Photoresist spin-coating process simulation method and device, computer equipment and storage medium

Abstract

The invention relates to a photoresist spin-coating process simulation method and device, computer equipment and a storage medium. The method comprises the following steps: establishing a fan-shaped simulation grid based on parameter information of a target wafer, position information of a photoresist injection port and an angle of the photoresist injection port; performing grid division on the fan-shaped simulation grid by adopting a two-dimensional adaptive grid encryption algorithm to obtain a target simulation grid; constructing a two-phase fluid mathematical model; the two-phase fluid comprises an air phase and a photoresist phase; and performing iterative solution on the two-phase fluid mathematical model based on the boundary condition of the target simulation grid, the photoresist spin-coating process parameters, the air fluid attribute and the photoresist fluid attribute, and determining a photoresist spin-coating process simulation image of the target wafer. According to the method, the two-phase junction of the two-phase fluid in the photoresist spin-coating process can be accurately encrypted, and the geometric constraint of a two-dimensional model is strictly followed, so that a reliable photoresist spin-coating process simulation result is obtained.

Description

Technical Field

[0001] The present application relates to the technical field of photolithography process simulation, and in particular to a method, device, computer equipment and storage medium for simulating a photoresist spin coating process. Background Art

[0002] In semiconductor manufacturing, photolithography is the core of integrated circuit planar processes, and the uniformity of the spin coating process, as a sub-process, impacts chip yield. Spin coating is commonly used for photoresist coating, utilizing the centrifugal force of wafer rotation to uniformly deposit the photoresist film. Photoresist thickness is affected by parameters such as wafer rotation speed and spin coating time. Traditional experimental optimization is costly and time-consuming. However, mature computational fluid dynamics (CFD) simulation technology can optimize process parameters, provide data support, and enable rapid design iterations to meet diverse product requirements. This improves the predictability and controllability of the manufacturing process, promotes innovation and product advancement, and is a key technical tool in modern micro-nanofabrication. Because photoresist thickness is precisely controlled by multiple process parameters, such as wafer rotation speed, spin coating time, humidity, and temperature, optimizing photoresist coating performance through traditional experiments is expensive and time-consuming. However, with the increasing maturity of CFD simulation technology, optimizing these process parameters has become possible. Simulation of the photoresist spin coating process provides process engineers with valuable data support when designing spin coating process parameters. By adjusting different parameters such as spin coating speed, rotation speed, photoresist concentration, etc. in the simulation model, the best combination of process parameters can be predicted and optimized to achieve the best pattern resolution and production efficiency. When simulating the photolithography process, the OpenFOAM (OpenSource Field Operation and Manipulation) software package is often used to simulate the two-phase fluid in the photolithography process. However, when simulating the two-phase fluid, OpenFOAM currently only supports adaptive mesh encryption of hexahedral units in the three-dimensional grid, and when adaptively encrypting the two-dimensional grid, the simulation results will be unstable. Therefore, how to obtain reliable simulation results of the photoresist spin coating process is a problem that needs to be solved. Summary of the Invention

[0003] Based on this, it is necessary to provide a photoresist spin coating process simulation method, device, computer equipment and storage medium that can improve the reliability of photoresist spin coating process simulation results in response to the above technical problems.

[0004] In a first aspect, the present application provides a method for simulating a photoresist spin coating process, the method comprising:

[0005] Based on the parameter information of the target wafer, the position information of the photoresist injection port and the angle of the photoresist injection port, a fan-shaped simulation grid is established;

[0006] Using a two-dimensional adaptive grid encryption algorithm to mesh the sector-shaped simulation grid to obtain a target simulation grid;

[0007] Constructing a two-phase fluid mathematical model; the two-phase fluid includes an air phase and a photoresist phase;

[0008] The two-phase fluid mathematical model is iteratively solved based on the boundary conditions of the target simulation grid, the photoresist spin coating process parameters, the air fluid properties and the photoresist fluid properties to determine the photoresist spin coating process simulation image of the target wafer.

[0009] In one embodiment, a sector-shaped simulation grid is established based on parameter information of a target wafer, position information of a photoresist injection port, and an angle of the photoresist injection port, including:

[0010] Determine grid parameters based on target wafer parameter information, photoresist injection port position information, and the angle of the photoresist injection port; the grid parameters include grid length, grid height, and grid center angle; the grid length is equal to the radius of the wafer, the grid height is equal to the height of the photoresist injection port, and the grid center angle is the angle of the photoresist injection port;

[0011] Based on the right-handed Cartesian coordinate system and the grid parameters, a sector-shaped simulation grid is established.

[0012] In one embodiment, establishing a sector-shaped simulation grid based on a right-handed Cartesian coordinate system and the grid parameters includes:

[0013] Determine the center point of the target wafer, use the center point as the origin of a right-handed Cartesian coordinate system, and construct coordinate axes of the right-handed Cartesian coordinate system based on the origin of the coordinate system; the coordinate axes include an x-axis, a y-axis, and a z-axis;

[0014] The y-axis of the right-handed Cartesian coordinate system is used as the axis of rotational symmetry;

[0015] Based on the rotational symmetry axis, a fan-shaped simulation grid is established in the x-axis direction of a right-handed Cartesian coordinate system according to the grid parameters.

[0016] In one embodiment, the unit in the z-axis direction of the right-handed Cartesian coordinate system is kept as 1.

[0017] In one embodiment, a two-dimensional adaptive mesh encryption algorithm is used to mesh the sector-shaped simulation mesh to obtain a target simulation mesh, including:

[0018] Determining a void boundary or a wedge boundary of the sector-shaped simulation grid based on a two-dimensional adaptive grid encryption algorithm, and determining a target interface of the sector-shaped simulation grid based on the void boundary or the wedge boundary;

[0019] The center position of each edge on the target interface is used as a segmentation node, and the sector-shaped simulation grid is meshed based on the segmentation node to obtain a target simulation grid.

[0020] In one embodiment, iteratively solving the two-phase fluid mathematical model based on the boundary conditions of the target simulation grid, the photoresist spin coating process parameters, the air fluid properties, and the photoresist fluid properties to determine the photoresist spin coating process simulation image of the target wafer includes:

[0021] Using a target solver in a two-dimensional adaptive mesh encryption algorithm, the two-phase fluid mathematical model is discretized based on boundary conditions of a target simulation mesh, photoresist spin coating process parameters, air fluid properties, and photoresist fluid properties to determine a discretized linear function;

[0022] Preprocessing the discretized linear function by a multi-grid preprocessor to determine a target linear function;

[0023] The target linear function is solved by a conjugate gradient method solver to determine a photoresist spin coating process simulation image of the target wafer.

[0024] In one embodiment, the above-mentioned photoresist spin coating process simulation method further includes:

[0025] Converting a photoresist spin coating process simulation image into a grayscale simulation image;

[0026] Converting the grayscale simulation image into a binary image;

[0027] Determining photoresist boundary pixels according to the binary image, and determining pixel coordinates of the photoresist boundary pixels;

[0028] A photoresist outline is drawn based on the pixel coordinates.

[0029] In a second aspect, the present application further provides a device for simulating a photoresist spin coating process, the device comprising:

[0030] A fan-shaped simulation grid determination module is used to establish a fan-shaped simulation grid based on parameter information of a target wafer, position information of a photoresist injection port, and an angle of the photoresist injection port;

[0031] a target simulation grid determination module, configured to perform grid division on the sector-shaped simulation grid using a two-dimensional adaptive grid encryption algorithm to obtain a target simulation grid;

[0032] A fluid model creation module, used to construct a two-phase fluid mathematical model; the two-phase fluid includes an air phase and a photoresist phase;

[0033] The simulation image determination module is used to iteratively solve the two-phase fluid mathematical model based on the boundary conditions of the target simulation grid, the photoresist spin coating process parameters, the air fluid properties and the photoresist fluid properties to determine the photoresist spin coating process simulation image of the target wafer.

[0034] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0035] A fan-shaped simulation grid determination module is used to establish a fan-shaped simulation grid based on parameter information of a target wafer, position information of a photoresist injection port, and an angle of the photoresist injection port;

[0036] a target simulation grid determination module, configured to perform grid division on the sector-shaped simulation grid using a two-dimensional adaptive grid encryption algorithm to obtain a target simulation grid;

[0037] A fluid model creation module, used to construct a two-phase fluid mathematical model; the two-phase fluid includes an air phase and a photoresist phase;

[0038] The simulation image determination module is used to iteratively solve the two-phase fluid mathematical model based on the boundary conditions of the target simulation grid, the photoresist spin coating process parameters, the air fluid properties and the photoresist fluid properties to determine the photoresist spin coating process simulation image of the target wafer.

[0039] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the following steps:

[0040] A fan-shaped simulation grid determination module is used to establish a fan-shaped simulation grid based on parameter information of a target wafer, position information of a photoresist injection port, and an angle of the photoresist injection port;

[0041] a target simulation grid determination module, configured to perform grid division on the sector-shaped simulation grid using a two-dimensional adaptive grid encryption algorithm to obtain a target simulation grid;

[0042] A fluid model creation module, used to construct a two-phase fluid mathematical model; the two-phase fluid includes an air phase and a photoresist phase;

[0043] The simulation image determination module is used to iteratively solve the two-phase fluid mathematical model based on the boundary conditions of the target simulation grid, the photoresist spin coating process parameters, the air fluid properties and the photoresist fluid properties to determine the photoresist spin coating process simulation image of the target wafer.

[0044] The above-mentioned photoresist spin-coating process simulation method, apparatus, computer equipment, and storage medium establish a sector-shaped simulation grid based on parameter information of a target wafer, the position information of a photoresist injection port, and the angle of the photoresist injection port; mesh the sector-shaped simulation grid using a two-dimensional adaptive mesh encryption algorithm to obtain a target simulation grid; construct a two-phase fluid mathematical model; the two-phase fluid includes an air phase and a photoresist phase; and iteratively solve the two-phase fluid mathematical model based on the boundary conditions of the target simulation grid, the photoresist spin-coating process parameters, the air fluid properties, and the photoresist fluid properties to determine a simulated image of the photoresist spin-coating process for the target wafer. This solves the problem that OpenFOAM currently only supports adaptive mesh encryption of hexahedral elements in a three-dimensional grid when simulating two-phase fluids, and that adaptive encryption of a two-dimensional grid results in unstable simulation results. The above-mentioned solution uses a two-dimensional adaptive mesh encryption algorithm to mesh the sector-shaped simulation grid to determine a target simulation grid, thereby determining a simulated image of the photoresist spin-coating process for the target wafer based on the target simulation grid and the two-phase fluid mathematical model. It can accurately encrypt the interface between the two phases of the two-phase fluid in the photoresist spin-coating process, avoid droplet breakup and numerical instability, maintain the integrity of the axisymmetric grid, and strictly follow the geometric constraints of the two-dimensional model, thereby obtaining reliable photoresist spin-coating process simulation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 2. A diagram showing an application environment of a photoresist spin coating process simulation method according to an embodiment;

[0046] Figure 2 1 is a schematic flow chart of a method for simulating a photoresist spin coating process in one embodiment;

[0047] Figure 3 An example diagram of a sector-shaped simulation grid in one embodiment;

[0048] Figure 4 is an example diagram of an octree refinement structure in one embodiment;

[0049] Figure 5 is an example diagram of a quadtree refinement structure in one embodiment;

[0050] Figure 6 is an example diagram of a quadtree refinement structure in another embodiment;

[0051] Figure 7 This is an example diagram of a photoresist spin coating process simulation image of a target wafer in one embodiment;

[0052] Figure 8 1 is a flow chart of a method for simulating a photoresist spin coating process in another embodiment;

[0053] Figure 9 This is an example diagram of a two-dimensional dam break calculation example in one embodiment;

[0054] Figure 10 An example diagram of a grid segmented image in one embodiment;

[0055] Figure 11 is a schematic diagram of a device for simulating a photoresist spin coating process according to an embodiment;

[0056] Figure 12 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0058] The photoresist spin coating process simulation method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 104 establishes a fan-shaped simulation grid based on the parameter information of the target wafer, the position information of the photoresist injection port, and the angle of the photoresist injection port; uses a two-dimensional adaptive grid encryption algorithm to mesh the fan-shaped simulation grid to obtain a target simulation grid; constructs a two-phase fluid mathematical model; the two-phase fluid includes an air phase and a photoresist phase; based on the boundary conditions of the target simulation grid, the photoresist spin coating process parameters, the air fluid properties, and the photoresist fluid properties, the two-phase fluid mathematical model is iteratively solved to determine the photoresist spin coating process simulation image of the target wafer, and the photoresist spin coating process simulation image is sent to the terminal 102 via the communication network. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablets, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car devices, etc. The portable wearable device may be a smart watch, a smart bracelet, a head-mounted device, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers.

[0059] In one embodiment, Figure 2As shown, a method for simulating a photoresist spin coating process is provided. This embodiment uses the method applied to a terminal as an example for illustration. It is understandable that the method can also be applied to a server, or to a system including a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0060] S210 , establishing a fan-shaped simulation grid based on parameter information of the target wafer, position information of the photoresist injection port, and angle of the photoresist injection port.

[0061] Among them, the parameter information of the target wafer includes the surface parameters and radius of the target wafer; the surface parameters include surface flatness. The photoresist injection port is a dedicated port for introducing photoresist to the surface of the target wafer; the photoresist injection port can be a nozzle, a needle or other type of delivery device. It only needs to ensure that the photoresist can be coated on the surface of the target wafer with a uniform thickness. No specific limitation is made here. The position information of the photoresist injection port includes the height of the photoresist injection port; the height of the photoresist injection port refers to the vertical distance of the photoresist injection port relative to the surface of the target wafer. The parameter information of the target wafer and the angle of the photoresist injection port can be set according to actual needs.

[0062] It should be noted that, in order to realize the simulation of the two-dimensional axisymmetric flow of photoresist spin coating, it is necessary to establish a grid area with a fan shape. The bottom surface of the fan-shaped simulation grid is the base of the target wafer, the top surface is the plane where the photoresist injection port is located, and the length is the radius of the target wafer. The fan-shaped simulation grid can be a fan-shaped space area in the columnar space surrounded by spatial extension with the base of the target wafer as the bottom surface and the plane where the photoresist injection port is located as the top surface as the simulation grid.

[0063] S220. Use a two-dimensional adaptive grid encryption algorithm to divide the sector-shaped simulation grid to obtain a target simulation grid.

[0064] Among them, the two-dimensional adaptive mesh encryption algorithm is a method for adaptively encrypting the mesh of a two-dimensional model during simulation. Adaptive mesh encryption is a technology that dynamically adjusts the mesh density. It aims to automatically optimize the mesh distribution according to changes in physical quantities during the calculation process, thereby improving calculation accuracy and efficiency. The core idea of ​​adaptive mesh encryption technology is to dynamically adjust the mesh density according to changes in physical quantities within the calculation domain, such as velocity gradients and pressure gradients. In areas where the flow field changes drastically, such as turbulence and boundary layers, the mesh is automatically encrypted to capture details; in areas with smaller changes, the mesh is automatically coarsened to save computing resources.

[0065] It should be noted that when simulating a 2D model, OpenFOAM can still divide the 2D model into a hexahedral grid, but it is necessary to constrain the unit of one coordinate dimension of the grid to be 1, that is, the coordinate dimension where the unit is constrained will not participate in the grid refinement calculation. For example, the sector simulation grid is as follows Figure 3 As shown, only one grid thickness is set in the z-axis direction, with the leftmost axis as the rotational symmetry axis. When photoresist is spin-coated on a target wafer, the photoresist flows from the photoresist injection port to the target wafer, and the substrate corresponding to the target wafer rotates around the rotational symmetry axis at a certain rotation speed to spin-coat the photoresist onto the entire target wafer surface.

[0066] The existing OpenFOAM adaptive mesh encryption algorithm performs mesh division and encryption on the 3D model, so it is based on the octree refinement scheme for mesh division. The octree refinement structure is as follows: Figure 4As shown in Figure 1. In three-dimensional space, hexahedral cells are subdivided using an octree structure. Each hexahedral cell is associated with a so-called refinement level, which determines whether the cell needs to be further divided to improve local mesh accuracy. When refinement is required, a hexahedral cell is evenly divided into eight smaller subcells, each of which maintains its hexahedral shape. These subcells share 36 faces, 12 of which are located within the original parent cell, while the remaining 24 faces are connected to adjacent cells. During the cell refinement process, in order to maintain the continuity and accuracy of the numerical simulation, the subcells inherit some key properties of the parent cell. Specifically, the cell center value of the subcell is initialized to the cell center value of the parent cell, where the cell center value of the subcell can be the volume average of physical quantities such as pressure or temperature. This is because most solvers in OpenFOAM are flux-based, and flux is usually represented as a surface field in the simulation, that is, a numerical value associated with the mesh surface. The flux is the physical quantity that passes through a unit area per unit time. Because most OpenFOAM solvers are flux-based, and fluxes are surface-dependent, during the cell refinement process, the cell center values ​​of child cells are initialized to the cell center values ​​of their parent cells to ensure continuity and accuracy of the numerical simulation. For flux-dependent surface fields, the values ​​on the refinement surfaces require special handling. These refinement surfaces, called "subsurfaces," are four new surfaces created by splitting a surface of the parent cell. During refinement, the initial values ​​of these subsurfaces are set to the values ​​of the corresponding parent surface. This approach ensures consistency and conservation of fluxes during mesh refinement while avoiding numerical discontinuities or errors caused by mesh changes. Through this meticulous value propagation and initialization mechanism, adaptive mesh refinement algorithms based on 3D models can improve the accuracy and reliability of complex flow simulations while maintaining computational efficiency. However, when meshing using an octree refinement scheme, the mesh is divided in the z-axis direction. Using this octree refinement scheme for the aforementioned sector-shaped simulation mesh can lead to large errors and difficulty in convergence when iterating the solution of the two-phase flow mathematical model based on this divided mesh.

[0067] Exemplarily, a two-dimensional adaptive mesh refinement algorithm is used to mesh the sector-shaped simulation mesh to obtain a target simulation mesh, including:

[0068] Based on a two-dimensional adaptive grid encryption algorithm, the empty boundary or wedge boundary of the sector-shaped simulation grid is determined, and the target interface of the sector-shaped simulation grid is determined based on the empty boundary or wedge boundary. The center position of each edge on the target interface is used as a splitting node, and the sector-shaped simulation grid is meshed based on the splitting nodes to obtain the target simulation grid.

[0069] It should be noted that in the OpenFOAM-based 2D adaptive mesh refinement algorithm, the quadtree mesh subdivision method is used instead of the original octree mesh subdivision method. The core of the quadtree subdivision method is to split cells to introduce new cells while ensuring that unnecessary splits are not performed in the z-axis direction when there is only one layer of mesh, thereby maintaining the 2D mesh structure and computational efficiency.

[0070] Specifically, the boundary conditions of the sector simulation grid are determined, and the empty boundary or wedge boundary of the sector simulation grid is determined based on the boundary conditions. The empty boundary belongs to the "Empty" boundary, and the wedge boundary belongs to the "Wedge" boundary. Among them, the solid wall boundary condition can be used as the boundary condition corresponding to the bottom surface of the sector simulation grid, the wedge boundary condition can be used as the boundary condition corresponding to the front and back surfaces of the sector simulation grid, and the atmospheric boundary condition can be used as the boundary condition corresponding to the top and right surfaces of the sector simulation grid. The solid wall boundary condition is used to describe the behavior of the photoresist fluid when it contacts the solid wall, that is, the target wafer surface. The wedge boundary condition is applicable to axisymmetric problems and is used to simulate geometric structures with axisymmetry. The atmospheric boundary condition is used to simulate the situation where the fluid flows into or out of the calculation domain. The interface where the empty boundary or wedge boundary is located is used as the target interface of the sector simulation grid; the center position of each edge on the target interface is used as the splitting node, and the sector simulation grid is meshed based on the splitting node to obtain the target simulation grid.

[0071] For example, Figure 5 As shown in the figure, after determining the empty boundary of the sector simulation grid, the center position of each edge of the sector simulation grid on the target interface located at the empty boundary is used as a splitting node. Based on the splitting nodes, the target interface is split into four new sub-surfaces. Each new sub-surface is assigned a new owner cell and neighbor cell. Other interfaces in the sector simulation grid, except the target interface, can be split into two new surfaces based on the splitting nodes. Based on this splitting method, four new internal surfaces can be created within the sector simulation grid. The addition of these internal surfaces ensures the integrity and connectivity of the grid cells and provides the necessary structural support for subsequent numerical calculations.

[0072] For example, Figure 6As shown in the figure, the wedge boundary of the sector simulation grid is determined. The sector simulation grid is not a standard hexahedron. The target interface located at the wedge boundary contains three vertices. During the refinement of the sector simulation grid, the target interface will be divided into two new faces, one of which contains four vertices and the other contains three vertices. In addition, an internal face containing three vertices should be added, one of which is the midpoint of the centerline edge. In the axisymmetric photoresist spin coating simulation, the cells on the centerline need to be meshed in the above way to ensure the geometric correctness of the grid and the accuracy of the calculation. Through the precise quadtree subdivision method, while ensuring the grid accuracy of key areas, the overall number of grids is optimized and the consumption of computing resources is reduced.

[0073] This sector-shaped meshing method effectively divides the sector-shaped simulation grid during photoresist spin-coating process simulation, generating a highly adaptable and accurate mesh to meet simulation requirements. This approach not only improves the local resolution of the target simulation grid but also maintains connectivity between target simulation grids, providing a foundation for efficient numerical simulation.

[0074] S230. Construct a two-phase fluid mathematical model.

[0075] The two-phase fluid includes an air phase and a photoresist phase.

[0076] It should be noted that the mathematical model of two-phase fluid can be constructed through OpenFOAM. The constant folder in OpenFOAM contains the physical property files required by the program. The process of photoresist spin coating can be regarded as a layer and motion process, so the laminar flow model can be used as the mathematical model of two-phase fluid. Among them, laminar flow is a type of fluid flow, which is characterized by the fluid flowing smoothly in a regular path, the fluid particles moving in a straight line along the direction parallel to the tube axis, and the layers do not interfere with each other. The mathematical model of two-phase fluid includes the continuity equation, the momentum equation and the interface tracking equation. Among them, the continuity equation is used to describe the principle of conservation of mass of the fluid; the momentum equation includes the Navier-Stokes equation, which is used to describe the continuity and momentum conservation of the fluid; the interface tracking equation includes the volume fraction equation, which is used to describe the position and shape of the interface between the two-phase fluid.

[0077] S240, iteratively solving the two-phase fluid mathematical model based on the boundary conditions of the target simulation grid, the photoresist spin coating process parameters, the air fluid properties, and the photoresist fluid properties, to determine a photoresist spin coating process simulation image for the target wafer.

[0078] In a right-handed Cartesian coordinate system, the fan-shaped simulation grid includes a bottom surface, a front surface, a rear surface, a top surface, and a right side surface. A solid wall boundary condition is used as the boundary condition corresponding to the bottom surface of the fan-shaped simulation grid; a wedge boundary condition is used as the boundary condition corresponding to the front and rear surfaces of the fan-shaped simulation grid; and an atmospheric boundary condition is used as the boundary condition corresponding to the top and right side surfaces of the fan-shaped simulation grid. The photoresist spin coating process parameters include the initial speed of photoresist injection, the initial rotation speed of the target wafer, and the process atmosphere. The initial speed of photoresist injection and the initial rotation speed of the target wafer can be set according to actual needs, and the process atmosphere is standard atmospheric pressure. The fluid properties corresponding to air include a fluid viscosity model, aerodynamic viscosity, and air fluid density. The fluid properties corresponding to photoresist include a fluid viscosity model, photoresist dynamic viscosity, photoresist fluid density, and ambient gravity. The aerodynamic viscosity, air fluid density, photoresist dynamic viscosity, and photoresist fluid density can be set according to actual needs. The fluid viscosity model may be a constant viscosity model. The constant viscosity model means that in the simulation of the photoresist spin coating process, the dynamic viscosity of the fluid is regarded as a constant and does not change with changes in temperature or pressure.

[0079] Specifically, the target solver in OpenFOAM is used to iteratively solve the two-phase fluid mathematical model based on the boundary conditions of the target simulation grid, the photoresist spin-coating process parameters, the air fluid properties, and the photoresist fluid properties until the convergence criteria are met, thereby determining the photoresist spin-coating process simulation image of the target wafer.

[0080] For example, the photoresist spin coating process simulation image of the target wafer obtained by the above method is as follows: Figure 7 As shown. It is understandable that Figure 7 The simulated image of the photoresist spin coating process shows no droplet breakup or numerical instability. The mesh is correctly encrypted, and the mesh at the axis of symmetry does not need to be changed. Therefore, the two-dimensional adaptive mesh encryption algorithm can correctly encrypt the wedge mesh at the axis of symmetry.

[0081] In the above-mentioned photoresist spin-coating process simulation method, a sector-shaped simulation grid is established based on parameter information of the target wafer, the position information of the photoresist injection port, and the angle of the photoresist injection port; the sector-shaped simulation grid is meshed using a two-dimensional adaptive mesh encryption algorithm to obtain a target simulation grid; a two-phase fluid mathematical model is constructed; the two-phase fluid includes an air phase and a photoresist phase; the two-phase fluid mathematical model is iteratively solved based on the boundary conditions of the target simulation grid, the photoresist spin-coating process parameters, the air fluid properties, and the photoresist fluid properties to determine a simulated image of the photoresist spin-coating process for the target wafer. This solves the problem that OpenFOAM currently only supports adaptive mesh encryption of hexahedral cells in a three-dimensional grid when simulating two-phase fluids, and that adaptive encryption of a two-dimensional grid results in unstable simulation results. The above-mentioned solution uses a two-dimensional adaptive mesh encryption algorithm to mesh the sector-shaped simulation grid to determine the target simulation grid, thereby determining a simulated image of the photoresist spin-coating process for the target wafer based on the target simulation grid and the two-phase fluid mathematical model. It can accurately encrypt the interface between the two phases of the two-phase fluid in the photoresist spin-coating process, avoid droplet breakup and numerical instability, maintain the integrity of the axisymmetric grid, and strictly follow the geometric constraints of the two-dimensional model, thereby obtaining reliable photoresist spin-coating process simulation results.

[0082] In one embodiment, Figure 8 As shown, based on the parameter information of the target wafer, the position information of the photoresist injection port, and the angle of the photoresist injection port, a fan-shaped simulation grid is established, including:

[0083] S310 , determining grid parameters based on parameter information of the target wafer, position information of the photoresist injection port, and angle of the photoresist injection port.

[0084] The grid parameters include grid length, grid height and grid center angle; the grid length is equal to the radius of the wafer, the grid height is equal to the height of the photoresist injection port, and the grid center angle is the angle of the photoresist injection port.

[0085] The target wafer parameter information includes the target wafer's center coordinates, surface parameters, and radius; the surface parameters include surface flatness. The photoresist injection port refers to a dedicated port used to introduce photoresist onto the target wafer surface. The angle of the photoresist injection port can be set according to actual needs. The grid parameters refer to the parameter information of the constructed fan-shaped simulation grid. The grid parameters include the length, height, and center angle of the fan-shaped simulation grid; the length of the fan-shaped simulation grid is equal to the radius of the target wafer, the height of the fan-shaped simulation grid is equal to the height of the photoresist injection port, and the center angle of the fan-shaped simulation grid is equal to the preset angle.

[0086] S320. Establish a sector-shaped simulation grid based on a right-handed Cartesian coordinate system and grid parameters.

[0087] The unit in the z-axis direction of the right-handed Cartesian coordinate system remains 1.

[0088] Exemplarily, a method for establishing a fan-shaped simulation grid can be: determining the center point of the target wafer, taking the center point as the origin of the coordinate system of the right-handed Cartesian coordinate system, and constructing the coordinate axes of the right-handed Cartesian coordinate system based on the origin of the coordinate system; the coordinate axes include the x-axis, the y-axis and the z-axis; taking the y-axis of the right-handed Cartesian coordinate system as the rotational symmetry axis; based on the rotational symmetry axis, establishing a fan-shaped simulation grid in the x-axis direction of the right-handed Cartesian coordinate system according to the grid parameters.

[0089] Specifically, a right-handed Cartesian coordinate system is constructed using the center coordinates of the target wafer as the coordinate origin. Based on the right-handed Cartesian coordinate system, the y-axis of the right-handed Cartesian coordinate system is used as the axis of rotational symmetry. An initial sector-shaped simulation grid is symmetrically distributed along the xy plane of the right-handed Cartesian coordinate system. The photoresist injection port is located on the side of the initial sector-shaped simulation grid closest to the axis of rotational symmetry. Based on the grid parameters of the sector-shaped simulation grid, a sector-shaped simulation grid is generated along the positive x-axis of the right-handed Cartesian coordinate system.

[0090] In the above scheme, the unit in the z-axis direction of the right-handed Cartesian coordinate system is kept at 1, which can realize the subsequent grid division of the sector-shaped simulation grid through the two-dimensional adaptive grid encryption algorithm, thereby ensuring the accuracy of the sector-shaped simulation grid division, thereby improving the reliability of the obtained photoresist spin coating process simulation image.

[0091] In one embodiment, a two-phase fluid mathematical model is iteratively solved based on boundary conditions of a target simulation grid, photoresist spin coating process parameters, air fluid properties, and photoresist fluid properties to determine a photoresist spin coating process simulation image for a target wafer, including:

[0092] The target solver in the two-dimensional adaptive grid encryption algorithm is used to discretize the two-phase fluid mathematical model based on the boundary conditions of the target simulation grid, photoresist spin-coating process parameters, air fluid properties and photoresist fluid properties to determine the discretized linear function; the discretized linear function is preprocessed by a multi-grid preprocessor to determine the target linear function; the target linear function is solved by the conjugate gradient method solver to determine the photoresist spin-coating process simulation image of the target wafer.

[0093] The target solver can be the interFoam solver in OpenFOAM, and the multigrid preconditioner can be GAMG (Geometric Algebraic Multigrid), which accelerates convergence by synergizing different mesh levels. This requires explicitly creating a hierarchy of meshes from fine to coarse. The conjugate gradient method (PCG) is an iterative algorithm that accelerates convergence by introducing a preconditioning matrix. It is primarily used for solving large-scale linear systems.

[0094] Specifically, the interFoam solver in OpenFOAM discretizes the two-phase fluid mathematical model based on the boundary conditions of the target simulation grid, the photoresist spin-coating process parameters, and the air and photoresist fluid properties to determine the discretized linear function. This discretized linear function is preprocessed using a multi-grid preprocessor to determine the target linear function. This target linear function is then solved using a conjugate gradient solver until convergence, resulting in the photoresist spin-coating process simulation image for the target wafer.

[0095] The above scheme iteratively solves the two-phase fluid mathematical model through the target solver, multi-grid preprocessor and conjugate gradient method in the two-dimensional adaptive grid encryption algorithm to obtain the photoresist spin-coating process simulation image of the target wafer, which can improve the acquisition efficiency of the photoresist spin-coating process simulation image.

[0096] In one embodiment, the above-mentioned photoresist spin coating process simulation method further includes:

[0097] Converting a photoresist spin coating process simulation image into a grayscale simulation image; converting the grayscale simulation image into a binary image; determining photoresist boundary pixels according to the binary image, and determining pixel coordinates of the photoresist boundary pixels; and drawing a photoresist outline based on the pixel coordinates.

[0098] Specifically, the simulated image of the photoresist spin-coating process details the distribution of photoresist on the target wafer surface under different process parameters. The simulated image of the photoresist spin-coating process is read and converted into a grayscale image. Converting a color image to a grayscale image simplifies subsequent processing, reduces computational effort, and improves processing speed. Based on the grayscale image, pixels in the image are divided into foreground and background based on a pre-set grayscale threshold. The foreground represents the photoresist area, and the background represents the non-photoresist area. Foreground pixels have grayscale values ​​greater than the grayscale threshold, while background pixels have grayscale values ​​less than or equal to the grayscale threshold. Converting the image to a binary image allows for a clearer outline of the photoresist that is subsequently extracted. In a binary image, the outline of the photoresist is defined by the edges of the pixels. By scanning the image pixels, the boundary between the photoresist and the background is identified. The pixels at the boundary between the photoresist and the background are tracked and their coordinates are recorded. The collected coordinates of the boundary pixels are used to draw a photoresist outline image on a new image.

[0099] It can be understood that the photoresist profile image clearly shows the distribution range and morphology of the photoresist on the wafer. The extracted profile can be used for further analysis to calculate key parameters such as the coverage area, thickness distribution and uniformity of the photoresist.

[0100] It is understood that the above-mentioned 2D adaptive mesh refinement algorithm can be widely applied to 2D simulation problems in various fields, including but not limited to fluid mechanics, heat transfer, and chemical reaction flows. For example, the above-mentioned 2D adaptive mesh refinement algorithm can also be used to capture phase interfaces in multiphase flows. In addition to photoresist spin coating simulations, in multiphase flow simulations such as gas-liquid two-phase flow and liquid-liquid extraction, accurate capture of phase interfaces is crucial for understanding fluid interactions and mass and heat transfer processes. The 2D adaptive mesh refinement algorithm can effectively capture subtle variations in phase interfaces, making simulation results more accurate to actual physical phenomena. The above-mentioned 2D adaptive mesh refinement algorithm can also be used for combustion problems. Physical quantities such as temperature and species concentration exhibit large gradients near the flame surface. The 2D adaptive mesh refinement algorithm automatically refines the mesh locally near the flame surface, better resolving flame surface details while maintaining a low computational load. The above-mentioned 2D adaptive mesh refinement algorithm can also be used for flow problems. Under certain operating conditions, the velocity field may exhibit large gradients. In particular, under supersonic conditions, the gradients of physical quantities before and after the shock wave are extremely large. The 2D adaptive mesh refinement algorithm can effectively capture the shock wave and efficiently restore the distribution of physical quantities in complex flow fields.

[0101] For example, the above-mentioned two-dimensional model simulation method based on the two-dimensional adaptive mesh encryption algorithm can also be used for the simulation of two-dimensional dam break. As a classic two-dimensional or three-dimensional multiphase and dynamic problem, the dam break problem is often used as a test case to verify and evaluate the accuracy and reliability of multiphase flow numerical methods and models. Many researchers will use the two-phase flow solver in OpenFOAM, such as interFoam, to simulate the dam break problem in order to test the performance of the numerical method used in capturing the free surface and calculating the interaction between phases. Figure 9 As shown, taking a simplified two-dimensional dam break (damBreak) example, the scenario corresponding to the two-dimensional dam break example is the collapse of the dam, the water flow tilts downstream, and impacts the downstream buildings. The physical process of the two-dimensional dam break example is: the initial state is a static water area to simulate the water storage of the reservoir, so that it is located on the left side of the model, and there is a small obstacle at the bottom of the water tank, that is, the protruding part at the bottom of the grid, simulating a building. During the simulation, at t=0s, the water column is allowed to collapse and flow freely under the action of gravity. During the collapse process, the water flow hits an obstacle at the bottom of the water tank to form a complex flow field structure, including a number of bubbles wrapped in water. The mesh segmentation image obtained by simulating the two-dimensional dam break example through the above-mentioned two-dimensional model simulation method based on the two-dimensional adaptive grid encryption algorithm is shown as follows. Figure 10As shown in Figure 2, a 2D model simulation method based on a 2D adaptive mesh refinement algorithm is used to simulate a 2D dam-break example. The algorithm dynamically refines and adjusts the mesh based on the movement and changes of the fluid. For example, in key areas, such as where water impacts obstacles, forming complex flow structures and bubbles, the algorithm automatically increases the mesh density, more accurately capturing the fluid boundaries and clearly presenting the actual flow pattern. After applying the 2D adaptive mesh refinement algorithm, the mesh is significantly denser in key areas and continuously adjusts to the fluid's movement, better adapting to the fluid's dynamic characteristics. By increasing the mesh density in these key areas, the numerical calculation can more accurately capture microscopic changes in the fluid, such as subtle fluctuations at the phase interface and the formation and evolution of bubbles. This improves computational accuracy, brings the simulation results closer to the actual physical process, and achieves a precise description of the complex flow field. Compared to meshing methods that directly increase the overall mesh density, the 2D adaptive mesh refinement algorithm refines the mesh only in key areas of concern, while maintaining a sparse mesh in areas where the fluid changes are relatively gentle, avoiding unnecessary waste of computational resources. This improves computational efficiency and shortens calculation time while ensuring accuracy, enabling more accurate solutions to 2D simulation problems within limited computing resources and time. This 2D adaptive mesh refinement algorithm dynamically refines the mesh in key areas, accurately capturing phase interface changes and effectively balancing computational accuracy and cost. This provides a precise and efficient solution for 2D simulation problems on the OpenFOAM platform, expanding the platform's application scope and offering a new approach to simulating complex 2D flow fields.

[0102] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0103] Based on the same inventive concept, the present application also provides a photoresist spin-coating process simulation device for implementing the aforementioned photoresist spin-coating process simulation method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations in one or more of the following photoresist spin-coating process simulation device embodiments can be found in the above-mentioned limitations on the photoresist spin-coating process simulation method, and will not be repeated here.

[0104] In one embodiment, Figure 11 As shown, a photoresist spin coating process simulation device is provided, including: a fan-shaped simulation grid determination module 1101, a target simulation grid determination module 1102, a fluid model creation module 1103 and a simulation image determination module 1104, wherein:

[0105] The fan-shaped simulation grid determination module 1101 is used to establish a fan-shaped simulation grid based on parameter information of the target wafer, position information of the photoresist injection port, and angle of the photoresist injection port;

[0106] A target simulation grid determination module 1102 is configured to perform grid division on the sector-shaped simulation grid using a two-dimensional adaptive grid encryption algorithm to obtain a target simulation grid;

[0107] The fluid model creation module 1103 is used to construct a two-phase fluid mathematical model; the two-phase fluid includes an air phase and a photoresist phase;

[0108] The simulation image determination module 1104 is used to iteratively solve the two-phase fluid mathematical model based on the boundary conditions of the target simulation grid, the photoresist spin coating process parameters, the air fluid properties and the photoresist fluid properties to determine the photoresist spin coating process simulation image of the target wafer.

[0109] Exemplarily, the fan-shaped simulation grid determination module 1101 is specifically configured to:

[0110] Determine grid parameters based on target wafer parameter information, photoresist injection port position information, and the angle of the photoresist injection port; the grid parameters include grid length, grid height, and grid center angle; the grid length is equal to the radius of the wafer, the grid height is equal to the height of the photoresist injection port, and the grid center angle is the angle of the photoresist injection port;

[0111] Based on the right-handed Cartesian coordinate system and grid parameters, a sector-shaped simulation grid is established.

[0112] Furthermore, the sector simulation grid determination module 1101 is further specifically configured to:

[0113] Determine the center point of the target wafer, use the center point as the origin of a right-handed Cartesian coordinate system, and construct the coordinate axes of the right-handed Cartesian coordinate system based on the origin of the coordinate system; the coordinate axes include an x-axis, a y-axis, and a z-axis;

[0114] The y-axis of the right-handed Cartesian coordinate system is used as the axis of rotational symmetry;

[0115] Based on the rotational symmetry axis, a fan-shaped simulation grid is established in the x-axis direction of the right-handed Cartesian coordinate system according to the grid parameters.

[0116] Furthermore, the unit in the z-axis direction of the right-handed Cartesian coordinate system is kept at 1.

[0117] Exemplarily, the target simulation grid determination module 1102 is specifically configured to:

[0118] Based on a two-dimensional adaptive grid encryption algorithm, a void boundary or a wedge boundary of the sector-shaped simulation grid is determined, and a target interface of the sector-shaped simulation grid is determined based on the void boundary or the wedge boundary;

[0119] The center position of each edge on the target interface is used as a split node, and the sector-shaped simulation grid is meshed based on the split nodes to obtain the target simulation grid.

[0120] Exemplarily, the simulation image determination module 1104 is specifically configured to:

[0121] The target solver in the two-dimensional adaptive mesh encryption algorithm is used to discretize the two-phase fluid mathematical model based on the boundary conditions of the target simulation grid, the photoresist spin coating process parameters, the air fluid properties, and the photoresist fluid properties, and the discretized linear function is determined.

[0122] Preprocessing the discretized linear function through a multi-grid preprocessor to determine the target linear function;

[0123] The target linear function is solved by the conjugate gradient method solver to determine the photoresist spin coating process simulation image of the target wafer.

[0124] Exemplarily, the above-mentioned photoresist spin coating process simulation device also includes a photoresist contour drawing module, which is used to: convert the photoresist spin coating process simulation image into a grayscale simulation image; convert the grayscale simulation image into a binary image; determine the photoresist boundary pixels based on the binary image, and determine the pixel coordinates of the photoresist boundary pixels; and draw the photoresist contour based on the pixel coordinates.

[0125] Each module in the above-mentioned photoresist spin-coating process simulation device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0126] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 12 As shown. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless means, and the wireless means can be achieved via Wi-Fi, mobile cellular networks, NFC (near-field communication), or other technologies. When executed by the processor, the computer program implements a method for simulating a photoresist spin-coating process. The display unit of the computer device is used to produce a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0127] Those skilled in the art will understand that Figure 12 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0128] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0129] Step 1: Based on the parameter information of the target wafer, the position information of the photoresist injection port, and the angle of the photoresist injection port, a fan-shaped simulation grid is established;

[0130] Step 2: Using a two-dimensional adaptive mesh encryption algorithm to mesh the sector simulation grid to obtain a target simulation grid;

[0131] Step 3: construct a two-phase fluid mathematical model; the two-phase fluid includes an air phase and a photoresist phase;

[0132] Step 4: Iteratively solve the two-phase fluid mathematical model based on the boundary conditions of the target simulation grid, the photoresist spin coating process parameters, the air fluid properties, and the photoresist fluid properties to determine the photoresist spin coating process simulation image of the target wafer.

[0133] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0134] Step 1: Based on the parameter information of the target wafer, the position information of the photoresist injection port, and the angle of the photoresist injection port, a fan-shaped simulation grid is established;

[0135] Step 2: Using a two-dimensional adaptive mesh encryption algorithm to mesh the sector simulation grid to obtain a target simulation grid;

[0136] Step 3: construct a two-phase fluid mathematical model; the two-phase fluid includes an air phase and a photoresist phase;

[0137] Step 4: Iteratively solve the two-phase fluid mathematical model based on the boundary conditions of the target simulation grid, the photoresist spin coating process parameters, the air fluid properties, and the photoresist fluid properties to determine the photoresist spin coating process simulation image of the target wafer.

[0138] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0139] Step 1: Based on the parameter information of the target wafer, the position information of the photoresist injection port, and the angle of the photoresist injection port, a fan-shaped simulation grid is established;

[0140] Step 2: Using a two-dimensional adaptive mesh encryption algorithm to mesh the sector simulation grid to obtain a target simulation grid;

[0141] Step 3: construct a two-phase fluid mathematical model; the two-phase fluid includes an air phase and a photoresist phase;

[0142] Step 4: Iteratively solve the two-phase fluid mathematical model based on the boundary conditions of the target simulation grid, the photoresist spin coating process parameters, the air fluid properties, and the photoresist fluid properties to determine the photoresist spin coating process simulation image of the target wafer.

[0143] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions.

[0144] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0145] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0146] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A photoresist spin coating process simulation method, characterized in that: include: Based on the parameter information of the target wafer, the position information of the photoresist injection port and the angle of the photoresist injection port, a fan-shaped simulation grid is established; Using a two-dimensional adaptive grid encryption algorithm to mesh the sector-shaped simulation grid to obtain a target simulation grid; Constructing a two-phase fluid mathematical model; the two-phase fluid includes an air phase and a photoresist phase; The two-phase fluid mathematical model is iteratively solved based on the boundary conditions of the target simulation grid, the photoresist spin coating process parameters, the air fluid properties and the photoresist fluid properties to determine the photoresist spin coating process simulation image of the target wafer.

2. The method according to claim 1, characterized in that Based on the target wafer parameter information, the location information of the photoresist injection port, and the angle of the photoresist injection port, a fan-shaped simulation grid is established, including: Determine grid parameters based on target wafer parameter information, photoresist injection port position information, and the angle of the photoresist injection port; the grid parameters include grid length, grid height, and grid center angle; the grid length is equal to the radius of the wafer, the grid height is equal to the height of the photoresist injection port, and the grid center angle is the angle of the photoresist injection port; Based on the right-handed Cartesian coordinate system and the grid parameters, a sector-shaped simulation grid is established.

3. The method according to claim 2, characterized in that Based on the right-handed Cartesian coordinate system and the grid parameters, a sector-shaped simulation grid is established, including: Determine the center point of the target wafer, use the center point as the origin of a right-handed Cartesian coordinate system, and construct coordinate axes of the right-handed Cartesian coordinate system based on the origin of the coordinate system; the coordinate axes include an x-axis, a y-axis, and a z-axis; The y-axis of the right-handed Cartesian coordinate system is used as the axis of rotational symmetry; Based on the rotational symmetry axis, a fan-shaped simulation grid is established in the x-axis direction of a right-handed Cartesian coordinate system according to the grid parameters.

4. The method according to claim 3, characterized in that The unit in the z-axis direction of the right-handed Cartesian coordinate system remains 1.

5. The method according to claim 1, wherein The sector-shaped simulation grid is meshed using a two-dimensional adaptive mesh encryption algorithm to obtain a target simulation grid, including: Determining a void boundary or a wedge boundary of the sector-shaped simulation grid based on a two-dimensional adaptive grid encryption algorithm, and determining a target interface of the sector-shaped simulation grid based on the void boundary or the wedge boundary; The center position of each edge on the target interface is used as a segmentation node, and the sector-shaped simulation grid is meshed based on the segmentation node to obtain a target simulation grid.

6. The method according to claim 1, characterized in that The method further comprises: iteratively solving the two-phase fluid mathematical model based on the boundary conditions of the target simulation grid, the photoresist spin coating process parameters, the air fluid properties, and the photoresist fluid properties to determine a photoresist spin coating process simulation image of the target wafer, including: Using a target solver in a two-dimensional adaptive mesh encryption algorithm, the two-phase fluid mathematical model is discretized based on boundary conditions of a target simulation mesh, photoresist spin coating process parameters, air fluid properties, and photoresist fluid properties to determine a discretized linear function; Preprocessing the discretized linear function by a multi-grid preprocessor to determine a target linear function; The target linear function is solved by a conjugate gradient method solver to determine a photoresist spin coating process simulation image of the target wafer.

7. The method according to claim 1, characterized in that Also includes: Converting a photoresist spin coating process simulation image into a grayscale simulation image; Converting the grayscale simulation image into a binary image; Determining photoresist boundary pixels according to the binary image, and determining pixel coordinates of the photoresist boundary pixels; A photoresist outline is drawn based on the pixel coordinates.

8. A photoresist spin coating process simulation device, characterized in that: The photoresist spin coating process simulation device comprises: A fan-shaped simulation grid determination module is used to establish a fan-shaped simulation grid based on parameter information of a target wafer, position information of a photoresist injection port, and an angle of the photoresist injection port; a target simulation grid determination module, configured to perform grid division on the sector-shaped simulation grid using a two-dimensional adaptive grid encryption algorithm to obtain a target simulation grid; A fluid model creation module, used to construct a two-phase fluid mathematical model; the two-phase fluid includes an air phase and a photoresist phase; The simulation image determination module is used to iteratively solve the two-phase fluid mathematical model based on the boundary conditions of the target simulation grid, the photoresist spin coating process parameters, the air fluid properties and the photoresist fluid properties to determine the photoresist spin coating process simulation image of the target wafer.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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