Simulation Method for AMC Flow Characteristics Inside FOUP Based on Mechanistic Model

Abstract

The present invention provides a simulation method for the internal AMC flow characteristics of a FOUP based on a mechanism model, which relates to the field of semiconductor technology and includes: establishing a mechanism model of the FOUP, and performing grid processing on the mechanism model of the FOUP by using a topological mapping segmentation method; selecting initial AMC simulation points through grid importance analysis, and optimizing the initial AMC simulation points by using the niche theory to obtain the layout of simulation sampling points; setting boundary conditions and initial conditions in combination with the actual working conditions of the FOUP, and performing simulation on the internal AMC flow characteristics of the FOUP based on the layout of simulation sampling points to obtain simulation results. The present invention can not only effectively solve the problem of simulating the internal AMC flow characteristics of the FOUP, but also provide a scientific basis and technical support for pollution control in the field of semiconductor manufacturing, and has important engineering value and application prospects.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor technology, and more particularly, to a method for simulating the internal AMC flow characteristics of a FOUP based on a mechanism model. Background Art

[0002] In the semiconductor manufacturing industry, the Front Opening Unified Pod (FOUP), as a standardized portable container, is widely used for storing and transporting wafers between different process equipment. Its core function is to provide a clean and stable microenvironment for wafers to avoid contamination of the wafer surface by external pollutants (such as airborne molecular contaminants, AMC), thereby ensuring the yield and reliability of chip manufacturing. During the operation of the FOUP, air management and pollutant diffusion are crucial factors. Especially during the accumulation and removal of pollutants inside the FOUP, factors such as air flow, air velocity, temperature, and humidity directly affect the distribution of pollutants. Particularly in semiconductor manufacturing, the management of airborne particles is of utmost importance, and the diffusion of any tiny particle or pollutant may lead to production defects. However, despite the strict sealing and filtering measures in the design of the FOUP, there are still potential flow and pollution problems, which may gradually accumulate during long-term transportation and affect the final wafer quality.

[0003] Although existing FOUP designs can control the diffusion of pollutants to a certain extent, in actual applications, the air flow characteristics and pollutant distribution inside the FOUP are still uneven. Most current simulation methods mainly focus on rough estimates of air flow and pollutant distribution, but these methods often ignore the effects of subtle flows and local pollution sources, resulting in simulation results that cannot fully and accurately predict the pollutant diffusion pattern inside the FOUP.

[0004] Regarding the problems in the related art, no effective solution has been proposed yet. Summary of the Invention

[0005] In view of this, the present invention provides a method for simulating the internal AMC flow characteristics of a FOUP based on a mechanism model to solve the above-mentioned problems.

[0006] To solve the above problems, the specific technical solution adopted by the present invention is as follows:

[0007] A method for simulating the internal AMC flow characteristics of a FOUP based on a mechanism model includes the following steps:

[0008] S1. Obtain the structural parameter information of the FOUP and establish a mechanism model of the FOUP, and perform grid processing on the mechanism model of the FOUP using the topological mapping segmentation method;

[0009] S2. Based on the mechanism model of the FOUP after grid processing, select the initial AMC simulation points through grid importance analysis, and optimize the initial AMC simulation points using the niche theory to obtain the layout of the simulation sampling points.

[0010] S3. Set the boundary conditions and initial conditions in combination with the actual working conditions of the FOUP, and perform a simulation of the internal AMC flow characteristics of the FOUP based on the layout of the simulation sampling points to obtain the simulation results.

[0011] Preferably, for obtaining the structural parameter information of the FOUP and establishing the mechanism model of the FOUP, the grid processing of the mechanism model of the FOUP using the topological mapping segmentation method includes the following steps:

[0012] S11. Collect the structural parameters of the FOUP based on the FOUP design document, and use modeling software to construct the mechanism model of the FOUP, including the air inlet, air outlet, tray position, and air flow channel.

[0013] S12. Determine the internal space of the FOUP based on the mechanism model of the FOUP, and use the Gaussian mixture model to divide the internal space of the FOUP to obtain a regular-shaped area and an irregular-shaped area.

[0014] S13. For the regular-shaped area and the irregular-shaped area, perform grid processing using the topological mapping segmentation method respectively.

[0015] S14. For the regular-shaped area and the irregular-shaped area after grid processing, connect the grids of the regular-shaped area and the irregular-shaped area through the grid fusion method.

[0016] Preferably, for determining the internal space of the FOUP based on the mechanism model of the FOUP and using the Gaussian mixture model to divide the internal space of the FOUP to obtain a regular-shaped area and an irregular-shaped area, it includes the following steps:

[0017] S121. Perform particle discretization processing on the internal space of the FOUP to obtain a meshless particle set.

[0018] S122. For each particle in the meshless particle set, find the adjacent particles within a preset search radius according to its spatial position to form a neighborhood set, and extract the particle features of the neighborhood particles of each particle. The particle features include particle coordinates, coordinate variance, and local particle density.

[0019] S123. According to the particle features, perform clustering analysis on each particle in the meshless particle set using two-dimensional singular value decomposition and the Gaussian mixture model to obtain the clustering results.

[0020] S124. Divide the internal space of the FOUP based on the clustering results to obtain regular-shaped regions and irregular-shaped regions.

[0021] Preferably, the steps of performing clustering analysis on each particle in the meshless particle set according to the particle characteristics by using two-dimensional singular value decomposition and Gaussian mixture model to obtain the clustering results include:

[0022] S1231. Organize the particle characteristics to form a particle characteristic matrix, and construct a covariance matrix according to the particle characteristic matrix;

[0023] S1232. Decompose the covariance matrix by using the two-dimensional singular value decomposition method to obtain eigenvectors and eigenvalues, and use the eigenvectors to transform the particle characteristic matrix to obtain a reduced-dimensional characteristic matrix;

[0024] S1233. Flatten the reduced-dimensional characteristic matrix of each particle into a one-dimensional vector, and construct a new characteristic data set;

[0025] S1234. Perform clustering analysis on the new data set by using the Gaussian mixture model to obtain the clustering results.

[0026] Preferably, the steps of performing meshing processing on the regular-shaped regions and irregular-shaped regions respectively by using the topological mapping segmentation method include:

[0027] S131. For the regular-shaped regions and irregular-shaped regions, construct initial computational domains homeomorphic to hexahedrons respectively;

[0028] S132. Adopt the planning strategy in the topological mapping segmentation method to decompose the initial computational domain into multiple sub-regions, and perform boundary parameterization processing on each sub-region to determine the parameter range;

[0029] S133. Based on the geometric shape characteristics of the regular-shaped regions and irregular-shaped regions, calculate the boundary function, and calculate the surface node coordinate values of the sub-regions according to the boundary function;

[0030] S134. Connect the surface node coordinates of each sub-region to form a quadrilateral mesh, and perform spatial splicing on the quadrilateral mesh to generate a hexahedron mesh.

[0031] Preferably, the dividing the internal space of the FOUP based on the clustering results to obtain regular-shaped regions and irregular-shaped regions includes:

[0032] Calculate the statistical characteristics of each cluster, including the mean and covariance matrix;

[0033] Based on the statistical features of each obtained cluster, the internal space of the FOUP is divided into regular-shaped regions and irregular-shaped regions.

[0034] Preferably, for the mechanism model of the FOUP after grid processing, initial AMC simulation points are selected through grid importance analysis, and the initial AMC simulation points are optimized using the niche theory to obtain the simulation sampling point layout, including the following steps:

[0035] S21. Collect the AMC historical data of the FOUP, combine with the emission characteristics of AMC, and identify and locate the positions of pollution sources;

[0036] S22. Based on the positions of pollution sources, use the mechanism model of the FOUP after grid processing to conduct importance analysis on the grids, and select initial AMC simulation points according to the analysis results;

[0037] S23. Use the niche theory to optimize the distribution of the initial AMC simulation points to generate the final simulation sampling point layout.

[0038] Preferably, the collection of the AMC historical data of the FOUP, combined with the emission characteristics of AMC, and the identification and location of the positions of pollution sources include the following steps:

[0039] S211. Based on the pre-configured monitoring system of the FOUP, obtain the AMC historical data for a preset time period and perform preprocessing, and the preprocessing includes data cleaning, missing value filling, and outlier processing;

[0040] S212. According to the preprocessed AMC historical data, identify and mark the over-standard events by analyzing the changes in pollutant concentrations;

[0041] S213. Based on the mechanism model of the FOUP, use computational fluid dynamics to simulate the airflow path inside the FOUP, and combine with the over-standard events to analyze the diffusion path of pollutants and judge the diffusion source of pollutants;

[0042] S214. Combine the equipment layout of the FOUP and the diffusion source of pollutants, and determine the position of the pollution source through a positioning algorithm.

[0043] Preferably, the importance analysis of the grids based on the positions of pollution sources using the mechanism model of the FOUP after grid processing and the selection of initial AMC simulation points according to the analysis results include the following steps:

[0044] S221. Determine the influence range of the pollution source on the internal environment of the FOUP based on the position of the pollution source;

[0045] S222. Conduct importance analysis on each grid cell inside the FOUP according to the influence range of the pollution source on the internal environment of the FOUP;

[0046] S223. Select key grid cells as the initial AMC simulation points according to the importance analysis results of the grid.

[0047] Preferably, the optimization of the distribution of the initial AMC simulation points by using the niche theory to generate the final layout of the simulation sampling points includes the following steps:

[0048] S231. For the initial AMC simulation points, use the niche overlap method to calculate the overlap degree of every two initial AMC simulation points in the influence range of the internal environment of the FOUP, and use the niche dominance method to calculate the niche dominance of each initial AMC simulation point relative to other initial AMC simulation points;

[0049] S232. Analyze the redundancy and competitive advantages between the simulation points by combining the results of niche overlap and niche dominance; <s

[0050] S233. Based on the analysis results, adjust the layout of the simulation points to obtain the final layout of the simulation sampling points.

[0051] The beneficial effects of the present invention are as follows: The present invention uses the topological mapping segmentation method to perform grid processing on the FOUP, decomposes the complex geometric structure into multiple small units, facilitates the refined analysis of each local area, can improve the spatial resolution of the simulation, make the results more accurate, identify the key areas with the greatest impact on pollution diffusion through the importance analysis of the gridded model, thereby selectively choosing the initial simulation points in a targeted manner, reducing the waste of unnecessary computing resources, capturing the physical laws inside the FOUP through the mechanism model, and simultaneously using the numerical simulation technology to simulate the flow characteristics, realizing the leap from theory to practice. It can not only effectively solve the problem of simulating the AMC flow characteristics inside the FOUP, but also provide a scientific basis and technical support for pollution control in the semiconductor manufacturing field, and has important engineering value and application prospects. Description of the Drawings

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. In the drawings:

[0053] Figure 1 is a flowchart of the method for simulating the AMC flow characteristics inside the FOUP based on the mechanism model according to the embodiment of the present invention. Detailed Embodiments

[0054] To enable those skilled in the art to better understand the technical solutions in this application, the following will clearly and completely describe the technical solutions in the embodiments of this application in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0055] According to an embodiment of the present invention, a method for simulating the internal AMC flow characteristics of a FOUP based on a mechanism model is provided.

[0056] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments. As Figure 1 shown, the method for simulating the internal AMC flow characteristics of a FOUP based on a mechanism model according to an embodiment of the present invention includes the following steps:

[0057] S1. Obtain the structural parameter information of the FOUP and establish a mechanism model of the FOUP, and perform grid processing on the mechanism model of the FOUP using the topological mapping segmentation method;

[0058] As a preferred embodiment, the step of obtaining the structural parameter information of the FOUP and establishing a mechanism model of the FOUP, and performing grid processing on the mechanism model of the FOUP using the topological mapping segmentation method includes the following steps:

[0059] S11. Collect the structural parameters of the FOUP based on the FOUP design document, and use modeling software to construct a mechanism model of the FOUP, including the air inlet, air outlet, tray position, and air flow channel;

[0060] It should be noted that key structural parameters are extracted from the design document, including: the position, size, and shape of the air inlet; the position, size, and shape of the air outlet; the size, number, and relative position of the trays; the size and layout of the air flow channel.

[0061] S12. Determine the internal space of the FOUP based on the mechanism model of the FOUP, and use the Gaussian mixture model to divide the internal space of the FOUP to obtain regular-shaped regions and irregular-shaped regions;

[0062] As a preferred embodiment, the step of determining the internal space of the FOUP based on the mechanism model of the FOUP, and using the Gaussian mixture model to divide the internal space of the FOUP to obtain regular-shaped regions and irregular-shaped regions includes the following steps:

[0063] S121. Perform particle discretization processing on the internal space of the FOUP to obtain a meshless particle set;

[0064] It should be noted that discretizing the internal space of the FOUP into particles is a method to convert the internal space of the FOUP into a particle representation. The meshless method is a discretization technique that does not rely on traditional grids but represents the space by distributing particles in the space. The particles are freely distributed in the space and can adapt to different physical phenomena in irregular geometries.

[0065] Each particle not only represents a position during discretization but also has physical properties, such as the three-dimensional coordinate position of the particle, the flow velocity, density, temperature, etc. of the particle.

[0066] S122. For each particle in the meshless particle set, find adjacent particles within a preset search radius according to its spatial position to form a neighborhood set, and extract particle features from the neighboring particles of each particle. The particle features include particle coordinates, coordinate variance, and local particle density.

[0067] Specifically, the search radius is a preset spatial range representing the maximum distance that a particle can sense in the space. S123. According to the particle features, perform clustering analysis on each particle in the meshless particle set using two-dimensional singular value decomposition and Gaussian mixture model to obtain a clustering result.

[0068] As a preferred embodiment, the step of performing clustering analysis on each particle in the meshless particle set using two-dimensional singular value decomposition and Gaussian mixture model according to the particle features to obtain a clustering result includes the following steps:

[0069] S1231. Organize the particle features to form a particle feature matrix, and construct a covariance matrix based on the particle feature matrix.

[0070] It should be noted that the covariance matrix is an important tool for measuring the mutual relationship between various features and is usually used to describe the distribution of the data set and the correlation between features. Before constructing the covariance matrix, it is usually necessary to standardize the particle feature matrix. The purpose of standardization is to ensure that each feature contributes equally to the covariance matrix and avoid some features with larger scales (such as coordinates) dominating the covariance calculation.

[0071] S1232. Decompose the covariance matrix using the two-dimensional singular value decomposition method to obtain eigenvectors and eigenvalues, and transform the particle feature matrix using the eigenvectors to obtain a reduced-dimensional feature matrix.

[0072] The transformation of the particle feature matrix is to transform the original particle feature matrix using the selected eigenvectors. Specifically, it is to multiply the original feature matrix by the transpose of the selected eigenvector matrix. Project the original high-dimensional feature space onto a low-dimensional space to obtain a reduced-dimensional feature matrix.

[0073] S1233. Flatten the dimensionality-reduced feature matrix of each particle into a one-dimensional vector and construct a new feature dataset;

[0074] It should be noted that after two-dimensional singular value decomposition (SVD) or other dimensionality reduction methods, each particle will correspond to a dimensionality-reduced feature matrix. This feature matrix is usually a low-dimensional representation, where each row (or column, depending on the specific dimensionality reduction method and implementation) of the feature matrix represents the features of a particle. In addition, the flattening operation arranges all the elements of the matrix in row (or column) order into a long vector. For example, if the dimensionality-reduced feature matrix of a particle is a 2×3 matrix, then the flattened one-dimensional vector will contain 6 elements.

[0075] S1234. Use the Gaussian mixture model to perform clustering analysis on the new dataset to obtain the clustering result.

[0076] S124. Based on the clustering result, divide the internal space of the FOUP into a regular-shaped region and an irregular-shaped region.

[0077] Specifically, after clustering by the Gaussian mixture model (GMM), the cluster (regular region or irregular region) to which each particle belongs has been obtained. The clustering result not only gives the cluster to which each particle belongs, but also can further describe the characteristics of each cluster through information such as the mean and covariance of the cluster, thereby providing a basis for region division.

[0078] In addition, divide the internal space of the FOUP according to the clustering result. Specifically, it includes:

[0079] (1) Calculate the statistical features of each cluster, including the mean and covariance matrix;

[0080] (2) Based on the features (mean and covariance matrix) of each obtained cluster, divide the internal space of the FOUP into two types of regions:

[0081] Regular-shaped region: Consider the clusters with concentrated means and small covariance matrices in the clustering result as regular-shaped regions. The particle distribution is uniform and the region shape is relatively regular.

[0082] Irregular-shaped region: Consider the clusters with dispersed means and large covariance matrices in the clustering result as irregular-shaped regions. The particle distribution is sparse and the region shape is asymmetric or complex.

[0083] S13. For the regular-shaped region and the irregular-shaped region, use the topological mapping segmentation method to perform grid processing respectively;

[0084] As a preferred embodiment, for the regular-shaped regions and irregular-shaped regions, the grid processing using the topological mapping segmentation method respectively includes the following steps:

[0085] S131. For the regular-shaped regions and irregular-shaped regions, respectively construct an initial computational domain homeomorphic to a hexahedron;

[0086] Specifically, regular-shaped regions usually have simple geometric boundaries (such as cuboids, cylinders, etc.), and their topological structures directly correspond to hexahedrons. When constructing the initial computational domain, it can be directly mapped to a standard hexahedron. The construction steps include:

[0087] (1) Parameterize the boundaries of the regular region into the six faces of a hexahedron. For example: a cuboid region can be mapped to a parameter space of [0,1]×[0,1]×[0,1]. A cylindrical region can be parameterized by polar coordinates and then mapped to a hexahedron (such as radially mapping to [0,1], axially mapping to [0,1], and circumferentially unfolded into a plane).

[0088] (2) Use a linear transformation (such as an affine transformation) to map the physical coordinates to the parameter coordinates.

[0089] The boundaries of irregular-shaped regions (such as complex mechanical parts, biological tissues) have large curvature changes and cannot be directly mapped to a hexahedron. It is necessary to decompose them into multiple parameterizable sub-regions through piecewise homeomorphic mapping or geometric deformation. The construction steps include:

[0090] Step 1, divide the irregular region into multiple sub-regions, and each sub-region is approximated as a regular shape (such as a quadrilateral, a hexahedron block). Step 2, construct a mapping homeomorphic to a hexahedron for each sub-region. For example, map the sub-region boundary to a unit square or cube through a radial basis function (RBF) or harmonic mapping. Step 3, minimize the mapping distortion (such as the Jacobian determinant approaching 1) to avoid distortion during mesh generation.

[0091] S132. Adopt the planning strategy in the topological mapping segmentation method to decompose the initial computational domain into multiple sub-regions, and perform boundary parameterization processing on each sub-region to determine the parameter range;

[0092] It should be noted that after constructing the initial computational domains of the regular region and the irregular region, the next step is to further decompose these regions into multiple sub-regions and perform boundary parameterization processing on each sub-region. Among them, the regular region is divided into grids in equal proportion to generate uniformly distributed sub-regions (for example, equally spaced division). The irregular region adopts adaptive division and is dynamically divided according to the boundary curvature or geometric features within the region. Where the curvature within the region is large, the mesh division is relatively dense; while where the curvature is small, the mesh division is relatively sparse.

[0093] S133. Calculate the boundary function based on the geometric shape features of the regular-shaped region and the irregular-shaped region, and calculate the surface node coordinate values of the sub-region according to the boundary function;

[0094] As a preferred embodiment, the calculation formula for calculating the boundary function based on the geometric shape features of the regular-shaped region and the irregular-shaped region, and calculating the surface node coordinate values of the sub-region according to the boundary function is:

[0095] ;

[0096] ;

[0097] ;

[0098] In the formula, X(r, s), Y(r, s), and Z(r, s) represent the XYZ coordinate values of the nodes on the surface of the sub-region in the Cartesian coordinate system, r and s represent two parameters in the natural coordinate system, which are used to represent the relative position of any point on the surface, a1(r) and a2(r) respectively represent the boundary functions related to the r direction on the surface boundary, a1(s) and a2(s) respectively represent the boundary functions related to the r direction on the surface boundary, X(0, 0), X(1, 0), X(0, 1), and X(1, 1) respectively represent the X coordinate values of the four corner points of the quadrilateral in the Cartesian coordinate system, Y(0, 0), Y(1, 0), Y(0, 1), and Y(1, 1) respectively represent the Y coordinate values of the four corner points of the quadrilateral in the Cartesian coordinate system, and Z(0, 0), Z(1, 0), Z(0, 1), and Z(1, 1) respectively represent the Z coordinate values of the four corner points of the quadrilateral in the Cartesian coordinate system.

[0099] S134. Connect the surface node coordinates of each sub-region to form a quadrilateral mesh, and perform spatial splicing on the quadrilateral mesh to generate a hexahedron mesh.

[0100] It should be noted that the surface of each sub-region obtains node coordinates through boundary parameterization, and these node coordinates are known in the local coordinate system. Connect the surface nodes of each sub-region to form a quadrilateral mesh. Once the quadrilateral mesh nodes of all sub-regions are calculated, the next step is to splice the meshes of these sub-regions to finally generate a hexahedron mesh.

[0101] S14. For the regular-shaped region and the irregular-shaped region after meshing, connect the meshes of the regular-shaped region and the irregular-shaped region through the mesh fusion method.

[0102] After grid processing, for regular-shaped regions and irregular-shaped regions, the Mesh Merging Method can be used to connect these two different-shaped meshes, thereby forming a coherent overall mesh.

[0103] S2. Based on the mechanism model of the FOUP after grid processing, initial AMC simulation points are selected through grid importance analysis, and the initial AMC simulation points are optimized using the niche theory to obtain the layout of simulation sampling points.

[0104] As a preferred implementation manner, the mechanism model of the FOUP after grid processing selects initial AMC simulation points through grid importance analysis, and uses the niche theory to optimize the initial AMC simulation points to obtain the layout of simulation sampling points, including the following steps:

[0105] S21. Collect the AMC historical data of the FOUP, and combine the emission characteristics of the AMC to identify and locate the source of pollution.

[0106] As a preferred implementation manner, the collection of the AMC historical data of the FOUP, combined with the emission characteristics of the AMC, to identify and locate the source of pollution includes the following steps:

[0107] S211. Based on the pre-configured monitoring system of the FOUP, obtain the AMC historical data for a preset time period and perform preprocessing, and the preprocessing includes data cleaning, missing value filling, and outlier processing.

[0108] It should be noted that the historical data of the AMC is collected through the monitoring system of the FOUP. These data include the concentration changes of pollutants in the internal environment of the FOUP, the air flow path, temperature and humidity, and other related data. The monitoring system can include gas sensors, flow meters, temperature and humidity sensors, etc.

[0109] S212. According to the preprocessed AMC historical data, by analyzing the changes in pollutant concentration, identify and mark the exceeded standard events.

[0110] S213. Based on the mechanism model of the FOUP, use computational fluid dynamics to simulate the air flow path inside the FOUP, and combine the exceeded standard events to analyze the diffusion path of the pollutants and judge the diffusion source of the pollutants.

[0111] Specifically, through CFD simulation, the flow situation of the air flow inside the FOUP can be calculated. The CFD model solves the situation of air flow and heat transfer through fluid dynamics equations (such as the Navier-Stokes equation). The simulation results will show the air flow path, including the velocity distribution and pressure distribution of the air flow, revealing the air flow characteristics inside the FOUP.

[0112] S214. Combine the equipment layout of the FOUP and the diffusion source of pollutants, and determine the location of the pollution source through a positioning algorithm.

[0113] Specifically, combining the equipment layout of the FOUP with the diffusion source of the pollution source to determine the final location of the pollution source includes: First, analyze the influence range of the pollution source in the equipment space according to the equipment layout of the FOUP and the air flow channel;

[0114] Then, combine the air flow path of the FOUP, the pollutant concentration distribution, the reverse speculation result and the equipment layout to determine the accurate location of the pollution source; Finally, through a comprehensive variety of analysis means, finally locate the specific location of the pollution source.

[0115] S22. Based on the location of the pollution source, use the FOUP mechanism model after grid processing to analyze the importance of the grids, and select the initial AMC simulation points according to the analysis results;

[0116] As a preferred implementation manner, the step of analyzing the importance of the grids based on the location of the pollution source, using the FOUP mechanism model after grid processing, and selecting the initial AMC simulation points according to the analysis results includes the following steps:

[0117] S221. Determine the influence range of the pollution source on the internal environment of the FOUP based on the location of the pollution source;

[0118] S222. Analyze the importance of each grid unit in the FOUP according to the influence range of the pollution source on the internal environment of the FOUP;

[0119] S223. According to the analysis results of the importance of the grids, select the key grid units as the initial AMC simulation points.

[0120] S23. Use the niche theory to optimize the distribution of the initial AMC simulation points to generate the final layout of the simulation sampling points.

[0121] As a preferred implementation manner, the step of using the niche theory to optimize the distribution of the initial AMC simulation points to generate the final layout of the simulation sampling points includes the following steps:

[0122] S231. For the initial AMC simulation points, use the niche overlap method to calculate the overlap degree of every two initial AMC simulation points on the influence range of the internal environment of the FOUP, and use the niche dominance method to calculate the niche dominance of each initial AMC simulation point relative to other initial AMC simulation points;

[0123] The calculation formula for using the niche overlap method to calculate the overlap degree of every two initial AMC simulation points on the influence range of the internal environment of the FOUP is:

[0124] ;

[0125] The calculation formula for using the niche advantage method to calculate the niche advantage of each initial AMC simulation point relative to other initial AMC simulation points is as follows:

[0126] ;

[0127] In the formula, G ij represents the overlap degree of the influence ranges of the initial AMC simulation point i and the initial AMC simulation point j in the FOUP internal environment, M represents the number of initial AMC simulation points, m represents the index value of the initial AMC simulation point, C i represents the influence range of the initial AMC simulation point i in the FOUP internal environment, C j represents the influence range of the initial AMC simulation point j in the FOUP internal environment, H represents the number of grids of the FOUP mechanism model, h represents the index value of the grid of the FOUP mechanism model, P ij represents the niche advantage of the initial AMC simulation point i relative to the initial AMC simulation point j, L i>j,hn represents the situation where the influence range of the initial AMC simulation point i in the FOUP internal environment is greater than that of the initial AMC simulation point j in the FOUP internal environment;

[0128] S232. Analyze the redundancy and competitive advantages between simulation points by combining the results of niche overlap and niche advantage;

[0129] It should be noted that redundancy reflects the repeatability and ineffectiveness of two or more initial AMC simulation points in the same area. If the influence ranges of two simulation points overlap greatly and their contributions to the pollutant diffusion path are similar, there is redundancy.

[0130] S233. Based on the analysis results, adjust the layout of the simulation points to obtain the final layout of the simulation sampling points.

[0131] Specifically, according to the redundancy analysis, delete or rearrange the redundant simulation points with a high overlap degree. By adjusting the positions of these redundant points, ensure the uniform distribution of the simulation points inside the FOUP and avoid unnecessary repeated calculations.

[0132] In addition, combining the analysis results of redundancy and competitive advantage, use optimization algorithms (such as genetic algorithms, simulated annealing, particle swarm optimization, etc.) to further adjust the layout of the simulation points to make them evenly distributed in the entire FOUP internal environment, reduce redundancy, and improve the coverage rate.

[0133] S3. Set boundary conditions and initial conditions in combination with the actual working conditions of the FOUP, and simulate the AMC flow characteristics inside the FOUP based on the layout of the simulation sampling points to obtain the simulation results.

[0134] Among them, the initial conditions are the settings of the internal fluid state of the FOUP at the start of the simulation. Common initial conditions include: the initial air velocity distribution inside the FOUP, the initial concentration distribution of pollutants, the initial temperature and humidity inside the FOUP, the distribution of the initial pressure, and so on.

[0135] In addition, based on the layout of the simulation sampling points, the CFD tool can accurately simulate the air flow characteristics and pollutant diffusion inside the FOUP.

[0136] In summary, by means of the above technical solutions of the present invention, the present invention uses the topological mapping segmentation method to perform grid processing on the FOUP, decomposes the complex geometric structure into multiple small units, facilitates the refined analysis of each local area, can improve the spatial resolution of the simulation, make the results more accurate, identify the key areas with the greatest impact on pollution diffusion through the importance analysis of the grid-based model, thereby selectively choosing the initial simulation points in a targeted manner, reducing the waste of unnecessary computing resources, capturing the physical laws inside the FOUP through the mechanism model, and simultaneously using the numerical simulation technology to simulate the flow characteristics, achieving the leap from theory to practice. It can not only effectively solve the problem of simulating the AMC flow characteristics inside the FOUP, but also provide a scientific basis and technical support for pollution control in the semiconductor manufacturing field, and has important engineering value and application prospects.

[0137] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program code.

[0138] The specific embodiments described above have further elaborated on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A simulation method for the internal AMC flow characteristics of FOUP based on a mechanism model, characterized in that Including the following steps: S1. Obtain the structural parameter information of the FOUP and establish a mechanism model of the FOUP, and perform grid processing on the mechanism model of the FOUP by using the topological mapping segmentation method; S2. Based on the mechanism model of the FOUP after grid processing, select the initial AMC simulation points through grid importance analysis, and optimize the initial AMC simulation points by using the niche theory to obtain the simulation sampling point layout; S3. Set the boundary conditions and initial conditions in combination with the actual working conditions of the FOUP, and perform simulation on the internal AMC flow characteristics of the FOUP based on the simulation sampling point layout to obtain the simulation results; The step of obtaining the structural parameter information of the FOUP and establishing a mechanism model of the FOUP, and performing grid processing on the mechanism model of the FOUP by using the topological mapping segmentation method includes the following steps: S11. Collect the structural parameters of the FOUP based on the FOUP design document, and use modeling software to construct a mechanism model of the FOUP, including the air inlet, air outlet, tray position and air flow channel; S12. Determine the internal space of the FOUP based on the mechanism model of the FOUP, and use the Gaussian mixture model to divide the internal space of the FOUP to obtain regular-shaped regions and irregular-shaped regions; S13. For the regular-shaped regions and irregular-shaped regions, use the topological mapping segmentation method to perform grid processing respectively; S14. For the regular-shaped regions and irregular-shaped regions after grid processing, connect the grids of the regular-shaped regions and irregular-shaped regions through the grid fusion method.

2. The simulation method for the internal AMC flow characteristics of the FOUP based on the mechanism model according to claim 1, wherein The step of determining the internal space of the FOUP based on the mechanism model of the FOUP, and using the Gaussian mixture model to divide the internal space of the FOUP to obtain regular-shaped regions and irregular-shaped regions includes the following steps: S121. Perform particle discretization processing on the internal space of the FOUP to obtain a meshless particle set; S122. For each particle in the meshless particle set, according to its spatial position, find adjacent particles by using a preset search radius to form a neighborhood set, and extract particle features from the neighborhood particles of each particle. The particle features include particle coordinates, coordinate variance and local particle density; S123. According to the particle features, perform clustering analysis on each particle in the meshless particle set by using two-dimensional singular value decomposition and Gaussian mixture model to obtain a clustering result; S124. Based on the clustering result, divide the internal space of the FOUP to obtain regular-shaped regions and irregular-shaped regions.

3. The simulation method for the internal AMC flow characteristics of the FOUP based on the mechanism model according to claim 2, wherein The step of performing clustering analysis on each particle in the meshless particle set by using two-dimensional singular value decomposition and Gaussian mixture model according to the particle features to obtain a clustering result includes the following steps: S1231. Organize the particle features to form a particle feature matrix, and construct a covariance matrix according to the particle feature matrix; S1232. Decompose the covariance matrix by using the two-dimensional singular value decomposition method to obtain eigenvectors and eigenvalues, and use the eigenvectors to transform the particle feature matrix to obtain a reduced-dimensional feature matrix; S1233. Flatten the dimensionality-reduced feature matrix of each particle into a one-dimensional vector and construct a new feature dataset; S1234. Use the Gaussian mixture model to perform clustering analysis on the new dataset to obtain the clustering result.

4. The simulation method for the internal AMC flow characteristics of a FOUP based on a mechanism model according to claim 2, characterized in that For the regular-shaped region and the irregular-shaped region, the grid processing using the topological mapping segmentation method respectively includes the following steps: S131. For the regular-shaped region and the irregular-shaped region, respectively construct an initial computational domain homeomorphic to a hexahedron; S132. Adopt the planning strategy in the topological mapping segmentation method to decompose the initial computational domain into multiple sub-regions, and perform boundary parameterization processing on each sub-region to determine the parameter range; S133. Based on the geometric shape characteristics of the regular-shaped region and the irregular-shaped region, calculate the boundary function, and calculate the surface node coordinate values of the sub-regions according to the boundary function; S134. Connect the surface node coordinates of each sub-region to form a quadrilateral grid, and perform spatial splicing on the quadrilateral grid to generate a hexahedron grid.

5. The simulation method for the internal AMC flow characteristics of the FOUP based on the mechanism model according to claim 3, wherein The regional division of the internal space of the FOUP based on the clustering result to obtain a regular-shaped region and an irregular-shaped region includes: Calculate the statistical characteristics of each cluster, including the mean and covariance matrix; Based on the statistical characteristics of each obtained cluster, divide the internal space of the FOUP into a regular-shaped region and an irregular-shaped region.

6. The simulation method for the AMC flow characteristics inside the FOUP based on the mechanism model according to claim 1, wherein Based on the mechanism model of the FOUP after grid processing, select the initial AMC simulation points through grid importance analysis, and optimize the initial AMC simulation points using the niche theory to obtain the simulation sampling point layout, including the following steps: S21. Collect the AMC historical data of the FOUP, combine the emission characteristics of the AMC, and identify and locate the pollution source location; S22. Based on the pollution source location, use the mechanism model of the FOUP after grid processing to perform importance analysis on the grid, and select the initial AMC simulation points according to the analysis results; S23. Use the niche theory to optimize the distribution of the initial AMC simulation points to generate the final simulation sampling point layout.

7. The simulation method for the AMC flow characteristics inside the FOUP based on the mechanism model according to claim 6, wherein The collection of the AMC historical data of the FOUP, combined with the emission characteristics of the AMC, to identify and locate the pollution source location includes the following steps: S211. Based on the pre-configured monitoring system of the FOUP, obtain the AMC historical data of the preset time period and perform preprocessing, and the preprocessing includes data cleaning, missing value filling, and outlier processing; S212. According to the preprocessed AMC historical data, identify and mark the over-standard events by analyzing the change of pollutant concentration; S213. Based on the mechanism model of the FOUP, use computational fluid dynamics to simulate the airflow path inside the FOUP, and combine the over-standard events to analyze the diffusion path of the pollutants and judge the diffusion source of the pollutants; S214. Combine the equipment layout of the FOUP and the diffusion source of the pollutants, and determine the pollution source location through the positioning algorithm.

8. The method for simulating the AMC flow characteristics inside the FOUP based on the mechanism model according to claim 7, wherein Based on the pollution source location, use the mechanism model of the FOUP after grid processing to perform importance analysis on the grid, and select the initial AMC simulation points according to the analysis results, including the following steps: S221. Determine the influence range of the pollution source on the internal environment of the FOUP based on the location of the pollution source; S222. Conduct importance analysis on each grid cell within the FOUP according to the influence range of the pollution source on the internal environment of the FOUP; S223. Select key grid cells as the initial AMC simulation points according to the importance analysis results of the grids.

9. The simulation method for the internal AMC flow characteristics of the FOUP based on the mechanism model according to claim 7, characterized in that The steps for optimizing the distribution of the initial AMC simulation points by using the niche theory to generate the final layout of the simulation sampling points are as follows: S231. For the initial AMC simulation points, calculate the overlap degree of every two initial AMC simulation points on the influence range of the internal environment of the FOUP by using the niche overlap method, and calculate the niche dominance of each initial AMC simulation point relative to other initial AMC simulation points by using the niche dominance method; S232. Analyze the redundancy and competitive advantages among the simulation points by combining the results of niche overlap and niche dominance; S233. Adjust the layout of the simulation points based on the analysis results to obtain the final layout of the simulation sampling points.

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