Method for testing and evaluating aging performance of metal oxide resistor disc

Abstract

The invention specifically relates to a method for testing and evaluating the aging performance of a metal oxide resistor disc, and relates to the technical field of performance evaluation of metal oxide resistor discs, and the method comprises the following steps: respectively obtaining a macro-loss value and a micro-loss value after macroscopic and microscopic analysis of a sample, carrying out normalization processing on the macro-loss value and the micro-loss value, and calculating the aging performance of the metal oxide resistor disc; a failure mechanism coefficient is obtained after weighted summation calculation, and a corresponding quality grade is matched based on the failure mechanism coefficient. The method comprises the following steps: constructing a digital twin baseline fusing electricity, a microstructure and thermal characteristics; electrical parameters such as a volt-ampere characteristic curve and a nonlinear coefficient are accurately obtained through standardized testing, microscopic data such as grain morphology and element distribution and thermal characteristic data such as steady-state temperature rise and temperature uniformity are analyzed, and a multi-dimensional reference system is formed; and an accurate reference is provided for aging comparison, and evaluation is ensured to be changed from single-parameter judgment to multi-dimensional comprehensive analysis.

Description

Technical Field

[0001] This invention relates to the field of performance evaluation technology for metal oxide resistors, and in particular to a method for testing and evaluating the aging performance of metal oxide resistors. Background Technology

[0002] Metal oxide resistors (such as ZnO resistors) are core components of overvoltage protection in power systems, and their aging performance directly determines the operational reliability and service life of the equipment. With increasing operating time, resistors undergo microstructural degradation under the influence of multiple factors such as electrical stress, thermal stress, and environmental stress. This degradation manifests as increased leakage current, worsened nonlinear characteristics, and decreased thermal stability, potentially leading to failure and power accidents.

[0003] Existing methods for evaluating the aging of resistors have many limitations: The test data is limited in scope, focusing mainly on monitoring electrical parameters, and lacks collaborative analysis of key data such as microstructure and thermal characteristics, making it difficult to establish a complete chain of performance degradation. The stress loading mode is fixed and a uniform static stress profile is used. It is impossible to make personalized adjustments based on the real-time response of the resistor, which can easily lead to confusion of the mechanism or low evaluation efficiency. Damage quantification standards are vague. Macroscopic failure characteristics (such as cracks and pores) and microscopic damage (such as element migration and grain boundary deterioration) rely heavily on qualitative descriptions and lack precise quantitative indicators. As a result, the evaluation results are highly subjective and have poor repeatability.

[0004] Therefore, a method for testing and evaluating the aging performance of metal oxide resistors is proposed to address the aforementioned problems. Summary of the Invention

[0005] The purpose of this invention is to provide a method for testing and evaluating the aging performance of metal oxide resistors in order to solve the above-mentioned problems.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A method for testing and evaluating the aging performance of metal oxide resistors, comprising: By obtaining electrical, microstructure, and thermal characteristics data of the resistive element through testing, a multi-dimensional digital twin baseline is established. Based on the failure mechanism library, a time-series stress profile is designed to induce grain boundary weakening, defect activation, and failure manifestation, thereby directionally stimulating the target failure mode. By simultaneously monitoring electrical, thermal, partial discharge, and acoustic emission, and combining dynamic feedback rules to adjust stress parameters in a personalized manner, adaptive evaluation can be achieved. Macroscopic failure localization and microscopic mechanism analysis were performed on failed samples to establish a complete causal relationship map of stress sequence, microscopic changes, parameter response and failure results; After macroscopic and microscopic analysis of the sample, macro-loss value and micro-loss value are obtained respectively. After normalization of macro-loss value and micro-loss value, weighted summation is performed to obtain failure mechanism coefficient, and corresponding quality level is matched based on failure mechanism coefficient.

[0007] Preferably, the step of obtaining electrical, microstructure, and thermal characteristic data of the resistive element through testing to establish a multi-dimensional digital twin baseline specifically includes: The complete current-voltage characteristic curve of the sample is measured to obtain the reference voltage, nonlinear coefficient, and leakage current and its resistive component at a specific percentage charge rate. Initial microstructure, grain size, and grain boundary phase distribution of some representative samples were observed and recorded to establish initial microstructure profiles; Measure the steady-state temperature rise curve of the sample under a preset power, or record the uniformity of its surface temperature distribution through thermal imaging; All acquired data are integrated to form the digital baseline of the resistor.

[0008] Preferably, the step of designing a time-series stress profile based on a failure mechanism library to induce grain boundary weakening, defect activation, and failure manifestation, and to directionally excite the target failure mode, specifically includes: Collect historical failure data of the target resistor, failure cases reported in the literature, and test results of similar products; Failure modes with a probability greater than a preset index are selected and a failure mechanism library is established. For each critical failure mode, determine the type of accelerating stress and the range of parameters; Develop a stress-mechanism mapping table to clarify the accelerating effect of different stress combinations on failure modes.

[0009] Preferably, the method further includes performing a timing stress profile: Phase 1: Determine the ambient temperature and relative humidity; apply a continuous AC voltage and frequency with preset values; Monitor the sample surface temperature in real time to ensure that the temperature does not exceed the preset threshold; periodically check the dew point in the environmental chamber; The shortest time is preset, and appropriate processing is performed based on the change in the resistive component of the leakage current within the shortest time: Phase Two: Set ambient humidity and temperature to preset standards; apply a preset standard lightning current impulse. By testing the resistive component of the leakage current and using infrared thermal imaging, we can confirm whether new hot spots have appeared or whether the resistive component of the leakage current has increased, and take appropriate actions based on the results. Phase Three: Humidity control is turned off, and the ambient temperature rises to the preset value; an AC voltage is applied and continuously increased for a preset duration until the sample fails or the termination criterion is met. Record the resistive component of the leakage current, power loss, and sample surface temperature field at preset time intervals.

[0010] Preferably, the adaptive evaluation is achieved by simultaneously monitoring electrical, thermal, partial discharge, and acoustic emission, combined with dynamic feedback rules to personalize the stress parameters, specifically including: Electrical parameters: Real-time monitoring of the full current waveform, and online calculation and extraction of the fundamental peak value of resistive current, third / fifth harmonic content, and power loss through synchronously acquired voltage signals; Thermal parameters: Infrared thermal imagers continuously monitor the temperature field on the sample surface to identify hot spots and their evolution; Using timestamp synchronization technology, electrical parameters, thermal parameters, and partial discharge / acoustic emission signals are correlated to the same time axis; Based on the established baseline data and combined with the results of preliminary experiments, early warning thresholds and emergency thresholds for each parameter are set.

[0011] Preferably, the step of performing macroscopic failure localization and microscopic mechanism analysis on the failed sample to establish a complete causal relationship map of stress sequence, microscopic changes, parameter response, and failure results specifically includes: Check the failure state of the sample, confirm whether the failure criteria are met, and record the time of failure and the stress parameters at that time. The camera photographs the front, back, and side of the sample, recording surface color changes, puncture marks, and the direction of cracks; Measure the geometric parameters of the failure points and analyze the correspondence between the failure points and the weak parts of the sample structure; Select the healthy region of the failed sample and cut the micro-region sample; SEM / EDS analysis, FIB and TEM analysis, and XRD micro-area analysis were performed. By correlating the recorded parameter mutation events with the timeline of the failure process, the chronological order of the key mutation events can be determined.

[0012] Preferably, the process of obtaining the macro loss value includes: Based on the image information of the sample, the image is divided into regions of a preset size to obtain sub-regions of the same size; Determine the number of cracks in the sub-region and the length of each crack. Sort the lengths of each crack in descending order of length, extract the longest crack length, determine the two ends of the crack, and connect the two ends with a straight line. Multiply the crack length by the distance between the two ends of the crack to obtain the crack quantification value. The crack quantization values ​​of each sub-region are obtained sequentially, and the crack quantization values ​​are sorted in descending order of numerical value. The four largest crack quantization values ​​and their corresponding sub-region locations are then extracted. Obtain the regional centers of the four sub-regions and mark each center as an endpoint. Connect the four endpoints with straight lines to form a spatial figure. If the resulting spatial figures lie on the same plane, calculate the area of ​​the figures and record it as the macro loss value. If the resulting spatial figures are not on the same plane, calculate the volume of the figure and record it as the macro loss value.

[0013] Preferably, the process of obtaining the micro-loss value includes: The failed sample was cut along a direction perpendicular to the electrode to obtain a cross-section containing the electrode-resistor interface. On the sample cross-section, select 3 to 5 analytical lines perpendicular to the electrode interface; Using EDS line scan mode, the scan starts from the inside of the electrode, passes vertically through the electrode-resistivity interface, and extends into the interior of the resistivity. Record the position coordinates and characteristic X-ray intensity of Ag element at each scan point; Simultaneously record the intensity of the background area as a reference; Determine the background intensity; Calculate the standard deviation of the background; When the Ag intensity at a certain point satisfies the following condition: the characteristic X-ray intensity of Ag element is greater than the background intensity + 3 × the standard deviation of the background, an Ag signal is considered to exist. Starting from the interface, move towards the interior of the resistor and find the last point where the strength of the Ag element at a distance d is ≥ 0.1 × (maximum Ag strength - background strength) + background strength; The distance from the interface to the point where the Ag strength meets the requirements is the penetration depth. A preset penetration depth threshold is set, and the penetration depth is subtracted from the penetration depth threshold. If the resulting value is greater than 0, it is retained and recorded as the penetration deviation value.

[0014] Preferably, the method further includes: A circular area with a radius of 2mm centered on the failure point is designated as Region 1, and a region ≥5mm away from the failure point and symmetrical to the electrode is designated as Region 2. The samples were cut and processed from region one and region two respectively; Select three sub-regions of preset sizes from region one using EDS surface scanning; Select three sub-regions of the same size from region two; Analyze key doping elements for each area scan region; For region 1, calculate the average of the three sub-regions. ; For region two, the average value of the three sub-regions is calculated similarly. ; Substituting into the formula, the abnormal quantification value is obtained. : ; After normalizing the permeation deviation value and the abnormal quantification value, the micro-loss value is obtained by weighted summation.

[0015] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention constructs a digital twin baseline that integrates electrical properties, microstructure, and thermal properties; accurately obtains electrical parameters such as current-voltage characteristic curves and nonlinear coefficients through standardized testing; analyzes microscopic data such as grain morphology and elemental distribution; and thermal property data such as steady-state temperature rise and temperature uniformity to form a multi-dimensional reference system; providing a precise benchmark for aging comparison and ensuring that the evaluation shifts from single-parameter judgment to multi-dimensional comprehensive analysis.

[0016] 2. This invention designs a targeted time-series stress profile, directionally excites failure modes such as ion migration based on a failure mechanism library, and adjusts stress in real time with dynamic rules. The macro-damage value is calculated by crack parameters and spatial graphics to quantify macroscopic defects, while the micro-damage value is combined with Ag penetration depth and elemental distribution anomalies to quantify microscopic damage, eliminate qualitative description errors, realize damage comparison under different scenarios, provide accurate data support for failure mechanism tracing, and improve the efficiency and scientific nature of assessment. Attached Figure Description

[0017] Further details, features, and advantages of this application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0018] Several embodiments of this application will now be described in more detail with reference to the accompanying drawings to enable those skilled in the art to implement this application. This application may be embodied in many different forms and for various purposes and should not be limited to the embodiments set forth herein. These embodiments are provided to make this application thorough and complete, and to fully convey the scope of this application to those skilled in the art. The embodiments described do not limit this application.

[0019] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having a meaning consistent with their meaning in the relevant field and / or the context of this specification, and shall not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0020] Example 1

[0021] Its specific implementation method is combined with the appendix Figure 1 Please provide a detailed explanation.

[0022] Appendix Figure 1 The flowchart of an aging performance testing and evaluation method for a metal oxide resistor provided in this embodiment of the invention illustrates the complete steps from obtaining electrical, microstructure, and thermal characteristic data of the resistor to obtaining the failure mechanism coefficient.

[0023] In this embodiment, it includes: By obtaining electrical, microstructure, and thermal characteristic data of the resistive element through standardized and high-precision testing, a multi-dimensional digital twin baseline is established to provide a benchmark for subsequent evaluation. Specifically, it includes: Initial electrical performance testing: Under standard laboratory conditions, accurately measure the complete current-voltage characteristic curve of the sample (from microamperes to milliamperes) and obtain the reference voltage ( ), nonlinear coefficients ( ), and a specific percentage charge rate (e.g., 80%). The leakage current and its resistive component under ( ); Testing environment: Strictly control laboratory temperature (23±2℃) and relative humidity (45%±5%) to avoid environmental factors interfering with measurement results; Test equipment: High-precision AC / DC voltage source (voltage accuracy ≤ ±0.1%), micro current meter (range 1nA-1A, accuracy ≤ ±0.5%), and waveform recorder (sampling rate ≥ 1MHz).

[0024] Test process: Gradually increase the voltage from 0V to the voltage value corresponding to sample U1mA, hold for 30s at each 5% voltage increase, record the corresponding current value, and plot the complete volt-ampere characteristic curve.

[0025] Calculate the nonlinear coefficient based on the current-voltage characteristic curve. ( , This is the voltage corresponding to a resistor leakage current of 0.1mA; For the corresponding Leakage current; This is the voltage corresponding to a resistor leakage current of 1mA. For the corresponding (Leakage current), select and (Calculation is performed using two feature points).

[0026] After applying a continuous AC voltage of 80% U1mA and running stably for 1 hour, the resistive and capacitive components of the leakage current are separated by harmonic analysis. The peak value, effective value, and fluctuation range of the resistive component of the leakage current are recorded (fluctuation ≤ ±3%).

[0027] Microstructure and composition analysis: Using scanning electron microscopy (SEM), X-ray diffraction (XRD), etc., the initial microstructure, grain size, and grain boundary phase distribution of some representative samples (or samples from the same batch) are observed and recorded to establish an initial microstructure profile; Sample preparation: Three representative samples were randomly selected from the same batch of resistor sheets. Test blocks with dimensions of 5mm×5mm×2mm were cut from the center and edge areas of each sample. The surface of the test blocks was ground and polished to remove the surface oxide layer and impurities.

[0028] Testing equipment: Scanning electron microscope, X-ray diffractometer, energy dispersive spectroscopy; Test content: SEM observation: Record grain morphology (columnar / equiaxed), average grain size (the average value of the diameter of at least 100 grains is obtained by statistical analysis using ImageJ software), grain boundary width (≤50nm), and whether there are defects such as pores and impurity particles at the grain boundaries.

[0029] XRD analysis: Identification of major phases (e.g.) , , (etc.), calculate the crystallinity (≥95%) and lattice constant of the phase, and determine whether there is an amorphous phase or impurity phase; EDS testing: Analyzes the elemental distribution in the grain boundary region and inside the grain, focusing on the initial content (≤100ppm) and uniformity of alkali metal ions such as Na and K.

[0030] Microscopic Archives: Establish the correspondence between sample location, microscopic morphology, and component distribution to form a visualized microscopic archive.

[0031] Thermal property calibration: Measure the steady-state temperature rise curve of the sample under a preset power, or record the uniformity of its surface temperature distribution through thermal imaging; Test environment: A constant temperature laboratory without forced convection (temperature 23±1℃) to avoid the influence of airflow on temperature rise measurement.

[0032] Test equipment: high-precision power source, infrared thermal imager, thermocouple.

[0033] Test process: Attach thermocouples to the center of the upper and lower surfaces of the sample and connect them to the data acquisition system; place the sample on an insulating support to ensure consistent heat dissipation conditions. Apply constant power (starting from 5W, and holding for 30 minutes for every 5W increase) until the sample surface temperature stabilizes (temperature change ≤0.5℃ within 10 minutes), and record the steady-state temperature rise (the difference between the sample surface temperature and the ambient temperature) at different power levels.

[0034] Infrared thermal imagers were used to capture the temperature field distribution on the sample surface, analyze the temperature uniformity (maximum temperature difference ≤2℃), and identify the presence of initial hot spots (areas with temperatures more than 1℃ higher than the surrounding area).

[0035] Establish a digital twin baseline: Integrate all acquired data to form a digital baseline for the resistor, which will serve as the benchmark for all subsequent performance degradation comparisons.

[0036] Data integration: Import electrical performance data, microstructure data, and thermal characteristic data into a unified database and establish a unique sample identifier (number + batch + test date).

[0037] Baseline construction: Statistical analysis of the mean values ​​of key parameters of samples in the same batch (e.g.) The mean, α mean, and resistive component mean of leakage current, along with their standard deviation (the standard deviation is required to be ≤5%), form the overall baseline for the batch.

[0038] For each sample, a personalized digital twin model is constructed, which includes the correlation mapping relationship between parameters, structure and thermal properties (such as the correlation between grain size and nonlinear coefficient).

[0039] Baseline validation: Randomly select 10% of the samples for repeated testing to verify the repeatability of the baseline data (repeated test error ≤ ±3%), ensuring the reliability of the baseline.

[0040] Based on the failure mechanism library, a time-series stress profile of grain boundary weakening, defect activation, and failure manifestation is designed to directionally excite the target failure mode and avoid mechanism confusion. Specifically, it includes: Mechanism analysis and profile design: Failure Mechanism Library Construction: Collect historical failure data of the target resistor (such as years of service, failure phenomena, and application scenarios), failure cases reported in the literature, and reliability test results of similar products; Fault tree analysis (FTA) and failure mode and effects analysis (FMEA) are used to screen out key failure modes with a probability greater than the preset index, and to clarify the inducing factors, development process and final manifestation of each failure mode (such as ion migration causing the resistive component of leakage current to continuously increase, eventually leading to thermal collapse). Establish a failure mechanism library, including information such as failure mode name, inducing factors, micro mechanism, macro characterization, and influence weight; For each critical failure mode, the most effective type of accelerating stress and parameter range are determined through theoretical analysis and preliminary experiments. Develop a stress-mechanism mapping table to clarify the accelerating effect of different stress combinations on failure modes (e.g., the acceleration factor of high temperature and humidity and voltage stress on ion migration is ≥100, which is much higher than the accelerating effect of single stress). Ensure that the stress parameters are within the sample's tolerance range (e.g., to avoid initial stress directly causing sample breakdown, the upper limit of stress needs to be determined through preliminary experiments).

[0041] It also includes the execution timing stress profile: Phase 1: Grain boundary weakening induction period Stress parameters: Ambient temperature 85±2℃, relative humidity 95%±3% (achieved using a programmable temperature and humidity chamber, temperature and humidity fluctuation ≤±1%); apply preset values ​​(e.g., 0.8 times). A continuous AC voltage with a frequency of 50Hz; Control conditions: Real-time monitoring of sample surface temperature to ensure it does not exceed a preset threshold (100℃). If the temperature exceeds the threshold, the voltage is automatically reduced by 0.05 times. Regularly check the dew point inside the environmental chamber to prevent condensation on the sample surface; Duration: The preset minimum time is 1000 hours, and corresponding processing is performed based on the change in the resistive component of the leakage current within the shortest time. If the rate of increase of the resistive component of the leakage current is ≤0.1% / h for 24 consecutive hours in the shortest time, it is determined that a steady state has been reached and the next stage can be entered in advance; if the resistive component of the leakage current continues to increase after the shortest time, the time limit is extended to 1500 hours. Monitoring frequency: The resistive component of the leakage current, sample surface temperature, and ambient temperature and humidity are recorded every 24 hours, and an infrared thermogram is taken every 100 hours.

[0042] Phase Two: Defect Activation and Propagation Period Stress parameters: Adjust the ambient humidity and temperature to the preset standard (maintain ambient humidity at 95%±3% and temperature at 60±2℃); apply a preset 8 / 20μs standard lightning current impulse (current waveform meets IEC60060-1 standard), with the impulse peak starting from 5kA and increasing by 5kA each time, for a total of 10 impulse levels (5kA, 10kA, ..., 50kA). Operating procedures: The time interval between each impact stage is preset (30 min) to ensure that the sample has sufficient time to dissipate heat after impact; the current waveform (sampling rate ≥ 100 MHz) and the voltage response across the sample are recorded simultaneously during impact to analyze the residual voltage change after impact; Target verification: Through resistive component testing of leakage current and infrared thermal imaging, confirm whether new hot spots have appeared or whether the resistive component of leakage current has significantly increased (growth rate ≥ 1% / level), and take appropriate actions based on the judgment results: If this occurs, the defect is determined to be activated; if there is no significant change after a 50kA impact, increase the impact to 60kA for 5 times. Phase Three: Accelerated Aging and Failure Manifestation Period Stress parameters: Humidity control is turned off, and the ambient temperature rises to the preset value (100±2℃); an AC power frequency voltage is applied and continuously increased for a preset duration until the sample fails or the termination criterion is met. The initial voltage is 0.9 times. It increases by 0.05 times every 24 hours. Until the sample becomes invalid or reaches 1.2 times the standard value. ; Failure criteria: When the resistive component of the leakage current of the sample exceeds 10 times the initial value, or when obvious breakdown marks (such as holes or cracks) appear on the surface, or when the power loss exceeds 5W, it is judged as a failure; if it does not fail after reaching 1.2 times U1mA, the test is terminated after 500 hours of continuous operation.

[0043] Data recording: The resistive component of leakage current, power loss, and sample surface temperature field are recorded at preset time intervals (every 8 hours). Intensive monitoring is conducted 1 hour before failure (recorded every 10 minutes) to fully record the parameter change trajectory of the failure process.

[0044] By synchronously monitoring multiple parameters such as electrical, thermal, partial discharge, and acoustic emission at high frequency, and combining dynamic feedback rules to adjust stress parameters in a personalized manner, adaptive and accurate assessment can be achieved. Specifically, it includes: Electrical parameters: Real-time monitoring of the full current waveform, and online calculation and extraction of the fundamental peak value of resistive current, third / fifth harmonic content, and power loss through synchronously acquired voltage signals; Thermal parameters: Infrared thermal imagers continuously monitor the temperature field on the sample surface to identify hot spots and their evolution; Other parameters: Optional monitoring quantities include partial discharge signal and acoustic emission signal; By employing timestamp synchronization technology, electrical parameters, thermal parameters, and partial discharge / acoustic emission signals are correlated onto the same time axis, which facilitates subsequent analysis of the temporal relationship of parameter abrupt changes. Based on the established baseline data and combined with the results of preliminary experiments, early warning thresholds and emergency thresholds for each parameter are set. The threshold adopts a dynamic adjustment mechanism. If the parameters of the sample change steadily during the test, the warning threshold can be appropriately increased (e.g., increased to 4 times the initial value); if the parameters fluctuate greatly, the warning threshold will be automatically reduced (e.g., reduced to 2.5 times the initial value). Preset warning thresholds and adjustment rules for key parameters, for example: Rule 1: If the growth rate of the fundamental peak resistive current exceeds the preset value (e.g., 5% / hour) in the second stage (after the impact), the voltage boost interval of the third stage power frequency voltage will be automatically shortened, or the voltage will be directly switched to the high stress level.

[0045] Rule 2: If the infrared thermal image shows local hot spots and the temperature continues to rise, the system will automatically pause the pressurization and enter a short-term depressurization and cooling phase. It will continue after the temperature stabilizes to observe its thermal recovery characteristics, which is itself an important aging characterization.

[0046] Rule 3: If a sudden burst of acoustic emission signal in a specific frequency band is detected, it may indicate the rapid propagation of microcracks. The system can automatically trigger a high-resolution recording of current and temperature data.

[0047] Through this feedback, the test profile changes from a pre-set fixed procedure to a personalized diagnostic path that is dynamically adjusted according to the real-time response of the sample.

[0048] Macroscopic failure localization and microscopic mechanism analysis were performed on failed samples to establish a complete causal relationship map of stress sequence, microscopic changes, parameter response and failure results; Specifically, it includes: Verify the failure status of the sample, confirm whether the failure criteria are met, and record the time of failure and the stress parameters (voltage, temperature, humidity) at that time. High-definition digital cameras were used to photograph the front, back, and sides of the samples, recording surface color changes (such as whether black or yellow areas appear), penetration marks (such as the diameter, location, and number of holes), and the orientation of cracks (such as extending from the edge to the center or distributed along the grain boundaries). Measure the geometric parameters of the failure point (such as hole diameter, crack length, and hot spot area) and analyze the correspondence between the failure point and the weak parts of the sample structure (such as edge chamfers and electrode coverage edges). Select the failed sample and cut micro-area samples with dimensions of 3mm×3mm×2mm along the center of the failure point, the edge of the failure point (1mm away from the failure point), and the healthy area away from the failure point (more than 5mm away from the failure point); The micro-area samples were ground (using 400#, 800#, 1200#, and 2000# sandpaper in successive grinding stages) and polished (using 1μm and 0.5μm diamond polishing paste), and the final surface roughness Ra ≤ 0.05μm; For SEM / EDS test samples, gold sputtering (gold layer thickness 5-10nm) is performed to improve conductivity; for FIB test samples, gold sputtering is not required, and focused ion beam cutting is performed directly. SEM / EDS analysis, FIB and TEM analysis, and XRD micro-area analysis were performed. SEM / EDS analysis: Observe the microstructure of the failure point area, focusing on analyzing whether there are phenomena such as grain boundary melting, grain detachment, pore expansion, and crack propagation (magnification 5000-50000 times). Line scan and area scan modes are used to analyze changes in elemental distribution along the failure path (such as whether Na⁺ and K⁺ aggregate towards grain boundaries, and whether the O element content decreases), and to calculate the migration distance and concentration gradient of elements.

[0049] FIB and TEM analysis: Using focused ion beam (FIB), cross-sectional slices with a thickness of 50-100 nm are cut in the failure point region to ensure that the slices contain a contrast between the failure path and the normal region; The microstructure of grain boundary phases (such as the presence of amorphization and second phase precipitation), the origin of microcracks (such as grain boundary junctions and around pores) and the propagation path were observed using transmission electron microscopy (TEM, accelerating voltage 200kV, resolution ≤0.2nm).

[0050] XRD micro-area analysis: Micro-area XRD tests (spot diameter ≤ 10 μm) were performed on the failure points and healthy areas to compare changes in phase composition (such as whether new oxide phases appear or whether crystallinity decreases) and to analyze lattice distortion (such as the amount of change in lattice constant).

[0051] By correlating the recorded parameter mutation events with the timeline of the failure process, the chronological order of the key mutation events can be determined. Phenomenon-mechanism correlation: If the resistive component of the leakage current continues to increase after the high temperature and high humidity stage, and SEM shows Na⁺ accumulation at the grain boundaries and an increase in grain boundary width, it can be determined that the high temperature and high humidity and the voltage stress cause ions to migrate to the grain boundaries, weakening the increase in the resistive component of the leakage current.

[0052] If a hot spot appears after a lightning current impact, and microcracks are observed by FIB, the causal relationship between the lightning current impact, mechanical-electrical stress, crack activation, poor local heat dissipation, and hot spot formation can be determined.

[0053] After macroscopic and microscopic analysis of the sample, the macro-loss value and micro-loss value are obtained respectively. After normalization of the macro-loss value and micro-loss value, the failure mechanism coefficient is obtained by weighted summation. The corresponding quality level is matched based on the failure mechanism coefficient. The process of obtaining macro loss values ​​includes: Based on the image information of the sample (including the front, back, and side), the image is divided into regions of a preset size to obtain sub-regions of the same size; Determine the number of cracks in the sub-region and the length of each crack. Sort the lengths of each crack in descending order of length, extract the longest crack length, determine the two ends of the crack, and connect the two ends with a straight line. Multiply the crack length by the distance between the two ends of the crack to obtain the crack quantification value. The crack quantization values ​​of each sub-region are obtained sequentially, and the crack quantization values ​​are sorted in descending order of numerical value. The four largest crack quantization values ​​and their corresponding sub-region locations are then extracted. Obtain the regional centers of the four sub-regions and mark each center as an endpoint. Connect the four endpoints with straight lines to form a spatial figure. If the resulting spatial figures lie on the same plane, calculate the area of ​​the figures and record it as the macro loss value. If the resulting spatial figures are not on the same plane, calculate the volume of the figure and record it as the macro loss value.

[0054] By using a standardized method for quantifying macroscopic failures, the macroscopic damage of resistors is transformed from traditional qualitative descriptions into precise quantitative data, thus completely solving the problem of difficulty in quantifying failure characteristics such as cracks and holes in traditional assessments.

[0055] By dividing the region, extracting the geometric parameters of the crack, constructing the spatial graphics and calculating the area / volume, it systematically integrates key information such as crack length and distribution range to form a unified macro-damage value index. This not only avoids errors caused by subjective judgment, but also achieves the comparability of macro-damage under different samples and different failure scenarios, making the macro-assessment of aging degree more objective and scientific.

[0056] The construction of this macro-loss value provides key macro-data support for the entire aging assessment system, complementing the subsequent micro-loss values ​​at the micro level.

[0057] Its precise quantification of the macroscopic failure range and degree can not only directly reflect the overall structural integrity degradation of the resistor, but also provide a clear target direction for microscopic mechanism analysis (such as focusing on the failure area corresponding to the macroscopic loss value to carry out microscopic detection). At the same time, as one of the core inputs for calculating the failure mechanism coefficient, it ensures the comprehensiveness and accuracy of the final quality level assessment and improves the practicality and credibility of the entire evaluation method.

[0058] The process of obtaining the micro-loss value includes: The failed sample was cut along a direction perpendicular to the electrode to obtain a cross-section containing the electrode-resistor interface. The cross-section is ground and polished to a mirror finish to ensure surface flatness (Ra≤0.05μm); The sample is treated with carbon or gold sputtering (thickness 5-10nm) to improve conductivity. On the sample cross-section, select 3 to 5 analytical lines perpendicular to the electrode interface and distribute them evenly across the entire interface width; avoid selecting areas with scratches, contamination, or obvious defects. Using EDS line scan mode, the scan starts from the inside of the electrode, passes vertically through the electrode-resistivity interface, and extends into the interior of the resistivity. Record the position coordinates (distance d from the interface) and the characteristic X-ray intensity of Ag element at each scan point; Simultaneously record the intensity of the background region (such as Zn, O) as a reference; The average Ag intensity at 5 consecutive points ≥80μm from the interface was determined as the background intensity. The standard deviation of the background is calculated as follows: Calculate the difference between each background reading and the average value; Square each difference to eliminate the positive and negative signs; Sum all the squared values; Divide the sum by (n-1) (this is the standard practice for calculating the sample standard deviation, known as Bessel correction); The square root of the result is the standard deviation, and its unit is the same as the strength unit.

[0059] The calculation process is a direct reference to existing technology and will not be elaborated here.

[0060] When the Ag intensity at a certain point satisfies the following condition: the characteristic X-ray intensity of Ag element is greater than the background intensity + 3 × the standard deviation of the background, it is considered that there is an Ag signal at that point. Starting from the interface (d=0), move inwards into the resistor and find the last point where the strength of the Ag element at a distance d is ≥0.1×(maximum Ag strength - background strength) + background strength; The distance from this point to the interface is the penetration depth. A preset penetration depth threshold is used. The penetration depth is subtracted from the penetration depth threshold. If the resulting value is greater than 0, it is retained and recorded as the penetration deviation value.

[0061] A standardized method for quantifying microscopic damage was established to accurately capture the Ag element penetration behavior at the electrode-resistor interface of the resistor sheet, thus solving the pain points of difficulty in quantifying interfacial diffusion damage and vague judgment criteria in traditional microscopic assessment.

[0062] Through a rigorous sample preparation process (mirror polishing, conductive treatment), scientific analytical line selection rules, and precise calculation of background intensity (3 times the standard deviation threshold) and penetration depth, it transforms microscopic element diffusion damage into quantifiable penetration deviation values, avoiding subjective judgment errors and enabling objective comparison of the degree of microscopic damage in different samples. This provides precise microscopic quantitative evidence for subsequent mechanism analysis.

[0063] The acquisition of this micro-damage value focuses on the core micro-failure mechanism of resistor aging (electrode metal penetration), forming a macro-micro bidirectional complementary damage assessment system with the macro-damage value.

[0064] Its quantified Ag penetration depth deviation can not only directly reflect the degree of degradation of the interface bonding performance, but also provide key microscopic evidence for the causal relationship of failure (such as the correspondence between penetration depth and leakage current growth). At the same time, as the core input for the calculation of micro-loss value, it ensures the scientific nature and pertinence of the final failure mechanism coefficient, and greatly improves the ability of the entire aging assessment method to identify micro-failure modes and the accuracy of assessment.

[0065] A circular area with a radius of 2mm centered on the failure point is designated as Region 1, and a region ≥5mm away from the failure point and symmetrical to the electrode is designated as Region 2. Samples with dimensions of 2mm×2mm were cut from Region 1 and Region 2 respectively, ground and polished to a mirror finish, cleaned and then carbon-sprayed. Using EDS surface scanning, select three sub-regions of preset size (500μm×500μm) from region one, avoiding obvious cracks or holes; Select three sub-regions of the same size from region two; For each area scanned region, the software automatically calculates the average atomic percentage of each element; The elements analyzed include key doping elements such as Bi, Mn, Sb, and Co, as well as Zn and O as matrix references; For region 1, calculate the average of the three sub-regions. ; For region two, the average value of the three sub-regions is calculated similarly. ; Substituting into the formula, the abnormal quantification value is obtained. : ; After normalizing the permeation deviation value and the abnormal quantification value, the micro-loss value is obtained by weighted summation.

[0066] The quantitative analysis of abnormal dopant element distribution overcomes the limitations of relying solely on Ag penetration depth to assess microscopic damage, achieving comprehensive coverage of microscopic failure mechanisms.

[0067] It adopts a comparative design between the failure region and the healthy region. Through standardized sampling (selection of symmetrical regions and avoidance of defective regions), statistical analysis of the mean of multiple sub-regions and logarithmic anomaly quantification formula, it accurately captures the distribution shift of key doping elements such as Bi and Mn. It transforms the fuzzy concept of element distribution uniformity degradation into a calculable quantitative index, avoiding the one-sidedness of damage assessment by a single micro-index, and making the micro-damage characterization more scientific and comprehensive.

[0068] This design further enhances the mechanism orientation of the micro-damage value, forming a two-dimensional micro-damage assessment system with the Ag penetration deviation value, which includes interfacial penetration and internal doping.

[0069] By quantifying the enrichment or loss of dopants in the failure region, it is possible to directly correlate them with core aging mechanisms such as grain boundary weakening and conductive pathway formation, providing crucial microscopic evidence for establishing a causal relationship map of failure. Simultaneously, the micro-loss value obtained through dual-dimensional weighting allows for a more precise correlation between macroscopic parameter changes and microscopic mechanism degradation, providing more robust microscopic data support for the final calculation of failure mechanism coefficients and quality grade matching.

[0070] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0071] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0072] It should be noted that, in this document, the use of relational terms such as "first" and "second" is merely for distinguishing one entity or operation from another, and does not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0073] It should be understood that, in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0074] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0075] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0076] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0077] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0078] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0079] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for testing and evaluating the aging performance of metal oxide resistors, characterized in that, include: By obtaining electrical, microstructure, and thermal characteristics data of the resistive element through testing, a multi-dimensional digital twin baseline is established. Based on the failure mechanism library, a time-series stress profile is designed to induce grain boundary weakening, defect activation, and failure manifestation, thereby directionally stimulating the target failure mode. By simultaneously monitoring electrical, thermal, partial discharge, and acoustic emission, and combining dynamic feedback rules to adjust stress parameters in a personalized manner, adaptive evaluation can be achieved. Macroscopic failure localization and microscopic mechanism analysis were performed on failed samples to establish a complete causal relationship map of stress sequence, microscopic changes, parameter response and failure results; After macroscopic and microscopic analysis of the sample, macro-loss value and micro-loss value are obtained respectively. After normalization of macro-loss value and micro-loss value, weighted summation is performed to obtain failure mechanism coefficient, and the corresponding quality level is matched based on failure mechanism coefficient.

2. The method for testing and evaluating the aging performance of a metal oxide resistor according to claim 1, characterized in that, By acquiring electrical, microstructure, and thermal property data of the resistive element through testing, a multi-dimensional digital twin baseline is established, specifically including: The complete current-voltage characteristic curve of the sample is measured to obtain the reference voltage, nonlinear coefficient, and leakage current and its resistive component at a specific percentage charge rate. Initial microstructure, grain size, and grain boundary phase distribution of some representative samples were observed and recorded to establish initial microstructure profiles; Measure the steady-state temperature rise curve of the sample under a preset power, or record the uniformity of its surface temperature distribution through thermal imaging; All acquired data are integrated to form the digital baseline of the resistor.

3. The method for testing and evaluating the aging performance of a metal oxide resistor according to claim 1, characterized in that, Based on a failure mechanism library, time-series stress profiles are designed to detect grain boundary weakening, defect activation, and failure manifestation, thereby directionally stimulating target failure modes, specifically including: Collect historical failure data of the target resistor, failure cases reported in the literature, and test results of similar products; Failure modes with a probability greater than a preset index are selected and a failure mechanism library is established. For each critical failure mode, determine the type of accelerating stress and the range of parameters; Develop a stress-mechanism mapping table.

4. The aging performance testing and evaluation method for a metal oxide resistor according to claim 3, characterized in that, It also includes the execution timing stress profile: Phase 1: Determine the ambient temperature and relative humidity; apply a continuous AC voltage and frequency with preset values; Monitor the sample surface temperature in real time to ensure that the temperature does not exceed the preset threshold; periodically check the dew point in the environmental chamber; The shortest time is preset, and appropriate processing is performed based on the change in the resistive component of the leakage current within the shortest time: Phase Two: Set ambient humidity and temperature to preset standards; apply preset standard lightning current impulse; By testing the resistive component of the leakage current and using infrared thermal imaging, we can confirm whether new hot spots have appeared or whether the resistive component of the leakage current has increased, and take appropriate actions based on the results. Phase Three: Humidity control is turned off, and the ambient temperature rises to the preset value; an AC voltage is applied and continuously increased for a preset duration until the sample fails or the termination criterion is met. Record the resistive component of the leakage current, power loss, and sample surface temperature field at preset time intervals.

5. The method for testing and evaluating the aging performance of a metal oxide resistor according to claim 1, characterized in that, By simultaneously monitoring electrical, thermal, partial discharge, and acoustic emission, and combining this with dynamic feedback rules to personalize stress parameters, adaptive evaluation is achieved, specifically including: Electrical parameters: Real-time monitoring of the full current waveform, and online calculation and extraction of the fundamental peak value of resistive current, third / fifth harmonic content, and power loss through synchronously acquired voltage signals; Thermal parameters: Infrared thermal imager continuously monitors the temperature field on the sample surface to identify hot spots and their evolution; Using timestamp synchronization technology, electrical parameters, thermal parameters, and partial discharge / acoustic emission signals are correlated to the same time axis; Based on the established baseline data and combined with the results of preliminary experiments, early warning thresholds and emergency thresholds for each parameter are set.

6. The method for testing and evaluating the aging performance of a metal oxide resistor according to claim 1, characterized in that, Macroscopic failure localization and microscopic mechanism analysis were performed on failed samples to establish a complete causal relationship map of stress sequence, microscopic changes, parameter response, and failure results, specifically including: Check the failure state of the sample, confirm whether the failure criteria are met, and record the time of failure and the stress parameters at that time. The camera photographs the front, back, and side of the sample, recording surface color changes, puncture marks, and the direction of cracks; Measure the geometric parameters of the failure points and analyze the correspondence between the failure points and the weak parts of the sample structure; Select the healthy region of the failed sample and cut the micro-region sample; SEM / EDS analysis, FIB and TEM analysis, and XRD micro-area analysis were performed. By correlating the recorded parameter mutation events with the timeline of the failure process, the chronological order of the key mutation events can be determined.

7. The method for testing and evaluating the aging performance of a metal oxide resistor according to claim 1, characterized in that, The process of obtaining macro loss values ​​includes: Based on the image information of the sample, the image is divided into regions of a preset size to obtain sub-regions of the same size; Determine the number of cracks in the sub-region and the length of each crack. Sort the lengths of each crack in descending order of length, extract the longest crack length, determine the two ends of the crack, and connect the two ends with a straight line. Multiply the crack length by the distance between the two ends of the crack to obtain the crack quantification value. The crack quantization values ​​of each sub-region are obtained sequentially, and the crack quantization values ​​are sorted in descending order of numerical value. The four largest crack quantization values ​​and their corresponding sub-region locations are then extracted. Obtain the center of each of the four sub-regions and mark each center as an endpoint. Connect the four endpoints with straight lines to form a spatial figure. If the resulting spatial figures lie on the same plane, calculate the area of ​​the figures and record it as the macro loss value. If the resulting spatial figures are not on the same plane, calculate the volume of the figure and record it as the macro loss value.

8. The method for testing and evaluating the aging performance of a metal oxide resistor according to claim 7, characterized in that, The process of obtaining the micro-loss value includes: The failed sample was cut along a direction perpendicular to the electrode to obtain a cross-section containing the electrode-resistor interface. On the sample cross-section, select 3 to 5 analytical lines perpendicular to the electrode interface; Using EDS line scan mode, the scan starts from the inside of the electrode, passes vertically through the electrode-resistivity interface, and extends into the interior of the resistivity. Record the position coordinates and characteristic X-ray intensity of Ag element at each scan point; Simultaneously record the intensity of the background area as a reference; Determine the background intensity; Calculate the standard deviation of the background; When the Ag intensity at a certain point satisfies the following condition: the characteristic X-ray intensity of Ag element is greater than the background intensity + 3 × the standard deviation of the background, an Ag signal is considered to exist. Starting from the interface, move towards the interior of the resistor and find the last point where the strength of the Ag element at a distance d is ≥ 0.1 × (maximum Ag strength - background strength) + background strength; The distance from the interface to the point where the Ag strength meets the requirements is the penetration depth. A preset penetration depth threshold is set, and the penetration depth is subtracted from the penetration depth threshold. If the resulting value is greater than 0, it is retained and recorded as the penetration deviation value.

9. The method for testing and evaluating the aging performance of a metal oxide resistor according to claim 8, characterized in that, Also includes: A circular area with a radius of 2mm centered on the failure point is designated as Region 1, and a region ≥5mm away from the failure point and symmetrical to the electrode is designated as Region 2. The samples were cut and processed from region one and region two respectively; Select three sub-regions of preset sizes from region one using EDS surface scanning; Select three sub-regions of the same size from region two; Analyze key doping elements for each area scan region; For region 1, calculate the average of the three sub-regions. ; For region two, the average value of the three sub-regions is calculated similarly. ; Substituting into the formula, the abnormal quantification value is obtained. : ； After normalizing the permeation deviation value and the abnormal quantification value, the micro-loss value is obtained by weighted summation.

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