Intelligent monitoring method and system for diode production quality based on multi-source data analysis

Abstract

The present invention discloses a method and system for intelligent monitoring of diode production quality based on multi-source data analysis, which relates to the field of production monitoring, including: obtaining real-time standard data of equipment parameters based on historical data of production equipment, and establishing a product qualification rate prediction model; generating predicted product qualification rates of each production equipment; obtaining performance data and appearance data of a product to be tested, and comparing them with standard performance thresholds and standard shape data respectively, to generate unqualified product data and qualification rates of the product to be tested; generating defective product environment association trends based on the unqualified product data; determining predicted deviation production equipment based on the predicted product qualification rates and the qualification rates of the product to be tested; generating an equipment difference index based on the real-time operating data of the predicted deviation production equipment and the performance data of the product to be tested; and establishing multiple monitoring standards by combining the defective product environment association trends, the equipment difference index and the real-time standard data of equipment parameters.

Description

Technical Field

[0001] The present invention relates to the field of production monitoring, and in particular to a method and system for intelligently monitoring diode production quality based on multi-source data analysis. Background Art

[0002] A diode is an electronic component made of semiconductor material with unidirectional conductivity. It is an indispensable basic component in modern electronic circuits. In the circuit, the rectifier diode can convert alternating current into direct current, providing a stable DC power supply for electronic equipment. The voltage regulator diode can stabilize the circuit voltage by using its reverse breakdown characteristics, ensuring that electronic components operate in a stable voltage environment. The light-emitting diode emits light when it is forward-conducted and is widely used in indicator lights, display screens and lighting fields.

[0003] Production quality monitoring of diodes is usually carried out after product production is completed, and processing is not carried out until unqualified products are discovered. This monitoring method makes it difficult to predict the occurrence of unqualified products in advance, does not fully consider the individual differences between the same type of equipment, ignores the connection between unqualified product defect data and production, and is difficult to trace the root cause of the unqualified products. As a result, the ability to prevent similar problems in subsequent production is weak. Summary of the Invention

[0004] In order to solve the above technical problems, a method for intelligent monitoring of diode production quality based on multi-source data analysis is provided. This technical solution solves the problem raised in the above background technology that it is difficult to predict the occurrence of unqualified products in advance, does not fully consider the individual differences between equipment of the same type, ignores the connection between unqualified product defect data and production, and is difficult to trace the root cause of the unqualified products, which in turn makes it difficult to prevent similar problems from occurring in subsequent production.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0006] A method for intelligently monitoring diode production quality based on multi-source data analysis, comprising:

[0007] Obtain and analyze diode production plans to generate standard performance thresholds and standard shape data;

[0008] Collect historical data of production equipment, obtain real-time standard data of equipment parameters, and establish a product qualification rate prediction model for the equipment;

[0009] Obtain real-time environmental data and process parameter data for each production equipment, and combine it with the equipment's product qualification rate prediction model to generate the predicted product qualification rate for each production equipment;

[0010] Collect the performance data and appearance data of the product to be tested, and compare them with the standard performance threshold and standard shape data respectively, to generate the data of unqualified products and the qualified rate of the products to be tested;

[0011] Generate defective product environmental correlation trends based on real-time environmental data of production equipment and unqualified product data;

[0012] Determine the production equipment for the predicted deviation based on the predicted product qualification rate and the qualified rate of the product to be tested;

[0013] Obtain real-time operating data of the predicted deviation production equipment, combine it with the performance data and appearance data of the product to be tested, and generate the equipment difference index;

[0014] By combining the real-time standard data of equipment parameters, the environmental correlation trend of defective products and the equipment difference index, multiple monitoring standards are established to monitor the real-time data of the equipment.

[0015] Preferably, the establishment of a product qualification rate prediction model for the equipment specifically includes:

[0016] Obtaining the production process of the diode and the production operation manual of each production equipment, extracting the characteristic steps of each production process and the reference parameters of each production equipment, wherein the reference parameters of the production equipment include equipment parameters and environmental parameters;

[0017] Classify the historical data of each production equipment according to process parameters and equipment environment data, and generate process parameter classification data and equipment environment data classification data;

[0018] Based on time, the process parameter classification data and equipment environment data classification data are sorted out, and the corresponding relationship between different process parameters and product qualification rate, as well as the corresponding relationship between different equipment environment data and product qualification rate, are listed;

[0019] According to the corresponding relationship between different process parameters and product qualification rate, as well as the corresponding relationship between different equipment environment data and product qualification rate, the product qualification rate change values ​​corresponding to the process parameter difference and the equipment environment data difference are obtained respectively and the average value is calculated. The degree of change of the product qualification rate corresponding to the change of the process parameter and the degree of change of the product qualification rate corresponding to the change of the equipment environment data are obtained, and recorded as the sensitivity of the product qualification rate to the process parameter and the sensitivity of the product qualification rate to the production equipment environment respectively;

[0020] Record the equipment corresponding to the characteristic steps of the production process as characteristic equipment, classify the historical data of the characteristic equipment into weekly time intervals, and generate time period historical data, which includes the time series data of the characteristic equipment process parameters and product quality data;

[0021] Analyze the historical data of the time period, taking one hour as an analysis node, and map the product quality data to the analysis node. Draw the product quality change curve with the analysis node as the horizontal axis and the product quality data as the vertical axis to generate the time fluctuation trend of the characteristic equipment.

[0022] Generating real-time standard data of equipment parameters according to the time fluctuation trend of the characteristic equipment and the reference parameters of the production equipment, wherein the real-time standard data of equipment parameters includes real-time standard data of equipment parameters and real-time standard data of equipment environment;

[0023] Using the real-time standard data of equipment parameters as a benchmark, the real-time environmental data and process parameter data of each production equipment are compared with the real-time standard data of equipment parameters, and the proportion of production equipment whose real-time environmental data and process parameter data are both greater than the real-time standard data of equipment parameters is obtained in the total number of production equipment, thereby generating the first product predicted qualification rate of the production equipment;

[0024] Based on the product qualification rate of production equipment, analyze the historical production data of production equipment to obtain the process interaction index and process environment impact index;

[0025] The preliminary product qualification rate relationship of the equipment is generated by comprehensively considering the sensitivity of the product qualification rate to the process parameters, the sensitivity of the product qualification rate to the production equipment environment, the process mutual influence index and the process environment influence index;

[0026] The electrostatic field strength and magnetic induction strength of each production equipment are used as the production environment of the production equipment, and the real-time alignment accuracy, processing time and packaging pressure of each production equipment are used as process parameter values. Substitute them into the production equipment product qualification rate relationship formula to obtain the predicted qualification rate of the second product;

[0027] Taking the first product predicted qualified rate and the second product predicted qualified rate as input variables, a product qualified rate prediction model for the equipment is established;

[0028] Among them, the relationship between the qualified rate of production equipment products is specifically as follows:

[0029]

[0030] Where y represents the predicted qualified rate of the second product, b i Indicates the sensitivity of the product qualification rate of the i-th process to the process parameters, x i represents the value of the i-th process parameter, a ij represents the process interaction index between the i-th process and the j-th process, x j represents the jth process parameter value, b j Indicates the sensitivity of the product qualification rate of the jth process to the process parameters, b ilrepresents the process environment impact index between the i-th process and the production environment l, x l It represents the specific value of the production environment l in which the production equipment is located, n represents the total number of processes required for the production equipment to generate products, and m represents the total number of environments that affect the production of products by the production equipment.

[0031] Preferably, generating defective product environment correlation trends specifically includes:

[0032] According to the diode production goals, clarify the diode usage scenarios and obtain the diode test environment thresholds;

[0033] Organizing the data of unqualified products to obtain production data and defect data of the unqualified products, wherein the defect data includes appearance characteristic values, forward voltage values, reverse current values, and breakdown voltage values;

[0034] Classify defective products according to defect data into single-defect defective products and multiple-defect defective products;

[0035] Based on the production data of unqualified products, analyze the defect data of single-defect unqualified products and obtain relevant production data corresponding to the defect data;

[0036] Combine the relevant production data corresponding to the defect data and the data of multi-defect unqualified products to determine the cause of the multi-defect unqualified products;

[0037] Adjust the real-time test environment based on the diode test environment threshold to obtain the specific values ​​of unqualified products under different test data. Compare the change in the specific value of the unqualified products with the change range of the test data to obtain the degree of change in the defect data of the unqualified products under different test environment data.

[0038] Combined with the relevant production data corresponding to the defect data and the degree of change in the defect data of unqualified products under different test environment data, the data relationship between product defect type and environment is determined, and the defect product environment association trend is generated.

[0039] Preferably, the determining of the cause of the occurrence of multi-defect unqualified products specifically includes:

[0040] Based on the unqualified product data, the multi-defect unqualified product data is classified to generate defect type combination data, defect severity data and defect frequency data respectively;

[0041] Using defect type combination data as the main judgment criterion, determine the problem process corresponding to the occurrence of multiple defective unqualified products;

[0042] Based on the defect severity data, determine whether the defects of the multi-defect unqualified products are serious. If so, conduct comprehensive quality traceability and process improvement for the multi-defect unqualified products. If not, adjust the production process parameters of the problem process corresponding to the multi-defect unqualified products;

[0043] Based on the defect frequency data, determine whether the defects of multi-defect unqualified products show periodic changes. If so, it means that the production line operators have non-standard operating procedures or there are quality problems with the production raw materials. Further analysis of the problem is needed based on the periodicity of the defects. If not, it means that the defects are normal losses in the production process.

[0044] Preferably, generating the device difference index specifically includes:

[0045] Obtain the product qualification rate of the predicted deviation production equipment;

[0046] Classify the historical data of production equipment according to the equipment's working time and equipment type to generate a group of equipment with the same working condition. The group of equipment with the same working condition means that the equipment type and working time in the same group are the same;

[0047] Obtain and compare the standard deviation of product qualification rates for equipment groups with the same operating conditions. If the standard deviation is greater than 0.02, it is determined that there is a difference between the equipment in question and the rest of the equipment in the same group. The equipment with the difference is identified, and the corresponding operating data and product data are obtained.

[0048] Extracting performance characteristic data and appearance characteristic data of products generated by different devices based on the generated product data, comparing them with standard performance thresholds and standard shape data respectively, and obtaining product performance difference and product shape data difference;

[0049] Obtain the equipment standard process parameters, combine them with the operating data corresponding to the differential equipment, subtract the operating data corresponding to the differential equipment from the equipment operating data in the equipment standard process parameters, and obtain the difference in the equipment operating process parameters;

[0050] The ratios of product performance difference and equipment operation process parameter difference, as well as product shape data difference and equipment operation process parameter difference are calculated respectively to generate the equipment difference index.

[0051] Preferably, the establishment of multiple monitoring standards to monitor real-time data of production equipment specifically includes:

[0052] The real-time standard data of equipment parameters, including the real-time standard data of equipment parameters and the real-time standard data of equipment environment, is used as the first standard data. The real-time standard data of equipment parameters is compared with the real-time environment data and process parameter data of the production equipment to determine whether the real-time standard data is lower than the first standard data. If so, an early warning is issued to notify the production line operator to adjust the equipment process parameters. If not, the product qualification rate of the production equipment is further predicted.

[0053] Real-time test environment information is acquired through sensors. Combined with the defective product environment correlation trend corresponding to the current test environment, performance data corresponding to the test environment data is generated as the second standard data. The performance data of the product to be tested in the test environment data is acquired through measuring instruments and compared with the second standard data to determine whether the performance data maintains a consistent trend with the second standard data. If so, the product is deemed unqualified and a warning is issued in advance. If not, the remaining products to be tested are tested.

[0054] Multiply the equipment difference index and the equipment standard process parameters to generate equipment process adaptation data, which is used as the third standard data. Compare it with the real-time process parameters of the production equipment to determine whether there are differences with the third standard data. If so, it means that the products produced by the equipment are at risk of being unqualified, and an early warning is issued. If not, continue to monitor the real-time process parameters of the equipment.

[0055] Furthermore, an intelligent monitoring system for diode production quality based on multi-source data analysis is proposed, including:

[0056] A data acquisition module, which is used to collect real-time environmental data and process parameters of production equipment, historical data of each production equipment, performance data and appearance data of the product to be tested, and transmit the data to the feature extraction module, data analysis module and model generation module;

[0057] A feature extraction module is used to extract key features from the received data, extract characteristic steps of the production process, extract defect data of single-defect unqualified products, and transmit the data to the data analysis module;

[0058] A data analysis module, which is used to organize the received data, generate a preliminary product qualification rate relationship equation for the equipment, analyze the relationship between equipment operation data, product data and environmental data, and transmit the data to the model generation module and the quality monitoring module;

[0059] A model generation module is used to establish a prediction model based on the received data, generate prediction data through the prediction model and real-time data, and transmit the prediction data to the quality monitoring module;

[0060] A quality monitoring module, which is used to generate monitoring standard data based on the received data, establish multiple monitoring standards, and transmit the data to the abnormal warning module;

[0061] An abnormality warning module is used to compare the received data, determine whether there is an abnormality and whether a warning needs to be issued based on the comparison results, and transmit the data to the intelligent decision-making module;

[0062] The intelligent decision-making module is used to analyze the received data, provide suggestions for the production process and formulate a maintenance plan for the production process equipment based on the data analysis results and the data transmitted by the abnormal warning module.

[0063] Preferably, the feature extraction module includes:

[0064] A first feature extraction unit, the first feature extraction unit is used to compare the process production process of each production equipment and extract characteristic steps in the production process;

[0065] a second feature extraction unit, configured to extract features according to a use environment of the diode and obtain a test environment threshold of the diode;

[0066] The third feature extraction unit is used to analyze the defect data of the unqualified products, extract the defect features of the unqualified products, and classify the unqualified products.

[0067] Preferably, the data analysis module includes:

[0068] a first analysis unit configured to analyze the product qualification rate of the production equipment based on the process parameters corresponding to the characteristic steps and the environmental data of the production equipment, and obtain a change relationship index between each process parameter and the product qualification rate, and a change relationship index between each production environment data of the production equipment and the product qualification rate;

[0069] A second analysis unit, configured to adjust the real-time test environment according to the test environment threshold of the diode, obtain the degree of change in defect data of unqualified products under different test environment data, and generate a defect product environment correlation trend based on the relevant production data corresponding to the defect data;

[0070] The third analysis unit is used to generate an equipment difference index according to the product performance difference, the product shape data difference and the equipment operation process parameter difference.

[0071] Preferably, the quality monitoring module includes:

[0072] a first quality monitoring module, configured to predict the product qualification rate of each production equipment based on a product qualification rate prediction model of the equipment, and compare the prediction with the actual product qualification rate of the production equipment to determine whether there is any abnormality in the product quality;

[0073] A second quality monitoring module, which is used to generate performance data corresponding to the real-time test environment data based on the real-time test environment information and the defective product environment correlation trend, and compare it with the performance data of the product to be tested in the real-time test environment data to determine whether there is any abnormality in the product quality;

[0074] The third quality monitoring module is used to generate equipment process adaptation data based on the equipment standard process parameters and the equipment difference index, and compare it with the real-time process parameters of the production equipment to determine whether there is any abnormality in the quality of the product.

[0075] Compared with the prior art, the present invention has the following beneficial effects:

[0076] The present invention proposes an intelligent monitoring method for diode production quality based on multi-source data analysis. According to the historical data of each production equipment, a product qualification rate prediction model of the equipment is established. The real-time environmental data and process parameters of each production equipment are combined to generate the predicted product qualification rate of each production equipment. The performance data and appearance data of the product to be tested are obtained and compared with the standard performance threshold and standard shape data respectively to generate unqualified product data and the qualification rate of the product to be tested. According to the real-time environmental data of the production equipment and the unqualified product data, the defective product environment correlation trend is generated. According to the predicted product qualification rate and the qualification rate of the product to be tested, the predicted deviation production equipment is determined. According to the real-time operation data of the predicted deviation production equipment, the performance data and appearance data of the product to be tested, the equipment difference index is generated. The defective product environment correlation trend, the equipment difference index and the product qualification rate prediction model of the equipment are combined to establish multiple monitoring standards. In this way, the occurrence of unqualified products can be effectively predicted, the individual differences between the same type of equipment are fully considered, the problem links in the production are accurately located, the root cause of the unqualified products is traced, and the prevention capability of similar problems in subsequent production is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 This is a flow chart of the diode production quality intelligent monitoring method based on multi-source data analysis proposed by the present invention;

[0078] Figure 2 A flowchart of the steps for establishing a product qualification rate prediction model for equipment in the present invention;

[0079] Figure 3A flowchart of the steps for generating defective product environment correlation trends in the present invention;

[0080] Figure 4 A flowchart of the steps for determining the cause of the occurrence of multi-defect unqualified products in the present invention;

[0081] Figure 5 A flow chart of the steps for generating a device difference index in the present invention;

[0082] Figure 6 A flowchart of the steps for establishing multiple monitoring standards in the present invention;

[0083] Figure 7 This is a structural diagram of the diode production quality intelligent monitoring system based on multi-source data analysis proposed by the present invention. DETAILED DESCRIPTION

[0084] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0085] Reference Figure 1 As shown, a method for intelligent monitoring of diode production quality based on multi-source data analysis includes:

[0086] Acquire and analyze diode production targets to generate standard performance thresholds and standard shape data;

[0087] Collect historical data of production equipment, obtain real-time standard data of equipment parameters, and establish a product qualification rate prediction model for the equipment;

[0088] Obtain real-time environmental data and process parameters of each production equipment, combine them with the equipment's product qualification rate prediction model, and generate the predicted product qualification rate of each production equipment;

[0089] Collect the performance data and appearance data of the product to be tested, and compare them with the standard performance threshold and standard shape data respectively, to generate the data of unqualified products and the qualified rate of the products to be tested;

[0090] Generate defective product environmental correlation trends based on real-time environmental data of production equipment and unqualified product data;

[0091] Determine the production equipment for the predicted deviation based on the predicted product qualification rate and the qualified rate of the product to be tested;

[0092] Obtain real-time operating data of the predicted deviation production equipment, combine it with the performance data and appearance data of the product to be tested, and generate the equipment difference index;

[0093] By combining the real-time standard data of equipment parameters, the environmental correlation trend of defective products and the equipment difference index, multiple monitoring standards are established to monitor the real-time data of the equipment.

[0094] This solution establishes a product qualification rate prediction model for each production equipment based on the historical data of each production equipment, and generates a predicted product qualification rate for each production equipment in combination with the real-time environmental data and process parameters of each production equipment. The performance data and appearance data of the product to be tested are obtained and compared with the standard performance threshold and standard shape data respectively to generate unqualified product data and the qualification rate of the product to be tested. The defective product environment correlation trend is generated based on the real-time environmental data and unqualified product data of the production equipment. The predicted deviation production equipment is determined based on the predicted product qualification rate and the qualification rate of the product to be tested. The equipment difference index is generated in combination with the real-time operation data of the predicted deviation production equipment and the performance data and appearance data of the product to be tested. The defective product environment correlation trend, the equipment difference index and the real-time standard data of equipment parameters are combined to establish multiple monitoring standards.

[0095] Reference Figure 2 As shown in the figure, a prediction model for the product qualification rate of the equipment is established, which specifically includes:

[0096] Obtaining the production process of the diode and the production operation manual of each production equipment, extracting the characteristic steps of each production process and the reference parameters of each production equipment, wherein the reference parameters of the production equipment include equipment parameters and environmental parameters;

[0097] Classify the historical data of each production equipment according to process parameters and equipment environment data, and generate process parameter classification data and equipment environment data classification data;

[0098] Based on time, the process parameter classification data and equipment environment data classification data are sorted out, and the corresponding relationship between different process parameters and product qualification rate, as well as the corresponding relationship between different equipment environment data and product qualification rate, are listed;

[0099] According to the corresponding relationship between different process parameters and product qualification rate, as well as the corresponding relationship between different equipment environment data and product qualification rate, the product qualification rate change values ​​corresponding to the process parameter difference and the equipment environment data difference are obtained respectively and the average value is calculated. The degree of change of the product qualification rate corresponding to the change of the process parameter and the degree of change of the product qualification rate corresponding to the change of the equipment environment data are obtained, and recorded as the sensitivity of the product qualification rate to the process parameter and the sensitivity of the product qualification rate to the production equipment environment respectively;

[0100] Record the equipment corresponding to the characteristic steps of the production process as characteristic equipment, classify the historical data of the characteristic equipment into weekly time intervals, and generate time period historical data, which includes the time series data of the characteristic equipment process parameters and product quality data;

[0101] Analyze the historical data of the time period, taking one hour as an analysis node, and map the product quality data to the analysis node. Draw the product quality change curve with the analysis node as the horizontal axis and the product quality data as the vertical axis to generate the time fluctuation trend of the characteristic equipment.

[0102] According to the time fluctuation trend of the characteristic equipment and the reference parameters of the production equipment, the real-time standard data of the equipment parameters are generated. The real-time standard data of the equipment parameters include the real-time standard data of the equipment parameters and the real-time standard data of the equipment environment. The process mutual influence index represents the relationship between the mutual influence between each process and the product qualification rate. The process environment influence index represents the relationship between the mutual influence between the process and the environment and the product qualification rate. Through historical production data, the specific value of the change in the product qualification rate of other equipment caused by the change in the process parameters of the same equipment under the same other conditions is obtained. The specific value of the change in the product qualification rate of other equipment is divided by the change value of the process parameter to generate the process mutual influence index. The specific value of the change in the product qualification rate of the equipment caused by the change in the environmental data of the equipment under the same other conditions is obtained. The specific value of the change in the product qualification rate of the equipment is divided by the data change value of the environment in which the equipment is located to generate the process environment influence index.

[0103] Using the real-time standard data of equipment parameters as a benchmark, the real-time environmental data and process parameter data of each production equipment are compared with the real-time standard data of equipment parameters, and the proportion of production equipment whose real-time environmental data and process parameter data are both greater than the real-time standard data of equipment parameters is obtained in the total number of production equipment, thereby generating the first product predicted qualification rate of the production equipment;

[0104] Based on the product qualification rate of production equipment, analyze the historical production data of production equipment to obtain the process interaction index and process environment impact index;

[0105] The preliminary product qualification rate relationship of the equipment is generated by comprehensively considering the sensitivity of the product qualification rate to the process parameters, the sensitivity of the product qualification rate to the production equipment environment, the process mutual influence index and the process environment influence index;

[0106] The electrostatic field strength and magnetic induction strength of each production equipment are used as the production environment of the production equipment, and the real-time alignment accuracy, processing time and packaging pressure of each production equipment are used as process parameter values. Substitute them into the production equipment product qualification rate relationship formula to obtain the predicted qualification rate of the second product;

[0107] Taking the first product predicted qualified rate and the second product predicted qualified rate as input variables, a product qualified rate prediction model for the equipment is established;

[0108] Among them, the relationship between the qualified rate of production equipment products is specifically as follows:

[0109]

[0110] Where y represents the predicted qualified rate of the second product, b i Indicates the sensitivity of the product qualification rate of the i-th process to the process parameters, x i represents the value of the i-th process parameter, a ij represents the process interaction index between the i-th process and the j-th process, x j represents the jth process parameter value, b j Indicates the sensitivity of the product qualification rate of the jth process to the process parameters, b il represents the process environment impact index between the i-th process and the production environment l, x l It represents the specific value of the production environment l in which the production equipment is located, n represents the total number of processes required for the production equipment to generate products, and m represents the total number of environments that affect the production of products by the production equipment.

[0111] It is understandable that the production process of diodes includes cutting process, oxidation process, photolithography process, diffusion process, electrode preparation and packaging welding. In the various production processes of diodes, although the production equipment maintains constant temperature and humidity to reduce the impact of the environment on the production process, some environmental impacts on production are inevitable, such as static electricity and electromagnetic interference. The friction of raw materials and the movement of people will generate static electricity, causing the semiconductor structure inside the diode chip to be broken down. The operation of large equipment will generate electromagnetic radiation, which will affect the precision equipment required for diode production. The transient pulse signal generated by external electromagnetic interference will interfere with the normal operation of the timing chip, causing the exposure time to be longer or shorter than the set value. If the exposure time is too long If the photoresist is overexposed, the pattern lines will become thicker, resulting in lower resolution. Conversely, if the exposure time is insufficient, the pattern cannot be fully formed, resulting in missing or blurred lines, which in turn affects the three-dimensional structural accuracy of the diode chip. Because the production of diodes is a delicate process, there is a mutual influence between processes. Taking the diffusion process and the photolithography process as an example, the PN junction depth and impurity distribution formed by the diffusion process directly affect the accuracy requirements of the photolithography process. In diode manufacturing, if the PN junction depth after diffusion does not meet the design standards, the alignment accuracy of the photolithography pattern will require higher requirements. The photolithography pattern needs to fall precisely on the area where the PN junction is located, otherwise it will cause problems such as short circuit or open circuit in the circuit.

[0112] Reference Figure 3 As shown, the defective product environment correlation trend is generated, including:

[0113] According to the diode production goals, clarify the diode usage scenarios and obtain the diode test environment thresholds;

[0114] Organizing the data of unqualified products to obtain production data and defect data of the unqualified products, wherein the defect data includes appearance characteristic values, forward voltage values, reverse current values, and breakdown voltage values;

[0115] Classify defective products according to defect data into single-defect defective products and multiple-defect defective products;

[0116] Based on the production data of unqualified products, analyze the defect data of single-defect unqualified products and obtain relevant production data corresponding to the defect data;

[0117] Combine the relevant production data corresponding to the defect data and the data of multi-defect unqualified products to determine the cause of the multi-defect unqualified products;

[0118] Adjust the real-time test environment based on the diode test environment threshold to obtain the specific values ​​of unqualified products under different test data. Compare the change in the specific value of the unqualified products with the change range of the test data to obtain the degree of change in the defect data of the unqualified products under different test environment data.

[0119] Combined with the relevant production data corresponding to the defect data and the degree of change in the defect data of unqualified products under different test environment data, the data relationship between product defect type and environment is determined, and the defect product environment association trend is generated.

[0120] It is understandable that for diodes, a qualified product needs to ensure that all indicators pass the test indicators perfectly. Therefore, there will be products that are eliminated due to failure of a single indicator and products that are eliminated due to failure of multiple indicators. Unqualified products with defects in both forward voltage and reverse current values ​​are due to problems in the manufacturing process of the PN junction, such as uneven doping or abnormal diffusion process, which results in a higher voltage being required to conduct when forward biased, and cannot effectively prevent current from passing when reverse biased. For unqualified products whose forward voltage and breakdown voltage values ​​do not meet the standards at the same time, there are problems with the material or structure of the diode. For example, the quality of the semiconductor material is poor or the depth of the PN junction does not meet the requirements. When the forward voltage is abnormal, it means that the diffusion process of the majority carriers is affected, and the abnormal breakdown voltage is because the PN junction cannot withstand the normal reverse electric field strength and is easily broken down. For products with defects in both reverse current and breakdown voltage values, it is due to impurities inside the diode. The content is too high or there are defects in the packaging process. Excessive impurities will form a leakage channel when reverse biased, increasing the reverse current and making it more likely to cause breakdown when the reverse voltage increases. If the forward voltage, reverse current and breakdown voltage of a defective product do not meet the standards, this usually indicates that there are systemic problems in the overall electrical performance of the product, which is caused by errors in multiple links in the production process, such as poor quality of raw materials, serious deviation of process parameters from the standards or equipment failure. Defects in appearance characteristics include package cracks, bent or oxidized pins. Package cracks will cause the chip to be affected by external factors such as moisture and impurities, thereby changing its electrical properties and causing changes in the forward voltage. Pin oxidation will increase contact resistance. When reverse biased, this additional resistance will cause deviations in the measured value of the reverse current. Poor quality of packaging materials or problems in the packaging process such as bubbles and delamination will affect the electric field distribution of the diode, thereby reducing the breakdown voltage.

[0121] Reference Figure 4 As shown in the figure, the reasons for the occurrence of multi-defect unqualified products include:

[0122] Based on the unqualified product data, the multi-defect unqualified product data is classified to generate defect type combination data, defect severity data and defect frequency data respectively;

[0123] Using defect type combination data as the main judgment criterion, determine the problem process corresponding to the occurrence of multiple defective unqualified products;

[0124] Based on the defect severity data, determine whether the defects of the multi-defect unqualified products are serious. If so, conduct comprehensive quality traceability and process improvement for the multi-defect unqualified products. If not, adjust the production process parameters of the problem process corresponding to the multi-defect unqualified products;

[0125] Based on the defect frequency data, determine whether the defects of multi-defect unqualified products show periodic changes. If so, it means that the production line operators have non-standard operating procedures or there are quality problems with the production raw materials. Further analysis of the problem is needed based on the periodicity of the defects. If not, it means that the defects are normal losses in the production process.

[0126] It is understandable that the defect type combination data counts the situations where different defect types appear simultaneously on the same diode. For example, some diodes are found to have both abnormal forward voltage values ​​and package cracks. This indicates that there is some correlation between the two defects. Perhaps the heat generated by the abnormal forward voltage value causes excessive thermal stress in the package material, resulting in cracks, or the package cracks cause the internal structure of the diode to be affected by external factors, resulting in abnormal forward voltage values. Defect severity data is usually classified according to the degree of impact of the defect on the function, performance, and reliability of the diode. For example, minor defects include tiny scratches on the appearance, unclear markings, etc., which have a negative impact on the electrical performance and basic usage of the diode. There is no impact at all. General defects such as slight oxidation of the pins will affect the welding quality and the stability of the electrical connection to a certain extent, but it can still be used normally with simple treatment. Severe defects such as internal short circuit or open circuit of the chip will directly cause the diode to malfunction and affect the performance of the entire circuit. The defect frequency data can intuitively show which defects are the most common and which are relatively rare. For example, if the frequency of the defect of excessive reverse leakage current is high, it means that there are factors affecting the reverse withstand voltage performance of the diode during the production process, such as oxide layer quality problems, improper chip surface treatment, etc., and it is necessary to improve the monitoring of these factors to improve the quality stability of the generated products.

[0127] Reference Figure 5 As shown, the device difference index is generated, including:

[0128] Obtain the product qualification rate of the predicted deviation production equipment;

[0129] Classify the historical data of production equipment according to the equipment's working time and equipment type to generate a group of equipment with the same working condition. The group of equipment with the same working condition means that the equipment type and working time in the same group are the same;

[0130] Obtain and compare the standard deviation of product qualification rates for equipment groups with the same operating conditions. If the standard deviation is greater than 0.02, it is determined that there is a difference between the equipment in question and the rest of the equipment in the same group. The equipment with the difference is identified, and the corresponding operating data and product data are obtained.

[0131] Extracting performance characteristic data and appearance characteristic data of products generated by different devices based on the generated product data, comparing them with standard performance thresholds and standard shape data respectively, and obtaining product performance difference and product shape data difference;

[0132] Obtain the equipment standard process parameters, combine them with the operating data corresponding to the differential equipment, subtract the operating data corresponding to the differential equipment from the equipment operating data in the equipment standard process parameters, and obtain the difference in the equipment operating process parameters;

[0133] The ratios of product performance difference and equipment operation process parameter difference, as well as product shape data difference and equipment operation process parameter difference are calculated respectively to generate the equipment difference index.

[0134] It is understandable that the same model of equipment may have differences in setting equipment parameters due to different operators. Even if the operation is carried out according to the same process standards, the actual operating performance of the equipment may be inconsistent with the standard parameters due to personal operating habits, deviations in parameter understanding, etc. For example, in the chip diffusion process, a slight deviation in the setting of diffusion temperature and time will cause changes in the impurity concentration and distribution inside the chip, thereby affecting the electrical properties of the diode such as the forward voltage drop and reverse breakdown voltage. This process will be further exacerbated by differences in equipment maintenance and parts replacement, resulting in performance differences between equipment of the same operating time and type.

[0135] Reference Figure 6 As shown, multiple monitoring standards are established to monitor the real-time data of production equipment, including:

[0136] The real-time standard data of equipment parameters, including the real-time standard data of equipment parameters and the real-time standard data of equipment environment, is used as the first standard data. The real-time standard data of equipment parameters is compared with the real-time environment data and process parameter data of the production equipment to determine whether the real-time standard data is lower than the first standard data. If so, an early warning is issued to notify the production line operator to adjust the equipment process parameters. If not, the product qualification rate of the production equipment is further predicted.

[0137] Real-time test environment information is acquired through sensors. Combined with the defective product environment correlation trend corresponding to the current test environment, performance data corresponding to the test environment data is generated as the second standard data. The performance data of the product to be tested in the test environment data is acquired through measuring instruments and compared with the second standard data to determine whether the performance data maintains a consistent trend with the second standard data. If so, the product is deemed unqualified and a warning is issued in advance. If not, the remaining products to be tested are tested.

[0138] Multiply the equipment difference index and the equipment standard process parameters to generate equipment process adaptation data, which is used as the third standard data. Compare it with the real-time process parameters of the production equipment to determine whether there are differences with the third standard data. If so, it means that the products produced by the equipment are at risk of being unqualified, and an early warning is issued. If not, continue to monitor the real-time process parameters of the equipment.

[0139] It is understandable that the production quality monitoring of diodes involves multiple aspects, from processing technology to testing and inspection. By taking the real-time standard data of equipment parameters as the first monitoring standard, the production quality of diodes can be monitored from the process production aspect. By taking the performance data corresponding to the real-time test environment data as the second standard data, the production quality of diodes can be monitored from the testing and inspection link. By taking the equipment process adaptation data as the third standard data, the production quality of diodes can be further monitored in the process production aspect, reducing the impact of differences between equipment on the quality monitoring of diode process production.

[0140] Further, refer to Figure 7 As shown, according to the same inventive concept as the diode production quality intelligent monitoring method based on multi-source data analysis, this solution proposes a diode production quality intelligent monitoring system based on multi-source data analysis, including:

[0141] A data acquisition module, which is used to collect real-time environmental data and process parameters of production equipment, historical data of each production equipment, performance data and appearance data of the product to be tested, and transmit the data to the feature extraction module, data analysis module and model generation module;

[0142] A feature extraction module is used to extract key features from the received data, extract characteristic steps of the production process, extract defect data of single-defect unqualified products, and transmit the data to the data analysis module;

[0143] A data analysis module, which is used to organize the received data, generate a preliminary product qualification rate relationship equation for the equipment, analyze the relationship between equipment operation data, product data and environmental data, and transmit the data to the model generation module and the quality monitoring module;

[0144] A model generation module is used to establish a prediction model based on the received data, generate prediction data through the prediction model and real-time data, and transmit the prediction data to the quality monitoring module;

[0145] A quality monitoring module, which is used to generate monitoring standard data based on the received data, establish multiple monitoring standards, and transmit the data to the abnormal warning module;

[0146] An abnormality warning module is used to compare the received data, determine whether there is an abnormality and whether a warning needs to be issued based on the comparison results, and transmit the data to the intelligent decision-making module;

[0147] The intelligent decision-making module is used to analyze the received data, provide suggestions for the production process and formulate a maintenance plan for the production process equipment based on the data analysis results and the data transmitted by the abnormal warning module.

[0148] Furthermore, the feature extraction module includes:

[0149] A first feature extraction unit, the first feature extraction unit is used to compare the process production process of each production equipment and extract characteristic steps in the production process;

[0150] a second feature extraction unit, configured to extract features according to a use environment of the diode and obtain a test environment threshold of the diode;

[0151] The third feature extraction unit is used to analyze the defect data of the unqualified products, extract the defect features of the unqualified products, and classify the unqualified products.

[0152] Furthermore, the data analysis module includes:

[0153] a first analysis unit configured to analyze the product qualification rate of the production equipment based on the process parameters corresponding to the characteristic steps and the environmental data of the production equipment, and obtain a change relationship index between each process parameter and the product qualification rate, and a change relationship index between each production environment data of the production equipment and the product qualification rate;

[0154] A second analysis unit, configured to adjust the real-time test environment according to the test environment threshold of the diode, obtain the degree of change in defect data of unqualified products under different test environment data, and generate a defect product environment correlation trend based on the relevant production data corresponding to the defect data;

[0155] The third analysis unit is used to generate an equipment difference index according to the product performance difference, the product shape data difference and the equipment operation process parameter difference.

[0156] Furthermore, the quality monitoring module includes:

[0157] a first quality monitoring module, configured to predict the product qualification rate of each production equipment based on a product qualification rate prediction model of the equipment, and compare the prediction with the actual product qualification rate of the production equipment to determine whether there is any abnormality in the product quality;

[0158] A second quality monitoring module, which is used to generate performance data corresponding to the real-time test environment data based on the real-time test environment information and the defective product environment correlation trend, and compare it with the performance data of the product to be tested in the real-time test environment data to determine whether there is any abnormality in the product quality;

[0159] The third quality monitoring module is used to generate equipment process adaptation data based on the equipment standard process parameters and the equipment difference index, and compare it with the real-time process parameters of the production equipment to determine whether there is any abnormality in the quality of the product.

[0160] In summary, the advantages of the present invention are that it can effectively predict the occurrence of unqualified products, fully consider the individual differences between equipment of the same type, accurately locate the problem links in production, facilitate tracing the root cause of the unqualified products, and improve the ability to prevent similar problems in subsequent production.

[0161] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent monitoring of diode production quality based on multi-source data analysis, characterized in that: include: Obtain and analyze diode production plans to generate standard performance thresholds and standard shape data; Collect historical data of production equipment, obtain real-time standard data of equipment parameters, and establish a product qualification rate prediction model for the equipment; Obtain real-time environmental data and process parameter data for each production equipment, and combine it with the equipment's product qualification rate prediction model to generate the predicted product qualification rate for each production equipment; Collect the performance data and appearance data of the product to be tested, and compare them with the standard performance threshold and standard shape data respectively, to generate the data of unqualified products and the qualified rate of the products to be tested; Generate defective product environmental correlation trends based on real-time environmental data of production equipment and unqualified product data; Determine the production equipment for the predicted deviation based on the predicted product qualification rate and the qualified rate of the product to be tested; Obtain real-time operating data of the predicted deviation production equipment, combine it with the performance data and appearance data of the product to be tested, and generate the equipment difference index; Combined with real-time standard data of equipment parameters, defective product environment correlation trends and equipment difference index, multiple monitoring standards are established to monitor the real-time data of equipment; The establishment of a product qualification rate prediction model for the equipment specifically includes: Obtaining the production process of the diode and the production operation manual of each production equipment, extracting the characteristic steps of each production process and the reference parameters of each production equipment, wherein the reference parameters of the production equipment include equipment parameters and environmental parameters; Classify the historical data of each production equipment according to process parameters and equipment environment data, and generate process parameter classification data and equipment environment data classification data; Based on time, the process parameter classification data and equipment environment data classification data are sorted out, and the corresponding relationship between different process parameters and product qualification rate, as well as the corresponding relationship between different equipment environment data and product qualification rate, are listed; According to the corresponding relationship between different process parameters and product qualification rate, as well as the corresponding relationship between different equipment environment data and product qualification rate, the product qualification rate change values ​​corresponding to the process parameter difference and the equipment environment data difference are obtained respectively and the average value is calculated. The degree of change of the product qualification rate corresponding to the change of the process parameter and the degree of change of the product qualification rate corresponding to the change of the equipment environment data are obtained, and recorded as the sensitivity of the product qualification rate to the process parameter and the sensitivity of the product qualification rate to the production equipment environment respectively; Record the equipment corresponding to the characteristic steps of the production process as characteristic equipment, classify the historical data of the characteristic equipment into weekly time intervals, and generate time period historical data, which includes the time series data of the characteristic equipment process parameters and product quality data; Analyze the historical data of the time period, taking one hour as an analysis node, and map the product quality data to the analysis node. Draw the product quality change curve with the analysis node as the horizontal axis and the product quality data as the vertical axis to generate the time fluctuation trend of the characteristic equipment. Generating real-time standard data of equipment parameters according to the time fluctuation trend of the characteristic equipment and the reference parameters of the production equipment, wherein the real-time standard data of equipment parameters includes real-time standard data of equipment parameters and real-time standard data of equipment environment; Using the real-time standard data of equipment parameters as a benchmark, the real-time environmental data and process parameter data of each production equipment are compared with the real-time standard data of equipment parameters, and the proportion of production equipment whose real-time environmental data and process parameter data are both greater than the real-time standard data of equipment parameters is obtained in the total number of production equipment, thereby generating the first product predicted qualification rate of the production equipment; Based on the product qualification rate of production equipment, analyze the historical production data of production equipment to obtain the process interaction index and process environment impact index; The preliminary product qualification rate relationship of the equipment is generated by comprehensively considering the sensitivity of the product qualification rate to the process parameters, the sensitivity of the product qualification rate to the production equipment environment, the process mutual influence index and the process environment influence index; The electrostatic field strength and magnetic induction strength of each production equipment are used as the production environment of the production equipment, and the real-time alignment accuracy, processing time and packaging pressure of each production equipment are used as process parameter values. Substitute them into the production equipment product qualification rate relationship formula to obtain the predicted qualification rate of the second product; Taking the first product predicted qualified rate and the second product predicted qualified rate as input variables, a product qualified rate prediction model for the equipment is established; Among them, the relationship between the qualified rate of production equipment products is specifically as follows: Where, It represents the predicted qualified rate of the second product, Indicates the The product qualification rate of each process is sensitive to the process parameters. Indicates the process parameter values, Indicates the The process and The process interaction index between the processes, Indicates the process parameter values, Indicates the The product qualification rate of each process is sensitive to the process parameters. Indicates the Process and production environment The process environment impact index between Indicates the production environment where the production equipment is located The specific value of Represents the total number of processes required by the production equipment to generate the product, Represents the total number of environments that impact the products produced by production equipment.

2. The method for intelligently monitoring diode production quality based on multi-source data analysis according to claim 1, characterized in that: Generating defective product environment correlation trends specifically includes: According to the diode production goals, clarify the diode usage scenarios and obtain the diode test environment thresholds; Organizing the data of unqualified products to obtain production data and defect data of the unqualified products, wherein the defect data includes appearance characteristic values, forward voltage values, reverse current values, and breakdown voltage values; Classify defective products according to defect data into single-defect defective products and multiple-defect defective products; Based on the production data of unqualified products, analyze the defect data of single-defect unqualified products and obtain relevant production data corresponding to the defect data; Combine the relevant production data corresponding to the defect data and the data of multi-defect unqualified products to determine the cause of the multi-defect unqualified products; Adjust the real-time test environment based on the diode test environment threshold to obtain the specific values ​​of unqualified products under different test data. Compare the specific value changes of unqualified products with the change range of test data to obtain the degree of change in defect data of unqualified products under different test environment data. Combined with the relevant production data corresponding to the defect data and the degree of change in the defect data of unqualified products under different test environment data, the data relationship between product defect type and environment is determined, and the defect product environment association trend is generated.

3. The method for intelligently monitoring diode production quality based on multi-source data analysis according to claim 2, characterized in that: The reasons for determining the occurrence of unqualified products with multiple defects include: Based on the unqualified product data, the multi-defect unqualified product data is classified to generate defect type combination data, defect severity data and defect frequency data respectively; Using defect type combination data as the main judgment criterion, determine the problem process corresponding to the occurrence of multiple defective unqualified products; Based on the defect severity data, determine whether the defects of the multi-defect unqualified products are serious. If so, conduct comprehensive quality traceability and process improvement for the multi-defect unqualified products. If not, adjust the production process parameters of the problem process corresponding to the multi-defect unqualified products; Based on the defect frequency data, determine whether the defects of multi-defect unqualified products show periodic changes. If so, it means that the production line operators have non-standard operating procedures or there are quality problems with the production raw materials. Further analysis of the problem is needed based on the periodicity of the defects. If not, it means that the defects are normal losses in the production process.

4. The method for intelligently monitoring diode production quality based on multi-source data analysis according to claim 3, characterized in that: The generating device difference index specifically includes: Obtain the product qualification rate of the predicted deviation production equipment; Classify the historical data of production equipment according to the equipment's working time and equipment type to generate a group of equipment with the same working condition. The group of equipment with the same working condition means that the equipment type and working time in the same group are the same; Obtain and compare the standard deviation of product qualification rates for equipment groups with the same operating conditions. If the standard deviation is greater than 0.02, it is determined that there is a difference between the equipment in question and the rest of the equipment in the same group. The equipment with the difference is identified, and the corresponding operating data and product data are obtained. Extracting performance characteristic data and appearance characteristic data of products generated by different devices based on the generated product data, comparing them with standard performance thresholds and standard shape data respectively, and obtaining product performance difference and product shape data difference; Obtain the equipment standard process parameters, combine them with the operating data corresponding to the differential equipment, subtract the operating data corresponding to the differential equipment from the equipment operating data in the equipment standard process parameters, and obtain the difference in the equipment operating process parameters; The ratios of product performance difference and equipment operation process parameter difference, as well as product shape data difference and equipment operation process parameter difference are calculated respectively to generate the equipment difference index.

5. The method for intelligently monitoring diode production quality based on multi-source data analysis according to claim 4, characterized in that: The establishment of multiple monitoring standards to monitor real-time data of production equipment specifically includes: The real-time standard data of equipment parameters, including the real-time standard data of equipment parameters and the real-time standard data of equipment environment, is used as the first standard data. The real-time standard data of equipment parameters is compared with the real-time environment data and process parameter data of the production equipment to determine whether the real-time standard data is lower than the first standard data. If so, an early warning is issued to notify the production line operator to adjust the equipment process parameters. If not, the product qualification rate of the production equipment is further predicted. Real-time test environment information is acquired through sensors. Combined with the defective product environment correlation trend corresponding to the current test environment, performance data corresponding to the test environment data is generated as the second standard data. The performance data of the product to be tested in the test environment data is acquired through measuring instruments and compared with the second standard data to determine whether the performance data maintains a consistent trend with the second standard data. If so, the product is deemed unqualified and a warning is issued in advance. If not, the remaining products to be tested are tested. Multiply the equipment difference index and the equipment standard process parameters to generate equipment process adaptation data, which is used as the third standard data. Compare it with the real-time process parameters of the production equipment to determine whether there are differences with the third standard data. If so, it means that the products produced by the equipment are at risk of being unqualified, and an early warning is issued. If not, continue to monitor the real-time process parameters of the equipment.

6. An intelligent monitoring system for diode production quality based on multi-source data analysis, characterized in that: A method for intelligently monitoring diode production quality based on multi-source data analysis according to any one of claims 1 to 5, comprising: A data acquisition module, which is used to collect real-time environmental data and process parameters of production equipment, historical data of each production equipment, performance data and appearance data of the product to be tested, and transmit the data to the feature extraction module, data analysis module and model generation module; A feature extraction module is used to extract key features from the received data, extract characteristic steps of the production process, extract defect data of single-defect unqualified products, and transmit the data to the data analysis module; A data analysis module, which is used to organize the received data, generate a preliminary product qualification rate relationship equation for the equipment, analyze the relationship between equipment operation data, product data and environmental data, and transmit the data to the model generation module and the quality monitoring module; A model generation module is used to establish a prediction model based on the received data, generate prediction data through the prediction model and real-time data, and transmit the prediction data to the quality monitoring module; A quality monitoring module, which is used to generate monitoring standard data based on the received data, establish multiple monitoring standards, and transmit the data to the abnormal warning module; An abnormality warning module is used to compare the received data, determine whether there is an abnormality and whether a warning needs to be issued based on the comparison results, and transmit the data to the intelligent decision-making module; The intelligent decision-making module is used to analyze the received data, provide suggestions for the production process and formulate a maintenance plan for the production process equipment based on the data analysis results and the data transmitted by the abnormal warning module.

7. The diode production quality intelligent monitoring system based on multi-source data analysis according to claim 6 is characterized in that: The feature extraction module includes: A first feature extraction unit, the first feature extraction unit is used to compare the process production process of each production equipment and extract characteristic steps in the production process; a second feature extraction unit, configured to extract features according to a use environment of the diode and obtain a test environment threshold of the diode; The third feature extraction unit is used to analyze the defect data of the unqualified products, extract the defect features of the unqualified products, and classify the unqualified products.

8. The diode production quality intelligent monitoring system based on multi-source data analysis according to claim 6 is characterized in that: The data analysis module includes: a first analysis unit configured to analyze the product qualification rate of the production equipment based on the process parameters corresponding to the characteristic steps and the environmental data of the production equipment, and obtain a change relationship index between each process parameter and the product qualification rate, and a change relationship index between each production environment data of the production equipment and the product qualification rate; A second analysis unit, configured to adjust the real-time test environment according to the test environment threshold of the diode, obtain the degree of change in defect data of unqualified products under different test environment data, and generate a defect product environment correlation trend based on the relevant production data corresponding to the defect data; The third analysis unit is used to generate an equipment difference index according to the product performance difference, the product shape data difference and the equipment operation process parameter difference.

9. The diode production quality intelligent monitoring system based on multi-source data analysis according to claim 6, characterized in that: The quality monitoring module includes: a first quality monitoring module, configured to predict the product qualification rate of each production equipment based on a product qualification rate prediction model of the equipment, and compare the prediction with the actual product qualification rate of the production equipment to determine whether there is any abnormality in the product quality; A second quality monitoring module, which is used to generate performance data corresponding to the real-time test environment data based on the real-time test environment information and the defective product environment correlation trend, and compare it with the performance data of the product to be tested in the real-time test environment data to determine whether there is any abnormality in the product quality; The third quality monitoring module is used to generate equipment process adaptation data based on the equipment standard process parameters and the equipment difference index, and compare it with the real-time process parameters of the production equipment to determine whether there is any abnormality in the quality of the product.

Images