A method and apparatus for selecting the material ratio of a semiconductor diaphragm valve

Abstract

This application provides a method and apparatus for selecting the material ratio of a semiconductor diaphragm valve. In this application, a sample set is constructed using the stability parameters of each stability item of the experimental material and the mass percentage of each component. Multiple models corresponding to each stability item are then constructed using the sample set, and each stability item is fitted to obtain a predictive model. Finally, the predictive model is used to predict the stability of the target mass percentage of each input component, thereby obtaining the components that meet the constraints. In this application, the predictive model is constructed using real data, and then constraints are set to select appropriate mass percentages of each component. Compared to conducting experiments on every material, this application helps to narrow the scope of experiments, thereby shortening the R&D cycle and reducing costs.

Description

Technical Field

[0001] This application relates to the field of computer technology, and more specifically, to a method and apparatus for selecting the material ratio of a semiconductor diaphragm valve. Background Technology

[0002] Diaphragm valves in semiconductor manufacturing processes are critical components for controlling the transport of ultrapure gases and chemical liquids. The performance of their seat or diaphragm materials directly determines the valve's reliability and lifespan. These materials are typically composites of various known polymer matrices (such as polytetrafluoroethylene or soluble polytetrafluoroethylene) and functional fillers (such as carbon fibers, glass microspheres, lubricants, etc.). The materials must simultaneously meet requirements for extremely low leakage rates (airtightness), long-term stable pressure holding capability (positive pressure holding), and dimensional and performance stability over a wide temperature range (e.g., -20°C to 150°C) (temperature resistance).

[0003] Currently, determining the optimal ratio of such high-performance composite materials mainly relies on the experience of materials experts and a large number of trial-and-error experiments. This method is not only time-consuming to develop, but also costly. Summary of the Invention

[0004] In view of this, embodiments of this application provide a method and apparatus for selecting the material ratio of a semiconductor diaphragm valve, so as to reduce the research and development cycle and cost.

[0005] In a first aspect, embodiments of this application provide a method for selecting the material ratio of a semiconductor diaphragm valve, the method comprising: Obtain the stability parameters of each stability item and the mass percentage of each component for each experimental material, wherein some experimental materials contain the same types of components; Based on the stability parameters of each stability item and the mass ratio, a sample set corresponding to each stability item is constructed. Each sample set corresponding to each stability item includes multiple sample data, and each sample data consists of the stability parameter corresponding to that stability item and the mass ratio of each experimental material. Input each sample data corresponding to the stability term into the following formula to obtain multiple models corresponding to the stability term; ; in, This refers to the stability parameter in a sample data set under this stability item. For constant terms, Let be the coefficient of the linear term of the i-th component in the sample data. Let be the coefficient of the quadratic term of the i-th component in the sample data. The coefficient of the interaction term between the i-th and j-th components in this sample data. The mass percentage of the i-th component in this sample data The mass percentage of the j-th component in this sample data The number of component types included in each experimental material. This is the random error term; By fitting multiple models corresponding to the stability term using the least squares method in multiple linear regression, the prediction model corresponding to the stability term is obtained. Using the target stability parameter range of each stability term as a constraint, the target mass ratio of each component is input into the prediction model to determine whether the target material meets the constraint.

[0006] Secondly, embodiments of this application provide a device for selecting the material ratio of a semiconductor diaphragm valve, the device comprising: The acquisition unit is used to acquire the stability parameters of each stability item and the mass percentage of each component for each experimental material, wherein some experimental materials include the same types of components; The construction unit is used to construct a sample set corresponding to each stability item based on the stability parameter of each stability item and the mass ratio. The sample set corresponding to each stability item includes multiple sample data, and each sample data consists of the stability parameter corresponding to that stability item and the mass ratio of each experimental material. The input unit is used to input each sample data corresponding to the stability term into the following formula to obtain multiple models corresponding to the stability term; ; in, This refers to the stability parameter in a sample data set under this stability item. For constant terms, Let be the coefficient of the linear term of the i-th component in the sample data. Let be the coefficient of the quadratic term of the i-th component in the sample data. The coefficient of the interaction term between the i-th and j-th components in this sample data. The mass percentage of the i-th component in this sample data The mass percentage of the j-th component in this sample data The number of component types included in each experimental material. This is the random error term; The fitting unit is used to fit multiple models corresponding to the stability term using the least squares method in multiple linear regression to obtain the prediction model corresponding to the stability term. The determination unit is used to input the target mass ratio of each component into the prediction model, using the target stability parameter range of each stability item as a constraint, to determine whether the target material meets the constraint conditions.

[0007] The technical solution provided in this application includes, but is not limited to, the following beneficial effects: In this application, a sample set is constructed using the stability parameters of each stability term of the experimental material and the mass ratio of each component. Multiple models corresponding to each stability term are then constructed using the sample set, and a prediction model for each stability term is obtained by fitting the model. Finally, the stability of the target mass ratio of each component is predicted using the prediction model, thereby obtaining the components that meet the constraints. In this application, the prediction model is constructed using real data, and then constraints are set and appropriate mass ratios of each component are selected. Compared with the method of conducting experiments on each material, this application is beneficial to narrowing the scope of experiments, thereby shortening the research and development cycle and reducing costs.

[0008] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A flowchart illustrating a method for selecting the material ratio of a semiconductor diaphragm valve, provided as an embodiment of this application; Figure 2 This is a schematic diagram of a device for selecting the material ratio of a semiconductor diaphragm valve, provided in an embodiment of this application. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0012] Figure 1 This application provides a flowchart illustrating a method for selecting the material ratio of a semiconductor diaphragm valve, as shown in the embodiments below. Figure 1 As shown, the method includes the following steps: Step 101: Obtain the stability parameters of each stability item and the mass percentage of each component for each experimental material. Some experimental materials contain the same types of components.

[0013] Step 102: Based on the stability parameters of each stability item and the mass ratio, construct a sample set corresponding to each stability item. Each sample set corresponding to each stability item includes multiple sample data, and each sample data consists of the stability parameters corresponding to that stability item and the mass ratio of each experimental material.

[0014] Step 103: Input each sample data corresponding to the stability term into Formula 1 to obtain multiple models corresponding to the stability term: Formula 1 in, This refers to the stability parameter in a sample data set under this stability item. For constant terms, Let be the coefficient of the linear term of the i-th component in the sample data. Let be the coefficient of the quadratic term of the i-th component in the sample data. The coefficient of the interaction term between the i-th and j-th components in this sample data. The mass percentage of the i-th component in this sample data The mass percentage of the j-th component in this sample data The number of component types included in each experimental material. This is the random error term.

[0015] Step 104: Use the least squares method in multiple linear regression to fit multiple models corresponding to the stability term to obtain the prediction model corresponding to the stability term. Step 105: Using the target stability parameter range of each stability item as a constraint, input the target mass ratio of each component into the prediction model to determine whether the target material meets the constraint conditions.

[0016] Specifically, for various experimental materials, each material is tested and experimented on to obtain the stability parameters of each stability item and the mass percentage of each component for each material. A sample set is constructed based on the data obtained above. For example, the stability items include: stability item 1, stability item 2, and stability item 3; the mass percentage of the components includes: mass percentage 1, mass percentage 2, and mass percentage 3. The sample data obtained from a certain experimental material includes: [stability item 1, mass percentage 1, mass percentage 2, mass percentage 3], [stability item 2, mass percentage 1, mass percentage 2, mass percentage 3], and [stability item 3, mass percentage 1, mass percentage 2, mass percentage 3]. That is, three sample data can be obtained for each sample.

[0017] When the experimental materials include experimental material 1 and experimental material 2, the sample data of experimental material 1 are [stability item 1, mass percentage 1, mass percentage 2, mass percentage 3], [stability item 2, mass percentage 1, mass percentage 2, mass percentage 3], and [stability item 3, mass percentage 1, mass percentage 2, mass percentage 3], and the sample data of experimental material 2 are [stability item 1, mass percentage 4, mass percentage 5, mass percentage 6], [stability item 2, mass percentage 4, mass percentage 5, mass percentage 6], and [stability item 3, mass percentage 4, mass percentage 5, mass percentage 6], which yields 3. There are three sample sets. Sample set 1 corresponding to stability item 1 includes: [stability item 1, quality percentage 1, quality percentage 2, quality percentage 3] and [stability item 1, quality percentage 4, quality percentage 5, quality percentage 6]. Sample set 2 corresponding to stability item 2 includes: [stability item 2, quality percentage 1, quality percentage 2, quality percentage 3] and [stability item 2, quality percentage 4, quality percentage 5, quality percentage 6]. Sample set 3 corresponding to stability item 3 includes: [stability item 3, quality percentage 1, quality percentage 2, quality percentage 3] and [stability item 3, quality percentage 4, quality percentage 5, quality percentage 6].

[0018] After obtaining multiple sample sets, for each sample set, each sample data in that set is input into Formula 1, thereby obtaining multiple models for the stability term to which that sample set belongs. Taking stability term 1 as an example, each sample data in the sample set of stability term 1 is substituted into Formula 1, thus obtaining multiple models for stability term 1. Then, the models corresponding to stability term 1 are fitted to obtain the model corresponding to stability term 1. , , , and Then get , , , and Substituting back into Formula 1, we obtain Prediction Model 1 for Stability Item 1. Similarly, we obtain Prediction Model 2 for Stability Item 2 and Prediction Model 3 for Stability Item 3. During prediction, the target mass percentage of each component is used as input and fed into Prediction Model 1, Prediction Model 2, and Prediction Model 3, respectively, to obtain Stability Parameter 1 for Stability Item 1, Stability Parameter 2 for Stability Item 2, and Stability Parameter 3 for Stability Item 3. Then, we determine whether Stability Parameter 1, Stability Parameter 2, and Stability Parameter 3 are all within their respective target stability parameter ranges. If they are, it indicates that the target mass percentage of the component may meet the actual performance requirements, and this can be used as a backup for actual testing experiments to determine whether the target mass percentage of the component meets the process requirements. Compared to conducting experiments on each material, this application is beneficial for narrowing the scope of experiments, thereby shortening the R&D cycle and reducing costs.

[0019] For example, three experimental materials are known, all of which consist only of polytetrafluoroethylene and carbon fiber. The stability parameters of these three materials were determined experimentally. The leakage rate will be used as an example to illustrate the stability parameters. Table 1 shows the obtained data:

[0020] Table 1 Substitute the data from Table 1 into Formula 2 (i.e., the expansion of Formula 1): Formula 2 The resulting models for leakage rates include:

[0021] The leakage rate is fitted using these three equations (more data points will be used in practice). , , , and Then get , , , and Substituting back into Equation 1, we obtain the prediction model for the leakage rate. By analogy, we can obtain the prediction models for other stability terms.

[0022] It should be noted that the constant term The coefficient of the linear term represents the baseline performance level after all effects have been balanced. The quadratic coefficient is used to reveal the independent main effect of each component on performance, i.e., whether it promotes or inhibits it. To reveal the excess effect of a single component, i.e., whether there is an optimal amount to add or whether too much is as bad as too little, interaction term coefficients. To reveal the synergistic or antagonistic effect between the two components, i.e., whether 1+1>2 or 1+1<2, the random error term. This represents variations that the model could not explain, such as measurement errors and minor process variations. Different results can be obtained from multiple models corresponding to a certain stability term. The mean is determined.

[0023] It should be noted that when the composition includes three components, such as polytetrafluoroethylene (A), carbon fiber (B), and graphite (C), the component variables are x1 = the mass percentage of A, x2 = the mass percentage of B, and x3 = the mass percentage of C. Substituting these variables into Formula 1, the expanded formula is: + + ; There are a total of 10 β parameters (1 constant + 3 linear + 3 quadratic + 3 interactive).

[0024] When there are 4 components, there are 6 possible pairwise combinations, resulting in a total of 15 β parameters. These include 1 constant term, 4 linear terms (β1, β2, β3, β4), and 4 quadratic terms (β...). 11 ,β 22 ,β 33 ,β 44 There are 6 interactive items.

[0025] It should be noted that, typically, the number of sample data for each stability term is greater than the number of β parameters.

[0026] In one feasible implementation, the stability parameters include: the leakage rate (in sccm) measured using a helium mass spectrometer leak detector, the decay rate (in Pa / s) corresponding to the pressure decay curve detected at a specific positive pressure (e.g., 6 Bar), and the heat distortion temperature (in °C) determined by a thermomechanical analyzer.

[0027] It should be noted that the calculations involved in this application are performed using dimensionless exponents, so there is no need to consider whether the dimensions on both sides of the equation are the same.

[0028] In one feasible implementation, the components of the experimental material include: Polymer matrix and functional fillers; The polymer matrix includes polytetrafluoroethylene or soluble polytetrafluoroethylene; the functional fillers include carbon fibers, glass microspheres, and lubricants.

[0029] In one feasible implementation, the method further includes: Obtain the restriction ranges set for each component; Each set of values ​​is formed by randomly selecting values ​​from each restricted interval. The values ​​included in each data set are normalized, and the normalized values ​​of the data set are used as the target quality proportion of the corresponding component.

[0030] For example, the limit range is set as [20%, 40%] for component A, [30%, 70%] for component B, and [10%, 30%] for component C. When the randomly selected values ​​are A=0.4, B=0.3, and C=0.2, the above three values ​​are normalized, i.e., 0.4 / (0.4+0.3+0.2)=0.44, 0.3 / (0.4+0.3+0.2)=0.33, and 0.2 / (0.4+0.3+0.2)=0.23. That is, when normalizing, the sum of the normalized values ​​is set to 1 as the limit. The obtained values ​​are then used as the target mass percentage of the components, i.e., a formula. The formula is then input into the prediction model to calculate whether each stability parameter meets the requirements.

[0031] In one feasible implementation, the method further includes: outputting the target mass percentage when the target material meets the constraints.

[0032] Specifically, in order to facilitate subsequent verification or to produce a finished product of the target material for experimentation, it is necessary to output and display the target material that meets the constraints.

[0033] Figure 2This application provides a schematic diagram of a device for selecting the material ratio of a semiconductor diaphragm valve, as shown in the embodiments of the present application. Figure 2 As shown, the device includes: The acquisition unit 21 is used to acquire the stability parameters of each stability item and the mass percentage of each component for each experimental material, wherein some experimental materials include the same types of components; Construction unit 22 is used to construct a sample set corresponding to each stability item based on the stability parameter of each stability item and the mass ratio, wherein the sample set corresponding to each stability item includes multiple sample data, and each sample data is composed of the stability parameter corresponding to the stability item of each experimental material and the mass ratio. Input unit 23 is used to input each sample data corresponding to the stability term into the following formula 3 to obtain multiple models corresponding to the stability term; Formula 3 in, This refers to the stability parameter in a sample data set under this stability item. For constant terms, Let be the coefficient of the linear term of the i-th component in the sample data. Let be the coefficient of the quadratic term of the i-th component in the sample data. The coefficient of the interaction term between the i-th and j-th components in this sample data. The mass percentage of the i-th component in this sample data The mass percentage of the j-th component in this sample data The number of component types included in each experimental material. This is the random error term; Fitting unit 24 is used to fit multiple models corresponding to the stability term using the least squares method in multiple linear regression to obtain the prediction model corresponding to the stability term. The determination unit 25 is used to input the target mass ratio of each component into the prediction model with the target stability parameter range of each stability item as a constraint condition, and determine whether the target material meets the constraint condition.

[0034] In one feasible implementation, the stability terms include: the leakage rate measured using a helium mass spectrometer leak detector, the decay rate corresponding to the pressure decay curve detected under a specific positive pressure, and the heat distortion temperature determined by a thermomechanical analyzer.

[0035] In one feasible implementation, the components of the experimental material include: Polymer matrix and functional fillers; The polymer matrix includes polytetrafluoroethylene or soluble polytetrafluoroethylene; the functional fillers include carbon fibers, glass microspheres, and lubricants.

[0036] In one feasible implementation, the device further includes: The processing unit is configured to obtain the constraint range set for each component; and to randomly select values ​​from each constraint range to form a value group; and to normalize the values ​​included in each data group, and to use the normalized values ​​of the data group as the target quality proportion of the corresponding component.

[0037] In one feasible implementation, the device further includes: The output unit is used to output the target mass percentage when the target material meets the constraint conditions.

[0038] about Figure 2 For explanations of the principles behind the content shown, please refer to [link / reference]. Figure 1 The detailed explanations of the relevant content shown will not be repeated here.

[0039] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0040] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0041] In addition, the functional units in the embodiments provided in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0042] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0043] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0044] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the technical scope disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A method for selecting the material ratio of a semiconductor diaphragm valve, characterized in that, The method includes: Obtain the stability parameters of each stability item and the mass percentage of each component for each experimental material, wherein some experimental materials contain the same types of components; Based on the stability parameters of each stability item and the mass ratio, a sample set corresponding to each stability item is constructed. Each sample set corresponding to each stability item includes multiple sample data, and each sample data consists of the stability parameter corresponding to that stability item and the mass ratio of each experimental material. Input each sample data corresponding to the stability term into the following formula to obtain multiple models corresponding to the stability term; ； in, This refers to the stability parameter in a sample data set under this stability item. For constant terms, Let be the coefficient of the linear term of the i-th component in the sample data. Let be the coefficient of the quadratic term of the i-th component in the sample data. The coefficient of the interaction term between the i-th and j-th components in this sample data. The mass percentage of the i-th component in this sample data The mass percentage of the j-th component in this sample data The number of component types included in each experimental material. This is the random error term; By fitting multiple models corresponding to the stability term using the least squares method in multiple linear regression, the prediction model corresponding to the stability term is obtained. Using the target stability parameter range of each stability term as a constraint, the target mass ratio of each component is input into the prediction model to determine whether the target material meets the constraint.

2. The selection method as described in claim 1, characterized in that, The stability parameters include: the leakage rate measured using a helium mass spectrometer leak detector, the decay rate corresponding to the pressure decay curve detected under a specific positive pressure, and the heat distortion temperature measured by a thermomechanical analyzer.

3. The selection method as described in claim 1, characterized in that, The components of the experimental materials include: Polymer matrix and functional fillers; The polymer matrix includes polytetrafluoroethylene or soluble polytetrafluoroethylene; the functional fillers include carbon fibers, glass microspheres, and lubricants.

4. The selection method as described in claim 1, characterized in that, The method further includes: Obtain the restriction ranges set for each component; Each set of values ​​is formed by randomly selecting values ​​from each restricted interval. The values ​​included in each data set are normalized, and the normalized values ​​of the data set are used as the target quality proportion of the corresponding component.

5. The selection method as described in claim 1, characterized in that, The method further includes: When the target material meets the constraints, the target mass percentage is output.

6. A device for selecting the material ratio of a semiconductor diaphragm valve, characterized in that, The device includes: The acquisition unit is used to acquire the stability parameters of each stability item and the mass percentage of each component for each experimental material, wherein some experimental materials include the same types of components; The construction unit is used to construct a sample set corresponding to each stability item based on the stability parameter of each stability item and the mass ratio. The sample set corresponding to each stability item includes multiple sample data, and each sample data consists of the stability parameter corresponding to that stability item and the mass ratio of each experimental material. The input unit is used to input each sample data corresponding to the stability term into the following formula to obtain multiple models corresponding to the stability term; ； in, This refers to the stability parameter in a sample data set under this stability item. For constant terms, Let be the coefficient of the linear term of the i-th component in the sample data. Let be the coefficient of the quadratic term of the i-th component in the sample data. The coefficient of the interaction term between the i-th and j-th components in this sample data. The mass percentage of the i-th component in this sample data The mass percentage of the j-th component in this sample data The number of component types included in each experimental material. This is the random error term; The fitting unit is used to fit multiple models corresponding to the stability term using the least squares method in multiple linear regression to obtain the prediction model corresponding to the stability term. The determination unit is used to input the target mass ratio of each component into the prediction model, using the target stability parameter range of each stability item as a constraint, to determine whether the target material meets the constraint conditions.

7. The selection device as claimed in claim 6, characterized in that, The stability parameters include: the leakage rate measured using a helium mass spectrometer leak detector, the decay rate corresponding to the pressure decay curve detected under a specific positive pressure, and the heat distortion temperature measured by a thermomechanical analyzer.

8. The selection device as claimed in claim 6, characterized in that, The components of the experimental materials include: Polymer matrix and functional fillers; The polymer matrix includes polytetrafluoroethylene or soluble polytetrafluoroethylene; the functional fillers include carbon fibers, glass microspheres, and lubricants.

9. The selection device as claimed in claim 6, characterized in that, The device further includes: The processing unit is configured to obtain the constraint range set for each component; and to randomly select values ​​from each constraint range to form a value group; and to normalize the values ​​included in each data group, and to use the normalized values ​​of the data group as the target quality proportion of the corresponding component.

10. The selection device as claimed in claim 6, characterized in that, The device further includes: The output unit is used to output the target mass percentage when the target material meets the constraint conditions.

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