Annealing monitoring method of bonding wire annealing equipment, electronic equipment and medium

Abstract

The invention provides an annealing monitoring method of bonding wire annealing equipment, electronic equipment and a medium, and relates to the technical field of annealing monitoring of the bonding wire annealing equipment, and the method comprises the steps: obtaining a temperature sequence obtained by collecting the temperature of a bonding wire in a preset sliding time window through each temperature sensor; obtaining an average temperature and a temperature fluctuation rate corresponding to each temperature sequence; obtaining an abnormal temperature sequence list B; if m is equal to 0, determining that annealing of the bonding wire in the preset sliding time window meets a preset annealing condition; otherwise, determining that the annealing of the bonding wire in the preset sliding time window does not meet the preset annealing condition; according to B, determining an abnormal area and an abnormal reason corresponding to the bonding wire in the annealing process of the bonding wire; according to the method, the consistency of the annealing quality of the bonding wire and the production efficiency can be remarkably improved, and the material performance defect and the yield loss caused by abnormal annealing are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of annealing monitoring of bonding wire annealing equipment, and particularly relates to an annealing monitoring method, an electronic device and a medium for bonding wire annealing equipment. Background Art

[0002] As a core material for realizing electrical connection between chips and external circuits in semiconductor packaging, the mechanical properties and microstructure of bonding wires directly affect the reliability and service life of packaged devices. Annealing treatment is a key process in the production of bonding wires, which can effectively eliminate the internal stress generated during the processing of bonding wires, optimize the grain arrangement, and improve the comprehensive performance of the material. At present, the monitoring of the bonding wire annealing process in the industry mainly relies on traditional temperature detection technologies, that is, only single or a small number of temperature sensors are set in the annealing equipment, and the annealing state is judged by collecting the single-point temperature value in real time and comparing it with a preset threshold; the comprehensiveness of temperature monitoring in this way is insufficient, and single-point data is prone to cover up abnormalities, resulting in missed detection of key temperature fluctuations; in addition, only by judging the instantaneous temperature threshold, the annealing state may be misjudged due to the failure to identify the fluctuation characteristics; when the traditional monitoring method detects temperature abnormalities, it can only send an alarm signal, unable to associate the abnormal data with the specific position in the annealing furnace, and it is even more difficult to analyze the cause of the abnormality, resulting in the process adjustment relying on manual experience, long repair cycle, and seriously affecting production efficiency and product yield. Summary of the Invention

[0003] For the above technical problems, the technical solution adopted by the present invention is as follows: According to the first aspect of the present application, an annealing monitoring method for bonding wire annealing equipment is provided. The bonding wire annealing equipment includes a gas annealing device and a conveying device. The conveying device is used to convey the bonding wire through the gas annealing device for gas annealing. A plurality of temperature sensors are arranged in sequence in the gas annealing device, and the temperature sensors are used to detect the temperature of the bonding wire; The method includes the following steps: S100, obtaining a temperature sequence of the bonding wire collected by each temperature sensor within a preset sliding time window.

[0004] S200, obtaining the average temperature and temperature volatility corresponding to each temperature sequence.

[0005] S300, if |η i，1 -T i |>ΔT or η i，2 >η’, then determining the i-th temperature sequence as an abnormal temperature sequence, obtaining an abnormal temperature sequence list B=(B1, B2,..., B j ,..., B m ), j = 1, 2,..., m; B jThe j-th abnormal temperature sequence is determined, m is the number of determined abnormal temperature sequences; η i，1 and η i，2 are respectively the average temperature and the temperature volatility corresponding to the i-th temperature sequence; T i is the standard temperature corresponding to the i-th temperature sensor, ΔT is the preset temperature difference threshold, and η’ is the preset temperature volatility threshold; i = 1, 2, …, n; n is the number of temperature sensors.

[0006] S400, if m = 0, it is determined that the annealing of the bonding wire within the preset sliding time window meets the preset annealing conditions; otherwise, it is determined that the annealing of the bonding wire within the preset sliding time window does not meet the preset annealing conditions, and S500 is entered.

[0007] S500, according to the relationship between each temperature in B and the standard temperature of the corresponding temperature sensor, determine the abnormal area and the cause of the abnormality corresponding to the bonding wire during the annealing process of the bonding wire.

[0008] According to another aspect of the present application, there is also provided a non-transitory computer-readable storage medium, in which at least one instruction or at least one program segment is stored, and at least one instruction or at least one program segment is loaded and executed by a processor to implement the annealing monitoring method of the above-mentioned bonding wire annealing device.

[0009] According to another aspect of the present application, there is also provided an electronic device, including a processor and the above-mentioned non-transitory computer-readable storage medium.

[0010] The present invention has at least the following beneficial effects: The annealing monitoring method of the bonding wire annealing device of the present invention, by arranging a number of temperature sensors arranged in sequence in the gas annealing device, obtaining the temperature sequences of each sensor within the preset sliding time window, and identifying abnormal temperature sequences based on the dual criteria of average temperature and temperature volatility, effectively solves the problems of insufficient comprehensiveness of temperature monitoring, single abnormal criterion, and lack of abnormal location and tracing ability in the prior art. Specifically, the combination of multi-sensor layout and sliding time window can capture the dynamic temperature changes in different regions of the bonding wire during continuous annealing in real time, avoiding missed detection of local abnormalities by single-point detection; through the dual threshold judgment of the difference between the average temperature and the standard temperature and the temperature volatility, it can not only monitor whether the temperature mean meets the process requirements, but also identify violent temperature fluctuations within a short time, improving the accuracy and comprehensiveness of abnormal identification; determining the abnormal area and cause of the bonding wire based on the list of abnormal temperature sequences, and associating possible abnormal factors, providing clear guidance for process adjustment and equipment maintenance, reducing the dependence on manual experience, shortening the fault handling cycle, and thus significantly improving the consistency of the bonding wire annealing quality and production efficiency, and reducing material performance defects and yield losses caused by annealing abnormalities. Description of the Drawings

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0012] Figure 1 It is a flowchart of the annealing monitoring method for the bonding wire annealing equipment provided by the embodiments of the present invention; Figure 2 It is a schematic structural diagram of the gas annealing device provided by the embodiments of the present invention; Symbol description: 100, gas annealing device, 110, temperature sensor, 200, bonding wire. Specific embodiments

[0013] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0014] It should be noted that based on this disclosure, those skilled in the art should understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement the device and / or practice the method. In addition, this device and / or this method can be implemented using other structures and / or functions in addition to one or more of the aspects described herein.

[0015] The following will refer to Figure 1 The flowchart of the annealing monitoring method of the bonding wire annealing equipment shown, and introduce an annealing monitoring method of a bonding wire annealing equipment.

[0016] In this embodiment, as Figure 2 shown, the bonding wire annealing equipment includes a gas annealing device 100 and a conveying device. The conveying device is used to convey the bonding wire 200 through the gas annealing device for gas annealing. A plurality of temperature sensors 110 are arranged in sequence in the gas annealing device 100, and the temperature sensors 110 are used to detect the temperature of the bonding wire 200.

[0017] In this embodiment, after the bonding wire 200 is heated in the heating furnace, the conveying device moves the heated bonding wire at a constant speed from the gas annealing device 100 to slowly cool it through the inert gas in the gas annealing device 100; a number of temperature sensors 110 are arranged in sequence on the inner wall of the gas annealing device 100, and the sampling frequency of the temperature sensors 110 is set according to the moving speed of the bonding wire 200, so that when the target position on the bonding wire 200 passes each temperature sensor 110, the temperature sensor 110 collects the temperature of the target position; the temperature sensor can be an infrared temperature sensor or a thermocouple temperature sensor, which can be selected according to actual needs.

[0018] It should be noted that those skilled in the art can heat and convey the bonding wire using existing heating furnaces and conveying devices according to actual needs, which will not be elaborated here.

[0019] The annealing monitoring method of this bonding wire annealing equipment may include the following steps: S100, obtaining the temperature sequence obtained by each temperature sensor collecting the temperature of the bonding wire within a preset sliding time window.

[0020] In this embodiment, the end time point of the preset sliding time window can be the dynamic current time point, and the length of the sliding time window can be set according to actual needs. For example, the length of the sliding time window is 20 seconds; the step size can also be set according to actual needs. For example, the step size of the sliding time window is 10 seconds.

[0021] A number of temperature sensors are arranged along the conveying direction of the bonding wire in the gas annealing device, and each sensor collects the temperature of the bonding wire at a fixed sampling frequency, such as 10Hz. The temperature data of each temperature sensor is dynamically intercepted through the preset sliding time window to form a temperature sequence. For example, each temperature sequence contains 100 temperature points.

[0022] S200, obtaining the average temperature and temperature volatility corresponding to each temperature sequence.

[0023] In this embodiment, two key indicators are calculated for each temperature sequence, namely the average temperature and the temperature volatility. The temperature volatility is obtained by calculating the variance of the corresponding temperature sequence; the average temperature can reflect the temperature steady state level of the corresponding temperature sensor area, and the temperature volatility reflects the fluctuation range of the temperature in the time dimension.

[0024] S300, if |η i，1 -T i |>ΔT or η i，2 >η’, then the i-th temperature sequence is determined as an abnormal temperature sequence, and the abnormal temperature sequence list B = (B1, B2,..., B j ,..., Bm ), j = 1, 2, …, m; B j is the j-th determined abnormal temperature sequence, and m is the number of determined abnormal temperature sequences; η i，1 and η i，2 are the average temperature and temperature volatility corresponding to the i-th temperature sequence in turn; T i is the standard temperature corresponding to the i-th temperature sensor, ΔT is the preset temperature difference threshold, and η' is the preset temperature volatility threshold; i = 1, 2, …, n; n is the number of temperature sensors.

[0025] In this embodiment, under normal steady-state conditions, the temperature at the same position on the bonding wire does not change significantly when passing through each temperature sensor in the gas annealing device, that is, the temperature values in the temperature sequence corresponding to each temperature sensor should be in a relatively stable state, and the temperature values are within a stable temperature range; for example: under normal conditions, the temperature values in the temperature sequence collected by the first temperature sensor in the gas annealing device are all 200 °C, with an upper and lower deviation of no more than 5 °C.

[0026] Based on this, if |η i，1 - T i | > ΔT, it means that most of the temperature values in the temperature sequence collected by the i-th temperature sensor within the preset sliding time window have changed significantly, belonging to an abnormal state, and the i-th temperature sequence is determined as an abnormal temperature sequence; if η i，2 > η', it means that the temperature values in the temperature sequence collected by the i-th temperature sensor within the preset sliding time window fluctuate greatly, which also belongs to an abnormal state, and the i-th temperature sequence is determined as an abnormal temperature sequence, thereby obtaining B.

[0027] S400, if m = 0, it is determined that the annealing of the bonding wire within the preset sliding time window meets the preset annealing conditions; otherwise, it is determined that the annealing of the bonding wire within the preset sliding time window does not meet the preset annealing conditions, and S500 is entered.

[0028] In this embodiment, if B is empty, it means that the temperature means and volatilities of all sensors meet the standards and the annealing is qualified; if B is non-empty, the annealing is unqualified, and the abnormal area and cause of the bonding wire need to be further analyzed.

[0029] S500, according to B, determine the abnormal area and cause of the bonding wire corresponding to the bonding wire during the annealing process.

[0030] Based on the abnormal list B, combined with the temperature data and the bonding wire conveying parameters, locate the specific abnormal area of the bonding wire during the annealing process, such as a certain section of wire, and analyze the cause of the abnormality.

[0031] In this embodiment, the entire process of gas annealing is covered by multiple sensors to avoid missing local anomalies in single-point monitoring. At the same time, the average temperature and volatility are monitored. For example, if the average value of a certain sensor meets the standard but the volatility is extremely high, such as a fluctuation of ±15°C, it can still be identified as an anomaly, avoiding potential hazards such as gas flow fluctuations being masked by instantaneous compliance. From data acquisition to anomaly alarm and then to positioning analysis, a complete monitoring link is formed to provide a direction for subsequent repair.

[0032] Further, step S500 may include the following steps: S510, obtain B j = (B j，1 , B j，2 , …, B j，p , …, B j，q ), p = 1, 2, …, q; where B j，p is the p-th temperature in the j-th abnormal temperature sequence, q is the number of temperatures in the abnormal temperature sequence; the acquisition time of B j，r is earlier than that of B j，r+1 ; r = 1, 2, …, q - 1.

[0033] In this embodiment, the temperatures in B j are arranged in the order of acquisition time. Among them, B j，1 is the earliest acquired temperature, and B j，q is the most recently acquired temperature.

[0034] S520, traverse B j , if |B j，p -QT j | > ΔT, then determine the position corresponding to B j，p on the bonding wire as the abnormal position to obtain the abnormal position list C j = (C j , C j，1 , …, C j，2 , …, C j，u , …, C j，f（j） ), u = 1, 2, …, f(j); where C j，u is the u-th abnormal position corresponding to B j , and f(j) is the number of abnormal positions corresponding to B j .

[0035] In this embodiment, under normal circumstances, when the same position on the bonding wire passes through different temperature sensor positions, there is a standard temperature corresponding to it; traverse B jFor each temperature point, if its deviation from the corresponding standard temperature exceeds the preset temperature difference threshold ΔT, e.g., ΔT = 5°C; then, based on the acquisition time of this temperature point and the bonding wire conveying speed, calculate its physical position on the bonding wire. For example, if the acquisition time is the 3rd second, the corresponding position is at 3 cm, and mark this position as an abnormal position to form an abnormal position list C j .

[0036] S530, obtain the temperature matrix A = (A1, A2, …, A i , …, A n ); where A i is the temperature sequence corresponding to the i-th temperature sensor; A i = (A i，1 , A i，2 , …, A i，p , …, A i，q ); A i，p is the p-th temperature in the temperature sequence corresponding to the i-th temperature sensor; each temperature sensor acquires the temperature at each preset position on the bonding wire.

[0037] Integrate the temperature sequences of all temperature sensors to obtain the global temperature matrix A; the temperature matrix A records the temperature data of each preset position on the bonding wire, such as the temperature at each 0.1 cm. For example, at the position of 2 cm, the temperature at sensor 1 is 300°C, and at sensor 2 is 280°C.

[0038] S540, based on C j and A, determine the corresponding abnormal region and the cause of the abnormality during the annealing process of the bonding wire.

[0039] Based on the abnormal position list C j and the temperature matrix A, further analyze the abnormal region, such as the 5 cm - 8 cm section of the bonding wire, and the cause of the abnormality, such as abnormal initial temperature, abnormal temperature sensor, or abnormal material.

[0040] In this embodiment, through time - position mapping, map the abstract temperature abnormality to the specific position of the bonding wire to process the specific position; the abnormal position list C j provides a specific object for subsequent analysis and avoids the analysis difficulty caused by data dispersion.

[0041] Further, step S540 may include the following steps: S541, perform duplicate removal processing on all abnormal positions to obtain the abnormal position list D = (D1, D2, …, D x , …, D y ), x = 1, 2, …, y; where D xx is the determined x-th abnormal position, and y is the number of determined abnormal positions.

[0042] Merge the repeated abnormal positions in different abnormal sequences. For example, if sensor 1 marks 5.1 cm and sensor 2 also marks 5.1 cm, then merge the same positions to obtain a unique list D of abnormal positions.

[0043] According to the sampling frequency of the temperature sensor, the conveying speed of the bonding wire, and the time points when the abnormal positions reach each sensor, extract the temperature data of the abnormal positions at each sensor from the global temperature matrix A to form the full-process temperature sequence of this position.

[0044] S542, determine the areas corresponding to all abnormal positions in D on the bonding wire as abnormal areas.

[0045] Determine the coverage range of all abnormal positions in D on the bonding wire as the abnormal area, such as the section from 5.1 cm to 6.3 cm.

[0046] The above steps have at least the following beneficial effects: Reduce redundant interference: The de-duplication process avoids the same position being marked multiple times, focuses on the core abnormal points, and improves the analysis efficiency; Regional-level positioning: Integrate discrete abnormal positions into continuous areas, which is convenient for targeted process adjustment or equipment maintenance.

[0047] Furthermore, after step S542, the method may further include the following steps: S542, according to the sampling frequency of the temperature sensor, the conveying speed of the bonding wire, and the time points when each abnormal position reaches each temperature sensor, determine the temperature sequence corresponding to each abnormal position from A to obtain the abnormal temperature sequence list E = (E1, E2,..., E x ,..., E y ); where E x is the abnormal temperature sequence corresponding to the x-th abnormal position.

[0048] S544, according to each abnormal temperature sequence in E, generate the abnormal temperature curve corresponding to each abnormal temperature sequence to obtain the abnormal temperature curve list F = (F1, F2,..., F x ,..., F y ); where F x is the abnormal temperature curve corresponding to E x .

[0049] In this embodiment, the temperature sequence of each abnormal position is converted into a temperature-time curve, with the horizontal axis being time and the vertical axis being temperature, to visually display the temperature change trend of this position during the annealing process.

[0050] S545. Obtain the similarity between each abnormal temperature curve in F and the standard temperature curve corresponding to the bonding wire to obtain a similarity list γ = (γ1, γ2,..., γ x , …, γ y ); where γ x is the similarity between F x and the standard temperature curve corresponding to the bonding wire.

[0051] By comparing the abnormal curve with the standard temperature curve, that is, the similarity of the temperature-time curve during the normal annealing of the bonding wire, the matching degree between the abnormal temperature curve and the standard temperature curve corresponding to the bonding wire can be calculated through the dynamic time warping (DTW) algorithm, and the matching degree is determined as the similarity.

[0052] S555. If γ x < γ' and |TF x，max - TR| < ΔT, then determine D x as the abnormal position of material abnormality; otherwise, determine D x as the abnormal position of temperature abnormality; where TF x，max is the highest temperature corresponding to F x , and TR is the standard temperature after the bonding wire is heated in the heating furnace.

[0053] In this embodiment, if γ x < γ', for example, γ' = 0.7, it means that the difference between F x and the standard temperature curve corresponding to the bonding wire is relatively large, and |TF x，max - TR| < ΔT means that the highest temperature is close to the standard value, that is, the temperature at which the bonding wire is heated in the heating furnace meets the preset conditions. Therefore, D x can be determined as the abnormal position of material abnormality; otherwise, determine D x as the abnormal position of temperature abnormality, for example: temperature sensor failure or the heating temperature of the heating furnace does not meet the preset conditions.

[0054] S556. If NUM1 / y > μ, then determine that the reason for the abnormal annealing of the bonding wire is material abnormality; otherwise, determine that the reason for the abnormal annealing of the bonding wire is initial temperature abnormality or temperature sensor abnormality; NUM1 is the number of abnormal positions of material abnormality, μ is a preset weight, and μ > 0.6.

[0055] Count the number NUM1 of abnormal positions of material abnormality. If NUM1 / y > μ, for example, μ = 0.8, that is, more than 80% of the abnormal positions are material problems, then the main reason is material abnormality; otherwise, it is initial temperature abnormality or temperature sensor abnormality.

[0056] The above steps have at least the following beneficial effects: Identify the root cause of the abnormality: Through curve similarity and maximum temperature analysis, the cause of the abnormality can be refined from temperature failure to material problems or equipment problems. For example, if the curve similarity of multiple positions of a batch of bonding wires is low but the maximum temperature is normal, it can be inferred that the material consistency is poor, such as high impurity content leading to abnormal thermal conductivity.

[0057] Basis for quantitative decision-making: The threshold of μ=0.8 is set based on industrial experience to ensure that most anomalies are judged as material problems only when they are caused by materials, avoiding misjudgment due to anomalies in individual locations.

[0058] Guide precise repair: If it is determined that the material is abnormal, the raw material supplier can be traced; if it is an equipment abnormality, the sensor or heating module can be repaired specifically to shorten the troubleshooting cycle.

[0059] Furthermore, η i，2 A i The corresponding variance.

[0060] In this embodiment, the variance directly reflects the degree to which the temperature value deviates from the mean, and is more sensitive than the standard deviation in capturing fluctuation energy. The mathematical definition of the variance is simple and suitable for real-time calculation. For example, each time a temperature point is added to the sliding window, the variance can be quickly updated to meet the real-time requirements of industrial scenarios.

[0061] Furthermore, the distances between any two adjacent temperature sensors are equal.

[0062] The equidistant layout ensures that the temperature monitoring density of each section in the gas annealing device is consistent, avoiding local monitoring blind spots caused by uneven sensor spacing; the equidistant arrangement makes the bonding wire position correspond linearly with the sensor number, eliminating the need for complex coordinate conversion and simplifying the calculation of abnormal positions.

[0063] Furthermore, the temperature sensor is an infrared temperature sensor.

[0064] The use of infrared temperature sensors eliminates the need to contact the wire, thus avoiding wire deformation or conveying speed fluctuations caused by mechanical contact. Infrared temperature sensors can work stably without contacting high-temperature areas, and their service life is longer than that of contact sensors. Infrared temperature measurement has a short response time and is suitable for dynamic temperature monitoring in scenarios where bonding wires are continuously conveyed, such as real-time capture of temperature fluctuations within 0.1 seconds.

[0065] In this embodiment, by setting a number of temperature sensors arranged in sequence in the gas annealing device, obtaining the temperature sequence of each sensor within the preset sliding time window, and identifying the abnormal temperature sequence based on the dual criteria of average temperature and temperature fluctuation rate, the problem of insufficient comprehensiveness of temperature monitoring, single abnormality judgment criteria and lack of abnormal location and traceability capability in the prior art is effectively solved. Specifically, the combination of multi-sensor layout and sliding time window can capture the dynamic temperature changes of different areas of the bonding wire during the continuous annealing process in real time, avoiding the single-point detection of local abnormalities; through the dual threshold judgment of the difference between the average temperature and the standard temperature and the temperature fluctuation rate, it can monitor whether the temperature mean meets the process requirements, and identify the drastic temperature fluctuations in a short time, thereby improving the accuracy and comprehensiveness of abnormal identification; based on the abnormal temperature sequence list, the abnormal area and cause of the bonding wire are determined, and possible abnormal factors are associated, providing clear guidance for process adjustment and equipment maintenance, reducing reliance on manual experience, shortening the fault handling cycle, thereby significantly improving the consistency of bonding wire annealing quality and production efficiency, and reducing material performance defects and yield losses caused by annealing abnormalities.

[0066] In an exemplary embodiment, after determining that the cause of the abnormal annealing of the bonding wire is the abnormal initial temperature or the abnormal temperature sensor in step S556 in the above embodiment, in order to improve the maintenance efficiency of the equipment, it is necessary to further determine the specific cause of the abnormality so as to repair the heating furnace or the temperature sensor in a targeted manner; based on this, the following method is provided: Q100, if the cause of the abnormal annealing of the bonding wire is the abnormal initial temperature or the abnormal temperature sensor, then the air pressure at the corresponding position of each temperature sensor each time the temperature is detected within the preset sliding time window is obtained to obtain the air pressure matrix HA; HA includes several rows and several columns, each row includes the air pressure detected at different detection times at the position corresponding to the same temperature sensor, and each column includes the air pressure at the position corresponding to different temperature sensors detected at the same detection time.

[0067] When it is preliminarily determined that the cause of annealing abnormality is abnormal initial temperature, such as abnormal heating of the bonding wire in the heating furnace or abnormal temperature sensor, the pressure data of each temperature sensor corresponding position in the preset sliding time window is collected, and the pressure matrix HA is constructed according to the dimension of sensor position × detection time. Each row corresponds to the sensor position, reflecting the spatial distribution, and each column corresponds to the detection time, reflecting the time series.

[0068] The pressure parameter is introduced as the environmental feature of the sensor location. The temporal correlation of the pressure data is retained through a sliding time window, which avoids the randomness of single-point data and provides dynamic pressure change characteristics for subsequent model prediction.

[0069] Q200, obtain several gas parameters, several pipeline parameters, and several environmental parameters corresponding to the gas annealing device to obtain the key parameter feature vector QA corresponding to the gas annealing device.

[0070] In this embodiment, an inert gas is introduced into the gas annealing device. There are several types of inert gases, and several gas parameters corresponding to the inert gas can be obtained, such as: flow rate, composition, temperature, etc.; pipeline parameters include: inner diameter, roughness, blockage rate, material, etc.; environmental parameters include: room temperature, humidity, air pressure, etc.; after collecting the above parameters, a corresponding key parameter feature vector QA can be generated; when generating QA, parameters with specific numerical values can be directly used as the corresponding elements in QA, and when they are type data, the corresponding elements in QA can be generated through secondary coding.

[0071] QA includes key parameters such as gas heating efficiency, pipeline transmission characteristics, and environmental heat dissipation conditions, solving the one-sided problem of traditional methods relying only on temperature data.

[0072] Q300, input QA and HA into a preset bonding wire temperature prediction model to obtain the predicted temperature matrix HB corresponding to HA; where HB has the same dimension as HA, and each element in HB represents the predicted temperature of the bonding wire at the corresponding temperature sensor position at the corresponding detection time.

[0073] In this embodiment, an integrated learning model, such as: XGBoost; or a lightweight neural network, such as: one-dimensional CNN-LSTM can be used as the preset bonding wire temperature prediction model to balance accuracy and real-time performance and meet the on-line monitoring requirements of the bonding wire production line.

[0074] It can be understood that during the bonding wire annealing process, when several gas parameters, several pipeline parameters, and several environmental parameters remain constant, the temperature change at the same temperature sensor position will cause the gas pressure to change. Therefore, obtaining the feature vectors corresponding to the above parameters and combining HA can inversely predict the predicted temperature at each temperature sensor.

[0075] The preset bonding wire temperature prediction model is a trained model. A large amount of known historical data can be used to train the initial model. The known historical data includes the corresponding several gas parameters, several pipeline parameters, and several environmental parameters, the temperature at the same position of the bonding wire at different sensor positions, and the gas pressure; it should be noted that those skilled in the art can use existing model training methods to train the initial model according to actual needs, which will not be elaborated here.

[0076] Input QA and HA into the preset temperature prediction model, and output a predicted temperature matrix HB with the same dimension as HA. Each element represents the theoretical temperature at the corresponding position and time.

[0077] By learning the mapping relationship between pressure, gas, pipeline, environmental parameters and temperature through a model, a mathematical representation of the equipment state - temperature field is constructed, breaking through the limitation of the separate analysis of temperature data and equipment parameters in the existing technology. HB, as the ideal temperature after excluding sensor anomalies or initial temperature anomalies, provides a comparison benchmark for the actual temperature deviation analysis, significantly enhancing the scientific nature of anomaly identification.

[0078] For Q400, perform an element - by - element subtraction of HB and A to obtain the temperature difference matrix ΔH; where A is the actual temperature matrix obtained by each temperature sensor collecting the actual temperature of the bonding wire within a preset sliding time window; A includes several rows and several columns, each row includes the temperatures detected by the same temperature sensor at different detection times, and each column includes the temperatures detected by different temperature sensors at the same detection time.

[0079] Perform an element - by - element subtraction of the actual temperature matrix A, that is, the sensor - measured data, and the prediction matrix HB, to obtain ΔH = A - HB. Each element represents the deviation value between the measured temperature and the theoretical temperature. ΔH directly reflects the degree of temperature deviation at each sensor position and each moment. A positive value indicates that the measured temperature is higher than the theoretical value, which may be due to sensor failure or local overheating. A negative value indicates that it is lower than the theoretical value, which may be due to insufficient gas flow or sensor failure. By retaining the spatio - temporal dimension deviation information in matrix form, it avoids the rough diagnosis of only analyzing the average temperature or single - point extreme values in traditional methods.

[0080] For Q500, analyze ΔH to determine the specific reasons for the abnormal annealing of the bonding wire.

[0081] Based on the spatio - temporal distribution characteristics of ΔH, distinguish initial temperature anomalies, such as equipment - end problems, and sensor anomalies, such as detection - end problems, to solve the problem of confusing misjudgment of the two types of anomalies in the existing technology.

[0082] Furthermore, step Q500 may include the following steps: For Q510, obtain ΔH=(ΔH1, ΔH2, …, ΔH i , …, ΔH n ), where i = 1, 2, …, n; where ΔH i is the temperature difference sequence corresponding to the i - th temperature sensor, and n is the number of temperature sensors; ΔH i includes the temperature difference between the predicted temperature and the actual temperature of the i - th temperature sensor at different detection times.

[0083] For Q520, obtain the average temperature difference corresponding to each temperature difference sequence in ΔH to obtain the average temperature difference list YA=(YA1, YA2, …, YA i , …, YA n ); where YA i is the average temperature difference corresponding to ΔHi The corresponding average temperature difference.

[0084] Q530, traverse YA. If YA i > ψ, then determine the i-th temperature sensor as an abnormal temperature sensor; where ψ is a preset average temperature difference threshold.

[0085] In this embodiment, ψ can be obtained by analyzing a large amount of historical data. For example, the value range of ψ is from -5°C to 5°C.

[0086] In the above steps, random noise is filtered by the mean value, continuous deviation is focused on, manual point-by-point inspection is avoided, and the diagnosis efficiency is improved; if there are multiple sensor abnormalities, such as adjacent sensors both exceeding the threshold, common problems of the detection system are prompted, such as poor contact of the sensor caused by vibration at the installation position.

[0087] Q540, according to the number and position of the abnormal temperature sensors, determine the specific reason for the abnormal annealing of the bonding wire.

[0088] Further, step Q540 may include the following steps: Q541, if it is determined that the number of abnormal temperature sensors is 0, then determine that the reason for the abnormal annealing of the bonding wire is the abnormal initial temperature.

[0089] If the number of abnormal sensors is 0, it means that the predicted temperature is consistent with the temperature actually measured by the sensor, and it can be determined as an abnormal initial temperature. For example, a failure of the heating module of the heating furnace causes an abnormal initial temperature of the bonding wire after it comes out of the heating furnace.

[0090] Q542, if it is determined that the number of abnormal temperature sensors is greater than 0, then delete the detected temperatures corresponding to the abnormal temperature sensors in A to obtain the corrected actual temperature matrix WA corresponding to A.

[0091] Delete the abnormal sensor data, generate the correction matrix WA, and further verify whether it is a simple sensor problem.

[0092] Q543, according to WA, determine whether the reason for the abnormal annealing of the bonding wire is the abnormal temperature sensor.

[0093] By the logic of first excluding sensor abnormalities and then analyzing the remaining data, misjudging equipment problems as sensor failures is avoided. For example, when the global temperature is abnormal due to gas flow fluctuations, the sensors are fault-free but the average temperatures are normal; the risk of misjudgment is reduced. For example, when a single sensor is abnormal, if the remaining temperature field conforms to the theoretical distribution after deleting its data, it can be diagnosed as an individual sensor failure rather than an equipment problem.

[0094] Further, step Q543 may include the following steps: Q10. Obtain the average temperature corresponding to each row in WA.

[0095] Q20. Use a preset fitting algorithm to fit the average temperature corresponding to each row in WA and the position of the temperature sensor corresponding to each row, so as to obtain the function relationship RG between the temperature and the sensor position corresponding to WA; the independent variable of RG is the sensor position, and the dependent variable is the temperature.

[0096] In this embodiment, the preset fitting algorithm can be a polynomial regression fitting algorithm and a non - linear least squares fitting algorithm; it should be noted that those skilled in the art can use the polynomial regression fitting algorithm and the non - linear least squares fitting algorithm according to actual needs to fit the average temperature corresponding to each row in WA and the position of the temperature sensor corresponding to each row, and obtain the function relationship RG between the temperature and the sensor position corresponding to WA, which will not be elaborated here.

[0097] Q30. According to RG and the position of the temperature sensor corresponding to the deleted detected temperature, determine the corrected temperature corresponding to each deleted detected temperature, and obtain the corrected temperature matrix WB corresponding to WA.

[0098] Through this step, the reasonable values corresponding to the abnormal temperature sensors can be supplemented.

[0099] Q40. According to WB, determine whether the reason for the abnormal annealing of the bonding wire is the abnormal temperature sensor.

[0100] Extract the spatial distribution characteristics of the normal temperature field through the fitting algorithm, such as uniform temperature rise or gradient temperature rise, to avoid the distortion of the overall temperature field analysis caused by local sensor abnormalities. Reasonably interpolate the abnormal sensor data to provide a complete data basis for subsequent comparison with the standard temperature matrix.

[0101] Further, step Q40 may include the following steps: Q41. Obtain the standard temperature matrix HK corresponding to the current type of bonding wire; HK includes several rows and several columns. Each row includes the standard temperatures corresponding to the same temperature sensor at different detection times, and each column includes the standard temperatures corresponding to different temperature sensors at each detection time.

[0102] Obtain the standard temperature matrix HK of the bonding wire, that is, the ideal temperature spatio - temporal distribution based on the process specifications.

[0103] Q42. Obtain the similarity ω between WB and HK.

[0104] Further, ω can be obtained through the following steps: Q421. Use the same feature extraction method to perform feature extraction on WB and HK respectively, so as to obtain the feature vector XL1 corresponding to WB and the feature vector XL2 corresponding to HK.

[0105] Q422, determine the similarity between XL1 and XL2 as ω.

[0106] Extract features from WB and HK, such as principal component analysis and convolutional features, and calculate the cosine similarity ω.

[0107] Q43, if ω > ω', then determine that the reason for the abnormal bonding wire annealing is the abnormal temperature sensor; otherwise, determine that the reasons for the abnormal bonding wire annealing are the abnormal initial temperature and the abnormal temperature sensor; where ω' is the preset similarity threshold of the temperature matrix.

[0108] If ω > ω', such as ω' = 0.9, it is determined as a simple sensor abnormality, that is, the corrected temperature field meets the standard; otherwise, it is determined as a mixed abnormality between the device end and the sensor end, such as abnormal initial temperature and partial sensor failures.

[0109] Through similarity calculation, directly compare the corrected temperature field with the ideal state, avoid subjective judgment, and improve the credibility of the diagnosis results; identify mixed abnormalities, and solve the problem in the prior art that it is impossible to distinguish between single abnormalities and compound abnormalities, such as local temperature abnormalities caused by abnormal heating modules in the heating furnace, and at the same time, individual sensor aging amplifies the deviation.

[0110] Furthermore, the distance between any two adjacent temperature sensors is equal.

[0111] The adjacent sensors are equally spaced, ensuring the uniformization of the spatial coordinates of the temperature field, facilitating the fitting algorithm modeling and the positioning of the abnormal position. For example, the equal spacing can simplify the gradient calculation.

[0112] In this embodiment, by obtaining the pressure matrix at the corresponding positions of each temperature sensor within the preset sliding time window, and combining the key parameter feature vectors composed of gas parameters, pipeline parameters, and environmental parameters, multi-dimensional information such as pressure changes, equipment operating states, and environmental factors during the annealing process is incorporated into the analysis system, changing the limitation of the traditional method that only relies on single-point temperature data and comprehensively reflecting the coupling effect of the complex physical field in the annealing device; by establishing an association mapping between the detection data and the equipment state parameters through the preset bonding wire temperature prediction model, generating a predicted temperature matrix and comparing it with the actual temperature matrix at the same position, based on the analysis of the difference matrix, it can accurately identify the abnormal initial temperature control and the abnormal temperature sensor, avoid misjudgment and accurately locate the root cause of the abnormality; through the systematic analysis of, quantifying the temperature deviation characteristics at different positions and different times, and quickly locking the abnormal link in combination with the spatio-temporal distribution laws of pressure and key parameters, overcoming the defect of insufficient multi-dimensional parameter fusion, realizing the accurate diagnosis of temperature abnormalities, reducing the increase in maintenance costs and production stagnation caused by blind troubleshooting, and ensuring the quality stability of the bonding wire annealing.

[0113] In addition, although the various steps of the methods in the present disclosure are described in a specific order in the drawings, this is not a requirement or implication that the steps must be performed in that specific order, or that all of the steps shown must be performed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution, etc.

[0114] Embodiments of the present invention also provide a non-transitory computer-readable storage medium. The storage medium can be disposed in an electronic device to store at least one instruction or at least one segment of a program related to a method in a method embodiment. The at least one instruction or the at least one segment of the program is loaded and executed by the processor to implement the method provided in the above embodiments.

[0115] The program product can adopt any combination of one or more readable media. The readable media can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0116] The computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries the readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable signal medium can also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0117] The program code contained on the readable medium can be transmitted by any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0118] The program code for performing the operations of the present application can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0119] Embodiments of the present invention also provide an electronic device, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0120] The electronic device is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0121] The electronic device is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one of the aforementioned processors, at least one of the aforementioned memories, and a bus connecting different system components (including the memory and the processor).

[0122] Wherein, the memory stores program code, and the program code can be executed by the processor, such that the processor executes the steps in various embodiments described in this specification.

[0123] The memory may include a readable medium in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0124] The memory may further include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules, and program data, and the implementation of a network environment may be included in each or some combination of these examples.

[0125] The bus may represent one or more of several types of bus structures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any bus structure in a variety of bus structures.

[0126] The electronic device can also communicate with one or more external devices (such as a keyboard, a pointing device, a Bluetooth device, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device, and / or communicate with any device that enables the electronic device to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication can be carried out through an input / output (I / O) interface. Moreover, the electronic device can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter. The network adapter communicates with other modules of the electronic device through a bus. It should be understood that although not shown in the figures, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0127] From the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software, or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0128] An embodiment of the present invention also provides a computer program product, which includes program code. When the program product runs on an electronic device, the program code is used to cause the electronic device to execute the steps in the methods according to various exemplary embodiments of the present invention described above in this specification.

[0129] Although some specific embodiments of the present invention have been described in detail by way of examples, those skilled in the art should understand that the above examples are only for illustration and not for limiting the scope of the present invention. Those skilled in the art should also understand that various modifications can be made to the embodiments without departing from the scope and spirit of the present invention.

Claims

1. An annealing monitoring method for a wire bonding annealing device, characterized in that The bonding wire annealing equipment includes a gas annealing device and a conveying device. The conveying device is used to convey the bonding wire through the gas annealing device for gas annealing. A plurality of temperature sensors arranged in sequence are provided in the gas annealing device, and the temperature sensors are used to detect the temperature of the bonding wire; The method includes the following steps: S100, obtaining a temperature sequence of the bonding wire collected by each temperature sensor within a preset sliding time window; S200, obtaining the average temperature and temperature volatility corresponding to each temperature sequence; S300, if |η i，1 -T i | > ΔT or η i，2 > η', then determine the i-th temperature sequence as an abnormal temperature sequence, and obtain a list of abnormal temperature sequences B = (B1, B2,..., B j ,..., B m ), where j = 1, 2,..., m; B j is the j-th abnormal temperature sequence determined, and m is the number of abnormal temperature sequences determined; η i，1 and η i，2 are the average temperature and temperature volatility corresponding to the i-th temperature sequence in turn; T i is the standard temperature corresponding to the i-th temperature sensor, ΔT is a preset temperature difference threshold, and η' is a preset temperature volatility threshold; i = 1, 2,..., n; n is the number of temperature sensors; S400, if m = 0, it is determined that the annealing of the bonding wire within the preset sliding time window meets the preset annealing conditions; otherwise, it is determined that the annealing of the bonding wire within the preset sliding time window does not meet the preset annealing conditions, and S500 is entered; S500, determining the abnormal area and the cause of the abnormality corresponding to the bonding wire during the annealing process of the bonding wire according to the relationship between each temperature in B and the standard temperature of the corresponding temperature sensor.

2. The annealing monitoring method of the bonding wire annealing equipment according to claim 1, characterized in that, Step S500 includes the following steps: S510, obtain B j = (B j，1 , B j，2 , …, B j，p , …, B j，q ), p = 1, 2, …, q; where B j，p is the p-th temperature in the j-th abnormal temperature sequence, q is the number of temperatures in the abnormal temperature sequence; the acquisition time of B j，r is earlier than the acquisition time of B j，r+1 ; r = 1, 2, …, q - 1; S520, traverse B j , if |B j，p -QT j | > ΔT, then determine the corresponding position on the bonding wire as the abnormal position to obtain the list C j，p of the abnormal positions corresponding to B j = (C j , C j，1 , …, C j，2 , …, C j，u , …, C j，f（j） ), where u = 1, 2, …, f(j); among them, C j，u is the u-th abnormal position corresponding to B j , and f(j) is the number of abnormal positions corresponding to B j ; QT j is the standard temperature of the temperature sensor corresponding to the j-th abnormal temperature sequence. S530, obtain the temperature matrix A = (A1, A2,..., A i ,..., A n ); where, A i is the temperature sequence corresponding to the i-th temperature sensor; A i = (A i，1 , A i，2 ,..., A i，p ,..., A i，q ); A i，p is the p-th temperature in the temperature sequence corresponding to the i-th temperature sensor; each temperature sensor collects the temperature at each preset position on the bonding wire; S540, determine the corresponding abnormal areas and causes during the annealing process of the bonding wire according to C j and A 3. The annealing monitoring method of the bonding wire annealing equipment according to claim 2, characterized in that, Step S540 includes the following steps: S541, perform duplicate removal on all abnormal positions to obtain an abnormal position list D = (D1, D2,..., D x ,..., D y ), where x = 1, 2,..., y; among them, D x is the x-th abnormal position determined, and y is the number of abnormal positions determined; S542, determining the area corresponding to all abnormal positions in D on the bonding wire as the abnormal area.

4. The annealing monitoring method of the bonding wire annealing equipment according to claim 3, characterized in that After step S542, the method further includes the following steps: S543. Determine the temperature sequence corresponding to each abnormal position from A according to the sampling frequency of the temperature sensor, the conveying speed of the bonding wire, and the time point when each abnormal position reaches each temperature sensor, and obtain the abnormal temperature sequence list E = (E1, E2,..., E x ,..., E y ); where E x is the abnormal temperature sequence corresponding to the x-th abnormal position; S544. Generate an abnormal temperature curve corresponding to each abnormal temperature sequence in E to obtain a list F=(F1, F2, …, F x , …, F y ); where F x is the abnormal temperature curve corresponding to E x . S545, obtain the similarity between each abnormal temperature curve in F and the corresponding standard temperature curve of the bonding wire to obtain a similarity list γ = (γ1, γ2,..., γ x ,..., γ y ); where γ x is the similarity between F x and the corresponding standard temperature curve of the bonding wire; S555, if γ x <γ’ and |TF x，max -TR| < ΔT, then determine D x as the abnormal position with material abnormality; otherwise, determine D x as the abnormal position with temperature abnormality; where TF x，max is the highest temperature corresponding to F x and TR is the standard temperature after the bonding wire is heated by the heating furnace; S556, if NUM1 / y > μ, it is determined that the cause of the abnormal annealing of the bonding wire is material abnormality; otherwise, it is determined that the cause of the abnormal annealing of the bonding wire is initial temperature abnormality or temperature sensor abnormality; NUM1 is the number of abnormal positions of material abnormality, μ is a preset weight, and μ > 0.

6.

5. The annealing monitoring method of the bonding wire annealing equipment according to claim 4, characterized in that, μ = 0.

8.

6. The annealing monitoring method of the bonding wire annealing equipment according to claim 2, characterized in that η i，2 is A i the corresponding variance.

7. The annealing monitoring method of the bonding wire annealing equipment according to claim 1, characterized in that The distance between any two adjacent temperature sensors is equal.

8. The annealing monitoring method of the bonding wire annealing equipment according to claim 1, characterized in that, The temperature sensing is an infrared temperature sensor.

9. A non-transitory computer-readable storage medium storing at least one instruction or at least one program segment, characterized in that, The at least one instruction or the at least one program segment is loaded and executed by a processor to implement the annealing monitoring method of the bonding wire annealing equipment as described in any one of claims 1-8.

10. An electronic device, characterized in that, Including a processor and the non-transitory computer-readable storage medium as described in claim 9.

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