A method and system for intelligently judging the material of silver bonding wire based on big data

Abstract

The present invention discloses a method and system for determining the material of an intelligent bonding silver wire based on big data, and relates to the technical field of material determination. The method comprises real-time monitoring of the current characteristic curve; analyzing and processing the current characteristic data to form a set of quantitative indicators for the degree of surface damage of the intelligent bonding silver wire; constructing a performance degradation model based on the quantitative indicators and combining the standard test data of the same batch of intelligent bonding silver wires; predicting the performance degradation, damage and aging of the intelligent bonding silver wire during long-term use based on the performance degradation model, and calculating the service life of the intelligent bonding silver wire under actual working conditions; generating a material quality assessment report for the intelligent bonding silver wire, and providing the material judgment result of the intelligent bonding silver wire. The present invention improves the accuracy and adaptability of silver wire performance assessment by dynamically adjusting test conditions, extracting multi-dimensional features, and correcting environmental factors, effectively solving the problem in the prior art that it is difficult to accurately assess damage and life due to complex working conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of material judgment, and in particular to a method and system for intelligently judging the material of a silver bonding wire based on big data. Background Art

[0002] With the rapid development of microelectronics, semiconductor manufacturing, and integrated circuit packaging technologies, bonding wire, as a core interconnect material, plays a vital role in the efficient operation and stability of electronic devices. Silver bonding wire, due to its excellent conductivity, thermal stability, and oxidation resistance, has become an important alternative to traditional gold and copper bonding wires.

[0003] However, when smart silver bonding wire is used for a long time in a complex working environment, it may be affected by factors such as oxidation, corrosion and stress fatigue, which may cause performance degradation and shortened life.

[0004] Existing bond wire material evaluation technologies primarily rely on macroscopic physical measurements, such as tensile testing and optical microscopy. While these methods can provide a certain degree of insight into the surface condition of bond wires, they lack real-time, accuracy, and specificity. In particular, they struggle to quantify the dynamic processes of material microstructural degradation and performance decline. Furthermore, research into the damage and aging patterns during long-term use is limited, hindering the ability to meet the demands of increasingly complex application scenarios.

[0005] Existing research often focuses on a single test condition, failing to effectively integrate diverse data from different batches or environmental conditions. This makes it difficult to construct highly adaptable and reliable performance degradation models. These shortcomings restrict the scientific and practical application of intelligent silver bonding wire quality assessment, indirectly impacting the overall reliability and production efficiency of high-precision electronic packaging. Summary of the Invention

[0006] In view of the problems existing in the above background technology, the present invention is proposed.

[0007] Therefore, the problem to be solved by the present invention is how to identify the damage of smart bonding silver wire during long-term use, evaluate its performance and predict its reliability.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0009] In the first aspect, an embodiment of the present invention provides a method for determining the material of an intelligent bonding silver wire based on big data, which includes placing the intelligent bonding silver wire in an electrochemical testing device, applying current to the surface, monitoring the current characteristic curve in real time, and synchronously recording multi-channel data; analyzing and processing the current characteristic data, extracting key characteristic parameters in the electrochemical reaction, and forming a set of quantitative indicators for the degree of surface damage of the intelligent bonding silver wire; based on the quantitative indicators, combined with the standard test data of the same batch of intelligent bonding silver wires, constructing a performance degradation model; based on the performance degradation model, predicting the performance degradation, damage and aging of the intelligent bonding silver wire during long-term use; generating a material quality assessment report for the intelligent bonding silver wire, and giving a material judgment result of the intelligent bonding silver wire.

[0010] As a preferred solution of the intelligent bonding silver wire material judgment method based on big data described in the present invention, the electrochemical testing process includes: dynamically adjusting the frequency of the applied current according to the real-time changes in the surface state of the intelligent bonding silver wire by setting the automatic adjustment function of the electrochemical testing equipment.

[0011] As a preferred solution of the intelligent bonding silver wire material judgment method based on big data of the present invention, the adjustment of the frequency of the current is carried out by considering the surface potential change rate, and the frequency of the applied current is set to , which is proportional to the rate of change of surface potential, and the calculation formula is:

[0012] ;

[0013] in, is the rate of change of surface potential, is the proportional coefficient.

[0014] As a preferred solution of the intelligent bonding silver wire material judgment method based on big data described in the present invention, wherein: the analysis and processing of the current characteristic data and the extraction of key characteristic parameters in the electrochemical reaction include the following: the key characteristic parameters include current peak value, corrosion resistance time and oxidation reaction rate; Kalman filtering is performed on the current signal to obtain a smoothed current signal; the current peak value in the current characteristic curve is extracted through the local extreme value detection algorithm; the corrosion resistance time is the time period during the electrochemical reaction when the current characteristic curve remains stable and there is no significant corrosion. By detecting the relatively stable interval of current change, the duration of the stable interval is defined as the corrosion resistance time; the oxidation reaction rate is calculated by the slope of the current curve to reflect the rate change of the electrochemical reaction.

[0015] As a preferred solution of the intelligent bonding silver wire material judgment method based on big data of the present invention, the calculation process of the corrosion resistance time is as follows:

[0016] ;

[0017] in, is the baseline value of the current, indicating the reference value of the stable period, For corrosion resistance time, is the smoothed current signal, is the threshold value of current change.

[0018] As a preferred solution of the intelligent silver bonding wire material judgment method based on big data described in the present invention, the construction of the performance degradation model includes: using a machine learning method to input quantitative indicators and standard test data into the model to predict the performance degradation trend of the intelligent silver bonding wire. The specific formula is as follows:

[0019] ;

[0020] in, For time The performance degradation prediction value at the moment, is the total number of decision trees in the random forest, For the The weight of a decision tree, For the A decision tree based on The output value of is a feature in the quantitative indicator set.

[0021] As a preferred solution of the big data-based intelligent bonding silver wire material judgment method described in the present invention, the prediction of the performance degradation, damage and aging of the intelligent bonding silver wire during long-term use includes the following steps: combining the performance degradation model, introducing environmental factors to correct the performance prediction results; based on the damage accumulation theory, calculating the cumulative damage value of the intelligent bonding silver wire.

[0022] In a second aspect, an embodiment of the present invention provides a smart silver bonding wire material determination system based on big data, which includes: an electrochemical reaction monitoring module for placing the smart silver bonding wire in an electrochemical testing device, applying current to the surface, monitoring the current characteristic curve in real time, and performing multi-channel data synchronous recording;

[0023] The data analysis and processing module is used to analyze and process the current characteristic data, extract the key characteristic parameters in the electrochemical reaction, and form a set of quantitative indicators for the degree of surface damage of the smart bonding silver wire;

[0024] A performance model building module, configured to build a performance degradation model based on the quantitative indicators and in combination with standard test data of the same batch of smart silver bonding wires;

[0025] A performance evaluation module, configured to predict the performance degradation, damage, and aging of the smart silver bonding wire during long-term use based on the performance degradation model;

[0026] The material evaluation report generation module is used to generate a material quality evaluation report for the smart bonding silver wire and provide the material judgment result of the smart bonding silver wire.

[0027] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the intelligent bonding silver wire material judgment method based on big data as described in the first aspect of the present invention are implemented.

[0028] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the intelligent bonding silver wire material judgment method based on big data as described in the first aspect of the present invention are implemented.

[0029] The beneficial effects of the present invention are as follows: the present invention utilizes an electrochemical sensor to detect the oxide layer or signs of corrosion on the surface of the silver wire in real time, and uses the rate of change of the surface potential as the basis for dynamically adjusting the applied current frequency, thereby improving the efficiency and accuracy of the test; through quantitative indicators and standard test data from the same batch, a machine learning method is used to construct a performance degradation model, thereby realizing the prediction of the performance degradation trend of the silver wire, solving the limitations of the traditional linear prediction method when facing nonlinear data, and improving the prediction accuracy of the performance change of the silver wire. The present invention improves the accuracy and adaptability of the silver wire performance evaluation by dynamically adjusting the test conditions, extracting multidimensional features, and correcting environmental factors, effectively solving the problem in the existing technology that it is difficult to accurately evaluate damage and life due to complex working conditions, and provides a generalizable technical framework for intelligent material evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0031] Figure 1 This is a flow chart of the intelligent bonding silver wire material judgment method based on big data.

[0032] Figure 2 This is the structural diagram of the intelligent bonding silver wire material judgment system based on big data. DETAILED DESCRIPTION

[0033] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0034] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0035] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0036] The present invention is described in detail with reference to schematic diagrams. For ease of illustration, cross-sectional views of device structures may be partially enlarged and not to scale when describing embodiments of the present invention. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0037] In the description of the present invention, it should be noted that the terms "upper, lower, inner, and outer" and other references to orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0038] In this disclosure, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be interpreted broadly. For example, they may refer to fixed, removable, or integral connections. They may also refer to mechanical, electrical, or direct connections, indirect connections through an intermediary, or internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this disclosure.

[0039] Example 1

[0040] Reference Figure 1~Figure 2, is the first embodiment of the present invention, which provides an intelligent bonding silver wire material judgment method based on big data, such as Figure 1 Shown, including,

[0041] S1: Place the smart bonding silver wire in an electrochemical testing device, apply a small current to the surface, and monitor the current characteristic curve in real time.

[0042] S1.1: By setting the electrochemical testing equipment's automatic adjustment function, the frequency of the applied current is dynamically adjusted based on the real-time changes in the surface condition of the smart silver bonding wire. First, an electrochemical sensor monitors the surface of the smart silver bonding wire for signs of oxide layer or corrosion in real time. This information serves as the basis for adjusting the current amplitude and frequency.

[0043] The frequency of the current can be adjusted by taking into account the rate of change of the surface potential to optimize the test efficiency and accuracy. The applied current frequency is set to , which is proportional to the rate of change of surface potential, and the calculation formula is:

[0044] ;

[0045] in, is the rate of change of surface potential, is the proportional coefficient, which indicates the sensitivity of potential change to frequency adjustment.

[0046] S1.2: Use high-frequency sampling technology to obtain current characteristic curves in real time and record multi-channel data synchronously.

[0047] For example, a high-frequency sampling rate (e.g., above 1 MHz) is used to capture the characteristic curve of current changing over time, ensuring that the subtle fluctuations in current generated during the electrochemical reaction process can be accurately captured.

[0048] Data is collected simultaneously at multiple sensor locations to ensure that the signal acquisition during the electrochemical reaction can cover the overall reaction of the smart bonding silver wire. For example, multiple electrochemical sensors can be set up to measure the current signals at different locations to ensure that the data of each channel is recorded synchronously, and the current data of each channel is synchronized using timestamps.

[0049] S2: Analyze and process the current characteristic data to extract key characteristic parameters in the electrochemical reaction, such as current peak, corrosion resistance time, oxidation reaction rate, etc., and form a set of quantitative indicators for the degree of surface damage of the smart bonding silver wire.

[0050] First, according to the current characteristic data collected in S1, the data is subjected to noise suppression through an adaptive filtering algorithm, that is, a Kalman filter is performed on the current signal to obtain a smoothed current signal, thereby removing the noise in the original data.

[0051] Based on the time-frequency domain analysis method, key parameters such as current peak value and corrosion resistance time are extracted from the current characteristic curve segment to generate preliminary damage index, including:

[0052] The current peak value in the current characteristic curve is extracted through the local extreme value detection algorithm;

[0053] The corrosion resistance time is defined as the period of time during which the current characteristic curve remains stable and there is no significant corrosion during the electrochemical reaction. The duration of the stable period is defined by detecting the relatively stable interval of current change. ,

[0054] That is, when the current fluctuation amplitude is less than the threshold Duration of time:

[0055] ;

[0056] in, is the baseline value of the current, indicating the reference value of the stable period, For corrosion resistance time, is the smoothed current signal, is the threshold value of current change.

[0057] The oxidation reaction rate is calculated by the slope of the current curve, which reflects the change in the rate of the electrochemical reaction. The oxidation reaction rate is related to the rate of change of the current and can be calculated by the following formula:

[0058] ;

[0059] in, is the rate of oxidation reaction, which reflects the intensity of surface oxidation reaction. is the rate of change of current.

[0060] The extracted current peak, corrosion resistance time and oxidation reaction rate are used as quantitative indicators of damage degree, and the final damage degree indicator set is generated.

[0061] S3: Based on the quantitative indicators obtained in step S2 and combined with the standard test data of the same batch of smart bonding silver wires, a performance degradation model is constructed.

[0062] It should be noted that the standard test data is taken from the long-term performance records of samples from the same batch in an experimental environment.

[0063] The performance degradation model uses machine learning methods (such as random forest or long short-term memory network LSTM) to convert quantitative indicators The performance degradation trend of smart silver bonding wire is predicted by inputting the model with standard test data. The specific formula is as follows:

[0064] ;

[0065] in, For time The performance degradation prediction value at the moment, is the total number of decision trees in the random forest, For the The weight of a decision tree, For the A decision tree based on The output value of is a feature in the quantitative indicator set.

[0066] Furthermore, the model parameters are adjusted through cross-validation to ensure the accuracy of the prediction results.

[0067] S4: Based on the performance degradation model, predict the performance degradation, damage and aging of the smart bonding wire during long-term use. The specific steps are as follows:

[0068] First, combined with the performance degradation model, environmental factors are introduced to correct the performance prediction results. The formula is:

[0069] ;

[0070] in, Corrected time The performance degradation prediction value at the moment, is the environmental factor sensitivity coefficient, The performance degradation prediction value is a weighted sum of the comprehensive environmental factor influence values, including temperature, current density and humidity. The revised performance degradation prediction value can reflect the performance changes of smart bonding silver wire in actual environment.

[0071] It can be seen that by introducing environmental factors and sensitivity coefficients, the coupling of multiple factors such as temperature is effectively incorporated into the calculation, which enhances the robustness and adaptability of the model. For example, when the humidity in the environment changes significantly, by adjusting The value can be modified dynamically This design improves the accuracy of silver wire performance evaluation under different working conditions and lays a reliable foundation for subsequent life prediction.

[0072] Secondly, based on the damage accumulation theory, the cumulative damage value of the smart bonding wire is calculated using the formula:

[0073] ;

[0074] in, is the cumulative damage value, is the total number of load cases, For the Actual working time under working conditions; For the Theoretical fatigue life under working conditions.

[0075] It should be noted that the use of Miner damage theory to accurately calculate the cumulative damage under multiple working conditions solves the problem of difficulty in quantifying material properties under complex load conditions. For example, under high temperature and high pressure conditions, the damage rate of silver wire may increase exponentially. The calculation effectively quantifies this change. In addition, by comprehensively considering the damage contributions of multiple operating conditions, it avoids dependence on a single operating condition and improves the reliability of the predicted life.

[0076] Furthermore, based on the cumulative damage value and performance correction model, the service life of the silver wire is predicted as follows:

[0077] ;

[0078] in, To predict the service life of smart bonding wire, is the long-term aging coefficient, determined by experimental fitting; is the total forecast time interval.

[0079] It should be noted that the present invention can predict the aging rate and damage type of silver wire under different working environments and usage conditions, and further evaluate the service life and long-term reliability.

[0080] S5: Generate a material quality assessment report for the smart bonding silver wire and provide a material judgment result for the smart bonding silver wire.

[0081] Specifically, the key quantitative indicators (current peak, corrosion resistance time, oxidation reaction rate) extracted from S2 are summarized to form a characteristic data table for each smart bonding silver wire;

[0082] The prediction results of the performance degradation model constructed from S3 are integrated to quantify the performance change trend of each silver wire during long-term use into a performance degradation curve. The long-term use prediction results calculated in the performance degradation model are applied to fit the performance change trend with the standard trend curve to determine whether its change rate is within the expected range.

[0083] The cumulative damage value is calculated using the damage accumulation theory. Combined with the long-term aging coefficient, the reliability of the silver wire is comprehensively evaluated. Smart bonding silver wire is divided into the following quality categories:

[0084] High quality (Class A): The cumulative damage value is less than 10%, the long-term performance degradation trend is stable, and the expected lifespan meets industry standards.

[0085] Available (Class B): The cumulative damage value is between 10% and 20%, and the performance degradation rate is slightly higher than the standard. It is recommended for use in low-risk environments.

[0086] Poor quality (Class C): The cumulative damage value is higher than 20%, there is a significant risk of performance degradation, and it is not recommended for use in critical components.

[0087] The quality category of each silver wire must be clearly marked in the evaluation report, and recommended application scenarios must be attached.

[0088] Furthermore, this embodiment also provides an intelligent bonding silver wire material judgment system based on big data, including:

[0089] The electrochemical reaction monitoring module is used to place the smart silver bonding wire in an electrochemical testing device, apply current to the surface, monitor the current characteristic curve in real time, and perform multi-channel data synchronous recording;

[0090] The data analysis and processing module is used to analyze and process the current characteristic data, extract the key characteristic parameters in the electrochemical reaction, and form a set of quantitative indicators for the degree of surface damage of the smart bonding silver wire;

[0091] A performance model building module, configured to build a performance degradation model based on the quantitative indicators and in combination with standard test data of the same batch of smart silver bonding wires;

[0092] A performance evaluation module, configured to predict the performance degradation, damage, and aging of the smart silver bonding wire during long-term use based on the performance degradation model;

[0093] The material evaluation report generation module is used to generate a material quality evaluation report for the smart bonding silver wire and provide the material judgment result of the smart bonding silver wire.

[0094] This embodiment also provides a computer device, which is suitable for the case of an intelligent bonding silver wire material judgment method based on big data, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the intelligent bonding silver wire material judgment method based on big data proposed in the above embodiment.

[0095] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0096] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for intelligently determining the material of silver bonding wire based on big data as proposed in the above embodiment is implemented.

[0097] In summary, the present invention utilizes electrochemical sensors to detect oxide layers or signs of corrosion on the surface of silver wire in real time, and uses the rate of change of surface potential as the basis for dynamically adjusting the frequency of applied current, thereby improving the efficiency and accuracy of the test. Through quantitative indicators and standard test data from the same batch, a machine learning method is used to construct a performance degradation model, thereby achieving the prediction of the performance degradation trend of silver wire. This solves the limitations of traditional linear prediction methods when faced with nonlinear data and improves the accuracy of predicting changes in silver wire performance. The present invention improves the accuracy and adaptability of silver wire performance evaluation by dynamically adjusting test conditions, extracting multidimensional features, and correcting environmental factors. This effectively solves the problem in existing technologies of difficulty in accurately assessing damage and lifespan due to complex working conditions, and provides a generalizable technical framework for intelligent material evaluation.

[0098] Example 2

[0099] Referring to Table 1, which is the second embodiment of the present invention, this embodiment provides an intelligent bonding silver wire material judgment method based on big data. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0100] First, the selected smart silver bonding wire sample was placed in an electrochemical testing device. The device's functional setup includes applying a low current and real-time monitoring of the current characteristic curve. Electrochemical sensors were used to monitor the surface of the silver wire for signs of oxide layer or corrosion, acquiring data in real time and performing dynamic analysis.

[0101] During the implementation process, the electrochemical test equipment's automatic adjustment function was first activated, adjusting the frequency of the applied current based on the real-time rate of change of the surface potential. The current frequency is adjusted based on the calculated rate of change of the surface potential. This dynamic adjustment helps optimize test efficiency and accuracy.

[0102] Next, high-frequency sampling technology (e.g., above 1 MHz) is used to acquire the current characteristic curve in real time, ensuring accurate capture of the subtle current fluctuations during the electrochemical reaction. Multiple sensors simultaneously collect data at different locations to ensure synchronous recording of data from each channel, using timestamps to synchronize the current data for each channel.

[0103] After collecting the current characteristic data, adaptive filtering algorithms (such as Kalman filtering) are used to remove noise and produce a smoothed current signal. Next, time-frequency domain analysis is used to extract key parameters such as current peak value, corrosion resistance time, and oxidation reaction rate from the current characteristic curve segments, forming a set of quantitative indicators of damage severity.

[0104] Based on data processing, a performance degradation model was constructed and combined with standard test data to predict the performance degradation trend of smart silver bonding wire. The influence of environmental factors (such as temperature and humidity) was introduced into the model to further refine the performance predictions and ensure their accuracy in real-world conditions.

[0105] Finally, based on damage accumulation theory, combined with actual operating time and theoretical fatigue life under different working conditions, the accumulated damage value of the silver wire is calculated to predict its service life. Based on this data, a quality assessment report for the smart bonding silver wire is generated, providing material judgment results and recommendations for its application in different scenarios. Some experimental data are as follows:

[0106] Table 1 Experimental data table

[0107] Smart bonding silver wire sample Current peak (mA) Corrosion resistance time (hours) Oxidation reaction rate (mA / s) Damage degree (index) Performance degradation prediction value (%) Cumulative damage value (%) Sample 1 12.5 80 0.05 8.2 5.3 8.0 Sample 2 13.0 75 0.04 7.5 6.0 9.0 Sample 3 12.8 85 0.06 8.0 4.8 7.5 Sample 4 11.9 72 0.03 6.5 6.3 9.2 Sample 5 14.1 90 0.05 9.0 5.2 7.0 Sample 6 13.5 88 0.04 8.1 5.5 8.5

[0108] Through the analysis of the above table data, it can be clearly seen that the big data-based intelligent bonding silver wire material judgment method adopted by the present invention has advantages in improving test accuracy and evaluation reliability. First, by real-time monitoring of the current characteristic curve and its dynamic adjustment of the current frequency, the present invention can optimize the test conditions under different silver wire states and accurately capture subtle changes in surface corrosion or oxidation. For example, the oxidation reaction rate of sample 3 is 0.06 mA / s, indicating that its oxidation reaction is relatively strong, and the corrosion resistance time is 85 hours, showing a long stable period. It is speculated that the material of this sample is relatively good.

[0109] Compared to traditional methods, this invention significantly improves the accuracy of current signal acquisition through multi-channel synchronous data acquisition and high-frequency sampling technology. In data processing, the use of a Kalman filter algorithm for denoising enables precise extraction of key parameters (such as current peak value and oxidation reaction rate). Quantitative extraction of these parameters effectively reflects the damage and corrosion status of the smart bonding wire.

[0110] By applying a performance degradation prediction model, combined with corrections for environmental factors, we can more accurately predict the long-term service life of silver wire. For example, Sample 4's cumulative damage value was 9.2%, indicating that it was approaching the critical value for performance degradation under relatively harsh operating conditions. Sample 1, on the other hand, had a lower damage value of only 8.0%, suggesting a relatively long service life. Including the influence of environmental factors (such as humidity and temperature) further enhances the model's adaptability and ensures accurate performance predictions under different operating conditions.

[0111] In general, the method of the present invention can not only monitor and analyze the electrochemical properties of smart bonding silver wires in real time, but also build a more accurate life prediction model based on long-term performance data and environmental factors, thereby improving the accuracy and reliability of smart bonding silver wire material judgment.

[0112] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for intelligently determining the material of silver bonding wire based on big data, characterized by: include, The smart silver bonding wire is placed in an electrochemical testing device, current is applied to the surface, the current characteristic curve is monitored in real time, and multi-channel data is recorded synchronously; Analyze and process the current characteristic data, extract the key characteristic parameters in the electrochemical reaction, and form a set of quantitative indicators for the degree of surface damage of the smart bonding silver wire; Based on the quantitative indicators and combined with standard test data of the same batch of smart silver bonding wires, a performance degradation model is constructed; Based on the performance degradation model, predict the performance degradation, damage and aging of the smart bonding wire during long-term use; Generate a material quality assessment report for the smart bonding silver wire and provide the material judgment results of the smart bonding silver wire; The analysis and processing of the current characteristic data to extract the key characteristic parameters in the electrochemical reaction includes the following: the key characteristic parameters include current peak value, corrosion resistance time and oxidation reaction rate; Kalman filtering is performed on the current signal to obtain a smoothed current signal; the current peak value in the current characteristic curve is extracted through a local extreme value detection algorithm; the corrosion resistance time is the time period during the electrochemical reaction when the current characteristic curve remains stable and there is no significant corrosion. By detecting the relatively stable interval of current change, the duration of the stable interval is defined as the corrosion resistance time; the oxidation reaction rate is calculated by the slope of the current curve to reflect the rate change of the electrochemical reaction.

2. The intelligent bonding silver wire material determination method based on big data according to claim 1, characterized in that: The electrochemical testing process includes: By setting the automatic adjustment function of the electrochemical testing equipment, the frequency of the applied current is dynamically adjusted according to the real-time changes in the surface state of the smart bonding silver wire.

3. The intelligent bonding silver wire material determination method based on big data according to claim 2, characterized in that: The frequency of the current is adjusted by taking into account the rate of change of the surface potential, and the frequency of the applied current is set to , which is proportional to the rate of change of surface potential, and the calculation formula is: ； in, is the rate of change of surface potential, is the proportional coefficient.

4. The intelligent bonding silver wire material determination method based on big data according to claim 3, characterized in that: The calculation process of the corrosion resistance time is as follows: ； in, is the baseline value of the current, indicating the reference value of the stable period, For corrosion resistance time, is the smoothed current signal, is the threshold value of current change.

5. The intelligent bonding silver wire material determination method based on big data according to claim 4, characterized in that: The construction of the performance degradation model includes: A machine learning method is used to input quantitative indicators and standard test data into the model to predict the performance degradation trend of smart silver bonding wire. The specific formula is as follows: ； in, For time The performance degradation prediction value at the moment, is the total number of decision trees in the random forest, For the The weight of a decision tree, For the A decision tree based on The output value of is a feature in the quantitative indicator set.

6. The intelligent bonding silver wire material determination method based on big data according to claim 5, characterized in that: The method of predicting the performance degradation, damage and aging of the smart silver bonding wire during long-term use includes the following steps: Combined with the performance degradation model, environmental factors are introduced to correct the performance prediction results; Based on the damage accumulation theory, the cumulative damage value of the smart bonding silver wire is calculated.

7. A big data-based intelligent silver bonding wire material judgment system, based on the big data-based intelligent silver bonding wire material judgment method according to any one of claims 1 to 6, characterized in that: Also includes, The electrochemical reaction monitoring module is used to place the smart silver bonding wire in an electrochemical testing device, apply current to the surface, monitor the current characteristic curve in real time, and perform multi-channel data synchronous recording; The data analysis and processing module is used to analyze and process the current characteristic data, extract the key characteristic parameters in the electrochemical reaction, and form a set of quantitative indicators for the degree of surface damage of the smart bonding silver wire; A performance model building module, configured to build a performance degradation model based on the quantitative indicators and in combination with standard test data of the same batch of smart silver bonding wires; A performance evaluation module, configured to predict the performance degradation, damage, and aging of the smart silver bonding wire during long-term use based on the performance degradation model; The material evaluation report generation module is used to generate a material quality evaluation report for the smart bonding silver wire and provide the material judgment result of the smart bonding silver wire.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent bonding silver wire material judgment method based on big data described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent bonding silver wire material judgment method based on big data described in any one of claims 1 to 6 are implemented.

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