Intelligent bonding copper wire production state monitoring method and system supported by Internet of Things

Abstract

The invention relates to the technical field of bonding copper wires, and discloses an Internet of Things supported intelligent bonding copper wire production state monitoring method and system, and the method comprises the steps: evaluating the equipment working condition performance corresponding to copper wire manufacturing equipment; the bonding process flow corresponding to the bonding copper wire is inquired, the quality variation degree corresponding to the bonding process flow is calculated, the process exquisite degree corresponding to the bonding process flow is evaluated, and the process stability performance corresponding to the bonding process flow is evaluated; carrying out physical and chemical test on the bonding copper wire sample to obtain physical and chemical test data, and respectively calculating the sample yield strength and the conductivity value corresponding to the bonding copper wire sample; and the bonding performance of the bonding copper wire is analyzed, and a production state monitoring report of the bonding copper wire is generated in combination with a preset Internet of Things database, equipment working condition performance, process stability performance and sample bonding performance. The main purpose of the invention is to solve the problem of low accuracy of monitoring the production state of the bonding copper wire.

Description

Technical Field

[0001] The invention relates to an intelligent bonding copper wire production status monitoring method and system supported by the Internet of Things, belonging to the technical field of bonding copper wires. Background Art

[0002] Bonding copper wire plays an extremely critical role in the field of electronic equipment manufacturing. It is widely used in many key links such as semiconductor packaging and integrated circuits. Its quality and performance are directly related to the overall reliability and operation effect of electronic equipment. With the rapid development of the electronics industry, the quality requirements for bonding copper wire are becoming increasingly stringent, and the production scale is also expanding. In order to ensure the production quality of bonding copper wire, improve production efficiency and optimize the production process, it is urgently necessary to use advanced Internet of Things technology to intelligently monitor and accurately control its production status.

[0003] The existing production status monitoring of bonding copper wire mainly relies on traditional manual inspections and simple manual analysis after sensor data collection. This method has many disadvantages. Due to its reliance on manual operation and experience judgment, it is difficult to quickly and accurately identify subtle changes in production abnormalities when faced with complex and changeable production conditions. In addition, the data processing speed of manual analysis is slow, and real-time dynamic monitoring and instant feedback of the production process cannot be achieved, resulting in limited production efficiency. At the same time, differences in analysis levels among different staff members may lead to inaccurate judgments on the production status, which in turn affects the stability and consistency of product quality, thereby reducing the accuracy of bonding copper wire production status monitoring. Summary of the invention

[0004] The present invention provides an intelligent bonding copper wire production status monitoring method and system supported by the Internet of Things, the main purpose of which is to solve the problem of low accuracy in the bonding copper wire production status monitoring.

[0005] To achieve the above object, the present invention provides an Internet of Things-supported intelligent bonding copper wire production status monitoring method, comprising: Obtaining a copper wire manufacturing device corresponding to the bonding copper wire, recording device operating condition data and device maintenance data corresponding to the copper wire manufacturing device, and evaluating device operating condition performance corresponding to the copper wire manufacturing device in combination with the device operating condition data and the device maintenance data; Query the bonding process flow corresponding to the bonding copper wire, collect the raw material bonding monitoring image and the finished copper wire image of the bonding copper wire in the bonding process flow in real time, calculate the quality variability corresponding to the bonding process flow based on the raw material bonding monitoring image, evaluate the process sophistication corresponding to the bonding process flow based on the finished copper wire image, and evaluate the process stability performance corresponding to the bonding process flow in combination with the quality variability and the process sophistication; In the bonding process, the bonding copper wire is sampled to obtain a bonding copper wire sample, and the bonding copper wire sample is subjected to physical and chemical testing to obtain physical and chemical testing data, wherein the physical and chemical testing data includes sample force measurement data and sample electrical measurement data, and the sample yield strength and electrical conductivity value corresponding to the bonding copper wire sample are calculated by combining the sample force measurement data and the sample electrical measurement data; The bonding performance of the bonding copper wire is analyzed in combination with the conductivity value and the yield strength of the sample, and a production status monitoring report of the bonding copper wire is generated in combination with a preset Internet of Things database, the equipment operating performance, the process stability performance and the sample bonding performance.

[0006] Optionally, combining the equipment operating condition data and the equipment maintenance data to evaluate the equipment operating condition performance corresponding to the copper wire manufacturing equipment includes: Identify the data tag corresponding to the equipment operating condition data to obtain the equipment operating condition tag; Extracting operating condition characterization parameters from the equipment operating condition data, and selecting key characterization parameters from the operating condition characterization parameters based on the equipment operating condition label; Calculating the equipment health corresponding to the copper wire manufacturing equipment according to the key characterization parameters and the equipment operating condition label; Calculating the failure risk rate corresponding to the copper wire manufacturing equipment according to the equipment maintenance data; In combination with the equipment health and the failure risk rate, the equipment operating performance corresponding to the copper wire manufacturing equipment is evaluated.

[0007] Optionally, the selecting, based on the equipment operating condition label, a key characterization parameter among the operating condition characterization parameters includes: Calculate the characterization parameter mean corresponding to the operating condition characterization parameter, and calculate the characterization variance value corresponding to the operating condition characterization parameter based on the characterization parameter mean; Analyze the semantics of the working condition labels corresponding to the equipment working condition labels, and calculate the label association degree between the equipment working condition labels based on the semantics of the working condition labels; In combination with the characterization variance value and the label association degree, a key characterization parameter among the operating condition characterization parameters is selected.

[0008] Optionally, calculating the failure risk rate corresponding to the copper wire manufacturing equipment according to the equipment maintenance data includes: Extracting the equipment maintenance frequency corresponding to the copper wire manufacturing equipment from the equipment maintenance data; Counting the number of devices corresponding to the copper wire manufacturing equipment, and querying the maintenance time span corresponding to the copper wire manufacturing equipment; Combined with the equipment maintenance frequency, the number of equipment and the maintenance time span, the failure risk rate corresponding to the copper wire manufacturing equipment can be calculated by the following formula: ; Among them, A represents the failure risk rate corresponding to the copper wire manufacturing equipment, B represents the equipment maintenance frequency, D represents the maintenance time span, and E represents the number of equipment.

[0009] Optionally, the calculating the quality variability corresponding to the bonding process flow based on the raw material bonding monitoring image includes: Obtaining a process processing purpose corresponding to the bonding process flow, and querying a bonding target quality corresponding to the process processing purpose; Performing image denoising processing on the raw material bonding monitoring image to obtain a denoised bonding monitoring image; Extracting a bonding color representation attribute corresponding to the denoised bonding monitoring image, and analyzing an actual bonding quality corresponding to the bonding color representation attribute; Calculating a mass difference value between the bonding target mass and the actual bonding mass; Based on the quality difference value, the quality variability corresponding to the bonding process flow is obtained.

[0010] Optionally, the evaluating the process sophistication corresponding to the bonding process flow based on the finished copper wire image includes: Dispatching a reference copper wire finished product image corresponding to the bonding process flow; Extracting finished product morphological features and finished product texture features from the finished copper wire image; Extracting reference morphological features and reference texture features from the reference copper wire finished product image; Calculating a process defect index corresponding to the bonding process flow by combining the finished product morphological features and the reference morphological features; Calculating the process deviation coefficient corresponding to the bonding process flow by combining the finished product texture features and the reference texture features; The process sophistication of the bonding process flow is evaluated by combining the process defect index and the process deviation coefficient.

[0011] Optionally, the calculating the process defect index corresponding to the bonding process flow by combining the finished product morphological features and the reference morphological features includes: Performing vectorization processing on the finished product morphological features and the reference morphological features respectively to obtain a first morphological feature vector and a second morphological feature vector; Calculate the characteristic deviation between the finished product morphological feature and the reference morphological feature by combining the first morphological feature vector and the second morphological feature vector; According to the characteristic deviation, the process defect index corresponding to the bonding process flow is calculated by the following formula: ; Among them, G represents the process defect index corresponding to the bonding process flow, It represents the feature deviation corresponding to the ath feature in the finished product morphological features, a represents the feature sequence number of the finished product morphological features, Q represents the number of features of the finished product morphological features, Indicates the maximum value in the feature deviation.

[0012] Optionally, the calculating the process deviation coefficient corresponding to the bonding process flow by combining the finished product texture feature and the reference texture feature includes: Calculating the texture entropies corresponding to the finished product texture feature and the reference texture feature respectively to obtain a first texture entropy and a second texture entropy; Calculating a texture weighting factor corresponding to the texture feature of the finished product; In combination with the texture weighting factor, the first texture entropy and the second texture entropy, the process deviation coefficient corresponding to the bonding process flow can be calculated by the following formula: ; Where F represents the process deviation coefficient corresponding to the bonding process flow, represents the texture weighting factor of the dth feature in the finished product texture feature, d represents the serial number corresponding to the finished product texture feature, r represents the number of features corresponding to the finished product texture feature, represents the texture entropy corresponding to the dth feature of the finished product texture feature in the first texture entropy, Represents the texture entropy corresponding to the dth feature of the reference texture feature in the second texture entropy.

[0013] Optionally, the combining the sample force measurement data and the sample electrical measurement data to respectively calculate the sample yield strength and the conductivity value corresponding to the bonding copper wire sample includes: Respectively performing data calibration processing on the sample force measurement data and the sample electrical measurement data to obtain calibrated force measurement data and calibrated electrical measurement data; Measuring the sample length and sample cross-sectional area corresponding to the bonding copper wire sample; Reading the sample yield force value corresponding to the bonding copper wire sample in the calibration force measurement data, and calculating the sample yield strength corresponding to the bonding copper wire sample by combining the sample yield force value and the sample cross-sectional area; Determining a sample resistance value corresponding to the bonding copper wire sample from the calibration electrical measurement data; The conductivity value corresponding to the bonding copper wire sample is calculated based on the sample length, the sample cross-sectional area and the sample resistance value.

[0014] An intelligent bonding copper wire production status monitoring system supported by the Internet of Things, the system comprising: A working condition performance evaluation module, used for obtaining the copper wire manufacturing equipment corresponding to the bonding copper wire, recording the equipment working condition data and equipment maintenance data corresponding to the copper wire manufacturing equipment, and evaluating the equipment working condition performance corresponding to the copper wire manufacturing equipment in combination with the equipment working condition data and the equipment maintenance data; A process performance evaluation module is used to query the bonding process flow corresponding to the bonding copper wire, collect the raw material bonding monitoring image and the finished copper wire image of the bonding copper wire in the bonding process flow in real time, calculate the quality variability corresponding to the bonding process flow based on the raw material bonding monitoring image, evaluate the process sophistication corresponding to the bonding process flow based on the finished copper wire image, and evaluate the process stability performance corresponding to the bonding process flow in combination with the quality variability and the process sophistication; A parameter calculation module, used for sampling the bonding copper wire in the bonding process to obtain a bonding copper wire sample, performing physical and chemical tests on the bonding copper wire sample to obtain physical and chemical test data, wherein the physical and chemical test data includes sample force measurement data and sample electrical measurement data, and combining the sample force measurement data and the sample electrical measurement data to respectively calculate the sample yield strength and the conductivity value corresponding to the bonding copper wire sample; The report production module is used to analyze the bonding performance of the bonding copper wire in combination with the conductive performance value and the yield strength of the sample, and generate a production status monitoring report of the bonding copper wire in combination with a preset Internet of Things database, the equipment operating performance, the process stability performance and the sample bonding performance.

[0015] Compared with the problems described in the background technology, the present invention evaluates the equipment operating performance corresponding to the copper wire manufacturing equipment by combining the equipment operating data and the equipment maintenance data, so as to understand the overall performance of the equipment corresponding to the copper wire manufacturing equipment, and then obtain the equipment status monitoring result of the bonding copper wire. The present invention calculates the quality variation corresponding to the bonding process flow based on the raw material bonding monitoring image, so as to understand the degree of fluctuation of the processing quality of the raw materials of the bonding copper wire in the bonding process flow, and then provides a basis for the subsequent evaluation of the process stability performance corresponding to the bonding process flow. The present invention combines the sample force measurement data and the sample electrical By measuring data and calculating the sample yield strength and conductivity value corresponding to the bonding copper wire sample, the mechanical and electrical characteristics of the bonding copper wire can be understood, which provides a basis for the subsequent analysis of the sample bonding performance of the bonding copper wire sample. The present invention analyzes the bonding performance of the bonding copper wire by combining the conductivity value and the sample yield strength, and can comprehensively analyze the comprehensive performance of the bonding copper wire sample during the processing. Combined with the preset Internet of Things database, the equipment operating performance, the process stability performance and the bonding performance, a production status monitoring report of the bonding copper wire is generated, thereby obtaining a more accurate production status monitoring result of the bonding copper wire. Therefore, the present invention proposes an intelligent bonding copper wire production status monitoring method and system supported by the Internet of Things to improve the accuracy of bonding copper wire production status monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a process flow of a method for monitoring the production status of an intelligent copper bonding wire supported by the Internet of Things provided in one embodiment of the present invention; Figure 2 A functional module diagram of the intelligent bonding copper wire production status monitoring system supported by the Internet of Things provided in one embodiment of the present invention.

[0017] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0018] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0019] The embodiment of the present application provides a method for monitoring the production status of intelligent bonding copper wires supported by the Internet of Things. The execution subject of the method for monitoring the production status of intelligent bonding copper wires supported by the Internet of Things includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for monitoring the production status of intelligent bonding copper wires supported by the Internet of Things can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. Embodiment 1:

[0020] Reference Figure 1 FIG. 1 is a flow chart of a method for monitoring the production status of a smart copper bonding wire supported by the Internet of Things according to an embodiment of the present invention. In this embodiment, the method for monitoring the production status of a smart copper bonding wire supported by the Internet of Things includes: S1. Obtain the copper wire manufacturing equipment corresponding to the bonding copper wire, record the equipment operating condition data and equipment maintenance data corresponding to the copper wire manufacturing equipment, and evaluate the equipment operating condition performance corresponding to the copper wire manufacturing equipment in combination with the equipment operating condition data and the equipment production capacity data.

[0021] The present invention evaluates the equipment operating condition performance corresponding to the copper wire manufacturing equipment by combining the equipment operating condition data and the equipment maintenance data, so as to understand the overall performance of the equipment corresponding to the copper wire manufacturing equipment, and then obtain the equipment status monitoring result of the bonding copper wire. It should be explained that the bonding copper wire is a key material used to realize electrical connection and other functions in electronic equipment, and the copper wire manufacturing equipment is a professional production device for producing the bonding copper wire. The equipment operating condition data is various parameter information reflecting the working status of the copper wire manufacturing equipment during operation, and the equipment maintenance data is the relevant records and parameters involved in the maintenance of the copper wire manufacturing equipment. The equipment operating condition performance is the comprehensive performance of the copper wire manufacturing equipment. Exemplarily, the recording of the equipment operating condition data and equipment maintenance data corresponding to the copper wire manufacturing equipment can be achieved through the recording system in the equipment.

[0022] In detail, the combining of the equipment operating condition data and the equipment maintenance data to evaluate the equipment operating condition performance corresponding to the copper wire manufacturing equipment includes: Identify the data tag corresponding to the equipment operating condition data to obtain the equipment operating condition tag; Extracting operating condition characterization parameters from the equipment operating condition data, and selecting key characterization parameters from the operating condition characterization parameters based on the equipment operating condition label; Calculating the equipment health corresponding to the copper wire manufacturing equipment according to the key characterization parameters and the equipment operating condition label; Calculating the failure risk rate corresponding to the copper wire manufacturing equipment according to the equipment maintenance data; In combination with the equipment health and the failure risk rate, the equipment operating performance corresponding to the copper wire manufacturing equipment is evaluated.

[0023] It should be explained that the data label is the category information corresponding to the equipment operating condition data, which is used to classify and identify different operating states of the equipment. The operating condition characterization parameter is the relevant parameter in the equipment operating condition data that can reflect the operating characteristics and conditions of the equipment. The key characterization parameter is the core parameter in the operating condition characterization parameter that plays a key role in judging the overall operating condition of the equipment. The equipment health represents a quantitative indicator of the overall operating condition and performance status of the copper wire manufacturing equipment. The failure risk rate represents a quantitative measurement of the possibility of failure of the copper wire manufacturing equipment within a certain period of time.

[0024] Furthermore, the identification of the data label corresponding to the equipment operating condition data can be achieved through a label identification tool, which is compiled by JAVA language; the extraction of the operating condition characterization parameters in the equipment operating condition data can be achieved through a principal component analysis method; the key characterization parameters are normalized according to the corresponding standard characterization parameters to obtain normalized parameters. Assuming that the normal range of the bonding temperature in the standard characterization parameters is 200-250 degrees Celsius and the current measured value is 230 degrees Celsius, the normalized parameter is (230-200) / (250-200)=0.6, and the label weight corresponding to the equipment operating condition label is calculated, and the label weight and the corresponding normalized parameter are multiplied and summed to obtain the equipment health corresponding to the copper wire manufacturing equipment; combined with the equipment health and the failure risk rate, the equipment operating condition performance corresponding to the copper wire manufacturing equipment is evaluated. If the equipment health is 0.8 and the failure risk rate is 0.2, the equipment operating condition performance is excellent on the whole.

[0025] Further, as an optional embodiment of the present invention, the selecting, based on the equipment operating condition label, a key characterization parameter among the operating condition characterization parameters includes: Calculate the characterization parameter mean corresponding to the operating condition characterization parameter, and calculate the characterization variance value corresponding to the operating condition characterization parameter based on the characterization parameter mean; Analyze the semantics of the working condition labels corresponding to the equipment working condition labels, and calculate the label association degree between the equipment working condition labels based on the semantics of the working condition labels; In combination with the characterization variance value and the label association degree, a key characterization parameter among the operating condition characterization parameters is selected.

[0026] It should be explained that the mean value of the characterization parameter is the average value of all data corresponding to the operating condition characterization parameter, which is used to reflect the average level of the parameter; the characterization variance value indicates the degree of discreteness of the data corresponding to the operating condition characterization parameter, reflecting the fluctuation of the parameter; the operating condition label semantics is the actual meaning contained in the textual expression corresponding to the equipment operating condition label; the label association degree represents a quantitative indicator of the degree of semantic similarity between the equipment operating condition labels.

[0027] Exemplarily, the mean value of the characterization parameter corresponding to the operating condition characterization parameter can be calculated by an average function; the characterization variance value corresponding to the operating condition characterization parameter can be calculated by a variance function; the operating condition label semantics corresponding to the equipment operating condition label can be analyzed by a semantic analysis method; the semantic similarity between the operating condition label semantics is calculated, and the semantic similarity is used as the label association degree between the equipment operating condition labels; the sum of the characterization variance value and the label association degree corresponding to the operating condition characterization parameter is calculated, and the key characterization parameter in the operating condition characterization parameter is selected according to the size of the sum.

[0028] Further, as an optional embodiment of the present invention, the calculating the failure risk rate corresponding to the copper wire manufacturing equipment according to the equipment maintenance data includes: Extracting the equipment maintenance frequency corresponding to the copper wire manufacturing equipment from the equipment maintenance data; Counting the number of devices corresponding to the copper wire manufacturing equipment, and querying the maintenance time span corresponding to the copper wire manufacturing equipment; Combined with the equipment maintenance frequency, the number of equipment and the maintenance time span, the failure risk rate corresponding to the copper wire manufacturing equipment can be calculated by the following formula: ; Among them, A represents the failure risk rate corresponding to the copper wire manufacturing equipment, B represents the equipment maintenance frequency, D represents the maintenance time span, and E represents the number of equipment.

[0029] It should be explained that the equipment maintenance frequency is the number of repairs corresponding to the copper wire manufacturing equipment, and the maintenance time span is a span description of the equipment failure data in the time dimension, such as in years. If it is a month, D is 1 / 12, and if it is a quarter, D is 1 / 4.

[0030] S2. Query the bonding process flow corresponding to the bonding copper wire, collect the raw material bonding monitoring image and the finished copper wire image of the bonding copper wire in the bonding process flow in real time, calculate the quality variability corresponding to the bonding process flow based on the raw material bonding monitoring image, evaluate the craftsmanship precision corresponding to the bonding process flow based on the finished copper wire image, and evaluate the process stability performance corresponding to the bonding process flow in combination with the quality variability and the craftsmanship precision.

[0031] The present invention calculates the quality variability corresponding to the bonding process flow based on the raw material bonding monitoring image, so as to understand the degree of fluctuation in the processing quality of the raw materials of the bonding copper wire in the bonding process flow, thereby providing a basis for the subsequent evaluation of the process stability performance corresponding to the bonding process flow. It should be explained that the bonding process flow is the processing process corresponding to the bonding copper wire, the raw material bonding monitoring image is the image of the raw material processing process of the bonding copper wire in the bonding process flow, and the copper wire finished product image is the molded product in each process obtained by processing the bonding copper wire in the bonding process flow. The quality variability represents a description of the quality fluctuation of the raw material processing corresponding to each process in the bonding process flow. Exemplarily, the bonding process flow corresponding to the bonding copper wire can be obtained by querying the manufacturer; the collection of the raw material bonding monitoring image and the copper wire finished product image of the bonding copper wire in the bonding process flow can be achieved by an industrial camera.

[0032] Specifically, the calculating of the quality variability corresponding to the bonding process flow based on the raw material bonding monitoring image includes: Obtaining a process processing purpose corresponding to the bonding process flow, and querying a bonding target quality corresponding to the process processing purpose; Performing image denoising processing on the raw material bonding monitoring image to obtain a denoised bonding monitoring image; Extracting a bonding color representation attribute corresponding to the denoised bonding monitoring image, and analyzing an actual bonding quality corresponding to the bonding color representation attribute; Calculating a mass difference value between the bonding target mass and the actual bonding mass; Based on the quality difference value, the quality variability corresponding to the bonding process flow is obtained.

[0033] It should be explained that the process processing purpose is the expected task direction to be achieved corresponding to the bonding process flow, the bonding target quality is the expected quality standard corresponding to the process processing purpose, the denoised bonding monitoring image is an image that is more conducive to analysis after the raw material bonding monitoring image is processed to remove noise interference, the bonding color characterization attribute is the property description of the color-related characteristics of the bonding area corresponding to the denoised bonding monitoring image, the actual bonding quality is the quality level of the bonding effect reflected based on the actual presentation status corresponding to the bonding color characterization attribute, and the quality difference value is the quantitative difference between the bonding target quality and the actual bonding quality, reflecting the degree of deviation between the two.

[0034] Furthermore, the acquisition of the process processing purpose corresponding to the bonding process flow can be achieved by consulting relevant process documents; the query of the bonding target quality corresponding to the process processing purpose can be achieved by referring to industry quality standards and specifications; the image denoising processing of the raw material bonding monitoring image can be achieved by using a median filtering algorithm; the extraction of the bonding color characterization attribute corresponding to the denoised bonding monitoring image can be achieved through spectral analysis technology; the analysis of the actual bonding quality corresponding to the bonding color characterization attribute can be achieved by combining materials science knowledge and comparing with standard samples; the quality difference value can be obtained by calculating the difference between the bonding target quality and the actual bonding quality; the quality difference value is converted into a percentage to obtain the quality variability corresponding to the bonding process flow.

[0035] The present invention evaluates the process sophistication corresponding to the bonding process flow based on the finished copper wire image, so as to understand the execution accuracy of each link in the bonding process flow, thereby improving the evaluation stability of the process stability performance corresponding to the subsequent bonding process flow. It should be explained that the process sophistication represents the execution accuracy of each link in the bonding process flow.

[0036] In detail, the process of evaluating the process sophistication corresponding to the bonding process flow based on the finished copper wire image includes: Dispatching a reference copper wire finished product image corresponding to the bonding process flow; Extracting finished product morphological features and finished product texture features from the finished copper wire image; Extracting reference morphological features and reference texture features from the reference copper wire finished product image; Calculating a process defect index corresponding to the bonding process flow by combining the finished product morphological features and the reference morphological features; Calculating the process deviation coefficient corresponding to the bonding process flow by combining the finished product texture features and the reference texture features; The process sophistication of the bonding process flow is evaluated by combining the process defect index and the process deviation coefficient.

[0037] It should be explained that the reference copper wire finished product image is a standard reference image corresponding to the bonding process flow for measuring the quality of actual production results. The finished product morphological features and the finished product texture features are respectively attribute descriptions in the copper wire finished product image that reflect the characteristics of the actual copper wire finished product in terms of shape, appearance and surface texture. The reference morphological features and the reference texture features are respectively attribute descriptions in the reference copper wire finished product image that reflect the shape, appearance and surface texture characteristics that the copper wire finished product should have under an ideal state. The process defect index indicates the degree of process defects reflected by the differences in morphology and other aspects between the actual and ideal states corresponding to the bonding process flow. The process deviation coefficient indicates the degree of process deviation reflected by the deviation of the actual texture features from the ideal texture features corresponding to the bonding process flow.

[0038] Exemplarily, the reference copper wire finished product image corresponding to the bonding process flow can be dispatched from the standard image database established within the enterprise or the archived qualified product image data; the finished product morphological features and finished product texture features in the copper wire finished product image can be extracted by using image analysis algorithms, such as geometric feature extraction algorithms based on shape recognition, grayscale co-occurrence matrix and other texture analysis algorithms, combined with computer vision technology; the reference morphological features and the reference texture features are extracted in the same principle as the finished product morphological features and the finished product texture features, and no further elaboration is made here; the process sophistication corresponding to the bonding process flow is evaluated in combination with the process defect index and the process deviation coefficient. If the process defect index is high, it indicates that the finished product has a large deviation from the reference in terms of morphology, such as irregular bonding point shape, abnormal bending of the copper wire, etc. At the same time, the process deviation coefficient is also large, which means that the texture features deviate significantly from the standard, such as poor texture uniformity, abnormal directionality, etc. Combining the two, it can be determined that the bonding process flow has a low process sophistication.

[0039] Further, as an optional embodiment of the present invention, the process defect index corresponding to the bonding process flow is calculated by combining the finished product morphological features and the reference morphological features, including: Performing vectorization processing on the finished product morphological features and the reference morphological features respectively to obtain a first morphological feature vector and a second morphological feature vector; Calculate the characteristic deviation between the finished product morphological feature and the reference morphological feature by combining the first morphological feature vector and the second morphological feature vector; According to the characteristic deviation, the process defect index corresponding to the bonding process flow is calculated by the following formula: ; Among them, G represents the process defect index corresponding to the bonding process flow, It represents the feature deviation corresponding to the ath feature in the finished product morphological features, a represents the feature sequence number of the finished product morphological features, Q represents the number of features of the finished product morphological features, Indicates the maximum value in the feature deviation.

[0040] It should be explained that the first morphological feature vector and the second morphological feature vector are respectively vector expressions corresponding to the finished product morphological features and the reference morphological features, and the feature deviation degree represents the degree of difference between the finished product morphological features and the reference morphological features. Exemplarily, the vectorization processing of the finished product morphological features and the reference morphological features can be achieved by a coding algorithm; the similarity between the first morphological feature vector and the second morphological feature vector is calculated, and 1-similarity is used to obtain the feature deviation degree between the finished product morphological features and the reference morphological features.

[0041] Further, as an optional embodiment of the present invention, the process deviation coefficient corresponding to the bonding process flow is calculated by combining the finished product texture feature and the reference texture feature, including: Calculating the texture entropies corresponding to the finished product texture feature and the reference texture feature respectively to obtain a first texture entropy and a second texture entropy; Calculating a texture weighting factor corresponding to the texture feature of the finished product; In combination with the texture weighting factor, the first texture entropy and the second texture entropy, the process deviation coefficient corresponding to the bonding process flow can be calculated by the following formula: ; Where F represents the process deviation coefficient corresponding to the bonding process flow, represents the texture weighting factor of the dth feature in the finished product texture feature, d represents the serial number corresponding to the finished product texture feature, r represents the number of features corresponding to the finished product texture feature, represents the texture entropy corresponding to the dth feature of the finished product texture feature in the first texture entropy, Represents the texture entropy corresponding to the dth feature of the reference texture feature in the second texture entropy.

[0042] It should be explained that the first texture entropy and the second texture entropy respectively represent the quantitative indicators reflecting the complexity of the texture corresponding to the finished product texture features and the reference texture features, and the texture weighting factor represents the quantitative coefficient corresponding to the finished product texture features based on the degree of importance of the finished product texture features to the overall process or quality. Exemplarily, the calculation of the texture entropy corresponding to the finished product texture features and the reference texture features can be achieved by extracting energy feature parameters based on the grayscale co-occurrence matrix, and then calculating the energy feature parameters using the Shannon entropy formula; the calculation of the texture weighting factor corresponding to the finished product texture features can be achieved by analyzing the degree of influence of the texture features on the key performance indicators of the bonding process (such as strength, conductivity, etc.).

[0043] The present invention evaluates the process stability performance corresponding to the bonding process flow by combining the quality variability and the process sophistication, thereby obtaining production performance monitoring of the bonding process flow and improving the accuracy of stability analysis of the bonding process flow. It should be explained that the process stability performance is a description of the stability corresponding to the bonding process flow. Exemplarily, if both the quality variability and the process sophistication are high, it means that the process stability performance corresponding to the bonding process flow is poor.

[0044] S3. In the bonding process, the bonding copper wire is sampled to obtain a bonding copper wire sample, and the bonding copper wire sample is subjected to physical and chemical tests to obtain physical and chemical test data, wherein the physical and chemical test data includes sample force measurement data and sample electrical measurement data, and the sample yield strength and electrical conductivity value corresponding to the bonding copper wire sample are calculated by combining the sample force measurement data and the sample electrical measurement data.

[0045] The present invention calculates the sample yield strength and the conductive performance value corresponding to the bonding copper wire sample respectively by combining the sample force measurement data and the sample electrical measurement data, so as to understand the mechanical and electrical characteristics of the bonding copper wire and provide a basis for the subsequent analysis of the sample bonding performance of the bonding copper wire sample. It should be explained that the bonding copper wire sample is a physical sample obtained after the bonding copper wire is actually processed in the bonding process for subsequent various performance tests. The sample force measurement data and the sample electrical measurement data are the data in the physical and chemical test data about the bonding copper wire sample obtained through mechanical testing and electrical testing respectively, which can reflect its force and conductive related characteristics. The sample yield strength and the conductive performance value respectively represent the strength index of the bonding copper wire sample corresponding to the beginning of plastic deformation when subjected to external force and the performance index of the ability to conduct current. Exemplarily, the physical and chemical testing of the bonding copper wire sample can be achieved by a universal material testing machine and an impedance analyzer.

[0046] In detail, the sample yield strength and the conductivity value corresponding to the bonding copper wire sample are calculated by combining the sample force measurement data and the sample electrical measurement data, respectively, including: Respectively performing data calibration processing on the sample force measurement data and the sample electrical measurement data to obtain calibrated force measurement data and calibrated electrical measurement data; Measuring the sample length and sample cross-sectional area corresponding to the bonding copper wire sample; Reading the sample yield force value corresponding to the bonding copper wire sample in the calibration force measurement data, and calculating the sample yield strength corresponding to the bonding copper wire sample by combining the sample yield force value and the sample cross-sectional area; Determining a sample resistance value corresponding to the bonding copper wire sample from the calibration electrical measurement data; The conductivity value corresponding to the bonding copper wire sample is calculated based on the sample length, the sample cross-sectional area and the sample resistance value.

[0047] It should be explained that the calibration force measurement data and the calibration electrical measurement data are respectively the data obtained after the sample force measurement data and the sample electrical measurement data have been calibrated by correction of deviations, removal of errors and other calibration operations; the sample yield force value is the value of the force corresponding to the bonding copper wire sample in the calibration force measurement data when the material begins to produce obvious plastic deformation. Exemplarily, the data calibration processing of the sample force measurement data and the sample electrical measurement data can be achieved by the mean substitution method, using the average value to replace the erroneous data; the measurement of the sample length and sample cross-sectional area corresponding to the bonding copper wire sample can be achieved by measuring tools, such as a vernier caliper and a laser diameter gauge.

[0048] Further, as an optional embodiment of the present invention, the conductivity value corresponding to the bonding copper wire sample is calculated by combining the sample length, the sample cross-sectional area and the sample resistance value, including: Combined with the sample length, the sample cross-sectional area and the sample resistance value, the conductivity value corresponding to the bonding copper wire sample is calculated by the following formula: ; Among them, N represents the conductivity value corresponding to the bonding copper wire sample, P represents the sample length, R represents the sample resistance value, and S represents the sample cross-sectional area.

[0049] S4. Analyze the bonding performance of the bonding copper wire in combination with the conductivity value and the yield strength of the sample, and generate a production status monitoring report of the bonding copper wire in combination with a preset Internet of Things database, the equipment operating performance, the process stability performance and the bonding performance.

[0050] The present invention analyzes the bonding performance of the bonding copper wire by combining the conductive performance value and the yield strength of the sample, and can comprehensively analyze the comprehensive performance of the bonding copper wire sample in the processing process, and generates a production status monitoring report of the bonding copper wire by combining the preset Internet of Things database, the equipment operating performance, the process stability performance and the bonding performance, thereby obtaining a more accurate production status monitoring result of the bonding copper wire. It should be explained that the bonding performance is the comprehensive performance of the bonding copper wire in the bonding production process, and the preset Internet of Things database is a data storage and management system pre-designed and configured specifically for Internet of Things applications, which is used to collect, store, manage and analyze various data generated by Internet of Things devices and related business processes. Exemplarily, in combination with the conductive performance value and the yield strength of the sample , analyze the bonding performance of the bonding copper wire. If the conductivity value is high and the yield strength of the sample meets the standard, it means that the resistance at the bonding point is small, which is conducive to current conduction, and it can withstand a certain mechanical stress, then the bonding performance is good; conversely, if the conductivity value is low and the yield strength of the sample is insufficient, then the bonding performance is low; combine the preset Internet of Things database, the equipment operating performance, the process stability performance and the sample bonding performance to generate a production status monitoring report of the bonding copper wire, compare the equipment operating performance, the process stability performance and the sample bonding performance with the data recorded in the preset Internet of Things database, and obtain an evaluation report corresponding to the equipment operating performance, the process stability performance and the sample bonding performance, summarize the evaluation reports, and obtain a production status monitoring report of the bonding copper wire.

[0051] Compared with the problems described in the background technology, the present invention evaluates the equipment operating performance corresponding to the copper wire manufacturing equipment by combining the equipment operating data and the equipment maintenance data, so as to understand the overall performance of the equipment corresponding to the copper wire manufacturing equipment, and then obtain the equipment status monitoring result of the bonding copper wire. The present invention calculates the quality variation corresponding to the bonding process flow based on the raw material bonding monitoring image, so as to understand the degree of fluctuation of the processing quality of the raw materials of the bonding copper wire in the bonding process flow, and then provides a basis for the subsequent evaluation of the process stability performance corresponding to the bonding process flow. The present invention combines the sample force measurement data and the sample electrical By measuring data and calculating the sample yield strength and conductivity value corresponding to the bonding copper wire sample, the mechanical and electrical characteristics of the bonding copper wire can be understood, which provides a basis for the subsequent analysis of the sample bonding performance of the bonding copper wire sample. The present invention analyzes the bonding performance of the bonding copper wire by combining the conductivity value and the sample yield strength, and can comprehensively analyze the comprehensive performance of the bonding copper wire sample during the processing. Combined with the preset Internet of Things database, the equipment operating performance, the process stability performance and the bonding performance, a production status monitoring report of the bonding copper wire is generated, thereby obtaining a more accurate production status monitoring result of the bonding copper wire. Therefore, the intelligent bonding copper wire production status monitoring method supported by the Internet of Things proposed in the present invention improves the accuracy of the bonding copper wire production status monitoring. Embodiment 2:

[0052] like Figure 2 , is a functional module diagram of an intelligent bonding copper wire production status monitoring system supported by the Internet of Things provided by an embodiment of the present invention.

[0053] The intelligent copper bonding wire production status monitoring system 100 supported by the Internet of Things of the present invention can be installed in an electronic device. According to the functions implemented, the intelligent copper bonding wire production status monitoring system 100 supported by the Internet of Things can include a working condition performance evaluation module 101, a process performance evaluation module 102, a parameter calculation module 103 and a report production module 104. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0054] In this embodiment, the functions of each module / unit are as follows: The working condition performance evaluation module 101 is used to obtain the copper wire manufacturing equipment corresponding to the bonding copper wire, record the equipment working condition data and equipment maintenance data corresponding to the copper wire manufacturing equipment, and evaluate the equipment working condition performance corresponding to the copper wire manufacturing equipment in combination with the equipment working condition data and the equipment maintenance data; The process performance evaluation module 102 is used to query the bonding process flow corresponding to the bonding copper wire, collect the raw material bonding monitoring image and the finished copper wire image of the bonding copper wire in the bonding process flow in real time, calculate the quality variability corresponding to the bonding process flow based on the raw material bonding monitoring image, evaluate the process sophistication corresponding to the bonding process flow based on the finished copper wire image, and evaluate the process stability performance corresponding to the bonding process flow in combination with the quality variability and the process sophistication; The parameter calculation module 103 is used to sample the bonding copper wire in the bonding process to obtain a bonding copper wire sample, perform physical and chemical tests on the bonding copper wire sample to obtain physical and chemical test data, wherein the physical and chemical test data includes sample force measurement data and sample electrical measurement data, and respectively calculate the sample yield strength and electrical conductivity value corresponding to the bonding copper wire sample by combining the sample force measurement data and the sample electrical measurement data; The report production module 104 is used to analyze the bonding performance of the bonding copper wire in combination with the conductive performance value and the sample yield strength, and generate a production status monitoring report of the bonding copper wire in combination with a preset Internet of Things database, the equipment operating performance, the process stability performance and the sample bonding performance.

[0055] In detail, each module described in the intelligent bonding copper wire production status monitoring system 100 supported by the Internet of Things described in the embodiment of the present application is used in the same manner as described above. Figure 1 The same technical means as the method for monitoring the production status of intelligent bonding copper wire supported by the Internet of Things described in the text can produce the same technical effects, so they will not be repeated here.

[0056] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. 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 solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for monitoring the production status of intelligent bonding copper wire supported by the Internet of Things, characterized in that: The method comprises: Obtaining a copper wire manufacturing device corresponding to the bonding copper wire, recording device operating condition data and device maintenance data corresponding to the copper wire manufacturing device, and evaluating device operating condition performance corresponding to the copper wire manufacturing device in combination with the device operating condition data and the device maintenance data; Query the bonding process flow corresponding to the bonding copper wire, collect the raw material bonding monitoring image and the finished copper wire image of the bonding copper wire in the bonding process flow in real time, calculate the quality variability corresponding to the bonding process flow based on the raw material bonding monitoring image, evaluate the process sophistication corresponding to the bonding process flow based on the finished copper wire image, and evaluate the process stability performance corresponding to the bonding process flow in combination with the quality variability and the process sophistication; In the bonding process, the bonding copper wire is sampled to obtain a bonding copper wire sample, and the bonding copper wire sample is subjected to physical and chemical testing to obtain physical and chemical testing data, wherein the physical and chemical testing data includes sample force measurement data and sample electrical measurement data, and the sample yield strength and electrical conductivity value corresponding to the bonding copper wire sample are calculated by combining the sample force measurement data and the sample electrical measurement data; The bonding performance of the bonding copper wire is analyzed in combination with the conductivity value and the yield strength of the sample, and a production status monitoring report of the bonding copper wire is generated in combination with a preset Internet of Things database, the equipment operating performance, the process stability performance and the sample bonding performance.

2. The method for monitoring the production status of the intelligent bonding copper wire supported by the Internet of Things according to claim 1, characterized in that: The combining of the equipment operating condition data and the equipment maintenance data to evaluate the equipment operating condition performance corresponding to the copper wire manufacturing equipment includes: Identify the data tag corresponding to the equipment operating condition data to obtain the equipment operating condition tag; Extracting operating condition characterization parameters from the equipment operating condition data, and selecting key characterization parameters from the operating condition characterization parameters based on the equipment operating condition label; Calculating the equipment health corresponding to the copper wire manufacturing equipment according to the key characterization parameters and the equipment operating condition label; Calculating the failure risk rate corresponding to the copper wire manufacturing equipment according to the equipment maintenance data; In combination with the equipment health and the failure risk rate, the equipment operating performance corresponding to the copper wire manufacturing equipment is evaluated.

3. The method for monitoring the production status of the intelligent bonding copper wire supported by the Internet of Things according to claim 2, characterized in that: The selecting, based on the equipment operating condition label, key characterization parameters from among the operating condition characterization parameters comprises: Calculate the characterization parameter mean corresponding to the operating condition characterization parameter, and calculate the characterization variance value corresponding to the operating condition characterization parameter based on the characterization parameter mean; Analyze the semantics of the working condition labels corresponding to the equipment working condition labels, and calculate the label association degree between the equipment working condition labels based on the semantics of the working condition labels; In combination with the characterization variance value and the label association degree, a key characterization parameter among the operating condition characterization parameters is selected.

4. The method for monitoring the production status of the intelligent bonding copper wire supported by the Internet of Things according to claim 2, characterized in that: The calculating, according to the equipment maintenance data, a failure risk rate corresponding to the copper wire manufacturing equipment comprises: Extracting the equipment maintenance frequency corresponding to the copper wire manufacturing equipment from the equipment maintenance data; Counting the number of devices corresponding to the copper wire manufacturing equipment, and querying the maintenance time span corresponding to the copper wire manufacturing equipment; Combined with the equipment maintenance frequency, the number of equipment and the maintenance time span, the failure risk rate corresponding to the copper wire manufacturing equipment can be calculated by the following formula: ; Among them, A represents the failure risk rate corresponding to the copper wire manufacturing equipment, B represents the equipment maintenance frequency, D represents the maintenance time span, and E represents the number of equipment.

5. The method for monitoring the production status of the intelligent bonding copper wire supported by the Internet of Things according to claim 1, characterized in that: The calculating the quality variability corresponding to the bonding process flow based on the raw material bonding monitoring image includes: Obtaining a process processing purpose corresponding to the bonding process flow, and querying a bonding target quality corresponding to the process processing purpose; Performing image denoising processing on the raw material bonding monitoring image to obtain a denoised bonding monitoring image; Extracting a bonding color representation attribute corresponding to the denoised bonding monitoring image, and analyzing an actual bonding quality corresponding to the bonding color representation attribute; Calculating a mass difference value between the bonding target mass and the actual bonding mass; Based on the quality difference value, the quality variability corresponding to the bonding process flow is obtained.

6. The method for monitoring the production status of the intelligent bonding copper wire supported by the Internet of Things according to claim 1, characterized in that: The step of evaluating the process sophistication of the bonding process based on the finished copper wire image includes: Dispatching a reference copper wire finished product image corresponding to the bonding process flow; Extracting finished product morphological features and finished product texture features from the finished copper wire image; Extracting reference morphological features and reference texture features from the reference copper wire finished product image; Calculating a process defect index corresponding to the bonding process flow by combining the finished product morphological features and the reference morphological features; Calculating the process deviation coefficient corresponding to the bonding process flow by combining the finished product texture features and the reference texture features; The process sophistication of the bonding process flow is evaluated by combining the process defect index and the process deviation coefficient.

7. The method for monitoring the production status of the intelligent bonding copper wire supported by the Internet of Things according to claim 6, characterized in that: The step of calculating the process defect index corresponding to the bonding process flow by combining the finished product morphological features and the reference morphological features includes: Performing vectorization processing on the finished product morphological features and the reference morphological features respectively to obtain a first morphological feature vector and a second morphological feature vector; Calculate the characteristic deviation between the finished product morphological feature and the reference morphological feature by combining the first morphological feature vector and the second morphological feature vector; According to the characteristic deviation, the process defect index corresponding to the bonding process flow is calculated by the following formula: ; Among them, G represents the process defect index corresponding to the bonding process flow, It represents the feature deviation corresponding to the ath feature in the finished product morphological features, a represents the feature sequence number of the finished product morphological features, Q represents the number of features of the finished product morphological features, Indicates the maximum value in the feature deviation.

8. The method for monitoring the production status of the intelligent bonding copper wire supported by the Internet of Things according to claim 6, characterized in that: The step of calculating the process deviation coefficient corresponding to the bonding process flow by combining the finished product texture feature and the reference texture feature includes: Calculating the texture entropies corresponding to the finished product texture feature and the reference texture feature respectively to obtain a first texture entropy and a second texture entropy; Calculating a texture weighting factor corresponding to the texture feature of the finished product; In combination with the texture weighting factor, the first texture entropy and the second texture entropy, the process deviation coefficient corresponding to the bonding process flow can be calculated by the following formula: ; Where F represents the process deviation coefficient corresponding to the bonding process flow, represents the texture weighting factor of the dth feature in the finished product texture feature, d represents the serial number corresponding to the finished product texture feature, r represents the number of features corresponding to the finished product texture feature, represents the texture entropy corresponding to the dth feature of the finished product texture feature in the first texture entropy, Represents the texture entropy corresponding to the dth feature of the reference texture feature in the second texture entropy.

9. The method for monitoring the production status of the intelligent bonding copper wire supported by the Internet of Things according to claim 1, characterized in that: The combining the sample force measurement data and the sample electrical measurement data to respectively calculate the sample yield strength and the conductivity value corresponding to the bonding copper wire sample comprises: Respectively performing data calibration processing on the sample force measurement data and the sample electrical measurement data to obtain calibrated force measurement data and calibrated electrical measurement data; Measuring the sample length and sample cross-sectional area corresponding to the bonding copper wire sample; Reading the sample yield force value corresponding to the bonding copper wire sample in the calibration force measurement data, and calculating the sample yield strength corresponding to the bonding copper wire sample by combining the sample yield force value and the sample cross-sectional area; Determining a sample resistance value corresponding to the bonding copper wire sample from the calibration electrical measurement data; The conductivity value corresponding to the bonding copper wire sample is calculated based on the sample length, the sample cross-sectional area and the sample resistance value.

10. The intelligent bonding copper wire production status monitoring system supported by the Internet of Things is characterized by: The system comprises: A working condition performance evaluation module, used for obtaining the copper wire manufacturing equipment corresponding to the bonding copper wire, recording the equipment working condition data and equipment maintenance data corresponding to the copper wire manufacturing equipment, and evaluating the equipment working condition performance corresponding to the copper wire manufacturing equipment in combination with the equipment working condition data and the equipment maintenance data; A process performance evaluation module is used to query the bonding process flow corresponding to the bonding copper wire, collect the raw material bonding monitoring image and the finished copper wire image of the bonding copper wire in the bonding process flow in real time, calculate the quality variability corresponding to the bonding process flow based on the raw material bonding monitoring image, evaluate the process sophistication corresponding to the bonding process flow based on the finished copper wire image, and evaluate the process stability performance corresponding to the bonding process flow in combination with the quality variability and the process sophistication; A parameter calculation module, used for sampling the bonding copper wire in the bonding process to obtain a bonding copper wire sample, performing physical and chemical tests on the bonding copper wire sample to obtain physical and chemical test data, wherein the physical and chemical test data includes sample force measurement data and sample electrical measurement data, and combining the sample force measurement data and the sample electrical measurement data to respectively calculate the sample yield strength and the conductivity value corresponding to the bonding copper wire sample; The report production module is used to analyze the bonding performance of the bonding copper wire in combination with the conductive performance value and the yield strength of the sample, and generate a production status monitoring report of the bonding copper wire in combination with a preset Internet of Things database, the equipment operating performance, the process stability performance and the sample bonding performance.

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