Quartz crystal oscillator quality analysis method and system based on intelligent manufacturing system big data

Abstract

The invention discloses a quartz crystal oscillator quality analysis method and system based on big data of an intelligent manufacturing system, and relates to the technical field of quartz crystal resonators. Appearance images and electrical performance parameters of all batches of quartz crystal oscillators are obtained from detection equipment, and appearance quality is judged according to appearance image parameters through an appearance quality judgment model; qualified products enter an electrical performance quality judgment link. By calculating electrical performance quality characteristic values such as a mean value, a standard deviation, a range and a target value, quality stability evaluation indexes such as a target fluctuation value and an overall discreteness value are obtained, and the electrical performance quality is judged by means of an electrical performance quality judgment model. Unqualified batches are substituted into a further analysis model to judge whether abnormity can be detected, screened and removed again or not, if yes, redetection is carried out according to a new detection rule, otherwise, relevant parameters are sent to technicians, and meanwhile recycling treatment is carried out.

Description

Technical Field

[0001] The present invention relates to the technical field of quartz crystal resonators, and in particular to a quartz crystal oscillator quality analysis method and system based on big data of an intelligent manufacturing system. Background Art

[0002] As the crystal oscillator industry develops towards high precision and high reliability, it promotes the quartz crystal resonator to shift to intelligent manufacturing. Through the deep integration of information technology and crystal oscillator manufacturing, the intelligent transformation of manufacturing process methods is promoted. Enterprises can obtain the 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 quartz crystal oscillator quality analysis method and system based on big data of intelligent manufacturing systems.

[0003] Existing technologies such as the invention patent application with announcement number: CN116468332A discloses a crystal resonator production quality evaluation system, which includes resonator image acquisition, apparent quality analysis, testing, test information collection, performance analysis, quality analysis and early warning terminal modules. By analyzing the apparent quality of the current batch of crystal resonators of a specified enterprise and randomly sampling and testing the sensitivity and stability of the target product, the defects of the existing technology are overcome and accurate quality analysis is achieved. This ensures the quality of products within the batch, improves the user experience, and thus enhances the reputation and sales of crystal resonator companies.

[0004] The existing technology still has the following defects, which are specifically reflected in: 1. In the existing technology, it relies on manual extraction of quartz crystal oscillator quality inspection data, and uses tools such as histograms and control charts to conduct random inspections, analysis and abnormality judgment. It is impossible to perform automatic big data full analysis and abnormality judgment on all quartz crystal oscillator electrical performance quality inspection data, making it difficult to fully grasp the product quality situation.

[0005] 2. In the existing technology, the sample size of manually extracted data is limited and cannot cover all the electrical performance quality test data of quartz crystal oscillators, which makes it difficult for the analysis results to fully and accurately reflect the quality status of the overall product. 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. It is difficult to accurately evaluate key electrical performance indicators such as frequency accuracy and resistance and capacitance stability, which increases product quality risks and makes it difficult to ensure that 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 quartz crystal oscillator quality analysis method and system based on big data of intelligent manufacturing system, which solves the problems existing in the background technology.

[0007] To solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a quartz crystal oscillator quality analysis method based on big data of intelligent manufacturing system, including: step one, information acquisition, obtaining relevant parameters of each quartz crystal resonator in each batch from the quartz crystal resonator intelligent manufacturing quality inspection equipment.

[0008] 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 quartz crystal resonator in a batch is qualified, execute step 3; if the appearance quality of a quartz crystal resonator in a batch is unqualified, recycle it.

[0009] Step three, electrical performance quality judgment, based on the relevant parameters of each quartz crystal resonator in this batch, calculate the characteristic value of the electrical performance quality of this batch of quartz crystal resonators, and then calculate the quality stability evaluation index of this batch of quartz crystal resonators, and then judge 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, execute step four.

[0010] Step 4: Specification adjustment and re-inspection: Based on the relevant parameters of each quartz crystal resonator in this batch, bring them into the further analysis model, and based on the output results of the further analysis model, determine whether each quartz crystal resonator in this batch can pass the re-testing and screening to eliminate the abnormalities. If each quartz crystal resonator in this batch cannot pass the re-testing and screening to eliminate the abnormalities, then send the relevant parameters of each quartz crystal resonator in this batch to the designated technicians. If each quartz crystal resonator in this batch can pass the re-testing and screening to eliminate the abnormalities, then re-test according to the new detection rules. If the electrical performance quality of this batch of quartz crystal resonators is still determined to be unqualified after re-testing according to the new detection rules, they will be recycled.

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

[0012] Preferably, the method of judging whether the appearance quality of each batch of quartz crystal resonators is qualified is specifically implemented by extracting the appearance image parameters of each batch of quartz crystal resonators, inputting them into the quartz crystal resonator appearance quality judgment model, and outputting the judgment result of the appearance quality of each batch of quartz crystal resonators.

[0013] The judgment result of the appearance quality of each quartz crystal resonator in each batch includes the values ​​of 1 and 0. If the judgment result of the appearance quality of a quartz crystal resonator in a batch is 1, then the appearance quality of the quartz crystal resonators in this batch is judged to be qualified. If the judgment result of the appearance quality of a quartz crystal resonator in a batch is 0, then the appearance quality of the quartz crystal resonators in this batch is judged to be unqualified.

[0014] Preferably, the characteristic values ​​of the electrical performance quality of the batch of quartz crystal resonators are calculated, and the specific implementation method is: the characteristic values ​​of the electrical performance quality include the mean, standard deviation, range, and target value of the resonant frequency, equivalent resistance, load capacitance, and temperature measurement frequency fitting of the quartz crystal resonator.

[0015] The mean value is calculated as follows: ,in Indicates the electrical performance quality of this batch of quartz crystal resonators. The mean of the eigenvalues, Indicates that the batch The electrical performance quality of a quartz crystal resonator eigenvalues, Indicates the number of quartz crystal resonators with qualified appearance quality in this batch. , is a positive integer greater than 2, Indicates the number of quartz crystal resonators in this batch that have qualified appearance quality. A number indicating the characteristic value of the electrical performance quality of a quartz crystal resonator. , is a positive integer greater than 2.

[0016] The standard deviation is calculated as follows: ,in Indicates the electrical performance quality of this batch of quartz crystal resonators. The standard deviation of the eigenvalues.

[0017] The range is calculated as follows: ,in Indicates the electrical performance quality of this batch of quartz crystal resonators. The range of the eigenvalues, Indicates the electrical performance quality of this batch of quartz crystal resonators. The maximum value of the eigenvalues, Indicates the electrical performance quality of this batch of quartz crystal resonators. The minimum value of the eigenvalues.

[0018] The target value is calculated as follows: ,in Indicates the electrical performance quality of this batch of quartz crystal resonators. The target value of the feature value, The electrical performance quality of the quartz crystal resonator that the customer needs is The upper specification limit for the characteristic value, The electrical performance quality of the quartz crystal resonator that the customer needs is The lower specification limit for the characteristic value.

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

[0020] The target volatility value is calculated as follows: ,in Indicates the electrical performance quality of this batch of quartz crystal resonators. The target volatility value for each eigenvalue.

[0021] The overall discreteness value is calculated as follows: ,in Indicates the electrical performance quality of this batch of quartz crystal resonators. The overall discreteness value of the eigenvalues.

[0022] Preferably, the method of judging whether the electrical performance quality of the batch of quartz crystal resonators is qualified is specifically implemented by extracting the quality stability evaluation index of the batch of quartz crystal resonators, inputting it into the electrical performance quality judgment model of the quartz crystal resonators, and outputting the judgment result of the electrical performance quality of the batch of quartz crystal resonators.

[0023] The judgment result of the electrical performance quality of the batch of quartz crystal resonators includes the values ​​of 1 and 0. If the judgment result of the electrical performance quality of the batch of quartz crystal resonators is 1, then the electrical performance quality of the batch of quartz crystal resonators is judged to be qualified. If the judgment result of the electrical performance quality of the batch of quartz crystal resonators is 0, then the electrical performance quality of the batch of quartz crystal resonators is judged to be unqualified.

[0024] Preferably, the determination of whether the quartz crystal resonators in the batch can be screened out through re-testing to eliminate abnormalities is specifically implemented by extracting characteristic values ​​of the electrical performance quality of the quartz crystal resonators in the batch, bringing them into a further analysis model, and outputting a judgment result of whether the quartz crystal resonators in the batch can be screened out through re-testing to eliminate abnormalities.

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

[0026] Preferably, the new detection rule is specifically: , ,in The electrical performance quality of the quartz crystal resonator is shown in the modified customer requirements. The upper specification limit for the characteristic value, The electrical performance quality of the quartz crystal resonator is shown in the modified customer requirements. The lower specification limit of the characteristic value, Represents a modification factor defined in the database.

[0027] Preferably, the recycling process is specifically implemented as follows: S1, heating the degumming agent in a water bath.

[0028] S2. Rinse with hot water.

[0029] S3. Heat in a citric acid water bath.

[0030] S4. Rinse with hot water.

[0031] S5. Bake.

[0032] S6. Ion cleaning.

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

[0034] The appearance quality judgment module 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 quartz crystal resonator in a batch is qualified, the electrical performance quality judgment module is executed; if the appearance quality of a quartz crystal resonator in a batch is unqualified, it is recycled.

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

[0036] The specification adjustment and re-inspection module is based on the relevant parameters of each quartz crystal resonator in this batch, which are brought 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-testing and screening to eliminate the abnormalities. If each quartz crystal resonator in this batch cannot pass the re-testing and screening to eliminate the abnormalities, the relevant parameters of each quartz crystal resonator in this batch will be sent to the designated technician. If each quartz crystal resonator in this batch can pass the re-testing and screening to eliminate the abnormalities, they will be re-tested according to the new detection rules. If the electrical performance quality of this batch of quartz crystal resonators is still determined to be unqualified after re-testing according to the new detection rules, they will be recycled.

[0037] The beneficial effects of the present invention are: 1. In the present invention, through the quartz crystal resonator appearance quality judgment model, the quartz crystal detection big data can be systematically and automatically fully analyzed and judged, breaking through the limitations of manual operation on the data processing scale, improving the overall data analysis efficiency, and achieving comprehensive control of product quality.

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

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 The present invention is a schematic flow chart of the steps for implementing the method.

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

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] Reference Figure 1 As shown, the present invention provides a quartz crystal oscillator quality analysis method based on big data of intelligent manufacturing system, including: step one, information acquisition, obtaining relevant parameters of each quartz crystal resonator in each batch from the quartz crystal resonator intelligent manufacturing quality inspection equipment.

[0044] In a specific embodiment, the relevant parameters of the quartz crystal resonator include: appearance image parameters and electrical performance parameters, wherein the appearance image parameters include the metal cover offset, the blackening area of ​​the welding wire, and the number of surface scratches, and the electrical performance parameters include the resonant frequency, equivalent resistance, load capacitance and temperature measurement frequency fitting degree.

[0045] 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 quartz crystal resonator in a batch is qualified, execute step 3; if the appearance quality of a quartz crystal resonator in a batch is unqualified, recycle it.

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

[0047] The judgment result of the appearance quality of each quartz crystal resonator in each batch includes the values ​​of 1 and 0. If the judgment result of the appearance quality of a quartz crystal resonator in a batch is 1, then the appearance quality of the quartz crystal resonators in this batch is judged to be qualified. If the judgment result of the appearance quality of a quartz crystal resonator in a batch is 0, then the appearance quality of the quartz crystal resonators in this batch is judged to be unqualified.

[0048] It should be noted that in the quartz crystal resonator appearance quality judgment model, the model expression is: ,in Indicates Batch No. The judgment result of the appearance quality of a quartz crystal resonator, , , Respectively represent Batch No. The metal cover offset of each quartz crystal resonator, the blackened area of ​​the solder wire, and the number of surface scratches. , , They represent the maximum threshold of the metal cover offset of the quartz crystal resonator, the maximum threshold of the blackening area of ​​the welding wire, and the maximum threshold of the number of surface scratches defined in the database, and are set by professionals.

[0049] In the present invention, through the quartz crystal resonator appearance quality judgment model, the quartz crystal detection big data can be systematically and automatically fully analyzed and judged, breaking through the limitations of manual operation in data processing scale, improving the overall data analysis efficiency, and achieving comprehensive control of product quality.

[0050] Step three, electrical performance quality judgment, based on the relevant parameters of each quartz crystal resonator in this batch, calculate the characteristic value of the electrical performance quality of this batch of quartz crystal resonators, and then calculate the quality stability evaluation index of this batch of quartz crystal resonators, and then judge 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, execute step four.

[0051] In a specific embodiment, the characteristic values ​​of the electrical performance quality of the batch of quartz crystal resonators are calculated, and the specific implementation method is: the characteristic values ​​of the electrical performance quality include the resonant frequency, equivalent resistance, load capacitance and the mean, standard deviation, range and target value of the temperature measurement frequency fitting of the quartz crystal resonator.

[0052] The mean value is calculated as follows: ,in Indicates the electrical performance quality of this batch of quartz crystal resonators. The mean of the eigenvalues, Indicates that the batch The electrical performance quality of a quartz crystal resonator eigenvalues, Indicates the number of quartz crystal resonators with qualified appearance quality in this batch. , is a positive integer greater than 2, Indicates the number of quartz crystal resonators in this batch that have qualified appearance quality. A number indicating the characteristic value of the electrical performance quality of a quartz crystal resonator. , is a positive integer greater than 2.

[0053] The standard deviation is calculated as follows: ,in Indicates the electrical performance quality of this batch of quartz crystal resonators. The standard deviation of the eigenvalues.

[0054] The range is calculated as follows: ,in Indicates the electrical performance quality of this batch of quartz crystal resonators. The range of the eigenvalues, Indicates the electrical performance quality of this batch of quartz crystal resonators. The maximum value of the eigenvalues, Indicates the electrical performance quality of this batch of quartz crystal resonators. The minimum value of the eigenvalues.

[0055] The target value is calculated as follows: ,in Indicates the electrical performance quality of this batch of quartz crystal resonators. The target value of the feature value, The electrical performance quality of the quartz crystal resonator that the customer needs is The upper specification limit for the characteristic value, The electrical performance quality of the quartz crystal resonator that the customer needs is The lower specification limit for the characteristic value.

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

[0057] The target volatility value is calculated as follows: ,in Indicates the electrical performance quality of this batch of quartz crystal resonators. The target volatility value for each eigenvalue.

[0058] It should be noted that using the target value when calculating the target volatility value can directly compare the degree of deviation between the actual data and the ideal standard. Using the target value when calculating the target volatility value can directly compare the degree of deviation 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. Combined with the mean and 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 product quality.

[0059] The overall discreteness value is calculated as follows: ,in Indicates the electrical performance quality of this batch of quartz crystal resonators. The overall discreteness value of the eigenvalues.

[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. Combined with the standard deviation, the discrete situation of the data can be evaluated more comprehensively. It can not only grasp the maximum fluctuation range of the data, but also deeply understand the discrete distribution of the data around the mean, thereby more accurately evaluating the overall discrete degree of the product's electrical performance quality and judging the stability of the production process and the consistency of product quality.

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

[0062] The judgment result of the electrical performance quality of the batch of quartz crystal resonators includes the values ​​of 1 and 0. If the judgment result of the electrical performance quality of the batch of quartz crystal resonators is 1, then the electrical performance quality of the batch of quartz crystal resonators is judged to be qualified. If the judgment result of the electrical performance quality of the batch of quartz crystal resonators is 0, then the electrical performance quality of the batch of quartz crystal resonators is judged to be unqualified.

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

[0064] It should be noted that the electrical performance quality judgment model of the quartz crystal resonator is expressed as follows: ,in Indicates the judgment result of the electrical performance quality of this batch of quartz crystal resonators. The first column represents the electrical performance quality of the quartz crystal resonator predefined in the database. The target volatility value of the eigenvalues, The first column represents the electrical performance quality of the quartz crystal resonator predefined in the database. The overall discreteness of the eigenvalues ​​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, bring them into the further analysis model, and based on the output results of the further analysis model, determine whether each quartz crystal resonator in this batch can pass the re-testing and screening to eliminate the abnormalities. If each quartz crystal resonator in this batch cannot pass the re-testing and screening to eliminate the abnormalities, then send the relevant parameters of each quartz crystal resonator in this batch to the designated technicians. If each quartz crystal resonator in this batch can pass the re-testing and screening to eliminate the abnormalities, then re-test according to the new detection rules. If the electrical performance quality of this batch of quartz crystal resonators is still determined to be unqualified after re-testing according to the new detection rules, they will be recycled.

[0066] In a specific embodiment, the determination of whether the quartz crystal resonators in the batch can pass the re-testing and screening to eliminate the abnormalities is specifically implemented by extracting the characteristic values ​​of the electrical performance quality of the quartz crystal resonators in the batch, bringing them into a further analysis model, and outputting the judgment result of whether the quartz crystal resonators in the batch can pass the re-testing and screening to eliminate the abnormalities.

[0067] The judgment result of whether the batch of quartz crystal resonators can pass the re-testing and screening to eliminate the abnormalities includes the values ​​of 1 and 0. If the judgment result of whether the batch of quartz crystal resonators can pass the re-testing and screening to eliminate the abnormalities is 1, it indicates that the batch of quartz crystal resonators can pass the re-testing and screening to eliminate the abnormalities. If the judgment result of whether the batch of quartz crystal resonators can pass the re-testing and screening to eliminate the abnormalities is 0, it indicates that the batch of quartz crystal resonators cannot pass the re-testing and screening to eliminate the abnormalities.

[0068] It should be noted that the further analysis model is expressed as follows: ,in Indicates whether the batch of quartz crystal resonators can pass the re-testing screening to eliminate abnormal ones. This represents the first step in further analyzing the electrical performance quality of the quartz crystal resonator in the model. The standard value of the characteristic value is set by professionals. .

[0069] In a specific embodiment, the new detection rule is specifically: , ,in The electrical performance quality of the quartz crystal resonator is shown in the modified customer requirements. The upper specification limit for the characteristic value, The electrical performance quality of the quartz crystal resonator is shown in the modified customer requirements. The lower specification limit of the characteristic value, Represents a modification factor defined in the database.

[0070] It should be noted that the modification factors defined in the database are set by professionals. The analysis method is as follows: the standard deviation is calculated from the batch quality big data of the electrical performance of the quartz crystal resonator, reflecting the degree of dispersion of the data relative to the mean. In the statistical normal distribution, 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, Used to determine a reasonable quality fluctuation range, therefore, here .

[0071] In a specific embodiment, the recycling treatment 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 heat them in a water bath for 60 minutes to dissolve the conductive glue and separate the wafer and the base. The degumming agent contains potassium hydroxide, and the ratio of the resonator to potassium hydroxide is 70g:1000g for the best effect.

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

[0073] S3. Heating in a citric acid water bath: Pour the rinsed quartz crystal resonator base into a box of citric acid and heat it in a water bath for 30 minutes to remove the potassium silicate on the surface of the resonator. The best effect is achieved when the ratio of quartz crystal resonator base to citric acid is 100g:500g.

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

[0075] S5, baking: put the rinsed quartz crystal resonator base into a 150-degree oven and bake for 30 minutes; after baking, transfer the quartz crystal resonator base to the corresponding tray.

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

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

[0078] The appearance quality judgment module 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 quartz crystal resonator in a batch is qualified, the electrical performance quality judgment module is executed; if the appearance quality of a quartz crystal resonator in a batch is unqualified, it is recycled.

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

[0080] The specification adjustment and re-inspection module is based on the relevant parameters of each quartz crystal resonator in this batch, which are brought 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-testing and screening to eliminate the abnormalities. If each quartz crystal resonator in this batch cannot pass the re-testing and screening to eliminate the abnormalities, the relevant parameters of each quartz crystal resonator in this batch will be sent to the designated technician. If each quartz crystal resonator in this batch can pass the re-testing and screening to eliminate the abnormalities, they will be re-tested according to the new detection rules. If the electrical performance quality of this batch of quartz crystal resonators is still determined to be unqualified after re-testing according to the new detection rules, they will be recycled.

[0081] It should be noted that the present invention also includes a database, specifically including: the maximum threshold of the metal cover offset of the quartz crystal resonator, the maximum threshold of the blackening area of ​​the welding wire, the maximum threshold of the number of surface scratches, the predefined electrical performance quality of the quartz crystal resonator, the maximum threshold of the number of surface scratches ... The target fluctuation value of the characteristic value, the electrical performance quality of the predefined quartz crystal resonator The overall discreteness of the eigenvalues, the electrical performance quality of the quartz crystal resonator in the model is further analyzed. The standard value of the eigenvalue 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 connected to the database at the same time.

[0083] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A quartz crystal oscillator quality analysis method based on big data of intelligent manufacturing system, characterized in that: include: Step 1: information acquisition: obtaining relevant parameters of each batch of quartz crystal resonators from the quartz crystal resonator intelligent manufacturing quality inspection equipment; Step 2: Appearance quality judgment: Based on the relevant parameters of each batch of quartz crystal resonators, judge whether the appearance quality of each batch of quartz crystal resonators is qualified. If the appearance quality of a quartz crystal resonator in a certain batch is qualified, execute step 3; if the appearance quality of a quartz crystal resonator in a certain batch is unqualified, recycle it; Step 3: Determine the electrical performance quality. Based on the relevant parameters of each quartz crystal resonator in the batch, calculate the characteristic value of the electrical performance quality of the quartz crystal resonator in the batch, and then calculate the quality stability evaluation index of the quartz crystal resonator in the batch, and then determine whether the electrical performance quality of the quartz crystal resonator in the batch is qualified. If the electrical performance quality of the quartz crystal resonator in the batch is unqualified, execute step 4. Step 4: Specification adjustment and re-inspection: Based on the relevant parameters of each quartz crystal resonator in this batch, bring them into the further analysis model, and based on the output results of the further analysis model, determine whether each quartz crystal resonator in this batch can pass the re-testing and screening to eliminate the abnormalities. If each quartz crystal resonator in this batch cannot pass the re-testing and screening to eliminate the abnormalities, then send the relevant parameters of each quartz crystal resonator in this batch to the designated technicians. If each quartz crystal resonator in this batch can pass the re-testing and screening to eliminate the abnormalities, then re-test according to the new detection rules. If the electrical performance quality of this batch of quartz crystal resonators is still determined to be unqualified after re-testing according to the new detection rules, they will be recycled.

2. According to the method for analyzing the quality of quartz crystal oscillators based on big data of intelligent manufacturing system in claim 1, it is characterized in that: The relevant parameters of the quartz crystal resonator include: appearance image parameters and electrical performance parameters, wherein the appearance image parameters include the metal cover offset, the blackened area of ​​the welding wire, and the number of surface scratches, and the electrical performance parameters include the resonant frequency, equivalent resistance, load capacitance and temperature measurement frequency fitting degree.

3. According to claim 2, a quartz crystal oscillator quality analysis method based on big data of intelligent manufacturing system is characterized in that: The specific implementation method of judging whether the appearance quality of each batch of quartz crystal resonators is qualified is as follows: Extracting appearance image parameters of each batch of quartz crystal resonators, inputting them into a quartz crystal resonator appearance quality judgment model, and outputting the judgment results of the appearance quality of each batch of quartz crystal resonators; The judgment result of the appearance quality of each quartz crystal resonator in each batch includes the values ​​of 1 and 0. If the judgment result of the appearance quality of a quartz crystal resonator in a batch is 1, then the appearance quality of the quartz crystal resonators in this batch is judged to be qualified. If the judgment result of the appearance quality of a quartz crystal resonator in a batch is 0, then the appearance quality of the quartz crystal resonators in this batch is judged to be unqualified.

4. The method for analyzing quartz crystal quality based on big data of intelligent manufacturing system according to claim 3 is characterized in that: The specific implementation method of calculating the characteristic value of the electrical performance quality of the batch of quartz crystal resonators is as follows: The characteristic values ​​of the electrical performance quality include the mean, standard deviation, range, and target value of the resonant frequency, equivalent resistance, load capacitance, and temperature measurement frequency fitting of the quartz crystal resonator; The mean value is calculated as follows: ,in Indicates the electrical performance quality of this batch of quartz crystal resonators. The mean of the eigenvalues, Indicates that the batch The electrical performance quality of a quartz crystal resonator eigenvalues, Indicates the number of quartz crystal resonators with qualified appearance quality in this batch. , is a positive integer greater than 2, Indicates the number of quartz crystal resonators in this batch that have qualified appearance quality. A number indicating the characteristic value of the electrical performance quality of a quartz crystal resonator. , is a positive integer greater than 2; The standard deviation is calculated as follows: ,in Indicates the electrical performance quality of this batch of quartz crystal resonators. The standard deviation of the eigenvalues; The range is calculated as follows: ,in Indicates the electrical performance quality of this batch of quartz crystal resonators. The range of the eigenvalues, Indicates the electrical performance quality of this batch of quartz crystal resonators. The maximum value of the eigenvalues, Indicates the electrical performance quality of this batch of quartz crystal resonators. The minimum value of eigenvalues; The target value is calculated as follows: ,in Indicates the electrical performance quality of this batch of quartz crystal resonators. The target value of the feature value, The electrical performance quality of the quartz crystal resonator that the customer needs is The upper specification limit for the characteristic value, The electrical performance quality of the quartz crystal resonator that the customer needs is The lower specification limit for the characteristic value.

5. The method for analyzing quartz crystal quality based on big data of intelligent manufacturing system according to claim 4 is characterized in that: The quality stability evaluation index of the batch of quartz crystal resonators specifically includes: target volatility value and overall discreteness value; The target volatility value is calculated as follows: ,in Indicates the electrical performance quality of this batch of quartz crystal resonators. The target volatility value of the feature value; The overall discreteness value is calculated as follows: ,in Indicates the electrical performance quality of this batch of quartz crystal resonators. The overall discreteness value of the eigenvalues.

6. The method for analyzing quartz crystal quality based on big data of intelligent manufacturing system according to claim 5 is characterized in that: The specific implementation method of judging whether the electrical performance quality of the batch of quartz crystal resonators is qualified is as follows: Extracting the quality stability evaluation index of the batch of quartz crystal resonators, inputting it into the quartz crystal resonator electrical performance quality judgment model, and outputting the judgment result of the electrical performance quality of the batch of quartz crystal resonators; The judgment result of the electrical performance quality of the batch of quartz crystal resonators includes the values ​​of 1 and 0. If the judgment result of the electrical performance quality of the batch of quartz crystal resonators is 1, then the electrical performance quality of the batch of quartz crystal resonators is judged to be qualified. If the judgment result of the electrical performance quality of the batch of quartz crystal resonators is 0, then the electrical performance quality of the batch of quartz crystal resonators is judged to be unqualified.

7. The method for analyzing quartz crystal quality based on big data of intelligent manufacturing system according to claim 6 is characterized in that: The specific implementation method of determining whether each quartz crystal resonator in the batch can be screened and abnormalities eliminated through re-testing is as follows: Extract the characteristic values ​​of the electrical performance quality of the batch of quartz crystal resonators, bring them into the further analysis model, and output the judgment result of whether the batch of quartz crystal resonators can be re-tested and screened to eliminate abnormalities; The judgment result of whether the batch of quartz crystal resonators can pass the re-testing and screening to eliminate the abnormalities includes the values ​​of 1 and 0. If the judgment result of whether the batch of quartz crystal resonators can pass the re-testing and screening to eliminate the abnormalities is 1, it indicates that the batch of quartz crystal resonators can pass the re-testing and screening to eliminate the abnormalities. If the judgment result of whether the batch of quartz crystal resonators can pass the re-testing and screening to eliminate the abnormalities is 0, it indicates that the batch of quartz crystal resonators cannot pass the re-testing and screening to eliminate the abnormalities.

8. The method for analyzing quartz crystal quality based on big data of intelligent manufacturing system according to claim 1 is characterized in that: The new detection rules are specifically: , ,in The electrical performance quality of the quartz crystal resonator is shown in the modified customer requirements. The upper specification limit for the characteristic value, The electrical performance quality of the quartz crystal resonator is shown in the modified customer requirements. The lower specification limit of the characteristic value, Represents a modification factor defined in the database.

9. The method for analyzing quartz crystal quality based on big data of intelligent manufacturing system according to claim 1, characterized in that: The recycling process is specifically implemented as follows: S1, degumming agent water bath heating; S2, hot water washing; S3, heating in a citric acid water bath; S4, hot water flushing; S5, baking; S6. Ion cleaning.

10. A system for executing the quartz crystal oscillator quality analysis method based on big data of intelligent manufacturing system according to any one of claims 1 to 9, characterized in that: include: The information acquisition module obtains the relevant parameters of each batch of quartz crystal resonators from the quartz crystal resonator intelligent manufacturing quality inspection equipment; The appearance quality judgment module 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 quartz crystal resonator in a batch is qualified, the electrical performance quality judgment module is executed. If the appearance quality of a quartz crystal resonator in a batch is unqualified, it is recycled; The electrical performance quality judgment module calculates the characteristic value of the electrical performance quality of the batch of quartz crystal resonators based on the relevant parameters of each quartz crystal resonator in the batch, and then calculates the quality stability evaluation index of the batch of quartz crystal resonators, and then judges whether the electrical performance quality of the batch of quartz crystal resonators is qualified. If the electrical performance quality of the batch of quartz crystal resonators is unqualified, the specification adjustment and re-inspection module is executed; The specification adjustment and re-inspection module is based on the relevant parameters of each quartz crystal resonator in this batch, which are brought 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-testing and screening to eliminate the abnormalities. If each quartz crystal resonator in this batch cannot pass the re-testing and screening to eliminate the abnormalities, the relevant parameters of each quartz crystal resonator in this batch will be sent to the designated technician. If each quartz crystal resonator in this batch can pass the re-testing and screening to eliminate the abnormalities, they will be re-tested according to the new detection rules. If the electrical performance quality of this batch of quartz crystal resonators is still determined to be unqualified after re-testing according to the new detection rules, they will be recycled.

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