Method and device for improving dispatching efficiency and electronic equipment

Abstract

The invention provides a method and device for improving dispatching efficiency and electronic equipment. The method comprises the steps that at least two influence factors influencing the dispatching efficiency of ion implantation equipment are acquired; determining a relative importance degree value between any two influence factors in the at least two influence factors; according to the relative importance degree value, determining one of the at least two influence factors with the maximum weight as a target influence factor; performing clustering analysis on the at least two sub-influence factors according to the conversion duration and the conversion frequency between any two sub-influence factors in the target influence factors, and dividing the at least two sub-influence factors into at least one clustering group; and according to the at least one clustering group and the to-be-executed ion implantation process, dispatching the ion implantation equipment, so that the ion implantation equipment carries out ion implantation according to the to-be-executed ion implantation process. The method for improving the dispatching efficiency provided by the invention has the effects of reducing the efficiency loss of the ion implantation equipment and improving the ion implantation efficiency.

Description

Technical Field

[0001] This application relates to the field of semiconductor technology, and in particular, to a method, device, and electronic device for improving dispatching efficiency. Background Art

[0002] To meet the needs of technological development, especially the development of artificial intelligence, its core driving force (chips) has gradually become diversified, and the process requirements for chips have become increasingly complex. For the ion implantation process of chips in semiconductor technology, as the complexity of process requirements gradually increases, the implantation elements, implantation frequencies, ion beam energies, etc. included in the ion implantation process will change accordingly.

[0003] In the related art, after receiving the next ion implantation process to be executed for dispatching, the ion implantation equipment is converted according to the implantation factors, implantation frequencies, ion implantation beam energy requirements, etc. of the next ion implantation process to be executed. The ion implantation equipment continuously receives the next ion implantation process to be executed and repeats the conversion of the ion implantation process. Since the switching methods between different influencing factors and between different sub-influencing factors of the same influencing factor are different, if dispatching is carried out without distinction based on the wafer in process (WIP) or adjusted based on manual experience, the switching time required to meet different switching methods may be relatively long, thereby reducing the ion implantation efficiency. Summary of the Invention

[0004] This application provides a method, device, and electronic device for improving dispatching efficiency, which can solve the problem of low ion implantation efficiency in the prior art.

[0005] In a first aspect, this application provides a method for improving dispatching efficiency, including: obtaining at least two influencing factors that affect the dispatching efficiency of the ion implantation equipment, where the at least two influencing factors are obtained from the process parameters of multiple ion implantation processes historically executed by the ion implantation equipment; determining the relative importance degree value between any two of the at least two influencing factors; according to the relative importance degree value, determining the one with the largest weight among the at least two influencing factors as the target influencing factor, where the target influencing factor includes at least two sub-influencing factors, and the sub-influencing factors correspond to the parameter values of the target influencing factor; performing cluster analysis on the at least two sub-influencing factors according to the conversion duration and conversion times between any two of the sub-influencing factors in the target influencing factor, and dividing the at least two sub-influencing factors into at least one cluster group, where the conversion time between any two sub-influencing factors in each cluster group is less than a preset duration; dispatching the ion implantation equipment according to the at least one cluster group and the ion implantation process to be executed, so that the ion implantation equipment performs ion implantation according to the ion implantation process to be executed.

[0006] In some embodiments of the present application, the obtaining of at least two influencing factors affecting the dispatching efficiency of the ion implantation equipment includes: obtaining the at least two influencing factors from the process parameters of a plurality of ion implantation processes executed historically according to a preset naming rule.

[0007] In some embodiments of the present application, the determining, according to the relative importance degree value, the one with the largest weight among the at least two influencing factors as the target influencing factor includes: constructing a judgment matrix according to the relative importance degree value, where the matrix elements in the judgment matrix represent the relative importance degree values of any two of the influencing factors; performing normalization processing on the judgment matrix; based on the normalized judgment matrix, obtaining a weight vector for each of the at least two influencing factors, where each vector element in the weight vector represents the relative weight of each influencing factor; and determining the influencing factor corresponding to the largest vector element in the weight vector as the target influencing factor.

[0008] In some embodiments of the present application, the obtaining, based on the normalized judgment matrix, a weight vector for each of the at least two influencing factors includes: adding up the matrix elements in each row of the normalized judgment matrix to generate a corresponding initial weight vector; and performing normalization processing on the initial weight vector to obtain a weight vector for each of the at least two influencing factors.

[0009] In some embodiments of the present application, after obtaining the weight vector for each of the at least two influencing factors based on the normalized judgment matrix, the method further includes: performing a consistency test on the relative weight of each influencing factor; and if the consistency verification passes, determining the influencing factor corresponding to the largest element value in the weight vector as the target influencing factor.

[0010] In some embodiments of the present application, the performing a consistency test on the relative weight of each influencing factor includes: obtaining the largest eigenvalue of the judgment matrix; determining a consistency test index for the relative weight according to the largest eigenvalue; and when the consistency test index meets a preset threshold, determining that the relative weight of each influencing factor meets the consistency requirement.

[0011] In some embodiments of the present application, clustering analysis is performed on the at least two sub-influence factors according to the conversion duration and the conversion times between any two of the sub-influence factors among the target influence factors, and the at least two sub-influence factors are divided into at least one clustering group, including: calculating the average distance between any two of the sub-influence factors according to the conversion duration and the conversion times; generating a hierarchical clustering dendrogram according to the average distance; and based on the hierarchical clustering dendrogram, clustering the at least two sub-influence factors according to a preset segmentation distance, and dividing the at least two sub-influence factors into at least one clustering group.

[0012] In some embodiments of the present application, the process parameters include implanted elements, implantation angles, implantation doses, implantation energies, and contamination levels.

[0013] In a second aspect, a device for improving dispatching efficiency is provided, including: an acquisition module, a first determination module, a second determination module, a grouping module, and a dispatching module; wherein, the acquisition module is configured to acquire at least two influence factors affecting the dispatching efficiency of the ion implantation equipment, and the at least two influence factors are obtained from the process parameters of a plurality of ion implantation processes historically executed by the ion implantation equipment; the first determination module is configured to determine the relative importance degree value between any two of the at least two influence factors; the second determination module is configured to determine, according to the relative importance degree value, the one with the largest weight among the at least two influence factors as the target influence factor, and the target influence factor includes at least two sub-influence factors, and the sub-influence factors correspond to the parameter values of the target influence factor; the classification module is configured to perform clustering analysis on the at least two sub-influence factors according to the conversion duration and the conversion times between any two of the sub-influence factors among the target influence factors, and divide the at least two sub-influence factors into at least one clustering group, and the conversion time between any two sub-influence factors in each clustering group is less than a preset duration; the dispatching module is configured to dispatch the ion implantation equipment according to the at least one clustering group and the ion implantation process to be executed, so that the ion implantation equipment performs ion implantation according to the ion implantation process to be executed.

[0014] In some embodiments of the present application, the acquisition module is configured to: acquire the at least two influence factors from the process parameters of a plurality of ion implantation processes historically executed according to a preset naming rule.

[0015] In some embodiments, the second determination module is configured to: construct a judgment matrix according to the relative importance value, where the matrix elements in the judgment matrix represent the relative importance values of any two of the influencing factors; perform normalization processing on the judgment matrix; based on the normalized judgment matrix, obtain a weight vector for each of the at least two influencing factors, where each vector element in the weight vector represents the relative weight of each influencing factor; and determine the influencing factor corresponding to the maximum vector element in the weight vector as the target influencing factor.

[0016] In some embodiments, the second determination module is configured to: add up the matrix elements in each row of the normalized judgment matrix to generate a corresponding initial weight vector; perform normalization processing on the initial weight vector to obtain a weight vector for each of the at least two influencing factors.

[0017] In some embodiments, the second determination module is configured to: after obtaining the weight vector for each of the at least two influencing factors based on the normalized judgment matrix, perform a consistency test on the relative weight of each influencing factor; if the consistency verification passes, determine the influencing factor corresponding to the maximum element value in the weight vector as the target influencing factor.

[0018] In some embodiments, the second determination module is configured to: obtain the maximum eigenvalue of the judgment matrix; determine a consistency test index for the relative weight according to the maximum eigenvalue; and when the consistency test index meets a preset threshold, determine that the relative weight of each influencing factor meets the consistency requirement.

[0019] In some embodiments, the grouping module is configured to: calculate the average distance between any two of the sub-influencing factors according to the conversion duration and the conversion times; generate a hierarchical clustering dendrogram according to the average distance; and based on the hierarchical clustering dendrogram, cluster the at least two sub-influencing factors according to a preset segmentation distance, and divide the at least two sub-influencing factors into at least one clustering group.

[0020] In some embodiments, in the above device, the process parameters include an implanting element, an implanting angle, an implanting dose, an implanting energy, and a contamination degree.

[0021] In a third aspect, an electronic device is provided, including: a processor and a memory, where the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method as described in the first aspect or its various implementation manners.

[0022] Through the technical solution provided by this application, first, at least two influencing factors affecting the dispatching efficiency of the ion implantation equipment are obtained, then the relative importance degree value between any two influencing factors is determined, and next, according to the relative importance degree value, the one with the largest weight among the at least two influencing factors is determined as the target influencing factor; next, clustering analysis is performed on at least two sub-influencing factors, and the at least two sub-influencing factors are divided into at least one clustering group. Finally, on the premise of meeting the actual ion implantation situation, dispatching can be performed for the ion implantation equipment according to the clustering group and the ion implantation process to be executed, so that the ion implantation equipment performs ion implantation according to the ion implantation process to be executed. In this way, by adjusting the execution order of the ion implantation process to be executed during the dispatching process, that is, adjusting the switching order of different sub-influencing factors, optimizing the switching method between different influencing factors of the ion implantation equipment, reducing the switching time required to meet different switching methods, reducing the efficiency loss of the ion implantation equipment, and improving the ion implantation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0024] Figure 1 is a flowchart of method 100 for improving dispatching efficiency shown in some embodiments of the present application;

[0025] Figure 2 is a flowchart of method 200 for improving dispatching efficiency shown in some embodiments of the present application;

[0026] Figure 3 is a flowchart of method 300 for improving dispatching efficiency shown in some embodiments of the present application;

[0027] Figure 4 is a flowchart of method 400 for improving dispatching efficiency shown in some embodiments of the present application;

[0028] Figure 5 is a schematic diagram of the first hierarchical clustering dendrogram shown in some embodiments of the present application;

[0029] Figure 6 is a schematic diagram of the second hierarchical clustering dendrogram shown in some embodiments of the present application;

[0030] Figure 7 is a schematic diagram of the third hierarchical clustering dendrogram shown in some embodiments of the present application;

[0031] Figure 8 is a block diagram of a device 800 for improving dispatching efficiency according to some embodiments of the present application;

[0032] Figure 9 is a schematic block diagram of an electronic device 900 according to some embodiments of the present application. Detailed implementation manners

[0033] Next, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0034] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein.

[0035] In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0036] Ion implantation equipment is used to perform precise process control according to the ion implantation process through process parameters such as implanted elements, implantation angles, implantation doses, implantation energies, and contamination levels. The ion implantation equipment can obtain the required ions from an ion source, accelerate them to obtain an ion beam current with an energy of several hundred to several thousand electron volts, perform ion implantation on semiconductor materials, large-scale integrated circuits, or devices, and dope the area near the surface to change the carrier concentration and conduction type.

[0037] In the related art, multiple elements may need to be implanted, multiple implantation frequencies may be adopted, and multiple ion beam energies may be used during the ion implantation process. Therefore, it is necessary to switch implanted elements, switch implantation frequencies, and switch ion beam energies during the actual process. Since the switching methods between different influencing factors and between different sub-influencing factors of the same influencing factor are different, if dispatching is performed without discrimination based on WIP or adjusted based on manual experience, the switching time required to meet different switching methods may be relatively long, thereby reducing the ion implantation efficiency.

[0038] To solve the above technical problems, the technical concept of the present invention is that, firstly, by means of process parameters such as implanted elements, implantation energy, and implantation angle involved in the ion implantation process, factors affecting the dispatching efficiency of the ion implantation equipment are obtained; then, target influencing factors among the influencing factors are determined, where the target influencing factor is the influencing factor with the largest weight affecting the dispatching efficiency; furthermore, hierarchical clustering is performed according to the sub-influencing factors in the target influencing factors to obtain grouping suggestions (at least one clustering group); finally, according to at least one clustering group and the ion implantation process to be executed, dispatching is carried out for the ion implantation equipment. In this way, by adjusting the execution order of the ion implantation processes to be executed during the dispatching process, that is, adjusting the switching order of different sub-influencing factors, the switching method between different influencing factors of the ion implantation equipment is optimized, the switching time required to meet different switching methods is reduced, the efficiency loss of the ion implantation equipment is reduced, and the ion implantation efficiency is improved.

[0039] It can be understood that for the specific application scenarios and requirements of the present application, the purpose is to enable those skilled in the art to manufacture and use the content of the present application.

[0040] In practical applications, the method for improving dispatching efficiency provided by the embodiments of the present application is applied to an ion implantation equipment. Below, with reference to the accompanying drawings, taking the execution entity as a device for improving dispatching efficiency as an example, the method for improving dispatching efficiency provided by the embodiments of the present application will be exemplarily described.

[0041] As Figure 1 shown, the method 100 for improving dispatching efficiency provided by the embodiments of the present application may include steps S110 to S150.

[0042] Step S110, obtain at least two influencing factors affecting the dispatching efficiency of the ion implantation equipment.

[0043] Among them, at least two influencing factors are obtained from the process parameters of multiple ion implantation processes historically executed by the ion implantation equipment. The process parameters may include implanted elements, implantation angle, implantation dose, implantation energy, and degree of contamination. At least two influencing factors are any two or more of the process parameters.

[0044] In the embodiments of the present application, multiple ion implantation processes historically executed by the ion implantation equipment can be obtained from the historical data of the ion implantation equipment, and then the influencing factors are extracted from the multiple ion implantation processes. Among them, the number of influencing factors is at least 2. The multiple ion implantation processes historically executed can be stored in the log of the ion implantation equipment. Since the historical data includes a large data base, in this way, the accuracy of the clustering result can be improved.

[0045] It can be understood that if there is only one influencing factor, then it can be determined that this influencing factor is the target influencing factor, and then step S140 is executed to obtain at least one clustering group, and finally step S150 is executed to dispatch work for the ion implantation equipment. However, in the actual use of ion implantation equipment, the number of influencing factors involved is usually greater than 1.

[0046] In some implementable ways, the specific implementation of step S110 can be: according to the preset naming rules, obtain at least two influencing factors from the process parameters of multiple ion implantation processes executed historically.

[0047] In the embodiments of the present application, the ion implantation process may include various process steps, process parameters, and conditions during the process of process processing. Exemplarily, the first process parameter is named: A0 000B 000C 0D00. According to the preset naming rules, the influencing factors among them include implanting element A, implanting energy B, implanting dose C, and implanting angle D. Exemplarily, the second process parameter is named A0 000B 000C 00E0. According to the preset naming rules, the influencing factors among them include implanting element A, implanting energy B, implanting dose C, and contamination degree E. Thus, at least two influencing factors can be obtained from the process parameters of multiple ion implantation processes executed historically by the ion implantation equipment according to the preset naming rules.

[0048] In this way, since the ion implantation process directly indicates how the ion implantation equipment performs ion implantation, the accuracy of the obtained influencing factors is relatively high. And obtaining the influencing factors from the process parameters of the historically executed ion implantation processes can further improve the accuracy of the obtained influencing factors. Furthermore, it improves the accuracy of clustering grouping, reduces the switching time required to meet different switching methods, and improves the ion implantation efficiency.

[0049] Step S120, determine the relative importance degree value between any two of the at least two influencing factors.

[0050] In the embodiments of the present application, the pairwise relative importance of at least two influencing factors can be judged by experience, and can also be judged by pairwise relative importance of at least two influencing factors based on the "Delphi method" on the basis of experience. Among them, the relative importance ratio scale is shown in Table 1, and the relative importance degree value between any two influencing factors can be any value from 1 to 9.

[0051] Table 1 Ratio scale of relative importance

[0052]

[0053] Step S130: Determine, according to the relative importance value, the one with the greatest weight among at least two influencing factors as the target influencing factor.

[0054] Among them, the target influencing factor includes at least two sub-influencing factors. The sub-influencing factors correspond to the parameter values of the target influencing factor.

[0055] It should be noted that the target influencing factor can correspond to multiple process parameter values, and each sub-influencing factor can correspond to one process parameter value.

[0056] Exemplarily, assume that the target influencing factor is the implanted element. The implanted element can include any one or more ionic elements among nitrogen ions, carbon ions, neon ions, silicon ions, oxygen ions, and gold ions. Each ionic element is a sub-influencing factor of the implanted element. Among them, the process parameter value corresponding to the nitrogen ion element can be 0, and the process parameter value corresponding to the carbon ion element can be 1.

[0057] Exemplarily, assume that the target influencing factor is the implantation angle. The implantation angle can include any one or more angles from 0 to 90 degrees, such as 3 degrees, 10 degrees, 40 degrees, and 85 degrees. Each angle is a sub-influencing factor of the implantation angle. Among them, the process parameter value corresponding to the 3-degree implantation angle can be 11, and the process parameter value corresponding to the 10-degree implantation angle can be 1010.

[0058] Exemplarily, assume that the target influencing factor is the implantation dose. The implantation dose is the number of ions (atoms) implanted on the surface of a silicon wafer per unit area (square centimeter). The implantation dose can include any one or more dose values among 1e11 ions / cm 2 、1e13 ions / cm 2 、1e15 ions / cm 2 and 1e18 ions / cm 2 Each dose value is a sub-influencing factor of the implantation dose. Among them, the process parameter value corresponding to the 1e11 ions / cm 2 implantation dose can be 1011, and the process parameter value corresponding to the 1e18 ions / cm 2 implantation dose can be 10010.

[0059] Exemplarily, assume that the target influencing factor is the implantation energy. The implantation energy can include any one or more energy values among 5 kev, 20 kev, 80 kev, 150 kev, and 200 kev. Each energy value is a sub-influencing factor of the implantation energy. Among them, the process parameter value corresponding to the 5-kev implantation energy can be 101, and the process parameter value corresponding to the 200-kev implantation energy can be 11001000.

[0060] Exemplarily, assume that the target influencing factor is the pollution level, which can include any one or more degree values among light pollution, medium pollution, and heavy pollution. Each degree value is a sub-influencing factor of the pollution level. Among them, the process parameter value corresponding to light pollution can be 0, and the process parameter value corresponding to medium pollution can be 1. The more serious the pollution level is when switching, the longer the cleaning time required.

[0061] In the embodiments of the present application, the analytic hierarchy process can be used to determine the target influencing factor. The analytic hierarchy process is a multi-criteria decision-making method, an evaluation method that combines quantitative analysis and qualitative analysis, and converts the relative scoring results into absolute scores through pairwise comparison. With this method, the weights of multiple factors can be obtained in a structured and systematic manner. It can be understood that the sub-criterion layer of the analytic hierarchy process is all the influencing factors among at least two influencing factors, including at least the implanted element, implantation angle, implantation dose, implantation energy, and pollution level.

[0062] In some implementable ways, as Figure 2 shown, in the embodiments of the present application, the above step S120 may include steps S210 to S240.

[0063] Step S210, generate a judgment matrix according to the relative importance value.

[0064] Among them, the matrix elements in the judgment matrix represent the relative importance values of any two influencing factors. The judgment matrix is a square matrix, and the number of rows and columns of the judgment matrix is the number of at least two influencing factors. If at least two influencing factors include the implanted element, implantation angle, implantation dose, implantation energy, and pollution level, then the judgment matrix is a 5×5 square matrix.

[0065] Exemplarily, as shown in Table 2 below, for the relative importance value of the implanted element, specifically including: the relative importance value of the implanted element relative to the implanted element is 1, the relative importance value of the implanted element relative to the implantation angle is 2, the relative importance value of the implanted element relative to the implantation energy is 2, the relative importance value of the implanted element relative to the implantation dose is 3, and the relative importance value of the implanted element relative to the pollution level is 3. Similarly, for the relative importance values of the implantation angle, implantation dose, implantation energy, and pollution level, their specific meanings are not elaborated here.

[0066] Table 2 Relative importance values between any two influencing factors

[0067] Influencing factors Implanting element Implanting corner Implanting energy Implanting dose Pollution degree Implanting element 1 2 2 3 3 Implanting corner 1 / 2 1 1 / 2 2 3 Implanting energy 1 / 2 2 1 1 / 2 2 Implanting dose 1 / 3 1 / 2 2 1 2 Pollution degree 1 / 3 1 / 3 1 / 2 1 / 2 1

[0068] It should be noted that after determining that the relative importance value of the injection element relative to the injection angle is 2, it can be determined that the relative importance value of the injection angle relative to the injection element is the reciprocal 1 / 2 of 2.

[0069] As can be seen from Table 2, the judgment matrix is where u i and u j represent different influencing factors, and u ij represents the relative importance value of u i relative to u j . The relative importance value of u ij is the reciprocal of the relative importance value of u ji .

[0070] Step S220: Perform normalization processing on the judgment matrix.

[0071] In the embodiments of the present application, the relative importance value is characterized by multiple feature variables, and the relative importance can be pre-processed by standardization, which can also be called normalization processing. Substantially, the variable values of the elements in the judgment matrix are scaled to the range of [0, 1] through data transformation. Specifically, min-max standardization, Z-score standardization, or column-wise standardization can be used.

[0072] Exemplarily, for each element value in the judgment matrix, according to the column-wise standardization formula each element value in the normalized matrix is calculated. Based on Table 2, each element value in the normalized matrix is shown in Table 3.

[0073] Table 3 Table of Element Values of the Normalized Matrix

[0074] Influencing factors Implanting element Implanting corner Implanting energy Implanting dose Pollution degree Implanting element 0.38 0.34 0.33 0.43 0.27 Implanting corner 0.19 0.17 0.08 0.29 0.27 Implanting energy 0.19 0.34 0.17 0.07 0.18 Implanting dose 0.13 0.09 0.33 0.14 0.18 Pollution degree 0.13 0.06 0.08 0.07 0.09

[0075] As can be seen from Table 3, based on the judgment matrix P, the normalized matrix is where u i and u j represent different influencing factors, and u ij represents the relative importance value of u i relative to u j .

[0076] Step S230: Based on the judgment matrix after normalization processing, obtain the weight vector of each of at least two influencing factors.

[0077] Among them, the weight vector is used to record the weight of each of at least two influencing factors. The weight vector is obtained by performing normalization processing on the judgment matrix. Each vector element in the weight vector represents the relative weight of each influencing factor.

[0078] In some implementable ways, such as Figure 3 shown, in the embodiment of the present application, the above step S230 may include step S310 and step S320.

[0079] Step S310: Add up the matrix elements in each row of the normalized judgment matrix to generate a corresponding initial weight vector.

[0080] Step S320: Normalize the initial weight vector to obtain the weight vector of each influencing factor among at least two influencing factors.

[0081] Exemplarily, for each element value in the normalized matrix, according to the formula Based on Table 3, add up the elements in the normalized matrix row by row. As shown in Table 4, the initial weight vector is obtained. Exemplarily, based on Table 4, normalize the initial weight vector. As shown in Table 5, the weight vector H = [0.35, 0.20, 0.19, 0.17, 0.09] is obtained. T .

[0082] Table 4 Initial Weight Table

[0083] Implanting element 1.75 Implanting corner 1.00 Implanting energy 0.95 Implanting dose 0.87 Pollution degree 0.43

[0084] Table 5 Weight Vector Table

[0085] Implanting element 0.35 Implanting corner 0.20 Implanting energy 0.19 Implanting dose 0.17 Pollution degree 0.09

[0086] In this way, by normalizing the normalized judgment matrix again to obtain the weight vector of each influencing factor among at least two influencing factors, the accuracy of the weight vector can be improved, thereby improving the accuracy of clustering and grouping, reducing the switching time required to meet different switching methods, and improving the ion implantation efficiency.

[0087] Step S240: Determine the influencing factor corresponding to the maximum vector element in the weight vector as the target influencing factor.

[0088] In some implementation ways of the present application, the influencing factor corresponding to the maximum vector element in the weight vector can be directly determined as the target influencing factor.

[0089] In some other implementation ways of the present application, the consistency of the relative weights of each influencing factor can also be tested first; if the consistency verification passes, then the influencing factor corresponding to the maximum element value in the weight vector is determined as the target influencing factor.

[0090] Specifically, the consistency test for the relative weights of each influencing factor may include: obtaining the maximum eigenvalue of the judgment matrix; determining the consistency test index of the relative weights according to the maximum eigenvalue; when the consistency test index meets the preset threshold, determining that the relative weights of each influencing factor meet the consistency requirement.

[0091] In the embodiment of the present application, according to the maximum eigenvalue formula Calculate the maximum eigenvalue of the judgment matrix. Exemplarily, as shown in Table 2 and Table 5, H = [0.35, 0.20, 0.19, 0.17, 0.09] T , AH = [1.91, 1.07, 1.02, 0.94, 0.45] T , where H is the weight vector and AH is the product of the judgment matrix and the weight vector, and λ max = 5.38 can be obtained.

[0092] In the embodiment of the present application, according to the consistency test formula CI = (λ max - n) / (n - 1), where n = 5, CI = (5.38 - 5) / (5 - 1) = 0.09. By referring to Table 6, RI = 1.12, and the consistency test index

[0093] Table 6 Average random consistency index values

[0094] n 1 2 3 4 5 6 7 8 9 RI 0 0 0.52 0.89 1.12 1.26 1.36 1.41 1.46

[0095] In the embodiment of the present application, if CR is less than the preset threshold (such as 0.1), the judgment matrix passes the consistency test; if CR is greater than or equal to the preset threshold (such as 0.1), the judgment matrix fails the consistency test.

[0096] Exemplarily, as shown in Table 5, the influencing factor corresponding to the maximum element value in the weight vector is the implanted element, that is, the target influencing factor is the implanted element.

[0097] In this way, the normalization process and the consistency test can improve the accuracy of the weights, thereby improving the accuracy of the clustering grouping, reducing the switching time required to meet different switching methods, and improving the ion implantation efficiency.

[0098] Step S140, perform clustering analysis on at least two sub-influencing factors according to the conversion duration and conversion times between any two sub-influencing factors in the target influencing factor, and divide the at least two sub-influencing factors into at least one clustering group.

[0099] Among them, the conversion time between any two sub-influencing factors in each clustering group is less than the preset duration.

[0100] In the embodiments of the present application, hierarchical clustering analysis method can be used to perform clustering analysis on at least two sub-influencing factors. Hierarchical clustering analysis divides a data set into different classes or clusters according to a specific criterion (such as distance), so that the data in the same class after clustering are as close together as possible, and the data in different classes are as separated as possible. The distance between different clusters can be calculated scientifically and reasonably and grouped according to the distance.

[0101] In the embodiments of the present application, the historical shipment records of the ion implantation equipment can be counted to obtain the sub-influencing factors included in the target influencing factor, the conversion time between any two sub-influencing factors, and the conversion times between any two sub-influencing factors within a preset time period.

[0102] Exemplarily, the target influencing factor is the implanted element, and the implanted elements include seven types: A, B, C, D, E, F, and G. The conversion time (in minutes) between any two implanted elements is shown in Table 7.

[0103] Table 7 Statistical table of conversion time between different implanted elements

[0104] Implanting element A B C D E F G A 1 1 1 41 38 1 3108 B 1 1 1 118 118 1 78 C 1 6 1 61 21 1 D 84 37 12 1 1 1 1750 E 56 37 15 18 1 1 72 F 1 1 57 544 164 2 G 5709 4343 3729 71 1

[0105] Exemplarily, the target influencing factor is the implanted element, and the implanted elements include seven types: A, B, C, D, E, F, and G. The conversion times between any two implanted elements are shown in Table 8. Among them, SUM in Table 8 represents the total conversion times between implanted elements.

[0106] Table 8 Statistical table of conversion times between different implanted elements

[0107]

[0108] In some implementable ways, as Figure 4 shown, in the embodiments of the present application, the above step S140 may include steps S410 to S430.

[0109] Step S410, calculate the average distance between any two sub-influencing factors according to the conversion time and the conversion times.

[0110] In the embodiments of the present application, first, according to the conversion time, calculate the distance between any two sub-influencing factors, and the calculation basis is Then, according to the conversion times, calculate the average distance between any two sub-influencing factors.

[0111] Step S420, generate a hierarchical clustering dendrogram according to the average distance.

[0112] Step S430: Based on the hierarchical clustering dendrogram, cluster at least two sub-influencing factors according to a preset segmentation distance, and divide the at least two sub-influencing factors into at least one clustering group.

[0113] Among them, the preset segmentation distance can be calculated based on the average distance.

[0114] In the embodiment of the present application, for the implanted element G, as shown in Table 7, the conversion duration from the implanted element G to the implanted element A, the conversion duration from the implanted element G to the implanted element B, the conversion duration from the implanted element G to the implanted element D, the conversion duration from the implanted element A to the implanted element G, and the conversion duration from the implanted element D to the implanted element G all exceed the preset segmentation distance. Then, according to the Figure 5 shown hierarchical clustering dendrogram, it can be known that for the implanted element G, it is not recommended to perform conversions with other implanted elements, and the implanted element G is separately divided into a clustering group.

[0115] In the embodiment of the present application, after the implanted element G is separately divided into a group, for the sub-influencing factors (A, B, C, D, E, and F) included in the target influencing factor, recalculate the average distance between any two sub-influencing factors according to the conversion duration and the number of conversions. Then, based on the recalculated average distance, regenerate the hierarchical clustering dendrogram. Then, according to the Figure 6 shown hierarchical clustering dendrogram, it can be known that for the implanted element F, it is not recommended to perform conversions with other implanted elements, and the implanted element F is separately divided into a clustering group.

[0116] In the embodiment of the present application, after the implanted element G is separately divided into a group, for the sub-influencing factors (A, B, C, D, and E) included in the target influencing factor, recalculate the average distance between any two sub-influencing factors according to the conversion duration and the number of conversions. Then, based on the recalculated average distance, regenerate the hierarchical clustering dendrogram. Then, according to the Figure 7 shown hierarchical clustering dendrogram, it can be known that for the implanted element D and the implanted element E, it is recommended to be divided into a clustering group, and the implanted element D and the implanted element E can be converted with each other.

[0117] In the embodiment of the present application, according to the Figure 7 shown hierarchical clustering dendrogram, it can be known that for the implanted element A, the implanted element B, and the implanted element C, it is recommended to be divided into a clustering group, and the implanted element A, the implanted element B, and the implanted element C can be converted with each other.

[0118] Step S150: Dispatch work to the ion implantation equipment according to at least one clustering group and the ion implantation process to be executed, so that the ion implantation equipment performs ion implantation according to the ion implantation process to be executed.

[0119] In the embodiments of the present application, when dispatching work for ion implantation equipment, it is also necessary to consider the number of ion implantation processes to be executed, the number of ion implantation equipment, the previous sub-influence factor set for the target influence factor in the target ion implantation equipment, the sub-influence factors that can be set for the target influence factor in the target ion implantation equipment, and the ion implantation processes that have been dispatched for the ion implantation equipment, etc.

[0120] For Scenario 1, for a single ion implantation process to be executed, the ion implantation process to be executed can be performed by multiple ion implantation equipment. The implementation method for Scenario 1 is as follows: Obtain the sub-influence factor to be dispatched in the target influence factor of the ion implantation process to be executed, search for the associated sub-influence factor that belongs to the same clustering group as the sub-influence factor to be dispatched, and dispatch the ion implantation process to be executed to the target ion implantation equipment corresponding to the associated sub-influence factor. The previous sub-influence factor set for the target influence factor in the target ion implantation equipment is the associated sub-influence factor.

[0121] Exemplarily, the target influence factor in the ion implantation process to be executed is the implanted element. According to the process parameter value in the ion implantation process to be executed, the sub-influence factor to be dispatched is implanted element A. The associated sub-influence factors that belong to the same clustering group as the sub-influence factor to be dispatched include implanted element A, implanted element B, and implanted element C. The ion implantation process to be executed can be performed by 3 ion implantation equipment. The previous sub-influence factors set for the target influence factor in the 3 ion implantation equipment are implanted element B, implanted element C, and implanted element F respectively. Since implanted element A, implanted element B, and implanted element C belong to the same clustering group, the ion implantation process to be executed can be dispatched to the ion implantation equipment corresponding to the previous sub-influence factor of implanted element B, or dispatched to the ion implantation equipment corresponding to the previous sub-influence factor of implanted element C.

[0122] For Scenario 2, for a batch of multiple ion implantation processes to be executed, the multiple ion implantation processes to be executed can be performed by one ion implantation equipment. The implementation method for Scenario 2 is as follows: Obtain the multiple sub-influence factors to be dispatched in the target influence factor of the multiple ion implantation processes to be executed. According to the multiple sub-influence factors to be dispatched and at least one clustering group, group the multiple ion implantation processes to be executed. Then, sequentially dispatch the ion implantation processes to be executed in the same process group continuously to the ion implantation equipment.

[0123] Exemplarily, the target influencing factor in the ion implantation process to be executed is the implanted element. According to the process parameter values in the ion implantation process to be executed, the sub-influencing factors to be dispatched are implanted element A, implanted element B, implanted element D, implanted element E, and implanted element G. According to at least one clustering grouping, 10 ion implantation processes to be executed corresponding to implanted element A and implanted element B are assigned to the first process grouping, 3 ion implantation processes to be executed corresponding to implanted element D and implanted element E are assigned to the second process grouping, and 20 ion implantation processes to be executed corresponding to implanted element G are assigned to the third process grouping. Determine the execution order of the process groupings (e.g., the first process grouping, the second process grouping, and the third process grouping), and continuously dispatch the 10 ion implantation processes to be executed in the first process grouping to the ion implantation equipment, then continuously dispatch the 3 ion implantation processes to be executed in the second process grouping to the ion implantation equipment, and continuously dispatch the 20 ion implantation processes to be executed in the third process grouping to the ion implantation equipment. Among them, the execution order of the process groupings can be any one of the 8 execution orders obtained by performing a full permutation on the first process grouping, the second process grouping, and the third process grouping.

[0124] For Case 3, for a batch of multiple ion implantation processes to be executed, each ion implantation process to be executed can be executed by multiple ion implantation equipment. The implementation method of Case 3 is to obtain the sub-influencing factors to be dispatched among the target influencing factors in the multiple ion implantation processes to be executed. According to the multiple sub-influencing factors to be dispatched and at least one clustering grouping, group the multiple ion implantation processes to be executed. According to the number of process groupings and the number of ion implantation equipment, with the goal of minimizing the total process conversion time for executing all ion implantation processes to be executed, determine the dispatching method for the ion implantation processes to be executed. The dispatching method is used to record how the ion implantation processes to be executed are dispatched to the ion implantation equipment.

[0125] Exemplarily, the target influencing factor in the ion implantation process to be executed is the implanted element. According to the process parameter values in the ion implantation process to be executed, the sub-influencing factors to be dispatched are implanted element A, implanted element B, implanted element D, implanted element E, and implanted element G. According to at least one clustering grouping, 100 ion implantation processes to be executed corresponding to implanted element A and implanted element B are assigned to the first process grouping, 3 ion implantation processes to be executed corresponding to implanted element D and implanted element E are assigned to the second process grouping, and 2 ion implantation processes to be executed corresponding to implanted element G are assigned to the third process grouping.

[0126] Among them, the numbers of ion implantation processes to be executed assigned to the three process groups are 100, 3, and 2 in sequence. The average intra-group process conversion times of the three process groups are 10 minutes, 100 minutes, and 2000 minutes in sequence. The inter-group process conversion time between the first process group and the second process group is 10 hours, the inter-group process conversion time between the first process group and the third process group is 2 hours, and the inter-group process conversion time between the second process group and the third process group is 50 hours.

[0127] Assume that the number of ion implantation devices corresponding to the ion implantation processes to be executed is 3, which is equal to the number of process groups, specifically the first ion implantation device, the second ion implantation device, and the third ion implantation device. Assume that the three process groups are assigned to the three ion implantation devices. The total intra-group process conversion times corresponding to the three ion implantation devices are 99×10 = 990 minutes, 2×100 = 200 minutes, and 1×2000 = 2000 minutes in sequence. Since the total intra-group process conversion time of the third process group (2000 minutes) is greater than the total intra-group process conversion time of the first process group (990 minutes) and also greater than the total intra-group process conversion time of the second process group (200 minutes), consider whether to dispatch one of the ion implantation processes to be executed in the third process group to other ion implantation devices to reduce the total process conversion time of the ion implantation processes to be executed. Since the inter-group process conversion time between the first process group and the third process group (2 hours) is less than the total intra-group process conversion time of the third process group (2000 minutes), dispatch one of the ion implantation processes to be executed in the third process group, as well as the ion implantation processes to be executed in the first process group, to the first ion implantation device. Dispatch the ion implantation processes to be executed in the second process group to the second ion implantation device, and dispatch the other ion implantation process to be executed in the third process group to the third ion implantation device.

[0128] Assume that the number of ion implantation devices corresponding to the ion implantation processes to be executed is 2, which is equal to the number of process groups, specifically the first ion implantation device and the second ion implantation device. Since the total intra-group process conversion times corresponding to the three ion implantation devices are 990 minutes, 200 minutes, and 2000 minutes in sequence. Since the inter-group process conversion time between the second process group and the third process group is 50 hours, do not preferentially consider dispatching the second process group and the third process group to the same ion implantation device to avoid a relatively long total process conversion time obtained by statistics.

[0129] In the first possible scenario, half of the to-be-executed ion implantation processes in the first process group and the to-be-executed ion implantation processes in the second process group are assigned to the first ion implantation device, and half of the to-be-executed ion implantation processes in the first process group and the to-be-executed ion implantation processes in the third process group are assigned to the second ion implantation device. Then, the process conversion time required for the first ion implantation device to execute the assigned ion implantation processes is (100÷2 - 1)×10 minutes + 10 hours + 200 minutes = 1290 minutes, and the process conversion time required for the second ion implantation device to execute the assigned ion implantation processes is (100÷2 - 1)×10 minutes + 2 hours + 2000 minutes = 2610 minutes. The total process conversion time is 1290 minutes + 2610 minutes = 3900 minutes.

[0130] In the second possible scenario, the to-be-executed ion implantation processes in the first process group and the second process group are assigned to the first ion implantation device, and the to-be-executed ion implantation processes in the third process group are assigned to the second ion implantation device. Then, the process conversion time required for the first ion implantation device to execute the assigned ion implantation processes is 990 minutes + 10 hours + 200 minutes = 1790 minutes, and the process conversion time required for the second ion implantation device to execute the assigned ion implantation processes is 2000 minutes. The total process conversion time is 1790 minutes + 2000 minutes = 3790 minutes.

[0131] In the third possible scenario, the to-be-executed ion implantation processes in the first process group and the third process group are assigned to the first ion implantation device, and the to-be-executed ion implantation processes in the second process group are assigned to the second ion implantation device. Then, the process conversion time required for the first ion implantation device to execute the assigned ion implantation processes is 990 minutes + 2 hours + 2000 minutes = 3110 minutes, and the process conversion time required for the second ion implantation device to execute the assigned ion implantation processes is 200 minutes. The total process conversion time is 3110 minutes + 200 minutes = 3310 minutes.

[0132] In the fourth possible scenario, the to-be-executed ion implantation processes in the first process group are assigned to the first ion implantation device, and the to-be-executed ion implantation processes in the second process group and the third process group are assigned to the second ion implantation device. Then, the process conversion time required for the first ion implantation device to execute the assigned ion implantation processes is 990 minutes, and the process conversion time required for the second ion implantation device to execute the assigned ion implantation processes is 200 minutes + 50 hours + 2000 minutes = 5200 minutes. The total process conversion time is 990 minutes + 5200 minutes = 6190 minutes.

[0133] Comparing the above four possible situations, among which, the total process conversion time of the third possible situation is the shortest. Therefore, the to-be-executed ion implantation processes in the first process group and the third process group are assigned to the first ion implantation equipment, and the to-be-executed ion implantation processes in the second process group are assigned to the second ion implantation equipment.

[0134] Similarly, assuming that the number of available ion implantation equipment is 4, which is greater than the number of process groups, the total process conversion time of different dispatching methods is counted, and the allocation method of the to-be-executed ion implantation processes corresponding to the shortest total process conversion time is determined to decide how to dispatch the ion implantation equipment.

[0135] Exemplarily, according to the conversion times between different implanted elements shown in Table 8, the conversion methods with a conversion ratio greater than 15% are retained, and the proportion is recalculated; assuming that there is 1 original machine for each ion implantation equipment corresponding to each implanted element, the time consumed for each element to complete ten processes is calculated. Using the non-discriminatory dispatching method for dispatching, the time taken for the ion implantation equipment to complete processing is shown in Table 9, and the total time spent is 1131 minutes. Dispatching the ion implantation equipment according to clustering grouping, the time taken for the ion implantation equipment to complete processing is shown in Table 10, and the total time spent is 113 minutes. Comparing the conversion time before and after clustering, the conversion time is shortened from 1131 min to 113 min, and the efficiency is increased by 90%. It can be seen that the existing dispatching method can be improved according to the hierarchical clustering analysis method, the maintenance time required for switching between different conditions can be reduced, and the equipment efficiency can be improved.

[0136] Table 9 Statistical table of the time taken for non-discriminatory dispatching to complete processing

[0137] Implanting element A B C D E F G SUM A 10 10 B 2 6 125 133 266 C 3 5 8 D 8 2 10 E 30 8 38 F 2 1 564 213 8 789 G 10 10

[0138] Table 10 Statistical table of the time taken for differential dispatching according to clustering grouping to complete processing

[0139]

[0140]

[0141] In the embodiments of the present application, the dispatching method adopted by the present application is not only applicable to ion implantation equipment, but also can be used for other production equipment with disordered dispatching, and two or more factors can be added for comprehensive clustering analysis to obtain the influence weights of multiple factors, and a more comprehensive dispatching method can be obtained according to the results.

[0142] The method for improving dispatching efficiency provided by the embodiments of the present application first obtains at least two influencing factors affecting the dispatching efficiency of the ion implantation equipment, then determines the relative importance degree value between any two influencing factors, and next determines, according to the relative importance degree value, the one with the largest weight among the at least two influencing factors as the target influencing factor; next, cluster analysis is performed on at least two sub-influencing factors, and the at least two sub-influencing factors are divided into at least one clustering group. Finally, on the premise of meeting the actual ion implantation situation, dispatching can be performed for the ion implantation equipment according to the clustering group and the ion implantation process to be executed, so that the ion implantation equipment performs ion implantation according to the ion implantation process to be executed. In this way, by adjusting the execution order of the ion implantation process to be executed during the dispatching process, that is, adjusting the switching order of different sub-influencing factors, optimizing the switching method between different influencing factors of the ion implantation equipment, reducing the switching time required to meet different switching methods, reducing the efficiency loss of the ion implantation equipment, and improving the ion implantation efficiency.

[0143] The above mainly introduces the solution of the embodiments of the present application from the perspective of the method. It can be understood that, combined with the various examples described in the embodiments of the present application, those skilled in the art should easily realize that the present application can be implemented in the form of hardware, computer software, or a combination of software and hardware (hardware and computer software). The device for improving dispatching efficiency includes at least one of the corresponding hardware structures and software modules for executing each function in order to implement the above functions. Whether a certain function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0144] The embodiments of the present application can perform functional unit division on the device for improving dispatching efficiency according to the above method examples. For example, each functional unit can be divided corresponding to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. It should be noted that the division of units in the embodiments of the present application is illustrative, only a logical function division, and there may be other division methods in actual implementation.

[0145] As Figure 8 shown, the embodiments of the present application also provide a device 800 for improving dispatching efficiency. The device 800 for improving dispatching efficiency may include an acquisition module 810, a first determination module 820, a second determination module 830, a grouping module 840, and a dispatching module 850.

[0146] The acquisition module 810 is configured to acquire at least two influencing factors that affect the dispatching efficiency of the ion implantation equipment, and the at least two influencing factors are acquired from the process parameters of a plurality of ion implantation processes historically executed by the ion implantation equipment; for example, as Figure 1 shown, the acquisition module 810 may be configured to execute step S110.

[0147] The first determination module 820 is configured to determine the relative importance degree value between any two of the at least two influencing factors; for example, as Figure 1 shown, the first determination module 820 may be configured to execute step S120.

[0148] The second determination module 830 is configured to determine, according to the relative importance degree value, the one with the largest weight among the at least two influencing factors as the target influencing factor, and the target influencing factor includes at least two sub-influencing factors, and the sub-influencing factors correspond to the parameter values of the target influencing factor; for example, as Figure 1 shown, the second determination module 830 may be configured to execute step S130.

[0149] The grouping module 840 is configured to perform clustering analysis on the at least two sub-influencing factors according to the conversion duration and conversion times between any two of the sub-influencing factors in the target influencing factor, and divide the at least two sub-influencing factors into at least one clustering group, and the conversion time between any two sub-influencing factors in each clustering group is less than a preset duration; for example, as Figure 1 shown, the classification module 840 may be configured to execute step S140.

[0150] The dispatching module 850 is configured to dispatch the ion implantation equipment according to the at least one clustering group and the ion implantation process to be executed, so that the ion implantation equipment performs ion implantation according to the ion implantation process to be executed. For example, as Figure 1 shown, the dispatching module 850 may be configured to execute step S150.

[0151] In some embodiments, the acquisition module 810 is configured to: acquire the at least two influencing factors from the process parameters of a plurality of ion implantation processes historically executed according to a preset naming rule.

[0152] In some embodiments, the second determination module 830 is configured to: construct a judgment matrix according to the relative importance degree values, where the matrix elements in the judgment matrix represent the relative importance degree values of any two of the influencing factors; perform normalization processing on the judgment matrix; based on the judgment matrix after the normalization processing, obtain a weight vector for each of the at least two influencing factors, where each vector element in the weight vector represents the relative weight of each influencing factor; and determine the influencing factor corresponding to the maximum vector element in the weight vector as the target influencing factor.

[0153] In some embodiments, the second determination module 830 is configured to: add the matrix elements of each row in the judgment matrix after the normalization processing to generate a corresponding initial weight vector; and perform normalization processing on the initial weight vector to obtain a weight vector for each of the at least two influencing factors.

[0154] In some embodiments, the second determination module 830 is configured to: after obtaining the weight vector for each of the at least two influencing factors based on the judgment matrix after the normalization processing, perform a consistency test on the relative weight of each influencing factor; if the consistency verification passes, determine the influencing factor corresponding to the maximum element value in the weight vector as the target influencing factor.

[0155] In some embodiments, the second determination module 830 is configured to: obtain the maximum eigenvalue of the judgment matrix; determine a consistency test index for the relative weight according to the maximum eigenvalue; and when the test index meets a preset threshold, determine that the relative weight of each influencing factor meets the consistency requirement.

[0156] In some embodiments, the grouping module 840 is configured to: calculate the average distance between any two of the sub-influencing factors according to the conversion duration and the conversion times; generate a hierarchical clustering dendrogram according to the average distance; and based on the hierarchical clustering dendrogram, cluster the at least two sub-influencing factors according to a preset segmentation distance, and divide the at least two sub-influencing factors into at least one clustering group.

[0157] In some embodiments, in the above device, the process parameters include implanted elements, implantation angles, implantation doses, implantation energies, and contamination degrees.

[0158] Regarding the device in the above embodiments, the specific manners in which each unit performs operations have been described in detail in the embodiments related to the method, and will not be elaborated herein.

[0159] Figure 9 It is a schematic structural diagram of an electronic device 900 provided by the present application. AsFigure 9 As shown, the electronic device may include a processor 91 and a memory 92 for storing executable instructions of the processor 91; wherein, the processor 91 is configured to execute the above instructions to implement the method for improving the dispatching efficiency in the above embodiments.

[0160] In addition, the electronic device may further include a communication bus 93 and at least one communication interface 94.

[0161] The processor 91 may be a central processing unit (CPU), a microprocessing unit, an application specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the solution of the present application.

[0162] The communication bus 93 is a signal path for transmitting information between the above components.

[0163] The communication interface 94 uses any device such as a transceiver for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0164] The memory 92 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 92 may exist independently and be connected to the processor 91 through the communication bus 93. The memory 92 may also be integrated with the processor 91.

[0165] Among them, the memory 92 is used to store instructions for executing the solution of this application, and is controlled by the processor 91 for execution. The processor 91 is used to execute the programs or instructions stored in the memory 92, so as to implement the functions in the method of this application.

[0166] As an example, in combination with Figure 8 , the functions implemented by the acquisition module 810, the first determination module 820, the second determination module 830, the grouping module 840, and the dispatching module 850 in the device for improving dispatching efficiency are the same as Figure 9 the functions of the processor 91 in

[0167] In a specific implementation, as an embodiment, the processor 91 may include one or more CPUs, such as Figure 9 CPU0 and CPU1 in

[0168] In a specific implementation, as an embodiment, the electronic device may include multiple processors 91, and each of these processors 91 may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. The processor 91 here may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0169] In a specific implementation, as an embodiment, the electronic device may further include an output device 95 and an input device 96. The output device 95 communicates with the processor 91 and can display information in various ways. For example, the output device 95 may be a liquid crystal display (LCD), a light emitting diode (LED) display device, a cathode ray tube (CRT) display device, or a projector, etc. The input device 96 communicates with the processor 91 and can accept user input in various ways. For example, the input device 96 may be a mouse, a keyboard, a touch screen device, or a sensing device, etc.

[0170] Those skilled in the art can understand that Figure 9 the structure shown in Figure 9 does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or adopt a different component layout.

[0171] In addition, the present application also provides a computer-readable storage medium, on which a program or instructions are stored. When the instructions in the above-readable storage medium are executed by a processor, the electronic device can execute the method for improving dispatch efficiency as provided in the above embodiments. Optionally, the readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0172] In addition, the present application also provides a computer program product, including a computer program / instructions. The computer program product is stored in a non-volatile readable storage medium. When the computer program product is executed by at least one processor, the electronic device executes the method for improving dispatch efficiency as provided in the above embodiments.

[0173] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include the common general knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and the embodiments are only regarded as exemplary, and the true scope and spirit of the present application are pointed out by the claims.

[0174] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A method for improving dispatching efficiency, characterized in that, Including: Obtain at least two influencing factors that affect the dispatching efficiency of the ion implantation equipment, where the at least two influencing factors are obtained from the process parameters of multiple ion implantation processes historically executed by the ion implantation equipment; Determine the relative importance degree value between any two of the at least two influencing factors; According to the relative importance degree value, determine the one with the largest weight among the at least two influencing factors as the target influencing factor, where the target influencing factor includes at least two sub-influencing factors, and the sub-influencing factors correspond to the parameter values of the target influencing factor; Perform cluster analysis on the at least two sub-influencing factors according to the conversion duration and conversion times between any two of the sub-influencing factors in the target influencing factor, and divide the at least two sub-influencing factors into at least one cluster group, where the conversion time between any two sub-influencing factors in each cluster group is less than a preset duration; Dispatch the ion implantation equipment according to the at least one cluster group and the ion implantation process to be executed, so that the ion implantation equipment performs ion implantation according to the ion implantation process to be executed.

2. The method for improving dispatching efficiency according to claim 1, wherein The obtaining of at least two influencing factors that affect the dispatching efficiency of the ion implantation equipment includes: According to a preset naming rule, obtain the at least two influencing factors from the process parameters of multiple ion implantation processes historically executed.

3. The method for improving dispatching efficiency according to claim 1, wherein The determining of the one with the largest weight among the at least two influencing factors as the target influencing factor according to the relative importance degree value includes: Construct a judgment matrix according to the relative importance degree value, where the matrix elements in the judgment matrix represent the relative importance degree values of any two of the influencing factors; Perform normalization processing on the judgment matrix; Based on the normalized judgment matrix, obtain the weight vector of each of the at least two influencing factors, where each vector element in the weight vector represents the relative weight of each influencing factor; Determine the influencing factor corresponding to the largest vector element in the weight vector as the target influencing factor.

4. The method for improving dispatching efficiency according to claim 3, characterized in that The obtaining of the weight vector of each of the at least two influencing factors based on the normalized judgment matrix includes: Add up the matrix elements in each row of the normalized judgment matrix to generate a corresponding initial weight vector; Perform normalization processing on the initial weight vector to obtain the weight vector of each of the at least two influencing factors.

5. The method for improving dispatching efficiency according to claim 3, characterized in that, After obtaining the weight vector of each of the at least two influencing factors based on the normalized judgment matrix, the method further includes: Perform consistency test on the relative weight of each influencing factor; If the consistency verification passes, determine the influencing factor corresponding to the largest element value in the weight vector as the target influencing factor.

6. The method for improving dispatching efficiency according to claim 5, characterized in that, The performing of the consistency test on the relative weight of each influencing factor includes: Obtain the maximum eigenvalue of the judgment matrix; Determine the consistency test index of the relative weight according to the maximum eigenvalue. When the consistency test index meets the preset threshold, it is determined that the relative weights of each of the influencing factors meet the consistency requirement.

7. The method for improving dispatching efficiency according to claim 1, characterized in that Performing clustering analysis on the at least two sub-influencing factors according to the conversion duration and the number of conversions between any two of the sub-influencing factors in the target influencing factor, and dividing the at least two sub-influencing factors into at least one clustering group, including: Calculating the average distance between any two of the sub-influencing factors according to the conversion duration and the number of conversions; Generating a hierarchical clustering dendrogram according to the average distance; On the basis of the hierarchical clustering dendrogram, clustering the at least two sub-influencing factors according to a preset segmentation distance, and dividing the at least two sub-influencing factors into at least one clustering group.

8. The method for improving dispatching efficiency according to any one of claims 1-7, characterized in that The process parameters include implanted elements, implantation angles, implantation doses, implantation energies, and degrees of contamination.

9. A device for improving dispatching efficiency, characterized in that, Including: An acquisition module, a first determination module, a second determination module, a grouping module, and a dispatching module; wherein, The acquisition module is configured to acquire at least two influencing factors affecting the dispatching efficiency of the ion implantation equipment, and the at least two influencing factors are obtained from the process parameters of a plurality of ion implantation processes previously executed by the ion implantation equipment; The first determination module is configured to determine the relative importance value between any two of the at least two influencing factors; The second determination module is configured to determine, according to the relative importance value, the one with the largest weight among the at least two influencing factors as the target influencing factor, the target influencing factor includes at least two sub-influencing factors, and the sub-influencing factors correspond to the parameter values of the target influencing factor; The classification module is configured to perform clustering analysis on the at least two sub-influencing factors according to the conversion duration and the number of conversions between any two of the sub-influencing factors in the target influencing factor, and divide the at least two sub-influencing factors into at least one clustering group, and the conversion time between any two sub-influencing factors in each clustering group is less than a preset duration; The dispatching module is configured to dispatch the ion implantation equipment according to the at least one clustering group and the ion implantation process to be executed, so that the ion implantation equipment performs ion implantation according to the ion implantation process to be executed.

10. An electronic device, characterized in that, Including: A processor and a memory, the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method according to any one of claims 1-8.

Images