Semi-automatic PIN correction method for MEMS probe card

Abstract

The invention relates to the technical field of semiconductor testing, and discloses a semi-automatic PIN correction method for an MEMS probe card, which comprises the following steps: fixing the probe card on a fixed platform, and adjusting the focal length and magnification of a large tool microscope through an operation interface, so that a probe is displayed in an image acquisition system; starting an image acquisition and processing system, acquiring initial images of the probe and the target PIN point, and extracting position and size parameters of the probe; the device comprises a large tool microscope which is used for carrying out high-resolution imaging on a probe and observing the relation between the probe and a target PIN point; and the probe card fixing platform is used for fixing the probe card. The technical scheme based on high-resolution image acquisition and real-time feedback adjustment is adopted, accurate alignment between the probe card and the target PIN point is achieved, and it is ensured that the probe card can be accurately in place in each step in the calibration process through real-time image feedback and accurate path optimization.

Description

Technical Field

[0001] The invention relates to the technical field of semiconductor testing, and in particular to a semi-automatic PIN correction method for a MEMS probe card. Background Art

[0002] In the field of semiconductor testing technology, the accuracy of the probe card directly determines the accuracy of the test. However, the existing technology faces multiple problems in the calibration process, mainly reflected in the low efficiency of manual dependence and path planning.

[0003] First, in the prior art, the calibration of the probe card usually relies on manual adjustment, which is not only time-consuming but also prone to human errors. Although some devices use high-resolution microscopes for imaging, due to the lack of an effective automated calibration mechanism, operators often need to manually correct the deviation between the probe and the target PIN point. This not only increases the complexity of the operation, but also makes it difficult to ensure the consistency of the accuracy of each adjustment. This manual operation method is particularly difficult to meet the requirements of high precision and high efficiency in mass production.

[0004] Secondly, traditional path planning methods often lack comprehensive consideration of path optimization. In the prior art, path planning is mostly based on simple geometric calculations or preset paths, which leads to lengthy and inaccurate path adjustments, especially when high-frequency and precise adjustments are required, which often wastes a lot of time and energy. In addition, existing path planning methods often ignore physical constraints such as acceleration and speed, and cannot perform global optimization, resulting in low path efficiency and may even cause unnecessary vibration or interference, thereby affecting the accuracy of calibration.

[0005] Furthermore, although some existing technologies have optimized path adjustment to some extent, the analysis and verification of path stability are often lacking. Path instability may cause the probe to fail to stably align with the target PIN point during multiple adjustments, or even repeatedly deviate from the target. Existing technologies rarely have effective theoretical support to ensure the stability of the path adjustment process, especially in application scenarios with high speed or high precision requirements, where path stability is crucial but often overlooked.

[0006] Therefore, the shortcomings of the existing technology are mainly manifested in: heavy reliance on manual labor, low efficiency in path optimization, and insufficient verification of path stability.

[0007] Therefore, the present invention proposes a semi-automatic PIN calibration method for a MEMS probe card to solve the deficiencies of the prior art. Summary of the invention

[0008] In view of the deficiencies in the prior art, the present invention provides a semi-automatic PIN correction method for a MEMS probe card, which solves the problems of heavy manual dependence, low path optimization efficiency, and insufficient path stability verification.

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: a semi-automatic PIN calibration method for a MEMS probe card, comprising the following steps: The probe card is fixed on a fixed platform, and the focus and magnification of the large tool microscope are adjusted through the operation interface so that the probe is displayed in the image acquisition system; Start the image acquisition and processing system, collect the initial images of the probe and the target PIN point, and extract the position and size parameters of the probe; According to the preset calibration standard, the motion control system automatically adjusts the position of the probe card so that the probe approaches the standard position; The user can make fine adjustments through the operation interface to adjust the position accuracy of the probe; Repeat the above steps to calibrate multiple probes on the probe card in sequence; After the calibration is completed, the image acquisition and processing system is used to collect the final image of the probe and the target PIN point, and compared and analyzed with the image before calibration. At the same time, the probe card path is adjusted through real-time image feedback to accurately align the probe card with the target PIN point and optimize the path of the probe card movement; The path optimization of the probe card movement is carried out by combining optimal control theory with particle swarm optimization algorithm; The stability of the path along which the probe card moves is analyzed using Lyapunov stability theory, and the path is further adjusted to verify the path optimization effect, so that the probe card is always accurately aligned with the target PIN point.

[0010] The present invention also provides a semi-automatic PIN calibration device for a MEMS probe card, comprising: A large tool microscope for high-resolution imaging of the probe and observation of the probe's relationship to the target PIN site; A probe card fixing platform, used for fixing the probe card; Motion control system for precise control of the position of the probe card, including X, Y, and Z axis motion; Image acquisition and processing system, used to collect microscope images in real time and calculate the deviation between the probe and the target PIN point; An operation interface, used to display the image and deviation information of the probe and the target PIN point, and receive the user's fine-tuning operation; The control system is used to coordinate the work of the above components, to adjust the probe card to the target PIN point position according to the image analysis results, and to perform path planning and optimization through the path optimization module.

[0011] The present invention provides a semi-automatic PIN calibration method for a MEMS probe card. It has the following beneficial effects: 1. The present invention adopts a technical solution based on high-resolution image acquisition and real-time feedback adjustment to achieve precise alignment between the probe card and the target PIN point. Through real-time image feedback and precise path optimization, it ensures that the probe card can be accurately positioned at every step in the calibration process. Compared with the existing solution that simply relies on manual fine-tuning, the present invention greatly reduces human errors, improves calibration accuracy, and ensures high-precision requirements in semiconductor testing.

[0012] 2. The present invention combines optimal control theory with the particle swarm optimization algorithm to intelligently optimize the path of the probe card, thereby greatly reducing the path time and energy consumption during the calibration process. Compared with the traditional path planning method, the particle swarm optimization algorithm can more effectively find the global optimal path, reduce unnecessary path adjustments, optimize the efficiency of the entire calibration process, reduce operational complexity, and improve work efficiency.

[0013] 3. The present invention introduces Lyapunov stability theory to analyze and verify the stability of the moving path of the probe card. Compared with the traditional method that only relies on intuition and simple algorithms, the present invention can more accurately ensure the stability of the path optimization process, effectively avoid the deviation problem caused by path instability during the calibration process of the probe card, and ensure that the probe card is always stably and accurately aligned with the target PIN point after each path adjustment.

[0014] 4. The present invention provides a highly adaptable calibration device that can support probe cards of different specifications and models. By adjusting the settings of the fixed platform and the microscope, the system can flexibly adapt to different types of probe cards. Compared with the dedicated equipment in the prior art, the present invention solves the problem that different models of probe cards cannot be flexibly adapted, improves the versatility and application scope of the device, and meets diverse calibration needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flow chart of the method of the present invention; Figure 2 It is a schematic diagram of the device structure of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the specification 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.

[0017] See also Figure 1The embodiment of the present invention provides a semi-automatic PIN calibration method for a MEMS probe card, comprising the following steps: S1. Fix the probe card on the fixed platform, adjust the focal length and magnification of the large tool microscope through the operation interface, and make the probe appear in the image acquisition system; S2, start the image acquisition and processing system, collect the initial images of the probe and the target PIN point, and extract the position and size parameters of the probe; S3. According to the preset calibration standard, the motion control system automatically adjusts the position of the probe card so that the probe is close to the standard position; S4, the user makes fine adjustments through the operation interface to adjust the position accuracy of the probe; S5, repeat the above steps to calibrate multiple probes on the probe card in sequence; S6. After the calibration is completed, the image acquisition and processing system is used to acquire the final image of the probe and the target PIN point, and the final image is compared and analyzed with the image before calibration. At the same time, the probe card path is adjusted through real-time image feedback to accurately align the probe card with the target PIN point and optimize the path of the probe card movement; S7, the path optimization of the probe card movement is carried out by combining the optimal control theory with the particle swarm optimization algorithm; S8. The stability of the path along which the probe card moves is analyzed by Lyapunov stability theory, and the path is further adjusted to verify the path optimization effect, so that the probe card is always accurately aligned with the target PIN point.

[0018] For step S1, first, in the initial step of the semi-automatic PIN calibration method for the MEMS probe card, the probe card to be calibrated needs to be fixed on a dedicated fixed platform, and the focal length and magnification of the large tool microscope are adjusted through the operation interface so that the probe is displayed in the image acquisition system. This step is the beginning of the entire calibration process, ensuring that subsequent calibration work can be carried out accurately and efficiently.

[0019] In this embodiment, the probe card is fixed on a dedicated fixed platform, which includes multiple vacuum suction holes to ensure that the probe card does not move during the calibration process. The bottom plate is designed as a porous structure, and the probe card is firmly adsorbed on the platform through a vacuum adsorption device to ensure its stability and avoid calibration errors caused by the movement of the probe card.

[0020] As an option, the fixing platform can have adjustable positioning devices to accommodate probe cards of different sizes or shapes. The inner diameter of the platform is adapted to the diameter of the probe card substrate, and precise positioning and fixing are achieved through a step structure, ensuring that the edge of the probe card fits perfectly with the positioning device of the platform. During the fixing process, the probe card substrate is adsorbed and firmly fixed to the platform through a vacuum adsorption mechanism, eliminating any possible probe position deviation.

[0021] In this step, the large tool microscope is adjusted to the appropriate focus and magnification so that the microscope can clearly display the details of the probe card. Through the operation interface, the user can control the focus and magnification of the microscope to ensure that the image acquisition system can accurately capture the relative position between the probe and the target PIN point.

[0022] Specifically, the purpose of adjusting the focus and magnification of the microscope is to ensure that the details of the probe are clearly visible for subsequent image acquisition and deviation calculation. In general, the adjusted image should have sufficient resolution to ensure that the relative position between the probe and the target PIN point can be accurately captured, thereby providing clear data support for the subsequent calibration steps.

[0023] In one possible implementation, the image acquisition system analyzes the probe and the target PIN point through image processing algorithms (such as edge detection or contour recognition algorithms). These image data will serve as the basis for the subsequent steps to determine the deviation between the probe and the target PIN point through precise calculations, and provide a basis for path optimization and adjustment.

[0024] In this step, the system collects images in real time, further providing necessary information for subsequent path optimization, deviation calculation and precision adjustment. This process is crucial to the entire semi-automatic calibration process because it determines the accuracy and precision of subsequent operations. The system can make adjustments based on real-time image feedback to ensure that each stage of calibration operation can meet the predetermined accuracy requirements.

[0025] In order to improve the adaptability and stability of the system, the system can be adaptively adjusted according to different probe types and target PIN point requirements. Through the operation interface, users can flexibly adjust microscope parameters and set different magnifications to further improve accuracy and ensure the quality of image data.

[0026] At this stage, the system provides an interactive operation interface, where users can adjust the focal length and magnification of the probe to enable the microscope to observe the probe in the best state. In the operation interface, the real-time image data can help users quickly judge the image clarity and magnification, so as to make timely adjustments to ensure that the subsequent steps can proceed smoothly.

[0027] In summary, step S1 ensures the stability and accuracy of the entire calibration process, and lays a solid foundation for subsequent image acquisition, deviation calculation, and path optimization by fixing the probe card and accurately adjusting the focal length and magnification of the microscope.

[0028] During this process, the fixing accuracy of the probe card and the adjustment of the microscope are crucial to the effectiveness of the calibration process. The design of the fixing platform, especially its vacuum adsorption function and positioning device, can ensure the position accuracy of the probe card. In actual operation, the platform can be designed with a structure with multiple adjustable holes or fixtures to accommodate different types of probe cards.

[0029] The details of adjusting the focus and magnification of the microscope involve real-time adjustment of parameters such as image resolution and frame rate. The image acquisition system needs to have high resolution and fast response capabilities to ensure that the relative position of the probe and the target PIN point can be clearly presented and accurately recorded during the fine-tuning process.

[0030] By displaying real-time images on the operating interface, the system can provide feedback on the deviation between the probe and the target, assisting the operator in adjusting the position of the microscope or platform, and ensuring that the system can operate accurately during the dynamic process.

[0031] During actual operation, the system will automatically adjust the magnification and focus settings according to different calibration requirements to adapt to the calibration requirements of different probes and targets.

[0032] Step S2 is crucial to the entire calibration process because it provides data for accurately measuring the deviation between the probe and the target PIN point through high-resolution image acquisition, ensuring the accuracy of the subsequent calibration process.

[0033] Specifically, in this embodiment, the image acquisition and processing system is first started, and the system uses a large tool microscope to collect images between the probe and the target PIN point in real time. The main purpose of this image capture is to extract the relative position, size and other related parameters between the probe and the target PIN point. The key parameters of the microscope imaging system, such as resolution, frame rate, exposure time, etc., are finely adjusted to ensure the clarity and accuracy of the image data.

[0034] Generally, the image acquisition system processes the image using edge detection or contour recognition algorithms to identify the boundary between the probe and the target PIN point. These identified boundary features will be further used to calculate the deviation between the probe and the target PIN point. As an option, the image acquisition and processing system can be equipped with a variety of image processing algorithms to filter, denoise, etc. the image through different algorithms to enhance the image quality.

[0035] Specifically, the workflow of the image acquisition and processing system includes: first, the initial image of the probe and the target PIN point is acquired; then, the image is edge-recognized and gray-scale processed by an algorithm; and then the deviation between the probe position and the target PIN point is calculated. These deviation values ​​include position deviation (such as deviation in the X and Y coordinate directions) and angle deviation (i.e., the angle error between the probe and the target).

[0036] In a possible implementation, the formula for calculating the deviation is: ; ; ; in, and Indicates the position deviation between the probe and the target PIN point in the X and Y directions; represents the angular deviation between the two; , are the coordinates of the target PIN point; , are the coordinates of the probes on the probe card, and and are the angles of the target PIN point and the probe, respectively.

[0037] In some embodiments, the system may also compare the deviation value with the tolerance standard to further verify whether the relative position of the probe and the target PIN point meets the accuracy requirements. In this case, the system will output the calibration results and provide a basis for the next step (such as path optimization or fine-tuning). The accuracy of the deviation calculation results directly affects the accuracy of subsequent steps, so this step requires sufficient calculation accuracy and efficient image processing capabilities.

[0038] During the image acquisition process, the large tool microscope used in this embodiment can provide high-resolution imaging, so that the clarity of each image acquisition reaches the micron level. This is crucial for the detection of tiny deviations, ensuring that the deviation between the probe and the target PIN point can be accurately calculated and fed back. The collaborative work between the microscope and the image acquisition system is the key to the efficient calibration of this method.

[0039] During the image processing, the system automatically optimizes the image according to the set algorithm, including but not limited to image enhancement, contrast adjustment, noise removal, etc., to improve the image quality and calculation accuracy. With the support of the image acquisition and processing system, the image deviation calculation results can achieve high accuracy, meeting the strict requirements for accuracy in the semiconductor industry.

[0040] Through this step, the system provides key data for subsequent motion control and path optimization. By feedbacking the precise deviation between the probe and the target PIN point, the system can accurately calculate the adjustment required for path optimization, ensuring that subsequent steps can be executed accurately.

[0041] The core of this step is to provide basic data for the subsequent calibration process through high-resolution image acquisition and accurate deviation calculation. The application of image processing algorithms can improve the clarity and accuracy of the image, making the deviation calculation between the probe and the target PIN point more accurate. This accuracy is crucial for subsequent path optimization and probe positioning, ensuring the efficiency and accuracy of the calibration process.

[0042] For step S3, in step S2, the image acquisition and deviation calculation steps complete the calculation of the deviation between the probe and the target PIN point, ensuring that the error of the probe position and angle is clearly defined. Based on these deviation information, step S3 controls the motion system to automatically adjust the position of the probe card and gradually guide the probe to the predetermined standard position.

[0043] Specifically, in this embodiment, the motion control system starts precise motion adjustment based on the above-mentioned deviation calculation results. The system first calculates the deviation value between the probe and the target PIN point, and generates the target position based on these data. In general, the deviation between the probe and the target PIN point may have errors in the X, Y, and Z coordinate axes and angles. As an option, the system automatically adjusts the position of the probe card through a multi-axis motion platform (including X, Y, and Z axes) to ensure that the probe is close to the standard position.

[0044] In a possible implementation, it is assumed that the initial deviations between the probe and the target PIN point are , and , the system will generate a preliminary path adjustment instruction based on these calculated deviations. The goal of this path adjustment is to minimize the deviation between the probe and the target PIN point. The following formula can be used to describe the adjustment strategy: ; ; ; in, , , Respectively represent the actual deviation between the current probe and the target PIN point in the X, Y, and angle directions. , , is the standard deviation value. The motion control system calculates these deviations in real time and gradually guides the probe card to align precisely to the target PIN point.

[0045] In actual operation, the motion control system executes corresponding control instructions based on these calculated deviation values. The control system will start the precise motion platform of the X, Y, and Z axes to gradually move the probe card closer to the target position. According to different control algorithms, the system can automatically perform complex movements such as translation and rotation to make the probe close to the standard position and ensure the target accuracy.

[0046] Additionally, as a possible option, the motion control system can be calibrated using different calibration standards during path adjustment, such as adjusting the target position based on a standard angle range or substrate alignment deviation tolerance to accommodate different types of probe cards.

[0047] During the implementation process, the motion control system continuously optimizes the precise positioning of the probe card through continuous feedback adjustment. The combination of real-time image feedback and position sensors ensures that the probe card can reach the desired target position after each adjustment, and the position and angle errors during the adjustment process are within the acceptable tolerance range.

[0048] In the specific implementation, the system not only controls the translation of the X, Y, and Z axes, but also can make fine angle adjustments to the probe card. During operation, the motion control system usually combines multiple sensors to ensure the precise positioning of the probe card in three-dimensional space. Under this multi-dimensional control system, the probe card can adaptively adjust according to real-time feedback to optimize the motion path and accuracy.

[0049] For example, the motion control system can combine accelerometers and gyroscopes to provide precise angle adjustment, ensuring that the probe card's angle adjustment also meets the accuracy requirements. During the adjustment process, the motion system analyzes real-time data and promptly corrects the probe card's moving path to avoid path deviation.

[0050] The key to this step is to ensure that the probe card can move to the target position quickly and accurately based on the image acquisition and deviation calculation results through a precise motion control system. Through the real-time feedback system, the position of the probe card is constantly corrected to ensure that it is accurately aligned with the target PIN point. The accuracy of motion control and adjustment directly affects the accuracy of subsequent calibration steps, so the precise control mechanism provided in this embodiment ensures the efficiency and accuracy of the entire calibration process.

[0051] Furthermore, precise motion control makes the probe card adjustment not only efficient, but also adaptable to the requirements of different probes. Through this precise control, the error of the entire calibration process is minimized, further improving the stability and reliability of the entire system and ensuring high-precision calibration results.

[0052] The core goal of step S4 is to further refine the position of the probe and ensure that the probe accuracy reaches the predetermined standard. Since the probe card has been moved close to the target position in step S3, step S4 makes fine adjustments through the user operation interface to correct the small error between the probe and the target PIN point.

[0053] Specifically, in this embodiment, the operation interface provides the user with the function of fine-tuning the probe position. The error between the probe and the target PIN point is displayed in real time through the image acquisition and processing system, and the user can manually adjust the position of the probe according to the displayed information. The image acquisition system continuously obtains image data of the probe and the target PIN point and calculates its deviation value. This deviation information is fed back to the operation interface in the form of images and data to help users perform precise operations.

[0054] Generally, the fine-tuning process involves fine-tuning the position of the probe in the X, Y coordinate axes and the angle direction. At this stage, the system ensures the effect of each fine-tuning through real-time feedback, avoiding accuracy problems caused by over-adjustment or misoperation. As an option, the system will automatically limit the amplitude of each adjustment during fine-tuning to avoid unnecessary errors caused by excessive adjustment amplitude.

[0055] Specifically, during the system adjustment process, by setting the maximum allowable deviation value, the system will gradually reduce the deviation between the probe and the target PIN point based on the real-time image data collected. The fine-tuning process works together through a sophisticated motion control system and an operating interface to ensure that each adjustment step can be precisely controlled to achieve the ideal alignment accuracy. If the deviation value exceeds the set tolerance range during the fine-tuning process, the system will automatically issue a warning signal to remind the user to make timely adjustments.

[0056] For example, suppose the initial calculated deviation between the probe and the target PIN point in the X direction is , the deviation in the Y direction is , the angle deviation is During the fine-tuning phase, the system will provide adjustment suggestions based on these deviation information. Suppose that after fine-tuning, the position of the probe is changed to , and , the system will update these data in real time and continue to adjust until the deviation between the probe and the target PIN point meets the accuracy requirements.

[0057] In a practical way, the system can set a final accuracy standard, such as the deviation value in the X and Y directions within a certain range. , the allowable range in the angular direction is If the deviation value exceeds this range after adjustment, the system will continue to make fine adjustments until the deviation between the probe and the target PIN point meets the standard.

[0058] The key to the fine-tuning process is how to accurately control the movement of the probe card. To this end, the system usually combines high-precision X and Y axis motion platforms and angle control devices to guide the fine-tuning process through real-time image data. Using these control devices, the system can accurately adjust the position of the probe card in each dimension and make corresponding corrections based on the deviation data.

[0059] For example, the system may use stepper motors or servo motors to control the small movements of the X, Y, and Z axes, ensuring that the amplitude of each adjustment is extremely small, so that the impact range of each fine-tuning is controlled to a minimum. This process ensures the gradual improvement of the probe accuracy through the combination of feedback control and real-time data analysis.

[0060] During the implementation process, the operation interface usually provides real-time feedback, showing the precise distance and angle between the probe and the target, helping the operator to better judge the adjustment range. The continuous work of the image acquisition and processing system can ensure that the actual effect after each fine-tuning is verified immediately, avoiding any mistakes in the fine-tuning process.

[0061] The core goal of this step is to gradually reduce the deviation between the probe and the target PIN point through the user's fine operation, and finally achieve precise alignment. Through real-time image feedback and fine motion control, the user can efficiently fine-tune the probe to ensure its position accuracy. The fine-tuning step makes the calibration process not limited to rough adjustments, but can carefully optimize the positioning accuracy of the probe, thereby improving the accuracy and stability of the entire semi-automatic calibration process.

[0062] By performing fine-tuning under conditions of strict precision requirements, the present invention can significantly improve the calibration accuracy, especially in multi-probe cards and complex test environments. The application of this step effectively ensures the accurate alignment of each probe, thereby improving the reliability of the test data.

[0063] In addition, the fine-tuning process enables the system to be adaptively adjusted according to different probe types and target requirements, thereby further enhancing the flexibility and applicability of the present invention and ensuring a wide range of engineering application possibilities.

[0064] Step S5 involves repeating the calibration of multiple probes on the probe card. This step is immediately after step S4, in which the user has fine-tuned a probe through the operation interface and ensured that its accuracy meets the predetermined standard. Therefore, the key to step S5 is to effectively extend the aforementioned calibration process to each probe on the probe card to ensure that all probes can meet the same accuracy requirements.

[0065] When calibrating multiple probes of the probe card, the system will first automatically perform the same operation for each probe in turn according to the calibration method performed in the previous step (such as steps S3 and S4). Specifically, the initial image of each probe and the target PIN point is first acquired by the image acquisition and processing system, and then the deviation between each probe and the target PIN point is calculated. Based on these deviation data, the system will automatically adjust the position of the probe card so that each probe can gradually approach the predetermined standard position.

[0066] As an option, the system may perform a separate calibration step for each probe to ensure that even when multiple probes on the probe card have different initial position deviations, they can still be accurately adjusted individually. In this case, the motion control system will generate path adjustment instructions for each probe separately, thereby adjusting the position of the probe card in turn according to the deviation data. During the adjustment process of each probe, the image acquisition and processing system continuously calculates the deviation between each probe and the target PIN point, and dynamically adjusts the path based on the calculation results.

[0067] Specifically, for each probe, the system combines optimal control theory with real-time image feedback to ensure that the accuracy error between the probe and the target PIN point is gradually reduced until the predetermined accuracy requirement is met. The image feedback of the image acquisition system plays a vital role in this process. It provides real-time data support for each path adjustment and corrects the probe position in a timely manner based on the feedback results.

[0068] In some embodiments, if the probes on multiple probe cards have different sizes or structural features, the system can automatically adjust the corresponding calibration standards according to the characteristics of each probe. These standards include but are not limited to the standard distance between the probe and the target PIN point, the standard angle range, and the alignment deviation tolerance of the probe substrate. In this way, the system can flexibly adapt to different types of probes and ensure that each probe can be accurately calibrated.

[0069] Through this series of steps, the calibration of multiple probes will be carried out according to the same high-precision standard, ensuring that after the entire probe card is calibrated, all probes can operate within the same accuracy range. This not only improves the calibration efficiency, but also ensures the accuracy and consistency of the entire calibration process.

[0070] In summary, the implementation of step S5 ensures the calibration accuracy of each probe through automated path adjustment and precise fine-tuning process, thereby improving the stability and reliability of the entire system.

[0071] Step S6 is immediately followed by step S5 to further ensure that the probe card is accurately aligned with the target PIN point. This step first uses the image acquisition and processing system to capture the final image of the probe and the target PIN point, and compares and analyzes it with the image before calibration to evaluate the calibration effect. Image comparison and analysis is a key step in verifying the calibration accuracy. It can clearly show the deviation between the probe and the target PIN point, providing an intuitive basis for whether the calibration meets the predetermined standard.

[0072] Specifically, first, the image acquisition and processing system acquires the final image of the probe and the target PIN point. During the image acquisition process, the system ensures that the image resolution and exposure time are set to the best settings to ensure the accuracy of the image data. During this process, parameters such as the magnification and focal length of the microscope continue to be adjusted to maintain the high quality of the image.

[0073] The system will then compare and analyze the captured image with the previously calibrated image. Through comparison, the system can calculate the difference between the probe and the target PIN point, which usually includes position deviation and angle deviation. The deviation data will be presented in real time in the system, and the deviation value will be further accurately calculated through image processing algorithms (such as image registration, edge detection, etc.).

[0074] Generally, the results of image comparison analysis are used to determine the success of calibration. If the image comparison shows a large deviation, the system will perform appropriate path optimization through a feedback mechanism. In this process, the image data not only provides the system with a basis for calibration accuracy verification, but also provides a key reference for subsequent path adjustment and optimization.

[0075] As an option, the image acquisition system can apply some advanced image processing algorithms, such as filtering, noise reduction, grayscale enhancement and other technologies, to further improve image clarity and data accuracy. The processed image can accurately display the relative position of the probe and the target PIN point, and provide higher quality data support for subsequent path adjustment.

[0076] In the second half of step S6, the system adjusts the path of the probe card through real-time image feedback. The purpose of this process is to ensure that the probe card is accurately aligned with the target PIN point by dynamically adjusting the path. Specifically, the system calculates the deviation between the current position of the probe card and the target PIN point based on the real-time feedback image information, and adjusts the position of the probe card through a precise motion control system. Through this feedback system, the position of the probe card is continuously optimized until the effect of precise alignment is achieved.

[0077] In addition, path optimization is not limited to position adjustment, but can also optimize the path as a whole based on feedback information. Based on real-time data, the system dynamically adjusts the path of the probe card to ensure that the target position is reached in a shorter time while minimizing energy consumption. Through path optimization algorithms (such as optimal control theory combined with particle swarm optimization algorithm), the system can effectively adjust the path of the probe card, reduce unnecessary path deviations, and ensure that the probe card stably reaches the target PIN point.

[0078] In some embodiments, during the path adjustment process, the system may consider physical constraints of acceleration and speed to avoid alignment instability caused by violent movement of the probe card. These constraints are automatically adjusted through optimization algorithms to ensure the smoothness and accuracy of the path adjustment.

[0079] In summary, the implementation of step S6 ensures the precise alignment of the probe and the target PIN point, and further improves the calibration accuracy and system stability through image comparison analysis and real-time path optimization. Through this series of precise operations, the performance of the probe card in the entire calibration process is effectively improved, ensuring that each probe can perform test operations accurately and stably.

[0080] Step S7 focuses on the optimization and adjustment of the probe card path. Specifically, the path optimization is performed by combining optimal control theory with the particle swarm optimization algorithm. The goal of this process is to accurately control the moving path of the probe card from the current position to the target PIN point, ensuring that the path is as short as possible and efficient, while avoiding excessive errors.

[0081] In general, the goal of path optimization is to minimize the time or energy consumption required for the path while maintaining sufficient accuracy. In this process, optimal control theory provides a mathematical framework for describing path optimization problems. Through optimal control theory, the system can calculate the most suitable control strategy based on the current state and the target state. The particle swarm optimization algorithm is applied on this basis, combined with global search capabilities, to further optimize the path planning.

[0082] Specifically, the particle swarm optimization algorithm searches the path space by simulating the behavior of a group of particles. These particles are iteratively updated in the path space according to the fitness function to find the optimal path. The fitness function usually measures the quality of the path based on objectives such as the length of the path, energy consumption, or time requirements. In some embodiments, the system sets multiple particles, each particle represents a possible path, and selects the optimal path by updating the speed and position of the particles. During the iteration process, the particles continuously optimize their paths to find the global optimal solution.

[0083] During the optimization process, the control algorithm and the particle swarm optimization algorithm complement each other. The control algorithm ensures that the probe card path matches the motion requirements, while the particle swarm optimization algorithm further optimizes the path through global search to avoid falling into the local optimal solution. For example, the particle swarm optimization algorithm can find a path that is not only the shortest in path length, but also meets the physical constraints of the system (such as acceleration, speed limit, etc.).

[0084] In one possible implementation, the particle swarm optimization algorithm uses the following steps: Initialize the particle swarm and set the particle position and velocity. The particle position represents the possible path. Calculate the fitness value of each particle. The fitness function may include the length of the path, the total time or the total energy consumption; Update the speed and position of the particles, and update the position of the particles based on the historical optimal position of each particle and the optimal position in the group; This process is repeated until convergence or the maximum number of iterations is reached, and finally the global optimal path is selected.

[0085] As an option, the particle swarm optimization algorithm can be combined with other local optimization algorithms (such as gradient descent, genetic algorithm, etc.) to enhance the efficiency and accuracy of path optimization.

[0086] Specifically, during the path optimization process, the system must not only consider the shortest path, but also the physical constraints in the path, such as acceleration, speed, etc. For the probe card, its motion path must not only reach the target, but also meet the motion limitations of the device to avoid vibration or unstable position of the probe due to excessive speed or acceleration.

[0087] In some embodiments, the system defines an optimization objective function of the path based on optimal control theory. This objective function usually includes the time, energy and accuracy requirements of the path. Assume that the objective function of the path is , which can be expressed as: ; in, To optimize the objective function, it is usually used to minimize the path time, energy consumption or other performance indicators; The time period for path optimization, which represents the total time from the beginning to the end of the path; is the objective function, which describes the performance at each moment on the path. It is the time Position at the moment and control input Function, control input It can be physical quantities such as acceleration and velocity; is the system state, which in this case represents the probe card at time The location at the moment; The control input is usually an external control variable that affects the state of the system. In this application, it may be a physical quantity such as speed or acceleration.

[0088] During the optimization process, the particle swarm optimization algorithm optimizes the path by adjusting the control inputs (such as speed, acceleration) while satisfying physical constraints such as maximum speed. and maximum acceleration , ensuring the stability and feasibility of the path.

[0089] In summary, this step effectively optimizes the path of the probe card by combining optimal control theory and particle swarm optimization algorithm, thereby ensuring that it moves to the target PIN point accurately and efficiently. The path optimization process takes into account physical constraints and the global optimal solution, providing a more accurate and stable control strategy for probe card calibration.

[0090] For step S8, the stability of the path of the probe card movement is analyzed by Lyapunov stability theory, and the path is further adjusted to verify the path optimization effect. This process is crucial to ensure that the probe card is always accurately aligned with the target PIN point, because the stability of the path directly affects the positioning accuracy of the probe card, which in turn determines the reliability of the entire calibration process.

[0091] In the above steps, the path of the probe card has been preliminarily optimized, and the optimal control theory and particle swarm optimization algorithm are used to optimize the path (step S7). Nevertheless, the stability of the path is still a key factor that needs further verification. In order to ensure the stability of the path optimization process, the Lyapunov stability theory is used for analysis. The core of the Lyapunov stability theory is to determine whether the path will be unstable during the optimization process by constructing the Lyapunov function, so as to take necessary adjustment measures.

[0092] Specifically, the Lyapunov stability theory analysis is carried out through the following steps: First, based on the optimized path, construct the Lyapunov function. The Lyapunov function is usually a positive definite function used to describe the energy or error measurement of the system. Set the Lyapunov function As system status The function of Represents the deviation between the probe card and the target PIN point. Specifically, the construction of the Lyapunov function can be shown as follows: ; in, To represent the Lyapunov function, it is usually used to measure the state of the system Energy or error measure of ; The state vector representing the system, usually the deviation between the probe card and the target PIN point, contains the position information of the system; Represents the state vector The transpose of , i.e. the row vector form; It is a symmetric positive definite matrix, usually used to measure the error of the system state; To ensure that the Lyapunov function satisfies the conditions, usually by introducing the coefficient To simplify the derivative calculation, making the derivative result more concise.

[0093] Second, calculate the derivative of the Lyapunov function , and use the derivative to analyze whether the path is stable. It can be expressed as: ; in, represents the Lyapunov function About Time The derivative of is often used to analyze the rate of change of the energy or error measure of a system; represents the Lyapunov function, usually the system state A function that describes the energy or error measure of the system; is the Lyapunov function for the system state The gradient (partial derivative) of Represents the state vector About Time The derivative of (i.e., the rate of change of the state); in general, if , the system is stable; if , the system is unstable and path adjustment is required; On this basis, by analyzing the derivative of the Lyapunov function, it is determined whether the probe card path is unstable. If the path is found to be unstable, the system will automatically adjust the path to ensure path stability by changing the control input or optimizing the path parameters, thereby avoiding the accumulation of path deviations.

[0094] In one possible implementation, the system combines path optimization and stability analysis to adjust path planning parameters in real time. Specifically, the system continuously performs stability analysis during the optimization process and adjusts the motion trajectory of the probe card based on feedback from Lyapunov stability theory. This adjustment not only eliminates unstable factors in the path, but also ensures that the path can always be accurately aligned with the target PIN point.

[0095] For example, if there is local instability in the optimized path, the derivative of the Lyapunov function may be positive, which means that there is an unstable part on the path. In this case, the system will adjust the path according to the calculation result of the derivative so that , ensuring path stability. The optimization and adjustment of the path not only relies on traditional optimal control theory and particle swarm optimization algorithm, but also combines the feedback provided by Lyapunov stability theory to further enhance the path optimization effect and ensure that the probe card is always accurately aligned with the target PIN point.

[0096] In summary, this embodiment combines Lyapunov stability theory to perform real-time stability analysis on the probe card path and make necessary path adjustments to ensure path stability and optimization effect. This method effectively solves the instability problem of the probe card during path optimization, ensuring that the probe card can always accurately align with the target PIN point during the entire calibration process, thereby improving the accuracy and reliability of calibration.

[0097] See also Figure 2 , and also provides a MEMS probe card semi-automatic PIN correction device, including: A large tool microscope for high-resolution imaging of the probe and observation of the probe's relationship to the target PIN site; A probe card fixing platform, used for fixing the probe card; Motion control system for precise control of the position of the probe card, including X, Y, and Z axis motion; Image acquisition and processing system, used to collect microscope images in real time and calculate the deviation between the probe and the target PIN point; An operation interface, used to display the image and deviation information of the probe and the target PIN point, and receive the user's fine-tuning operation; The control system is used to coordinate the work of the above components, to adjust the probe card to the target PIN point position according to the image analysis results, and to perform path planning and optimization through the path optimization module.

[0098] Specifically, a large tool microscope is used to provide high-resolution images to accurately observe the relative position of the probe and the target PIN point. The microscope has adjustable focus and magnification. Through the control of the operation interface, the accuracy of image acquisition can be adjusted in real time to meet the correction requirements of different types of probe cards and target PIN points.

[0099] The probe card fixing platform adopts a porous bottom plate design with multiple vacuum holes on the platform to firmly fix the probe card and ensure that the probe card will not shift during the calibration process. The fixing ring design precisely fits the probe card substrate to ensure that the probe card remains stable under the action of the motion control system.

[0100] Vacuum adsorption device: By adjusting the vacuum suction force, the fixed platform can adapt to probe cards of different sizes and ensure that they will not move during the entire calibration process.

[0101] Fixed ring structure: The inner diameter of the ring structure is slightly larger than the diameter of the probe card substrate, ensuring the stability of the probe card and avoiding deviations caused by inaccurate positioning.

[0102] The motion control system precisely controls the position of the probe card, including precision motion control of the X, Y, and Z axes. The system ensures that the probe card can be fine-tuned along the predetermined path through a high-precision platform. By adjusting the control system, the probe card is precisely aligned with the target PIN point in three-dimensional space.

[0103] Three-dimensional control system: Controls the translational motion of the X, Y, and Z axes, and has angle control function, so that the probe card can align and maintain precise docking with the target PIN point.

[0104] The image acquisition and processing system collects images of the probe and the target PIN point in real time, analyzes the image data through image processing algorithms (such as edge detection, contour recognition algorithms, etc.), and calculates the deviation between the probe and the target PIN point. The system provides deviation data to the motion control system to ensure that the probe card can accurately adjust its position as needed.

[0105] The operation interface provides an intuitive user interaction platform, through which users can control the focus and magnification of the microscope, and view the image and the deviation information between the probe and the target PIN point in real time. In addition, users can make fine adjustments on the operation interface to ensure that the probe card is accurately aligned with the target.

[0106] The control system coordinates the work of all components, ensures that the probe card accurately adjusts its position according to the image analysis results, and performs path planning and optimization through the path optimization module. The control system can coordinate the work of multiple probe cards and optimize the path of the probe card based on real-time feedback, so that the probe card can accurately align with the target PIN point in the shortest time.

[0107] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A semi-automatic PIN calibration method for a MEMS probe card, characterized in that: The following steps are involved: The probe card is fixed on a fixed platform, and the focus and magnification of the large tool microscope are adjusted through the operation interface so that the probe is displayed in the image acquisition system; Start the image acquisition and processing system, collect the initial images of the probe and the target PIN point, and extract the position and size parameters of the probe; According to the preset calibration standard, the motion control system automatically adjusts the position of the probe card so that the probe approaches the standard position; The user can make fine adjustments through the operation interface to adjust the position accuracy of the probe; Repeat the above steps to calibrate multiple probes on the probe card in sequence; After the calibration is completed, the image acquisition and processing system is used to collect the final image of the probe and the target PIN point, and compared and analyzed with the image before calibration. At the same time, the probe card path is adjusted through real-time image feedback to accurately align the probe card with the target PIN point and optimize the path of the probe card movement; The path optimization of the probe card movement is carried out by combining optimal control theory with particle swarm optimization algorithm; The stability of the path along which the probe card moves is analyzed using Lyapunov stability theory, and the path is further adjusted to verify the path optimization effect, so that the probe card is always accurately aligned with the target PIN point.

2. A semi-automatic PIN calibration method for a MEMS probe card according to claim 1, characterized in that: The preset calibration standards include: Standard distance between the probe and the target PIN point; Standard angular range of the probe; Alignment deviation tolerance of probe substrate.

3. A semi-automatic PIN calibration method for a MEMS probe card according to claim 1, characterized in that: The path optimization and adjustment steps include: Generate a preliminary path based on the calculated deviation between the probe and the target PIN point; Use optimal control theory to adjust the path so that it meets the predetermined time or energy consumption requirements; Adjust the motion parameters in the path through physical constraints of acceleration and velocity; Real-time feedback correction path for precise alignment of the probe card with the target PIN point.

4. A semi-automatic PIN calibration method for a MEMS probe card according to claim 3, characterized in that: The optimal control theory adjustment path step includes: According to the deviation between the probe and the target PIN point, the path optimization objective function is set; Use optimal control theory to solve the time minimization or energy minimization problem of the path; During the solution process, the motion trajectory of the path is adjusted through acceleration and velocity physical constraints; Generate the optimal path and update it in real time to ensure that the path meets the control requirements.

5. A semi-automatic PIN calibration method for a MEMS probe card according to claim 1, characterized in that: The particle swarm optimization algorithm steps include: Initialize the particle swarm, set the particle position, velocity and fitness function, and the fitness function is calculated based on the deviation between the probe and the target PIN point; According to the calculated fitness value, the speed and position of each particle are updated, and the particle moves along the optimal path in the search space; Select the best particle according to the fitness value, and use it as the current optimal solution, and save the position of the particle; Repeatedly update the particle's velocity and position until the stop condition is met or the set accuracy requirement is reached; Finally, the globally optimal particle is selected to generate the optimal path, which is used to guide the probe card to accurately align with the target PIN point.

6. A semi-automatic PIN calibration method for a MEMS probe card according to claim 1, characterized in that: The step of adjusting the probe card path by real-time image feedback comprises: Collect image data between the probe and the target PIN point in real time and analyze the deviation in the image; Calculate the deviation value based on the image processing results and update the path planning parameters; The motion trajectory of the probe card is adjusted according to the updated path parameters to ensure that it is accurately aligned with the target PIN point.

7. A semi-automatic PIN calibration method for a MEMS probe card according to claim 1, characterized in that: The stability steps of the Lyapunov stability theory analysis path include: Based on the optimized path, construct the Lyapunov function; By calculating the derivative of the Lyapunov function, it is determined that there is an unstable state in the path planning process; The path is adjusted according to Lyapunov stability theory to ensure that the path remains stable during the optimization process.

8. A semi-automatic PIN calibration method for a MEMS probe card according to claim 7, characterized in that: The Lyapunov function formula is: ； in, is a positive definite matrix, representing the energy or error measure of the system; is the state vector of the system, representing the deviation between the probe card and the target PIN point; is the state vector The transpose of represents the dot product operation of the row vector and the column vector.

9. A semi-automatic PIN calibration device for a MEMS probe card, applied to a semi-automatic PIN calibration method for a MEMS probe card as claimed in any one of claims 1 to 8, characterized in that: include: A large tool microscope for high-resolution imaging of the probe and observation of the probe's relationship to the target PIN site; A probe card fixing platform, used for fixing the probe card; Motion control system for precise control of the position of the probe card, including X, Y, and Z axis motion; Image acquisition and processing system, used to collect microscope images in real time and calculate the deviation between the probe and the target PIN point; An operation interface, used to display the image and deviation information of the probe and the target PIN point, and receive the user's fine-tuning operation; The control system is used to coordinate the work of the above components, to adjust the probe card to the target PIN point position according to the image analysis results, and to perform path planning and optimization through the path optimization module.

10. A semi-automatic PIN calibration device for a MEMS probe card according to claim 9, characterized in that: The probe card fixing platform comprises: A porous bottom plate, wherein a plurality of vacuum holes are arranged on the bottom plate; A fixing ring, wherein the fixing ring is a structure with a central through hole, the inner diameter of which is slightly larger than the diameter of the probe card substrate and fits with the edge of the probe card substrate; The adsorption device fixes the probe card substrate on the fixed platform by vacuum adsorption.

Images