FCVA film preparation visual simulation method based on ion beam subsurface growth

Abstract

The invention discloses an FCVA film preparation visual simulation method based on ion beam subsurface growth, and the method comprises the steps: building an FCVA equipment three-dimensional model and a multi-field coupling simulation model, building an ion beam subsurface growth multi-scale simulation model, generating a subsurface growth full-process visual animation, and building a database for matching with technological parameters. Visual simulation and process optimization of the whole process of subsurface growth in FCVA thin film preparation are achieved, and the method is suitable for high-performance thin film preparation process optimization, subsurface growth mechanism research, FCVA equipment research and development and other scenes.

Description

Technical Field

[0001] This invention relates to the fields of vacuum coating simulation and thin film growth technology, specifically to a visualization simulation method for FCVA (Filtered Cathodic Vacuum Arc) thin film preparation based on ion beam subsurface growth, which is applicable to scenarios such as high-performance thin film preparation process optimization, subsurface growth mechanism research, and FCVA equipment development. Background Technology

[0002] FCVA technology, with its advantages of high deposition particle energy and good film density, is widely used in hard coatings, microelectronics, and optical thin films. Traditional FCVA film growth mainly relies on surface growth modes (island, layer, and hybrid growth modes), which have problems such as limited film-substrate adhesion, difficulty in interface control, and limitations in high-temperature growth.

[0003] In recent years, "subsurface growth based on ion beam technology" has emerged as a fourth thin film growth method, surpassing the traditional three growth modes, demonstrating significant technological advantages: by injecting deposited atoms into the substrate subsurface layer using high-energy ions, atomic mixing and subsurface nucleation are achieved through collisional cascade effects, which can greatly improve film adhesion, synthesize metastable materials, and achieve low-temperature growth. However, the subsurface growth process involves complex multi-physics coupling (electric field, magnetic field, ion dynamics, atomic-level mixing), and its growth mechanism (ion implantation depth, subsurface nucleation law, and mixed layer formation process) is difficult to observe directly through experiments. At the same time, the electric and magnetic field distributions of the FCVA device directly affect the subsurface growth effect by regulating ion energy and trajectory. Traditional process optimization methods that rely on experimental trial and error suffer from high cost, long cycle, and low efficiency.

[0004] Existing FCVA simulation techniques mostly focus on macroscopic simulations of surface growth processes, lacking accurate simulations of key physical processes in subsurface growth (ion implantation, atomic mixing, and defect evolution), and failing to visualize the entire process of electric field-magnetic field-subsurface growth. Therefore, there is an urgent need for an FCVA visualization simulation method based on the ion beam subsurface growth mechanism to accurately simulate the subsurface growth process under multi-field coupling, providing an intuitive and efficient technical tool for process optimization and mechanism research. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the first objective of this invention is to provide a visual simulation method for FCVA thin film preparation based on ion beam subsurface growth. The second objective is to provide an application of this simulation method in the optimization of the FeCoNiCrMn high-entropy alloy thin film preparation process. This method can achieve accurate simulation and visualization of electric-magnetic field distribution, ion trajectory, subsurface atomic mixing, defect evolution, and the entire thin film growth process, making it suitable for the process development and performance prediction of high-entropy alloy thin films.

[0006] To achieve the above objectives, the present invention employs the following technical solution:

[0007] This invention provides a visualization simulation method for FCVA thin film preparation based on ion beam subsurface growth. By constructing a multi-scale simulation system from the equipment level to the atomic level, a three-dimensional model of the FCVA equipment and an electric-magnetic field coupling simulation model are first established. Then, a multi-scale simulation model of ion beam subsurface growth is established. Finally, a visualization animation of the entire subsurface growth process is generated, realizing accurate simulation and visualization of the growth process of FeCoNiCrMn high-entropy alloy thin films.

[0008] The simulation method of this invention employs multiphysics coupling simulation technology. First, it accurately calculates the electromagnetic field distribution near the filter channel and substrate using an electric-magnetic field coupling model, providing accurate field distribution input for ion trajectory simulation. Then, it simulates the collision cascade process between FeCoNiCrMn high-entropy alloy ions and substrate atoms through molecular dynamics simulation, simulating subsurface atomic mixing, defect evolution, and thin film growth mechanisms. Finally, it intuitively displays the entire physical process through multi-view dynamic visualization. The simulation model established by this invention has high accuracy, intuitive visualization effects, and can accurately predict the impact of different process parameters on thin film growth quality.

[0009] The solution in this invention does not impose too many restrictions on the process parameters involved in the simulation, such as arc current size, bias voltage, magnetic field strength, deposition time, etc.

[0010] In a preferred embodiment, the substrate material used in the simulation is an iron-based material. This preferred embodiment is based on the fact that FeCoNiCrMn high-entropy alloy thin films are commonly used for surface modification of steel materials.

[0011] In a preferred embodiment, the high-entropy alloy is a FeCoNiCr-based high-entropy alloy, such as FeCoNiCrMn, FeCoNiCrCu, FeCoNiCrAl, or FeCoNiCrTi, with FeCoNiCrMn being the most preferred, as its constituent atomic radii are similar (Fe: Co: Ni: Cr: Mn: It combines high strength and high ductility, and its single face-centered cubic solid solution structure is stable. Its comprehensive performance is the most balanced in the system, making it an ideal model material for basic research and engineering applications.

[0012] Based on size Taking the Fe substrate model as an example, in the actual modeling process, a complete cell structure is first established, then the actual surface roughness state is simulated by randomly removing surface atoms, and finally energy minimization is performed to obtain a stable initial structure.

[0013] In a preferred embodiment, the composition of the FeCoNiCrMn high-entropy alloy ions is designed according to an equal atomic ratio, that is, the atomic percentage of each element is 20%.

[0014] The inventors discovered that selecting FeCoNiCrMn high-entropy alloys with equal atomic ratios not only better aligns with the definition of high entropy, but also accurately simulates the unique lattice distortion effect and diffusion behavior of high-entropy alloys. However, improper composition design can lead to simulation results that do not match the actual characteristics of high-entropy alloys. For example, when the atomic ratio of a certain element is significantly higher, it may be impossible to accurately reproduce the slow diffusion effect and lattice distortion characteristics of high-entropy alloys.

[0015] In the preferred embodiment, the MEAM potential function parameters used in the simulation need to be verified by experimental data to ensure that they can accurately describe the interactions between Fe-Co-Ni-Cr-Mn multi-component atoms.

[0016] In a preferred embodiment, the permittivity of the vacuum environment in the electric-magnetic field coupling simulation is set to 8.854 × 10⁻⁶. - 12 F / m. In actual simulation, reasonable boundary conditions are first set, and then the field distribution is solved using the finite element method.

[0017] In a preferred embodiment, the ion trajectory simulation process involves setting the initial ion energy to 50-100 eV, the incident angle to 30-90 degrees, and the implantation depth to 1-5 nm, and then solving for the ion trajectory in the electromagnetic field using a numerical integration method. In this invention, reasonable initial ion parameters are first set, and then the ion trajectory is obtained by solving the equations of motion. Adjusting the initial parameters ensures an accurate ion implantation distribution. Setting the initial energy too high may lead to excessive damage, while setting it too low may fail to accurately simulate the actual implantation process.

[0018] In a preferred embodiment, the thickness of the ion implantation layer is approximately 10 atomic layers.

[0019] In a preferred embodiment, the bias voltage is set to -50V to -200V, more preferably -80V to -150V, and even more preferably -100V. Controlling the bias voltage within the range of this invention is essential to accurately simulate the growth process of obtaining high-quality thin films.

[0020] In a preferred embodiment, the molecular dynamics simulation process involves setting the simulation temperature to 300-400K, the deposition time to 50-200ps, and the ensemble type to NVE-NVT-NPT. During the deposition of FeCoNiCrMn high-entropy alloy thin films, the simulation temperature needs to be controlled. If it is too low, atomic diffusion will be insufficient, while if it is too high, excessive thermal defects may occur.

[0021] In the actual simulation process, energy minimization is performed first, and then the temperature is gradually increased to the target temperature. After the system stabilizes, deposition simulation is carried out.

[0022] In a preferred embodiment, the simulated thickness of the FeCoNiCrMn high-entropy alloy thin film is 5-20 nm.

[0023] This invention also provides the application of the above simulation method in the optimization of the FeCoNiCrMn high-entropy alloy thin film preparation process.

[0024] Compared with existing technologies, the beneficial effects of this invention are as follows:

[0025] This invention employs a multi-scale simulation method. First, it accurately calculates the electromagnetic field distribution using an electric-magnetic field coupling model, providing accurate input conditions for ion trajectory simulation. Then, it simulates the implantation, mixing, and thin film growth process of FeCoNiCrMn high-entropy alloy ions using molecular dynamics. By controlling process parameters such as bias voltage, it can effectively predict the microstructure, defect density, and interfacial bonding strength of the thin film, thereby obtaining simulation results with high accuracy, strong predictability, and good visualization.

[0026] Meanwhile, due to the significant differences in elemental size and chemical complexity in FeCoNiCrMn high-entropy alloys, the simulation can accurately reproduce severe lattice distortion effects, slow diffusion effects, and high-entropy effects. The differences in chemical potential between different elements generate complex energy landscapes during atomic migration, which greatly affect atomic arrangement and defect evolution. Furthermore, the synergistic effect between multiple components leads to significant structural amplitude modulation decomposition, which restricts grain growth and thus promotes the formation of dense nanocrystalline structures.

[0027] Due to the synergistic effect described above, the simulation method provided by this invention has excellent prediction accuracy and visualization effect.

[0028] The simulation method of this invention has high computational efficiency and wide applicability. It can effectively guide the optimization of the preparation process of FeCoNiCrMn high-entropy alloy thin films, and significantly reduce the experimental research and development costs and cycle. Attached Figure Description

[0029] Figure 1 This is a flowchart illustrating the method for depositing FeCoNiCrMn high-entropy alloy thin films using molecular dynamics simulations via FCVA.

[0030] Figure 2 The electric and magnetic field distributions in the simulated FCVA device are shown.

[0031] Figure 3 This is a schematic diagram of the structural model of FeCoNiCrMn involved in this invention.

[0032] Figure 4 The distribution and number of defects during the cascading collision process.

[0033] Figure 5 Stress-strain curves of FeCoNiCrMn Detailed Implementation

[0034] The present invention provides a visualization simulation method for the preparation of FCVA thin films based on ion beam subsurface growth, the method comprising the following steps:

[0035] Figure 1 This is a multi-scale simulation flowchart of the subsurface growth process of the FeCoNiCrMn high-entropy alloy described in this invention. The diagram illustrates the complete technical path from COMSOL electromagnetic field modeling and LAMMPS deposition simulation to Ovito post-processing visualization, covering key steps such as electric field-magnetic field coupling solution, ion trajectory tracking, atomic-level collision cascade simulation, defect evolution analysis, and visualization animation generation, realizing a full-chain visualization simulation from macroscopic field distribution to atomic-level structure evolution.

[0036] 1. Construct a 3D model and a multi-field coupled simulation model for the FCVA device.

[0037] Obtain structural composition information of the FCVA equipment, including the dimensions, materials, and relative positions of components such as the cathode target, filter channel, magnetic field coil, substrate support, and vacuum chamber, as well as information on the equipment's working principle, covering the arc discharge mechanism, magnetic field filtering principle, and ion beam acceleration method. Using SolidWorks modeling software, establish a three-dimensional simulation model that includes a cathode arc source model, a magnetic field filtering system model, an ion acceleration electrode model, and a substrate model, ensuring that the model's geometric dimensions are consistent with the actual equipment, and optimize the model's complexity to suit simulation requirements.

[0038] Based on Maxwell's equations and the law of electromagnetic induction, an FCVA electric-magnetic field coupling simulation model was established using COMSOL Multiphysics software. Key parameters of the device were input, including the number of turns of the magnetic field coil, the magnitude of the current, the electrode voltage, and the size of the filter channel. Boundary conditions were set, covering the dielectric constant of the vacuum environment, the electrode potential distribution, and the magnetic field constraint range. The electric field function and magnetic field function were solved using the finite element method to obtain the electric field intensity distribution, magnetic field line trajectory, and electromagnetic force distribution data within the filter channel and near the substrate. This provides accurate field distribution input for subsequent simulation of FeCoNiCrMn high-entropy alloy ion trajectories.

[0039] 2. Establish a multi-scale simulation model for ion beam subsurface growth.

[0040] Based on the electric and magnetic field distribution data output by COMSOL, the governing equations for the ion motion of FeCoNiCrMn high-entropy alloys were constructed. Figure 2 Taking into account the electric field force, Lorentz force and inter-particle collision force on the ions, the Runge-Kutta method in the numerical integration method is used to solve the ion trajectory and obtain the energy distribution, incident angle and implantation depth data of the ions when they reach the substrate.

[0041] Based on molecular dynamics principles, a multi-atom ionic model of the substrate-FeCoNiCrMn high-entropy alloy was established using LAMMPS software. Figure 3 The simulation program inputs key parameters such as ion energy, implantation depth, and incident angle, sets the interatomic interaction potential of FeCoNiCrMn high-entropy alloy, and describes it using the MEAM potential function. It simulates the collision cascade process between FeCoNiCrMn high-entropy alloy ions and substrate atoms, including ion implantation, substrate atom displacement, subsurface atom mixing, and the generation and evolution of vacancies / interstitial defects. By tracking the formation and changes of subsurface clusters, it simulates the complete process of metastable phase nucleation, mixed layer formation, and film growth from the subsurface in FeCoNiCrMn high-entropy alloy, outputting key subsurface growth parameters such as mixed layer thickness, defect density, film density, and interfacial bonding strength.

[0042] Historical experimental data on the subsurface growth of FeCoNiCrMn high-entropy alloy thin films prepared by FCVA were obtained, including process parameters, measured electric / magnetic field data, surface roughness, Young's modulus, hardness, residual stress, and film adhesion test data. The experimental process parameters were input into the simulation model to obtain predicted data. The difference between the predicted data and the measured data was calculated, and an accurate simulation model was obtained through parameter correction.

[0043] 3. Visual animation generation of the entire process of subsurface growth in FeCoNiCrMn high-entropy alloys

[0044] The electric and magnetic field distribution data output by COMSOL are exported, and the electric field intensity distribution and magnetic field line trajectory are displayed using a color gradient. Based on the ion motion trajectory data of FeCoNiCrMn high-entropy alloy, the entire process of ions being generated from the cathode arc source, transported through the filtering channel, accelerated under the action of electric and magnetic fields, and injected into the subsurface layer of the substrate is dynamically displayed using a particle model.

[0045] Based on atomic trajectory data from LAMMPS simulations, including atomic coordinates, stress distribution, potential energy, and kinetic energy parameters, an atomic-level dynamic scene of sub-surface growth of FeCoNiCrMn high-entropy alloy was constructed using Ovito post-processing software. Fe was set as the substrate atom, and the deposited atoms were colored according to type to dynamically display the multi-component deposition process: First, five component ions, Co, Ni, Cr, Fe, and Mn, were sequentially injected into the Fe substrate from a designated deposition area at a specific velocity, inducing substrate atomic displacement and initial defect formation. Second, the five component atoms mixed and interdiffused in the sub-surface layer through a collisional cascade effect, forming a FeCoNiCrMn high-entropy alloy mixed layer. Finally, the defect evolution process was monitored in real time, including vacancy formation, interstitial atom generation, and recombination. A layered effect was used to display the structural evolution of the substrate surface and sub-surface layer, and the thickness of the mixed layer, defect density, elemental concentration distribution, and atomic-level stress-energy field changes were dynamically labeled.

[0046] Figure 4 The graph shows the defect distribution (left) and defect number (right) of FeCoNiCrMn high-entropy alloy ions during the cascade collision process, reflecting the atomic-level stress accumulation and release behavior in the subsurface region during ion implantation-induced collisional cascades. This is used to assess the local mechanical stability and defect formation tendency during film growth.

[0047] Figure 5 The stress-strain response curves of uniaxial tensile stress on FeCoNiCrMn high-entropy alloy thin films obtained by LAMMPS simulation are shown.

[0048] The atomic trajectory data output by LAMMPS was post-processed and visualized using Ovito software to dynamically display the evolution of key physical quantities such as dislocations, stacking faults, cascade collisions, stress distribution, and atomic temperature field generated during the ion implantation process of FeCoNiCrMn high-entropy alloy. At the same time, the dislocation line length, defect number and distribution, cluster size distribution, and content distribution of different crystal structures were quantitatively statistically visualized, realizing an intuitive and quantitative characterization of the microstructure evolution and defect evolution mechanism during the subsurface growth of FeCoNiCrMn high-entropy alloy.

Claims

1. A visualization simulation method for the fabrication of FCVA thin films based on ion beam subsurface growth, characterized in that, The method includes: Obtain structural and operational information of the FCVA device, and establish a three-dimensional simulation model including a cathode arc source model, a magnetic field filtering system model, an ion acceleration electrode model, and a substrate model; based on Maxwell's equations and the law of electromagnetic induction, use COMSOL software to establish an electric-magnetic field coupling simulation model, and solve for the electric and magnetic field distribution data. A multi-scale simulation model for ion beam subsurface growth was constructed, including: solving the ion trajectory and energy of FeCoNiCrMn high-entropy alloy using numerical integration based on electric field-magnetic field distribution data; establishing a multi-atom model of FeCoNiCrMn high-entropy alloy ions on the substrate using LAMMPS software based on molecular dynamics principles to simulate subsurface atomic mixing and defect evolution; and finally, correcting the model based on experimental data. Multi-field distribution data, ion trajectory data, and atomic-level simulation data are imported into visualization software to generate a multi-view, interactive visualization animation of the entire subsurface growth process; an FCVA subsurface growth simulation database is constructed, and the optimal process parameters are matched according to the key requirements input by the user and displayed on the visualization interface.

2. The method according to claim 1, characterized in that, The electric field-magnetic field coupling simulation model is solved using the finite element method. The boundary conditions include the dielectric constant of the vacuum environment, the electrode potential distribution, and the magnetic field constraint range. The output data include the electric field strength and magnetic field line trajectory data within the filter channel and near the substrate.

3. The method according to claim 1, characterized in that, The governing equations for the ion motion of the FeCoNiCrMn high-entropy alloy consider electric field force, Lorentz force, and inter-particle collision force. Through SRIM simulation, the cascade collision trajectories of different atoms in the FeCoNiCrMn deposition layer, the distribution of recoil atoms with depth, the distribution of vacancy damage with depth, phonon energy, and sputtering yield can be obtained.

4. The method according to claim 1, characterized in that, The subsurface atomic-level simulation uses the MEAM potential function to describe the atomic interactions of FeCoNiCrMn high-entropy alloys. The NVE-NVT-NPT ensemble simulates the ion implantation, atomic mixing, and annealing evolution processes, and outputs parameters such as stress, radial distribution function, number of defects, cluster size, and dislocation line length.

5. The method according to claim 1, characterized in that, The visualization animation supports color gradient display of electric and magnetic field distribution, dynamic simulation of FeCoNiCrMn high-entropy alloy ion trajectory, and layered transparent display of subsurface atomic-level processes. It also features viewpoint switching, time axis control, and real-time annotation of key parameters.

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