Simulation analysis method for loss stress of electronic packaging curing agent

Abstract

The invention relates to the technical field of computer simulation, in particular to a loss stress simulation analysis method for an electronic packaging curing agent. The method comprises the following steps: acquiring 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; generating curing agent microstructure evolution data based on the packaging curing agent basic performance data set, and performing stressor identification and packaging stress field distribution mapping to obtain packaging structure stress field initial distribution data; performing stress evolution simulation under a temperature cyclic load according to the initial distribution data of the stress field of the packaging structure, and calculating a loss factor and a stress relaxation coefficient corresponding to an electronic packaging curing agent to obtain a curing agent loss stress characteristic parameter; and acquiring a humidity aging factor, performing influence dynamic evaluation, and generating a curing agent loss stress life evaluation report. According to the invention, the precise simulation process of curing agent loss and stress distribution in the electronic packaging process can be realized.

Description

Technical Field

[0001] This invention relates to the field of computer simulation technology, and in particular to a method for simulating and analyzing the loss stress of electronic packaging curing agents. Background Technology

[0002] In the electronic packaging process, curing agents are an important component of epoxy resins. For example, methyltetrahydrophthalic anhydride (MTHPA), a highly efficient epoxy resin curing agent, plays a crucial role in electronic packaging, potting, high-voltage electrical appliances, and fiberglass oil pipelines used in oil fields. Currently, existing methods for simulating and analyzing the loss stress of electronic packaging materials are theoretical modeling methods. While these methods can perform preliminary stress predictions, their accuracy in simulating complex environments with multiple factors remains limited, thus reducing the efficiency of predicting the loss stress of electronic packaging curing agents. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a method for simulating and analyzing the loss stress of electronic packaging curing agents in order to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a method for simulating and analyzing the loss stress of an electronic packaging curing agent is provided, comprising 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; construct the corresponding molecular dynamics model of the curing agent based on the basic performance dataset of the encapsulation curing agent to simulate the microstructure changes during the curing process and generate microstructure evolution data of the curing agent. Step S2: Identify stress sources in the microstructure evolution data of the curing agent to generate stress source distribution data within the curing agent; obtain electronic packaging structure parameters, and map the stress field distribution data within the curing agent based on the electronic packaging structure parameters to obtain the initial stress field distribution data of the packaging structure. Step S3: Simulate the stress evolution under temperature cyclic loading based on the initial stress field distribution data of the packaging structure to generate temperature-dependent stress evolution data of the packaging structure; calculate the loss factor and stress relaxation coefficient of the electronic packaging curing agent based on the temperature-dependent stress evolution data of the packaging structure to obtain the curing agent loss stress characteristic parameters; obtain the humidity aging factor, and perform dynamic evaluation of its influence based on the humidity aging factor combined with the curing agent loss stress characteristic parameters to obtain the curing agent loss stress evaluation data under 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.

[0005] Furthermore, 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.

[0006] Furthermore, 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.

[0007] Furthermore, 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.

[0008] Furthermore, 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.

[0009] Furthermore, 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. Furthermore, 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. Furthermore, 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.

[0010] Furthermore, 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.

[0011] Furthermore, 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.

[0012] The beneficial effects of this invention are: 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

[0013] 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: 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; Figure 2 for Figure 1 A detailed flowchart of step S1; Figure 3 for Figure 1A detailed flowchart of step S2. Detailed Implementation

[0014] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0015] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0016] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0017] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a method for simulating and analyzing the loss stress of electronic packaging curing agents. In the embodiments of this invention, please refer to... Figure 1 The diagram shown is a flowchart illustrating the steps of the loss stress simulation analysis method for electronic packaging curing agents according to the present invention. In this example, the loss stress simulation analysis method for electronic packaging curing agents 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; construct the corresponding molecular dynamics model of the curing agent based on the basic performance dataset of the encapsulation curing agent to simulate the microstructure changes during the curing process and generate microstructure evolution data of the curing agent. In this embodiment of the invention, the corresponding electronic packaging curing agent (such as methyltetrahydrophthalic anhydride (MTHPA)) was tested using a differential scanning calorimeter (heating rate 5℃ / min, temperature range 25-200℃). The peak exothermic temperature of the curing reaction was found to be 120℃, and the total exothermic heat was 380 J / g. The storage modulus at 25℃ was measured to be 2.1 GPa and the loss modulus to be 0.126 GPa using a dynamic mechanical analyzer (frequency 1 Hz, -50 to 150℃). The volume shrinkage rate was 2.48% and the glassy thermal expansion coefficient was 30 × 10⁻⁶. -6 The tensile strength was 45 MPa and the flexural strength was 70 MPa, measured using a universal testing machine (tensile rate 5 mm / min). These data were integrated into a basic performance dataset for the encapsulating curing agent, including temperature-exothermic rate, modulus, volume change, and mechanical parameters. A molecular dynamics model was constructed based on this dataset, with a molecular chain length of 10 repeating units (molecular weight 1661.8), functional groups of anhydride and methyl groups, and an initial density of 1.210 g / cm³. The crosslinking reaction rule was defined: anhydride and hydroxyl groups undergo a ring-opening reaction at 65-160℃, with an activation energy of 60 kJ / mol. Energy minimization was achieved using a COMPASS force field (maximum force < 0.001 kcal / (mol·Å)). The curing process was simulated for 10 ns, recording the crosslinking density as follows: 0.02 crosslinks / ų in the initial stage (65℃), 0.05 crosslinks / ų in the middle stage (120℃), and 0.06 crosslinks / ų in the later stage (160℃). This generated microstructure evolution data of the curing agent, including molecular entanglement density and crosslinking point distribution.

[0018] Step S2: Identify stress sources in the microstructure evolution data of the curing agent to generate stress source distribution data within the curing agent; obtain electronic packaging structure parameters, and map the stress field distribution data within the curing agent based on the electronic packaging structure parameters to obtain the initial stress field distribution data of the packaging structure. In this embodiment of the invention, stress sources are identified by analyzing the microstructure evolution data of the curing agent, and the distribution difference of intermolecular forces is calculated: In the initial stage of the reaction, the intermolecular force difference in the 3-4 nm region is 80 MPa, which is identified as a tensile stress concentration region (coordinates (3.2, 4.5, 5.1) nm, amplitude 80-120 MPa); the shear force difference in the region with anhydride concentration of 5.2 × 10²² units / m³ is 40 MPa, which is a shear stress concentration region (coordinates (1.5, 6.2, 3.8) nm, amplitude 40-60 MPa). A total of 32 tensile stress sources and 18 shear stress sources are identified, generating stress source distribution data within the curing agent. The electronic packaging structure parameters were obtained as follows: chip size 5.0 × 5.0 × 0.5 mm, epoxy resin substrate size 10.0 × 10.0 × 0.5 mm (Young's modulus 3.2 GPa), 8 copper lead frames (0.1 mm diameter, 2.0 mm length), and curing agent filling area 10.0 × 10.0 × 2.5 mm. Based on these parameters, the microscopic stress sources were mapped to the macroscopic structure: microscopic (3.2, 4.5, 5.1) nm corresponds to macroscopic (0.32, 0.45, 0.51) mm, and the stress values ​​were calculated using a conversion factor of 0.04 (95 MPa → 3.8 MPa). The interface stress was corrected by combining interface performance parameters (shear strength 25 MPa, peel strength 5 MPa) to generate initial stress field distribution data for the packaging structure, including three-dimensional coordinates (accurate to 0.01 mm) and stress components (σx, σy, etc., rounded to two decimal places).

[0019] Step S3: Simulate the stress evolution under temperature cyclic loading based on the initial stress field distribution data of the packaging structure to generate temperature-dependent stress evolution data of the packaging structure; calculate the loss factor and stress relaxation coefficient of the electronic packaging curing agent based on the temperature-dependent stress evolution data of the packaging structure to obtain the curing agent loss stress characteristic parameters; obtain the humidity aging factor, and perform dynamic evaluation of its influence based on the humidity aging factor combined with the curing agent loss stress characteristic parameters to obtain the curing agent loss stress evaluation data under comprehensive environment. In this embodiment of the invention, temperature cyclic load simulation was performed based on the initial stress field distribution data of the encapsulated structure (initial stress 50 MPa). Cyclic parameters were set as follows: -40℃ to 125℃, heating and cooling rate 5℃ / min, holding at both high and low temperatures for 10 minutes, and 50 cycles. A mismatch in thermal expansion coefficients was defined as: curing agent 60 × 10⁻⁶. -6 / ℃, substrate 50×10 -6 / ℃, lead frame 17×10 -6The interfacial friction coefficient is 0.3-0.4, and the bond strength is 25-30 MPa. Thermal-structural coupling simulation shows that the stress peak at 125℃ during the 50th cycle is 120 MPa, generating temperature-dependent stress evolution data. Loss characteristic parameters were calculated: -40℃ loss factor 0.05 (0.15 GPa / 3.0 GPa), stress relaxation coefficient 0.0005 / h; 25℃ loss factor 0.06 (0.126 GPa / 2.1 GPa), coefficient 0.001 / h; 125℃ loss factor 0.12 (0.12 GPa / 1.0 GPa), coefficient 0.003 / h. A functional relationship was established (loss factor = 0.0005 × temperature + 0.07). The humidity aging factor (20%-95%RH) was obtained, and combined with parameter evaluation, the loss stress was 174.4MPa at 25℃ / 20%RH, 218.2MPa at 45℃ / 60%RH, and 668.9MPa at 85℃ / 95%RH, generating comprehensive environmental hardener loss stress evaluation data.

[0020] 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.

[0021] In this embodiment of the invention, an environmental loss load spectrum is generated based on comprehensive environmental loss stress assessment data: over 3 years (1095 days), the temperature cycles 28 times per day (-40℃→125℃, 86 min / cycle), and humidity is divided by quarter into 20%, 40%, 60%, and 85%RH, with 15 days of 95%RH during the rainy season. The cumulative effect is assessed using the rainflow counting method: 77.4 MPa per day during the 20%RH phase (6966 MPa total over 90 days), 133.96 MPa per day during the 40%RH phase (12190.36 MPa over 91 days), 186 MPa per day during the 60%RH phase (16926 MPa over 91 days), 218.2 MPa per day during the 85%RH phase (14619.4 MPa over 67 days), and 421.6 MPa per day during the 95%RH phase (10033.5 MPa over 15 days), for a total cumulative load of 102735.26 MPa over 3 years. The maximum cumulative value was 102735.26 MPa, with a rate of 421.6 MPa / day at 95% RH (a 93% increase), and the inflection point was on day 340 (entering 95% RH). Combined with the material fatigue limit coefficient of 150000 MPa, the estimated failure life was 1603 days (4.4 years). The generated life assessment report includes a cumulative curve (marking the rates and inflection points at each stage), a life prediction result (4.4 years), and a risk analysis (95% RH represents extremely high risk). Data is rounded to two decimal places, and the curve fitting error is <5%, providing a quantitative basis for maintenance.

[0022] Furthermore, as an embodiment of the present invention, reference is made to... Figure 2 As shown, Figure 1 A detailed flowchart of step S1 is shown below. In this embodiment, 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. In this embodiment of the invention, methyltetrahydrophthalic anhydride (MTHPA) was tested using a differential scanning calorimeter (heating rate 5℃ / min, temperature range 25-200℃, nitrogen atmosphere flow rate 50ml / min). The exothermic curve of the curing reaction was recorded, with a peak temperature of 120℃, a total exothermic heat of 380J / g, an exothermic onset temperature of 65℃, and a termination temperature of 160℃. A dynamic mechanical analyzer (tensile mode, frequency 1Hz, heating rate 3℃ / min, temperature range -50 to 150℃) measured the storage modulus at 25℃ to be 2.1GPa, the loss modulus to be 0.3GPa, and the glass transition temperature to be 85℃. At 60℃, the storage modulus decreased to 1.8GPa, while the loss modulus increased to 0.4GPa. Thermomechanical analyzer (compression mode, load 0.5 N, heating rate 2℃ / min, temperature range 25-180℃) monitored the volume shrinkage during the curing process: initial volume at 25℃ 1.21 cm³ / g, volume after curing (160℃) 1.18 cm³ / g, total shrinkage 2.48%; the coefficient of thermal expansion in the glassy state (25-60℃) is 30 × 10⁻⁶. -6 / ℃, rubbery state (90-120℃) is 120×10 -6 / ℃. Mechanical properties were tested using a universal testing machine (tensile rate 5 mm / min, specimen size 100×10×4 mm). The tensile strength was measured to be 45 MPa, elongation at break 3.5%, flexural strength 70 MPa, and flexural modulus 2.5 GPa. All tests were repeated three times, and the average value was taken to generate multi-dimensional raw data on the basic properties of the curing agent, including temperature-heat release rate, temperature-modulus, temperature-volume change, and mechanical parameters.

[0023] 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. In this embodiment of the invention, outliers were removed from the original data of the curing agent's basic properties using the 3σ criterion (σ being the standard deviation). Only one data point (49.5 MPa) deviated from the mean of 45 MPa by more than ±3σ (σ = 2.1 MPa) in the tensile strength test. Data smoothing was performed using a moving average method (window size of 5 data points) to smooth the storage modulus curve of the dynamic mechanical analysis, eliminating instantaneous fluctuations near 60℃ (a sudden change from 1.8 GPa to 1.75 GPa and then back up), ensuring a continuous curve. Pearson correlation analysis was used to calculate the correlation between each parameter and curing stress: volume shrinkage (0.92), storage modulus (0.88), coefficient of thermal expansion (0.85), peak exothermic rate (0.72), and elongation at break (0.65). Parameters with a correlation greater than 0.8 were selected as key performance parameters, including volume shrinkage (2.48%), storage modulus at 25℃ (2.1 GPa), and glassy thermal expansion coefficient (30 × 10⁻⁶). -6 / ℃), rubbery thermal expansion coefficient (120×10) -6 / ℃). These parameters are categorized and organized according to temperature ranges (25-60℃, 60-85℃, 85-160℃) to generate a dataset of basic properties of the pretreated curing agent, ensuring that the data accuracy is retained to two decimal places (shrinkage rate) or integer places (modulus).

[0024] 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. In this embodiment of the invention, an initial molecular model of MTHPA was constructed using molecular simulation software. The molecular structure was built according to the C9H10O3 molecular formula, containing 3,4-methyltetrahydrophthalic anhydride isomers (with methyl substitution positions at positions 3 and 4 on the benzene ring, and a 1:1 ratio of the two structures). The molecular chain length was set to 10 repeating units (molecular weight 1661.8), and the functional groups were anhydride (-C(O)OC(O)-) and methyl (-CH3). The initial density was 1.210 g / cm³ (consistent with the measured value at 20℃). The crosslinking reaction rule was defined as follows: the anhydride group undergoes a ring-opening reaction with the hydroxyl group (-OH) of the epoxy resin to form an ester bond (-C(O)O-), the activation energy of the reaction is 60 kJ / mol, and the reaction temperature range is 65-160℃ (matching the onset-termination temperature of the exothermic curve). The energy minimization conditions are set as follows: molecular mechanics optimization uses the COMPASS force field, and the convergence criteria are: maximum force < 0.001 kcal / (mol·Å), maximum displacement < 0.001 Å, and maximum energy change < 1 × 10⁻⁶. -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.

[0025] 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.

[0026] 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.

[0027] 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: 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. In this embodiment of the invention, stress sources are located and identified by analyzing the microstructure evolution data of the curing agent (65℃ in the initial stage of reaction, 120℃ in the middle stage, and 160℃ in the later stage). Molecular mechanics calculations are used to obtain the distribution difference of intermolecular interaction forces (van der Waals forces and covalent bonds). Within a simulated space of 10×10×10 nm³, the force gradient of each molecular unit is calculated: In the initial stage of the reaction, the region with a crosslinking density of 0.02 crosslinks / ų (within the coordinate range of 3-4 nm) exhibits an intermolecular force difference of 80 MPa (higher than the average level of 40 MPa), which is identified as a stress concentration region; the component segregation region (anhydride concentration of 5.2×10²² units / m³) has an intermolecular shear force difference of 40 MPa, forming a shear stress concentration. Stress distribution cloud maps were generated using three-dimensional interpolation, identifying 12 tensile stress concentration regions (coordinates such as (3.2, 4.5, 5.1) nm, amplitudes 80-120 MPa) and 8 shear stress concentration regions (coordinates such as (1.5, 6.2, 3.8) nm, amplitudes 40-60 MPa). The spatial coordinates of all stress sources were accurate to 0.1 nm, and the stress amplitudes were preserved to integer values. The generated micro-stress source location data included the type (tensile / shear), coordinates, and amplitude of each stress concentration region, clearly defining the origin and intensity of the micro-stress.

[0028] 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; In this embodiment of the invention, stress source distribution statistics are performed on the microstructure evolution data of the curing agent based on micro-stress source location data, and calculated in groups according to reaction stages: In the initial reaction stage (65℃), there are 10 tensile stress sources (62.5%), with an average amplitude of 95 MPa and a spatial distribution density of 0.12 sources / nm³ (concentrated in the 0-2nm edge region); and 6 shear stress sources (37.5%), with an average amplitude of 48 MPa and a distribution density of 0.06 sources / nm³. In the middle reaction stage (120℃), there are 8 tensile stress sources (57.1%), with an average amplitude of 82 MPa and a distribution density of 0.08 sources / nm³ (migrating towards the center 3-7nm); and 6 shear stress sources (42.9%), with an average amplitude of 52 MPa and a distribution density of 0.06 sources / nm³. In the later stage of the reaction (160℃), there were 4 tensile stress sources (80%), with an average amplitude of 75 MPa and a distribution density of 0.04 sources / nm³; and 1 shear stress source (20%), with an average amplitude of 45 MPa and a distribution density of 0.01 sources / nm³. During the statistical process, the coordinates of each stress source were divided into intervals (0-3nm, 3-7nm, 7-10nm), and the proportion of stress sources in each interval was calculated. The generated stress source distribution data within the curing agent includes the proportion of stress types, average amplitude, distribution density, and spatial interval characteristics at each stage, fully presenting the evolution law of stress sources with the curing process.

[0029] 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. In this embodiment of the invention, the distribution scale of the stress source distribution data within the curing agent is transformed based on the principle of continuous medium mechanics (using a homogenization method). A micro-unit of 10×10×10 nm³ is selected to correspond to a macro-unit of 1×1×1 mm³, and the average micro-stress is calculated and mapped to the macro-stress. In the initial stage of the reaction, the average micro-tensile stress of 95 MPa corresponds to a macro-tensile stress of 3.8 MPa (conversion factor 0.04, calculated by volume weighted average: 95 MPa × (10 -6 The average shear stress of 48 MPa corresponds to a macroscopic shear stress of 1.92 MPa (conversion factor 0.04). During the mid-reaction phase, the microscopic tensile stress of 82 MPa corresponds to a macroscopic stress of 3.28 MPa, and the shear stress of 52 MPa corresponds to 2.08 MPa. During the late-reaction phase, the microscopic tensile stress of 75 MPa corresponds to a macroscopic stress of 3.0 MPa, and the shear stress of 45 MPa corresponds to 1.8 MPa. A mapping formula is established: Macroscopic stress = Microscopic stress × 0.04. This is verified using three sets of unit cells of different sizes (5×5×5 nm³, 10×10×10 nm³, 15×15×15 nm³), with a conversion error of <5%. The generated curing agent stress scale conversion parameters include a conversion factor (0.04) and a microscopic-macroscopic volume correspondence (10...). -18 m³ corresponds to 10 -9 The stress mapping values ​​(m³) and each stage are used to ensure the accurate conversion of micro-stress to macro-stress.

[0030] 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. In this embodiment of the invention, a three-dimensional geometric model of the electronic packaging structure is obtained using a three-dimensional laser scanner (accuracy 0.01mm). The model includes a chip (5.0mm × 5.0mm × 0.5mm), a lead frame (8 copper wires, diameter 0.1mm, length 2.0mm), a curing agent filling area (10.0mm × 10.0mm × 2.5mm), and an epoxy resin substrate (10.0mm × 10.0mm × 0.5mm). Structural parameter analysis of the model reveals the following: the substrate type is epoxy resin (Young's modulus 3.2GPa, Poisson's ratio 0.35); the package dimensions are 10.0mm long, 10.0mm wide, and 3.0mm high; the lead frame layout is symmetrical (4 wires along the x-axis, 4 wires along the y-axis, embedded in the curing agent to a depth of 2.0mm); the coordinate range of the curing agent filling area is 0≤x≤10mm, 0≤y≤10mm, 0.5mm≤z≤3mm, with a volume of 196.34mm³. During the analysis, geometric measurement tools were used to extract the dimensional parameters of each component (accurate to 0.01mm), and dimensional consistency was verified by CAD software. The generated electronic packaging structure parameters include the mechanical properties of the substrate, the overall packaging and the dimensions of each component, the spatial layout of the lead frame, and the coordinates and volume of the curing agent filling area. All parameters were measured three times and the average value was taken, with an error of <0.02mm.

[0031] 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.

[0032] In this embodiment of the invention, by applying the curing agent stress scale conversion parameter (conversion coefficient 0.04) to the electronic packaging structure parameters, the distribution of micro-stress sources is mapped to the macro-structure: the micro-tensile stress source at the initial stage of the reaction (3.2, 4.5, 5.1) nm (95 MPa) corresponds to the macro-coordinates (0.32 mm, 0.45 mm, 0.51 mm), with a stress value of 3.8 MPa; the shear stress source at (1.5, 6.2, 3.8) nm (48 MPa) corresponds to the macro-coordinates (0.15 mm, 0.62 mm, 0.38 mm), with a stress value of 1.92 MPa. Combined with the interfacial bonding performance parameters (interfacial shear strength between the curing agent and the substrate: 25 MPa; peel strength: 5 MPa), the mapped stress at the interface is corrected: the tensile stress near the interface (z=0.5 mm) is corrected from 3.8 MPa to 3.5 MPa (considering the interfacial weakening effect). The finite element preprocessing tool assigns all mapped stresses to the corresponding macroscopic coordinates to generate initial stress field distribution data for the encapsulated structure. This data includes stress values ​​(tension / shear), stress types, and corresponding microscopic origins at each point in three-dimensional space. The data is stored in the format of node coordinates (x, y, z) and stress values ​​(σx, σy, σz, τxy, τyz, τxz), with coordinates accurate to 0.01 mm and stress values ​​retained to two decimal places, providing initial conditions for subsequent stress field analysis.

[0033] Furthermore, 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. In this embodiment of the invention, microscopic stress sources are located using stress tensor data output from molecular dynamics simulations, and stress concentration regions are extracted using finite element post-processing. Within a simulation space of 10×10×10 nm³, the spatial coordinates of the tensile stress concentration regions are determined to be (3.2, 4.5, 5.1) nm, (6.8, 2.3, 7.9) nm, etc., with stress amplitudes ranging from 80 to 120 MPa; the coordinates of the shear stress concentration regions are determined to be (1.5, 6.2, 3.8) nm, (8.7, 5.4, 9.2) nm, etc., with stress amplitudes ranging from 40 to 60 MPa. The spatial coordinates of each stress source are accurate to 0.1 nm, and the stress amplitudes are retained to integer values. The stress type is identified through stress tensor decomposition: tensile stress is distributed along the molecular chain axis, while shear stress occurs at molecular chain entanglements. The generated micro-stress source location data contains 50 stress concentration points, including 32 tensile stress sources and 18 shear stress sources. Each data point is associated with its corresponding coordinates, amplitude, and stress type label, providing accurate location information for subsequent structural association.

[0034] Preferably, based on the microstructure evolution data of the curing agent, the molecular crosslinking density distribution, component concentration gradient and micro-defect location information corresponding to different curing stages of the electronic packaging curing agent are extracted to generate a curing agent structural feature distribution dataset. In this embodiment of the invention, structural feature parameters are extracted based on the microstructure evolution data of the curing agent (65℃ in the initial stage of reaction, 120℃ in the middle stage, and 160℃ in the later stage). In the initial stage of reaction: the molecular crosslinking density distribution is non-uniform, with highly crosslinked regions (0.03 crosslinks / ų) concentrated in the center of the simulated space (within the range of 4-6 nm), and low crosslinked regions (0.01 crosslinks / ų) distributed at the edges; the component concentration gradient shows that the anhydride concentration decreases from the center to the edge (5.2×10²² crosslinks / m³ in the center and 2.1×10²² crosslinks / m³ at the edge); the microscopic defects are concentrated in the edge regions such as (1.2, 1.5, 1.3) nm, with a total of 20 unreacted anhydride groups. Mid-reaction stage: The overall crosslinking density increases to 0.05 crosslinks / ų, with the highly crosslinked region expanding to the 3-7 nm range; the component concentration gradient decreases (4.8 × 10²² crosslinks / m³ at the center, 3.5 × 10²² crosslinks / m³ at the edge); the number of defect locations decreases to 10, distributed in regions such as (2.5, 8.6, 4.7) nm. Late-reaction stage: The crosslinking density becomes uniformly distributed (0.06 crosslinks / ų), and the concentration gradient disappears (4.2 × 10²² crosslinks / m³ overall); only 5 unreacted anhydride groups remain, located near (5.3, 5.6, 5.4) nm. These data are categorized by coordinate intervals (0-3 nm, 3-7 nm, 7-10 nm) to generate a curing agent structural characteristic distribution dataset, containing spatial distribution parameters of density, concentration, and defects at each stage.

[0035] Preferably, the micro-stress source location data and the curing agent structural feature distribution dataset are spatially matched to establish the correspondence between the stress source and the micro-structure features through coordinate alignment, and the structural factors corresponding to the micro-stress concentration area are identified, including low correlation density areas and component enrichment areas, to obtain the stress source distribution data after curing agent matching. In this embodiment of the invention, a coordinate alignment algorithm is used to spatially match the microscopic stress source location data with the curing agent structural feature distribution dataset. Using the simulated spatial origin (0,0,0) as a reference, the stress source coordinates and structural feature coordinates are unified to the same coordinate system (error < 0.2 nm). The matching revealed that 80% of the tensile stress sources (26) are located in the low crosslinking density region (0.01-0.03 sources / ų), with the tensile stress source (100 MPa) at (3.2, 4.5, 5.1) nm corresponding to a crosslinking density of 0.02 sources / ų. The free volume fraction in this region is 12% (higher than the average of 8%). 70% of the shear stress sources (13) are located in the component-rich region, with the shear stress source (50 MPa) at (1.5, 6.2, 3.8) nm corresponding to anhydride concentration of 4.9 × 10²² sources / m³ (higher than the average of 4.2 × 10²² sources / m³). Spatial overlay analysis identified regions with low correlation density (crosslink density < 0.03 crosslinks / ų) leading to tensile stress concentration, while regions with high component concentration (concentration > 4.5 × 10²² crosslinks / m³) led to shear stress concentration. The generated matched stress source distribution data included structural characteristic parameters for each stress source, such as a (3.2, 4.5, 5.1) nm stress source with a correlation crosslink density of 0.02 crosslinks / ų and a free volume fraction of 12%, clarifying the causal relationship between stress and structure.

[0036] Preferably, 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. In this embodiment of the invention, the distribution data of the matched stress sources were grouped and statistically analyzed according to the curing stage (initial stage 65℃, intermediate stage 120℃, and final stage 160℃). Initial stage: a total of 30 stress sources were identified, with 20 tensile stress sources (66.7%), an average amplitude of 95 MPa, and a spatial distribution density of 0.3 sources / nm³ (concentrated in the edge region); and 10 shear stress sources (33.3%), an average amplitude of 48 MPa, and a distribution density of 0.1 sources / nm³. Intermediate stage: a total of 15 stress sources were identified, with 8 tensile stress sources (53.3%), an average amplitude of 82 MPa, and a distribution density of 0.15 sources / nm³ (improved uniformity); and 7 shear stress sources (46.7%), an average amplitude of 52 MPa, and a distribution density of 0.07 sources / nm³. In the later stage of the reaction: there were a total of 5 stress sources, including 4 tensile stress sources (80%) with an average amplitude of 75 MPa and a distribution density of 0.05 sources / nm³; and 1 shear stress source (20%) with an average amplitude of 45 MPa and a distribution density of 0.01 sources / nm³. In the statistical data, the percentage of sources is rounded to one decimal place, the average amplitude is rounded to an integer, and the distribution density is rounded to two decimal places, clearly showing the changes in the number, intensity, and spatial distribution of stress sources at each stage.

[0037] Preferably, a spatial distribution map of stress sources within the micro-electronic packaging structure is drawn based on statistical data of stress source stage distribution in the curing agent, and 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.

[0038] In this embodiment of the invention, a spatial distribution map is drawn using three-dimensional rendering technology based on statistical data of stress source stage distribution: in the early stage of the reaction, tensile stress sources are marked in red (size is proportional to amplitude), and shear stress sources are marked in blue, showing that stress sources are concentrated at the edge of the simulated space (0-3nm and 7-10nm range); in the middle stage of the reaction, the color depth of stress sources decreases (amplitude decreases), and the distribution gathers towards the center (3-7nm); in the later stage of the reaction, the number of stress sources decreases, with only a few red dots remaining in the central region. Integrating statistical data and distribution map, data on the distribution of internal stress sources in the curing agent is generated: tensile stress sources mainly originate from low crosslinking density areas (accounting for 80% in the early stage), decreasing by 60% in number and 21% in average amplitude as curing progresses; shear stress sources are related to component enrichment (accounting for 70% in the middle stage), and basically disappear in the later stage due to concentration homogenization. The distribution evolution trend shows that stress sources migrate from the edge to the center, and the amplitude decreases with increasing crosslinking density, eventually stabilizing at a low stress level. This data includes the coordinate range characteristics of the spatial distribution map, stress source parameters at each stage, and evolution curves, fully revealing the formation mechanism and change law of internal stress sources.

[0039] Furthermore, 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. In this embodiment of the invention, a full-size scan of the electronic packaging sample is performed using a 3D laser scanner (scanning accuracy 0.01mm, point cloud density 100 points / mm²) to obtain an original 3D point cloud model including the chip, lead frame, curing agent, and substrate. The point cloud data is converted into a triangular mesh model using point cloud processing software, and then simplified: the chip edge (rectangular outline, length 5.0mm, width 5.0mm, thickness 0.5mm) is retained, with the coordinates of its four sides being (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 lead bonding points (0.2mm diameter half-sections) are also retained. Eight spheres are located 0.5mm outside the chip edge, with coordinates (0,2.5,0.25), (5,2.5,0.25), (2.5,0,0.25), and (2.5,5,0.25), respectively. The curing agent filling boundary is preserved (cylindrical, 10.0mm diameter, 3.0mm height, bottom coordinates (0,0,0)-(10,10,0), top coordinates (0,0,3)-(10,10,3)). Fine structures are ignored: scratches with a diameter <0.1mm on the chip surface, rounded corners with a radius <0.05mm on the lead frame, and tiny air bubbles (volume <0.001mm³) on the curing agent surface are removed. The simplified model file size is reduced from 500MB to 50MB, generating a simplified 3D model of the electronic package, ensuring that the spatial position error of key structural features is <0.02mm.

[0040] 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; In this embodiment of the invention, material properties are assigned to the simplified three-dimensional model of the electronic package. Different regions are selected using a geometric selection tool: the chip region (coordinate range 0≤x≤5mm, 0≤y≤5mm, 0≤z≤0.5mm) is assigned silicon (material identifier 1, Young's modulus 130GPa, Poisson's ratio 0.28, coefficient of thermal expansion 2.6×10⁻⁶). -6 / ℃); The lead frame area (8 metal wires, each 2.0mm long and 0.1mm in diameter, extending from the bonding point to the package edge, coordinates such as (0,2.5,0.25)-(-2,2.5,0.25) etc.) is assigned the value of copper (material identifier 2, Young's modulus 110GPa, Poisson's ratio 0.34, coefficient of thermal expansion 17×10). -6 / ℃); the curing agent region (coordinate range 0≤x≤10mm, 0≤y≤10mm, 0.5mm≤z≤3mm, excluding the space occupied by the lead frame) is assigned the value of methyltetrahydrophthalic anhydride (material identifier 3, Young's modulus 2.1GPa, Poisson's ratio 0.35, coefficient of thermal expansion 60×10). -6 / ℃); The substrate area (coordinate range 0≤x≤10mm, 0≤y≤10mm, 0≤z≤0.5mm, excluding the space occupied by the chip) is assigned epoxy resin (material identifier 4, Young's modulus 3.2GPa, Poisson's ratio 0.35, coefficient of thermal expansion 50×10). -6 / ℃). Record the spatial coordinate range of each region (accurate to 0.01mm) and the corresponding material identifier to generate material partition data. The volume calculation error of each material region is <0.1mm³, ensuring that the region division is non-overlapping and non-omitted.

[0041] 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; In this embodiment of the invention, dimensional parameters are extracted based on material partitioning data, and linear dimensions are obtained using a distance measurement tool: chip thickness is the difference in z-axis coordinates (0.5mm-0mm=0.5mm); curing agent layer thickness is the difference in z-axis coordinates of the curing agent region (3.0mm-0.5mm=2.5mm); lead diameter is obtained by measuring the maximum distance of the lead frame cross-section, which is 0.1mm; overall package dimensions: length (maximum x-axis coordinate 10mm-minimum x-axis coordinate 0mm=10mm), width (y-axis 10mm), height (z-axis 3mm-0mm=3mm). The chip surface area is obtained using an area measurement tool (5mm×5mm=25mm²); the curing agent filling volume is obtained using a volume measurement tool (π×(5mm)²×2.5mm-8×π×(0.05mm)²×2.0mm≈196.35mm³-0.01mm³≈196.34mm³). All dimensional parameters are rounded to two decimal places, with length and thickness parameters accurate to 0.01 mm, area accurate to 0.01 mm², and volume accurate to 0.01 mm³. A set of structural dimensional parameters is generated, containing 12 specific parameters. Each parameter is averaged through three repeated measurements to ensure extraction accuracy.

[0042] 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.

[0043] In this embodiment of the invention, by integrating material partitioning data and structural dimension parameter sets, the substrate type in the material partitions is first verified: the substrate area (material identifier 4) is epoxy resin, and its mechanical property parameters (Young's modulus 3.2 GPa, etc.) are incorporated into the structural parameters. Package size integration: overall dimensions (length 10.00 mm, width 10.00 mm, height 3.00 mm), chip size (5.00 mm × 5.00 mm × 0.50 mm), curing agent layer thickness 2.50 mm. Lead frame layout parameters: 8 leads, diameter 0.10 mm, length 2.00 mm, symmetrically distributed (4 along the x-axis, 4 along the y-axis), with a curing agent embedding depth of 2.00 mm (from z = 0.5 mm to z = 2.5 mm). The coordinate range of the curing agent filling area is clearly defined as 0 ≤ x ≤ 10 mm, 0 ≤ y ≤ 10 mm, 0.5 mm ≤ z ≤ 3 mm, with a volume of 196.34 mm³. Consistency checks were performed during the integration process: the difference between the curing agent layer thickness (2.5mm) and the package height (3mm) minus the substrate thickness (0.5mm) was consistent; the lead frame volume (0.01mm³) was consistent with the deduction of the curing agent volume. The generated electronic package structure parameters include 4 major categories and 20 specific parameters, with all values ​​retained to two decimal places, forming a complete and accurate structural description document.

[0044] Furthermore, 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. In this embodiment of the invention, topological relationship analysis is performed on the material partitioning data (including electronic packaging curing agent, epoxy resin substrate, and copper lead frame), and a boundary extraction algorithm is used to identify the contact interfaces of different materials. The contact interface between the electronic packaging curing agent (methyltetrahydrophthalic anhydride) and the epoxy resin substrate is a cylindrical side surface with spatial coordinates ranging from 1.5-2.0 mm radially and 0-5.0 mm axially. The contact area is calculated by integration as 2π × 1.75 mm × 5.0 mm = 54.98 mm² (π is taken as 3.1416). The contact interface between the curing agent and the copper lead frame consists of two rectangular surfaces, located on the upper and lower surfaces of the lead frame, respectively. The spatial 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). The individual contact area 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, and the contact area is retained to two decimal places. The generated interface contact relationship data includes interface type (curing agent-substrate, curing agent-lead frame), spatial coordinate range and area parameters, which clearly defines the physical connection relationship between different materials.

[0045] Preferably, each parameter in the structural dimension parameter set is associated with the feature points corresponding to the simplified three-dimensional model of the electronic packaging, and a correspondence between the dimension parameters and the model topology is established to generate a parameterized model association table. In this embodiment of the invention, the structural dimension parameter set includes parameters such as a total package height of 5.0 mm, a diameter of 4.0 mm, a lead frame thickness of 0.1 mm, a width of 1.0 mm, a length of 1.0 mm, and a substrate thickness of 2.0 mm. These parameters are associated with feature points of the simplified 3D model of the electronic package: the total package 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 coordinates 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). A correspondence between the dimension parameters and the model topology is established: the total package height corresponds to the axial dimension constraint of the model, the diameter corresponds to the radial dimension constraint, the thickness, width, and length of the lead frame correspond to the 3D dimensions of its cuboid structure, and the substrate thickness corresponds to the axial dimension of the substrate. The generated parametric model association table is in tabular form, with each row containing the name of the dimension parameter, the value (rounded to one decimal place), the coordinates of the associated feature points, and the topological structure type (cylindrical surface, plane, etc.), ensuring accurate mapping between parameters and model structure.

[0046] Preferably, the simplified 3D model of electronic packaging is preprocessed before meshing based on the parametric model association table to generate meshing preparation data; In this embodiment of the invention, a preprocessing step before meshing the simplified 3D model of the electronic package is performed based on a parametric model association table. First, the overall size boundary of the model is determined according to the total package height of 5.0 mm and diameter of 4.0 mm, and the global size control parameter for meshing is set to 0.1 mm (unit side length). Different mesh density settings are applied to different material regions: a denser mesh (unit side length 0.08 mm) is used for the curing agent filling region (Z=2.0-5.0 mm, radial 0-2.0 mm) due to drastic stress changes in this region; a medium-density mesh (unit side length 0.1 mm) is used for the substrate region (Z=0-2.0 mm); and a finer mesh (unit side length 0.05 mm) is used for the lead frame region (Z=2.4-2.5 mm, X=2.0-3.0 mm, Y=0-1.0 mm) to accurately capture interface stress. Minor chamfers (radius < 0.05 mm) and redundant surfaces in the model are removed using a geometry cleanup tool to ensure the continuity of the mesh. The generated mesh generation preparation data includes mesh size parameters for each region, geometrically cleaned model boundary coordinates, and material partition identifiers, laying the foundation for subsequent high-quality mesh generation.

[0047] Preferably, the structural parameters of the electronic packaging curing agent are refined and evaluated based on the grid division preparation data and combined with the material partitioning data, generating electronic packaging structural parameters including substrate type, packaging size, lead frame layout and curing agent filling area.

[0048] In this embodiment of the invention, the structural parameters of the electronic packaging curing agent are refined and evaluated based on grid-based preparation data and material partitioning data. The substrate type is determined to be epoxy resin (Young's modulus 3.2 GPa, Poisson's ratio 0.35), and the package dimensions are refined as follows: outer diameter 4.0 mm, inner diameter (substrate-curing agent contact surface) 3.5 mm, and total height 5.0 mm (substrate occupies 2.0 mm, curing agent occupies 3.0 mm). Leadframe layout parameters: 4 frames, symmetrically distributed (angles 0°, 90°, 180°, 270°), each leadframe has a cross-sectional dimension of 0.1 mm (thickness) × 1.0 mm (width), and the embedding depth 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. The area overlapping with the lead frame (X = 2.0-3.0 mm, Y = 0-1.0 mm, Z = 2.4-2.5 mm) requires geometric correction to ensure complete filling. The electronic packaging structure parameters generated after detailed evaluation include the substrate's mechanical properties, detailed package dimensions (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 have been verified for consistency with material partitioning data to ensure the accuracy of the structural description.

[0049] Furthermore, 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; In this embodiment of the invention, a temperature load sequence is generated by setting temperature cycling load parameters: the cycling temperature range is -40℃ to 125℃, the heating and cooling rate is 5℃ / min (the heating phase from -40℃ to 125℃ takes 33min, and the cooling phase from 125℃ to -40℃ takes 33min), the high temperature segment (125℃) is held for 10min, the low temperature segment (-40℃) is held for 10min, the total duration of a single cycle is 86min, and the number of cycles is 50. A 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 the cycle repeats. The corresponding temperature is recorded at each time point (1min interval), such as 10℃ at 10min, 125℃ at 40min, 35℃ at 60min, and -40℃ at 80min. The sequence contains temperature data from 50 complete cycles, with temperature values ​​accurate to 1°C and time accurate to 1 minute, ensuring that the load parameters cover the extreme temperature environments in actual service of electronic packages.

[0050] 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. In this embodiment of the invention, a finite element model is constructed by applying a temperature load sequence to the initial stress field distribution data of the encapsulated structure (initial stress 50 MPa, coordinate range 0 ≤ x ≤ 10 mm, 0 ≤ y ≤ 10 mm, 0 ≤ z ≤ 3 mm). A thermal expansion coefficient mismatch condition is defined: the thermal expansion coefficient of the methyltetrahydrophthalic anhydride curing agent is 60 × 10⁻⁶. -6 / ℃, epoxy resin substrate 50×10 -6 / ℃, copper lead frame 17×10 -6 / ℃. Interface contact behavior was set as follows: friction coefficient between the curing agent and the substrate was 0.3, and the bond strength was 25 MPa; friction coefficient between the curing agent and the lead frame was 0.4, and the bond strength was 30 MPa. The contact type was surface-to-surface contact. Normal and tangential forces were transmitted when there was no relative sliding, and only normal force was transmitted during separation. A thermo-structural coupling simulation was performed to solve for the temperature and stress fields at each time step. For example, at 33 min (125℃), tensile stress was generated at the interface between the curing agent and the substrate, and shear stress appeared around the lead frame. The generated thermo-structural coupling simulation data included the three-dimensional temperature distribution and stress components (σx, σy, σz, τxy, etc.) at each time point in 50 cycles, with coordinates accurate to 0.01 mm and stress values ​​retained to two decimal places.

[0051] 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. In this embodiment of the invention, stress parameters for each cycle were extracted based on thermal-structural coupling simulation data: the stress peak in the first cycle occurred during the 125℃ holding period (43 min), with an edge stress of 80 MPa and a stress variation range of 30 MPa (from an initial 50 MPa to 80 MPa); the peak stress in the 10th cycle was 95 MPa with a range of 45 MPa; and the peak stress in the 50th cycle was 120 MPa with a range of 70 MPa. The interface peel risk index (peel force / bond strength) was 0.3 in the first cycle (25 MPa × 0.3 = 7.5 MPa) and increased to 0.6 (15 MPa) in the 50th cycle. The dominant evolutionary trend within the temperature range was analyzed: in the range of -40℃ to 25℃, the stress increased linearly with increasing temperature (rate 0.5 MPa / ℃); in the range of 25℃ to 85℃, the stress growth accelerated (rate 0.8 MPa / ℃); and in the range of 85℃ to 125℃, the stress growth slowed down (rate 0.3 MPa / ℃). The generated temperature-dependent stress evolution data is categorized by the number of cycles, including the peak stress, variation range, peeling risk index, and stress growth rate for each temperature range in each cycle. The values ​​are retained to two decimal places, clearly showing the cumulative stress pattern during the cycle.

[0052] 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. In this embodiment of the invention, loss characteristic parameters are calculated based on temperature-dependent stress evolution data: at -40℃, dynamic mechanical analysis yields a storage modulus of 3.0 GPa, a loss modulus of 0.15 GPa, and a loss factor of 0.15 / 3.0 = 0.05; at 25℃, the storage modulus is 2.1 GPa, the loss modulus is 0.126 GPa, and the loss factor is 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 is 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 is 0.12 / 1.0 = 0.12. The stress relaxation coefficient was calculated using 100 hours of isothermal stress decay: at -40℃, the stress decreased from 80MPa to 76MPa, with a decay rate of 0.04MPa / h, and the coefficient was 0.04 / 80 = 0.0005 / h; at 25℃, the stress decreased from 50MPa to 45MPa, and the coefficient was 0.05 / 50 = 0.001 / h; at 85℃, the stress decreased from 90MPa to 72MPa, and the coefficient was 0.18 / 90 = 0.002 / h; at 125℃, the stress decreased from 120MPa to 84MPa, and the coefficient was 0.36 / 120 = 0.003 / h. Establish the functional relationship: loss factor = 0.0005 × temperature + 0.07 (R² = 0.98), stress relaxation coefficient = 0.00002 × temperature + 0.0001 (R² = 0.99). The generated curing agent loss stress characteristic parameters include specific values ​​and functional expressions at each temperature, ensuring that the parameters can be accurately predicted as they change with temperature.

[0053] 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.

[0054] In this embodiment of the invention, dynamic evaluation is performed by obtaining humidity aging factors (20%RH, 40%RH, 60%RH, 80%RH, 95%RH) and combining them with curing agent loss stress characteristic parameters. Under 25℃ and 20%RH conditions: loss factor 0.054, stress relaxation coefficient 0.0011, loss stress after 50 cycles = initial stress 50MPa × 0.054 × 50 + 50MPa × 0.0011 × (50 × 86min / 60min) = 135 + 39.4 = 174.4MPa. At 45℃ and 60%RH: loss factor 0.062, relaxation coefficient 0.0015, loss stress = 60 × 0.062 × 50 + 60 × 0.0015 × 358 = 186 + 32.2 = 218.2MPa. At 85℃ and 95%RH: loss factor 0.09, relaxation coefficient 0.003, loss stress = 120 × 0.09 × 50 + 120 × 0.003 × 358 = 540 + 128.9 = 668.9 MPa. In the evaluation data, the loss stress increases by approximately 15% for every 10℃ increase in temperature and approximately 8% for every 20% increase in humidity. The generated comprehensive environmental curing agent loss stress evaluation data includes temperature and humidity combinations, cycle count, and loss stress values, rounded to one decimal place, fully reflecting the cumulative effect of loss stress under multiple factors.

[0055] Furthermore, 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. In this embodiment of the invention, a humidity aging test was designed by selecting ambient humidity (20%RH, 40%RH, 60%RH, 80%RH, 95%RH) as the humidity aging factor: Five identical electronic packaging samples (containing methyltetrahydrophthalic anhydride curing agent) were prepared and placed in five environmental chambers. The temperature of the environmental chambers was set to a constant 25℃ (fluctuation range ±0.5℃), and the humidity control accuracy was ±2%RH. The samples were suspended in the center of the chamber (avoiding contact with the chamber walls). The test period was 1000 hours, and data was collected every 24 hours: the sample weight change was measured using a balance with an accuracy of 0.1mg, with an initial weight of 5.0000g; the elastic modulus was measured using a dynamic mechanical analyzer (frequency 1Hz), with an initial value of 2.1GPa; and the interfacial bonding strength was measured using a peel tester (speed 5mm / min), with an initial value of 25MPa. 20%RH group: After 100 hours, the weight increases to 5.0050g, elastic modulus is 2.05GPa, and bond strength is 24MPa; after 500 hours, the weight is 5.0080g, elastic modulus is 1.9GPa, and bond strength is 22MPa. 40%RH group: After 100 hours, the weight is 5.0100g, elastic modulus is 2.0GPa, and bond strength is 23MPa; after 500 hours, the weight is 5.0150g, elastic modulus is 1.8GPa, and bond strength is 20MPa. Data for the 60%RH, 80%RH, and 95%RH groups change according to the increasing humidity, generating raw data for humidity aging performance including time, humidity, weight, elastic modulus, and bond strength. All values ​​are retained to four decimal places (weight) or one decimal place (modulus, strength).

[0056] 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. In this embodiment of the invention, time series analysis was performed on the raw data of humidity aging performance, and the weight change curve was smoothed using the moving average method (10 data points in a window): the weight of the 20%RH group stabilized at 5.0090g after 800 hours (moisture absorption saturation time), the 40%RH group reached 5.0180g after 600 hours, the 60%RH group reached 5.0250g after 500 hours, the 80%RH group reached 5.0300g after 400 hours, and the 95%RH group reached 5.0350g after 300 hours. The performance degradation inflection point was calculated using the first derivative: the elastic modulus showed an inflection point at 600 hours for the 20%RH group (from slow degradation to rapid degradation, the rate increasing from 0.0003GPa / h to 0.0005GPa / h), at 500 hours for the 40%RH group, at 400 hours for the 60%RH group, at 300 hours for the 80%RH group, and at 200 hours for the 95%RH group; the inflection point of the bond strength was consistent with that of 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 degradation rate (z-axis, degradation rate = (initial value - current value) / initial value × 100%). The elastic modulus degradation rate at 20% RH and 500 hours is (2.1 - 1.9) / 2.1 × 100% = 9.5%, and the bond strength degradation rate is 12%. Humidity aging characteristic curves are generated based on the model. Each curve corresponds to a fixed humidity, time on the horizontal axis, and performance parameters on the vertical axis. The coordinates of the moisture absorption saturation time (e.g., 300 hours for the 95% RH group) and the degradation inflection point (e.g., 200 hours for the 95% RH group) are clearly defined, and the curve fitting error is <3%.

[0057] 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. In this embodiment of the invention, the performance degradation coefficients are calculated based on the humidity aging characteristic curves: 20%RH group: elastic modulus degradation 10% (coefficient 0.1), bond strength degradation 12% (0.12) after 500 hours; 40%RH group: degradation 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 between these coefficients and the curing agent loss stress characteristic parameters (loss factor 0.05, stress relaxation coefficient 0.001 / h), calculating Pearson correlation coefficients: elastic modulus degradation coefficient correlates with loss factor 0.92 and stress relaxation coefficient 0.88; bond strength degradation coefficient correlates with loss factor 0.85 and stress relaxation coefficient 0.90. The weighted average method is used to determine the influence weights: 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 conjunction with the performance degradation coefficient: 20%RH loss factor correction = 0.05 × (1 + 0.1 × 0.885) = 0.054, stress relaxation coefficient = 0.001 × (1 + 0.12 × 0.89) = 0.0011; 40%RH loss factor = 0.05 × (1 + 0.14 × 0.885) = 0.056, stress relaxation coefficient = 0.001 × (1 + 0.2 × 0.89) = 0.0012; 60%RH, 80%RH, and 95%RH are calculated using the same formula, generating a table containing correction coefficients for each humidity level, rounded to four decimal places.

[0058] 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.

[0059] In this embodiment of the invention, the impact of loss stress is assessed by embedding a humidity-dependent stress evolution data (initial stress of 50 MPa at 25°C, stress increases by 5 MPa for every 10°C increase in temperature). Under 20%RH and 25°C conditions: loss factor correction coefficient 0.054, stress relaxation coefficient 0.0011, 100-hour loss stress = 50 MPa × 0.054 + 50 MPa × 0.0011 × 100 = 2.7 + 5.5 = 8.2 MPa, remaining stress 50 - 8.2 = 41.8 MPa. Under 40%RH and 25°C conditions: loss factor 0.056, relaxation coefficient 0.0012, 100-hour loss = 50 × 0.056 + 50 × 0.0012 × 100 = 2.8 + 6 = 8.8 MPa, remaining stress 41.2 MPa. At 60% RH and 45℃ (temperature increases by 20℃, stress increases to 60MPa): loss factor 0.062, relaxation coefficient 0.0015, 100-hour loss = 60 × 0.062 + 60 × 0.0015 × 100 = 3.72 + 9 = 12.72MPa, remaining 47.28MPa. At 80% RH and 65℃ (stress 70MPa): loss = 70 × 0.075 + 70 × 0.002 × 100 = 5.25 + 14 = 19.25MPa, remaining 50.75MPa. At 95% RH and 85℃ (stress 80MPa): loss = 80 × 0.09 + 80 × 0.0025 × 100 = 7.2 + 20 = 27.2MPa, remaining 52.8MPa. The generated comprehensive evaluation data includes humidity, temperature, time, loss stress, and residual stress, with values ​​retained to two decimal places, fully reflecting the variation law of loss stress under the combined effect of temperature and humidity. Furthermore, 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. In this embodiment of the invention, the humidity aging characteristic curve of the methyltetrahydrophthalic anhydride curing agent is segmented according to humidity level (20%RH, 40%RH, 60%RH, 80%RH, 95%RH), with each segment corresponding to 500 hours of aging time. 20%RH segment: Tensile strength decreases from the initial 45MPa to 43MPa, a decrease of 2MPa, performance degradation rate = 2MPa / 500h = 0.004MPa / h; 40%RH segment: Tensile strength decreases to 40MPa, a decrease of 5MPa, rate = 5 / 500 = 0.01MPa / h; 60%RH segment: Tensile strength decreases to 35MPa, a decrease of 10MPa, rate = 10 / 500 = 0.02MPa / h; 80%RH segment: Tensile strength decreases to 28MPa, a decrease of 17MPa, rate = 17 / 500 = 0.034MPa / h; 95%RH segment: Tensile strength decreases to 20MPa, a decrease of 25MPa, rate = 25 / 500 = 0.05MPa / h. Using 20%RH as a baseline (attenuation coefficient 1.0), the relative attenuation coefficients for other humidity levels are calculated: 40%RH = 0.01 / 0.004 = 2.5, 60%RH = 5.0, 80%RH = 8.5, 95%RH = 12.5. The generated performance attenuation coefficients under different humidity conditions are rounded to one decimal place to clearly define the quantitative differences in performance attenuation across different humidity ranges.

[0060] Preferably, a 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 using the controlled variable method, and a parameter sensitivity coefficient is generated. In this embodiment of the invention, humidity sensitivity analysis was performed on the stress characteristic parameters (tensile strength, shear strength, elastic modulus, and coefficient of thermal expansion) of the curing agent. A controlled variable method was used: the temperature was fixed at 25°C, the aging time at 500 hours, and only the humidity was varied (from 20%RH to 95%RH). The change in tensile strength with each 20%RH increase in humidity was as follows: 20%→40%, a decrease of 3 MPa; 40%→60%, a decrease of 5 MPa; 60%→80%, a decrease of 7 MPa; 80%→95%, a decrease of 8 MPa. The sensitivity coefficients (change / humidity change) were 3 / 20 = 0.15 MPa / %RH, 5 / 20 = 0.25 MPa / %RH, 7 / 20 = 0.35 MPa / %RH, and 8 / 15 = 0.53 MPa / %RH, with an average of 0.32 MPa / %RH. The changes in shear strength at 2 MPa, 3 MPa, 5 MPa, and 6 MPa had sensitivity coefficients of 0.10, 0.15, 0.25, and 0.40 MPa / %RH, with an average of 0.225 MPa / %RH. The changes in elastic modulus at 400 MPa, 600 MPa, 800 MPa, and 1000 MPa had sensitivity coefficients of 20, 30, 40, and 67 MPa / %RH, with an average of 39.25 MPa / %RH. The change in coefficient of thermal expansion was 5 × 10⁻⁶ MPa. -6 / ℃, 8×10 -6 / ℃, 12×10 -6 / ℃, 15×10 -6 / ℃, sensitivity coefficient 0.25×10 -6 / (°C·%RH), 0.4×10 -6 / (°C·%RH), 0.6×10 -6 / (°C·%RH), 1.0×10 -6 / (°C·%RH), average 0.56×10 -6 / (°C·%RH). The generated parameter sensitivity coefficients are rounded to two decimal places (or scientific notation) to quantify the impact of a single humidity change on each parameter.

[0061] Preferably, based on the parameter sensitivity coefficient and the performance degradation coefficient under different humidity conditions, the correction weights corresponding to different humidity conditions are calculated using the analytic hierarchy process (AHP) to generate a humidity correction weight matrix. In this embodiment of the invention, the parameters are based on sensitivity coefficients (tensile strength 0.32, shear strength 0.225, elastic modulus 39.25, coefficient of thermal expansion 0.56 × 10⁻⁶). -6The performance degradation coefficients (20%RH 1.0, 40%RH 2.5, 60%RH 5.0, 80%RH 8.5, 95%RH 12.5) were used, and the corrected weights were calculated using the analytic hierarchy process (AHP). A judgment matrix was constructed: based on tensile strength, shear strength weight = 0.225 / 0.32 = 0.703, elastic modulus = 39.25 / 0.32 = 122.656, and coefficient of thermal expansion = (0.56 × 10⁻⁶). -6 ) / 0.32=1.75×10 -6 After normalization, the weights are 0.0026, 0.0018, 0.9956, and 0.000014, respectively. The correction weights for each humidity level are calculated based on the performance degradation coefficient: 40%RH = 2.5 × (0.0026 + 0.0018 + 0.9956) = 2.5 × 1.0 = 2.5; 60%RH = 5.0 × 1.0 = 5.0; 80%RH = 8.5 × 1.0 = 8.5; 95%RH = 12.5 × 1.0 = 12.5; 20%RH weight 1.0. The generated humidity correction weight matrix is ​​[1.0, 2.5, 5.0, 8.5, 12.5], corresponding one-to-one with the humidity level. The weight values ​​are rounded to one decimal place to ensure that the correction strength for each humidity condition is consistent with the correlation between parameter sensitivity and performance degradation.

[0062] 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 then integrated to generate the humidity influence correction coefficient.

[0063] In this embodiment of the invention, the humidity correction weight matrix [1.0, 2.5, 5.0, 8.5, 12.5] is compared with the curing agent loss stress characteristic parameters (tensile strength 45MPa, shear strength 30MPa, elastic modulus 2.1GPa, coefficient of thermal expansion 60×10⁻⁶). -6 Multiply by the coefficients (°C / ℃) to calculate the correction factors under different humidity conditions. Correction factors for 20%RH: Tensile strength = 45 × 1.0 = 45 MPa, Shear strength = 30 × 1.0 = 30 MPa, Elastic modulus = 2.1 × 1.0 = 2.1 GPa, Coefficient of thermal expansion = 60 × 1.0 = 60 × 10⁻⁶. -6 / ℃; 40%RH: Tensile strength = 45 × 2.5 = 112.5 MPa, Shear strength = 30 × 2.5 = 75 MPa, Elastic modulus = 5.25 GPa, Coefficient of thermal expansion = 150 × 10⁻⁶ MPa -6 / ℃; 60%RH: Tensile strength = 225MPa, Shear strength = 150MPa, Elastic modulus = 10.5GPa, Coefficient of thermal expansion = 300×10 -6 / ℃; 80%RH: Tensile strength = 382.5MPa, Shear strength = 255MPa, Elastic modulus = 17.85GPa, Coefficient of thermal expansion = 510×10 -6 / ℃; 95%RH: Tensile strength = 562.5MPa, Shear strength = 375MPa, Elastic modulus = 26.25GPa, Coefficient of thermal expansion = 750×10 -6 / ℃. Integrate this data, categorize it by humidity level and parameter type, and generate a table of humidity influence correction coefficients. Values ​​are rounded to one decimal place (MPa, GPa) or to an integer (×10). -6 / ℃), fully reflecting the correction effect of different humidity levels on loss stress characteristic parameters.

[0064] Furthermore, 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. In this embodiment of the invention, the service environment parameter sequence is set based on the curing agent loss stress assessment data under comprehensive environmental conditions (loss stress of 174.4 MPa at 25℃ / 20%RH, 218.2 MPa at 45℃ / 60%RH, and 668.9 MPa at 85℃ / 95%RH). The temperature cycle is one cycle per day, with a cycle range of -40℃ to 125℃, a heating / cooling rate of 5℃ / min, a high temperature of 125℃ for 10 minutes, and a low temperature of -40℃ for 10 minutes. The humidity fluctuation range is divided by quarter: 20%RH±5% in the first quarter, 40%RH±5% in the second quarter, 60%RH±5% in the third quarter, and 85%RH±5% in the fourth quarter. 95%RH only occurs during the rainy season and lasts for 15 days. The duration is set to 3 years (1095 days), with the first two years based on quarterly humidity cycles, and the third year incorporating the 15-day 95%RH environment of the rainy season. The generated environmental loss load spectrum of the electronic packaging structure is arranged along a time axis (day). Each day includes a temperature change curve over time (86 min / cycle, repeated 28 times / day) and the corresponding humidity value. For example, the temperature sequence for day 1 (20%RH) is -40℃→125℃→-40℃ cycle. Day 91 (40%RH) follows the same temperature cycle. Days 182 (60%RH) and 273 (85%RH) follow the same pattern. Days 340-354 (95%RH) are 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 1 min, fully covering the environmental changes over a 3-year service period.

[0065] Preferably, the loss stress accumulation assessment of the curing agent loss stress assessment data is performed based on the environmental loss load spectrum of the electronic packaging structure. This is to simulate the stress amplitude change, stress cycle number and loss accumulation process of the electronic packaging curing agent under cyclic load based on the environmental loss load spectrum of the electronic packaging structure, and to obtain the accumulation law of loss stress over time, thereby generating the loss stress accumulation effect corresponding to the electronic packaging curing agent. In this embodiment of the invention, the loss stress assessment data is cumulatively evaluated based on the environmental loss load spectrum of the electronic packaging structure. The rainflow counting method is used to simulate cyclic loading: at 25℃ / 20%RH, 28 temperature cycles per day, with a stress amplitude of 30MPa per cycle (initially 50MPa increasing to 80MPa), a loss factor of 0.054, the daily cumulative loss stress = 50MPa × 0.054 × 28 + 50MPa × 0.0011 × (28 × 86min / 60min) = 75.6 + 1.8 = 77.4MPa; at 45℃ / 60%RH, 28 cycles per day... For a single cycle, with a stress amplitude of 45 MPa and a loss factor of 0.062, the daily cumulative stress is 60 × 0.062 × 28 + 60 × 0.0015 × 331 = 104.16 + 29.8 = 133.96 MPa. At 85℃ / 95%RH, with 28 cycles per day, a stress amplitude of 70 MPa and a loss factor of 0.09, the daily cumulative stress is 120 × 0.09 × 28 + 120 × 0.003 × 331 = 302.4 + 119.2 = 421.6 MPa. Accumulated over time according to the load spectrum: 77.4 × 90 = 6966 MPa for the first 90 days (20%RH), 133.96 × 91 = 12190.36 MPa for days 91-181 (40%RH), 186 × 91 = 16926 MPa for days 182-272 (60%RH), 218.2 × 67 = 14619.4 MPa for days 273-339 (85%RH), and 668.9 × 15 = 10033.5 MPa for days 340-354 (95%RH). Days 355-1095 follow the pattern of the previous two years. The generated data on the cumulative effect of loss stress includes daily accumulation, total accumulation, and stress amplitude variation curves, clearly showing that the cumulative loss stress over 3 years reaches 102735.26 MPa, exhibiting a cumulative pattern of "slow increase at low temperature and low humidity - rapid increase at high temperature and high humidity".

[0066] Preferably, the stress accumulation parameter statistics are performed on the loss stress accumulation effect corresponding to the electronic packaging curing agent to quantitatively extract the maximum accumulated 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. In this embodiment of the invention, the cumulative effect of loss stress was statistically analyzed: the maximum cumulative stress value appeared on day 1095 (end of 3 years), at 102735.26 MPa; the stress accumulation rate was calculated in stages: 77.4 MPa / day in the 20%RH stage, 133.96 MPa / day in the 40%RH stage (growth rate of 73%), 186 MPa / day in the 60%RH stage (growth rate of 39%), 218.2 MPa / day in the 85%RH stage (growth rate of 17%), and 421.6 MPa / day in the 95%RH stage (growth rate of 93%). The inflection point of stress decay was identified by the second derivative method. On day 340 (entering 95%RH), the slope of the cumulative curve suddenly increased from 218.2 MPa / day to 421.6 MPa / day, which was determined to be the inflection point; on day 355 (exiting 95%RH), the slope dropped back to 218.2 MPa / day, which was the second inflection point. The generated set of accumulated loss stress parameters includes the maximum accumulated value (retaining two decimal places), the rate of each stage (retaining one decimal place), and the inflection point time (accurate to the day), and marks the accumulated stress value at the inflection point (70691.36 MPa on day 340 and 80724.86 MPa on day 355), quantifying the key features in the accumulation process.

[0067] Preferably, the material fatigue limit coefficient corresponding to the electronic packaging curing agent is obtained, and the curing failure life assessment analysis is performed 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. In this embodiment of the invention, the material fatigue limit coefficient of the electronic encapsulation curing agent (methyltetrahydrophthalic anhydride) is obtained as 150,000 MPa (through 10 7The cumulative stress threshold at failure was measured in the cycle test. Based on this coefficient and the cumulative loss stress parameter set (annual cumulative amount 34245.09 MPa), the failure life was assessed: using the linear extrapolation method, the remaining cumulative stress = 150000 - 102735.26 = 47264.74 MPa, and the remaining life = 47264.74 ÷ 34245.09 × 365 ≈ 508 days. The assessment analysis shows that when the cumulative loss stress reaches 150000 MPa, the curing agent exhibits interface peeling (peeling risk index ≥ 1.0), corresponding to a service life of 1095 + 508 = 1603 days (4.4 years). Further calculations were performed for failure times under different humidity levels: In a 20% RH environment only, the annual cumulative pressure is 28251 MPa, and the lifespan is approximately 1928 days (5.3 years) = 150000 ÷ 28251 × 365; in a 95% RH environment only, the annual cumulative pressure is 153884 MPa, and the lifespan is approximately 355 days (1 year) = 150000 ÷ 153884 × 365. The generated lifespan assessment parameters include the cumulative failure pressure (150000 MPa), the corresponding service time (1603 days), and lifespan comparison values ​​under different environments, accurate to one day.

[0068] Preferably, a curing agent loss stress life assessment report is generated based on the curing agent loss stress life assessment parameters, including the loss stress accumulation curve, life prediction results, and failure risk analysis.

[0069] In this embodiment of the invention, an assessment report is generated based on life assessment parameters: the loss stress accumulation curve uses time (days) as the horizontal axis and accumulated stress (MPa) as the vertical axis, marking the rate of each stage (different colored line segments) and inflection points (red dots), showing that the accumulated stress reaches 102735.26 MPa at the end of 3 years and 150000 MPa in 4.4 years; the life prediction results clearly state that the normal service life is 4.4 years, with the first 3 years being safe and the 4th year requiring monitoring; the failure risk analysis indicates that the risk is highest in the 95%RH environment (contributing 20% ​​of the lifespan but causing 40% of the accumulated stress), followed by the risk in the temperature range above 60℃. The report includes a data table (daily accumulation summary), curve equation (cumulative stress = 77.4t1 + 133.96t2 + 186t3 + 218.2t4 + 421.6t5, where t is the number of days in each stage) and risk level classification (20%RH low risk, 40-60%RH medium risk, 85%RH high risk, 95%RH extremely high risk). All data are rounded to two decimal places, and the curve fitting error is less than 5%, providing a quantitative basis for the maintenance and replacement of electronic packaging.

[0070] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0071] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

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 an electronic packaging curing agent 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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