Adaptive detection method and system for aperture of probe card

Abstract

The invention discloses a self-adaptive detection method and system for the aperture of a probe card, and relates to the field of aperture detection. The method comprises the following steps: obtaining an image in a target hole; obtaining a cleaning verification result in the target hole; when the target in-hole cleaning verification result is passed, real-time objective table control data and real-time target positioning data are obtained; according to the multi-stage detection feature verification constraint, obtaining a detection feature verification result; when the detection feature verification result is qualified, obtaining a detection equipment control decision; and detecting the target probe card based on a self-adaptive aperture detection algorithm to obtain a target aperture detection report. By adopting the method, the technical problems that an existing probe card aperture detection method is high in detection error, poor in precision and difficult to meet the requirement for high precision of probe card aperture detection are solved, and the technical effects of improving the aperture detection accuracy of the probe card and reducing the aperture detection error of the probe card are achieved.

Description

Technical Field

[0001] The present application relates to the field of aperture detection technology, and in particular to the field of aperture adaptive detection, and specifically to a method and system for adaptively detecting the aperture of a probe card. Background Art

[0002] With the rapid development of modern science and technology, the requirements for process accuracy in the field of microelectronics manufacturing are increasing day by day. In the manufacturing process of semiconductor devices, probe cards, as a key tool, are widely used in wafer testing, chip packaging and other links. Probe card aperture detection, as a key link to ensure the stability of probe card performance and improve test accuracy, has received widespread attention in recent years.

[0003] In summary, the present method solves the technical problems of the existing probe card aperture detection method, which has high detection error and poor precision and is difficult to meet the high-precision requirements for probe card aperture detection. Summary of the invention

[0004] Based on this, it is necessary to provide a probe card aperture adaptive detection method and system that can improve the aperture detection accuracy of the probe card and reduce the aperture detection error of the probe card in order to solve the above technical problems.

[0005] In a first aspect, a method for adaptively detecting the aperture of a probe card is provided, the method comprising: loading specification model attribute information and aperture detection accuracy parameters of a target probe card; acquiring in-aperture image data of the target probe card according to an image acquisition device to obtain an in-aperture image acquisition result, and performing image enhancement on the in-aperture image acquisition result according to a Retinex algorithm to obtain an in-aperture image of a target; performing in-aperture cleaning verification on the target probe card according to the in-aperture image of the target based on an in-aperture cleaning detector of the probe card to obtain an in-aperture cleaning verification result of the target aperture; when the in-aperture cleaning verification result of the target aperture is passed, placing the target probe card on a stage of the probe card aperture detection device, Real-time stage control data and real-time target positioning data are obtained; based on the specification model attribute information, according to the multi-level detection feature verification constraints, the real-time stage control data and the real-time target positioning data are subjected to detection feature verification to obtain a detection feature verification result; when the detection feature verification result is qualified, the real-time stage control data and the real-time target positioning data are input into a pre-constructed detection equipment control decision model to obtain a detection equipment control decision; based on an adaptive aperture detection algorithm, the target probe card is detected according to the aperture detection accuracy parameters, the detection equipment control decision and the probe card aperture detection device to obtain a target aperture detection report.

[0006] In a second aspect, an adaptive detection system for the aperture of a probe card is provided, the system comprising: an attribute information loading module, the attribute information loading module is used to load the specification model attribute information and aperture detection accuracy parameters of the target probe card; a target in-hole image acquisition module, the target in-hole image acquisition module is used to acquire the in-hole image data of the target probe card according to the image acquisition device, obtain the in-hole image acquisition result, and perform image enhancement on the in-hole image acquisition result according to the Retinex algorithm to obtain the target in-hole image; a target in-hole cleaning verification module, the target in-hole cleaning verification module is used to perform in-hole cleaning verification on the target probe card according to the target in-hole image based on the probe card in-hole cleaning detector, and obtain the target in-hole cleaning verification result; a control positioning data acquisition module, the control positioning data acquisition module is used to place the target probe card in the probe card aperture detection area when the target in-hole cleaning verification result is passed. The device obtains real-time stage control data and real-time target positioning data on the stage of the device; a detection result acquisition module for obtaining the detection result to be verified, the module is used to perform detection feature verification on the real-time stage control data and the real-time target positioning data based on the specification model attribute information and according to the multi-level detection feature verification constraint, so as to obtain the detection feature verification result; a detection equipment control decision module, the detection equipment control decision module is used to input the real-time stage control data and the real-time target positioning data into a pre-built detection equipment control decision model when the detection feature verification result is qualified, so as to obtain the detection equipment control decision; a target aperture detection report acquisition module, the target aperture detection report acquisition module is used to detect the target probe card based on the adaptive aperture detection algorithm according to the aperture detection accuracy parameter, the detection equipment control decision and the probe card aperture detection device, so as to obtain the target aperture detection report.

[0007] According to a third aspect, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps according to the first aspect when executing the computer program.

[0008] According to a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps described in the first aspect are implemented.

[0009] The above-mentioned adaptive detection method and system for the aperture of a probe card adopts this method to solve the technical problems that the existing probe card aperture detection method has high detection error and poor accuracy, and is difficult to meet the high-precision requirements for probe card aperture detection, thereby achieving the technical effect of improving the aperture detection accuracy of the probe card and reducing the aperture detection error of the probe card.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A schematic diagram of a flow chart of a method for adaptively detecting the aperture of a probe card in one embodiment;

[0012] Figure 2 A schematic diagram of a flow chart of adjusting an abnormality detection device in a method for adaptively detecting an aperture of a probe card in one embodiment;

[0013] Figure 3 is a structural block diagram of an adaptive detection system for a probe card aperture in one embodiment;

[0014] Figure 4 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment.

[0015] Explanation of the reference numerals: attribute information loading module 11, target hole image acquisition module 12, target hole cleaning verification module 13, control positioning data acquisition module 14, detection result acquisition module for verification 15, detection equipment control decision module 16, target aperture detection report acquisition module 17. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0017] like Figure 1 As shown, the present application provides a method for adaptively detecting the aperture of a probe card, the method comprising:

[0018] Load the specification model attribute information and aperture detection accuracy parameters of the target probe card;

[0019] The probe card, also known as the wafer probe card, is the key interface between the tester and the chip under test in wafer testing. It is widely used in wafer testing of technology products such as memory, logic, consumer, drive, and communication ICs. It is mainly composed of PCB, probes, and functional components. According to different needs, it also includes electronic components, reinforcement boards and other components. During wafer testing, the tester contacts the wafer through the probe card and transmits test signals to obtain the electrical signals of the wafer. The principle of using the probe card is to directly contact the probes on it with the pads or bumps on the chip, derive chip signals, and then cooperate with peripheral test instruments and software control to achieve automated measurement of the wafer. The present application provides an adaptive detection method for the aperture of a probe card, which improves the accuracy of the aperture detection of the probe card and reduces the aperture detection error of the probe card.

[0020] The target probe card refers to the probe card arbitrarily selected by the staff for research in this application, recorded as the target probe card, and the specification model attribute information and aperture detection accuracy parameters of the target probe card are obtained, wherein the specification model attribute information includes the size, material, number of probes and probe layout of the target probe card, and the physical properties of the target probe card are obtained. The aperture detection accuracy parameter refers to the minimum resolution or maximum error that the probe card can achieve when performing aperture detection, which is used to ensure the accuracy and reliability of the test; the specification model attribute information and aperture detection accuracy parameters of the target probe card are obtained by referring to relevant product manuals, technical specifications, etc.; data support is provided for the subsequent improvement of the aperture detection accuracy of the target probe card.

[0021] The image acquisition device acquires the in-hole image data of the target probe card to obtain an in-hole image acquisition result, and performs image enhancement on the in-hole image acquisition result according to a Retinex algorithm to obtain an in-hole image of the target;

[0022] The image acquisition device refers to an instrument for acquiring images, such as a microscope camera, etc. The image acquisition device is used to photograph the in-hole area of ​​the target probe card to obtain in-hole image data, that is, the in-hole image acquisition result; the Retinex algorithm is an image enhancement algorithm based on color constancy, which is used to improve the contrast and brightness distribution of the image. The in-hole image acquisition result is processed by the Retinex algorithm, and the in-hole image acquisition result is enhanced by estimating and removing the illumination component in the image. The enhanced in-hole image acquisition result is obtained as the target in-hole image. The image acquisition device and the Retin ex algorithm are used to effectively acquire and enhance the in-hole image of the target probe card, so as to more clearly observe and analyze the structure and details in the hole.

[0023] Based on the probe card hole cleaning detector, performing hole cleaning verification on the target probe card according to the target hole image to obtain the target hole cleaning verification result;

[0024] The probe card hole cleaning detector includes a probe card hole cleaning evaluation unit and a probe card hole cleaning verification unit;

[0025] Inputting the target hole internal image into the probe card hole internal cleaning evaluation unit to obtain the target hole internal cleaning evaluation coefficient;

[0026] Inputting the cleaning evaluation coefficient in the target hole into the cleaning verification unit in the probe card hole to generate the cleaning verification result in the target hole;

[0027] The probe card hole cleaning verification unit includes a probe card hole cleaning verification constraint rule, and the probe card hole cleaning verification constraint rule includes that when the target hole cleaning evaluation coefficient satisfies the probe card hole cleaning evaluation constraint, the target hole cleaning verification result obtained is passed;

[0028] The probe card hole cleaning verification constraint rule also includes obtaining a target hole cleaning verification result of failure when the target hole cleaning evaluation coefficient does not meet the probe card hole cleaning evaluation constraint, and generating a probe card hole cleaning optimization instruction.

[0029] The probe card in-hole cleaning detector is a module used to detect the cleanliness of the in-hole area of ​​the target probe card and evaluate the cleanliness of the in-hole, including a probe card in-hole cleaning evaluation unit and a probe card in-hole cleaning verification unit. The probe card in-hole cleaning evaluation unit is used to analyze the input target in-hole image and extract features related to the cleanliness of the hole, such as the number, size, distribution, etc. of the particles in the hole. According to the embedded calculation method of the probe card in-hole cleaning evaluation unit, the cleanliness of the hole of the target probe card, that is, the cleaning evaluation coefficient, is obtained. The significance of the cleaning verification is that the cleanliness of the sample directly affects the accuracy of the measurement result. If there are impurities or residues in the aperture, the measured value may be too large or too small. The probe card in-hole cleaning verification unit has built-in probe card in-hole cleaning verification constraint rules, which define the qualified standard for the cleanliness of the hole. If the cleaning evaluation coefficient satisfies the cleaning evaluation constraint, that is, the cleanliness of the hole reaches the qualified standard, the verification unit will generate a target hole cleaning verification result of "pass". If the cleaning evaluation coefficient does not meet the cleaning evaluation constraint, that is, there are non-standard contaminants in the hole, the verification unit will generate a target hole cleaning verification result of "fail", that is, the probe card hole cleaning evaluation constraint. In the probe card hole cleaning verification unit, the probe card hole cleaning evaluation constraint is used to compare with the cleaning evaluation coefficient. When the target hole cleaning verification result is "fail", a probe card hole cleaning optimization instruction is generated at the same time. The probe card hole cleaning optimization instruction includes a cleaning method, etc. The comparison results include "pass" and "fail", both of which are called the target hole cleaning verification result. The target hole image is input into the probe card hole cleaning evaluation unit, the target hole cleaning evaluation coefficient is obtained, the target hole cleaning evaluation coefficient is input into the probe card hole cleaning verification unit, and verification is performed according to the built-in cleaning verification constraint rules to obtain the verification result. According to the verification result, the target hole cleaning verification result is obtained, and the cleaning optimization instruction is generated as needed, so that the cleanliness of the probe card hole can be efficiently and accurately verified to ensure that the probe card meets the required cleaning standards before use.

[0030] Based on big data, multiple probe card hole cleaning evaluation record data sets are loaded;

[0031] Based on multiple image feature learning operators, the multiple probe card in-hole cleaning evaluation record data sets are trained and tested respectively to obtain multiple in-hole cleaning evaluation models that meet preset in-hole cleaning evaluation accuracy constraints;

[0032] Obtaining a plurality of in-hole cleaning evaluation accuracy parameters corresponding to the plurality of in-hole cleaning evaluation models, and performing proportion calculation based on the plurality of in-hole cleaning evaluation accuracy parameters to construct an in-hole cleaning evaluation output incentive model, wherein the in-hole cleaning evaluation output incentive model includes a plurality of in-hole cleaning evaluation output incentive coefficients;

[0033] embedding the plurality of in-hole cleaning evaluation models and the in-hole cleaning evaluation output excitation model into the in-hole cleaning evaluation unit of the probe card;

[0034] Inputting the target hole image into the multiple hole cleaning evaluation models to obtain multiple hole cleaning evaluation coefficients;

[0035] The multiple in-hole cleaning evaluation coefficients are input into the in-hole cleaning evaluation output incentive model, and the multiple in-hole cleaning evaluation coefficients are weighted calculated according to the multiple in-hole cleaning evaluation output incentive coefficients to generate the target in-hole cleaning evaluation coefficient.

[0036] According to big data, a plurality of probe card hole cleaning evaluation record data sets are obtained, wherein the probe card hole cleaning evaluation record data sets refer to a data set formed by integrating the record data of the hole cleaning evaluation of a plurality of probe cards in the past time period, and are used to train a plurality of subsequent hole cleaning evaluation models. According to the characteristics of the probe card hole image, a plurality of operators suitable for image feature learning are selected, such as convolutional neural networks, autoencoders and other machine learning algorithms suitable for image recognition. The plurality of probe card hole cleaning evaluation record data sets are trained according to a plurality of different machine learning algorithms. By adjusting model parameters and optimizing algorithms, the plurality of hole cleaning evaluation models are enabled to accurately identify the hole cleanliness, and a plurality of hole cleaning evaluation accuracy parameters are output. For example, the target hole image is input into the plurality of hole cleaning evaluation models, and a plurality of different hole cleaning evaluation coefficients obtained by the models of the target hole image under different algorithm training are obtained; a plurality of hole cleaning evaluation accuracy parameters corresponding to the plurality of hole cleaning evaluation models are obtained, that is, the model accuracy of the plurality of hole cleaning evaluation models is obtained, wherein the model accuracy refers to the model accuracy rate, which is an indicator for measuring the ability of the machine learning model to make predictions or decisions based on data. For example, the plurality of probe card hole cleaning evaluation record data sets are used. The in-hole cleaning evaluation model is trained, and the output result of the in-hole cleaning evaluation model conforms to the ratio of the number of the plurality of probe card in-hole cleaning evaluation record data sets to the number of the plurality of probe card in-hole cleaning evaluation record data sets, which is the in-hole cleaning evaluation accuracy parameter; and a proportion calculation is performed based on the plurality of in-hole cleaning evaluation accuracy parameters to construct an in-hole cleaning evaluation output incentive model, wherein the in-hole cleaning evaluation output incentive model includes a plurality of in-hole cleaning evaluation output incentive coefficients, and the plurality of in-hole cleaning evaluation output incentive coefficients refer to the ratio of the in-hole cleaning evaluation accuracy parameter to the sum of the plurality of in-hole cleaning evaluation accuracy parameters. value, embed the multiple in-hole cleaning evaluation models and the in-hole cleaning evaluation output incentive model into the in-hole cleaning evaluation unit of the probe card; input the multiple in-hole cleaning evaluation coefficients into the in-hole cleaning evaluation output incentive model, and perform weighted calculation on the multiple in-hole cleaning evaluation coefficients according to the multiple in-hole cleaning evaluation output incentive coefficients to generate the target in-hole cleaning evaluation coefficient. By using big data and machine learning technology, a more accurate and reliable probe card in-hole cleaning evaluation system can be constructed. The system can comprehensively consider the outputs of multiple models and perform weighted calculation according to the performance of each model, thereby improving the accuracy and stability of the evaluation.

[0037] When the cleaning verification result in the target hole is passed, the target probe card is placed on a stage of a probe card aperture detection device to obtain real-time stage control data and real-time target positioning data;

[0038] The stage refers to a component device specially designed for positioning and supporting the probe card to be tested. The probe card aperture detection device refers to a device for subsequent aperture measurement of the target probe card. When the target hole cleaning verification result is passed, it proves that the hole cleaning of the target probe card is qualified, and the target probe card can be tested for the next project. When the stage is working, real-time stage control data is generated, which records the movement trajectory, height, inclination and other parameters of the stage, reflecting the real-time state of the stage when adjusting the position of the probe card. The real-time target positioning data is obtained through high-precision sensors, which capture the image or signal of the probe card in real time, and determine the specific position of each hole within the synchronization time through algorithm processing. The real-time target positioning data provides key coordinate information for subsequent aperture measurement, ensuring the accuracy of the measurement. When the target hole cleaning verification result is passed, the target probe card is placed on the stage of the probe card aperture detection device, and real-time stage control data and real-time target positioning data are obtained to ensure the accuracy and reliability of the aperture measurement.

[0039] Based on the specification model attribute information and in accordance with the multi-level detection feature verification constraint, the real-time stage control data and the real-time target positioning data are subjected to detection feature verification to obtain a detection feature verification result;

[0040] The multi-level detection feature verification constraint is a detection rule and condition formulated according to the specification model attribute information, which is used to verify the real-time stage control data and the real-time target positioning data. The multi-level detection feature verification constraint includes the accuracy requirements of the stage movement trajectory, the error range of the positioning data, the recognition accuracy of specific hole positions, etc. In the detection feature verification process, the key features of the real-time stage control data and the real-time target positioning data are extracted, such as the coordinate points of the movement trajectory, the coordinate positions of the positioning data, etc. The key features are compared and verified with the multi-level detection feature verification constraints. If the data features meet the requirements of the verification constraints, it is considered that the verification is passed; if the data features do not meet the requirements, a corresponding error prompt will be generated for further adjustment and optimization.

[0041] Based on the specification model attribute information, stage control anomaly evaluation records of the same type of probe cards are collected to obtain a stage control anomaly evaluation record set of the same type, and a stage control anomaly evaluation network that satisfies the first convergence constraint is trained based on the stage control anomaly evaluation record set of the same type;

[0042] Based on the specification model attribute information, the positioning anomaly evaluation records of the same type of probe cards are collected to obtain a same type of positioning anomaly evaluation record set, and a positioning anomaly evaluation network that satisfies the second convergence constraint is trained based on the same type of positioning anomaly evaluation record set;

[0043] Inputting the real-time stage control data into the stage control abnormality evaluation network to obtain a real-time stage control abnormality evaluation coefficient;

[0044] Inputting the real-time target positioning data into the positioning anomaly evaluation network to obtain a real-time target positioning anomaly evaluation coefficient;

[0045] Comparing the multi-level detection feature verification constraint with the real-time stage control abnormality evaluation coefficient and the real-time target positioning abnormality evaluation coefficient to generate the detection feature verification result;

[0046] Wherein, the multi-level detection feature verification constraints include a stage control abnormality evaluation threshold and a target positioning abnormality evaluation threshold;

[0047] When the real-time stage control abnormality evaluation coefficient is less than the stage control abnormality evaluation threshold, and the real-time target positioning abnormality evaluation coefficient is less than the target positioning abnormality evaluation threshold, the generated detection feature verification result is qualified;

[0048] When the real-time stage control abnormality evaluation coefficient is greater than / equal to the stage control abnormality evaluation threshold, and / or the real-time target positioning abnormality evaluation coefficient is greater than / equal to the target positioning abnormality evaluation threshold, the generated detection feature verification result is unqualified.

[0049] According to the specification model attribute information of the target probe card, the stage control anomaly evaluation records of the same probe card are collected. The stage control anomaly evaluation record set includes various abnormal situations that occurred in the stage control of the same probe card during the detection process in the past time period, which provides a basis for the training of the stage control anomaly evaluation network. Through the same stage control anomaly evaluation record set, a stage control anomaly evaluation network that meets the first convergence constraint is trained. The first convergence constraint refers to the model accuracy of the stage control anomaly evaluation network. When the accuracy of the stage control anomaly evaluation network meets the first convergence constraint, the stage control anomaly evaluation network is acquired; the same positioning anomaly evaluation record set refers to various abnormal situations that occurred in the positioning data of the same probe card during the detection process. According to the above technology, based on the same positioning anomaly evaluation record set, a positioning anomaly evaluation network that meets the second convergence constraint is trained. The positioning anomaly evaluation network can learn and identify abnormal patterns of positioning data. The second convergence constraint refers to the model accuracy of the positioning anomaly evaluation network, which provides support for abnormal evaluation of real-time data. Input the real-time stage control data into the stage control abnormality evaluation network to obtain the real-time stage control abnormality evaluation coefficient; input the real-time target positioning data into the positioning abnormality evaluation network to obtain the real-time target positioning abnormality evaluation coefficient; wherein the real-time stage control abnormality evaluation coefficient includes the abnormality of the real-time stage data, and similarly, the target positioning abnormality evaluation coefficient includes the position abnormality of the real-time probe card; compare the multi-level detection feature verification constraint with the real-time stage control abnormality evaluation coefficient and the real-time target positioning abnormality evaluation coefficient to generate the detection feature verification result, wherein the multi-level detection feature verification constraint includes the stage control abnormality evaluation threshold and the target positioning abnormality evaluation threshold, if the real-time stage control abnormality evaluation coefficient is less than the stage control abnormality evaluation threshold, and the real-time target positioning abnormality evaluation coefficient is less than the target positioning abnormality evaluation threshold, then the generated detection feature verification result is qualified; otherwise, the generated detection feature verification result is unqualified. Through the above method, it can be ensured that the stage control and target positioning data meet the requirements before the aperture detection, thereby improving the accuracy and reliability of the detection.

[0050] When the detection feature verification result is qualified, the real-time stage control data and the real-time target positioning data are input into a pre-built detection equipment control decision model to obtain a detection equipment control decision;

[0051] The detection equipment control decision model is used to perform aperture detection on the target probe card that has been checked for cleanliness in the hole and the stage and no abnormality is found. The detection equipment control decision model is constructed based on the neural network model, and historical stage control data, historical target positioning data and historical detection equipment control decisions are obtained. The historical detection equipment control decision refers to the historical parameters of the stage for detecting the target probe card under the conditions of the corresponding historical stage control data and historical target positioning data in the past time. The historical stage control data and historical target positioning data are input into the detection equipment control decision model, and the detection equipment control decision model is optimized according to the corresponding historical detection equipment control decision. When the model accuracy of the detection equipment control decision model reaches the preset requirements, the detection equipment control decision model is obtained. When the detection feature verification result is qualified, the real-time stage control data and the real-time target positioning data are input into the pre-constructed detection equipment control decision model, and the detection equipment control decision is output. The detection equipment control decision is the adjustment parameter of the stage, such as the inclination angle and height of the stage. The above method is helpful to achieve more accurate and efficient aperture detection.

[0052] like Figure 2 As shown, when the detection feature verification result is unqualified, abnormal detection equipment and equipment abnormal feature data are generated;

[0053] Performing equipment abnormality compensation analysis based on the equipment abnormality feature data to determine equipment abnormality compensation adjustment data;

[0054] The abnormality detection device is adjusted based on the device abnormality compensation adjustment data.

[0055] When the detection feature verification result is unqualified, the detection abnormality device and the device abnormality feature data are generated, the detection abnormality device includes an abnormal stage and an abnormal target probe card placed on the stage, and the device abnormality feature data includes the abnormal conditions of the stage and the abnormal conditions of the target probe card, such as abnormal fluctuations in the detection data, values ​​exceeding a preset range, specific patterns that do not conform to normal patterns, etc. It should be clear that when any of the stage and the target probe card is abnormal, the detection abnormality device and the device abnormality feature data will be generated; based on the device abnormality feature data, device abnormality compensation analysis is performed, including Statistical analysis of abnormal data, pattern recognition, and abnormal diagnosis based on expert systems, etc., through equipment abnormality compensation analysis, can determine the specific cause of equipment abnormality and calculate the corresponding equipment abnormality compensation adjustment data. The equipment abnormality compensation adjustment data is a set of parameters used to adjust the state of the repair equipment, including adjusting the sensitivity of the detection equipment, the positioning accuracy of the calibration equipment, etc.; based on the equipment abnormality compensation adjustment data, the system adjusts the abnormal detection equipment, and the adjustment process includes automatically adjusting the parameter settings of the equipment, triggering the self-calibration program of the equipment, etc. By adjusting the abnormal detection equipment, the equipment abnormality is eliminated and restored to a normal working state. When the detection feature verification result is unqualified, the system effectively responds to equipment abnormalities by generating equipment abnormality feature data, performing equipment abnormality compensation analysis, and adjusting the abnormal detection equipment based on the analysis results, ensuring the accuracy of the detection process and the stable operation of the equipment.

[0056] Based on the adaptive aperture detection algorithm, the target probe card is detected according to the aperture detection accuracy parameter, the detection equipment control decision and the probe card aperture detection device to obtain a target aperture detection report.

[0057] The adaptive aperture detection algorithm can set appropriate detection thresholds and accuracy requirements according to the aperture detection accuracy parameters. Combined with the detection equipment control decision, it can guide the detection equipment to perform detection according to the optimal path and strategy, including adjusting the movement trajectory of the stage, optimizing positioning accuracy, controlling the movement speed of the probe card, etc. When the probe card aperture detection device is used to detect the target probe card, the adaptive aperture detection algorithm will generate a detailed target aperture detection report, including the aperture measurement results, and provide detailed information on the detection equipment performance, detection accuracy and abnormal conditions, which will help operators to have an in-depth understanding of the detection results and provide strong support for subsequent quality control and process improvements.

[0058] Inputting the aperture detection accuracy parameter into a pre-constructed aperture detection frequency measurement table to obtain a matching aperture detection frequency;

[0059] Taking the matching aperture detection frequency as the detection frequency constraint, controlling the probe card aperture detection device to perform multiple detections on the target probe card according to the detection device control decision, and obtaining multiple groups of aperture detection data that meet the detection frequency constraint;

[0060] Collecting real-time environmental information and real-time equipment monitoring information corresponding to the multiple sets of aperture detection data to obtain multiple sets of aperture detection scene data;

[0061] Based on the multiple sets of aperture detection scene data, adaptively compensate for detection scene anomalies on the multiple sets of aperture detection data to obtain multiple aperture detection results;

[0062] Data fusion is performed based on the multiple aperture detection results to generate the target aperture detection report.

[0063] The aperture detection accuracy parameters are input into a pre-constructed aperture detection frequency measurement table to determine the appropriate detection frequency of the target probe card for specific accuracy requirements. The aperture detection frequency measurement table is constructed based on historical data, and the historical data includes the aperture detection accuracy parameters and the corresponding detection frequencies. The table can automatically match the corresponding aperture detection times according to different accuracy parameters, that is, match the aperture detection frequency, to ensure that the detection process will not miss important information due to too low a frequency, nor waste resources due to too high a frequency. Taking the matching aperture detection frequency as the detection frequency constraint, the probe card aperture detection device is controlled according to the detection equipment control decision to perform multiple detections on the target probe card. In the multiple detections, the detection equipment will operate according to the parameters and strategies specified in the detection equipment control decision to ensure that each detection can accurately reflect the aperture condition of the target probe card. Through multiple detections, multiple groups of aperture detection data that meet the detection frequency constraint can be obtained; real-time environmental information and real-time equipment monitoring information corresponding to the multiple groups of aperture detection data are collected, that is, environmental information and equipment monitoring information when the multiple groups of aperture detection data are collected, including environmental factors such as temperature, humidity, and air pressure during detection, as well as monitoring data such as the operating status, temperature, and vibration of the equipment, to form multiple groups of aperture detection scene data; adaptive detection scene anomaly compensation is performed on the multiple groups of aperture detection data based on the multiple groups of aperture detection scene data, in order to eliminate or reduce the influence of environmental factors and equipment status on the detection results, and to obtain more accurate multiple aperture detection results by identifying and compensating for abnormal deviations caused by scene factors. Data fusion is performed based on multiple aperture detection results. Data fusion combines multiple detection results to form a more comprehensive and accurate conclusion. By considering the data differences and correlations under different detection conditions, more robust and reliable aperture detection results can be obtained, and a target aperture detection report is generated. The target aperture detection report includes detailed detection data, analysis results and conclusions. The matching detection frequency is obtained by inputting aperture detection accuracy parameters, and the probe card aperture detection device is controlled to perform multiple detections. Real-time environmental information and equipment monitoring information are collected, and adaptive detection scene anomaly compensation is performed. Finally, data fusion is performed to generate a report, which improves the quality and efficiency of the detection process.

[0064] Traversing the multiple groups of aperture detection data and the multiple groups of aperture detection scene data, extracting a first group of aperture detection data and a first group of aperture detection scene data corresponding to the first group of aperture detection data, wherein the first group of aperture detection scene data includes first aperture detection environment information and first aperture detection device monitoring information;

[0065] Performing anomaly recognition and evaluation on the first aperture detection environment information, and determining a first environment anomaly recognition result and a first environment anomaly evaluation coefficient;

[0066] Performing abnormality identification and evaluation on the monitoring information of the first aperture detection device to obtain a first device abnormality identification result and a first device abnormality evaluation coefficient;

[0067] If the first environment anomaly evaluation coefficient is greater than / equal to the environment anomaly evaluation threshold, and the first device anomaly evaluation coefficient is less than the device anomaly evaluation threshold, the first environment anomaly recognition result is input into the environment anomaly compensation analysis model to obtain first environment anomaly compensation data, and the first set of aperture detection scene data is adaptively compensated based on the first environment anomaly compensation data to obtain a first aperture detection result, and the first aperture detection result is added to the multiple aperture detection results;

[0068] If the first environment abnormality evaluation coefficient is less than the environment abnormality evaluation threshold, and the first device abnormality evaluation coefficient is greater than / equal to the device abnormality evaluation threshold, the first device abnormality identification result is input into the device abnormality compensation analysis model to obtain first device abnormality compensation data, and the first set of aperture detection scene data is adaptively compensated based on the first device abnormality compensation data to generate the first aperture detection result;

[0069] If the first environment abnormality evaluation coefficient is greater than / equal to the environment abnormality evaluation threshold, and the first device abnormality evaluation coefficient is greater than / equal to the device abnormality evaluation threshold, based on the first environment abnormality recognition result and the first device abnormality recognition result, abnormality compensation fusion is performed according to the environment abnormality compensation analysis model and the device abnormality compensation analysis model to obtain first fused abnormality compensation data, and the first group of aperture detection scene data is adaptively compensated based on the first fused abnormality compensation data to obtain the first aperture detection result;

[0070] If the first environment abnormality evaluation coefficient is less than the environment abnormality evaluation threshold, and the first equipment abnormality evaluation coefficient is less than the equipment abnormality evaluation threshold, a first detection data mapping instruction is obtained, and the first set of aperture detection data is output as the first aperture detection result according to the first detection data mapping instruction.

[0071] Traversing the multiple groups of aperture detection data and the multiple groups of aperture detection scene data, extracting a first group of aperture detection data and a first group of aperture detection scene data corresponding to the first group of aperture detection data, wherein the first group of aperture detection scene data includes first aperture detection environment information and first aperture detection device monitoring information, the first group of aperture detection data refers to any group of data extracted from the multiple groups of aperture detection data for research, the first aperture detection environment information and the first aperture detection device monitoring information are the corresponding data of the first group of aperture detection data, performing abnormal identification and evaluation on the first aperture detection environment information refers to checking whether environmental parameters such as temperature, humidity, air pressure, etc. are within a normal range, and whether there are sudden changes or abnormal fluctuations; determining a first environmental abnormality identification result and a first environmental abnormality evaluation coefficient, the first environmental abnormality identification result refers to an identified abnormal environmental condition, the first environmental abnormality evaluation coefficient refers to an evaluation parameter for the abnormal environmental condition, constructing an environmental abnormality compensation analysis model, the environmental abnormality compensation analysis model is constructed based on a neural network model, and the input data is the first environmental abnormality identification result, The output data is the first environmental anomaly compensation data. The historical environmental anomaly recognition result data set and the historical environmental anomaly compensation data are obtained to train the environmental anomaly compensation analysis model. When the model accuracy of the environmental anomaly compensation analysis model reaches the requirement, the environmental anomaly compensation analysis model is obtained. Similarly, the equipment anomaly compensation analysis model can be constructed. If the environmental anomaly evaluation coefficient is greater than or equal to the environmental anomaly evaluation threshold, and the equipment anomaly evaluation coefficient is less than the equipment anomaly evaluation threshold, it means that the environment has a greater impact on the detection result and the equipment state is normal. At this time, the environmental anomaly recognition result is input into the environmental anomaly compensation analysis model to obtain the first environmental anomaly compensation data, and the first set of aperture detection scene data is adaptively compensated to obtain the first aperture detection result; if the environmental anomaly evaluation coefficient is less than the environmental anomaly evaluation threshold, and the equipment anomaly evaluation coefficient is greater than or equal to the equipment anomaly evaluation threshold, it means that the abnormal equipment state has a greater impact on the detection result. At this time, the equipment anomaly recognition result is input into the equipment anomaly compensation analysis model to obtain the first equipment anomaly compensation data, and corresponding adaptive compensation is performed; if both exceed their respective thresholds, it means that both the environment and the equipment have abnormal influences. At this time, based on the environmental anomaly identification results and the equipment anomaly identification results, two compensation analysis models are used to perform anomaly compensation fusion to obtain the first fused anomaly compensation data, and adaptive compensation is performed. Anomaly compensation fusion means adjusting both anomalies and obtaining parameters as the first fused anomaly compensation data; if neither exceeds their respective thresholds, it means that the environment and equipment status are normal, and no compensation is required at this time, and the first set of aperture detection data is directly used as the first aperture detection result. The obtained aperture detection result, that is, the first aperture detection result, is added to multiple aperture detection results for subsequent data fusion and report generation.The same processing is performed on each set of aperture detection data and the corresponding scene data in turn until all data are processed, accurately reflecting the actual situation of aperture detection, reducing the impact of environmental and equipment abnormalities on the detection results, and improving the accuracy and reliability of the report. The method solves the technical problems of the existing probe card aperture detection method, such as high detection error and poor accuracy, which are difficult to meet the high-precision requirements of probe card aperture detection, and achieves the technical effect of improving the accuracy of probe card aperture detection and reducing the error of probe card aperture detection.

[0072] like Figure 3 As shown, the embodiment of the present application includes an adaptive detection system for the aperture of a probe card, the system comprising:

[0073] An attribute information loading module 11, wherein the attribute information loading module 11 is used to load the specification model attribute information and aperture detection accuracy parameters of the target probe card;

[0074] A target hole image acquisition module 12, wherein the target hole image acquisition module 12 is used to acquire hole image data of the target probe card according to an image acquisition device, obtain a hole image acquisition result, and perform image enhancement on the hole image acquisition result according to a Retinex algorithm to acquire a target hole image;

[0075] A target hole cleaning verification module 13, wherein the target hole cleaning verification module 13 is used to perform hole cleaning verification on the target probe card according to the target hole image based on a probe card hole cleaning detector to obtain a target hole cleaning verification result;

[0076] A control positioning data acquisition module 14, wherein the control positioning data acquisition module 14 is used to place the target probe card on a stage of a probe card aperture detection device when the cleaning verification result in the target hole is passed, and obtain real-time stage control data and real-time target positioning data;

[0077] A detection result acquisition module 15 for obtaining detection results to be verified, the detection result acquisition module 15 is used to perform detection feature verification on the real-time stage control data and the real-time target positioning data based on the specification model attribute information and according to the multi-level detection feature verification constraint to obtain a detection feature verification result;

[0078] A detection equipment control decision module 16, wherein the detection equipment control decision module 16 is used to input the real-time stage control data and the real-time target positioning data into a pre-built detection equipment control decision model to obtain a detection equipment control decision when the detection feature verification result is qualified;

[0079] The target aperture detection report acquisition module 17 is used to detect the target probe card based on the adaptive aperture detection algorithm, according to the aperture detection accuracy parameters, the detection equipment control decision and the probe card aperture detection device, to obtain a target aperture detection report.

[0080] Furthermore, the embodiment of the present application also includes:

[0081] The probe card hole cleaning detector includes a module, and the probe card hole cleaning detector includes a module for the probe card hole cleaning detector including a probe card hole cleaning evaluation unit and a probe card hole cleaning verification unit;

[0082] A target hole cleaning evaluation coefficient obtaining module, wherein the target hole cleaning evaluation coefficient obtaining module is used to input the target hole image into the probe card hole cleaning evaluation unit to obtain the target hole cleaning evaluation coefficient;

[0083] A cleaning verification result generation module, the cleaning verification result generation module is used to input the cleaning evaluation coefficient in the target hole into the cleaning verification unit in the probe card hole to generate the cleaning verification result in the target hole;

[0084] A cleaning evaluation coefficient judgment module, wherein the cleaning evaluation coefficient judgment module is used in which the cleaning verification unit in the probe card hole includes a cleaning verification constraint rule in the probe card hole, and the cleaning verification constraint rule in the probe card hole includes that when the cleaning evaluation coefficient in the target hole satisfies the cleaning evaluation constraint in the probe card hole, the cleaning verification result in the target hole is passed;

[0085] A cleaning optimization instruction generation module, wherein the cleaning optimization instruction generation module is used for the cleaning verification constraint rule in the probe card hole and also includes that when the cleaning evaluation coefficient in the target hole does not meet the cleaning evaluation constraint in the probe card hole, the cleaning verification result in the target hole is obtained as failure, and a cleaning optimization instruction in the probe card hole is generated.

[0086] Furthermore, the embodiment of the present application also includes:

[0087] A cleaning evaluation record data set loading module, wherein the cleaning evaluation record data set loading module is used to load a plurality of probe card hole cleaning evaluation record data sets based on big data;

[0088] An in-hole cleaning evaluation model constraint module, wherein the in-hole cleaning evaluation model constraint module is used to train and test the multiple probe card in-hole cleaning evaluation record data sets based on multiple image feature learning operators to obtain multiple in-hole cleaning evaluation models that meet preset in-hole cleaning evaluation accuracy constraints;

[0089] A cleaning evaluation output incentive module, the cleaning evaluation output incentive module is used to obtain a plurality of in-hole cleaning evaluation accuracy parameters corresponding to the plurality of in-hole cleaning evaluation models, and perform a proportion calculation based on the plurality of in-hole cleaning evaluation accuracy parameters to construct an in-hole cleaning evaluation output incentive model, wherein the in-hole cleaning evaluation output incentive model includes a plurality of in-hole cleaning evaluation output incentive coefficients;

[0090] A probe card hole cleaning evaluation unit module, the probe card hole cleaning evaluation unit module is used to embed the plurality of hole cleaning evaluation models and the hole cleaning evaluation output incentive model into the probe card hole cleaning evaluation unit;

[0091] a hole cleaning evaluation coefficient obtaining module, wherein the hole cleaning evaluation coefficient obtaining module is used to input the target hole image into the multiple hole cleaning evaluation models to obtain multiple hole cleaning evaluation coefficients;

[0092] A weighted calculation module for in-hole cleaning evaluation coefficients, wherein the weighted calculation module for in-hole cleaning evaluation coefficients is used to input the multiple in-hole cleaning evaluation coefficients into the in-hole cleaning evaluation output incentive model, perform weighted calculation on the multiple in-hole cleaning evaluation coefficients according to the multiple in-hole cleaning evaluation output incentive coefficients, and generate the target in-hole cleaning evaluation coefficient.

[0093] Furthermore, the embodiment of the present application also includes:

[0094] A stage control anomaly evaluation network module, the stage control anomaly evaluation network module is used to collect stage control anomaly evaluation records of the same type of probe cards based on the specification model attribute information, obtain a stage control anomaly evaluation record set of the same type, and train a stage control anomaly evaluation network that satisfies the first convergence constraint based on the same type of stage control anomaly record set;

[0095] A positioning anomaly evaluation network module, the positioning anomaly evaluation network module is used to collect positioning anomaly evaluation records of the same type of probe cards based on the specification model attribute information, obtain a same type of positioning anomaly evaluation record set, and train a positioning anomaly evaluation network that satisfies a second convergence constraint based on the same type of positioning anomaly evaluation record set;

[0096] A control abnormality evaluation coefficient acquisition module, the control abnormality evaluation coefficient acquisition module is used to input the real-time stage control data into the stage control abnormality evaluation network to obtain a real-time stage control abnormality evaluation coefficient;

[0097] A positioning anomaly evaluation coefficient module, wherein the positioning anomaly evaluation coefficient module is used to input the real-time target positioning data into the positioning anomaly evaluation network to obtain a real-time target positioning anomaly evaluation coefficient;

[0098] A detection feature verification result generation module, the detection feature verification result generation module is used to compare the multi-level detection feature verification constraint with the real-time stage control abnormality evaluation coefficient and the real-time target positioning abnormality evaluation coefficient to generate the detection feature verification result;

[0099] A positioning anomaly evaluation threshold acquisition module, wherein the positioning anomaly evaluation threshold acquisition module is used in which the multi-level detection feature verification constraint includes a stage control anomaly evaluation threshold and a target positioning anomaly evaluation threshold;

[0100] A qualified result generation module, wherein the qualified result generation module is used to generate the detection feature verification result as qualified when the real-time stage control abnormality evaluation coefficient is less than the stage control abnormality evaluation threshold, and the real-time target positioning abnormality evaluation coefficient is less than the target positioning abnormality evaluation threshold;

[0101] An unqualified result generation module is used to generate the detection feature verification result as unqualified when the real-time stage control abnormality evaluation coefficient is greater than / equal to the stage control abnormality evaluation threshold, and / or the real-time target positioning abnormality evaluation coefficient is greater than / equal to the target positioning abnormality evaluation threshold.

[0102] Furthermore, the embodiment of the present application also includes:

[0103] An abnormal data generation module, the abnormal data generation module is used to generate abnormal detection equipment and equipment abnormality feature data when the detection feature verification result is unqualified;

[0104] An abnormality compensation adjustment data module, the abnormality compensation adjustment data module is used to perform equipment abnormality compensation analysis based on the equipment abnormality feature data to determine equipment abnormality compensation adjustment data;

[0105] An abnormal device adjustment module is used to adjust the abnormal device detected based on the device abnormality compensation adjustment data.

[0106] Furthermore, the embodiment of the present application also includes:

[0107] A matching aperture detection frequency acquisition module, wherein the matching aperture detection frequency acquisition module is used to input the aperture detection accuracy parameter into a pre-constructed aperture detection frequency measurement table to obtain a matching aperture detection frequency;

[0108] A multiple detection module, the multiple detection module is used to control the probe card aperture detection device to perform multiple detections on the target probe card according to the detection device control decision, taking the matching aperture detection frequency as the detection frequency constraint, to obtain multiple groups of aperture detection data that meet the detection frequency constraint;

[0109] An aperture detection scene data acquisition module, the aperture detection scene data acquisition module is used to collect real-time environmental information and real-time equipment monitoring information corresponding to the multiple sets of aperture detection data to obtain multiple sets of aperture detection scene data;

[0110] An aperture detection result acquisition module, the aperture detection result acquisition module is used to perform adaptive detection scene abnormality compensation on the multiple sets of aperture detection data based on the multiple sets of aperture detection scene data to obtain multiple aperture detection results;

[0111] A target aperture detection report generation module is used to perform data fusion based on the multiple aperture detection results to generate the target aperture detection report.

[0112] Furthermore, the embodiment of the present application also includes:

[0113] An aperture detection data acquisition module, the aperture detection data acquisition module is used to traverse the multiple groups of aperture detection data and the multiple groups of aperture detection scene data, extract a first group of aperture detection data, and a first group of aperture detection scene data corresponding to the first group of aperture detection data, wherein the first group of aperture detection scene data includes first aperture detection environment information and first aperture detection equipment monitoring information;

[0114] An abnormality recognition and evaluation module, the abnormality recognition and evaluation module is used to perform abnormality recognition and evaluation on the first aperture detection environment information, and determine a first environment abnormality recognition result and a first environment abnormality evaluation coefficient;

[0115] An abnormality identification and evaluation module, the abnormality identification and evaluation module is used to perform abnormality identification and evaluation on the monitoring information of the first aperture detection device to obtain a first device abnormality identification result and a first device abnormality evaluation coefficient;

[0116] an environment anomaly compensation data acquisition module, wherein if the first environment anomaly evaluation coefficient is greater than / equal to an environment anomaly evaluation threshold, and the first device anomaly evaluation coefficient is less than the device anomaly evaluation threshold, the first environment anomaly recognition result is input into an environment anomaly compensation analysis model to obtain first environment anomaly compensation data, and based on the first environment anomaly compensation data, the first set of aperture detection scene data is adaptively compensated to obtain a first aperture detection result, and the first aperture detection result is added to the multiple aperture detection results;

[0117] an aperture detection result generating module, wherein if the first environment abnormality evaluation coefficient is less than the environment abnormality evaluation threshold, and the first device abnormality evaluation coefficient is greater than / equal to the device abnormality evaluation threshold, the first device abnormality identification result is input into the device abnormality compensation analysis model to obtain first device abnormality compensation data, and the first set of aperture detection scene data is adaptively compensated based on the first device abnormality compensation data to generate the first aperture detection result;

[0118] An adaptive compensation module, wherein if the first environment abnormality evaluation coefficient is greater than / equal to the environment abnormality evaluation threshold, and the first device abnormality evaluation coefficient is greater than / equal to the device abnormality evaluation threshold, based on the first environment abnormality recognition result and the first device abnormality recognition result, an abnormality compensation fusion is performed according to the environment abnormality compensation analysis model and the device abnormality compensation analysis model to obtain first fused abnormality compensation data, and the first set of aperture detection scene data is adaptively compensated based on the first fused abnormality compensation data to obtain the first aperture detection result;

[0119] An aperture detection result output module, wherein the aperture detection result output module is used to obtain a first detection data mapping instruction if the first environment abnormality evaluation coefficient is less than the environment abnormality evaluation threshold, and the first equipment abnormality evaluation coefficient is less than the equipment abnormality evaluation threshold, and output the first group of aperture detection data as the first aperture detection result according to the first detection data mapping instruction.

[0120] For a specific embodiment of an adaptive detection system for the aperture of a probe card, please refer to the embodiment of an adaptive detection method for the aperture of a probe card described above, which will not be described in detail here. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0121] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store news data and data such as time attenuation factors. The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement an adaptive detection method for the aperture of a probe card.

[0122] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0123] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, steps of a method for adaptively detecting an aperture of a probe card are implemented.

[0124] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of a method for adaptively detecting the aperture of a probe card are implemented.

[0125] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0126] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for adaptively detecting the aperture of a probe card, characterized in that: The method comprises: Load the specification model attribute information and aperture detection accuracy parameters of the target probe card; The image acquisition device acquires the in-hole image data of the target probe card to obtain an in-hole image acquisition result, and performs image enhancement on the in-hole image acquisition result according to a Retinex algorithm to obtain an in-hole image of the target; Based on the probe card hole cleaning detector, performing hole cleaning verification on the target probe card according to the target hole image to obtain the target hole cleaning verification result; When the cleaning verification result in the target hole is passed, the target probe card is placed on a stage of a probe card aperture detection device to obtain real-time stage control data and real-time target positioning data; Based on the specification model attribute information and in accordance with the multi-level detection feature verification constraint, the real-time stage control data and the real-time target positioning data are subjected to detection feature verification to obtain a detection feature verification result; When the detection feature verification result is qualified, the real-time stage control data and the real-time target positioning data are input into a pre-built detection equipment control decision model to obtain a detection equipment control decision; Based on the adaptive aperture detection algorithm, the target probe card is detected according to the aperture detection accuracy parameter, the detection equipment control decision and the probe card aperture detection device to obtain a target aperture detection report.

2. The method according to claim 1, characterized in that Based on the probe card hole cleaning detector, the target probe card is subjected to hole cleaning verification according to the target hole image to obtain the target hole cleaning verification result, including: The probe card hole cleaning detector includes a probe card hole cleaning evaluation unit and a probe card hole cleaning verification unit; Inputting the target hole internal image into the probe card hole internal cleaning evaluation unit to obtain the target hole internal cleaning evaluation coefficient; Inputting the cleaning evaluation coefficient in the target hole into the cleaning verification unit in the probe card hole to generate the cleaning verification result in the target hole; The probe card hole cleaning verification unit includes a probe card hole cleaning verification constraint rule, and the probe card hole cleaning verification constraint rule includes that when the target hole cleaning evaluation coefficient satisfies the probe card hole cleaning evaluation constraint, the target hole cleaning verification result obtained is passed; The probe card hole cleaning verification constraint rule also includes obtaining a target hole cleaning verification result of failure when the target hole cleaning evaluation coefficient does not meet the probe card hole cleaning evaluation constraint, and generating a probe card hole cleaning optimization instruction.

3. The method according to claim 2, characterized in that Inputting the target hole image into the probe card hole cleaning evaluation unit to obtain the target hole cleaning evaluation coefficient, including: Based on big data, multiple probe card hole cleaning evaluation record data sets are loaded; Based on multiple image feature learning operators, the multiple probe card in-hole cleaning evaluation record data sets are trained and tested respectively to obtain multiple in-hole cleaning evaluation models that meet preset in-hole cleaning evaluation accuracy constraints; Obtaining a plurality of in-hole cleaning evaluation accuracy parameters corresponding to the plurality of in-hole cleaning evaluation models, and performing proportion calculation based on the plurality of in-hole cleaning evaluation accuracy parameters to construct an in-hole cleaning evaluation output incentive model, wherein the in-hole cleaning evaluation output incentive model includes a plurality of in-hole cleaning evaluation output incentive coefficients; embedding the plurality of in-hole cleaning evaluation models and the in-hole cleaning evaluation output excitation model into the in-hole cleaning evaluation unit of the probe card; Inputting the target hole image into the multiple hole cleaning evaluation models to obtain multiple hole cleaning evaluation coefficients; The multiple in-hole cleaning evaluation coefficients are input into the in-hole cleaning evaluation output incentive model, and the multiple in-hole cleaning evaluation coefficients are weighted calculated according to the multiple in-hole cleaning evaluation output incentive coefficients to generate the target in-hole cleaning evaluation coefficient.

4. The method according to claim 1, characterized in that Based on the specification model attribute information and according to the multi-level detection feature verification constraint, the real-time stage control data and the real-time target positioning data are subjected to detection feature verification to obtain a detection feature verification result, including: Based on the specification model attribute information, stage control anomaly evaluation records of the same type of probe cards are collected to obtain a stage control anomaly evaluation record set of the same type, and a stage control anomaly evaluation network that satisfies the first convergence constraint is trained based on the stage control anomaly evaluation record set of the same type; Based on the specification model attribute information, the positioning anomaly evaluation records of the same type of probe cards are collected to obtain a same type of positioning anomaly evaluation record set, and a positioning anomaly evaluation network that satisfies the second convergence constraint is trained based on the same type of positioning anomaly evaluation record set; Inputting the real-time stage control data into the stage control abnormality evaluation network to obtain a real-time stage control abnormality evaluation coefficient; Inputting the real-time target positioning data into the positioning anomaly evaluation network to obtain a real-time target positioning anomaly evaluation coefficient; Comparing the multi-level detection feature verification constraint with the real-time stage control abnormality evaluation coefficient and the real-time target positioning abnormality evaluation coefficient to generate the detection feature verification result; Wherein, the multi-level detection feature verification constraints include a stage control abnormality evaluation threshold and a target positioning abnormality evaluation threshold; When the real-time stage control abnormality evaluation coefficient is less than the stage control abnormality evaluation threshold, and the real-time target positioning abnormality evaluation coefficient is less than the target positioning abnormality evaluation threshold, the generated detection feature verification result is qualified; When the real-time stage control abnormality evaluation coefficient is greater than / equal to the stage control abnormality evaluation threshold, and / or the real-time target positioning abnormality evaluation coefficient is greater than / equal to the target positioning abnormality evaluation threshold, the generated detection feature verification result is unqualified.

5. The method according to claim 1, characterized in that The method comprises: When the detection feature verification result is unqualified, generating detection abnormal equipment and equipment abnormal feature data; Performing equipment abnormality compensation analysis based on the equipment abnormality feature data to determine equipment abnormality compensation adjustment data; The abnormality detection device is adjusted based on the device abnormality compensation adjustment data.

6. The method according to claim 1, characterized in that Based on the adaptive aperture detection algorithm, the target probe card is detected according to the aperture detection accuracy parameter, the detection equipment control decision and the probe card aperture detection device to obtain a target aperture detection report, including: Inputting the aperture detection accuracy parameter into a pre-constructed aperture detection frequency measurement table to obtain a matching aperture detection frequency; Taking the matching aperture detection frequency as the detection frequency constraint, controlling the probe card aperture detection device to perform multiple detections on the target probe card according to the detection device control decision, and obtaining multiple groups of aperture detection data that meet the detection frequency constraint; Collecting real-time environmental information and real-time equipment monitoring information corresponding to the multiple sets of aperture detection data to obtain multiple sets of aperture detection scene data; Based on the multiple sets of aperture detection scene data, adaptively compensate for detection scene anomalies on the multiple sets of aperture detection data to obtain multiple aperture detection results; Data fusion is performed based on the multiple aperture detection results to generate the target aperture detection report.

7. The method according to claim 6, characterized in that Based on the multiple sets of aperture detection scene data, adaptive detection scene abnormality compensation is performed on the multiple sets of aperture detection data to obtain multiple aperture detection results, including: Traversing the multiple groups of aperture detection data and the multiple groups of aperture detection scene data, extracting a first group of aperture detection data and a first group of aperture detection scene data corresponding to the first group of aperture detection data, wherein the first group of aperture detection scene data includes first aperture detection environment information and first aperture detection device monitoring information; Performing anomaly recognition and evaluation on the first aperture detection environment information, and determining a first environment anomaly recognition result and a first environment anomaly evaluation coefficient; Performing abnormality identification and evaluation on the monitoring information of the first aperture detection device to obtain a first device abnormality identification result and a first device abnormality evaluation coefficient; If the first environment anomaly evaluation coefficient is greater than / equal to the environment anomaly evaluation threshold, and the first device anomaly evaluation coefficient is less than the device anomaly evaluation threshold, the first environment anomaly recognition result is input into the environment anomaly compensation analysis model to obtain first environment anomaly compensation data, and the first set of aperture detection scene data is adaptively compensated based on the first environment anomaly compensation data to obtain a first aperture detection result, and the first aperture detection result is added to the multiple aperture detection results; If the first environment abnormality evaluation coefficient is less than the environment abnormality evaluation threshold, and the first device abnormality evaluation coefficient is greater than / equal to the device abnormality evaluation threshold, the first device abnormality identification result is input into the device abnormality compensation analysis model to obtain first device abnormality compensation data, and the first set of aperture detection scene data is adaptively compensated based on the first device abnormality compensation data to generate the first aperture detection result; If the first environment abnormality evaluation coefficient is greater than / equal to the environment abnormality evaluation threshold, and the first device abnormality evaluation coefficient is greater than / equal to the device abnormality evaluation threshold, based on the first environment abnormality recognition result and the first device abnormality recognition result, abnormality compensation fusion is performed according to the environment abnormality compensation analysis model and the device abnormality compensation analysis model to obtain first fused abnormality compensation data, and the first group of aperture detection scene data is adaptively compensated based on the first fused abnormality compensation data to obtain the first aperture detection result; If the first environment abnormality evaluation coefficient is less than the environment abnormality evaluation threshold, and the first equipment abnormality evaluation coefficient is less than the equipment abnormality evaluation threshold, a first detection data mapping instruction is obtained, and the first set of aperture detection data is output as the first aperture detection result according to the first detection data mapping instruction.

8. An adaptive detection system for a probe card aperture, characterized in that: The system is used to perform the method according to any one of claims 1 to 7, and the system comprises: An attribute information loading module, wherein the attribute information loading module is used to load the specification model attribute information and aperture detection accuracy parameters of the target probe card; A target hole image acquisition module, the target hole image acquisition module is used to acquire hole image data of the target probe card according to an image acquisition device, obtain a hole image acquisition result, and perform image enhancement on the hole image acquisition result according to a Retinex algorithm to obtain a target hole image; A target hole cleaning verification module, wherein the target hole cleaning verification module is used to perform hole cleaning verification on the target probe card according to the target hole image based on the probe card hole cleaning detector to obtain a target hole cleaning verification result; A control positioning data acquisition module, wherein the control positioning data acquisition module is used to place the target probe card on a stage of a probe card aperture detection device when the cleaning verification result in the target hole is passed, and obtain real-time stage control data and real-time target positioning data; A detection result acquisition module for being verified, the detection result acquisition module for being verified is used to perform detection feature verification on the real-time stage control data and the real-time target positioning data based on the specification model attribute information and according to the multi-level detection feature verification constraint to obtain a detection feature verification result; A detection equipment control decision module, wherein when the detection feature verification result is qualified, the detection equipment control decision module is used to input the real-time stage control data and the real-time target positioning data into a pre-built detection equipment control decision model to obtain a detection equipment control decision; A target aperture detection report acquisition module is used to detect the target probe card based on an adaptive aperture detection algorithm, according to the aperture detection accuracy parameters, the detection equipment control decision and the probe card aperture detection device, to obtain a target aperture detection report.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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