Optimization method for wafer transfer mechanical finger structure and wafer transfer mechanical finger structure

Abstract

The present invention discloses a method for optimizing the structure of a wafer transfer robot finger and a wafer transfer robot finger structure, comprising the following steps: establishing a mathematical model for the wafer transfer robot finger structure; S2, setting constraints and value ranges for structural parameters; S3, constructing an objective function; and S4, obtaining the optimal solution of the objective function through an optimization method to obtain optimal structural parameters. By establishing a mathematical model for the wafer transfer robot finger structure and defining a volume function, the present invention can accurately calculate and predict the volume of the structure, thereby optimizing the volume during the design phase, reducing unnecessary material use, and lowering structural weight. The optimized robot finger structure, due to its reduced weight, can reduce inertia during movement, thereby improving transmission efficiency and response speed, reducing energy consumption, reducing inertial sway, and improving transmission accuracy and stability.

Description

Technical Field

[0001] The present invention relates to the technical field of wafer transfer machinery, and in particular to an optimization method for a wafer transfer machinery finger structure and a wafer transfer machinery finger structure. Background Art

[0002] With the rapid development of the semiconductor industry, wafer transfer robot finger structures, as key components in the wafer handling process, have a direct impact on production efficiency and wafer safety. Wafer transfer robot finger structures typically utilize a cantilever beam design, whose motion varies with acceleration. Traditional wafer transfer robot finger structures are not optimized for size and weight, resulting in a heavy structure that affects transfer efficiency and response speed. Inertial vibration also reduces transfer accuracy and stability. Summary of the Invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a method for optimizing the structure of a wafer transfer robot finger.

[0004] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0005] A method for optimizing a wafer transfer robot finger structure includes: S1, establishing a mathematical model for the wafer transfer robot finger structure: , is the volume of the wafer transfer mechanical finger structure, A set of structural parameters of the wafer-transmitting robotic finger structure; is the volume function of the wafer transfer robot finger structure; S2, sets the constraints and value range of the structural parameters; S3, constructs the objective function , To find the minimum function; S4, obtain the optimal solution of the objective function through optimization method to obtain the optimal structural parameters.

[0006] Furthermore,

[0007]

[0008] ; is the distance between the two symmetrical circular holes and the bottom surface, is the distance from the first step to the bottom, is the distance from the second step to the bottom, is the distance from the intersection line of the arc and the rectangle to the bottom surface, is the radius of the hollow arc, is the radius of the great circle, is the thickness of the mechanical finger, For 4 equal circular holes, The ventilation holes are high. is the outer diameter of the vent hole, is the inner diameter of the vent hole, is the distance between the centers of the two circular holes, The distance to the narrow steps above. is the distance of the widest step below, is the cross-sectional width of the ventilation channel, It is the angle between the center of the two upper ventilation holes and the center of the arc.

[0009] Furthermore, the constraints are: ; ； ； ； ； ； ； ； ； .

[0010] Furthermore, step S4 specifically includes: S41, establishing an initial population ,in is the population size, It is the G generation The vector of individuals, ; It has D-dimensional components, each of which corresponds to a type of structural parameter. = , D is the number of types of structural parameters; G is the number of iterations, the initial value of G is 0, the initial population The number of iterations is 0; the vector is composed of all structural parameters of the structural parameter set, and the structural parameters satisfy the constraints and the value range; S42, select the best individual from the latest generation population ; All individuals of the latest generation population All learn from the optimal individual according to the learning rule to obtain the learned individual ;Compare and The fitness value of the new generation population is the population with low fitness value. Individuals in ; S43, all individuals in the new generation population learn from each other according to the learning rule, and obtain the learned individuals ;Compare and The fitness value of the new generation of individuals with low fitness value Individual ; S44, randomly optimize the proportion of individuals in the new generation population to obtain new individuals ,Compare and The fitness value of the new generation of individuals with low fitness value Individual ;renew value, , and judge whether the G value is less than the iteration threshold : If yes, return to step S42; if not, stop the iteration and select the individual with the highest fitness value in the latest population as the optimal individual. The structural parameters corresponding to the optimal individual are the optimal structural parameters.

[0011] Furthermore, before returning to step S42, the method further includes performing adaptive boundary update: setting the upper boundary vector = and ; and Update according to the following formula: ; .

[0012] Furthermore, the new individuals are obtained by randomly optimizing the proportion of individuals in the new generation population. , specifically including: according to the following formula to obtain ： ； ； ； and for The best and worst individuals in for The maximum difference between two individuals in ; and for The best and worst individuals in for The maximum difference between two individuals in ; is a random operator vector.

[0013] Furthermore, ; ; k = ; ; is the iteration angle, and m is the iteration index.

[0014] Furthermore, the learning rule of step S42 is: ;

[0015] is the mean vector of all individuals in the population, , is the random coefficient; is the learning weight value.

[0016] Furthermore, the adaptation value is the structural parameter corresponding to the individual substituted into the volume function Obtained function value.

[0017] The present invention also provides a wafer transfer robot finger structure, wherein the structural parameters of the wafer transfer robot finger structure are optimal structural parameters obtained by the optimization method of the wafer transfer robot finger structure.

[0018] The present invention has the following beneficial effects:

[0019] By establishing a mathematical model of the wafer transfer robot finger structure and defining a volume function, the structure's volume can be accurately calculated and predicted, allowing for volume optimization during the design phase, reducing unnecessary material usage and lowering structural weight. The optimized robot finger structure, due to its reduced weight, can reduce inertia during motion, thereby improving transmission efficiency and response speed, and reducing energy consumption. By setting constraints and value ranges for structural parameters, it is possible to ensure that while reducing weight, the stiffness and strength of the structure are not sacrificed, thereby enhancing stability during transmission and reducing wafer damage caused by inertial sway. The objective function in the optimization method minimizes volume while also taking into account structural stability and precision requirements. By solving for the optimal structural parameters, the finger structure is optimized, reducing the mass and inertia of the wafer transfer robot finger structure, effectively improving transmission efficiency and response speed, reducing inertial sway, and improving transmission accuracy and stability.

[0020] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0022] Figure 1 It is a schematic diagram of the overall process of the present invention;

[0023] Figure 2 It is a partial flow diagram of the present invention;

[0024] Figure 3 It is the trend diagram of iteration angle;

[0025] Figure 4 It is a structural diagram of the wafer transfer mechanical finger structure;

[0026] Figure 5 This is a perspective view of the internal structure of the wafer transfer mechanical finger structure;

[0027] Figure 6 This is a schematic diagram of the size parameters of the wafer transfer robot finger structure;

[0028] Figure 7 This is a convergence trend diagram of the fitness value of an embodiment of the present invention. DETAILED DESCRIPTION

[0029] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0031] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0032] In addition, the descriptions of "first", "second", etc. in the present invention are for descriptive purposes only and should not be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but this must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0033] Please refer to Figure 1 In a preferred embodiment of the present invention, a method for optimizing the structure of a wafer transfer robot finger includes steps S1, S2, S3 and S4.

[0034] S1, establish a mathematical model for the wafer transfer robot finger structure: , is the volume of the wafer transfer mechanical finger structure, A set of structural parameters of the wafer-transmitting mechanical finger structure; is the volume function of the wafer transfer robot finger structure.

[0035] S2, set the constraints and value range of structural parameters.

[0036] S3, build the target function , To find the minimum function, the optimization goal is to make The smaller the value, the better. When determining the value, it is necessary to take the trend of smaller values ​​to set the target for subsequent optimization direction and judgment. That is, when there are 3 values, substitute the three values ​​into , the one with the smallest value is the best choice at present.

[0037] S4, obtain the optimal solution of the objective function through optimization method to obtain the optimal structural parameters.

[0038] The present invention provides a method for optimizing the structure of a wafer transfer robot finger. By establishing a mathematical model of the wafer transfer robot finger structure and defining a volume function, the volume of the structure can be accurately calculated and predicted, thereby optimizing the volume during the design phase, reducing unnecessary material use, and lowering structural weight. The optimized robot finger structure, due to its reduced weight, can reduce inertia during movement, thereby improving transmission efficiency and response speed, and reducing energy consumption. By setting constraints and value ranges for structural parameters, it can ensure that while reducing weight, the rigidity and strength of the structure are not sacrificed, thereby enhancing stability during the transmission process and reducing wafer damage caused by inertial sway. The objective function in the optimization method minimizes the volume as the optimization goal, while taking into account the stability and precision requirements of the structure. By solving the optimal structural parameters, the finger structure is optimized, the mass and motion inertia of the wafer transfer robot finger structure are reduced, effectively improving transmission efficiency and response speed, reducing inertial sway, and improving transmission accuracy and stability.

[0039] Reference Figure 4 and Figure 5The wafer transfer robot finger structure includes a rectangular section 100 at the bottom. The upper end of the rectangular section 100 is connected to an upwardly narrowing trapezoidal section 200. The upper end of the trapezoidal section 200 is connected to a long strip section 300. Attached to the upper ends of the long strip section 300 are symmetrically arranged circular fingers 400. The circular fingers 400 are connected at one end and disconnected at the other end, forming a notch 410. The circular fingers 400 on both sides have the same center and inner and outer diameters. Each circular finger 400 is provided with two downwardly concave annular ventilation holes 500. The ventilation holes 500 are symmetrically arranged. A circular hole 110 is formed on one side of the rectangular section 100. A ventilation channel 600 is provided within the wafer transfer robot finger structure. One end of the ventilation channel 600 connects to the circular hole 110 and extends to the trapezoidal section 200, the long strip section 300, and the circular fingers 400 in sequence, ultimately connecting to the ventilation hole 500. One end of the arc finger 400 away from the long strip section 300 is a semicircular arc, and the center of the semicircular arc is concentric with the center of the adjacent ventilation hole 500.

[0040] In a specific embodiment of the present invention,

[0041]

[0042] It is derived from the following formula:

[0043] ;

[0044] ;

[0045] ;

[0046] .

[0047] Indicates the approximate total volume of the wafer transfer robot fingers, Represents the volume of 4 equal circular ventilation holes 500, Indicates the approximate volume of the ventilation channel 600.

[0048] The set of structural parameters is ;reference Figure 6 , is the distance between the two symmetrical circular holes and the bottom surface, that is, the distance from the circular hole 110 to the bottom of the rectangular segment 100. It is the distance from the first step to the bottom, that is, the distance from the junction of the rectangular section 100 and the trapezoidal section 200 to the bottom of the rectangular section 100 . It is the distance from the second step to the bottom, that is, the distance from the junction of the trapezoidal segment 200 and the elongated segment 300 to the bottom of the rectangular segment 100 . It is the distance from the intersection line of the arc and the rectangle to the bottom surface, that is, the distance from the junction of the upper end of the long segment 300 and the arc finger 400 to the bottom of the rectangular segment 100. is the distance from the center of the arc to the bottom surface, that is, the distance from the center of the arc finger 400 to the bottom of the rectangular segment 100. is the radius of the hollow arc, that is, the radius of the inner circle of the arc finger 400. is the radius of the distribution circle where the centers of the four ventilation holes are 500, is the radius of the large arc, that is, the radius of the outer circle of the arc finger 400. is the thickness of the mechanical finger, The depth of the four equal ventilation holes is 500. is the height (thickness) of the ventilation channel 600, The outer diameter of the vent hole is 500. The inner diameter of the vent hole is 500. The distance between the centers of the two circular holes is 110. The distance of the narrow step above (the width of the long section is 300), is the width of the lower step (width of the rectangular segment 100), is the cross-sectional width of the ventilation channel 600, is the angle between the center of the two upper ventilation holes 110 and the center of the arc, such as Figure 7 As shown, the angle between the center of the two ventilation holes 110 below and the center of the arc is also .

[0049] In a specific embodiment of the present invention, the constraint conditions are:

[0050] ; ； ； ； ； ； ； ； ； .

[0051] The value range is: ; ； ； ； ； ； ； ； ； ； ； ； ； ； ； .

[0052] Reference Figure 2 In a specific embodiment of the present invention, step S4 specifically includes steps S41, S42, S43, and S44.

[0053] S41, establish an initial population ,in is the population size, It is the G generation individual vectors, ; It has D-dimensional components, each of which corresponds to a type of structural parameter. = , D is the number of types of structural parameters, is a vector The component of the kth dimension, k= G is the number of iterations, the initial value of G is 0, and the initial population The number of iterations is 0; the components of all dimensions of the vector correspond to all structural parameters of the structural parameter set, and the structural parameters satisfy the constraint conditions and the value range.

[0054] In an embodiment of the present invention, the structural parameter set is , there are 16 variable parameters, so the D value is 16. , , , Corresponding respectively , , , , , , , , , , , , , , , A selection of values ​​for these 16 parameters.

[0055] S42, from the latest generation population Select the best individual ; The optimal individual is in the population, and the corresponding structural parameters of the individual are substituted into the volume function The function value obtained is the minimum. All individuals of the latest generation population All learn from the optimal individual according to the learning rule to obtain the learned individual ;Compare and The fitness value of the new generation population is the population with low fitness value. Individuals in When the first execution is executed, the latest generation population is In a specific embodiment of the present invention, the adaptation value is the structural parameter corresponding to the individual vector substituted into the volume function The updated logic of step S42 can be implemented by the following formula:

[0056] When hour, ,otherwise . is the individual vector The function value of the volume function corresponding to the corresponding structural parameters.

[0057] In a specific embodiment of the present invention, the learning rule of step S42 is:

[0058] ; It is a population The average vector of all individuals in , , is the random coefficient; is the learning weight value. , It means taking any value between 0 and 1. () indicates that the value is rounded to the nearest integer. It can be understood that the value of any dimension in the vector needs to be within the value range. If it exceeds the value range, the boundary value outside the corresponding boundary is taken. ,but The value range is -25, if If the calculation exceeds 25, then The value is 25.

[0059] S43, all individuals in the new generation population learn from each other according to the learning rule, and obtain the learned individuals ;Compare and The fitness value of the new generation of individuals with low fitness value Individual Steps S41, S42 and S43 are improved steps based on the TLBO algorithm.

[0060] Specifically, the learning rule of step S43 is:

[0061] .

[0062] , is a random value. and Are two from the current population Two individual vectors randomly selected from , and When hour, ,otherwise .

[0063] In step S43, individual updates can be performed according to the following formula:

[0064] When hour, ,otherwise .

[0065] S44, randomly optimize the proportion of individuals in the new generation population to obtain new individuals ,Compare and The fitness value of the new generation of individuals with low fitness value Individual ;renew value, , here the G value is updated, that is, the three iterations of steps S42, S43 and S44 are counted into the number of iterations, the number of iterations is updated to obtain the latest number of iterations, and it is determined whether the G value is less than the iteration threshold : If so, return to step S42, indicating that the number of iterations is not enough and further iterative updates are required.

[0066] If not, the iteration is stopped, indicating that the number of iterations has reached the standard, and the individual with the lowest fitness value (that is, the lowest volume function value) in the latest population can be selected as the optimal individual, and the structural parameters corresponding to the optimal individual are the optimal structural parameters.

[0067] In a specific embodiment of the present invention, in step S44, before returning to step S42, it further includes performing adaptive boundary update: setting the upper boundary vector = and the lower boundary vector . is the value of the first dimension of the upper boundary vector, is the value of the i-th dimension of the upper boundary vector, i . is the value of the first dimension of the lower boundary vector, is the value of the i-th dimension of the lower boundary vector, i The values ​​of each dimension in the population individual vector need to be within the value range of the upper boundary vector and the lower boundary vector corresponding to the dimension, such as If the value exceeds this range during subsequent calculation and update, the boundary value of the exceeded boundary can be taken. Each component of all individual vectors satisfies the following formula: , express The component of the jth dimension, .

[0068] The initial value ranges of the parameters of the finger structure are as follows: ; ； ； ； ； ； ； ； ； ； ； ； ； ； ； . That is, it can be understood as, The initial value of , The initial value is .

[0069] Of course, when step S44 is reached, three iterations have been performed. and It needs to be updated according to the following formula:

[0070] ;

[0071] .

[0072] for The component of the jth dimension of ; for The j-th dimension of the component. That is, after obtaining the latest generation population in step S44, the maximum and minimum values ​​of each dimension corresponding to the individuals of the new population are used as the subsequent value range of the dimension. The value of each dimension of the individual is the value of the structural parameter of the corresponding species. That is, the individuals of the latest generation of the population The value of the component of the jth dimension in , and so on. is the function that takes the maximum value, It is a function to remove the minimum value, so that the value range can be gradually converged to improve the optimization efficiency.

[0073] In a specific embodiment of the present invention, the new individuals are obtained by randomly optimizing the proportion of individuals in the new generation population. , specifically including:

[0074] According to the following formula ：

[0075] ；

[0076] ；

[0077] ；

[0078] and for The best individual vector and the worst individual vector in the equation are the best individual vector and the worst individual vector, which represent the corresponding volume function. The function value of is the smallest and largest in the population.

[0079] for The maximum difference vector between two individuals in ;

[0080] and for The best individual vector and the worst individual vector in , for The maximum difference vector between two individuals in . is the random operator vector, which has the same number of dimensions as the individual vector, 16 dimensions.

[0081] In a specific embodiment of the present invention, ; for The component of the kth dimension, k= .

[0082] ; k = ;

[0083] ; is the iteration angle, and m is the iteration index.

[0084] The operator designed In the following In the process of gradual increase, at the beginning and end stages, along with This improves the algorithm's ability to explore. On the other hand, in the middle stage, along with The increase of decreases rapidly, which can improve the development ability of the algorithm. The value can make the algorithm achieve a balance between exploration and development capabilities. Figure 3 As shown, when m=8, The changing trend of Figure 7 As shown, the horizontal axis is the number of iterations, and the vertical axis is the volume function corresponding to the optimal individual obtained The function value is log10, that is, log10 ( ), before joining After the operator is applied, the fitness value initially decreases rapidly as the number of iterations increases, indicating that this method can achieve better values ​​in a shorter time. Furthermore, it can also achieve better values ​​with a relatively large number of iterations. Therefore, this method can achieve convergence when solving the problem, demonstrating its effectiveness in practical applications.

[0085] The present invention also provides a wafer transfer robot finger structure, wherein the structural parameters of the wafer transfer robot finger structure are optimal structural parameters obtained by an optimization method of the wafer transfer robot finger structure.

[0086] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the structure of wafer transfer mechanical fingers, characterized in that: The steps include: S1, establish a mathematical model for the wafer transfer robot finger structure: , is the volume of the wafer transfer mechanical finger structure, A set of structural parameters of the wafer-transmitting mechanical finger structure; is the volume function of the wafer transfer robot finger structure; S2, setting the constraints and value range of structural parameters; S3, build the target function , To find the minimum function; S4, obtaining the optimal solution of the objective function by an optimization method to obtain the optimal structural parameters; Step S4 specifically includes: S41, establish an initial population ,in is the population size, It is the G generation The vector of individuals, ; It has D-dimensional components, each of which corresponds to a type of structural parameter. = , D is the number of types of structural parameters; G is the number of iterations, the initial value of G is 0, the initial population The number of iterations is 0; S42, select the best individual from the latest generation population ; All individuals of the latest generation population All learn from the optimal individual according to the learning rule to obtain the learned individual ; Compare and The fitness value of the new generation population is the population with low fitness value. Individuals in ; S43, all individuals in the new generation population learn from each other according to the learning rule, and obtain the learned individuals ;Compare and The fitness value of the new generation of individuals with low fitness value Individual ; S44, randomly optimize the proportion of individuals in the new generation population to obtain new individuals ,Compare and The fitness value of the new generation of individuals with low fitness value Individual ;renew value, , and judge whether the G value is less than the iteration threshold : If yes, return to step S42; if not, stop the iteration and select the individual with the highest fitness value in the latest population as the optimal individual. The structural parameters corresponding to the optimal individual are the optimal structural parameters.

2. The method for optimizing the wafer transfer robot finger structure according to claim 1, characterized in that: ； is the distance between the two symmetrical circular holes and the bottom surface, is the distance from the first step to the bottom, is the distance from the second step to the bottom, is the distance from the intersection line of the arc and the rectangle to the bottom surface, is the radius of the hollow arc, is the radius of the great circle, is the thickness of the mechanical finger, For 4 equal circular holes, The ventilation holes are high. is the outer diameter of the vent hole, is the inner diameter of the vent hole, is the distance between the centers of the two circular holes, The distance to the narrow steps above. is the distance of the widest step below, is the cross-sectional width of the ventilation channel, It is the angle between the center of the two upper ventilation holes and the center of the arc.

3. The method for optimizing the wafer transfer robot finger structure according to claim 2, characterized in that: The constraints are: ； ； ； ； ； ； ； ； ； 。 4. The method for optimizing the wafer transfer robot finger structure according to claim 1, characterized in that: Before returning to step S42, the method further includes performing adaptive boundary update: Let the upper boundary vector = and the lower boundary vector ; and Update according to the following formula: ； 。 5. The method for optimizing the wafer transfer robot finger structure according to claim 1, characterized in that: The new individuals of the new generation population are randomly optimized to obtain new individuals. , specifically including: According to the following formula ： ； ； ； and for The best and worst individuals in for The maximum difference between two individuals in ; and for The best and worst individuals in for The maximum difference between two individuals in ; is a random operator vector.

6. The method for optimizing the wafer transfer robot finger structure according to claim 5, characterized in that: ； ；k= ； ; is the iteration angle, and m is the iteration index.

7. The method for optimizing the wafer transfer robot finger structure according to claim 1, characterized in that: The learning rule of step S42 is: ; is the mean vector of all individuals in the population, , is the random coefficient; is the learning weight value.

8. The method for optimizing the wafer transfer robot finger structure according to claim 1, characterized in that: The fitness value is the structural parameter corresponding to the individual substituted into the volume function Obtained function value.

9. A wafer transfer mechanical finger structure, characterized in that: The structural parameters of the wafer transfer robot finger structure are the optimal structural parameters obtained by the optimization method of the wafer transfer robot finger structure according to any one of claims 1 to 8.

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