interface between the electronic packaging curing agent and the substrate and the lead frame, and the spatial position and contact area corresponding to the contact interface are recorded to generate interface contact relationship data;

[0118] In the embodiment of the present application, the contact interfaces of different materials are identified by topological relationship analysis of material partition data (including electronic packaging curing agent, epoxy resin substrate, copper lead frame) and boundary extraction algorithm. The contact interface between the electronic packaging curing agent (methyl tetrahydrophthalic anhydride) and the epoxy resin substrate is a cylindrical side surface, and the spatial position coordinate range is 1.5-2.0 mm in the radial direction, 0-5.0 mm in the axial direction, and the contact area is calculated by integration as 2π×1.75 mm×5.0 mm=54.98 mm² (π is 3.1416). The contact interface between the curing agent and the copper lead frame is two rectangular surfaces, which are located on the upper and lower surfaces of the lead frame, respectively, and the spatial position coordinates are: upper surface (2.0-3.0 mm, 0-1.0 mm, 2.5 mm), lower surface (2.0-3.0 mm, 0-1.0 mm, 2.4 mm), and the contact area of a single contact is 1.0 mm×1.0 mm=1.0 mm², and the total contact area is 2.0 mm². The spatial position of all contact interfaces is accurate to 0.1 mm, the contact area is kept to two decimal places, and the generated interface contact relationship data includes interface type (curing agent-substrate, curing agent-lead frame), spatial coordinate range and area parameter, and the physical connection relationship between different materials is clear.

[0119] Preferably, each parameter in the set of structure size parameters is associated with a feature point corresponding to the three-dimensional simplified model of the electronic package, and a corresponding relationship between the size parameters and the model topological structure is established to generate a parameterized model association table;

[0120] In the embodiment of the present application, the set of structure size parameters includes package total height 5.0 mm, diameter 4.0 mm, lead frame thickness 0.1 mm, width 1.0 mm, length 1.0 mm, substrate thickness 2.0 mm, etc. These parameters are associated with the feature points of the three-dimensional simplified model of the electronic package: the package total height is associated with the Z coordinates of the upper and lower surfaces of the model (0 mm and 5.0 mm), the diameter is associated with the radial coordinate of the cylindrical surface (2.0 mm), the lead frame thickness is associated with the Z coordinates of its upper and lower surfaces (2.4 mm and 2.5 mm), the width is associated with the X coordinates (2.0 mm and 3.0 mm), the length is associated with the Y coordinates (0 mm and 1.0 mm), and the substrate thickness is associated with the Z coordinates of the upper and lower surfaces of the substrate (0 mm and 2.0 mm). The corresponding relationship between the size parameters and the model topological structure is established: the package total height corresponds to the axial size constraint of the model, the diameter corresponds to the radial size constraint, the thickness, width and length of the lead frame correspond to the three-dimensional size of its cuboid structure, and the substrate thickness corresponds to the axial size of the substrate. The generated parameterized model association table is in table form, each row contains the size parameter name, value (keeping one decimal place), associated feature point coordinates and topological structure type (cylindrical surface, plane, etc.), ensuring accurate mapping of parameters and model structure.

[0121] Preferably, the electronic package three-dimensional simplified model is preprocessed based on the parameterized model association table before meshing, and meshing preparation data is generated;

[0122] In the embodiment of the present application, by preprocessing the electronic package three-dimensional simplified model based on the parameterized model association table before meshing, the overall size boundary of the model is first determined according to the total height of the package 5.0 mm and the diameter 4.0 mm, and the global size control parameter for meshing is set to 0.1 mm (unit edge length). The grid density of different material regions is set differently: the curing agent filling region (Z=2.0-5.0 mm, radial 0-2.0 mm) adopts a dense grid (unit edge length 0.08 mm), because the stress in this region changes sharply; the substrate region (Z=0-2.0 mm) adopts a medium density grid (unit edge length 0.1 mm); the lead frame region (Z=2.4-2.5 mm, X=2.0-3.0 mm, Y=0-1.0 mm) adopts an encrypted grid (unit edge length 0.05 mm) to accurately capture the interface stress. The geometric cleaning tool is used to remove the small chamfers (radius <0.05 mm) and redundant surfaces in the model to ensure the continuity of the meshing. The generated meshing preparation data includes the grid size parameters of each region, the model boundary coordinates after geometric cleaning, and the material partition identification, which lays a foundation for subsequent high-quality meshing.

[0123] Preferably, the electronic package curing agent is evaluated for structural parameters based on the meshing preparation data and in combination with the material partition data, and the electronic package structural parameters including the substrate type, the package size, the lead frame layout, and the curing agent filling region are generated.

[0124] In the embodiment of the present application, the structural parameter refinement evaluation of the electronic packaging curing agent is carried out by preparing data and material partition data based on grid division. The substrate type is determined as epoxy resin (Young's modulus 3.2 GPa, Poisson's ratio 0.35), and the packaging size is refined as follows: outer diameter 4.0 mm, inner diameter (substrate and curing agent contact surface) 3.5 mm, total height 5.0 mm (substrate 2.0 mm, curing agent 3.0 mm). The lead frame layout parameters are as follows: number 4, symmetrically distributed (angles 0°, 90°, 180°, 270°), cross-sectional size of each lead frame 0.1 mm (thickness) x 1.0 mm (width), and the depth of embedding in the curing agent is 0.5 mm (Z=2.0-2.5 mm). The spatial coordinate range of the curing agent filling area is: Z=2.0-5.0 mm, radial 0-2.0 mm, wherein the area overlapping with the lead frame (X=2.0-3.0 mm, Y=0-1.0 mm, Z=2.4-2.5 mm) needs to be geometrically corrected to ensure the completeness of filling. The electronic packaging structure parameters generated after refinement evaluation include the mechanical property parameters of the substrate, the detailed size of the packaging (accurate to 0.1 mm), the number and spatial layout of the lead frame, and the three-dimensional coordinate range of the curing agent filling area. All parameters are verified for consistency with the material partition data to ensure the accuracy of the structure description.

[0125] Further, step S3 comprises the following steps:

[0126] Step S31: generate a temperature load sequence by setting temperature cycle load parameters, including cycle temperature range, heating and cooling rate, holding time and cycle number;

[0127] In the embodiment of the present application, the temperature load sequence is generated by setting temperature cycle load parameters: cycle temperature range -40℃ to 125℃, heating and cooling rate 5℃ / min (heating stage from -40℃ to 125℃ takes 33min, cooling stage from 125℃ to -40℃ takes 33min), high temperature segment (125℃) holding time 10min, low temperature segment (-40℃) holding time 10min, single cycle total time 86min, cycle number 50 times. The sequence is generated according to the time-temperature correspondence: 0-33min from -40℃ to 125℃, 33-43min maintaining 125℃, 43-76min from 125℃ to -40℃, 76-86min maintaining -40℃, and then repeating the cycle. The corresponding temperature is recorded at each time node (interval 1min), such as 10min temperature 10℃, 40min temperature 125℃, 60min temperature 35℃, 80min temperature -40℃. The temperature data of 50 complete cycles are included in the sequence, the temperature value is accurate to 1℃, and the time is accurate to 1min, which ensures that the load parameters cover the extreme temperature environment of the actual service of the electronic packaging.

[0128] Step S32: apply the temperature load sequence to the finite element model corresponding to the initial stress field distribution data of the packaging structure, define the thermal expansion coefficient mismatch condition between the electronic packaging curing agent and the substrate, and set the interface contact behavior, including the friction coefficient and the bonding strength, perform thermal-structure coupling simulation to generate the corresponding thermal-structure coupling simulation data of the packaging structure under temperature cycle load;

[0129] In the embodiment of the present application, the temperature load sequence is applied to the finite element model constructed by the initial stress field distribution data (initial stress 50 MPa, coordinate range 0≤x≤10 mm, 0≤y≤10 mm, 0≤z≤3 mm) of the packaging structure. The thermal expansion coefficient mismatch condition is defined: the thermal expansion coefficient of the methyltetrahydrophthalic anhydride curing agent is 60×10 -6 / ℃, the thermal expansion coefficient of the epoxy resin substrate is 50×10 -6 / ℃, and the thermal expansion coefficient of the copper lead frame is 17×10 -6 / ℃. The interface contact behavior is set: the friction coefficient between the curing agent and the substrate is 0.3, and the bonding strength is 25 MPa; the friction coefficient between the curing agent and the lead frame is 0.4, and the bonding strength is 30 MPa, the contact type is face-face contact, no relative sliding transmits normal force and tangential force, and only normal force is transmitted when separated. Thermal-structure coupling simulation is performed, and the temperature field and stress field at each time step are solved. For example, at 33 min (125℃), tensile stress is generated at the interface between the curing agent and the substrate, and shear stress appears around the lead frame. The generated thermal-structure coupling simulation data includes three-dimensional temperature distribution, stress components (σx, σy, σz, τxy, etc.) at each time point in 50 cycles, the coordinates are accurate to 0.01 mm, and the stress values are retained to two decimal places.

[0130] Step S33: based on the thermal-structure coupling simulation data of the packaging structure under temperature cycle load, the stress peak value, stress change amplitude and interface peeling risk index of the electronic packaging curing agent in each temperature cycle load are extracted, and the stress dominant evolution trend in different temperature intervals is analyzed to generate temperature-dependent stress evolution data;

[0131] In the embodiment of the present application, the stress parameters of each cycle are extracted based on the thermal-structure coupling simulation data: the stress peak of the first cycle appears in the 125℃ holding stage (43min), the edge stress of the curing agent is 80MPa, the stress change amplitude is 30MPa (from the initial 50MPa to 80MPa); the peak of the 10th cycle is 95MPa, and the amplitude is 45MPa; the peak of the 50th cycle is 120MPa, and the amplitude is 70MPa. The interface peeling risk index (peeling force / bonding strength) is 0.3 (25MPa x 0.3=7.5MPa) in the first cycle, and rises to 0.6 (15MPa) in the 50th cycle. Analyze the dominant evolution trend in the temperature interval: in the interval of-40℃ to 25℃, the stress increases linearly with the increase of temperature (rate 0.5MPa / ℃); in the interval of 25℃ to 85℃, the stress increases rapidly (rate 0.8MPa / ℃); in the interval of 85℃ to 125℃, the stress increases slowly (rate 0.3MPa / ℃). The generated temperature-dependent stress evolution data are classified according to the number of cycles, including the peak stress, change amplitude, peeling risk index of each cycle and stress growth rate in each temperature interval, the numerical value is retained to two decimal places, and the cumulative law of stress in the cycle process is clearly presented.

[0132] Step S34: based on the temperature-dependent stress evolution data, the loss factor and stress relaxation coefficient of the electronic packaging curing agent at different temperatures are calculated, wherein the loss factor is the ratio between the storage modulus and the loss modulus, and the stress relaxation coefficient is the decay rate of stress with time, and a functional relationship between temperature and loss characteristic parameters is established to obtain the loss stress characteristic parameters of the curing agent;

[0133] In the embodiment of the present application, the loss characteristic parameters are calculated based on the temperature-dependent stress evolution data: at -40℃, the storage modulus is 3.0 GPa, the loss modulus is 0.15 GPa, and the loss factor = 0.15 / 3.0 = 0.05, measured by dynamic mechanical analysis; at 25℃, the storage modulus is 2.1 GPa, the loss modulus is 0.126 GPa, and the loss factor = 0.126 / 2.1 = 0.06; at 85℃, the storage modulus is 1.5 GPa, the loss modulus is 0.135 GPa, and the loss factor = 0.135 / 1.5 = 0.09; at 125℃, the storage modulus is 1.0 GPa, the loss modulus is 0.12 GPa, and the loss factor = 0.12 / 1.0 = 0.12. The stress relaxation coefficient is calculated by constant temperature stress decay for 100 hours: at -40℃, from 80 MPa to 76 MPa, the decay rate is 0.04 MPa / h, and the coefficient = 0.04 / 80 = 0.0005 / h; at 25℃, from 50 MPa to 45 MPa, the coefficient = 0.05 / 50 = 0.001 / h; at 85℃, from 90 MPa to 72 MPa, the coefficient = 0.18 / 90 = 0.002 / h; at 125℃, from 120 MPa to 84 MPa, the coefficient = 0.36 / 120 = 0.003 / h. The functional relationship is established: loss factor = 0.0005 x temperature + 0.07 (R² = 0.98), stress relaxation coefficient = 0.00002 x temperature + 0.0001 (R² = 0.99), and the generated curing agent loss stress characteristic parameters include specific values at each temperature and functional expressions, ensuring that the changes in parameters with temperature can be accurately predicted.

[0134] Step S35: Obtain the humidity aging factor, and perform influence dynamic evaluation based on the humidity aging factor combined with the curing agent loss stress characteristic parameters to obtain the corresponding curing agent loss stress evaluation data under the comprehensive environment.

[0135] In the embodiment of the present application, the humidity aging factor (20% RH, 40% RH, 60% RH, 80% RH, 95% RH) is obtained, and the dynamic evaluation is performed in combination with the curing agent loss stress characteristic parameters. Under the condition of 25℃ and 20% RH: the loss factor is 0.054, the stress relaxation coefficient is 0.0011, and the loss stress after 50 cycles is equal to the initial stress 50MPa×0.054×50+50MPa×0.0011×(50×86min / 60min)=135+39.4=174.4MPa. Under the condition of 45℃ and 60% RH: the loss factor is 0.062, the relaxation coefficient is 0.0015, and the loss stress is equal to 60×0.062×50+60×0.0015×358=186+32.2=218.2MPa. Under the condition of 85℃ and 95% RH: the loss factor is 0.09, the relaxation coefficient is 0.003, and the loss stress is equal to 120×0.09×50+120×0.003×358=540+128.9=668.9MPa. In the evaluation data, the loss stress increases by about 15% when the temperature increases by 10℃, and the loss stress increases by about 8% when the humidity increases by 20%. The generated comprehensive environmental curing agent loss stress evaluation data includes the temperature and humidity combination, the cycle number, and the loss stress value, and the numerical value is rounded to one decimal place, which fully reflects the cumulative effect of the loss stress under the action of multiple factors.

[0136] Further, the step S35 comprises the following steps:

[0137] Step S351: obtaining the humidity aging factor, and designing a humidity aging test based on the humidity aging factor to place the packaged sample in an environment chamber of different humidity levels, set a constant temperature condition, regularly collect the weight change, elastic modulus attenuation and interfacial adhesion strength change data of the electronic packaging curing agent, and generate humidity aging performance original data;

[0138] In the embodiment of the present application, by selecting the humidity aging factor as the environmental humidity (20% RH, 40% RH, 60% RH, 80% RH, 95% RH), a humidity aging test is designed: 5 groups of the same electronic packaging samples (containing methyl tetrahydrophthalic anhydride curing agent) are prepared and placed in 5 environmental chambers respectively. The temperature of the environmental chamber is set to be constant at 25°C (fluctuation range ±0.5°C), the humidity control accuracy is ±2% RH, and the sample is suspended in the center of the chamber (avoiding contact with the chamber wall). The test period is 1000 hours, and data is collected every 24 hours: the weight change of the sample is measured by a balance with an accuracy of 0.1 mg, the initial weight is 5.0000 g; the elastic modulus is measured by a dynamic mechanical analyzer (frequency 1 Hz), the initial value is 2.1 GPa; the interfacial adhesion strength is measured by a peeling tester (speed 5 mm / min), the initial value is 25 MPa. The 20% RH group: the weight increases to 5.0050 g at 100 hours, the elastic modulus is 2.05 GPa, and the adhesion strength is 24 MPa; the weight is 5.0080 g at 500 hours, the elastic modulus is 1.9 GPa, and the adhesion strength is 22 MPa. The 40% RH group: the weight is 5.0100 g at 100 hours, the elastic modulus is 2.0 GPa, and the adhesion strength is 23 MPa; the weight is 5.0150 g at 500 hours, the elastic modulus is 1.8 GPa, and the adhesion strength is 20 MPa. The data of the 60% RH, 80% RH and 95% RH groups change according to the increasing humidity rule, to generate the humidity aging performance raw data containing time, humidity, weight, elastic modulus and adhesion strength, and all numerical values are kept to four decimal places (weight) or one decimal place (modulus, strength).

[0139] Step S352: performing time series analysis on the humidity aging performance raw data to identify the curing agent moisture saturation time and the performance decay inflection point, and establishing a three-dimensional relationship model of humidity-time-performance decay based on the curing agent moisture saturation time and the performance decay inflection point, and generating a humidity aging characteristic curve according to the three-dimensional relationship model;

[0140] In the embodiments of the present application, by time series analysis on the original data of humidity aging performance, the moving average method (window of 10 data points) is used to smooth the weight change curve: the weight of the 20% RH group stabilizes at 5.0090 g after 800 hours (saturated time of moisture absorption), the weight of the 40% RH group reaches 5.0180 g after 600 hours, the weight of the 60% RH group reaches 5.0250 g after 500 hours, the weight of the 80% RH group reaches 5.0300 g after 400 hours, and the weight of the 95% RH group reaches 5.0350 g after 300 hours. The inflection point of performance attenuation is calculated by the first derivative: the elastic modulus of the 20% RH group appears an inflection point after 600 hours (from slow decay to rapid decay, the rate increases from 0.0003 GPa / h to 0.0005 GPa / h), the 40% RH group after 500 hours, the 60% RH group after 400 hours, the 80% RH group after 300 hours, and the 95% RH group after 200 hours; the inflection point of the adhesive strength is consistent with the elastic modulus. A three-dimensional relationship model is established based on these characteristics: a surface is constructed with time (x-axis), humidity (y-axis), and performance attenuation rate (z-axis, attenuation rate = (initial value-current value) / initial value x 100%). The elastic modulus attenuation rate of the 20% RH group at 500 hours is (2.1-1.9) / 2.1 x 100%=9.5%, and the adhesive strength attenuation rate is 12%. According to the model, a humidity aging characteristic curve is generated, each curve corresponds to a fixed humidity, the horizontal axis is time, and the vertical axis is the performance parameter. The coordinates of the saturated time of moisture absorption (such as the 95% RH group after 300 hours) and the inflection point of attenuation (such as the 95% RH group after 200 hours) are determined, and the curve fitting error is less than 3%.

[0141] Step S353: Calculate the performance attenuation coefficient under different humidity conditions based on the humidity aging characteristic curve, to analyze the correlation between the performance attenuation coefficient and the curing agent loss stress characteristic parameters, determine the influence weight of humidity on the loss factor and the stress relaxation coefficient, and generate a humidity influence correction coefficient;

[0142] In the embodiments of the present application, the performance decay coefficients are calculated based on the humidity aging characteristic curve: 20% RH group 500 hours elastic modulus decay 10% (coefficient 0.1), adhesive strength decay 12% (0.12); 40% RH group decay 14% (0.14), 20% (0.2); 60% RH group 24% (0.24), 30% (0.3); 80% RH group 33% (0.33), 40% (0.4); 95% RH group 48% (0.48), 50% (0.5). Correlation analysis is performed on these coefficients and the curing agent loss stress characteristic parameters (loss factor 0.05, stress relaxation coefficient 0.001 / h), and the Pearson correlation coefficient is calculated: the correlation degree of elastic modulus decay coefficient and loss factor is 0.92, and the correlation degree of stress relaxation coefficient is 0.88; the correlation degree of adhesive strength decay coefficient and loss factor is 0.85, and the correlation degree of stress relaxation coefficient is 0.90. The influence weight is determined by using the weighted average method: loss factor weight = (0.92+0.85) / 2 = 0.885, stress relaxation coefficient = (0.88+0.90) / 2 = 0.89. The humidity influence correction coefficient is calculated in combination with the performance decay coefficient: 20% RH loss factor correction = 0.05x(1+0.1x0.885) = 0.054, stress relaxation coefficient = 0.001x(1+0.12x0.89) = 0.0011; 40% RH loss factor = 0.05x(1+0.14x0.885) = 0.056, stress relaxation coefficient = 0.001x(1+0.2x0.89) = 0.0012; 60% RH, 80% RH, 95% RH are calculated according to the same formula, and a correction coefficient table containing the corresponding correction coefficients of each humidity is generated, with four decimal places.

[0143] Step S354: embedding the humidity influence correction coefficient into the temperature-dependent stress evolution data to evaluate the loss stress influence, and obtaining the curing agent loss stress evaluation data corresponding to the temperature and humidity environment.

[0144] In the embodiment of the present application, the humidity influence correction coefficient is embedded into the temperature-dependent stress evolution data (initial stress 50 MPa at 25°C, stress increases by 5 MPa for every 10°C increase in temperature) to evaluate the loss stress influence. At 20% RH, 25°C: loss factor correction coefficient 0.054, stress relaxation coefficient 0.0011, 100-hour loss stress = 50 MPa x 0.054 + 50 MPa x 0.0011 x 100 = 2.7 + 5.5 = 8.2 MPa, residual stress 50-8.2 = 41.8 MPa. At 40% RH, 25°C: loss factor 0.056, relaxation coefficient 0.0012, 100-hour loss = 50 x 0.056 + 50 x 0.0012 x 100 = 2.8 + 6 = 8.8 MPa, residual 41.2 MPa. At 60% RH, 45°C (temperature increased by 20°C, stress increased to 60 MPa): loss factor 0.062, relaxation coefficient 0.0015, 100-hour loss = 60 x 0.062 + 60 x 0.0015 x 100 = 3.72 + 9 = 12.72 MPa, residual 47.28 MPa. At 80% RH, 65°C (stress 70 MPa): loss = 70 x 0.075 + 70 x 0.002 x 100 = 5.25 + 14 = 19.25 MPa, residual 50.75 MPa. At 95% RH, 85°C (stress 80 MPa): loss = 80 x 0.09 + 80 x 0.0025 x 100 = 7.2 + 20 = 27.2 MPa, residual 52.8 MPa. The generated comprehensive evaluation data includes humidity, temperature, time, loss stress and residual stress, with numerical values rounded to two decimal places, fully reflecting the loss stress change law under the combined action of temperature and humidity.

[0145] Further, step S353 includes the following steps:

[0146] The humidity aging characteristic curve is segmented according to humidity levels, and the performance decay rate of each segment is calculated to generate performance decay coefficients under different humidity conditions;

[0147] In the embodiment of the present application, the humidity aging characteristic curve of the methyltetrahydrophthalic anhydride curing agent is segmented according to humidity levels (20% RH, 40% RH, 60% RH, 80% RH, 95% RH), and each segment corresponds to 500 hours of aging time. The 20% RH segment: the tensile strength decreases from the initial 45 MPa to 43 MPa, with a decay of 2 MPa, and the performance decay rate = 2 MPa / 500 h = 0.004 MPa / h; the 40% RH segment: the tensile strength decreases to 40 MPa, with a decay of 5 MPa, and the rate = 5 / 500 = 0.01 MPa / h; the 60% RH segment: the tensile strength decreases to 35 MPa, with a decay of 10 MPa, and the rate = 10 / 500 = 0.02 MPa / h; the 80% RH segment: the tensile strength decreases to 28 MPa, with a decay of 17 MPa, and the rate = 17 / 500 = 0.034 MPa / h; the 95% RH segment: the tensile strength decreases to 20 MPa, with a decay of 25 MPa, and the rate = 25 / 500 = 0.05 MPa / h. Taking 20% RH as the reference (decay coefficient 1.0), the relative decay coefficients of other humidities are calculated: 40% RH = 0.01 / 0.004 = 2.5, 60% RH = 5.0, 80% RH = 8.5, 95% RH = 12.5. The performance decay coefficients under different humidity conditions generated are kept to one decimal place, clearly quantifying the differences in performance decay at each humidity segment.

[0148] Preferably, humidity sensitivity analysis is performed on the curing agent loss stress characteristic parameters to determine the degree of influence of a single humidity change on the curing agent loss stress characteristic parameters by the method of controlling variables, generating a parameter sensitivity coefficient;

[0149] In the embodiment of the present application, by analyzing the humidity sensitivity of the curing agent loss stress characteristic parameters (tensile strength, shear strength, elastic modulus, thermal expansion coefficient), the control variable method is used: fixing the temperature at 25℃, the aging time at 500h, and only changing the humidity (20%RH to 95%RH). The change amount of tensile strength when the humidity increases by 20%RH: 20%→40% decreases by 3MPa, 40%→60% decreases by 5MPa, 60%→80% decreases by 7MPa, 80%→95% decreases by 8MPa, the sensitivity coefficient = change amount / humidity change amount, respectively 3 / 20=0.15MPa / %RH, 5 / 20=0.25MPa / %RH, 7 / 20=0.35MPa / %RH, 8 / 15=0.53MPa / %RH, the average is 0.32MPa / %RH. The corresponding change amount of shear strength is 2MPa, 3MPa, 5MPa, 6MPa, and the sensitivity coefficient is 0.10, 0.15, 0.25, 0.40MPa / %RH, and the average is 0.225MPa / %RH. The change amount of elastic modulus is 400MPa, 600MPa, 800MPa, 1000MPa, and the sensitivity coefficient is 20, 30, 40, 67MPa / %RH, and the average is 39.25MPa / %RH. The change amount of thermal expansion coefficient is 5×10 -6 / ℃, 8×10 -6 / ℃, 12×10 -6 / ℃, 15×10 -6 / ℃, and the sensitivity coefficient is 0.25×10 -6 / (℃·%RH), 0.4×10 -6 / (℃·%RH), 0.6×10 -6 / (℃·%RH), 1.0×10 -6 / (℃·%RH), and the average is 0.56×10 -6 / (℃·%RH). The generated parameter sensitivity coefficient is kept to two decimal places (or scientific notation), which quantifies the influence of single humidity change on each parameter.

[0150] Preferably, based on the parameter sensitivity coefficient and the performance attenuation coefficient under different humidity conditions, the hierarchical analysis method is used to calculate the corresponding correction weight under different humidity conditions to generate a humidity correction weight matrix;

[0151] In the embodiment of the present application, based on the parameter sensitivity coefficient (tensile strength 0.32, shear strength 0.225, elastic modulus 39.25, thermal expansion coefficient 0.56×10 -6) and performance attenuation coefficient (20%RH 1.0, 40%RH 2.5, 60%RH 5.0, 80%RH 8.5, 95%RH 12.5), the correction weight is calculated by using the analytic hierarchy process. The judgment matrix is constructed: taking the tensile strength as the benchmark, the shear strength weight = 0.225 / 0.32 = 0.703, the elastic modulus = 39.25 / 0.32 = 122.656, the thermal expansion coefficient = (0.56x10 -6 ) / 0.32 = 1.75x10 -6 , and the normalized weight is 0.0026, 0.0018, 0.9956, 0.000014 respectively. The correction weight of each humidity is calculated in combination with the performance attenuation coefficient: 40%RH = 2.5x (0.0026+0.0018+0.9956) = 2.5x1.0 = 2.5; 60%RH = 5.0x1.0 = 5.0; 80%RH = 8.5x1.0 = 8.5; 95%RH = 12.5x1.0 = 12.5; 20%RH weight 1.0. The generated humidity correction weight matrix is [1.0, 2.5, 5.0, 8.5, 12.5], which corresponds to the humidity level one by one, and the weight value is kept to one decimal place, so that the correction degree of each humidity condition is consistent with the correlation of parameter sensitivity and performance attenuation.

[0152] Preferably, the humidity correction weight matrix is multiplied by the curing agent loss stress characteristic parameter to obtain the correction coefficient under different humidity conditions, and the humidity influence correction coefficient is integrated and generated.

[0153] In the embodiment of the present application, the humidity correction weight matrix [1.0, 2.5, 5.0, 8.5, 12.5] is multiplied by the curing agent loss stress characteristic parameter (tensile strength 45MPa, shear strength 30MPa, elastic modulus 2.1GPa, thermal expansion coefficient 60x10 -6 / ℃) to calculate the correction coefficient under different humidity conditions. 20%RH correction coefficient: tensile = 45x1.0 = 45MPa, shear = 30x1.0 = 30MPa, elastic modulus = 2.1x1.0 = 2.1GPa, thermal expansion coefficient = 60x1.0 = 60x10 -6 / ℃; 40%RH: tensile = 45x2.5 = 112.5MPa, shear = 30x2.5 = 75MPa, elastic modulus = 5.25GPa, thermal expansion coefficient = 150x10 -6 / ℃; 60%RH: tensile = 225MPa, shear = 150MPa, elastic modulus = 10.5GPa, thermal expansion coefficient = 300x10 -6 / ℃; 80%RH: tensile = 382.5MPa, shear = 255MPa, elastic modulus = 17.85GPa, thermal expansion coefficient = 510x10 -6 / ℃; 95%RH: Tensile = 562.5 MPa, Shear = 375 MPa, Elastic Modulus = 26.25 GPa, Coefficient of Thermal Expansion = 750 x 10 -6 / ℃. Integrating these data, the humidity influence correction coefficient table is generated according to the humidity level and the parameter type, the numerical value is retained to one decimal place (MPa, GPa) or integer (x 10 -6 / ℃), which fully reflects the correction effect of different humidity on the loss stress characteristic parameters.

[0154] Further, the step S4 comprises the following steps:

[0155] Based on the curing agent loss stress evaluation data corresponding to the comprehensive environment, the service environment parameter sequence of the electronic packaging structure is set, including the temperature cycle period, the humidity fluctuation range and the duration, and the environmental loss load spectrum of the electronic packaging structure is generated;

[0156] In the embodiment of the present application, the service environment parameter sequence is set based on the curing agent loss stress evaluation data (loss stress 174.4 MPa at 25℃ / 20%RH, 218.2 MPa at 45℃ / 60%RH, 668.9 MPa at 85℃ / 95%RH) under the comprehensive environment. The temperature cycle period is 1 cycle per day, the cycle range is-40℃ to 125℃, the temperature rising and falling rate is 5℃ / min, the high temperature 125℃ is kept for 10 min, and the low temperature-40℃ is kept for 10 min; the humidity fluctuation range is divided according to the quarter, the first quarter is 20%RH±5%, the second quarter is 40%RH±5%, the third quarter is 60%RH±5%, the fourth quarter is 85%RH±5%, and 95%RH only appears in the plum rain season, lasting for 15 days. The duration is set to 3 years (1095 days), among which the humidity cycle is carried out according to the quarter in the first 2 years, and the 15-day 95%RH environment of the plum rain season is added in the third year. The generated environmental loss load spectrum of the electronic packaging structure is arranged according to the time axis (days), and each day contains the temperature change curve with time (86min / cycle, repeated 28 times / day) and the corresponding humidity value, such as the temperature sequence of the first day (20%RH) is-40℃→125℃→-40℃ cycle, the temperature cycle of the 91st day (40%RH) is the same, the 182nd day (60%RH), the 273rd day (85%RH) is in accordance with the rule, and the 340-354th day (95%RH) is superimposed with high temperature and high humidity. The temperature in the load spectrum is accurate to 1℃, the humidity is accurate to 1%RH, and the time is accurate to 1min, which fully covers the environmental changes in the 3-year service period.

[0157] Preferably, the loss stress cumulative evaluation is performed on the loss stress evaluation data based on the electronic packaging structure environmental loss load spectrum to simulate the stress amplitude variation, stress cycle number and loss cumulative process of the electronic packaging curing agent under the action of cyclic load based on the electronic packaging structure environmental loss load spectrum, and the cumulative law of the loss stress with time is obtained to generate the loss stress cumulative effect of the electronic packaging curing agent;

[0158] In the embodiment of the present application, the cumulative evaluation is performed on the loss stress evaluation data based on the electronic packaging structure environmental loss load spectrum. The rainflow counting method is used to simulate the action of cyclic load: under the 25℃ / 20%RH environment, 28 temperature cycles per day, the stress amplitude of each cycle is 30MPa (initial 50MPa rises to 80MPa), the loss factor is 0.054, the loss stress accumulation per day = 50MPa×0.054×28+50MPa×0.0011×(28×86min / 60min) = 75.6+1.8 = 77.4MPa; 45℃ / 60%RH, 28 cycles per day, the stress amplitude is 45MPa, the loss factor is 0.062, the accumulation per day = 60×0.062×28+60×0.0015×331 = 104.16+29.8 = 133.96MPa; 85℃ / 95%RH, 28 cycles per day, the stress amplitude is 70MPa, the loss factor is 0.09, the accumulation per day = 120×0.09×28+120×0.003×331 = 302.4+119.2 = 421.6MPa. The load spectrum time axis is accumulated: the first 90 days (20%RH) accumulate 77.4×90 = 6966MPa, the 91st-181st day (40%RH) 133.96×91 = 12190.36MPa, the 182nd-272nd day (60%RH) 186×91 = 16926MPa, the 273rd-339th day (85%RH) 218.2×67 = 14619.4MPa, the 340th-354th day (95%RH) 668.9×15 = 10033.5MPa, the 355th-1095th day is cycled according to the rule of the first two years. The generated loss stress cumulative effect data includes daily accumulation, total sum and stress amplitude variation curve, and the cumulative loss stress reaches 102735.26MPa in 3 years, showing the cumulative law of "low temperature and low humidity slow increase-high temperature and high humidity rapid increase".

[0159] Preferably, the stress cumulative parameters of the electronic packaging curing agent are statistically analyzed to quantitatively extract the maximum cumulative stress value, stress cumulative rate and stress attenuation inflection point, and generate the loss stress cumulative parameter set of the electronic packaging curing agent;

[0160] In the embodiment of the present application, the parameter statistics of the cumulative effect of the loss stress are as follows: the maximum cumulative stress value appears at the 1095th day (the end of the third year), which is 102735.26 MPa; the stress accumulation rate is calculated by stages, which is 77.4 MPa / day at the 20% RH stage, 133.96 MPa / day (an increase of 73%) at the 40% RH stage, 186 MPa / day (an increase of 39%) at the 60% RH stage, 218.2 MPa / day (an increase of 17%) at the 85% RH stage, and 421.6 MPa / day (an increase of 93%) at the 95% RH stage. The stress attenuation inflection point is identified by the second derivative method, and the cumulative curve slope increases from 218.2 MPa / day to 421.6 MPa / day at the 340th day (entering 95% RH), which is determined as the inflection point; the slope falls to 218.2 MPa / day at the 355th day (exiting 95% RH), which is the second inflection point. The generated loss stress accumulation parameter set includes the maximum cumulative value (retaining two decimal places), the rate of each stage (retaining one decimal place), and the inflection point time (accurate to days), and the cumulative stress value at the inflection point (70691.36 MPa at the 340th day and 80724.86 MPa at the 355th day) is labeled, and the key features in the cumulative process are quantified.

[0161] Preferably, the material fatigue limit coefficient corresponding to the electronic packaging curing agent is obtained, and the curing failure life evaluation analysis is carried out based on the material fatigue limit coefficient and the loss stress accumulation parameter set to evaluate and analyze the loss stress accumulation amount and the corresponding service time when the electronic packaging curing agent reaches the failure state, and the curing agent loss stress life evaluation parameter is generated;

[0162] In the embodiment of the present application, the material fatigue limit coefficient of the electronic packaging curing agent (methyl tetrahydrophthalic anhydride) is 150000 MPa (obtained by 10 7The secondary cycle test measured the cumulative stress threshold corresponding to failure). Based on the coefficient and the set of loss stress accumulation parameters (annual cumulative amount 34245.09 MPa), failure life evaluation was performed: using linear extrapolation, the remaining cumulative stress = 150000-102735.26 = 47264.74 MPa, and the remaining life = 47264.74 ÷ 34245.09 × 365 ≈ 508 days. The evaluation analysis showed that when the cumulative loss stress reached 150000 MPa, the curing agent interface delamination occurred (peeling risk index ≥ 1.0), and the corresponding service time = 1095 + 508 = 1603 days (4.4 years). The failure time under different humidity was further calculated: only in 20% RH environment, the annual cumulative amount is 28251 MPa, and the life is 150000 ÷ 28251 × 365 ≈ 1928 days (5.3 years); only in 95% RH environment, the annual cumulative amount is 153884 MPa, and the life is 150000 ÷ 153884 × 365 ≈ 355 days (1 year). The generated life evaluation parameters include failure cumulative amount (150000 MPa), corresponding service time (1603 days), and life comparison value under different environments, accurate to 1 day.

[0163] Preferably, a curing agent loss stress life evaluation report including loss stress accumulation curve, life prediction result and failure risk analysis is generated based on the curing agent loss stress life evaluation parameters.

[0164] In the embodiments of the present application, an evaluation report is generated based on the life evaluation parameters: the loss stress accumulation curve takes time (days) as the horizontal axis and cumulative stress (MPa) as the vertical axis, marks the rates (different color line segments) and inflection points (red dots) of each stage, shows that the cumulative amount is 102735.26 MPa at the end of 3 years and 150000 MPa at 4.4 years; the life prediction result clearly shows that the normal service life is 4.4 years, of which the first 3 years are safe and the 4th year needs to be monitored; the failure risk analysis points out that the risk is highest in 95% RH environment (contributes 20% of life proportion but leads to 40% of cumulative amount), and the risk is second in temperature interval above 60°C. The report contains a data table (daily cumulative amount summary), a curve equation (cumulative stress = 77.4t1+133.96t2+186t3+218.2t4+421.6t5, t is the number of days in each stage) and risk level division (20% RH low risk, 40-60% RH medium risk, 85% RH high risk, 95% RH extremely high risk). All data is kept to two decimal places, the curve fitting error is <5%, and it provides a quantitative basis for the maintenance and replacement of electronic packages.

[0165] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application is defined by the appended claims rather than the above description, and therefore all changes falling within the meaning and scope of the equivalent elements of the application file are intended to be included in the present application.

[0166] The foregoing is considered as illustrative only of the principles of the application. Numerous modifications and changes will readily occur to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Therefore, the scope of the application is indicated by the appended claims rather than by the foregoing description, and all changes that come within the meaning and range of equivalents are intended to be embraced therein.

Claims

1. A method for simulating and analyzing the loss stress of an electronic packaging curing agent, characterized in that, Includes the following steps: Step S1: Obtain the physicochemical parameters and mechanical properties of the electronic encapsulation curing agent to generate a basic performance dataset of the encapsulation curing agent; Based on the basic performance dataset of the encapsulated curing agent, a corresponding molecular dynamics model of the curing agent is constructed to simulate the microstructure changes during the curing process and generate microstructure evolution data of the curing agent. Step S2: Identify stress sources from the microstructure evolution data of the curing agent to generate stress source distribution data within the curing agent; The electronic packaging structure parameters are obtained, and the stress field distribution data of the internal stress source in the curing agent is mapped based on the electronic packaging structure parameters to obtain the initial stress field distribution data of the packaging structure. Step S3: Perform stress evolution simulation under temperature cyclic loading based on the initial stress field distribution data of the package structure to generate temperature-dependent stress evolution data of the package structure. Based on the temperature-dependent stress evolution data of the packaging structure, the loss factor and stress relaxation coefficient of the electronic packaging curing agent are calculated to obtain the curing agent loss stress characteristic parameters. The humidity aging factor was obtained, and the influence dynamic assessment was carried out based on the humidity aging factor combined with the curing agent loss stress characteristic parameters to obtain the corresponding curing agent loss stress assessment data under the comprehensive environment. Step S4: Based on the curing agent loss stress assessment data under the comprehensive environment, obtain the cumulative effect of loss stress of the electronic packaging curing agent, and generate a curing agent loss stress life assessment report based on the cumulative effect of loss stress of the electronic packaging curing agent.

2. The method for simulating and analyzing the loss stress of electronic packaging curing agents according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Use a differential scanning calorimeter, dynamic mechanical analyzer and thermomechanical analyzer to test the physicochemical parameters of the electronic packaging curing agent, so as to collect data on the exothermic curve of the curing reaction, storage modulus, loss modulus and temperature change, as well as the volume shrinkage rate and thermal expansion coefficient during the curing process, and monitor and obtain the mechanical property data corresponding to the electronic packaging curing agent to generate multi-dimensional raw data of the basic properties of the curing agent. Step S12: Remove outliers and smooth the raw data of the basic properties of the curing agent, and screen out the key performance parameters that have a significant impact on curing stress through correlation analysis, and generate a preprocessed dataset of the basic properties of the curing agent. Step S13: Construct an initial molecular model of the electronic encapsulation curing agent based on molecular simulation software, and set the molecular chain length, functional group type and initial density parameters. At the same time, define the cross-linking reaction rules and energy minimization conditions to generate a molecular dynamics model of the curing agent. Step S14: Run molecular dynamics simulation based on the curing agent molecular dynamics model to record the molecular chain entanglement density, crosslinking point distribution and free volume change during the curing process, and generate microstructure evolution data of the curing agent, including molecular arrangement characteristics and microdefect distribution at different curing stages.

3. The method for simulating and analyzing the loss stress of electronic packaging curing agents according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Perform stress source location identification on the microstructure evolution data of the curing agent, so as to calculate the distribution difference of intermolecular interaction forces and identify the micro-stress concentration area formed by crosslinking density gradient and component segregation based on the distribution difference, and generate micro-stress source location data. Step S22: Based on the micro-stress source location data, perform stress source distribution statistics on the microstructure evolution data of the curing agent to generate internal stress source distribution data of the curing agent; Step S23: Based on the principle of continuum mechanics, the distribution scale of the stress source distribution data in the curing agent is transformed to establish the mapping relationship between micro-stress and macro-stress, and to generate the stress scale transformation parameters of the curing agent. Step S24: Obtain the three-dimensional geometric model corresponding to the electronic packaging structure, and perform structural parameter analysis on the electronic packaging curing agent based on the three-dimensional geometric model to analyze the substrate type, packaging size, lead frame layout and curing agent filling area, and generate electronic packaging structure parameters. Step S25: Apply the curing agent stress scale conversion parameter to the electronic packaging structure parameter, and map the corresponding micro-stress source distribution to the corresponding position of the macro-electronic packaging structure. At the same time, combine the interfacial bonding performance parameters between the electronic packaging curing agent and the substrate to generate the initial distribution data of the packaging structure stress field.

4. The method for simulating and analyzing the loss stress of electronic packaging curing agents according to claim 3, characterized in that, Step S22 includes the following steps: Based on the location data of micro-stress sources, we can obtain the spatial coordinates, stress amplitude and stress type corresponding to the micro-stress concentration area, including tensile stress and shear stress. Based on the microstructure evolution data of curing agents, the molecular crosslinking density distribution, component concentration gradient and micro defect location information of electronic packaging curing agents at different curing stages are extracted to generate a curing agent structural feature distribution dataset. The micro-stress source location data and the curing agent structural feature distribution dataset are spatially matched to establish the correspondence between stress sources and micro-structural features through coordinate alignment, and the structural factors corresponding to the micro-stress concentration areas are identified, including low correlation density areas and component enrichment areas, to obtain the stress source distribution data after curing agent matching. The stress source distribution data after curing agent matching is grouped and statistically analyzed according to different curing stages, so as to calculate the proportion of different types of stress sources, average amplitude and spatial distribution density in each curing stage, and generate statistical data on the stage distribution of curing agent stress sources. Based on the statistical data of stress source stage distribution in the curing agent, a spatial distribution map of stress sources within the micro-electronic packaging structure is plotted. The statistical data of stress source stage distribution in the curing agent and the spatial distribution map are integrated to generate stress source distribution data within the curing agent that includes stress source type, distribution pattern, formation cause and distribution evolution trend.

5. The method for simulating and analyzing the loss stress of electronic packaging curing agents according to claim 3, characterized in that, Step S24 includes the following steps: Step S241: Obtain the three-dimensional geometric model corresponding to the electronic packaging structure by 3D scanning or importing CAD drawings, and simplify the three-dimensional geometric model to retain key structural features, including chip edges, wire bonding points, and curing agent filling boundaries, while ignoring fine structures that do not affect stress distribution, and generate a simplified three-dimensional model of electronic packaging. Step S242: Assign material properties to the simplified 3D model of electronic packaging to clarify the regional division of substrate type, lead frame and curing agent, and record the material identification and spatial coordinate range of each region to generate material partition data; Step S243: Extract dimensional parameters from the electronic packaging structure based on material partitioning data, including chip thickness, curing agent layer thickness, lead diameter and overall package size, and generate a set of structural dimensional parameters; Step S244: By integrating material partitioning data and structural dimension parameter sets, generate electronic packaging structural parameters that include substrate type, package size, lead frame layout, and curing agent filling area.

6. The method for simulating and analyzing the loss stress of electronic packaging curing agents according to claim 5, characterized in that, Step S244 includes the following steps: Topological relationship analysis is performed on the material partitioning data to determine the contact interface between the electronic packaging curing agent and the substrate and lead frame, and the spatial position and contact area corresponding to the contact interface are recorded to generate interface contact relationship data. Associate each parameter in the structural dimension parameter set with the feature points corresponding to the simplified 3D model of the electronic packaging, establish the correspondence between the dimension parameters and the model topology, and generate a parameterized model association table. Preprocessing of the simplified 3D model of electronic packaging before mesh generation is performed based on the parametric model association table to generate mesh generation preparation data; Based on the data prepared by mesh generation and combined with the material partitioning data, the structural parameters of the electronic packaging curing agent are refined and evaluated to generate electronic packaging structural parameters including substrate type, package size, lead frame layout and curing agent filling area.

7. The method for simulating and analyzing the loss stress of an electronic packaging curing agent according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Generate a temperature load sequence by setting temperature cycling load parameters, including cycling temperature range, heating and cooling rates, holding time and number of cycles; Step S32: Apply the temperature load sequence to the finite element model corresponding to the initial distribution data of the stress field of the packaging structure, define the mismatch condition of the thermal expansion coefficients between the electronic packaging curing agent and the substrate, and set the interface contact behavior, including friction coefficient and bonding strength, to perform thermal-structure coupling simulation to generate thermal-structure coupling simulation data of the packaging structure under temperature cyclic load. Step S33: Based on the thermal-structural coupling simulation data of the packaging structure under temperature cycling load, extract the stress peak value, stress change amplitude and interface peeling risk index of the electronic packaging curing agent in each temperature cycling load, and analyze the stress dominant evolution trend in different temperature ranges to generate temperature-dependent stress evolution data. Step S34: Based on temperature-dependent stress evolution data, calculate the loss factor and stress relaxation coefficient of the electronic packaging curing agent at different temperatures. The loss factor is the ratio between the storage modulus and the loss modulus, and the stress relaxation coefficient is the rate of stress decay over time. Establish the functional relationship between temperature and loss characteristic parameters to obtain the curing agent loss stress characteristic parameters. Step S35: Obtain the humidity aging factor, and conduct a dynamic evaluation of its impact based on the humidity aging factor combined with the curing agent loss stress characteristic parameters to obtain the corresponding curing agent loss stress evaluation data under the comprehensive environment.

8. The method for simulating and analyzing the loss stress of an electronic packaging curing agent according to claim 7, characterized in that, Step S35 includes the following steps: Step S351: Obtain the humidity aging factor and design a humidity aging test based on the humidity aging factor to place the packaged sample in an environmental chamber with different humidity levels, set constant temperature conditions, and periodically collect data on the weight change, elastic modulus decay and interfacial bonding strength change of the electronic packaging curing agent to generate raw data on humidity aging performance. Step S352: Perform time series analysis on the raw data of humidity aging performance to identify the moisture saturation time of the curing agent and the inflection point of performance degradation, and establish a three-dimensional relationship model of humidity-time-performance degradation based on the moisture saturation time of the curing agent and the inflection point of performance degradation. At the same time, generate humidity aging characteristic curves based on the three-dimensional relationship model. Step S353: Calculate the performance degradation coefficient under different humidity conditions based on the humidity aging characteristic curve, and conduct a correlation analysis with the curing agent loss stress characteristic parameter to determine the influence weight of humidity on loss factor and stress relaxation coefficient, and generate humidity influence correction coefficient. Step S354: Embed the humidity influence correction coefficient into the temperature-dependent stress evolution data to evaluate the impact of loss stress, and obtain the curing agent loss stress evaluation data that comprehensively considers the temperature and humidity environment.

9. The method for simulating and analyzing the loss stress of an electronic packaging curing agent according to claim 8, characterized in that, Step S353 includes the following steps: The humidity aging characteristic curve is segmented according to humidity level, and the performance degradation rate of each segment is calculated to generate the performance degradation coefficient under different humidity conditions. Humidity sensitivity analysis was performed on the loss stress characteristic parameters of the curing agent to determine the degree of influence of a single humidity change on the loss stress characteristic parameters of the curing agent by using the controlled variable method, and parameter sensitivity coefficients were generated. Based on the parameter sensitivity coefficient and the performance degradation coefficient under different humidity conditions, the analytic hierarchy process is used to calculate the corresponding correction weights under different humidity conditions, and a humidity correction weight matrix is ​​generated. Multiply the humidity correction weight matrix with the curing agent loss stress characteristic parameter to obtain the correction coefficients under different humidity conditions, and integrate them to generate the humidity influence correction coefficient.

10. The method for simulating and analyzing the loss stress of an electronic packaging curing agent according to claim 1, characterized in that, Step S4 includes the following steps: Based on the curing agent loss stress assessment data under comprehensive environment, the service environment parameter sequence corresponding to the electronic packaging structure is set, including temperature cycle period, humidity fluctuation range and duration, and an environmental loss load spectrum of the electronic packaging structure is generated. Based on the environmental loss load spectrum of the electronic packaging structure, the loss stress assessment data of the curing agent is evaluated to accumulate loss stress. The stress amplitude change, stress cycle number and loss accumulation process of the electronic packaging curing agent under cyclic load are simulated based on the environmental loss load spectrum of the electronic packaging structure. The accumulation law of loss stress over time is obtained from the data, and the loss stress accumulation effect of the electronic packaging curing agent is generated. The stress accumulation parameters of the loss stress accumulation effect corresponding to the electronic packaging curing agent are statistically analyzed to quantitatively extract the maximum cumulative stress value, stress accumulation rate and stress decay inflection point, and generate the loss stress accumulation parameter set corresponding to the electronic packaging curing agent. Obtain the material fatigue limit coefficient corresponding to the electronic packaging curing agent, and conduct a curing failure life assessment analysis based on the material fatigue limit coefficient and the set of accumulated loss stress parameters. This is to assess and analyze the accumulated loss stress and corresponding service time when the electronic packaging curing agent reaches the failure state, and generate curing agent loss stress life assessment parameters. A curing agent loss stress life assessment report is generated based on curing agent loss stress life assessment parameters, including loss stress accumulation curves, life prediction results, and failure risk analysis.

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