A Quality Analysis Method and System for Quartz Crystal Oscillators Based on Big Data in Intelligent Manufacturing Systems

Abstract

The present invention discloses a method and system for quartz crystal oscillator quality analysis based on big data of intelligent manufacturing systems, which relates to the technical field of quartz crystal resonators. The present invention obtains the appearance images and electrical performance parameters of each batch of quartz crystal oscillators from detection equipment, uses an appearance quality judgment model to judge the appearance quality based on the appearance image parameters, and qualified products enter the electrical performance quality judgment link. By calculating electrical performance quality characteristic values such as mean, standard deviation, range, target value, etc., quality stability evaluation indicators such as target volatility value and overall discreteness value are obtained, and an electrical performance quality judgment model is used to judge the electrical performance quality. For unqualified batches, they are brought into a further analysis model to determine whether they can be re-detected and screened to eliminate abnormalities. If so, they are re-inspected according to the new detection rules, otherwise the relevant parameters are sent to technical personnel, and at the same time, recycling treatment is carried out.

Description

Technical Field

[0001] The present invention relates to the technical field of quartz crystal resonators, and specifically relates to a method and system for analyzing the quality of quartz crystal oscillators based on big data of intelligent manufacturing systems. Background Art

[0002] As the crystal oscillator industry develops towards high precision and high reliability, it promotes the transformation of quartz crystal resonators to intelligent manufacturing. Through the deep integration of information technology and crystal oscillator manufacturing, after promoting the intelligent transformation of manufacturing process methods, enterprises can obtain big data analysis and evaluation of the electrical performance quality of quartz crystal resonators through intelligent manufacturing systems to predict whether the electrical performance quality of quartz crystal resonators meets the product quality requirements of high precision and high stability. Therefore, it is particularly important to invent a method and system for analyzing the quality of quartz crystal oscillators based on big data of intelligent manufacturing systems.

[0003] The prior art, such as an invention patent application with the publication number of CN116468332A, discloses a production quality evaluation system for crystal resonators, which includes a resonator image acquisition, apparent quality analysis, testing, test information collection, performance analysis, quality analysis, and warning terminal modules. By analyzing the apparent quality of the current batch of crystal resonators of a specified enterprise and randomly sampling and detecting the sensitivity and stability of the target products, it overcomes the defects of the prior art and realizes accurate quality analysis. This ensures the product quality within the batch, improves the user experience, and further enhances the reputation and sales volume of crystal oscillator enterprises.

[0004] The prior art still has the following defects, specifically manifested as: 1. In the prior art, relying on manual extraction of quartz crystal oscillator quality inspection data and using tools such as histograms and control charts for sampling analysis and outlier detection, it is impossible to automatically perform big data full analysis and outlier detection on all quartz crystal oscillator electrical performance quality inspection data, and it is difficult to comprehensively grasp the product quality situation.

[0005] 2. In the prior art, the sample size of manually extracted data is limited and cannot cover all quartz crystal oscillator electrical performance quality inspection data, which makes the analysis results difficult to comprehensively and accurately reflect the overall product quality situation. In terms of the accuracy of identifying the electrical performance quality of quartz crystal oscillator products, manual analysis is limited by human subjective judgment and limited data analysis capabilities, and it is difficult to accurately evaluate key electrical performance indicators such as frequency accuracy and resistance capacitance stability, increasing the product quality risk and making it difficult to ensure that the product quality meets the requirements of high precision and high stability. Summary of the Invention

[0006] The purpose of the present invention is to provide a method and system for analyzing the quality of quartz crystal oscillators based on big data of intelligent manufacturing systems, which solves the problems existing in the background art.

[0007] To solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a method for analyzing the quality of quartz crystal oscillators based on big data of intelligent manufacturing systems, including: Step 1, information acquisition, obtaining the relevant parameters of each quartz crystal oscillator in each batch from the intelligent manufacturing quality inspection equipment for quartz crystal resonators.

[0008] Step 2, appearance quality judgment, based on the relevant parameters of each quartz crystal oscillator in each batch, judging whether the appearance quality of each quartz crystal oscillator in each batch is qualified. If the appearance quality of a certain quartz crystal oscillator in a certain batch is qualified, then execute Step 3. If the appearance quality of a certain quartz crystal oscillator in a certain batch is unqualified, then carry out recycling treatment.

[0009] Step 3, electrical performance quality judgment, based on the relevant parameters of each quartz crystal oscillator in this batch, calculating the characteristic value of the electrical performance quality of the quartz crystal oscillators in this batch, and then calculating the quality stability evaluation index of the quartz crystal oscillators in this batch, and then judging whether the electrical performance quality of the quartz crystal oscillators in this batch is qualified. If the electrical performance quality of the quartz crystal oscillators in this batch is unqualified, then execute Step 4.

[0010] Step 4, specification adjustment and re-inspection, based on the relevant parameters of each quartz crystal oscillator in this batch, inputting into a further analysis model, and based on the output result of the further analysis model, determining whether each quartz crystal oscillator in this batch can pass the re-detection and screening to eliminate anomalies. If each quartz crystal oscillator in this batch cannot pass the re-detection and screening to eliminate anomalies, then send the relevant parameters of each quartz crystal oscillator in this batch to the designated technical personnel. If each quartz crystal oscillator in this batch can pass the re-detection and screening to eliminate anomalies, then re-detect according to the new detection rules. If it is still determined that the electrical performance quality of the quartz crystal oscillators in this batch is unqualified according to the new detection rules, then carry out recycling treatment.

[0011] Preferably, the relevant parameters of the quartz crystal resonator include: appearance image parameters and electrical performance parameters, where the appearance image parameters include the offset of the metal lid, the blackened area of the bonding wire, and the number of surface scratches, and the electrical performance parameters include the resonance frequency, equivalent resistance, load capacitance, and temperature measurement frequency fitting degree.

[0012] Preferably, the specific implementation method for judging whether the appearance quality of each quartz crystal oscillator in each batch is qualified is: extracting the appearance image parameters of each quartz crystal oscillator in each batch and inputting them into the appearance quality judgment model of the quartz crystal oscillator to output the judgment result of the appearance quality of each quartz crystal oscillator in each batch.

[0013] The judgment results of the appearance quality of each batch of quartz crystal resonators include values of 1 and 0. If the judgment result of the appearance quality of a certain batch of a certain quartz crystal resonator is 1, it is determined that the appearance quality of this batch of this quartz crystal resonator is qualified; if the judgment result of the appearance quality of a certain batch of a certain quartz crystal resonator is 0, it is determined that the appearance quality of this batch of this quartz crystal resonator is unqualified.

[0014] Preferably, the method for calculating the characteristic value of the electrical performance quality of this batch of quartz crystal resonators is specifically implemented as follows: The characteristic value of the electrical performance quality includes the mean, standard deviation, range, and target value of the resonance frequency, equivalent resistance, load capacitance, and temperature measurement frequency fitting degree of the quartz crystal resonator.

[0015] The formula for the mean is: , where represents the mean of the th characteristic value of the electrical performance quality of this batch of quartz crystal resonators, represents the th quartz crystal resonator of this batch, and the th characteristic value of its electrical performance quality, represents the number of the quartz crystal resonator with qualified appearance quality in this batch, , is a positive integer greater than 2, represents the number of quartz crystal resonators with qualified appearance quality in this batch, represents the number of the characteristic value of the electrical performance quality of the quartz crystal resonator, , is a positive integer greater than 2.

[0016] The formula for the standard deviation is: , where represents the standard deviation of the th characteristic value of the electrical performance quality of this batch of quartz crystal resonators.

[0017] The formula for the range is: , where represents the range of the th characteristic value of the electrical performance quality of this batch of quartz crystal resonators, represents the maximum value of the th characteristic value of the electrical performance quality of this batch of quartz crystal resonators, represents the minimum value of the th characteristic value of the electrical performance quality of this batch of quartz crystal resonators.

[0018] The formula for the target value is: , where The target value of the th eigenvalue representing the electrical performance quality of this batch of quartz crystal resonators, The upper specification limit of the th eigenvalue representing the electrical performance quality of the quartz crystal resonator required by the customer, The lower specification limit of the th eigenvalue representing the electrical performance quality of the quartz crystal resonator required by the customer.

[0019] Preferably, the quality stability evaluation index of this batch of quartz crystal resonators specifically includes: the target volatility value and the overall discreteness value.

[0020] The formula for the target volatility value is: , where represents the target volatility value of the th eigenvalue representing the electrical performance quality of this batch of quartz crystal resonators.

[0021] The formula for the overall discreteness value is: , where represents the overall discreteness value of the th eigenvalue representing the electrical performance quality of this batch of quartz crystal resonators.

[0022] Preferably, the specific implementation method for determining whether the electrical performance quality of this batch of quartz crystal resonators is qualified is: extracting the quality stability evaluation index of this batch of quartz crystal resonators and inputting it into the electrical performance quality judgment model of the quartz crystal resonator to output the judgment result of the electrical performance quality of this batch of quartz crystal resonators.

[0023] The judgment result of the electrical performance quality of this batch of quartz crystal resonators contains numerical values of 1 and 0. If the judgment result of the electrical performance quality of this batch of quartz crystal resonators is 1, it is determined that the electrical performance quality of this batch of quartz crystal resonators is qualified; if the judgment result of the electrical performance quality of this batch of quartz crystal resonators is 0, it is determined that the electrical performance quality of this batch of quartz crystal resonators is unqualified.

[0024] Preferably, the specific implementation method for determining whether each quartz crystal resonator in this batch can be screened and abnormal ones can be eliminated through re-detection is: extracting the eigenvalue of the electrical performance quality of this batch of quartz crystal resonators and inputting it into the further analysis model to output the judgment result of whether this batch of quartz crystal resonators can be screened and abnormal ones can be eliminated through re-detection.

[0025] The judgment result of whether the batch of quartz crystal resonators can pass the re - detection and screening to eliminate anomalies includes numerical values of 1 and 0. If the judgment result of whether the batch of quartz crystal resonators can pass the re - detection and screening to eliminate anomalies is 1, it indicates that the batch of quartz crystal resonators can pass the re - detection and screening to eliminate anomalies. If the judgment result of whether the batch of quartz crystal resonators can pass the re - detection and screening to eliminate anomalies is 0, it indicates that the batch of quartz crystal resonators cannot pass the re - detection and screening to eliminate anomalies.

[0026] Preferably, the new detection rule is specifically as follows: , , where represents the upper specification limit of the th eigenvalue of the electrical performance quality of the quartz crystal resonator with the modified customer requirements, represents the lower specification limit of the th eigenvalue of the electrical performance quality of the quartz crystal resonator with the modified customer requirements, represents the modification factor defined in the database.

[0027] Preferably, the recycling process is specifically implemented as follows: S1. Water - bath heating of the degreasing agent.

[0028] S2. Hot water rinsing.

[0029] S3. Water - bath heating of citric acid.

[0030] S4. Hot water rinsing.

[0031] S5. Baking.

[0032] S6. Ion cleaning.

[0033] The second aspect of the present invention provides a system for a quartz crystal oscillator quality analysis method based on big data of an intelligent manufacturing system, including: an information acquisition module, which acquires relevant parameters of each batch of quartz crystal resonators from the intelligent manufacturing quality detection equipment of the quartz crystal resonators.

[0034] An appearance quality judgment module, which judges whether the appearance quality of each batch of quartz crystal resonators is qualified based on the relevant parameters of each batch of quartz crystal resonators. If the appearance quality of a certain batch of a certain quartz crystal resonator is qualified, the electrical performance quality judgment module is executed. If the appearance quality of a certain batch of a certain quartz crystal resonator is unqualified, recycling processing is performed.

[0035] The electrical performance quality judgment module calculates the characteristic values of the electrical performance quality of the quartz crystal resonators in this batch based on the relevant parameters of each quartz crystal resonator in this batch, and then calculates the quality stability evaluation index of the quartz crystal resonators in this batch, and then judges whether the electrical performance quality of the quartz crystal resonators in this batch is qualified. If the electrical performance quality of the quartz crystal resonators in this batch is unqualified, the specification adjustment and re-inspection module will be executed.

[0036] The specification adjustment and re-inspection module, based on the relevant parameters of each quartz crystal resonator in this batch, brings them into the further analysis model. Based on the output results of the further analysis model, it determines whether each quartz crystal resonator in this batch can pass the re-detection and screening to eliminate abnormalities. If each quartz crystal resonator in this batch cannot pass the re-detection and screening to eliminate abnormalities, the relevant parameters of each quartz crystal resonator in this batch will be sent to the designated technical personnel. If each quartz crystal resonator in this batch can pass the re-detection and screening to eliminate abnormalities, it will be re-detected according to the new detection rules. If it is still determined that the electrical performance quality of the quartz crystal resonators in this batch is unqualified according to the new detection rules, recycling treatment will be carried out.

[0037] The beneficial effects of the present invention are as follows: 1. In the present invention, through the appearance quality judgment model of quartz crystal resonators, it is possible to systematically and automatically analyze and judge abnormalities in the large data of quartz crystal detection, break through the limitations of manual operation in data processing scale, improve the overall data analysis efficiency, and achieve comprehensive control of product quality.

[0038] 2. In the present invention, in terms of the accuracy of identifying the electrical performance quality of quartz crystal oscillator products, intelligent algorithms and professional data operation logics are used, which are not interfered by subjective factors, and can accurately evaluate key indicators such as frequency accuracy and resistance-capacitance stability. It reduces the product quality risk, strongly ensures that the product quality meets the strict standards of high precision and high stability, and provides a solid support for the reliable operation of the product in various complex application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0040] Figure 1 It is a schematic flow chart of the implementation steps of the method of the present invention.

[0041] Figure 2 It is a schematic connection diagram of the system structure of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0042] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0043] Referring to Figure 1 As shown, the present invention provides a method for analyzing the quality of quartz crystal oscillators based on big data of intelligent manufacturing systems, including: Step 1, information acquisition, obtaining relevant parameters of each batch of quartz crystal resonators from the intelligent manufacturing quality inspection equipment for quartz crystal resonators.

[0044] In a specific embodiment, the relevant parameters of the quartz crystal resonator include: appearance image parameters and electrical performance parameters, where the appearance image parameters include the offset of the metal lid, the blackened area of the bonding wire, and the number of surface scratches, and the electrical performance parameters include the resonance frequency, equivalent resistance, load capacitance, and temperature measurement frequency fitting degree.

[0045] Step 2, appearance quality judgment, based on the relevant parameters of each batch of quartz crystal resonators, judging whether the appearance quality of each batch of quartz crystal resonators is qualified. If the appearance quality of a certain batch of a certain quartz crystal resonator is qualified, then execute Step 3. If the appearance quality of a certain batch of a certain quartz crystal resonator is unqualified, then perform recycling processing.

[0046] In a specific embodiment, the method for judging whether the appearance quality of each batch of quartz crystal resonators is qualified is specifically implemented as: extracting the appearance image parameters of each batch of quartz crystal resonators and inputting them into the appearance quality judgment model of the quartz crystal resonator to output the judgment result of the appearance quality of each batch of quartz crystal resonators.

[0047] The judgment result of the appearance quality of each batch of quartz crystal resonators includes numerical values of 1 and 0. If the judgment result of the appearance quality of a certain batch of a certain quartz crystal resonator is 1, it is determined that the appearance quality of this batch of this quartz crystal resonator is qualified. If the judgment result of the appearance quality of a certain batch of a certain quartz crystal resonator is 0, it is determined that the appearance quality of this batch of this quartz crystal resonator is unqualified.

[0048] It should be noted that in the appearance quality judgment model of the quartz crystal resonator, the model expression is: , where represents the judgment result of the appearance quality of the th batch and the th quartz crystal resonator, , , respectively represent the metal lid offset, wire bonding blackening area, and surface scratch number of the th quartz crystal resonator in the batch, respectively, and

[0049] respectively represent the highest thresholds of the metal lid offset, wire bonding blackening area, and surface scratch number of the quartz crystal resonator defined in the database, which are set by professionals.

[0050] In the present invention, through the appearance quality judgment model of the quartz crystal resonator, it is possible to systematically and automatically perform a full analysis and outlier detection on the large amount of quartz crystal detection data, breaking through the limitation of manual operation in terms of data processing scale, improving the overall data analysis efficiency, and achieving a comprehensive control of product quality.

[0051] In a specific embodiment, the specific implementation method for calculating the characteristic value of the electrical performance quality of the quartz crystal resonators in this batch is as follows: The characteristic value of the electrical performance quality includes the mean, standard deviation, range, and target value of the resonance frequency, equivalent resistance, load capacitance, and temperature measurement frequency fitting degree of the quartz crystal resonator.

[0052] The formula for the mean is: , where represents the mean of the th characteristic value of the electrical performance quality of the quartz crystal resonators in this batch, represents the th quartz crystal resonator in this batch, th characteristic value of the electrical performance quality, represents the serial number of the quartz crystal resonator with qualified appearance quality in this batch, , is a positive integer greater than 2, represents the number of quartz crystal resonators with qualified appearance quality in this batch, represents the serial number of the characteristic value of the electrical performance quality of the quartz crystal resonator, , is a positive integer greater than 2.

[0053] The formula for the standard deviation is: , where represents the standard deviation of the th eigenvalue of the electrical performance quality of this batch of quartz crystal resonators.

[0054] The range mentioned above, the calculation formula is: , where represents the range of the th eigenvalue of the electrical performance quality of this batch of quartz crystal resonators, represents the maximum value of the th eigenvalue of the electrical performance quality of this batch of quartz crystal resonators, represents the minimum value of the th eigenvalue of the electrical performance quality of this batch of quartz crystal resonators.

[0055] The target value mentioned above, the calculation formula is: , where represents the target value of the th eigenvalue of the electrical performance quality of this batch of quartz crystal resonators, represents the upper specification limit of the th eigenvalue of the electrical performance quality of the quartz crystal resonator required by the customer, represents the lower specification limit of the th eigenvalue of the electrical performance quality of the quartz crystal resonator required by the customer.

[0056] In a specific embodiment, the quality stability evaluation index of this batch of quartz crystal resonators specifically includes: target volatility value and overall discreteness value.

[0057] The target volatility value, the calculation formula is: , where represents the target volatility value of the th eigenvalue of the electrical performance quality of this batch of quartz crystal resonators.

[0058] It should be noted that when calculating the target volatility value, using the target value can directly compare the deviation degree between the actual data and the ideal standard. When calculating the target volatility value, using the target value can directly compare the deviation degree between the actual data and the ideal standard. When calculating the target volatility value, the standard deviation is used to quantify the fluctuation range of the data. Combining the mean value and the target value, the standard deviation can more accurately reflect the fluctuation of the product's electrical performance around the target value and judge the stability of the product quality.

[0059] The overall discreteness value, the calculation formula is: , where represents the overall discreteness value of the th eigenvalue of the electrical performance quality of this batch of quartz crystal resonators.

[0060] It should be noted that when calculating the overall discrete value, the range can provide a simple and intuitive indicator to quickly understand the fluctuation span of the data. Combining with the standard deviation can more comprehensively evaluate the discrete situation of the data, which can not only grasp the maximum fluctuation range of the data, but also deeply understand the discrete distribution of the data around the mean value, so as to more accurately evaluate the overall discrete degree of the electrical performance quality of the product and judge the stability of the production process and the consistency of the product quality.

[0061] In a specific embodiment, the specific implementation method for determining whether the electrical performance quality of this batch of quartz crystal resonators is qualified is as follows: extract the quality stability evaluation index of this batch of quartz crystal resonators and input it into the electrical performance quality judgment model of the quartz crystal resonator, and output the judgment result of the electrical performance quality of this batch of quartz crystal resonators.

[0062] The judgment result of the electrical performance quality of this batch of quartz crystal resonators includes numerical values of 1 and 0. If the judgment result of the electrical performance quality of this batch of quartz crystal resonators is 1, it is determined that the electrical performance quality of this batch of quartz crystal resonators is qualified; if the judgment result of the electrical performance quality of this batch of quartz crystal resonators is 0, it is determined that the electrical performance quality of this batch of quartz crystal resonators is unqualified.

[0063] In the present invention, in terms of identifying the accuracy of the electrical performance quality of quartz crystal oscillator products, intelligent algorithms and professional data operation logics are used, which are not interfered by subjective factors, and can accurately evaluate key indicators such as frequency accuracy and resistance-capacitance stability. It reduces the product quality risk and strongly ensures that the product quality meets the strict standards of high precision and high stability, providing a solid support for the reliable operation of the product in various complex application scenarios.

[0064] It should be noted that the electrical performance quality judgment model of the quartz crystal resonator has the following model expression: , where represents the judgment result of the electrical performance quality of this batch of quartz crystal resonators, represents the th eigenvalue target volatility value of the electrical performance quality of the quartz crystal resonator predefined in the database, represents the th eigenvalue overall discreteness value of the electrical performance quality of the quartz crystal resonator predefined in the database, which is set by professionals. For example, here , .

[0065] Step 4. Specification adjustment and re-inspection. Based on the relevant parameters of each quartz crystal resonator in this batch, input them into the further analysis model. Based on the output results of the further analysis model, determine whether each quartz crystal resonator in this batch can pass the re-detection and screening to eliminate anomalies. If each quartz crystal resonator in this batch cannot pass the re-detection and screening to eliminate anomalies, then send the relevant parameters of each quartz crystal resonator in this batch to the designated technical personnel. If each quartz crystal resonator in this batch can pass the re-detection and screening to eliminate anomalies, then re-detect according to the new detection rules. If it is still determined that the electrical performance quality of the quartz crystal resonators in this batch is unqualified according to the new detection rules, then carry out recycling treatment.

[0066] In a specific embodiment, the method for determining whether each quartz crystal resonator in this batch can pass the re-detection and screening to eliminate anomalies is specifically implemented as follows: Extract the characteristic values of the electrical performance quality of the quartz crystal resonators in this batch, input them into the further analysis model, and output the judgment result on whether each quartz crystal resonator in this batch can pass the re-detection and screening to eliminate anomalies.

[0067] The judgment result on whether each quartz crystal resonator in this batch can pass the re-detection and screening to eliminate anomalies includes numerical values of 1 and 0. If the judgment result on whether each quartz crystal resonator in this batch can pass the re-detection and screening to eliminate anomalies is 1, it indicates that each quartz crystal resonator in this batch can pass the re-detection and screening to eliminate anomalies. If the judgment result on whether each quartz crystal resonator in this batch can pass the re-detection and screening to eliminate anomalies is 0, it indicates that each quartz crystal resonator in this batch cannot pass the re-detection and screening to eliminate anomalies.

[0068] It should be noted that the further analysis model has the model expression: , where represents the judgment result on whether each quartz crystal resonator in this batch can pass the re-detection and screening to eliminate anomalies, represents the standard value of the th characteristic value of the electrical performance quality of the quartz crystal resonator in the further analysis model, which is set by professionals. Here .

[0069] In a specific embodiment, the new detection rules are specifically as follows: , , where represents the upper specification limit of the th characteristic value of the electrical performance quality of the quartz crystal resonator for the modified customer requirements, represents the lower specification limit of the th characteristic value of the electrical performance quality of the quartz crystal resonator for the modified customer requirements, represents the modification factor defined in the database.

[0070] It should be noted that the modification factors defined in the database are set by professionals, and the analysis method is as follows: The standard deviation is calculated from the big data of the batch quality of the electrical performance of the quartz crystal resonator, which reflects the degree of dispersion of the data relative to the mean. In the normal distribution of statistics, about 99.73% of the data will fall within the range of the mean ± 3 times the standard deviation. In the quality control of quartz crystal oscillators, it is used to determine a reasonable quality fluctuation range. Therefore, here .

[0071] In a specific embodiment, the recycling process is specifically implemented as follows: S1. Degumming agent water bath heating: Pour the quartz crystal resonator base and the degumming agent into a box at the same time, and perform water bath heating for 60 minutes to dissolve the conductive glue and separate the wafer from the base. The component of the degumming agent is potassium hydroxide, and the ratio of the resonator to potassium hydroxide is 70g:1000g, which has the best effect.

[0072] S2. Hot water rinsing: Pour the degumming agent into a spare box, and rinse the quartz crystal resonator base 5 times with 50-degree hot water to rinse off the residual degumming agent and the wafer.

[0073] S3. Citric acid water bath heating: Pour the rinsed quartz crystal resonator base into a box containing citric acid, and perform water bath heating for 30 minutes to remove potassium silicate on the surface of the resonator. The ratio of the quartz crystal resonator base to citric acid is 100g:500g, which has the best effect.

[0074] S4. Hot water rinsing: Pour the citric acid into a spare box, and rinse the quartz crystal resonator base 5 times with 50-degree hot water to rinse off the residual citric acid and decomposition products.

[0075] S5. Baking: Put the rinsed quartz crystal resonator base into an oven at 150 degrees and bake for 30 minutes; after baking, transplant the quartz crystal resonator base into the corresponding tray.

[0076] S6. Ion cleaning: Put the transplanted quartz crystal resonator base into the equipment for ion cleaning to remove foreign objects in the resonator cavity, and finally perform vacuum packaging.

[0077] Reference Figure 2 , the second aspect of the present invention provides a system for a quartz crystal oscillator quality analysis method based on big data of an intelligent manufacturing system, including: an information acquisition module, which acquires relevant parameters of each quartz crystal resonator in each batch from the intelligent manufacturing quality detection equipment of the quartz crystal resonator.

[0078] Appearance quality judgment module, based on the relevant parameters of each quartz crystal resonator in each batch, determines whether the appearance quality of each quartz crystal resonator in each batch is qualified. If the appearance quality of a certain quartz crystal resonator in a certain batch is qualified, the electrical performance quality judgment module is executed. If the appearance quality of a certain quartz crystal resonator in a certain batch is unqualified, recycling treatment is carried out.

[0079] Electrical performance quality judgment module, based on the relevant parameters of each quartz crystal resonator in this batch, calculates the characteristic value of the electrical performance quality of the quartz crystal resonators in this batch, and then calculates the quality stability evaluation index of the quartz crystal resonators in this batch, and then determines whether the electrical performance quality of the quartz crystal resonators in this batch is qualified. If the electrical performance quality of the quartz crystal resonators in this batch is unqualified, the specification adjustment and re-inspection module is executed.

[0080] Specification adjustment and re-inspection module, based on the relevant parameters of each quartz crystal resonator in this batch, inputs them into the further analysis model. Based on the output result of the further analysis model, it determines whether each quartz crystal resonator in this batch can pass the re-detection and screening to eliminate abnormalities. If each quartz crystal resonator in this batch cannot pass the re-detection and screening to eliminate abnormalities, the relevant parameters of each quartz crystal resonator in this batch are sent to the designated technical personnel. If each quartz crystal resonator in this batch can pass the re-detection and screening to eliminate abnormalities, it is re-detected according to the new detection rules. If it is still determined that the electrical performance quality of the quartz crystal resonators in this batch is unqualified according to the new detection rules, recycling treatment is carried out.

[0081] It should be noted that the present invention also includes a database, specifically including: the highest threshold value of the metal lid offset of the quartz crystal resonator, the highest threshold value of the blackening area of the bonding wire, the highest threshold value of the number of surface scratches, the predefined target volatility value of the th characteristic value of the electrical performance quality of the quartz crystal resonator, the predefined overall discreteness value of the th characteristic value of the electrical performance quality of the quartz crystal resonator, the standard value of the th characteristic value of the electrical performance quality of the quartz crystal resonator in the further analysis model, and the defined modification factor.

[0082] The information acquisition module is connected to the appearance quality judgment module, the appearance quality judgment module is connected to the electrical performance quality judgment module, the electrical performance quality judgment module is connected to the specification adjustment and re-inspection module, and the appearance quality judgment module, the electrical performance quality judgment module, and the specification adjustment and re-inspection module are all connected to the database.

[0083] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of this technology can make various modifications, supplements, or use similar methods to replace the specific embodiments described. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should fall within the protection scope of the present invention.

Claims

1. A method for analyzing the quality of quartz crystal oscillators based on big data of intelligent manufacturing systems, characterized in that, Including: Step 1, Information acquisition: Obtain the relevant parameters of each quartz crystal resonator in each batch from the intelligent manufacturing quality inspection equipment for quartz crystal resonators. Step 2, Appearance quality judgment: Based on the relevant parameters of each quartz crystal resonator in each batch, judge whether the appearance quality of each quartz crystal resonator in each batch is qualified. If the appearance quality of a certain quartz crystal resonator in a certain batch is qualified, then execute Step 3. If the appearance quality of a certain quartz crystal resonator in a certain batch is unqualified, then perform recycling processing. Step 3, Electrical performance quality judgment: Based on the relevant parameters of each quartz crystal resonator in this batch, calculate the characteristic values of the electrical performance quality of the quartz crystal resonators in this batch, and then calculate the quality stability evaluation index of the quartz crystal resonators in this batch, and then judge whether the electrical performance quality of the quartz crystal resonators in this batch is qualified. If the electrical performance quality of the quartz crystal resonators in this batch is unqualified, then execute Step 4. The specific implementation method for calculating the characteristic values of the electrical performance quality of the quartz crystal resonators in this batch is as follows: The characteristic values of the electrical performance quality include the mean, standard deviation, range, and target value of the resonance frequency, equivalent resistance, load capacitance, and temperature measurement frequency fitting degree of the quartz crystal resonator. The mean value is calculated by the formula: , where represents the mean value of the th eigenvalue of the electrical performance quality of this batch of quartz crystal resonators, represents the th quartz crystal resonator of this batch, and the th eigenvalue of its electrical performance quality, represents the number of the quartz crystal resonators with qualified appearance quality in this batch, , is a positive integer greater than 2, represents the quantity of the quartz crystal resonators with qualified appearance quality in this batch, represents the serial number of the eigenvalue of the electrical performance quality of the quartz crystal resonator, , is a positive integer greater than 2; The standard deviation is calculated by the formula: , where represents the standard deviation of the -th eigenvalue of the electrical performance quality of this batch of quartz crystal resonators; The range is calculated by the formula: , where represents the range of the th eigenvalue of the electrical performance quality of this batch of quartz crystal resonators, represents the maximum value of the th eigenvalue of the electrical performance quality of this batch of quartz crystal resonators, represents the minimum value of the th eigenvalue of the electrical performance quality of this batch of quartz crystal resonators; The target value is calculated by the formula: , where represents the target value of the th eigenvalue of the electrical performance quality of this batch of quartz crystal resonators, represents the upper specification limit of the th eigenvalue of the electrical performance quality of the quartz crystal resonator required by the customer, represents the lower specification limit of the th eigenvalue of the electrical performance quality of the quartz crystal resonator required by the customer; The quality stability evaluation index of the quartz crystal resonators in this batch specifically includes: target volatility value and overall discreteness value. The target volatility value is calculated by the formula: , where represents the target volatility value of the -th eigenvalue of the electrical performance quality of this batch of quartz crystal resonators; The overall discreteness value is calculated by the formula: , where represents the overall discreteness value of the th eigenvalue of the electrical performance quality of this batch of quartz crystal resonators; Step 4, Specification adjustment and re-inspection: Based on the relevant parameters of each quartz crystal resonator in this batch, input them into the further analysis model. Based on the output result of the further analysis model, determine whether each quartz crystal resonator in this batch can pass the re-detection and screening to eliminate abnormalities. If each quartz crystal resonator in this batch cannot pass the re-detection and screening to eliminate abnormalities, then send the relevant parameters of each quartz crystal resonator in this batch to the designated technical personnel. If each quartz crystal resonator in this batch can pass the re-detection and screening to eliminate abnormalities, then re-detect according to the new detection rules. If it is still determined that the electrical performance quality of the quartz crystal resonators in this batch is unqualified after re-detecting according to the new detection rules, then perform recycling processing. The new detection rule is specifically as follows: , , where represents the upper specification limit of the th eigenvalue of the electrical performance quality of the quartz crystal resonator for the modified customer requirements, represents the lower specification limit of the th eigenvalue of the electrical performance quality of the quartz crystal resonator for the modified customer requirements, represents the modification factor defined in the database.

2. The quality analysis method of quartz crystal oscillators based on big data of intelligent manufacturing systems according to claim 1, wherein The relevant parameters of the quartz crystal resonator include: appearance image parameters and electrical performance parameters. Among them, the appearance image parameters include the offset of the metal lid, the blackened area of the bonding wire, and the number of surface scratches. Among them, the electrical performance parameters include resonance frequency, equivalent resistance, load capacitance, and temperature measurement frequency fitting degree.

3. The method for analyzing the quality of quartz crystal oscillators based on big data of an intelligent manufacturing system according to claim 2, wherein The specific implementation method for judging whether the appearance quality of each quartz crystal resonator in each batch is qualified is as follows: Extract the appearance image parameters of each quartz crystal resonator in each batch and input them into the quartz crystal resonator appearance quality judgment model to output the judgment results of the appearance quality of each quartz crystal resonator in each batch. The judgment results of the appearance quality of each quartz crystal resonator in each batch contain numerical values of 1 and 0. If the judgment result of the appearance quality of a certain quartz crystal resonator in a certain batch is 1, then it is determined that the appearance quality of the quartz crystal resonator in this batch is qualified. If the judgment result of the appearance quality of a certain quartz crystal resonator in a certain batch is 0, then it is determined that the appearance quality of the quartz crystal resonator in this batch is unqualified.

4. A method for analyzing the quality of quartz crystal oscillators based on big data of an intelligent manufacturing system according to claim 3, characterized in that, To determine whether the electrical performance quality of this batch of quartz crystal resonators is qualified, the specific implementation method is as follows: Extract the quality stability evaluation indicators of this batch of quartz crystal resonators, input them into the electrical performance quality judgment model of quartz crystal resonators, and output the judgment result of the electrical performance quality of this batch of quartz crystal resonators; The judgment result of the electrical performance quality of this batch of quartz crystal resonators includes numerical values of 1 and 0. If the judgment result of the electrical performance quality of this batch of quartz crystal resonators is 1, it is determined that the electrical performance quality of this batch of quartz crystal resonators is qualified. If the judgment result of the electrical performance quality of this batch of quartz crystal resonators is 0, it is determined that the electrical performance quality of this batch of quartz crystal resonators is unqualified.

5. The method for analyzing the quality of quartz crystal oscillators based on big data of an intelligent manufacturing system according to claim 4, wherein To determine whether each quartz crystal resonator in this batch can be screened out for abnormalities through re-detection, the specific implementation method is as follows: Extract the characteristic values of the electrical performance quality of this batch of quartz crystal resonators, input them into the further analysis model, and output the judgment result of whether this batch of quartz crystal resonators can be screened out for abnormalities through re-detection; The judgment result of whether this batch of quartz crystal resonators can be screened out for abnormalities through re-detection includes numerical values of 1 and 0. If the judgment result of whether this batch of quartz crystal resonators can be screened out for abnormalities through re-detection is 1, it indicates that this batch of quartz crystal resonators can be screened out for abnormalities through re-detection. If the judgment result of whether this batch of quartz crystal resonators can be screened out for abnormalities through re-detection is 0, it indicates that this batch of quartz crystal resonators cannot be screened out for abnormalities through re-detection.

6. The method for analyzing the quality of quartz crystal oscillators based on big data of intelligent manufacturing systems according to claim 1, wherein, The recycling process, the specific implementation method is as follows: S1. Heat the degreaser in a water bath; S2. Rinse with hot water; S3. Heat citric acid in a water bath; S4. Rinse with hot water; S5. Bake; S6. Ion cleaning.

7. A system for implementing the method for analyzing the quality of quartz crystal oscillators based on big data of an intelligent manufacturing system according to any one of claims 1 to 6, characterized in that, It includes: An information acquisition module, which acquires the relevant parameters of each quartz crystal resonator in each batch from the intelligent quality inspection equipment for quartz crystal resonators; An appearance quality judgment module, which judges whether the appearance quality of each quartz crystal resonator in each batch is qualified based on the relevant parameters of each quartz crystal resonator in each batch. If the appearance quality of a certain quartz crystal resonator in a certain batch is qualified, the electrical performance quality judgment module is executed. If the appearance quality of a certain quartz crystal resonator in a certain batch is unqualified, recycling processing is performed; An electrical performance quality judgment module, which calculates the characteristic values of the electrical performance quality of this batch of quartz crystal resonators based on the relevant parameters of each quartz crystal resonator in this batch, and then calculates the quality stability evaluation indicators of this batch of quartz crystal resonators, and then judges whether the electrical performance quality of this batch of quartz crystal resonators is qualified. If the electrical performance quality of this batch of quartz crystal resonators is unqualified, the specification adjustment and re-inspection module is executed; Specification adjustment and re-inspection module. Based on the relevant parameters of each quartz crystal resonator in this batch, it is input into the further analysis model. Based on the output results of the further analysis model, it is determined whether each quartz crystal resonator in this batch can pass the re-inspection and screening to eliminate abnormalities. If each quartz crystal resonator in this batch cannot pass the re-inspection and screening to eliminate abnormalities, the relevant parameters of each quartz crystal resonator in this batch will be sent to the designated technical personnel. If each quartz crystal resonator in this batch can pass the re-inspection and screening to eliminate abnormalities, it will be re-inspected according to the new inspection rules. If it is still determined that the electrical performance quality of the quartz crystal resonators in this batch is unqualified after re-inspection according to the new inspection rules, recovery treatment will be carried out.

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