Wafer scanning device motor precision parameter collaborative optimization selection method and system

Abstract

This invention provides a collaborative optimization selection method and system for motor accuracy parameters in a wafer scanning device, relating to the fields of high-precision motion control and electromechanical system design. Based on the structure of the wafer scanning device, kinematic equations are established. The kinematic model calculates the actual spatial coordinates of any point on the wafer based on the command and actual angles of the first, second, and third motors. Then, constraints are defined to generate three-dimensional motor tracking error combinations. For each generated three-dimensional motor tracking error combination, it is substituted into the kinematic model. Optimal combination selection is performed to obtain the optimal motor accuracy parameter selection scheme. The end-effector trajectory is discretized and traversed with a set step size to generate L displacement target points. For each displacement target point, the optimal feasible combination is obtained, and the tracking error group with the smallest average value is the optimal motor selection scheme.

Description

Technical Field

[0001] This invention relates to the field of high-precision motion control and electromechanical system design technology, and in particular to a collaborative optimization selection method and system for the precision parameters of a wafer scanning device motor. Background Technology

[0002] In a precision wafer scanning system where multiple motors work in tandem, the tracking errors of each motor eventually accumulate and affect the positioning accuracy of points on the wafer. Existing motor selection methods have the following drawbacks:

[0003] 1. Over-design: In order to ensure end-effector accuracy, current technology often selects models with higher precision than allowed for all motors. This leads to a sharp increase in system cost and waste.

[0004] 2. Empiricism and Separate Design: Existing selection techniques rely on engineers' experience to assign accuracy indicators individually to each motor, lacking system-level, quantitative collaborative optimization. This makes it impossible to guarantee that the accuracy-cost combination of the three motors achieves global optimality while meeting end-point accuracy constraints.

[0005] 3. Ignoring Coupling and Nonlinearity: For precision wafer scanning devices, the end-effector displacement is controlled by the coordinated coupling of multiple motors. The impact of the errors of these multiple motors on the end-effector accuracy is not fixed, but rather varies nonlinearly with the position and orientation of the motion mechanism. Traditional simplified calculations or worst-case estimation methods are too conservative and cannot fully exploit the system's accuracy potential. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a collaborative optimization selection method and system for motor accuracy parameters of a wafer scanning device. This invention aims to solve how to scientifically and quantitatively determine a set of cost-optimal motor accuracy parameter combinations that satisfy: (1) the positioning error of each point on the wafer does not exceed the permissible range at any working position; (2) the accuracy requirements of the three motors are matched to avoid an imbalance state where the tracking difference between the three motors is too large, thereby minimizing the total system cost.

[0007] The specific technical solution of the present invention is as follows:

[0008] On one hand, the present invention provides a method for collaborative optimization and selection of motor accuracy parameters of a wafer scanning device, comprising the following steps:

[0009] Step S1: System modeling and error mapping establishment;

[0010] The system modeling specifically involves: establishing a kinematic model of the wafer scanning device;

[0011] The wafer scanning device includes a first motor, a second motor, and a third motor. The first motor and the second motor work together to drive the planar linkage mechanism to realize the two-dimensional planar motion of the wafer, and the third motor independently drives the wafer to rotate around its normal to control the torsion angle.

[0012] The kinematic model is specifically as follows: Based on the structure of the wafer scanning device, kinematic equations are established; the wafer scanning device structure is a five-bar linkage, where A is the first motor mounting point; E is the second motor mounting point; F is the third motor mounting point; B, C, and D are hinge structures; and F is the wafer mounting center. The specific kinematic equations are as follows:

[0013]

[0014] In the formula, (X) A Y A (X) represents the coordinates of the first motor. E Y E (X) represents the coordinates of the second motor. F Y F (X) represents the coordinates of the third motor. B Y B (X) C Y C (X) D Y D L1, L2, L3, L4, and L5 are the x-coordinates of the hinge positions of the five-bar linkage; L1, L2, L3, L4, and L5 are the lengths of the links in the five-bar linkage; θ1, θ2, and θ5 are the rotation angles of the first, second, and third motors, respectively; and a, b, and c are process variables. BD Let θ be the distance between hinge points B and D. θ3 and θ4 are the angles formed by links BC and CD with the horizontal direction, respectively.

[0015] The error mapping is established specifically by the kinematic model calculating the actual spatial coordinates of any point on the wafer based on the command angles and actual angles of the first motor, the second motor, and the third motor.

[0016] Step S2: Define constraints: Determine the N sample points on the wafer that need to be verified, and the maximum allowable position deviation threshold δ_max for the wafer sample points.

[0017] Step S3: Generate a three-dimensional motor tracking error combination:

[0018] Step S31: Set an initial tracking error search range [-ε] for the first motor and the second motor. 12 , +ε 12Within the tracking error search range, a discretization traversal is performed with a set step size to generate M tracking error pairs (Δθ1, Δθ2) for the first and second motors. Where -ε 12 , +ε 12 Δθ1 and Δθ2 are the lower and upper limits of the tracking error search range for the first and second motors, respectively; Δθ1 and Δθ2 are the actual tracking errors obtained by traversing the first and second motors with a set step size, respectively. Specifically: when the wafer center reaches a certain position, the required rotation angles (θ1, θ2) for the first and second motors are obtained, and the tracking error search range [-ε] is added to (θ1, θ2). 12 , +ε 12 ], so that the actual rotation angle of the motor is within (θ1±-ε 12 ,θ2±+ε 12 ), and in (θ1±-ε 12 ,θ2±+ε 12 Within the interval, a set step size is used to traverse the data to obtain the actual tracking error pair (Δθ1, Δθ2). Step S32: For each (Δθ1, Δθ2), a tracking error search range [-ε3, +ε3] is set for the third motor, and discretization is performed within this tracking error search range with a set step size to generate K tracking errors Δθ3 for the third motor; thus, each group (Δθ1, Δθ2, Δθ3) constitutes a three-dimensional motor tracking error combination; where -ε3 and +ε3 are the lower limit and upper limit of the tracking error search range of the third motor, respectively, and Δθ3 is the actual tracking error obtained by the third motor through the traversal with a set step size;

[0019] Step S4: Global Error Verification and Feasibility Screening:

[0020] For each three-dimensional motor tracking error combination (Δθ1, Δθ2, Δθ3) generated in step S3, substitute it into the kinematic model established in S1;

[0021] Traverse the N predefined sample points on the wafer and calculate the displacement deviation between the actual position and the theoretical target position of each sample point under the three-dimensional motor tracking error combination. If the displacement deviation of all N sample points is less than or equal to δ_max, the three-dimensional motor tracking error combination is marked as a feasible combination and enters the candidate pool; otherwise, it is discarded.

[0022] Step S5: Optimal combination selection: Among all feasible combinations that have entered the candidate pool, calculate the variance of the three tracking error values ​​for each three-dimensional motor tracking error combination; select the feasible combination with the smallest variance as the optimal motor accuracy parameter selection scheme.

[0023] Step S6: Discretize the end motion trajectory with a set step size to generate L displacement target points. Repeat S2-S5 for each displacement target point to obtain the optimal feasible combination for each displacement target point. The tracking difference group with the smallest average value is the optimal motor selection scheme.

[0024] On the other hand, a collaborative optimization selection system for the accuracy parameters of a wafer scanning device motor is provided to implement the aforementioned collaborative optimization selection method for the accuracy parameters of a wafer scanning device motor. The system includes one or more processors and a memory, wherein the memory is used to store instructions, and when the instructions are executed by the one or more processors, the one or more processors execute the collaborative optimization selection method for the accuracy parameters of the wafer scanning device motor.

[0025] Thirdly, this application proposes a computer-readable storage medium storing executable instructions that, when executed, cause a processor to perform the collaborative optimization selection method for the accuracy parameters of the wafer scanning device motor.

[0026] The beneficial effects of adopting the above technical solution are as follows:

[0027] This invention provides a collaborative optimization selection method for the accuracy parameters of a wafer scanning device motor, which has the following beneficial effects:

[0028] 1. Significantly reduce costs. Through scientific calculations, while ensuring that the terminal accuracy requirements are met, the excessively high accuracy requirements for some motors are relaxed, so that the accuracy parameters of the three motors are balanced and matched. This avoids blindly selecting expensive ultra-high precision motors in pursuit of design margins, thereby significantly reducing the procurement cost of the entire motion system.

[0029] 2. The design process is made more scientific and quantifiable. The original experience-based and vague design process is transformed into a model- and algorithm-based, quantifiable, and automated optimization process, which reduces the uncertainty of human factors and improves the reliability and optimality of the design.

[0030] 3. Ensure optimal performance redundancy. The solution found by this method is a combination of redundancy and balance that satisfies accuracy constraints. It provides scientific and optimal accuracy redundancy for factors such as manufacturing tolerances and long-term wear, rather than blind redundancy.

[0031] 4. Improve design efficiency. By automating the traversal, verification, and filtering processes through computer programs, the optimal solution can be obtained quickly, shortening the design cycle. Attached Figure Description

[0032] Figure 1 Schematic diagram of the wafer scanning device according to an embodiment of the present invention;

[0033] Figure 2 A graphical schematic diagram of the tracking differential group of the first motor and the second motor in an embodiment of the present invention;

[0034] Where (a) is a graphical schematic diagram of the first motor tracking differential group, and (b) is a graphical schematic diagram of the second motor tracking differential group;

[0035] Figure 3 A graphical schematic diagram of the tracking differential group of the first motor, the second motor, and the third motor in an embodiment of the present invention. Detailed Implementation

[0036] The specific implementation methods of this application will be further described in detail below with reference to the accompanying drawings and embodiments.

[0037] Example 1:

[0038] On one hand, the present invention provides a method for collaborative optimization and selection of motor accuracy parameters of a wafer scanning device, comprising the following steps:

[0039] Step S1: System modeling and error mapping establishment;

[0040] The system modeling specifically involves: establishing a kinematic model of the wafer scanning device;

[0041] The wafer scanning device includes a first motor, a second motor, and a third motor. The first motor and the second motor work together to drive the planar linkage mechanism to realize the two-dimensional planar motion of the wafer, and the third motor independently drives the wafer to rotate around its normal to control the torsion angle.

[0042] The kinematic model is specifically as follows: Based on the structure of the wafer scanning device, kinematic equations are established; the wafer scanning device structure is a five-bar linkage, where A is the first motor mounting point; E is the second motor mounting point; F is the third motor mounting point; B, C, and D are hinge structures; and F is the wafer mounting center. The specific kinematic equations are as follows:

[0043]

[0044] In the formula, (X) A Y A (X) represents the coordinates of the first motor. E Y E (X) represents the coordinates of the second motor. F Y F (X) represents the coordinates of the third motor. B Y B (X) C Y C (X)D Y D L1, L2, L3, L4, and L5 are the x-coordinates of the hinge positions of the five-bar linkage; L1, L2, L3, L4, and L5 are the lengths of the links in the five-bar linkage; θ1, θ2, and θ5 are the rotation angles of the first, second, and third motors, respectively; and a, b, and c are process variables. BD Let θ be the distance between hinge points B and D. θ3 and θ4 are the angles formed by links BC and CD with the horizontal direction, respectively.

[0045] The error mapping is established by the kinematic model calculating the actual spatial coordinates of any point on the wafer based on the command angles and actual angles (including tracking errors) of the first motor, the second motor, and the third motor.

[0046] In this embodiment, the calculation process is as follows: The position of the wafer center is obtained by the kinematic model. Let the wafer radius be R, and obtain the coordinates of all points on the wafer.

[0047] Step S2: Define constraints: Determine the N sample points on the wafer that need to be verified, and the maximum allowable position deviation threshold δ_max for the wafer sample points.

[0048] Step S3: Generate a three-dimensional motor tracking error combination:

[0049] Step S31: Set an initial tracking error search range of [-ε1, +ε1] for the first motor and [-ε2, +ε2] for the second motor. Within this tracking error search range, perform discretization traversal with a set step size to generate M tracking error pairs (Δθ1, Δθ2) for the first and second motors. Here, -ε1, +ε1, -ε2, and +ε2 are the lower and upper limits of the tracking error search range for the first and second motors, respectively; Δθ1 and Δθ2 are the actual tracking errors obtained by traversing the first and second motors with the set step size, respectively. Specifically: when the wafer center reaches a certain position, the required rotation angles (θ1, θ2) for the first and second motors are obtained. Based on (θ1, θ2), a tracking error search range [ε1, ε2] is added to make the actual rotation angle of the motors within (θ1 ± -ε2). 12 ,θ2±+ε 12 ), and in (θ1±-ε 12 ,θ2±+ε 12 Within the interval, a set step size is used to traverse the data to obtain the actual tracking error pair (Δθ1, Δθ2). In this embodiment, the theoretical two motor rotation angles (θ1, θ2) when the wafer center reaches a certain position are used. Based on these, deviations [-ε1, +ε1] and [-ε2, +ε2] are added, so that the actual motor rotation angle is within (θ1 ± -ε1). 12,θ2±+ε 12 Within the range, traverse with step sizes of ε1 / n and ε2 / n to obtain 2n*2n tracking error pairs (Δθ1, Δθ2). Step S32: For each (Δθ1, Δθ2), a tracking error search range [-ε3, +ε3] is set for the third motor, and discretization is performed within this tracking error search range with a set step size to generate K tracking errors Δθ3 for the third motor; thus, each group (Δθ1, Δθ2, Δθ3) constitutes a three-dimensional motor tracking error combination; where -ε3 and +ε3 are the lower limit and upper limit of the tracking error search range of the third motor, respectively, and Δθ3 is the actual tracking error obtained by the third motor through the traversal with a set step size;

[0050] Specifically, a third motor is added. In this embodiment, the rotation angle of the third motor is required to be θ3. Similarly, a deviation of [-ε3, +ε3] is added, and it is combined with the first and second motors. This allows the actual rotation angles of the three motors to traverse within (θ1±ε1, θ2±ε2, θ3±ε3), resulting in a three-dimensional tracking difference set (Δθ1, Δθ2, Δθ3) with a step size of ε3 / n and a number of 2n*2n*2n.

[0051] Step S4: Global Error Verification and Feasibility Screening:

[0052] For each three-dimensional motor tracking error combination (Δθ1, Δθ2, Δθ3) generated in step S3, substitute it into the kinematic model established in S1;

[0053] Traverse the N predefined sample points on the wafer and calculate the displacement deviation between the actual position and the theoretical target position of each sample point under the three-dimensional motor tracking error combination. If the displacement deviation of all N sample points is less than or equal to δ_max, the three-dimensional motor tracking error combination is marked as a feasible combination and enters the candidate pool; otherwise, it is discarded.

[0054] Step S5: Optimal combination selection: Among all feasible combinations that have entered the candidate pool, calculate the variance of the three tracking error values ​​for each three-dimensional motor tracking error combination; select the feasible combination with the smallest variance as the optimal motor accuracy parameter selection scheme.

[0055] Step S6: Discretize the end motion trajectory with a set step size to generate L displacement target points. Repeat S2-S5 for each displacement target point to obtain the optimal feasible combination for each displacement target point. The tracking difference group with the smallest average value is the optimal motor selection scheme.

[0056] When the actual rotation angle of the motor traverses through 2n*2n*2n combinations, the actual position of point G in the kinematic model supplemented above will also have 2n*2n*2n. This section describes the positional trajectory of point G.

[0057] For each displacement point, there is usually more than one combination of three-dimensional motor tracking errors that can meet the error requirements. For example, when the three-dimensional motor tracking error combinations (e1(1), e2(1), e3(1)) and (e1(2), e2(2), e3(2)) can both satisfy the requirement that the distance from the actual point to the theoretical point is less than δ_max, calculate (e1(1), e2(1), e3(1)) / 3 and (e1(2), e2(2), e3(2)) / 3, and compare the two average values. The tracking error pair containing the smaller average value is the better solution.

[0058] This approach means that the precision requirements of the three motors are most similar, avoiding performance overkill or performance bottlenecks, thereby achieving the lowest total system cost.

[0059] In this embodiment, as shown Figure 1 As shown, L1 is the length of arm 1; L2 is the length of arm 2; L3 and L5 are the two parts of arm 3 respectively; L4 is the length of arm 4; A is the location of the first motor mounting bracket; E is the location of the second motor mounting bracket; B, C, and D are hinge structures; F is the wafer mounting center, and F is also the mounting point of the third motor, used to adjust the wafer torsion angle; θ1 is the rotation angle of the first motor, and θ2 is the rotation angle of the second motor; S1 is the ordinate of point E with point A as the origin; S2 is the abscissa of point E with point A as the origin.

[0060] Example 2:

[0061] Taking the design of a certain type of wafer scanning device as an example, the terminal accuracy requirement is: the positioning error of any point on the wafer ≤ δ_max.

[0062] S1: Based on the three-dimensional model of the device, and using robot kinematics theory, its forward kinematic mathematical model is established, as shown in the appendix. Figure 1 As shown, the input is the angle of the three motors, expressed as the sum of the command value and the error value; the output is the (X, Y) coordinates of the wafer center point and the rotation angle φ.

[0063] S2: Select N sample points uniformly on the wafer and set δ_max.

[0064] S3: Set the tracking error search range for the first and second motors to [-ε1, +ε1] and [-ε2, +ε2] degrees, with step sizes ε1 / n and ε2 / n degrees, generating approximately 2n*2n (Δθ1, Δθ2) pairs. For each pair, the tracking error of the third motor traverses within [-ε3, +ε3] degrees with a step size of ε3 / n, generating 2n Δθ3 pairs. A total of 2n*2n*2n error combinations are generated, such as... Figure 3 As shown.

[0065] S4: Write a program to automatically substitute 2n*2n*2n combinations into the model sequentially and calculate the error of N sample points for each combination. The calculation reveals that approximately a of these combinations satisfy the requirement that the error of all N sample points ≤ δ_max.

[0066] S5: The program calculates the variance of (Δθ1, Δθ2, Δθ3) among these a feasible combinations. Finally, the group with the smallest variance is selected as (e1(1), e2(1), e3(1)).

[0067] S6: Discretize the end motion trajectory with a certain step size to generate Q displacement target points. Repeat S2 - S5 for each displacement target point to obtain Q tracking difference groups of displacement target points. Finally, select the tracking difference group with the smallest average value as (e1(2), e2(2), e3(2)).

[0068] Conclusion: The procurement department is advised to select three types of motors with tracking error accuracy less than e1(2), e2(2) and e3(2) respectively, and apply them to the three axes of the mechanical system. This will ensure the system's final accuracy, and the accuracy levels of the three motors are closest under this combination, resulting in the lowest total cost.

[0069] Example 3:

[0070] This embodiment proposes an electronic device, including: one or more processors, and a memory, wherein the memory is used to store instructions, and when the instructions are executed by the one or more processors, the one or more processors execute the aforementioned collaborative optimization selection method for the accuracy parameters of a wafer scanning device motor.

[0071] The electronic device can be a mobile phone, computer, or tablet computer, etc., and includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, implements the ride-hailing pick-up point dynamic prediction and multi-strategy configuration method as described in the embodiments. It is understood that the electronic device may also include input / output (I / O) interfaces and communication components.

[0072] The processor is used to execute all or part of the steps in the ride-hailing pick-up point dynamic prediction and multi-strategy configuration method as described in the above embodiments. The memory is used to store various types of data, which may include, for example, instructions for any application or method in the electronic device, as well as application-related data.

[0073] The processor can be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic components, and is used to execute the collaborative optimization selection method for the motor accuracy parameters of a wafer scanning device described in the above embodiments.

[0074] Example 4:

[0075] This embodiment proposes a computer-readable storage medium that stores executable instructions. When these instructions are executed, if they are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0076] The computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the collaborative optimization selection method for the motor accuracy parameters of a wafer scanning device as described in various embodiments of this application.

[0077] The aforementioned storage media include: flash memory, hard disk, multimedia card, card-type memory (e.g., SD (Secure Digital Memory Card) or DX (Memory Data Register, MDR) memory, random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, server, APP (Application) application store, and other media capable of storing program verification codes. These media store computer programs, and when executed by a processor, they can implement the various steps of the aforementioned collaborative optimization selection method for the motor accuracy parameters of a wafer scanning device.

[0078] Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a computer program product.

[0079] The various embodiments in this application are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0080] The scope of protection of this application is not limited to the embodiments described above. Obviously, those skilled in the art can make various modifications and variations to this disclosure without departing from the scope and spirit of this disclosure. If such modifications and variations fall within the scope of the methods disclosed herein and their equivalents, then the intent of this disclosure also includes such modifications and variations.

Claims

1. A method for collaborative optimization and selection of motor accuracy parameters in a wafer scanning device, characterized in that, Includes the following steps: Step S1: System modeling and error mapping establishment; The system modeling specifically involves: establishing a kinematic model of the wafer scanning device; The error mapping is established specifically by: the kinematic model calculating the actual spatial coordinates of any point on the wafer based on the command angles and actual angles of the first motor, the second motor, and the third motor; Step S2: Define constraints: Determine the N sample points that need to be verified on the wafer, and the maximum allowable position deviation threshold δ_max for the wafer sample points; Step S3: Generate a three-dimensional motor tracking error combination: Step S4: Global Error Verification and Feasibility Screening: For each three-dimensional motor tracking error combination (Δθ1, Δθ2, Δθ3) generated in step S3, substitute it into the kinematic model established in S1; Step S5: Optimal combination selection: Among all feasible combinations that have entered the candidate pool, calculate the variance of the three tracking error values ​​for each three-dimensional motor tracking error combination; select the feasible combination with the smallest variance as the optimal motor accuracy parameter selection scheme. Step S6: Discretize the end motion trajectory with a set step size to generate L displacement target points. Repeat S2-S5 for each displacement target point to obtain the optimal feasible combination for each displacement target point. The tracking difference group with the smallest average value is the optimal motor selection scheme.

2. The method for collaborative optimization and selection of motor accuracy parameters for a wafer scanning device according to claim 1, characterized in that, The wafer scanning device in step S1 includes a first motor, a second motor, and a third motor. The first motor and the second motor work together to drive the planar linkage mechanism to realize the two-dimensional planar motion of the wafer, and the third motor independently drives the wafer to rotate around its normal to control the torsion angle.

3. The method for collaborative optimization and selection of motor accuracy parameters for a wafer scanning device according to claim 2, characterized in that, The kinematic model described in step S1 is as follows: Based on the structure of the wafer scanning device, kinematic equations are established; the wafer scanning device structure is a five-bar linkage, where A is the first motor mounting point; E is the second motor mounting point; F is the third motor mounting point; B, C, and D are hinge structures; and F is the wafer mounting center. The kinematic equations are as follows: ； In the formula, (X A Y A (X) represents the coordinates of the first motor. E Y E (X) represents the coordinates of the second motor. F Y F (X) represents the coordinates of the third motor. B Y B (X) C Y C (X) D Y D θ1, θ2, θ5 are the x-coordinates of the hinge positions of the five-bar linkage; L1, L2, L3, L4, L5 are the lengths of the links in the five-bar linkage; θ1, θ2, θ5 are the rotation angles of the first motor, the second motor, and the third motor, respectively; a, b, c are process variables; L BD Let θ be the distance between hinge points B and D, and θ3 and θ4 be the angles formed by link BC and link CD with the horizontal direction, respectively.

4. The method for collaborative optimization and selection of motor accuracy parameters for a wafer scanning device according to claim 2, characterized in that, Step S3 specifically includes the following steps: Step S31: Set an initial tracking error search range [-ε] for the first motor and the second motor. 12 , +ε 12 Within the tracking error search range, a discretization traversal is performed with a set step size to generate M tracking error pairs (Δθ1, Δθ2) for the first and second motors; where -ε 12 , +ε 12 Δθ1 and Δθ2 are the lower and upper limits of the tracking error search range for the first and second motors, respectively; Δθ1 and Δθ2 are the actual tracking errors obtained by traversing the first and second motors with a set step size, respectively. Step S32: For each (Δθ1, Δθ2), a tracking error search range [-ε3, +ε3] is set for the third motor, and the tracking error is discretized and traversed within this tracking error search range with a set step size to generate K tracking errors Δθ3 for the third motor; thus, each group (Δθ1, Δθ2, Δθ3) constitutes a three-dimensional motor tracking error combination; where -ε3 and +ε3 are the lower limit and upper limit of the tracking error search range of the third motor, respectively, and Δθ3 is the actual tracking error obtained by traversing the third motor with a set step size.

5. The method for collaborative optimization and selection of motor accuracy parameters for a wafer scanning device according to claim 2, characterized in that, Step S31 specifically involves: when the wafer center reaches a certain position, obtaining the required rotation angles (θ1, θ2) for the first and second motors, and increasing the tracking error search range [-ε] based on (θ1, θ2). 12 , +ε 12 ], so that the actual rotation angle of the motor is within (θ1±-ε 12 ,θ2±+ε 12 ), and in (θ1±-ε 12 ,θ2±+ε 12 The actual tracking error pair (Δθ1, Δθ2) is obtained by traversing the interval with a set step size.

6. The method for collaborative optimization and selection of motor accuracy parameters for a wafer scanning device according to claim 4, characterized in that, Step S4 specifically involves: traversing the N predefined sample points on the wafer and calculating the displacement deviation between the actual position and the theoretical target position of each sample point under the three-dimensional motor tracking error combination; if the displacement deviation of all N sample points is less than or equal to δ_max, then the three-dimensional motor tracking error combination is marked as a feasible combination and enters the candidate pool; otherwise, it is discarded.

7. A collaborative optimization selection system for the accuracy parameters of a wafer scanning device motor, used to implement the collaborative optimization selection method for the accuracy parameters of a wafer scanning device motor as described in claim 1, characterized in that, include: One or more processors, and a memory for storing instructions that, when executed by the one or more processors, cause the one or more processors to perform the collaborative optimization selection method for the accuracy parameters of the wafer scanning device motor.

8. A computer-readable storage medium, implemented based on the collaborative optimization selection system for the motor accuracy parameters of a wafer scanning device as described in claim 7, characterized in that, The device stores executable instructions that, when executed, cause the processor to perform the collaborative optimization selection method for the accuracy parameters of the wafer scanning device motor.

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