Plasma immersion ion implantation method suitable for mold coating

Abstract

The invention belongs to the technical field of automobile part mold surface enhancement, and particularly relates to a plasma immersion ion implantation method suitable for a mold coating, which comprises the following steps of: dynamically adjusting gas flow through a gas flow control module according to a vacuum degree requirement; an output result of the sputtering power optimization algorithm is input into the working temperature setting model; output data of the working temperature setting model are transmitted to the ion energy control system; output data and surface cleaning demand data of the ion energy control system are used for argon ion bombardment parameter optimization; output data and surface state data of the argon ion bombardment parameter optimization module are transmitted to a hydrogen etching process control system; and the output data of the hydrogen etching process control system is input into the composite film layer structure design module to realize the designed composite film layer structure. According to the invention, by introducing an immersion ion implantation technology, dynamic regulation and control of key parameters such as gas flow, sputtering power, working temperature and ion energy are realized.

Description

Technical Field

[0001] The invention belongs to the technical field of surface enhancement of automobile parts moulds, and in particular relates to a plasma immersion ion implantation method suitable for mould coating. Background Art

[0002] The role of ion plating in the surface enhancement of automotive parts molds is mainly reflected in the following aspects: 1) Improving surface hardness and wear resistance: Ion plating technology can significantly improve the surface hardness of the mold by depositing a layer of high-hardness film on the mold surface, thereby enhancing its wear resistance. This helps to extend the service life of the mold and reduce performance degradation caused by wear. Enhanced corrosion resistance: 2) Ion plating technology can form a dense protective film on the mold surface, effectively isolating the corrosive medium, thereby significantly improving the corrosion resistance of the mold. This is especially important for automotive parts molds working in harsh environments.

[0003] When introducing immersion ion implantation technology into the ion plating system, multiple key parameters need to be optimized. The first is the gas flow rate. Too large a flow rate will cause the vacuum to decrease and affect the coating quality; too small a flow rate will affect the ion implantation effect. The second is the sputtering power. Too high a power will cause the substrate temperature to rise and cause the substrate to deform; too low a power will affect the deposition rate of the coating. The third is the working temperature. Too high a temperature will cause the substrate to soften and deform, while too low a temperature will affect the film structure and adhesion. Finally, the ion energy, i.e., the bias voltage, will cause the substrate to be damaged if the energy is too high, while too low an energy will affect the depth of ion implantation. In the preparation process of nickel-based and chromium-based coatings, argon ion bombardment and hydrogen etching are two key links. Too high a bombardment energy will cause the substrate to be damaged, while too low a bombardment energy will make it difficult to remove the surface contamination layer; too long a bombardment time will cause excessive sputtering of the substrate, while too short a bombardment time will result in poor cleaning effect. Improper control of hydrogen etching parameters will cause hydrogen embrittlement of the substrate or increase of surface roughness. In addition, improper control of the deposition rate of the main film layer will cause the film structure to be loose and porous or the adhesion to decrease. The design of the composite film structure also needs to balance internal stress, hardness, toughness and other properties. Summary of the invention

[0004] The purpose of the present invention is to overcome the above problems existing in the conventional technology and to provide a plasma immersion ion implantation method suitable for mold coating.

[0005] In order to achieve the above technical objectives and the above technical effects, the present invention is implemented through the following technical solutions:

[0006] A plasma immersion ion implantation method suitable for mold coating comprises the following steps:

[0007] S1. According to the vacuum degree requirement of the ion plating system, the gas flow rate is dynamically adjusted through the gas flow control module;

[0008] S2, the output of the sputtering power optimization algorithm is input into the operating temperature setting model;

[0009] S3, the output data of the working temperature setting model is transmitted to the ion energy control system;

[0010] S4, the output data of the ion energy control system and the surface cleaning requirement data are used for the optimization of argon ion bombardment parameters;

[0011] S5, the output data of the argon ion bombardment parameter optimization module and the surface state data are transmitted to the hydrogen etching process control system;

[0012] S6. The output data of the hydrogen etching process control system is input into the composite film layer structure design module to realize the designed composite film layer structure.

[0013] Furthermore, in step S1, the gas flow control module monitors the pressure in the vacuum chamber in real time, and when the immersion ion implantation technology is introduced, the gas flow is automatically adjusted to ensure that the vacuum degree is maintained within a preset range; the flow control module is linked with the vacuum monitoring system, and when a drop in the vacuum degree is detected, the gas flow is immediately reduced to avoid affecting the coating quality; at the same time, the flow control module communicates with the ion implantation system, adjusts the gas flow according to the implantation requirements, and achieves the best ion implantation effect while ensuring the vacuum degree.

[0014] Furthermore, in step S2, the sputtering power optimization algorithm comprehensively considers the material properties of the substrate, the coating requirements and the vacuum data, and adaptively adjusts the sputtering power parameters; the sputtering power optimization algorithm first obtains the heat resistance data of the substrate from the material database. If the heat resistance of the substrate is low, the power is limited to a safe range; at the same time, the sputtering power optimization algorithm considers the coating deposition rate requirements and increases the power within the tolerance range of the substrate; the degree of power increase is determined according to the preset deposition rate target value and the maximum tolerance temperature of the substrate, and the optimal power value is obtained through iterative calculation; the power optimization algorithm interacts with the temperature monitoring system in real time, and continuously adjusts the power parameters to ensure that the substrate temperature does not exceed the critical temperature threshold.

[0015] Furthermore, in step S2, the calculation formula of the sputtering power after the sputtering power optimization algorithm is optimized is as follows:

[0016]

[0017] Among them, P opt represents the optimized sputtering power, P max Indicates the maximum safe limit of power, T max Indicates the maximum tolerable temperature of the substrate, T amb Indicates the ambient temperature, R th represents thermal resistance;

[0018] v dep = k·P opt

[0019] v dep represents the deposition rate, k represents the proportionality constant related to power, P opt represents the optimized sputtering power;

[0020] T real =T amb +P opt ·R th

[0021] T real represents the real-time monitored substrate temperature, T amb Indicates the ambient temperature, P opt represents the optimized sputtering power, R th Represents thermal resistance.

[0022] Furthermore, in step S3, the ion energy control system optimizes and controls the ion energy, i.e., the bias voltage, in combination with the coating process requirements and the substrate material properties; the system first establishes a correlation model between the ion energy, the implantation depth, and the degree of substrate damage, from which a suitable energy range is determined; during the coating process, the bias voltage monitoring device collects the bias voltage data in real time, and the control system compares it with a preset energy range; if the bias voltage exceeds the optimization range, the control system immediately adjusts the bias voltage parameters to ensure that the ion implantation depth meets the requirements while avoiding damage to the substrate; the ion energy control system also evaluates the surface cleanliness of the substrate and generates surface cleaning demand data.

[0023] Furthermore, in step S4, during the preparation of nickel-based and chromium-based coatings, the parameter optimization module first analyzes the material properties of the substrate and the degree of surface contamination, and extracts the optimal bombardment energy range and time from the database; during the bombardment process, the spectrometer obtains surface composition information in real time, and the data analysis module determines the removal of the contamination layer; if the contamination layer is not completely removed, the system automatically extends the bombardment time; if it is detected that the substrate component signal is enhanced, the bombardment energy is reduced or the time is shortened in time to avoid substrate damage or excessive sputtering; after the bombardment, the system evaluates the surface state and generates surface state data.

[0024] Furthermore, in step S5, the hydrogen etching process control system establishes an optimization model of hydrogen atom flow, substrate temperature and etching time according to the surface state data, and calculates the best process parameter combination; during the etching process, the in-situ monitoring device collects data such as hydrogen atom flow and substrate temperature in real time, and the control system compares these data with the optimization model; if it is detected that the parameters deviate from the optimal range, the system immediately adjusts the hydrogen flow, heating power or etching time to ensure that the ideal surface morphology and performance are obtained, while avoiding problems such as hydrogen embrittlement of the substrate or increased surface roughness.

[0025] Furthermore, in step S6, the composite film layer structure design module simulates and optimizes different material combinations and thickness ratios through finite element analysis and material performance database to obtain the best film layer structure that takes into account internal stress, hardness, toughness and other properties; the optimization result is transmitted to the deposition control system, which accurately controls the deposition rate and time of each layer of material to achieve the designed composite film layer structure; at the same time, the online stress monitoring system collects stress data in real time and compares it with the design requirements; if it is detected that the stress deviates from the expected value, the feedback control module immediately fine-tunes the deposition parameters; the specific parameters that can be adjusted include the deposition rate of each layer of material, the deposition time, the substrate temperature and the ion bombardment energy to ensure that a composite coating with excellent performance is obtained.

[0026] The beneficial effects of the present invention are:

[0027] The present invention realizes the dynamic regulation of key parameters such as gas flow, sputtering power, working temperature and ion energy by introducing immersion ion implantation technology. The present invention first uses the gas flow control module to monitor the vacuum degree in real time, and adaptively adjusts the gas flow to ensure the best ion implantation effect. Subsequently, the sputtering power optimization algorithm comprehensively considers the substrate characteristics and coating requirements, and dynamically adjusts the power parameters. The working temperature setting model calculates and maintains the optimal temperature range according to different substrate and coating requirements. The ion energy control system ensures that the injection depth meets the requirements while avoiding substrate damage through bias optimization. In the preparation process of nickel-based and chromium-based coatings, the present invention optimizes the argon ion bombardment and hydrogen etching processes, and realizes the effective removal of the surface contamination layer and the acquisition of the ideal morphology. Finally, the composite film layer structure design module optimizes the film layer structure through finite element analysis and material performance database, and realizes the balance of internal stress, hardness and toughness. The present invention significantly improves the coating quality and efficiency through data interaction and feedback control between modules, and provides strong support for the preparation of high-performance composite coatings.

[0028] Of course, any product implementing the present invention does not necessarily need to achieve all of the above advantages at the same time. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] This embodiment provides a plasma immersion ion implantation method suitable for mold coating, comprising the following steps:

[0031] S1. According to the vacuum degree requirement of the ion plating system, the gas flow rate is dynamically adjusted through the gas flow control module;

[0032] S2, the output of the sputtering power optimization algorithm is input into the operating temperature setting model;

[0033] S3, the output data of the working temperature setting model is transmitted to the ion energy control system;

[0034] S4, the output data of the ion energy control system and the surface cleaning requirement data are used for the optimization of argon ion bombardment parameters;

[0035] S5, the output data of the argon ion bombardment parameter optimization module and the surface state data are transmitted to the hydrogen etching process control system;

[0036] S6. The output data of the hydrogen etching process control system is input into the composite film layer structure design module to realize the designed composite film layer structure.

[0037] In this embodiment, the gas flow control module monitors the pressure in the vacuum chamber in real time. When the immersion ion implantation technology is introduced, the gas flow rate is automatically adjusted to ensure that the vacuum degree is maintained within the preset range. The flow control module is linked with the vacuum degree monitoring system. When the vacuum degree drops, the gas flow rate is immediately reduced to avoid affecting the coating quality. At the same time, the flow control module communicates with the ion implantation system to adjust the gas flow rate according to the implantation requirements to achieve the best ion implantation effect while ensuring the vacuum degree.

[0038] Acquire the real-time pressure data in the vacuum chamber and use it as the input parameter of the gas flow control module. The gas flow control module determines whether the current pressure is within the normal range based on the preset vacuum range. If the pressure is lower than the lower limit of the preset range, the gas flow control module reduces the gas flow until the pressure returns to the normal range. If the pressure is higher than the upper limit of the preset range, the gas flow control module increases the gas flow until the pressure returns to the normal range. When the immersion ion implantation technology is introduced, the gas flow control module obtains the injection requirement parameters of the ion implantation system. Based on the injection requirement parameters and the current pressure data, the gas flow control module uses a machine learning algorithm, such as a support vector machine or a neural network, to predict the optimal gas flow value. The predicted optimal gas flow value is passed to the gas flow control module as a control instruction to achieve the best ion implantation effect while ensuring the vacuum degree.

[0039] Specifically, the real-time pressure data acquisition in the vacuum chamber can be achieved through a vacuum gauge installed on the chamber. Common vacuum gauges include Pirani vacuum gauges, thermocouple vacuum gauges, and ionization vacuum gauges. For example, you can choose a Pirani vacuum gauge, which is suitable for measurements in the low vacuum range and can convert the measured pressure value into an electrical signal to facilitate subsequent data processing and control. The core of the gas flow control module is the flow control valve. Common flow control valves include needle valves, proportional valves, and mass flow controllers. For this example, you can choose a mass flow controller, which can accurately control the flow of the gas and automatically adjust the valve opening according to the input set value. The preset vacuum range needs to be determined according to the specific process requirements. For example, for a certain coating process, the vacuum degree may be required to be maintained at 1.0×10 -4 Pa to 5.0×10 -4 If the current pressure is lower than the lower limit of the preset range, for example, the current pressure is 0.5×10 -4 Pa, below the lower limit 1.0×10 -4 Pa, the gas flow control module will reduce the gas flow. This is because the vacuum is too high, indicating that the number of gas molecules in the chamber is too small, and it is necessary to reduce the gas flow to reduce the extraction speed of gas molecules, so as to increase the pressure. On the contrary, if the current pressure is higher than the upper limit of the preset range, for example, the current pressure is 6.0×10 -4 Pa, higher than the upper limit 5.0×10 -4Pa, the gas flow control module will increase the gas flow to speed up the extraction of gas molecules and reduce the pressure in the cavity. After the introduction of immersion ion implantation technology, the control of gas flow becomes more complicated. The required parameters of ion implantation include the type, energy, and dose of the implanted ions. These parameters will affect the pressure change in the cavity, so the gas flow needs to be adjusted according to these parameters. For example, the higher the injection energy, the greater the probability of collision between ions and gas molecules, which will cause the pressure to rise, so the gas flow needs to be increased accordingly. At this time, a machine learning algorithm can be used to predict the optimal gas flow value. The support vector machine algorithm can be selected. First, a large amount of historical data needs to be collected, including ion implantation parameters, vacuum degree, gas flow, etc. Then, the support vector machine model is trained using these data to establish a mapping relationship between input parameters (ion implantation parameters and current pressure data) and output parameters (optimal gas flow value). When a new ion implantation task arrives, the injection requirement parameters and current pressure data are input into the trained model to obtain the predicted optimal gas flow value. The predicted optimal gas flow value is passed as a control instruction to the gas flow control module. For example, if the optimal gas flow value predicted by the model is 20sccm, the value is passed to the mass flow controller to adjust the gas flow to 20sccm. This can achieve the best ion implantation effect under the premise of ensuring the vacuum degree, because the machine learning algorithm can learn the best gas flow control strategy based on historical data, thereby improving the efficiency and accuracy of ion implantation. The reason why the vacuum degree must be guaranteed is that the vacuum degree will affect the depth and uniformity of ion implantation. If the vacuum degree is too low, the ions will collide with gas molecules before reaching the target, resulting in energy loss and scattering, which will affect the implantation effect. Compared with the traditional PID control method, the machine learning algorithm for gas flow control can better adapt to complex nonlinear relationships and improve control accuracy and efficiency. Traditional PID control requires adjusting parameters based on experience, while the machine learning algorithm can automatically learn the best control strategy in a data-driven way, reduce human intervention, and improve the robustness of the control system.

[0040] In step S2, the sputtering power optimization algorithm comprehensively considers the substrate material properties, coating requirements and vacuum data, and adaptively adjusts the sputtering power parameters. The algorithm first obtains the heat resistance data of the substrate from the material database. If the heat resistance of the substrate is low, the power is limited to a safe range. At the same time, the algorithm considers the coating deposition rate requirements and increases the power within the tolerance range of the substrate. The degree of power increase is determined according to the preset deposition rate target value and the maximum tolerance temperature of the substrate, and the optimal power value is obtained by iterative calculation. The power optimization algorithm interacts with the temperature monitoring system in real time to continuously adjust the power parameters to ensure that the substrate temperature does not exceed the critical temperature threshold.

[0041] Obtain the gas flow data output by the gas flow control module and use it as an input parameter of the sputtering power optimization algorithm. Obtain the material property data of the substrate to be coated from the material database, including parameters such as heat resistance and maximum tolerance temperature. Obtain the specific requirements of this coating process, including parameters such as target coating thickness and deposition rate, and use them as another set of inputs for the algorithm. Obtain the vacuum degree monitoring data in the vacuum chamber in real time and include it in the input parameter set of the algorithm. According to the heat resistance data of the substrate, determine whether the heat resistance of the substrate is low. If the heat resistance is low, limit the sputtering power to a safe threshold range. Under the premise of ensuring the safety of the substrate, according to the preset deposition rate target value and the maximum tolerance temperature of the substrate, through iterative calculation, obtain an optimal sputtering power value that takes into account deposition efficiency and substrate safety. Feed the real-time temperature data of the substrate collected by the temperature monitoring system back to the power optimization algorithm in real time, and dynamically adjust the sputtering power parameters through closed-loop control to ensure that the substrate temperature is always maintained below the critical threshold of safety.

[0042] Specifically, the gas flow data output by the gas flow control module can be used to optimize the sputtering power. For example, if the gas flow rate is too large, the density of the sputtering gas molecules is too high, which will increase the probability of collision between the sputtering particles and the gas molecules, thereby reducing the sputtering efficiency. Therefore, the sputtering power needs to be adjusted according to the gas flow data to obtain the best sputtering efficiency. Assuming that the sputtering rate is the highest when the gas flow rate is 20sccm, 20sccm can be used as an input parameter of the sputtering power optimization algorithm. The material property data of the substrate to be coated, such as heat resistance and maximum tolerance temperature, are crucial for the optimization of sputtering power. The heat resistance of the substrate determines the maximum temperature it can withstand. If this temperature is exceeded, the substrate may be deformed or damaged. Assuming that the substrate to be coated is high-carbon low-alloy cold working die steel, its maximum tolerance temperature is 600℃. Then when setting the sputtering power, it is necessary to ensure that the temperature of the substrate does not exceed this value. The specific requirements of the coating process, such as the target coating thickness and deposition rate, are also important input parameters of the sputtering power optimization algorithm. The target coating thickness determines the sputtering time, while the deposition rate determines the sputtering power. For example, if the target coating thickness is 1μm and the deposition rate is 0.1μm / min, the sputtering time is 10 minutes. In order to achieve this deposition rate, it is necessary to set the appropriate sputtering power according to the material properties of the substrate and parameters such as the gas flow rate. The vacuum degree in the vacuum chamber also has an important influence on the sputtering process. The higher the vacuum degree, the longer the mean free path of the sputtered particles, and the easier it is to reach the surface of the substrate, thereby increasing the deposition rate. Therefore, it is necessary to obtain the vacuum degree monitoring data in the vacuum chamber in real time and incorporate it into the input parameter set of the algorithm. For example, if the vacuum degree is too low, the sputtering power needs to be reduced to avoid collisions between sputtered particles and gas molecules. For substrates with low heat resistance, the sputtering power needs to be limited within a safe threshold range. For example, for high-carbon low-alloy cold working die steel substrates, the sputtering power can be limited to less than 100W to ensure that the substrate temperature does not exceed its maximum tolerance temperature. Under the premise of ensuring the safety of the substrate, it is necessary to obtain an optimal sputtering power value that takes into account both deposition efficiency and substrate safety through iterative calculation based on the preset deposition rate target value and the maximum tolerable temperature of the substrate. For example, assuming that the target deposition rate is 0.1μm / min and the maximum tolerable temperature of the substrate is 600°C, the sputtering power can be gradually increased and the substrate temperature can be monitored until an optimal sputtering power value is found that can achieve the target deposition rate and ensure that the substrate temperature does not exceed 600°C. The real-time temperature data of the substrate is very important for the dynamic adjustment of the sputtering power. Through closed-loop control, the sputtering power parameters can be dynamically adjusted according to the real-time temperature data of the substrate to ensure that the substrate temperature is always maintained below the safe critical threshold. For example, if the substrate temperature exceeds the preset threshold, the sputtering power needs to be reduced immediately to avoid overheating of the substrate.By comprehensively considering factors such as gas flow, substrate material properties, coating process requirements, vacuum degree, and substrate real-time temperature, and using methods such as iterative calculation and closed-loop control, the sputtering power can be optimized to obtain high-quality coatings. For example, for a high-carbon low-alloy cold-working die steel substrate, the final sputtering power may be 80W, the gas flow rate is 20sccm, and the vacuum degree is 5×10. -4 Pa. Under this condition, a coating with a thickness of 1 μm and a uniform surface can be obtained.

[0043] In this embodiment, the output of the sputtering power optimization algorithm is input into the operating temperature setting model. The model calculates the optimal operating temperature range based on different substrate materials and coating performance requirements. The temperature control system collects the temperature data in the cavity in real time and compares it with the optimal temperature range calculated by the model. Through the feedback control mechanism, the temperature control system dynamically adjusts the power of the heating and cooling devices to keep the cavity temperature within the optimal range. This not only avoids the softening and deformation of the substrate caused by excessively high temperatures, but also prevents the film structure and adhesion from being affected by too low temperatures.

[0044] The calculation formula of the sputtering power after optimization by the sputtering power optimization algorithm is as follows:

[0045]

[0046] Among them, P opt represents the optimized sputtering power, P max Indicates the maximum safe limit of power, T max Indicates the maximum tolerable temperature of the substrate, T amb Indicates the ambient temperature, R th represents thermal resistance;

[0047] v dep = k·P opt

[0048] v dep represents the deposition rate, k represents the proportionality constant related to power, P opt represents the optimized sputtering power;

[0049] T real =T amb +P opt ·R th

[0050] T real represents the real-time monitored substrate temperature, T amb Indicates the ambient temperature, P opt represents the optimized sputtering power, R th Represents thermal resistance.

[0051] The output result of the sputtering power optimization algorithm is obtained as the input parameter of the working temperature setting model. According to the material properties of the substrate and the performance requirements of the coating, the optimal working temperature range is calculated through the working temperature setting model. The temperature control system collects temperature data from multiple locations in the cavity in real time and calculates the average temperature value. The average cavity temperature collected in real time is compared with the optimal working temperature range to calculate the temperature deviation value. If the temperature deviation value exceeds the preset threshold, the power value that needs to be adjusted for the heating device or cooling device is calculated by the PID control algorithm according to the direction and size of the deviation. The temperature control system dynamically controls the output power of the heating device and the cooling device according to the calculated power adjustment value to achieve precise control of the cavity temperature. Continuously perform closed-loop feedback adjustment of temperature collection, comparison, and control to stabilize the cavity temperature within the optimal working temperature range, ensure that the substrate does not soften and deform, and at the same time ensure that the coating film structure and adhesion meet the performance requirements.

[0052] In this embodiment, the ion energy control system optimizes and controls the ion energy, i.e., the bias voltage, in combination with the coating process requirements and the substrate material characteristics. The system first establishes a correlation model between the ion energy, the implantation depth, and the degree of substrate damage, from which the appropriate energy range is determined. During the coating process, the bias voltage monitoring device collects the bias voltage data in real time, and the control system compares it with the preset energy range. If the bias voltage exceeds the optimization range, the control system immediately adjusts the bias voltage parameters to ensure that the ion implantation depth meets the requirements while avoiding damage to the substrate. The ion energy control system also evaluates the surface cleanliness of the substrate and generates surface cleaning demand data.

[0053] According to the output data of the working temperature setting model and the requirements of the coating process, a correlation model between ion energy, implantation depth and substrate damage is established to determine the appropriate ion energy range. During the coating process, the bias monitoring device collects bias data in real time and transmits it to the ion energy control system. The ion energy control system compares the bias data collected in real time with the preset energy range to determine whether the bias exceeds the optimization range. If the bias exceeds the optimization range, the ion energy control system immediately adjusts the bias parameters to return it to the appropriate energy range to ensure that the ion implantation depth meets the requirements of the coating process while avoiding damage to the substrate. The ion energy control system also evaluates the surface cleanliness of the substrate, identifies surface contaminants through machine vision algorithms, and quantifies the degree of contamination. According to the quantified degree of contamination, the ion energy control system generates corresponding surface cleaning requirement data and transmits it to the cleaning control module. The cleaning control module adjusts the cleaning process parameters, such as cleaning time, detergent concentration, etc., according to the surface cleaning requirement data to ensure that the substrate surface meets the cleanliness requirements required for coating, providing a good foundation for the subsequent ion implantation process.

[0054] Specifically, ion energy has a crucial influence on the thin film deposition process. It determines the depth of ion implantation into the substrate and the degree of damage to the substrate. Too low ion energy will cause the ions to be unable to be effectively implanted into the substrate, affecting the adhesion and structural density of the film layer. Too high ion energy will cause excessive damage to the substrate, such as lattice defects, increased surface roughness, etc., and even cause the substrate to soften and deform. Therefore, it is necessary to establish a correlation model between ion energy, implantation depth and substrate damage degree. During the coating process, the bias monitoring device collects bias data in real time and transmits it to the ion energy control system. For example, the bias monitoring device collects bias data once per second and transmits the data to the ion energy control system. The ion energy control system compares the bias data collected in real time with the preset ion energy range (for example, 50eV to 100eV). Assuming that the ion energy corresponding to the current bias is 80eV, within the preset energy range, the ion energy control system determines that the bias is normal and does not need to be adjusted. If the bias value measured by the bias monitoring device corresponds to an ion energy of 120eV, it exceeds the upper limit of the preset energy range. The ion energy control system immediately adjusts the bias parameters, such as reducing the bias value to return the ion energy to the appropriate range, such as adjusting the bias to return the ion energy to 90eV. In addition, the ion energy control system is also responsible for evaluating the surface cleanliness of the substrate. For example, the camera equipped with the system collects images of the substrate surface. The machine vision algorithm identifies surface contaminants, such as dust, oil, etc., and quantifies the degree of contamination. For example, the algorithm quantifies the degree of contamination on the substrate surface from 0 to 10, where 0 indicates complete cleanliness and 10 indicates severe contamination. Assuming that there is a small amount of dust on the surface of the substrate, the algorithm quantifies it as level 3. The ion energy control system generates corresponding surface cleaning demand data based on the quantified degree of contamination and transmits it to the cleaning control module. For example, the degree of contamination level 3 is converted into a cleaning time of 5 minutes and a detergent concentration of 2%. The cleaning control module adjusts the cleaning process parameters based on the received cleaning demand data to ensure that the substrate surface meets the cleanliness requirements required for coating. For example, the cleaning control module controls the spray system to spray a detergent with a concentration of 2% for 5 minutes to clean the substrate surface. After cleaning, the system will perform cleanliness evaluation again until the surface cleanliness of the substrate meets the coating requirements. For example, the cleanliness reaches level 0 or level 1. This can provide a good foundation for the subsequent ion implantation process and ensure the coating quality.

[0055] In this embodiment, the output data of the ion energy control system and the surface cleaning requirement data are used for argon ion bombardment parameter optimization. During the preparation of nickel-based and chromium-based coatings, the parameter optimization module first analyzes the material properties of the substrate and the degree of surface contamination, and extracts the optimal bombardment energy range and time from the database. During the bombardment process, the spectrometer obtains surface composition information in real time, and the data analysis module determines the removal of the contamination layer. If the contamination layer is not completely removed, the system automatically extends the bombardment time; if it is detected that the substrate component signal is enhanced, the bombardment energy is reduced or the time is shortened in time to avoid substrate damage or excessive sputtering. After the bombardment, the system evaluates the surface state and generates surface state data.

[0056] According to the characteristics of the substrate and the degree of contamination, the initial bombardment energy and time parameters are obtained from the preset bombardment parameter database. During the argon ion bombardment process, the spectrometer collects surface composition information in real time and determines the degree of removal of the contamination layer through the support vector machine algorithm. If the contamination layer is not completely removed, the decision tree algorithm is used to determine the extent of extending the bombardment time based on the residual amount of the contamination layer. If the spectrometer detects that the substrate signal intensity exceeds the preset threshold, the neural network algorithm is used to determine the extent of reducing the bombardment energy and shortening the time to avoid damage to the substrate. After the bombardment, the surface cleanliness is evaluated by the clustering algorithm based on the surface composition data collected by the spectrometer to obtain the surface state parameters. The optimized bombardment energy, time parameters and surface state parameters are uploaded to the central database for optimizing the bombardment parameter database and guiding subsequent coating preparation. According to the surface state parameters, it is determined whether a secondary bombardment cleaning is required. A second round of bombardment is performed according to the updated database parameters until the ideal surface cleanliness is achieved.

[0057] Specifically, argon ion bombardment technology is often used to clean the surface of the substrate before thin film deposition. The principle is to use high-energy argon ions to bombard the surface of the substrate to remove adsorbed pollutants, such as oxide layers, organic matter and residual gases. The setting of bombardment parameters is crucial, which directly affects the cleaning effect and the degree of damage to the substrate. First, the initial bombardment parameters need to be determined according to the characteristics of the substrate and the degree of contamination. The characteristics of the substrate include material type, crystal structure, hardness, etc. The degree of contamination can be evaluated by indicators such as the type of pollutant, thickness, and coverage area. For example, for a silicon wafer with a slightly oxidized surface, a lower bombardment energy (e.g., 500eV) and a shorter bombardment time (e.g., 30 seconds) can be selected. For a metal substrate that is severely contaminated with oil, a higher bombardment energy (e.g., 1000eV) and a longer bombardment time (e.g., 60 seconds) are required. These initial parameters are usually stored in a preset bombardment parameter database and can be adjusted according to actual conditions. During the argon ion bombardment process, the bombardment process needs to be monitored in real time. The energy spectrometer is a commonly used surface analysis tool that can provide real-time information on the elemental composition and content of the substrate surface. By analyzing the energy spectrum data, the degree of removal of the contamination layer can be determined. The support vector machine algorithm can be used to identify the characteristic peaks in the energy spectrum data and distinguish between substrate signals and contaminant signals. For example, a support vector machine model can be trained to identify the characteristic peaks of silicon as substrate signals and the characteristic peaks of oxygen as contaminant signals. By comparing the intensities of these two signals, the degree of removal of the oxide layer can be determined. If the contamination layer is not completely removed, the bombardment time needs to be extended. The decision tree algorithm can determine the extent of extending the bombardment time based on the residual amount of the contamination layer. For example, if the energy spectrum data shows that the residual amount of the oxide layer is high, the decision tree algorithm may recommend extending the bombardment time by 30 seconds. If the residual amount is low, it may only recommend extending it by 10 seconds. During the bombardment process, if the energy spectrometer detects that the substrate signal intensity exceeds the preset threshold, it means that the bombardment depth has approached the substrate, and the bombardment energy and time need to be reduced to avoid damage to the substrate. The neural network algorithm can determine the extent of reducing the bombardment energy and shortening the time based on the substrate signal intensity. For example, if the substrate signal intensity is close to saturation, the neural network algorithm may recommend reducing the bombardment energy by 200eV and shortening the bombardment time by 15 seconds. After the bombardment, the surface cleanliness needs to be evaluated. The clustering algorithm can classify different surface states according to the surface composition data collected by the spectrometer. For example, the surface state can be divided into four categories: "clean", "slightly contaminated", "moderately contaminated" and "severely contaminated". The clustering algorithm can classify the current surface state into one of the categories based on the content of various elements in the energy spectrum data and generate the corresponding surface state parameters. The optimized bombardment parameters and surface state parameters will be uploaded to the central database to optimize the bombardment parameter database and guide subsequent coating preparation.

[0058] In this embodiment, the hydrogen etching process control system establishes an optimization model of hydrogen atom flow, substrate temperature and etching time based on the surface state data, and calculates the best process parameter combination. During the etching process, the in-situ monitoring device collects data such as hydrogen atom flow and substrate temperature in real time, and the control system compares these data with the optimization model. If it is detected that the parameters deviate from the optimal range, the system immediately adjusts the hydrogen flow, heating power or etching time to ensure the ideal surface morphology and performance, while avoiding problems such as hydrogen embrittlement of the substrate or increased surface roughness.

[0059] The surface state data output by the argon ion bombardment parameter optimization module is obtained. Based on the surface state data, a machine learning algorithm is used to establish an optimization model for hydrogen atom flow, substrate temperature and etching time. The optimal process parameter combination, including the optimal hydrogen atom flow, the optimal substrate temperature and the optimal etching time, is calculated by the optimization model. During the hydrogen etching process, the in-situ monitoring device collects parameter data such as hydrogen atom flow and substrate temperature in real time. The parameter data collected in real time is compared with the optimal parameter combination in the optimization model to determine whether the actual parameters deviate from the optimal range. If the actual parameters deviate from the optimal range, the hydrogen flow, heating power or etching time is automatically adjusted by the control system according to the degree of deviation, so that the actual parameters converge to the optimal parameter combination. Repeat steps 3-5 until the etching process is completed and the ideal surface morphology and performance are obtained. During the etching process, if it is detected that the substrate temperature is too high or the hydrogen atom flow is too large, it is determined that there is a risk of hydrogen embrittlement of the substrate or increased surface roughness, and the control system immediately reduces the heating power or hydrogen flow to avoid damage to the substrate and surface.

[0060] Specifically, the surface state data after argon ion bombardment, such as surface roughness, residual contaminant content, lattice damage degree, etc., will be used to guide the subsequent hydrogen atom etching process. Machine learning algorithms, such as support vector regression (SVR), can establish optimization models for hydrogen atom flow, substrate temperature and etching time based on these surface state data. For example, the etching result data under different hydrogen atom flow rates, substrate temperatures and etching times are collected, including surface morphology, roughness, lattice structure, etc., as well as the corresponding surface state data after argon ion bombardment. Use these data to train the SVR model and establish a mapping relationship between the surface state and the optimal hydrogen etching parameters. Assume that after argon ion bombardment, the surface roughness of a nickel-based coating is 1.5nm, the residual oxide content is 2%, and the lattice damage depth is 0.5nm. Input these data into the trained SVR model, and the model predicts that the optimal hydrogen atom flow rate is 5sccm, the optimal substrate temperature is 300℃, and the optimal etching time is 10 minutes. During the hydrogen etching process, in-situ monitoring devices, such as mass spectrometers and thermocouples, collect parameter data such as hydrogen atomic flow, substrate temperature, and chamber pressure in real time. Assume that during the etching process, the mass spectrometer detects a hydrogen atomic flow of 4.5sccm and the thermocouple measures a substrate temperature of 290°C. Compare these real-time data with the optimal parameter combination predicted by the SVR model (5sccm, 300°C, 10 minutes). Since the actual hydrogen atomic flow and substrate temperature are slightly lower than the optimal values, the control system automatically adjusts the hydrogen flow and heating power. For example, the control system increases the hydrogen flow from 4.5sccm to 4.8sccm and increases the heating power by 5%, gradually raising the substrate temperature to 300°C. The control system continuously monitors the parameters and makes fine adjustments based on the actual situation until the etching time reaches 10 minutes. During the etching process, a safety monitoring mechanism is crucial. For example, if the thermocouple detects that the substrate temperature exceeds 350°C, the system will determine that there is a risk of hydrogen embrittlement of the substrate and immediately reduce the heating power until the temperature returns to a safe range. Similarly, if the mass spectrometer detects that the hydrogen atomic flow rate exceeds 8sccm, the system will determine that there is a risk of increased surface roughness and immediately reduce the hydrogen flow rate to avoid damage to the substrate surface. Through this closed-loop control and real-time monitoring, it can be ensured that the hydrogen etching process is always carried out within the optimal parameter range, and ultimately obtain the ideal surface morphology and performance, such as low surface roughness, no residual contaminants, complete lattice structure, etc., laying a good foundation for subsequent coating preparation. Different substrates and coating materials require different hydrogen etching parameters. For example, chromium-based coatings may require higher substrate temperatures and longer etching times to achieve ideal cleaning effects. Therefore, it is necessary to adjust the optimization model and control strategy according to the specific material properties and application requirements.

[0061] In this embodiment, the composite film layer structure design module simulates and optimizes different material combinations and thickness ratios through finite element analysis and material performance database to obtain the optimal film layer structure that takes into account internal stress, hardness, toughness and other properties. The optimization results are transmitted to the deposition control system, which accurately controls the deposition rate and time of each layer of material to achieve the designed composite film layer structure. At the same time, the online stress monitoring system collects stress data in real time and compares it with the design requirements. If it is detected that the stress deviates from the expected, the feedback control module immediately fine-tunes the deposition parameters. The specific parameters that can be adjusted include the deposition rate, deposition time, substrate temperature and ion bombardment energy of each layer of material to ensure that a composite coating with excellent performance is obtained.

[0062] According to the output data of the hydrogen etching process control system, the finite element analysis method is used, combined with the material property parameters in the material performance database, to establish a mechanical model of the composite film structure, and simulate the performance indicators such as internal stress, hardness and toughness under different material combinations and thickness ratios. Through iterative optimization algorithms, such as genetic algorithms or particle swarm optimization algorithms, under the constraints of the mechanical model, the optimal material combination and thickness ratio that take into account the performance such as internal stress, hardness and toughness are found, and the optimized composite film structure design parameters are obtained. The optimized composite film structure design parameters are transmitted to the deposition control system, and the corresponding deposition process parameters, including deposition rate, deposition time, substrate temperature and ion bombardment energy, are determined according to the characteristics of each layer of material, and the precise control of each parameter is achieved through a closed-loop control algorithm. During the deposition process of the composite film layer, the online stress monitoring system is used to collect the internal stress data of the film layer in real time, and it is compared with the expected stress value during the optimization design to calculate the stress deviation. If the stress deviation exceeds the preset threshold, the feedback control module is triggered, and the fuzzy control or PID control algorithm is used to adaptively adjust the deposition rate, deposition time, substrate temperature or ion bombardment energy according to the direction and size of the stress deviation, so that the internal stress of the film layer returns to the expected level. While adjusting the deposition parameters, machine learning algorithms such as support vector machines or neural networks are used to train the existing process parameters and stress monitoring data, and a nonlinear mapping relationship between deposition parameters and film stress is established to guide the optimization and adjustment of subsequent process parameters. Through the iterative execution of the above closed-loop feedback control and intelligent optimization strategy, a high-quality composite coating with uniform internal stress distribution and expected performance indicators such as hardness and toughness is finally obtained.

[0063] Specifically, the design of composite film structure usually needs to balance multiple mechanical performance indicators, such as internal stress, hardness and toughness. In order to achieve this balance, the finite element analysis method combined with the optimization algorithm can be used for design. First, it is necessary to establish a mechanical model of the composite film structure based on the output data of the hydrogen etching process control system, such as surface morphology, roughness, etc. For example, a composite film composed of a ceramic layer and a metal layer, its mechanical model needs to consider the brittleness of the ceramic layer and the ductility of the metal layer, as well as the interface bonding strength between them. The material property database can provide the property parameters of these materials, such as elastic modulus, Poisson's ratio, yield strength, etc. Suppose you want to design a composite film composed of titanium nitride (TiN) and aluminum (Al). TiN has high hardness and wear resistance, while Al has good ductility and toughness. Finite element software, such as ANSYS or ABAQUS, can be used to establish a mechanical model of the composite film. In the model, the thickness ratio of the TiN and Al layers can be set as a variable. Next, it is necessary to simulate the mechanical performance indicators under different material combinations and thickness ratios. For example, the thickness ratio of the TiN and Al layers can be changed to 1:1, 2:1, and 1:2, respectively, and the stress distribution, deformation, and changes in hardness and toughness under external loads can be simulated. Assume that the simulation results show that when the thickness ratio of the TiN and Al layers is 1:1, the composite film has lower internal stress, but the hardness and toughness indicators are moderate. When the TiN layer is thicker, the hardness increases, but the internal stress also increases; when the Al layer is thicker, the toughness increases, but the hardness decreases. In order to find the best material combination and thickness ratio, an iterative optimization algorithm, such as a genetic algorithm or a particle swarm optimization algorithm, can be used. These algorithms can automatically search for the best thickness ratio under given constraints, such as the maximum allowable internal stress value. Assume that after optimization by a genetic algorithm, the optimal thickness ratio of TiN and Al is obtained to be 1.5:1, at which time the composite film takes into account lower internal stress, higher hardness, and a certain toughness. After obtaining the optimized composite film structure design parameters, they need to be transmitted to the deposition control system. The deposition control system needs to determine the corresponding deposition process parameters according to the characteristics of each layer of material. For example, for the TiN layer, the magnetron sputtering method can be selected, and the deposition rate is set to 0.5nm / s, the substrate temperature is 300℃, and the ion bombardment energy is 500eV. For the Al layer, the electron beam evaporation method can be selected, and the deposition rate is set to 1nm / s, the substrate temperature is 200℃, and no ion bombardment is required. During the deposition of the composite film layer, it is necessary to use an online stress monitoring system to collect the internal stress data of the film layer in real time. Assume that when the TiN layer is deposited, it is monitored that the internal stress gradually increases and exceeds the preset threshold of 1GPa. At this time, the feedback control module will be triggered, and the fuzzy control or PID control algorithm can be used to adjust the deposition parameters. For example, the deposition rate or substrate temperature can be reduced to reduce the internal stress.Assume that by lowering the substrate temperature to 250°C, the internal stress is successfully reduced to 0.8 GPa. In order to improve the efficiency and accuracy of the control system, machine learning algorithms, such as support vector machines or neural networks, can be used to train the existing process parameters and stress monitoring data. In this way, a nonlinear mapping relationship between deposition parameters and film stress can be established to guide the optimization and adjustment of subsequent process parameters. For example, by training a support vector machine model, the internal stress value of the film layer under specific deposition parameters can be predicted, so that the parameters can be adjusted in advance to avoid excessive internal stress. Through the iterative execution of the above closed-loop feedback control and intelligent optimization strategy, a high-quality composite coating with uniform internal stress distribution and expected performance indicators such as hardness and toughness can be obtained. For example, the composite coating finally obtained has an internal stress of less than 0.5 GPa, a hardness of 20 GPa, and a toughness of 10 MPa·m^0.5, which meets the design requirements. Different substrates and coating materials require different deposition parameters and control strategies, so they need to be adjusted according to the specific situation. For example, when depositing chromium-based coatings, higher substrate temperatures and longer deposition times may be required.

[0064] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A plasma immersion ion implantation method suitable for mold coating, characterized in that: The steps include: S1. According to the vacuum degree requirement of the ion plating system, the gas flow rate is dynamically adjusted through the gas flow control module; S2, the output of the sputtering power optimization algorithm is input into the operating temperature setting model; S3, the output data of the working temperature setting model is transmitted to the ion energy control system; S4, the output data of the ion energy control system and the surface cleaning requirement data are used for the optimization of argon ion bombardment parameters; S5, the output data of the argon ion bombardment parameter optimization module and the surface state data are transmitted to the hydrogen etching process control system; S6. The output data of the hydrogen etching process control system is input into the composite film layer structure design module to realize the designed composite film layer structure.

2. The method according to claim 1, characterized in that: In step S1, the gas flow control module monitors the pressure in the vacuum chamber in real time. When the immersion ion implantation technology is introduced, the gas flow is automatically adjusted to ensure that the vacuum degree is maintained within a preset range. The flow control module is linked with the vacuum monitoring system. When a drop in vacuum degree is detected, the gas flow is immediately reduced to avoid affecting the coating quality. At the same time, the flow control module communicates with the ion implantation system to adjust the gas flow according to the implantation requirements to achieve the best ion implantation effect while ensuring the vacuum degree.

3. The method according to claim 1, characterized in that In step S2, the sputtering power optimization algorithm comprehensively considers the material properties of the substrate, the coating requirements and the vacuum data, and adaptively adjusts the sputtering power parameters; the sputtering power optimization algorithm first obtains the heat resistance data of the substrate from the material database. If the heat resistance of the substrate is low, the power is limited to a safe range; at the same time, the sputtering power optimization algorithm considers the coating deposition rate requirements and increases the power within the tolerance range of the substrate; the degree of power increase is determined according to the preset deposition rate target value and the maximum tolerance temperature of the substrate, and the optimal power value is obtained through iterative calculation; the power optimization algorithm interacts with the temperature monitoring system in real time, and continuously adjusts the power parameters to ensure that the substrate temperature does not exceed the critical temperature threshold.

4. The method according to claim 1, characterized in that In step S2, the sputtering power after optimization by the sputtering power optimization algorithm is calculated as follows: Among them, Popt represents the optimized sputtering power, Pmax represents the maximum safe limit of power, and Tma x Indicates the maximum tolerable temperature of the substrate, Tam b Indicates the ambient temperature, Rt h represents thermal resistance; bdep=k·Popt vdep represents the deposition rate, k represents the proportionality constant related to the power, and Popt represents the optimized sputtering power; Treal=Tamb+Popt·Rh Treal represents the real-time monitored substrate temperature, Tamb represents the ambient temperature, Popt represents the optimized sputtering power, and Rt h Represents thermal resistance.

5. The method according to claim 1, characterized in that: In step S3, the ion energy control system optimizes and controls the ion energy, i.e., the bias voltage, in combination with the coating process requirements and the characteristics of the substrate material. The system first establishes a correlation model between the ion energy, the implantation depth, and the degree of substrate damage, from which the appropriate energy range is determined. During the coating process, the bias voltage monitoring device collects the bias voltage data in real time, and the control system compares it with the preset energy range. If the bias voltage exceeds the optimization range, the control system immediately adjusts the bias voltage parameters to ensure that the ion implantation depth meets the requirements while avoiding damage to the substrate. The ion energy control system also evaluates the surface cleanliness of the substrate and generates surface cleaning demand data.

6. The method according to claim 1, characterized in that In step S4, during the preparation of nickel-based and chromium-based coatings, the parameter optimization module first analyzes the material properties of the substrate and the degree of surface contamination, and extracts the optimal bombardment energy range and time from the database; during the bombardment process, the spectrometer obtains surface composition information in real time, and the data analysis module determines the removal of the contamination layer; if the contamination layer is not completely removed, the system automatically extends the bombardment time; if it is detected that the substrate component signal is enhanced, the bombardment energy is reduced or the time is shortened in time to avoid substrate damage or excessive sputtering; after the bombardment, the system evaluates the surface state and generates surface state data.

7. The method according to claim 1, characterized in that In step S5, the hydrogen etching process control system establishes an optimization model of hydrogen atom flow, substrate temperature and etching time according to the surface state data, and calculates the best process parameter combination; During the etching process, the in-situ monitoring device collects data such as hydrogen atom flow and substrate temperature in real time, and the control system compares these data with the optimization model; if it is detected that the parameters deviate from the optimal range, the system immediately adjusts the hydrogen flow, heating power or etching time to ensure the ideal surface morphology and performance, while avoiding problems such as substrate hydrogen embrittlement or increased surface roughness.

8. The method according to claim 1, characterized in that In step S6, the composite membrane structure design module simulates and optimizes different material combinations and thickness ratios through finite element analysis and material performance database to obtain the best membrane structure that takes into account internal stress, hardness, toughness and other properties; The optimization results are transmitted to the deposition control system, which accurately controls the deposition rate and time of each layer of material to achieve the designed composite film structure. At the same time, the online stress monitoring system collects stress data in real time and compares it with the design requirements. If it is detected that the stress deviates from the expected value, the feedback control module immediately fine-tunes the deposition parameters. The specific parameters that can be adjusted include the deposition rate of each layer of material, deposition time, substrate temperature and ion bombardment energy to ensure the acquisition of a composite coating with excellent performance.

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