A wafer component driving detection method and system based on multi-hole plates and multiple sensors

Abstract

This invention relates to the field of semiconductor manufacturing equipment inspection technology, and discloses a wafer component driving inspection method and system based on a perforated plate and multiple sensors. The method involves determining the inspection strategy based on the type of the component to be inspected, initializing the working mode of the sensor assembly, co-calibrating the sensor assembly, marking the initial position in the inspection coordinate system, initiating the driving component's action based on the inspection strategy, and having the sensor assembly collect motion data of the component to be inspected. The collected motion data are fused to reconstruct a three-dimensional motion trajectory, and the accuracy index of the component to be inspected is determined based on the deviation. The perforated plate enables standardized installation and positioning of the sensors and driving components, and the combination of multi-sensor co-calibration and data fusion technology solves the problems of low coverage and difficulty in data spatial alignment in traditional inspection methods. This method offers advantages such as improved inspection accuracy and coverage, multi-sensor data spatial alignment, and enhanced dynamic collaborative inspection capabilities.

Description

Technical Field

[0001] This invention relates to the field of semiconductor manufacturing equipment inspection technology, specifically to a wafer component driving inspection method and system based on a multi-hole plate and multiple sensors. Background Technology

[0002] Wafer processing equipment is a core component in semiconductor manufacturing, performing complex processes such as wafer transport, positioning, etching, and deposition through the coordinated action of multiple precision internal components. Due to the inherent thinness and fragility of wafers, typically on the order of hundreds of micrometers, exceeding the permissible precision of internal actuators can easily lead to uneven wafer stress and localized stress concentration, resulting in wafer breakage or microcracks and significantly reducing product yield. Current technologies for maintaining equipment precision typically employ selective component inspection strategies, relying on experience or historical fault data to independently calibrate only high-risk components before equipment startup. However, this method has significant limitations. The stability of the wafer processing process depends on the timing and spatial coupling of multiple component actions. Using a single sensor cannot cover the multi-dimensional characteristics of component actions, resulting in low inspection accuracy and coverage for wafer processing components. Furthermore, existing inspection systems lack a unified benchmark platform, leading to inaccurate sensor placement and difficulty in spatially aligning data collected by different sensors, further impacting the reliability of inspection results. Meanwhile, traditional methods cannot achieve dynamic coordination between the driving and detection processes, making it difficult to accurately capture the true motion state of components under complex operating conditions. To address these issues, existing technologies urgently need improvement. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to provide a wafer component driving inspection method based on a multi-hole plate and multiple sensors. This method offers advantages such as improved inspection accuracy and coverage, spatial alignment of multi-sensor data, and enhanced dynamic collaborative inspection capabilities.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A wafer component driving detection method based on a perforated plate and multiple sensors includes: fixing the component to be detected onto a perforated plate using a mounting base and electrically connecting a driving component to the component; determining a detection strategy based on the type of the component to be detected and initializing the operating mode of the corresponding sensor assembly based on the detection strategy; performing collaborative calibration on the sensor assembly; marking the initial position of the component to be detected and the calibrated position of the sensors in a detection coordinate system; activating the driving component based on the detection strategy, the driving component driving the component to be detected to perform actions, while the sensor assembly collects the action data of the component to be detected; performing data fusion on the collected action data to reconstruct the three-dimensional motion trajectory of the component to be detected; calculating the deviation between the three-dimensional motion trajectory and the standard trajectory to determine the accuracy index of the component to be detected.

[0006] In this invention, preferably, the perforated plate includes a central n-level through hole, a perimeter n+i-level through hole, 30°, 45°, 60° oblique holes, and threaded holes, all distributed in an equally spaced array.

[0007] In this invention, preferably, the detection strategy includes a driving process and process parameters. The driving process is determined based on the actual operation of the component to be detected. The driving process includes linear motion, steering motion, clamping motion, and pressure motion driven in sequence. The process parameters include corresponding action parameters for each driving process. The linear motion parameters include motion distance, speed, acceleration, and action time. The steering motion parameters include rotation angle, angular velocity, action time, and rotation axis offset. The clamping motion parameters include clamping stroke, clamping force, action time, and clamping parallelism. The pressure motion parameters include pressure value, pressing depth, force control response time, and holding time.

[0008] In this invention, preferably, the sensor assembly includes a visual array sensor, a strain sensor, a laser interferometer, a capacitance sensor, and a laser displacement sensor.

[0009] In this invention, preferably, the collaborative calibration of the sensor components specifically includes static calibration:

[0010] Activate each sensor within the sensor assembly and restore it to its initial state. In the initial state, each sensor acquires initial data from the standard point. Match the initial data acquired by the vision array sensor with the standard point in the overlapping area of ​​the multi-camera field of view to establish the transformation relationship between the coordinate system of each camera and the reference coordinate system of the perforated plate. The laser interferometer calibrates the beam turning error through the angle calibration block of the oblique aperture and uses the standard point to calibrate the orthogonality of the X / Y / Z axis optical paths.

[0011] In this invention, preferably, the collaborative calibration of the sensor components also includes linkage calibration: based on the measurement data of the reference array, the errors of each sensor and the influence coefficients of the sensor errors on other sensors are calculated to form a coupling error matrix. During the calibration process, multiple sensors in the sensor matrix are linked and adjusted based on the coupling error matrix.

[0012] In this invention, preferably, the collaborative calibration of the sensor components further includes dynamic calibration:

[0013] When the sensor collects different motion data, the error prediction model outputs the current sensor error compensation value based on the parameters of the current driving process, thereby achieving real-time calibration during motion.

[0014] In this invention, preferably, the data fusion includes: fusing identical state data collected by a laser interferometer, a capacitance sensor, and a laser displacement sensor according to a certain weight based on weighted Kalman filtering, and obtaining displacement data of the wafer component after fusion; and fusing visual sensor and strain sensor data based on DS evidence theory, and obtaining end pressure data of the wafer component after fusion.

[0015] In this invention, preferably, the trajectory generation and comparison module specifically includes the following steps: based on the working conditions corresponding to the driving strategy and the data fused by the data fusion strategy, extracting the corresponding quantization data of the wafer component; performing mathematical modeling based on the quantization data in the detection coordinate system to generate a three-dimensional motion trajectory; calculating the deviation between the three-dimensional motion trajectory and the standard trajectory to obtain the deviation index.

[0016] A wafer component driving and inspection system based on a perforated plate and multiple sensors includes: a test fixture, comprising two side plates and a perforated plate, the perforated plate being fixed to the side plates, and a top plate being provided on the top of the perforated plate; a driving component, comprising a power unit and a transmission unit, fixedly mounted on the test fixture, for driving the movement of the wafer component; a sensor assembly, comprising a vision array sensor, a laser interferometer, a capacitance sensor, a laser displacement sensor, and a strain sensor; and a control unit, communicatively connected to the driving component and the sensor assembly, the control unit comprising a data processing module, a trajectory generation and comparison module, a decision module, and a driving module. The data processing module receives data collected by the sensor assembly and performs data preprocessing and data fusion on the collected data. The fused data is then used by the trajectory generation and comparison module to reconstruct and compare the motion trajectory to obtain a trajectory deviation index. The decision module determines the driving strategy of the driving component and the acquisition strategy of the sensor assembly based on the wafer component type, and sends the driving strategy and sensor strategy to the driving module. The driving module controls the driving component and the sensor assembly to operate according to the corresponding strategies.

[0017] In this invention, preferably, the visual array sensor includes: a front-view camera mounted on the top plate of the perforated plate with its lens pointing vertically downwards; two side-view cameras mounted on the left and right side plates of the perforated plate respectively, with their lenses facing each other horizontally; and an oblique camera mounted behind the perforated plate through 30° oblique holes, with its lens at a 45° angle to the horizontal plane, to capture images of the contact area between the actuator and the simulated wafer. The perforated plate is equipped with n+i level through-holes at its four corners to capture images of the wafer component movement from different angles.

[0018] In this invention, preferably, the sensor assembly includes a strain sensor, which is attached to the surface of the wafer component that contacts the wafer.

[0019] In this invention, preferably, the laser head of the laser interferometer is fixed to a side plate connected to the perforated plate via an adjustable bracket, and its beam is parallel to the X-axis direction of the perforated plate. A reflector is disposed on the end effector of the wafer component. The deflector of the laser interferometer is installed on the edge of the perforated plate through a 45° oblique aperture, and the beam is deflected parallel to the Y-axis direction of the perforated plate. A vertical optical path bracket is installed on the right side of the side plate, and the beam is vertically upward. The corresponding reflector is fixed to the bottom of the end effector.

[0020] In this invention, preferably, the probes of the capacitive sensors are vertically upward and equally spaced at eight points on the same circle; the laser displacement sensors are respectively installed at the X-axis and Y-axis endpoints of the porous plate; the strain sensors are attached to the surface of the wafer component that contacts the wafer, and multiple capacitive sensors are arranged in a ring on the porous plate.

[0021] In this invention, preferably, the power unit includes a servo motor and a precision cylinder. The servo motor and the precision cylinder are fixed to the side of the perforated plate by a U-shaped frame. The servo motor is used to drive the wafer component to perform steering motion, and the precision cylinder is used to drive the wafer component to perform linear motion.

[0022] The transmission unit includes a magnetic coupling drive, which is located at the bottom of the perforated plate and connected to the wafer component through the oblique holes of the perforated plate.

[0023] The data processing module is equipped with a preprocessing strategy, which includes:

[0024] Time alignment: Unify the data collected by multiple sensors to the same sampling rate, and associate and store multiple data collected at the same time as a group, with each group corresponding to a sampling time;

[0025] Spatial unification: The data collected by the sensors are mapped to the detection coordinate system according to the installation position of the sensors on the perforated plate;

[0026] Noise filtering: High-frequency vibration noise is filtered out through wavelet transform.

[0027] Compared with the prior art, the beneficial effects of the present invention are:

[0028] The method of this invention achieves standardized installation and positioning of sensors and driving components through a perforated plate. Combined with multi-sensor collaborative calibration and data fusion technology, it solves the problems of low coverage and difficulty in data spatial alignment in traditional detection methods. It has the advantages of improving detection accuracy and coverage, realizing multi-sensor data spatial alignment, and enhancing dynamic collaborative detection capabilities. Attached Figure Description

[0029] Figure 1 This is a schematic flowchart of the wafer component driving and detection method based on a multi-hole plate and multiple sensors described in this invention.

[0030] Figure 2 This is a schematic diagram of the test fixture described in this invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] It should be noted that when a component is described as "fixed to" another component, it can be directly on the other component or may have a component in between. When a component is described as "connected to" another component, it can be directly connected to the other component or may have a component in between. When a component is described as "set on" another component, it can be directly set on the other component or may have a component in between. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0034] Please see Figure 1A preferred embodiment of the present invention provides a wafer component driving detection method based on a perforated plate and multiple sensors. The method involves fixing the component to be detected onto a perforated plate 1 via a mounting base and electrically connecting a driving component to the component; determining a detection strategy based on the type of the component to be detected; initializing the operating mode of the corresponding sensor assembly based on the detection strategy; performing collaborative calibration on the sensor assembly; marking the initial positions of the component to be detected and the calibrated sensor positions in a detection coordinate system; initiating the driving component's action based on the detection strategy, the driving component drives the component to be detected to perform actions, while the sensor assembly collects action data of the component to be detected; fusing the collected action data to reconstruct the three-dimensional motion trajectory of the component to be detected; calculating the deviation between the three-dimensional motion trajectory and a standard trajectory to determine the accuracy index of the component to be detected.

[0035] Among them, the perforated plate 1 refers to a positioning substrate with a multi-level through-hole array. Specifically, it can be made of aluminum alloy material to form a composite structure of central n-level through-holes 2 and surrounding n+i-level through-holes 3, adapting to various component installation requirements through different hole diameter combinations. The detection strategy refers to a set of drive parameters customized according to the component's motion characteristics. Specifically, it can be matched with the component type through a preset parameter database to dynamically generate control commands including linear motion speed and rotation angle thresholds. Collaborative calibration is the process of eliminating errors in multi-sensor systems. It mainly establishes coordinate system transformation relationships through static calibration, and then combines dynamic error compensation to achieve sensor data synchronization. Data fusion mainly involves data collected from multi-source heterogeneous sensors.

[0036] Specifically, the component to be tested is fixed to the perforated plate 1 via an adjustable mounting base, and the mounting position is precisely adjusted using a through-hole array. The drive component outputs control signals according to the detection strategy to reproduce the composite motion of the component under actual working conditions. After collaborative calibration, the multi-sensor assembly collects displacement, stress, and deformation data from different spatial dimensions. After time alignment and spatial unification, the collected data is fused using multiple algorithms to generate a three-dimensional motion trajectory model. This model is spatially matched with a standard trajectory, and the trajectory offset is quantitatively calculated as a basis for accuracy evaluation. The perforated plate 1 enables rapid reconstruction of the detection fixture, and the multi-sensor collaborative calibration eliminates system errors, effectively improving the consistency of the detection environment. Dynamic data fusion technology breaks through the measurement dimension limitations of a single sensor, and can simultaneously acquire multiple physical quantity data such as displacement, stress, and deformation, more accurately reflecting the actual motion state of the component.

[0037] This application's method enables full-dimensional detection of the motion characteristics of wafer fabrication components, overcoming the misjudgment problem caused by the single measurement parameter in traditional methods. A multi-sensor collaborative calibration mechanism ensures a unified spatial benchmark for the detection data, avoiding conflicts between multi-source data. Three-dimensional motion trajectory reconstruction technology accurately restores the complex motion state of the components, providing reliable data support for accuracy evaluation. The dynamic data fusion method effectively improves the environmental adaptability of the detection system, meeting the accuracy detection requirements under different operating conditions.

[0038] Please see Figure 2 In this embodiment, the perforated plate 1 includes central n-level through holes 2 distributed in an equally spaced array, surrounding n+i-level through holes 3, 30°, 45°, and 60° oblique holes 4, and threaded holes 5.

[0039] Among them, the central n-level through-hole 2 refers to a two-dimensional array of holes with equal spacing formed around the geometric center of the perforated plate 1. Specifically, it can be implemented using a rectangular grid distribution with a spacing of 5mm, used to provide reference positioning points for the sensor components and ensure uniform coverage of the core measurement area. The surrounding n+i-level through-hole 3 refers to an extended array with more holes in the edge area of ​​the perforated plate 1 than in the central area. Specifically, it can be implemented by adding 3 holes on each side, used to adapt to the detection requirements of wafer components of different sizes and enhance the adjustability of the peripheral sensors. The 30°, 45°, and 60° angled holes 4 refer to channel structures that penetrate the perforated plate 1 at specific tilt angles. Specifically, it can be implemented by installing built-in angle calibration blocks, used to guide the optical measurement path in non-orthogonal directions and obtain the three-dimensional motion characteristics of the wafer components. The threaded hole 5 refers to a fixed hole with an internal thread structure, specifically implemented using the M3 standard thread specification, used for quick disassembly and assembly of the sensor bracket and to achieve fine-tuning of the position, forming a dynamically reconfigurable detection platform.

[0040] Specifically, the central n-level through-holes 2 form a reference positioning network through an equally spaced array, providing a stable coordinate reference system for the vision array sensor, while also offering flexible options for the installation of wafer components and sensor assemblies. The surrounding n+i-level through-holes 3, by expanding the number of holes, allow the laser displacement sensor 12 to adjust its installation position according to the wafer component size, avoiding measurement blind spots. The 30°, 45°, and 60° angled holes 4 are respectively equipped with angle calibration blocks, enabling the laser interferometer 10 beam to be redirected at non-orthogonal angles, accurately capturing the rotational motion trajectory of the wafer component. The threaded holes 5 cooperate with the adjustable bracket to facilitate the installation of various sensors, drive components, and the wafer component under test, as well as to achieve rapid positioning and spacing adjustment of the capacitive sensor 11 in the ring array. Through the synergistic effect of the above hole structure, the perforated plate 1 can support the synchronous data acquisition of multiple types of sensors in different spatial dimensions, eliminating the problem of lost three-dimensional motion features caused by traditional single-plane layouts. Through the design of the composite hole structure, the sensor can simultaneously collect data from orthogonal and non-orthogonal directions. For example, the 45° oblique hole 4 is used to guide the laser interferometer 10 to measure the axial offset of the wafer holder. Combined with the visual positioning data of the central through-hole array, the accurate reconstruction of the three-dimensional motion trajectory is achieved.

[0041] This application solves the problem of incomplete multi-dimensional data acquisition caused by limited sensor layout. It achieves benchmark positioning through a central through-hole array, adapts the peripheral sensor installation through the surrounding extended through-holes, supports three-dimensional motion measurement with the oblique hole 4, and provides dynamic adjustment capability with the threaded hole 5, forming a detection platform that covers the motion characteristics of the wafer components in the whole space, significantly improving the spatial coverage of detection data and the accuracy of motion trajectory reconstruction.

[0042] In this embodiment, the detection strategy includes a driving process and process parameters. The driving process is determined based on the actual operation of the component to be detected. The driving process includes linear motion, steering motion, clamping motion, and pressure motion driven in sequence. The process parameters include the corresponding action parameters for each driving process. The linear motion parameters include motion distance, speed, acceleration, and action time; the steering motion parameters include rotation angle, angular velocity, action time, and rotation axis offset; the clamping motion parameters include clamping stroke, clamping force, action time, and clamping parallelism; and the pressure motion parameters include pressure value, pressing depth, force control response time, and holding time.

[0043] The driving process refers to a standardized sequence of actions decomposed from the actual motion pattern of the component under test in the wafer processing equipment. In a specific embodiment, this is achieved through the coordinated control of a servo motor and a cylinder to ensure that the detection action is consistent with the actual working conditions. Process parameters refer to the quantitative indicators corresponding to each type of driving action. In a specific embodiment, these are dynamically adjusted through sensor feedback and preset thresholds, and the acceleration parameter can be calibrated in real time using encoder data. The combination of motion distance and speed in the linear motion parameters characterizes displacement accuracy. The dynamic response characteristics are related to acceleration and action time, and trapezoidal velocity curve control avoids vibration caused by sudden stops. The rotation axis offset in the steering motion parameters is used to capture mechanical assembly errors, specifically measured by a laser interferometer 10 to measure the rotation center position deviation. The clamping parallelism in the clamping motion parameters is used to evaluate the uniformity of the contact surface, and a dual-axis force sensor monitors the clamping force distribution. The force control response time in the pressure motion parameters is used to evaluate contact stability, and the pressure output can be adjusted by a PID algorithm.

[0044] Specifically, the driving process is decomposed into four basic motions to match the actual working conditions of the wafer components. When the detection component is a robotic arm, the robotic arm's transmission process corresponds to a combination of linear and steering motions, while the vacuum chuck positioning corresponds to a combination of clamping and pressure motions. A multi-dimensional parameter system for each type of motion comprehensively characterizes the action state from the perspectives of spatial displacement, time series, and mechanical properties. The rotation angle and angular velocity parameters in the steering motion can simultaneously monitor the static accuracy and dynamic stability of the rotation trajectory. The combination of clamping stroke and clamping force parameters can identify gripper loosening caused by wear, and the clamping parallelism parameter calculates flatness error using multi-point strain data. The pressing depth and holding time parameters of the pressure motion can assess the deformation recovery characteristics of the contact surface, and the force control response time parameter reflects the actuator's ability to suppress pressure fluctuations. Through synergistic analysis between parameters, composite deviations that cannot be detected by a single sensor can be identified, such as trajectory drift caused by the combined effects of the robotic arm's rotation axis offset and acceleration anomalies in this embodiment. This solution achieves full-dimensional coverage of the wafer component's motion characteristics by defining four types of driving processes and their multi-dimensional parameter system. For example, existing technologies only measure the rotation angle in steering motion, while this solution introduces an additional parameter of rotation axis offset, which can detect non-concentric rotation problems caused by bearing wear. In pressure motion detection, existing technologies only focus on the steady-state pressure value, while this solution can identify the impact load at the moment of contact by using a combination of parameters of pressure depth and force control response time.

[0045] This application addresses the accuracy and coverage deficiencies caused by insufficient detection dimensions. By rigorously matching the driving process with actual working conditions, deviations between simulated and real actions are avoided. Through multi-dimensional parameter definition, simultaneous monitoring of characteristics such as spatial displacement, dynamic response, and mechanical distribution is achieved. For example, in clamping motion detection, the clamping parallelism parameter can identify plane tilt at the 0.1 mm level, thus providing early warning of uneven wafer stress. In pressure motion, the holding time parameter can assess the creep characteristics of the contact surface, preventing positioning failures due to material relaxation. This solution enables detection data to accurately reflect the true state of components under complex actions, providing a precise benchmark for multi-sensor collaborative calibration.

[0046] In this embodiment, the sensor assembly includes a vision array sensor, a strain sensor, a laser interferometer 10, a capacitance sensor 11, and a laser displacement sensor 12.

[0047] In one specific embodiment, the visual array sensor includes: a front-view camera 8, mounted on the top plate 7 at the upper end of the perforated plate 1, with its lens pointing vertically downwards; two side-view cameras 9, respectively mounted on the side plates 6 on the left and right sides of the perforated plate 1, with their lenses facing each other horizontally; and an oblique camera, mounted behind the perforated plate 1 through a 30° oblique aperture 4, with its lens at a 45° angle to the horizontal plane, to capture the contact area between the actuator and the simulated wafer, thereby capturing the spatial pose changes and deformation characteristics of the contact area during the movement of the wafer component. The laser head of the laser interferometer 10 is fixed to the side plate 6 connected to the perforated plate 1 via an adjustable bracket, and its beam is parallel to the X-axis direction of the perforated plate 1. A reflector is disposed on the end effector of the wafer component. The deflector of the laser interferometer 10 is installed on the edge of the perforated plate 1 through a 45° oblique aperture 4, and its beam is deflected parallel to the Y-axis direction of the perforated plate 1. A vertical optical path bracket is installed on the right side of the side plate 6, and the beam is pointing vertically upwards. The corresponding reflector is fixed to the bottom of the end effector. The probe of the capacitive sensor 11 is vertically upward and evenly spaced at eight points on the same circle, used to detect minute changes in the gap between the wafer component and the substrate during movement. Laser displacement sensors 12 are installed at the X-axis and Y-axis endpoints of the porous plate 1 to supplement the acquisition of displacement information in specific directions. Strain sensors are attached to the surface where the wafer component contacts the wafer; these strain sensors can be implemented using resistance strain gauges or fiber Bragg grating sensors, and are used to monitor the micromechanical distribution at the wafer-actuator contact surface.

[0048] Specifically, by constructing a multimodal sensor combination system, a vision array sensor captures the macroscopic motion trajectory of wafer components from different angles, a strain sensor simultaneously monitors the stress distribution on the contact surface, a laser interferometer 10 and a capacitive sensor 11 acquire high-precision displacement data and gap changes respectively, and a laser displacement sensor 12 performs redundant measurements on key axial displacements. The various sensors complement each other in spatial distribution and measurement dimensions. The combination of the vision sensor and the strain sensor can simultaneously acquire motion trajectories and contact stresses, while the cooperation between the laser interferometer 10 and the capacitive sensor 11 can simultaneously monitor displacement and gap changes. The collaborative configuration of multi-source heterogeneous sensors covers multiple dimensions of physical quantities such as displacement, angle, stress, and gap, providing a complete raw dataset for subsequent data fusion. This solution, through the collaborative work of multiple sensors, not only covers kinematic parameters but also achieves simultaneous monitoring of mechanical properties and gap states, overcoming the limitations of single sensors in the frequency domain, spatial domain, and types of physical quantities.

[0049] Through the above technical solution, this application effectively improves the dimensional coverage capability of wafer component motion detection, enabling the simultaneous acquisition of multi-dimensional data such as displacement, angle, stress, and gap, providing comprehensive data support for subsequent accuracy evaluation. This solution solves the problem of insufficient accuracy caused by the lack of data dimensions in traditional detection methods, and is particularly suitable for precision component detection scenarios that require monitoring the coupling relationship between contact mechanical properties and motion trajectory.

[0050] In this embodiment, the collaborative calibration of the sensor assembly specifically includes static calibration: starting each sensor in the sensor assembly and restoring it to its initial state; in the initial state, each sensor acquires initial data from the standard point; the initial data acquired by the visual array sensor is used to match the standard point in the overlapping area of ​​the multi-camera field of view, and the transformation relationship between the coordinate system of each camera and the reference coordinate system of the perforated plate 1 is established; the laser interferometer 10 calibrates the beam turning error through the angle calibration block of the oblique hole 4, and uses the standard point to calibrate the orthogonality of the X / Y / Z axis optical paths.

[0051] Restoring to the initial state refers to eliminating zero-drift errors through sensor reset operations. This can be achieved by sending calibration commands or triggering a hardware reset circuit, returning all sensors to their factory-preset reference state. Standard points are set on the perforated plate 1, with the central n-level through-hole 2 serving as the standard point to provide a unified spatial reference for multiple sensors. Standard point matching in the overlapping areas of multiple camera fields of view involves aligning coordinates using standard points observed by multiple cameras. This can be achieved by using image feature extraction algorithms to calculate the coordinates of corresponding points under different camera perspectives, and then using a coordinate transformation matrix to unify the multi-view data space. The angle calibration block of the oblique aperture 4 is a physical calibration device with precise angle markings. Specifically, it can be a mirror bracket installed within the oblique aperture 4 of the perforated plate 1, such as a mirror mount with a 45° angle marking, used to compensate for the beam steering error of the laser interferometer 10.

[0052] Specifically, the static calibration process is executed in four levels: First, hardware reset returns each sensor to its initial reference state, eliminating initial errors caused by zero drift or environmental interference. Then, using preset standard points on the perforated plate 1, all sensors are triggered to synchronously acquire initial data, establishing a physical reference for the multi-source data. For the vision array sensor, target coordinates are extracted from the overlapping areas of the multi-camera fields of view, and a coordinate transformation algorithm is used to map each camera's coordinate system to the reference coordinate system of the perforated plate 1, eliminating spatial deviations caused by differences in installation angles. For the laser interferometer 10, the beam steering angle is adjusted using the angle calibration block within the oblique aperture 4, and the orthogonality of the X / Y / Z axis optical paths is verified using standard points, compensating for measurement deviations caused by optical path deflection or assembly errors. By integrating standard target points and angle calibration blocks on the perforated plate 1, synchronous calibration of multiple sensors under a unified physical reference is achieved, solving the coupling problem of cross-sensor coordinate system transformation errors and optical path steering errors.

[0053] Through the above technical solution, this application effectively eliminates measurement deviations caused by inconsistencies in the initial states of multiple sensors, establishes a unified spatial coordinate system across sensors, and compensates for the optical path turning angle error of the laser interferometer 10. This enables precise alignment of multi-view data from the vision array sensor, ensures the orthogonality of the optical path of the laser interferometer 10, and provides a highly consistent measurement benchmark for subsequent multi-source data fusion, thereby improving the overall accuracy of wafer component motion detection.

[0054] In this embodiment, the coordinated calibration of the sensor components also includes linkage calibration. Based on the measurement data of the reference array, the errors of each sensor and the influence coefficient of the sensor errors on other sensors are calculated to form a coupling error matrix. During the calibration process, multiple sensors in the sensor matrix are adjusted in linkage based on the coupling error matrix.

[0055] The measurement data of the reference array refers to the multi-sensor synchronous measurement dataset obtained through preset spatial reference points. Specifically, it can be achieved by using standard point coordinates generated by a high-precision calibration board or a laser tracker, which is used to establish the error correlation between multiple sensors.

[0056] The coupling error matrix is ​​a mathematical expression model that reflects the mutual influence between the errors of each sensor. Specifically, it can be calculated using multiple regression analysis or principal component analysis to obtain the error transmission coefficient, which is used to quantify the degree of cross-interference between sensors.

[0057] Specifically, in this embodiment, a visual array sensor, a laser interferometer 10, a capacitance sensor 11, a laser displacement sensor 12, and a strain sensor are used to collect data from the robotic arm. During data collection while the robotic arm is moving, and during the linkage calibration process, the robotic arm is controlled to move the reference target along a preset trajectory, and data is collected synchronously.

[0058] Static data acquisition: The robotic arm remains in 30 fixed poses for 5 seconds. The front-view camera 8 acquires the coordinates of grid feature points, the prism pixel positions of the side-view camera 9, and the deformation image of the contact area from the oblique camera. The laser interferometer 10 acquires the displacement of the robotic arm's end effector along the X, Y, and Z axes. The eight probes of the capacitive sensor 11 acquire the gap value between the bottom of the robotic arm and the perforated plate 1. The laser displacement sensor 12 acquires the end effector displacement in the X and Y axes. The strain sensor acquires the strain value of the suction cup contact area at the end effector of the robotic arm. The robotic arm performs sinusoidal motion along the X / Y / Z axes at a speed of 50 mm / s for 10 cycles, recording the dynamic measurement values ​​of each sensor, i.e., time-series data. A coupling error matrix is ​​constructed based on the motion. Using the measurement values ​​of the laser interferometer 10 as a reference, the errors and coupling coefficients of each sensor are calculated.

[0059] Among them, the visual sensor: the grid point coordinate error Δu of the front-view camera 8 = measured pixel - true pixel; the prism positioning error Δp ​​of the side-view camera 9 = measured position - positioning value of the laser interferometer 10; the gap error Δd of the capacitive sensor 11. i =Measured value - Actual height of the target protrusion; Displacement error ΔLx of laser displacement sensor 12 = Measured value - True value of the X-axis of laser interferometer 10, ΔLy is similar; Error Δε of strain sensor i=Measured value - Known strain of calibration plate. Analyzing the error correlation between sensors, the installation angle error of the front-view camera 8 will cause a change in the prism positioning error of the side-view camera 9. In this embodiment, every 0.1 degree angle error corresponds to a positioning error of ±2μm, with a corresponding influence coefficient k1=2μm / 0.1°; the temperature drift of the capacitive sensor 11, with every 1°C change causing a gap error of ±0.05μm, will synchronously affect the Z-axis measurement of the laser displacement sensor 12. Every 1°C temperature change corresponds to a displacement error of ±0.03μm, with an influence coefficient k2=0.03μm / 0.05μm. The contact pressure error of the strain sensor will cause the deformation image resolution error of the oblique camera, with an influence coefficient k3=1.5 pixels / 0.1N.

[0060] Using each sensor as a reference, an 8×8 matrix is ​​formed, with the following components arranged from top left to bottom: front-view camera 8, left-side view camera 9, right-side view camera 9, oblique camera, laser interferometer 10, capacitive sensor 11, laser displacement sensor 12, and strain sensor. The matrix elements are the aforementioned influence coefficients.

[0061] Based on the coupling error matrix, collaborative parameter corrections were performed on multiple sensors: Based on the coupling coefficient k1 between the forward-looking camera 8 and the side-looking camera 9, the extrinsic parameter matrix of the side-looking camera 9 was adjusted: the mounting angles of the left and right side-looking cameras 9 were corrected by -0.05°, and the distortion coefficient of the forward-looking camera 8 was updated from k1=0.02 to k1=0.005, reducing the positioning error of both from ±5μm to ±1.2μm. Combining the coupling coefficient k3 between the oblique camera and the strain sensor, the image scaling factor of the oblique camera was corrected from 1.02 to 0.998, and the sensitivity coefficient of the strain sensor was simultaneously calibrated from 1.28 to 1.27, reducing the deformation resolution error by 60%.

[0062] To address the ring distribution characteristics of the capacitive sensor 11, based on the influence of adjacent capacitors in the coupling error matrix, the error d1 affects d2 by ±0.03μm. Simultaneously, the zero-point offsets of the eight capacitive sensors 11 are adjusted as follows: d1 is corrected by +0.02μm, d2 by -0.01μm, d3 by +0.018μm, d4 by -0.005μm, d5 by +0.012μm, d6 by -0.015μm, d7 by +0.008μm, and d8 by +0.015μm. This improves the gap measurement consistency from ±0.2μm to ±0.08μm.

[0063] By incorporating the temperature coupling coefficient k2, a temperature compensation formula is embedded in the laser displacement sensor 12: Lx correction value = Lx measured value - 0.03 × (average temperature drift of capacitor), reducing the X / Y axis displacement error from ±3μm to ±0.8μm. Then, using the Z-axis data of the laser interferometer 10 as a reference, the Z-axis coordinate transformation parameters of the forward-looking camera 8 are corrected from 0.05mm per pixel to 0.0498mm per pixel. Simultaneously, the 45° angle parameter of the oblique camera is adjusted to 44.98° to ensure multi-sensor consistency in spatial pose calculation.

[0064] Under 20 new poses, the deviations between the measured values ​​of each sensor and the true values ​​of the laser interferometer 10 are as follows: visual sensor positioning error ≤ ±1.5μm, capacitive sensor 11 gap error ≤ ±0.1μm, and strain sensor error ≤ ±5με. When the robotic arm performs random trajectory motion, the actuator pose calculation error of multi-sensor fusion decreases from ±8μm before calibration to ±2μm, and the time synchronization error of contact area deformation capture is ≤0.5ms. By quantifying the mutual influence between sensors through a coupling error matrix, the "compensation offset" caused by single sensor calibration is avoided, improving the global consistency of robotic arm motion measurement by more than 70%, and meeting the sensor collaboration requirements for high-precision operation of wafer components.

[0065] Through the above technical solution, this application solves the problem of decreased measurement accuracy caused by error coupling during multi-sensor collaborative calibration, ensuring the consistency of wafer component motion trajectory detection data in the spatial coordinate system. For example, in the dynamic detection of the end effector of a wafer robot, the measurement deviation between the vision array sensor and the laser interferometer 10 is synchronously corrected, avoiding distortion of three-dimensional trajectory reconstruction caused by error transmission from a single sensor, thereby improving the reliability of accuracy assessment of wafer processing equipment.

[0066] In this embodiment, when the sensor collects different motion data, the error prediction model outputs the current sensor error compensation value based on the parameters of the current driving process, thereby achieving real-time calibration during motion.

[0067] Dynamic calibration refers to adjusting the sensor error compensation value in real time during the movement of wafer components. Specifically, it can be achieved by using an adaptive algorithm based on the correlation between driving parameters and sensor errors. By collecting the action parameters of the driving components in real time and inputting them into the error prediction model, compensation values ​​are dynamically generated to offset measurement deviations caused by environmental interference or mechanical vibration.

[0068] The error prediction model refers to the mapping relationship between sensor errors and driving parameters established through machine learning or statistical modeling. Specifically, an improved LSTM-linear hybrid model is adopted. For dynamic parameters such as acceleration and velocity, a single LSTM layer is used to capture temporal correlations. This single LSTM layer has 16 neurons. For static parameters such as temperature and humidity, a linear regression module is used to model them. For example, for every 1°C increase in temperature, the error of capacitive sensor 11 increases by 0.02μm. The output layer ensures the linkage of compensation values ​​from multiple sensors by coupling the weights of the error matrix. The LSTM-linear hybrid model is trained using prior training data. When the driving component performs linear or turning motion, the system acquires the current action parameters in real time. The X / Y / Z axis velocity, acceleration, motor current, temperature, humidity, average error of capacitive sensor 11 in the previous three sampling periods, error of laser displacement sensor 12, and visual positioning error are input into the LSTM-linear hybrid model. After calculation by the LSTM-linear hybrid model, the real-time error compensation values ​​of the eight capacitive sensors 11, the X / Y axis compensation value of laser displacement sensor 12, and the pixel coordinate correction of the visual array sensor are output. Therefore, the sensor can continuously correct for the accumulated errors caused by mechanical vibration or temperature changes during dynamic detection, ensuring that the measured data is consistent with the actual motion state of the wafer components. This solution achieves real-time error compensation during motion by dynamically correlating the driving parameters with the sensor error, thus solving the problem of sensor accuracy degradation caused by changes in the dynamic characteristics of the mechanical system.

[0069] Through the above technical solution, this application can automatically correct sensor measurement errors during the continuous movement of wafer components, avoiding trajectory reconstruction deviations caused by error accumulation. For example, when the wafer robot performs high-speed gripping actions, dynamic calibration can effectively suppress the influence of vibration on the laser displacement sensor 12, thereby accurately capturing the micron-level displacement changes of the end effector and preventing wafer positioning errors or clamping force loss caused by measurement distortion.

[0070] In this embodiment, data fusion includes fusing the same state data collected by the laser interferometer 10, the capacitance sensor 11 and the laser displacement sensor 12 according to weights based on weighted Kalman filtering to obtain displacement data, and fusing the visual sensor and strain sensor data based on DS evidence theory to obtain end pressure data.

[0071] Weighted Kalman filtering refers to a method for optimizing and fusing multi-source homogeneous data by dynamically adjusting the weight coefficients of each sensor. Specifically, it can be implemented using an iterative update algorithm for the covariance matrix. By real-time evaluation of the measurement noise level of each sensor and the allocation of weight coefficients, high-frequency sampling data and contact measurement data become complementary. DS evidence theory refers to a method for fusing cross-dimensional heterogeneous data through confidence function modeling. Specifically, it can be implemented using basic probability allocation functions and evidence synthesis rules, forming reliable decisions by eliminating data conflicts between sensors.

[0072] Specifically, addressing the characteristic of laser interferometer 10, capacitance sensor 11, and laser displacement sensor 12 having the same physical quantities but different measurement errors in displacement detection scenarios, a weighted Kalman filter is used to establish a state-space model. Through dynamic correction of predicted and observed values, the high-frequency characteristics of optical measurement are combined with the anti-interference characteristics of contact measurement to eliminate the impact of single-point measurement errors on overall trajectory reconstruction. For the problem of mismatch between visual image features and strain sensor scalar data dimensions in pressure detection scenarios, a pressure state recognition framework is constructed. The deformation features of the contact area captured by the visual sensor are converted into spatial distribution confidence, which is then synthesized with the stress values ​​measured by the strain sensor to solve the problem of misjudgment caused by local occlusion or temperature drift.

[0073] Compared to existing technologies, traditional methods employ independent calibration of a single sensor or simple weighted average fusion, failing to consider the error coupling relationship between optical and contact sensors, and also neglecting the dimensional differences between image data and physical quantity data. This solution utilizes a hierarchical fusion mechanism to achieve dynamic error compensation at the homogeneous data layer and establish a cross-dimensional decision model at the heterogeneous data layer, forming a multi-dimensional data collaborative verification mechanism.

[0074] Through the above technical solution, this application effectively solves the problem of insufficient fusion accuracy of multi-sensor data due to differences in acquisition principles, reduces the restoration error of the three-dimensional motion trajectory of wafer components to the micrometer level, and accurately identifies the pressure distribution state of the end effector and the wafer contact surface, avoiding the risk of wafer breakage caused by local stress concentration.

[0075] In this embodiment, the specific implementation steps of the trajectory generation and comparison module include: extracting the corresponding quantization data of the wafer component based on the working conditions corresponding to the driving strategy and the data fused by the data fusion strategy; generating a three-dimensional motion trajectory by mathematical modeling based on the quantization data in the detection coordinate system; calculating the deviation between the three-dimensional motion trajectory and the standard trajectory to obtain the deviation index.

[0076] The detection coordinate system refers to the coordinate system used to unify the spatial reference of multiple sensors. Specifically, it can be implemented using the transformation matrix between the perforated plate 1 reference coordinate system and the coordinate systems of each sensor, eliminating the influence of sensor installation position differences on the data spatial reference. Quantitative data refers to the displacement, angle, and acceleration parameters extracted from the fused data. Specifically, it can be achieved by filtering characteristic parameters with high correlation to the current driving process through operating condition analysis, avoiding redundant data processing. Mathematical modeling refers to the algorithm that transforms discrete parameters into continuous trajectories. Specifically, it can employ a coupled model of rigid body kinematics and elastic deformation, reflecting both the overall motion characteristics of the component and capturing the micro-deformation of the end effector. The standard trajectory refers to the motion trajectory that the wafer processing equipment should achieve under ideal conditions. Specifically, it can be achieved by extracting spatiotemporal characteristic parameters from the equipment control log and establishing parameterized equations. The deviation index refers to the comprehensive deviation between the actual trajectory and the standard trajectory. Specifically, it can be generated as a quantitative evaluation index by calculating the weighted root mean square error of spatial position, velocity, and acceleration.

[0077] Specifically, this technical solution achieves high-precision trajectory reconstruction through the dual constraints of condition-driven and data fusion. First, based on the condition type corresponding to the driving strategy, quantitative parameters matching that condition are selected from the fused data. For example, displacement and acceleration parameters are extracted for linear motion conditions, and angle and angular velocity parameters are extracted for rotational conditions. Then, a mathematical model is established in the detection coordinate system. Coordinate system mapping eliminates spatial reference differences between multiple sensors, and a kinematic model is used to transform discrete parameters into a continuous three-dimensional trajectory. Finally, a trajectory deviation index is used as a quantitative evaluation indicator. By calculating the comprehensive deviation between the actual trajectory and the standard trajectory in multiple dimensions such as spatial position, motion velocity, and acceleration, a quantifiable accuracy evaluation system is formed. The mathematical modeling stage employs a coupled model of rigid body kinematics and elastic deformation, reflecting both the overall motion characteristics of the component and capturing the micro-deformation of the end effector, thereby improving the fidelity of trajectory reproduction.

[0078] In some specific implementations, the mathematical modeling of the standard trajectory can be based on the spatiotemporal feature parameters extracted from the equipment control logs to establish parameterized equations. For example, a straight trajectory is described by a quadratic equation of initial velocity and acceleration, while a circular trajectory is described by a trigonometric function equation of radius and angular velocity. The establishment of the detection coordinate system can be achieved through multi-camera field-of-view matching of the vision array sensor, uniformly mapping the data collected by each sensor to the reference coordinate system of the perforated plate 1. The calculation of the deviation index can be based on the weighted root mean square error algorithm, assigning different weight coefficients to errors in different dimensions to reflect the degree of influence of wafer component movement accuracy on process stability. This scheme effectively improves the fidelity of trajectory reconstruction by using a detection coordinate system as a multi-sensor data space reference and introducing a rigid body and elastic deformation coupling model. In addition, traditional accuracy assessments often rely on human experience judgment, while this scheme uses the deviation index to achieve quantitative calculation of multi-dimensional errors, providing an objective decision-making basis for wafer component maintenance.

[0079] Through the above technical solution, this application solves the problem of inaccurate 3D motion trajectory reconstruction after multi-sensor data fusion. By detecting coordinate system mapping and coupling models, spatial reference differences and micro-deformation interference are eliminated, achieving high-fidelity trajectory reproduction. Simultaneously, the deviation index based on multi-dimensional error calculation provides a quantitative basis for evaluating the motion accuracy of wafer components, avoiding subjective errors from human experience judgment and improving the reliability and operability of the detection results.

[0080] Another preferred embodiment of the present invention provides a wafer component driving and inspection system based on a perforated plate 1 and multiple sensors, including a test frame, a driving component, a sensor assembly, and a control unit. The test frame consists of two side plates 6 and a perforated plate 1, with the perforated plate 1 fixed on the side plates 6 and a top plate 7 on top. The driving component includes a power unit and a transmission unit, which are fixed on the test frame. The sensor assembly includes a vision array sensor, a laser interferometer 10, a capacitance sensor 11, a laser displacement sensor 12, and a strain sensor. The control unit is communicatively connected to the driving component and the sensor assembly, and includes a data processing module, a trajectory generation and comparison module, a decision module, and a driving module.

[0081] The perforated plate 1 refers to a mounting substrate with a multi-level array of through holes. Specifically, it can adopt a combination structure of equally spaced central through holes, oblique edge holes 4, and threaded holes 5, providing a modular mounting interface for sensors and drive components. The power unit refers to the actuator that drives the movement of the wafer components. Specifically, it can adopt a combination of a servo motor and a precision cylinder, achieving precise control of linear and rotational movements through magnetic coupling transmission. The sensor assembly refers to a multi-physical quantity collaborative sensing network. Specifically, it can achieve multi-view coverage through a visual array sensor, measure spatial displacement through a combination of a laser interferometer 10 and a laser displacement sensor 12, detect contact pressure through a capacitance sensor 11, and monitor surface stress through a strain sensor. The control unit refers to an intelligent data processing and decision-making center. Specifically, it can dynamically match detection strategies through a decision module, control the timing of drive component actions through a drive module, and fuse multi-source data to generate a three-dimensional trajectory model through a data processing module.

[0082] Specifically, the test frame provides a modular mounting base for sensors and drive components through the combination structure of side plate 6 and perforated plate 1. The through-hole layout of perforated plate 1 supports flexible configuration of sensors at different angles. The drive component adopts a separate design for the power unit and transmission unit. The power unit is fixed to the side of perforated plate 1 by a U-shaped frame, which ensures drive stability and avoids interference with the sensor layout. The sensor assembly achieves multi-view coverage through a vision array sensor. A combination of laser interferometer 10 and laser displacement sensor 12 measures spatial displacement. Capacitance sensor 11 detects contact pressure, and strain sensor monitors surface stress, forming a multi-physical quantity collaborative sensing network. The control unit dynamically matches the detection strategy through the decision module. The drive module precisely controls the timing and amplitude of the drive component's actions according to the strategy parameters. The data processing module uses time alignment, spatial unification, and noise filtering preprocessing strategies to eliminate differences in multi-source data. The trajectory generation and comparison module restores the real motion trajectory through data fusion and 3D modeling. Finally, the component accuracy is quantitatively evaluated through the deviation index.

[0083] Compared to existing technologies, traditional detection systems typically employ a single sensor operating independently, acquiring only single-dimensional motion data. This solution, however, integrates multiple types of sensors, including a vision array and a laser interferometer, through a porous plate structure (1), constructing a multi-dimensional data acquisition network. Existing technologies lack dynamic calibration mechanisms, while this solution achieves sensor-linked calibration and error compensation through a control unit, effectively eliminating coupling errors between multiple sensors. Traditional methods rely on manual experience to select detection components, while this solution automatically matches detection strategies through a decision module, achieving a fully automated detection process.

[0084] Through the above technical solution, this application solves the accuracy defects caused by insufficient coverage dimension of a single sensor in wafer component inspection, and realizes the collaborative acquisition and dynamic compensation of multiple physical quantity data. The porous plate 1 structure provides a standardized interface for sensor layout, ensuring spatial consistency of multi-source data; the multi-module collaborative working mechanism of the control unit effectively improves the reconstruction accuracy of complex motion trajectories; the automated strategy matching function of the decision module significantly improves the applicability and working efficiency of the inspection system.

[0085] In this embodiment, the power unit includes a servo motor and a precision cylinder. The servo motor and precision cylinder are fixed to the vertical side plate 6 on the rear side of the perforated plate 1 via a U-shaped bracket. The servo motor drives the wafer component to perform steering movements, and the precision cylinder drives the wafer component to perform linear movements. The transmission unit includes a magnetic coupling transmission, which is located at the bottom of the perforated plate 1 and connected to the wafer component through the oblique holes 4 of the perforated plate 1, thereby achieving high-precision attitude adjustment of the wafer component.

[0086] Specifically, the servo motor can be a permanent magnet synchronous servo motor with an absolute encoder. A U-shaped frame rigidly fixes the output shaft to ensure it is coaxial with the wafer component's steering axis, and a PID algorithm corrects speed fluctuations in real time. Specifically, the servo motor is horizontally mounted on the upper half of the U-shaped frame, with its output shaft passing through a pre-set through-hole in the side plate 6. Its axis is parallel to the Z-axis of the perforated plate 1, ensuring that the steering drive force is transmitted along the wafer component's rotation axis. The precision cylinder can be a compact cylinder with a magnetic scale. The cylinder body is connected to the side of the U-shaped frame via a floating joint to compensate for radial forces caused by installation errors. The precision cylinder is vertically mounted on the lower half of the U-shaped frame, with its cylinder body parallel to the side plate 6. The piston rod extends along the X-axis of the perforated plate 1, aligned with the wafer component's translation mechanism, avoiding additional radial force during linear motion. The magnetic coupling drive consists of a driving wheel and a driven wheel with built-in neodymium iron boron magnetic rings. The driving wheel is connected to the drive shaft, and the driven wheel is connected to the rotating shaft of the wafer component. The driving wheel is fixed to the lower surface of the perforated plate 1 via a bearing seat, and the center of its magnetic ring is concentric with the oblique hole 4. The driven wheel is embedded in a groove on the upper surface of the perforated plate 1, forming a 1mm air gap with the driving wheel through the oblique hole 4 to ensure magnetic field coupling efficiency. The axes of both the driving wheel and the driven wheel coincide with the axis of the oblique hole 4, allowing power to be transmitted to the wafer component at a 30° angle, avoiding conflict with the detection areas of the capacitive sensor 11 and the laser displacement sensor 12 (X / Y axis endpoints) on the base plate.

[0087] Specifically, to address the combined "steering-translation" motion requirements of wafer components, a servo motor drives the steering shaft via a key connection, converting electrical pulse signals into continuous rotation angles within the 0-90° range to meet the fine-tuning requirements during wafer alignment. A precision cylinder connects to the wafer component translation mechanism via a piston rod, achieving 0-30mm linear motion within a 0.2-0.6MPa air pressure range, while a buffer valve in the air circuit eliminates end-of-pipe impact. A magnetic coupling transmission unit transmits the power from the servo motor and precision cylinder to the wafer component via a magnetic field, avoiding contact wear and particulate contamination associated with traditional mechanical transmissions, while utilizing an air gap to isolate vibration between the active and driven ends.

[0088] This application can effectively drive the movement of wafer components and control the steering and positioning error within ±0.02°, with a linear motion repeatability of ±0.005mm. Furthermore, no particles are generated during the transmission process, meeting the requirements for high-precision operation and reducing the impact of inadequate driving on the accuracy of wafer component detection.

[0089] In this embodiment, the data processing module is equipped with a preprocessing strategy, which includes: time alignment, spatial unification, and noise filtering. Time alignment involves unifying data collected by multiple sensors to the same sampling rate and associating multiple data points collected at the same time into a single group, with each group corresponding to a sampling time. Spatial unification maps the data collected by the corresponding sensors to the detection coordinate system based on the sensor's installation position on the perforated plate 1. Noise filtering uses wavelet transform to filter high-frequency vibration noise.

[0090] Specifically, time alignment can be achieved through high-precision time stamp synchronization and interpolation algorithms. For devices with different sampling rates, such as the laser interferometer 10, capacitive sensor 11, and vision sensor, the highest sampling rate is used as a benchmark, and linear interpolation or cubic spline interpolation is used to supplement the missing data of low frame rate sensors. Then, the timestamps of all sensors are unified through a hardware trigger signal, so that the measurement data at the same physical moment are associated as a group, and each group of data contains sampling time stamps accurate to the microsecond level.

[0091] Spatial unification is achieved by constructing a spatial transformation matrix using sensor calibration parameters. A three-dimensional detection coordinate system is established with the geometric center of the perforated plate 1 as the origin. The X-axis is along the front-to-back direction of the perforated plate 1, the Y-axis is along the left-to-right direction, and the Z-axis is vertically upward. Based on the installation parameters of each sensor, such as the vertical installation height of the front-view camera 8, the beam direction of the laser interferometer 10, and the annular distribution radius of the capacitive sensor 11, the pixel coordinates (u,v) of the visual sensor are converted into physical coordinates (X,Y,Z) through translation and rotation matrices. The gap value of the capacitive sensor 11 is mapped to the Z-axis height of the corresponding position at the bottom of the wafer, making all data comparable in the same spatial dimension.

[0092] The noise filtering is achieved using wavelet transform because it can distinguish between signal and noise in both the time and frequency domains. A suitable wavelet basis for high-frequency vibration noise filtering is selected, and the dynamic displacement signal of the laser displacement sensor 12 and the stress signal of the strain sensor are decomposed into 3-5 layers of wavelets. The high-frequency coefficients are processed by a soft threshold function to reconstruct the signal, effectively filtering out the 100-1000Hz high-frequency noise generated by the operation of the servo motor and the airflow of the cylinder, while retaining useful signal details such as the small deformation of the wafer and the sudden change in contact pressure.

[0093] Specifically, in the scenario of wafer component motion detection:

[0094] Time alignment ensures that when the robotic arm moves to X=50mm, the displacement value of the laser interferometer 10, the gap value of the capacitive sensor 11, and the image features of the vision sensor can be accurately correlated, avoiding asynchronous data fusion caused by time deviations, such as matching the current pressure data with displacement data from 10ms ago. Spatial unification resolves the coordinate difference between the wafer left edge displacement measured by the side-view camera 9 and the center displacement measured by the laser interferometer 10. A transformation matrix maps the two to the global coordinate system, enabling multi-dimensional data comparison at the same location. Noise filtering addresses vibration interference during the high-speed movement of the robotic arm, reducing the standard deviation of the gap measurement of the capacitive sensor 11 from 0.1μm to 0.03μm, providing more stable observations for subsequent weighted Kalman filtering.

[0095] This solution employs a three-level collaborative processing approach: achieving microsecond-level synchronization in time, establishing global coordinate association in space, and filtering noise to preserve microscale features, thus laying a data foundation with spatiotemporal consistency and a high signal-to-noise ratio for heterogeneous data fusion.

[0096] Through the above preprocessing strategy, the time synchronization error of each sensor data is reduced, the overall spatial coordinates are consistent, and the noise energy is reduced by ≥60%, which effectively supports the displacement fusion accuracy of subsequent weighted Kalman filtering and the pressure recognition accuracy of DS evidence theory, providing reliable data input for high-precision motion control of wafer components.

[0097] In some other preferred embodiments of the present invention, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, causes the processor to perform the steps of the method as described in the above embodiments.

[0098] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0099] The above description is a detailed description of the preferred embodiments of the present invention. However, the embodiments are not intended to limit the scope of the patent application of the present invention. All equivalent changes or modifications made under the technical spirit of the present invention should fall within the patent scope covered by the present invention.

Claims

1. A wafer component driving and detection method based on a multi-hole plate and multiple sensors, characterized in that, include: The component to be tested is fixed on the porous plate by the mounting base, and the driving component is electrically connected to the component to be tested; The detection strategy is determined based on the type of the component to be detected, and the working mode of the corresponding sensor component is initialized based on the detection strategy. The sensor components are calibrated collaboratively. Mark the initial positions of the component to be tested and the calibrated position of the sensor in the detection coordinate system; The driving component is activated based on the detection strategy. The driving component drives the component under test to perform actions, while the sensor components collect the action data of the component under test. The collected motion data is fused to reconstruct the three-dimensional motion trajectory of the component under test; Calculate the deviation between the three-dimensional motion trajectory and the standard trajectory to determine the accuracy index of the component to be tested.

2. The wafer component driving and detection method based on a multi-hole plate and multiple sensors according to claim 1, characterized in that, The perforated plate includes central n-level through holes arranged in an equally spaced array, surrounding n+i-level through holes, 30°, 45°, and 60° oblique holes, and threaded holes.

3. The wafer component driving and detection method based on a multi-hole plate and multiple sensors according to claim 2, characterized in that, The detection strategy includes a driving process and process parameters. The driving process is determined based on the actual operation of the component to be detected. The driving process includes linear motion, steering motion, clamping motion, and pressure motion driven in sequence. The process parameters include the corresponding action parameters for each driving process. The linear motion parameters include motion distance, speed, acceleration, and action time. The steering motion parameters include rotation angle, angular velocity, action time, and rotation axis offset. The clamping motion parameters include clamping stroke, clamping force, action time, and clamping parallelism. The pressure motion parameters include pressure value, pressing depth, force control response time, and holding time.

4. The wafer component driving and detection method based on a multi-hole plate and multiple sensors according to claim 1, characterized in that, The sensor assembly includes a vision array sensor, a strain sensor, a laser interferometer, a capacitance sensor, and a laser displacement sensor.

5. The wafer component driving and detection method based on a multi-hole plate and multiple sensors according to claim 1, characterized in that, The collaborative calibration of the sensor components specifically includes static calibration: Activate the corresponding sensors within the sensor assembly and restore them to their initial state. In the initial state, each sensor collects initial data from the standard point; The initial data collected by the visual array sensor is used to match standard points in the overlapping area of ​​the multi-camera field of view, and the transformation relationship between the coordinate system of each camera and the reference coordinate system of the perforated plate is established. The laser interferometer calibrates the beam steering error through the angle calibration block with the oblique aperture, and uses standard points to calibrate the orthogonality of the X / Y / Z axis optical paths.

6. The wafer component driving and detection method based on a multi-hole plate and multiple sensors according to claim 5, characterized in that, The collaborative calibration of the sensor components also includes linkage calibration: Based on the measurement data of the reference array, the errors of each sensor and the influence coefficients of the sensor errors on other sensors are calculated to form a coupling error matrix. During the calibration process, multiple sensors in the sensor matrix are adjusted in a coordinated manner based on the coupling error matrix.

7. The wafer component driving and detection method based on a multi-hole plate and multiple sensors according to claim 5, characterized in that, Co-calibrating the sensor components also includes dynamic calibration: When the sensor collects different motion data, the error prediction model outputs the current sensor error compensation value based on the parameters of the current driving process, thereby achieving real-time calibration during motion.

8. The wafer component driving and detection method based on a multi-hole plate and multiple sensors according to claim 1, characterized in that, The data fusion includes: Based on weighted Kalman filtering, identical state data collected by laser interferometer, capacitive sensor and laser displacement sensor are fused according to a certain weight, and the fused data is used to obtain the displacement data of wafer components. Based on the DS evidence theory, data from visual sensors and strain sensors are fused to obtain end pressure data for wafer components.

9. The wafer component driving and detection method based on a multi-hole plate and multiple sensors according to claim 1, characterized in that, The specific steps for determining the precision index of the component to be tested include: Based on the operating conditions corresponding to the driving strategy, and based on the data fusion strategy after data fusion, extract the corresponding quantitative data of the wafer components; The detection coordinate system is used to generate a three-dimensional motion trajectory by mathematical modeling based on quantized data. The deviation between the three-dimensional motion trajectory and the standard trajectory is calculated to obtain the deviation index.

10. A wafer component driving and inspection system based on a multi-hole plate and multiple sensors, characterized in that, include: The test frame includes two side plates and a perforated plate, the perforated plate is fixed on the side plates, and a top plate is also provided on the top of the perforated plate; A driving component, comprising a power unit and a transmission unit, is fixedly mounted on the test fixture and is used to drive the wafer component to move. The sensor assembly includes a vision array sensor, a laser interferometer, a capacitance sensor, a laser displacement sensor, and a strain sensor; The control unit is communicatively connected to the drive component and sensor assembly. The control unit includes a data processing module, a trajectory generation and comparison module, a decision module, and a drive module. The data processing module receives data collected by the sensor assembly and performs data preprocessing and fusion. The fused data is then used by the trajectory generation and comparison module to reconstruct and compare the motion trajectory to obtain a trajectory deviation index. The decision module determines the drive strategy of the drive component and the acquisition strategy of the sensor assembly based on the wafer component type, and sends the drive strategy and sensor strategy to the drive module. The drive module controls the drive component and sensor assembly to operate according to the corresponding strategies.

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