A method for determining process parameters for a cross-scale coupled thin film physical vapor deposition

Abstract

The application discloses a kind of thin film physical vapor deposition process parameter determination methods of cross-scale coupling, this method takes material attribute, equipment structure and equipment parameter as input, simulates the desorption and initial movement process of particle at evaporation source, obtains the boundary condition of evaporation particle;Boundary condition is mapped to macroscopic scale free molecule flow transport model again, the transport behavior of particle in chamber and the spatial flux distribution and incident state before reaching substrate of substrate surface are calculated, which is mapped to microscale molecular dynamics calculation as boundary condition, the deposition and surface evolution process of particle are calculated, and film coating uniformity, deposition rate are obtained.Further under the constraint of preset threshold, equipment parameters and material parameters are iteratively adjusted and determined inversely, and the process parameters meeting the target requirements are output.This method can provide reliable basis for physical vapor deposition equipment design and process condition optimization, and covers the whole process and realizes cross-scale coupling, has stronger engineering guiding significance.

Description

Technical Field

[0001] This invention relates to the fields of semiconductor manufacturing, optical thin film preparation and material surface treatment technology, and specifically to a method for determining process parameters of thin film physical vapor deposition that is coupled across scales. Background Technology

[0002] Physical vapor deposition (PVD) is widely used in semiconductor manufacturing, optical thin films, and material surface treatment technologies. In actual deposition processes, film performance and consistency are highly dependent on equipment structure (such as the shape of the evaporation source crucible, the location of the evaporation source, the baffle structure, the substrate position and rotation method), process conditions (such as the heating conditions of the evaporation source and the baffle opening strategy), and the inherent properties of the material (such as adsorption energy and surface diffusion capacity). Especially under high vacuum conditions, the transport of deposited particles within the chamber exhibits characteristics of a rarefied molecular flow. Particle transport and the chamber structure together determine the flux distribution on the substrate surface, which in turn affects key process parameters such as film thickness uniformity, deposition rate, and surface morphology evolution. Therefore, how to quickly and accurately assess the operating conditions and determine key process parameters during equipment design and process development is a core issue in improving deposition consistency and process repeatability.

[0003] In existing technologies, macroscopic deposition transport models can calculate particle flux and film thickness distribution at the device scale, but they usually require pre-assuming the emission boundary conditions of the evaporation source and simplifying microscopic processes such as adhesion, rebound, migration, and rearrangement on the substrate surface, resulting in limited accuracy in predicting film thickness uniformity, deposition rate, and morphology evolution. On the other hand, microscopic molecular dynamics simulations can describe atomic-level evaporation-desorption, incident deposition, and surface evolution mechanisms, but due to limitations in computational scale and size, they cannot directly cover the transport processes and device structural influences at the chamber scale, and therefore cannot be directly used for overall system performance evaluation and parameter optimization.

[0004] Therefore, there is an urgent need for a cross-scale computational method that can bridge the micro and macro scales and couple the entire process from evaporation source emission to chamber transport to substrate deposition. This method would allow the micro-scale simulation output to serve as boundary conditions for the macro-scale transport model, and the substrate incident state obtained from macro-scale transport to be backfilled into the micro-scale deposition model. This would enable the acquisition of process evaluation indicators such as film thickness uniformity and deposition rate under a unified process. Furthermore, there is an urgent need for a method that iteratively adjusts and inverts process conditions, equipment parameters, and material parameters under threshold constraints to support deposition equipment design and process window development. Summary of the Invention

[0005] This invention aims to overcome the shortcomings of existing technologies and provide a cross-scale coupled calculation and key process parameter determination method for physical vapor deposition (PVD) processes. This method combines microscale particle motion simulation with macroscale free molecular flow transport calculations to achieve joint calculation of the entire process from evaporation source emission, chamber transport, and substrate surface deposition and growth. Under the constraint of preset thresholds, it inversely determines equipment parameters, material parameters, and process conditions, providing a reliable basis for PVD equipment design, parameter adjustment, and process optimization.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for determining cross-scale coupled thin film physical vapor deposition process parameters, comprising the following steps: Step 1: Construct a geometric model of the physical vapor deposition (PVD) equipment. The geometric model includes at least a vacuum chamber, an evaporation source crucible with a specific shape, a rotatable substrate, and an evaporation source baffle. Set the equipment parameters, including the substrate stage rotation speed, the evaporation source heating curve, and the evaporation source baffle opening time. Set the parameters of the material to be deposited, including at least the element types and composition, crystal structure, lattice constant, and adsorption energy. Step 2: At the microscale, molecular dynamics methods are used to calculate the motion and desorption behavior of particles at the evaporation source during the heating process to obtain evaporation boundary condition parameters. The evaporation boundary condition parameters include at least the particle evaporation rate, particle ejection velocity distribution, particle ejection angle distribution, and particle morphology characteristics on the surface of the evaporation source. Step 3: Map the evaporation boundary condition parameters obtained in Step 2 to the source terms and boundary conditions of the macroscopic particle transport model. Use the free molecular flow transport model to calculate the transport process of particles in the chamber, and obtain the particle spatial flux distribution and the incident state parameters before the particles reach the substrate surface. The incident state parameters include at least the incident velocity distribution and the incident angle distribution.

[0007] Step 4: Using the particle spatial flux distribution and incident state parameters obtained in Step 3 as boundary conditions for molecular dynamics simulation, calculate the particle deposition behavior and surface evolution growth process on the substrate surface at the microscale, and output process evaluation indicators, which include at least film thickness uniformity and deposition rate. Step 5: Determine whether the process evaluation indicators obtained in Step 4 meet the preset requirements; if they do, output the corresponding equipment parameters and material parameters as process parameters; if they do not meet the requirements, proceed to Step 6. Step Six: Adjust the equipment and material parameters, then return to Step Two for iterative calculations until the preset requirements are met or the maximum number of iterations is reached.

[0008] Further, step one is achieved through the following steps: constructing a geometric model of the equipment, the geometric model including the three-dimensional structure of the physical vapor deposition equipment process chamber, and including a vacuum chamber and an evaporation source installed in the vacuum chamber, an evaporation source baffle installed on the evaporation source, a substrate stage and a substrate installed on the substrate stage, the substrate stage being rotatably installed in the vacuum chamber, and controlling the rotation of the substrate stage to drive the rotation of the substrate; in the geometric model, the equipment parameters also include the position and size of the extraction port, in order to analyze the influence of the extraction boundary conditions on the particle transport process.

[0009] The evaporation source is either an evaporation boat or a crucible; the shape of the evaporation source is constructed by combining elements through Boolean operations to closely resemble actual production conditions.

[0010] Furthermore, before the atomic arrangement state of the material to be evaporated in the evaporation source in step two is used as the initial condition, energy minimization calculation is performed to make the system undergo surface contraction and tend to a stable state under the cohesive effect between atoms, thereby obtaining the initial configuration for subsequent heating evaporation calculation; the system is heated from room temperature to working temperature according to the preset heating curve to simulate the thermal excitation desorption process of particles, and the particle evaporation rate, emission velocity distribution, emission angle distribution and particle morphology characteristics on the surface of the evaporation source are obtained.

[0011] The preset heating curve is determined by constant power heating, linearly increasing power heating (e.g., y=ax+b, until y reaches the preset or maximum power, where a and b are coefficients, y is power, and x is time), or custom power heating (e.g., 0-1s 50W, 1-2s 100W, 2-3s 200W, 3-4s 300W, 4-5s 400W, 5-6s 500W).

[0012] Furthermore, in step two, in order to more realistically simulate the atomic evaporation process and avoid unnecessary interactions between gas phase atoms after evaporation, the gas phase atoms are removed from the statistical region of the calculation system after they are freed from the surface of the evaporation source, so as to improve the calculation efficiency and the stability of evaporation statistics.

[0013] Furthermore, the particle evaporation rate, emission velocity distribution, emission angle distribution, and particle morphology characteristics on the evaporation source surface obtained in step two are used as particle source term parameters of the macroscopic particle transport model (i.e., particle emission flux at each position on the evaporation source surface and the initial velocity vector distribution of particles emitted at each position). Combined with the chamber geometry model and baffle opening strategy from step one, particle trajectory statistics or flux calculations are performed to obtain the spatial flux distribution on the substrate surface and the incident velocity distribution and incident angle distribution of particles before they reach the substrate surface.

[0014] Among them, the particle morphology characteristics of the evaporation source surface include the roughness of the evaporation surface, normal variation, local protrusions / pits and other morphological features.

[0015] Furthermore, the opening strategy of the evaporation source baffle in step three is related to the temperature state of the evaporation source. This opening strategy can be triggered by the instantaneous temperature of the evaporation source reaching a preset threshold, or by the evaporation source temperature being above the preset threshold for a preset duration. The preset threshold is determined based on the material properties; for example, if the evaporation condition for copper is 1500K, then the preset threshold is set to 1500K. Furthermore, in step three, the substrate rotation speed is set to a constant rotation speed, or varies with time according to a preset acceleration and deceleration curve, in order to simulate the influence of different rotation strategies on the flux time averaging effect and film thickness uniformity.

[0016] Furthermore, in step three, the surface shape of the top surface of the evaporation source melt is obtained by describing it with an analytical function or by fitting an interpolation curve, and a three-dimensional surface is generated by rotation to simulate the wetting and climbing behavior of the melt at the crucible wall and its influence on the ejection boundary conditions.

[0017] Furthermore, the interaction between the particles and the chamber wall in step three can be set to multiple modes, including but not limited to complete adhesion, complete reflection, and a combination of adhesion and reflection according to a preset probability, in order to characterize the influence of different wall materials or wall states on particle transport.

[0018] Further, in step three, the number of particles arriving at different regions of the substrate is recorded by a particle counter, and the particle incident angle distribution is calculated based on the particle velocity vector when arriving at the substrate, so as to obtain the incident state parameters for setting the deposition boundary conditions in step four.

[0019] Furthermore, the calculation processes in steps two, three, and four are controlled by grid adaptation and convergence criteria, with a relative tolerance of <0.001.

[0020] Furthermore, the parameter adjustment algorithm in step six adopts the gradient descent method, or the preset parameter space adopts a grid search strategy to obtain a set of key process parameters or a process window that meets the preset threshold constraints.

[0021] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for determining process parameters of a cross-scale coupled thin film physical vapor deposition process.

[0022] The beneficial effects of this invention are as follows: This invention provides a method for determining process parameters of thin film physical vapor deposition that is coupled across scales, with research scales spanning 10... -10 m to 10 -1Spanning ten orders of magnitude, this invention enables full-process coverage and key indicator prediction of physical vapor deposition (PVD). By coupling the microscopic evaporation and emission from the evaporation source, particle transport in the rarefied environment within the chamber, and the microscopic deposition and growth process on the substrate surface across multiple scales, it achieves consistent simulation of the entire "source-transport-deposition" process. This avoids the biases caused by simplified assumptions about evaporation boundary conditions, incident states, and surface behavior in traditional single-scale modeling. Furthermore, this invention combines mechanisms such as post-evaporation gas particle deletion, wall interaction mode setting, baffle opening and closing strategies, and substrate rotation strategies to ensure the characterization of key physical processes while balancing computational efficiency and engineering usability. Under preset threshold constraints, it can achieve key process parameter inversion and process window determination through parameter adjustment strategies such as grid search or gradient descent, thus providing a reliable basis and guidance for the structural design and process condition optimization of PVD equipment. Attached Figure Description

[0023] Figure 1 A schematic flowchart of a method for determining process parameters of thin film physical vapor deposition across scales, provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the geometric model of the process chamber, evaporation source, and substrate stage in an embodiment of the present invention; Figure 3 This is a schematic diagram of the evaporation source crucible structure in an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the particle state changes of Cu particles in the evaporation source during the heating process in an embodiment of the present invention; Figure 5 The particle emission angle is shown in the embodiment of the present invention. Figure 6 This is a schematic diagram showing the number of remaining particles in the evaporation source and the number of evaporated particles in an embodiment of the present invention; Figure 7 This is a schematic diagram of the film thickness distribution on the substrate surface during Cu evaporation for 60 seconds in an embodiment of the present invention; Figure 8 This is a schematic diagram of particle transport distribution within the cavity in an embodiment of the present invention; Figure 9 This is a schematic diagram of the Cu particle deposition morphology on the substrate surface in an embodiment of the present invention; Figure 10 This is a graph showing the change of film thickness over time in an embodiment of the present invention. Detailed Implementation

[0024] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but the implementation of the present invention is not limited thereto.

[0025] This invention provides a method for determining process parameters in a cross-scale coupled thin-film physical vapor deposition process, and details its application in simulating copper (Cu) evaporation deposition. See [link to relevant documentation]. Figure 1 Specifically, it includes the following steps: Step 1: Construct the geometric model of the evaporation source, equipment parameters, and material parameters.

[0026] Constructing a three-dimensional geometric model, such as Figure 2 and Figure 3 As shown, it includes a cuboid vacuum chamber, four evaporation source crucibles located at the bottom of the chamber, and a substrate stage located above (the substrate is mounted on the substrate stage, but is not shown in the figure). The evaporation source structure is as follows. Figure 3 The diagram shows a cavity consisting of a cylinder and a hemispherical recess. The top surface of the evaporating material (molten copper) is defined as the emission surface of the evaporating substance. An opening for a vacuum pump (such as a molecular pump) is provided on the cavity wall. Material parameters: Copper molecular weight 63.5 g / mol, density 8900 kg / m³ 3 Its heat capacity is 385 J / (kg·K), its thermal conductivity is 401 W / (m·K), and its lattice constant is 3.61 × 10⁻⁶. -10 m, crystal structure is face-centered cubic (Fm-3m); substrate material is silicon, defined as molecular weight 28.1 g / mol and density 2300 kg / m³. 3 Its heat capacity is 704 J / (kg·K), its thermal conductivity is 148 W / (m·K), and its lattice constant is 5.43 × 10⁻⁶. -10 m, with a crystal structure of diamond cubic (Fd-3m), and the adsorption energy of copper on the silicon surface is -1.17 eV.

[0027] The equipment parameters were set as follows: evaporation temperature Tevap = 2000 K, ambient temperature Tamb = 293.15 K, the evaporation source baffle was opened when the outgoing particle flux remained stable within 5% for 5 consecutive seconds, the equipment maintained constant power to maintain the temperature after the evaporation source temperature reached 2000 K, the simulation time was t = 60 s, and the substrate rotation speed was 5 rpm.

[0028] The threshold for step five is set as follows: coating speed > 100 nm / min, coating uniformity ≤ 10%.

[0029] Step 2: Calculation of microscopic particle evaporation process Energy minimization calculations are performed on the atomic arrangement within the evaporation source to cause the system to undergo surface contraction and tend towards a stable state under interatomic cohesion, such as... Figure 4 As shown in (A), the atomic arrangement changes significantly with increasing temperature. Figure 4(B) in the diagram represents the evaporation source interface when heated to 500K. Figure 4 (C) in the figure represents the evaporation source interface when heated to 1000 K. Figure 4 (D) in the diagram represents the evaporation source interface when heated to 2000K, where particles begin to exhibit significant evaporation. Figure 4 As shown in (E), the atomic changes at the operating temperature of 2000 K were statistically analyzed. The overall information within the statistical region is as follows: initial number of atoms: 122786; final number of atoms: 119554; number of atoms evaporated: 3232; evaporation rate: 0.54 atoms / ps; evaporation source surface morphology is convex. By statistically analyzing the position and velocity of each evaporating particle passing through the four walls of the cuboid, the particle emission angle and emission velocity can be obtained. The particle emission angle is shown in... Figure 5 As shown, the number of remaining particles in the evaporation source is as follows: Figure 6 As shown in (A), the number of particles evaporated at different times is statistically represented as follows: Figure 6 As shown in (B), the particle emission velocity is calculated based on the number of particles emitted per unit time.

[0030] Step 3: Macroscale particle transport and spatial flux distribution calculation Substrate stage rotation model setup: Define the substrate boundary as a specified mesh displacement in the free molecular flow model. Set it to rotate about the central axis (z-axis) with an angular velocity RPM_test. Use automatic remeshing or smoothing methods to handle mesh distortion caused by rotation and ensure computational stability. Specific coordinate settings are as follows: (x) (X - x0) ×(cos(omega_test×t) - 1) - (Y - y0) ×sin(omega_test×t); (y) (X - x0) ×sin(omega_test×t) + (Y - y0) ×(cos(omega_test×t) -1); (z)0.

[0031] Omega_test represents the angular velocity, t represents the time, Omega_test×t represents the angle rotated, X and Y represent the original coordinates of the grid point, and x0 and y0 represent the center of rotation.

[0032] Based on the calculation results in step two, the initial number of particles N=3232 is set in the free molecular flow model. The velocity vector and particle spatial distribution are consistent with the calculation of the microscale model. The average velocity of air molecules at the pump inlet of the chamber is set to 470m / s, and the actual particle capture coefficient is 0.54.

[0033] The dynamic mesh, particle boundary conditions, and free molecular flow physics are coupled. A transient solver is set with a time range of (0, 1, 60) seconds to calculate the film thickness at the time the particle reaches the substrate stage surface. Figure 7 As shown, the flux is calculated as film thickness / time, and the particle transport distribution within the chamber is as follows. Figure 8 As shown.

[0034] Step 4: Calculation of microscale particle deposition process A silicon wafer substrate model was constructed: the computational cell size was defined based on the lattice constant. Si atoms were generated in the lower half of the computational cell, and a certain thickness region at the bottom was defined as a fixed layer to constrain the substrate. A deposition atom generation region was set above the substrate for subsequent particle injection. System interactions were implemented using mixed potential functions: Si-Si used the SW potential, Cu-Cu used the EAM potential, and Si-Cu used the Lennard-Jones cutoff potential. A nearest neighbor list and output strategy were set to record system energy, temperature, and atomic trajectories. Energy minimization was then performed on the initial structure, and pre-relaxation at room temperature under the NVT ensemble was performed to obtain a stable initial state. Figure 9 As shown in (A) in the diagram.

[0035] During the deposition stage, Cu deposition particles were injected into a pre-defined region based on the particle terminal velocity obtained in step three, using the solved flux distribution. Simultaneously, the deposition particles were subjected to isothermal control to simulate water cooling of the substrate stage. During the calculation, a strategy of ignoring particle loss was employed to ensure numerical stability for particles exiting the computational chamber. After deposition, relaxation was performed at room temperature to obtain a stable post-deposition structure, ultimately outputting the morphology (e.g., ...). Figure 9 (as shown in (B)) and deposition rate (as shown in...) Figure 10 Statistical analysis of indicators such as (as shown in the figure).

[0036] Step 5: Determine if the requirements have been met. In this case, the coating speed of 200 nm / min is greater than the preset requirement, and the calculated coating uniformity is approximately 10%, which meets the preset requirement and is the derived operating condition. The coating uniformity is calculated by subtracting the minimum film thickness from the maximum film thickness and then dividing by the average film thickness.

[0037] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for determining process parameters of a cross-scale coupled thin film physical vapor deposition process.

[0038] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0039] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0040] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0041] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0042] The above embodiments are only used to illustrate the design concept and features of the present invention, and their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes or modifications made based on the principles and design ideas disclosed in the present invention are within the protection scope of the present invention.

Claims

1. A method for determining process parameters of thin film physical vapor deposition (PVD) with cross-scale coupling, characterized in that, Includes the following steps: Step 1: Construct a geometric model of the physical vapor deposition (PVD) equipment, which includes at least a vacuum chamber, an evaporation source, a rotatable substrate, and an evaporation source baffle; set the equipment parameters, including the substrate stage rotation speed, the evaporation source heating curve, and the evaporation source baffle opening time; set the parameters of the material to be deposited, including at least the element types and composition, crystal structure, lattice constant, and adsorption energy. Step 2: At the microscale, molecular dynamics methods are used to calculate the motion and desorption behavior of particles at the evaporation source during the heating process to obtain evaporation boundary condition parameters. The evaporation boundary condition parameters include at least the particle evaporation rate, particle ejection velocity distribution, particle ejection angle distribution, and particle morphology characteristics on the surface of the evaporation source. Step 3: Map the evaporation boundary condition parameters obtained in Step 2 to the source terms and boundary conditions of the macroscopic particle transport model. Use the free molecular flow transport model to calculate the transport process of particles in the chamber, and obtain the particle spatial flux distribution and the incident state parameters before the particles reach the substrate surface. The incident state parameters include at least the incident velocity distribution and the incident angle distribution. Step 4: Using the particle spatial flux distribution and incident state parameters obtained in Step 3 as boundary conditions for molecular dynamics simulation, calculate the particle deposition behavior and surface evolution growth process on the substrate surface at the microscale, and output process evaluation indicators, which include at least film thickness uniformity and deposition rate. Step 5: Determine whether the process evaluation indicators obtained in Step 4 meet the preset requirements; if they do, output the corresponding equipment parameters and material parameters as process parameters; if they do not meet the requirements, proceed to Step 6. Step Six: Adjust the equipment and material parameters, then return to Step Two for iterative calculations until the preset requirements are met or the maximum number of iterations is reached.

2. The method according to claim 1, characterized in that, Step one is achieved through the following steps: A geometric model of the equipment is constructed, which includes the three-dimensional structure of the process chamber of the physical vapor deposition equipment, and includes an evaporation source, an evaporation source baffle, a rotatable substrate stage, and a substrate mounted on the substrate stage. In the geometric model, the equipment parameters also include the position and size of the extraction port, so as to analyze the influence of the extraction boundary conditions on the particle transport process.

3. The method according to claim 1, characterized in that, Step two is achieved through the following steps: Substitute the material parameters and evaporation source geometry model set in step one into the molecular dynamics model to establish the initial atomic configuration and perform energy minimization or equilibrium calculations; raise the system from room temperature to working temperature according to the preset heating curve to simulate the thermal excitation desorption process of particles and obtain the particle evaporation rate, emission velocity distribution, emission angle distribution, and particle morphology characteristics on the surface of the evaporation source.

4. The method according to claim 1, characterized in that, Step three is achieved through the following steps: The particle evaporation rate, emission velocity distribution, emission angle distribution, and particle morphology characteristics on the evaporation source surface obtained in step two are used as particle source term parameters in the macroscopic particle transport model. Combined with the chamber geometry model and baffle opening strategy in step one, particle trajectory statistics or flux calculations are performed to obtain the spatial flux distribution on the substrate surface and the incident velocity distribution and incident angle distribution of particles before they reach the substrate surface.

5. The method according to claim 1, characterized in that, Step three considers the influence of substrate rotation on film thickness uniformity by introducing substrate rotation motion to perform time-varying calculations on particle transport processes; the implementation of substrate rotation motion includes simulating substrate rotation using a dynamic mesh method to obtain the flux distribution on the substrate surface under rotation conditions.

6. The method according to claim 1, characterized in that, Step four is achieved through the following steps: Using the incident velocity distribution and incident angle distribution obtained in step three as boundary conditions for the injection of deposited particles, the deposited particles are released onto the substrate surface in the molecular dynamics model and their evolution over time is performed. The particle adhesion, migration and rearrangement deposition behavior and surface morphology evolution process are calculated. The deposition rate is calculated based on the number of deposited atoms or the change in film height, and the film thickness uniformity is calculated based on the film thickness distribution.

7. The method according to claim 1, characterized in that, The criteria for step five include that the deposition rate is not less than a preset deposition rate threshold and the film thickness uniformity index is not greater than a preset uniformity threshold; when the above threshold criteria are met, the process evaluation index is determined to meet the preset requirements.

8. The method according to claim 1, characterized in that, Step six uses the gradient descent method for parameter adjustment, or a grid search method for the preset parameter space.

9. The method according to claim 1, characterized in that, The calculation processes in steps two, three, and four employ grid adaptation and convergence criterion control, with a relative tolerance of <0.

001.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for determining process parameters of a cross-scale coupled thin film physical vapor deposition process as described in any one of claims 1-9.

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