Optimization method and optimization system of wafer cleaning cooling system

Abstract

The application discloses a wafer cleaning and cooling system optimization method and system in the field of information technology, comprising the following steps: according to the numerical simulation result, manufacturing different fan blade structures and refrigerant nozzle samples, building a refrigeration system performance test platform, testing the wafer surface temperature distribution characteristics under different wafer processing process temperatures by using a high-speed thermocouple array and an infrared thermal imager, verifying the wafer cooling uniformity control effect of the optimization design, and correcting and improving the numerical model; comprehensively considering the numerical simulation and experimental test results, establishing a quantitative correlation model between the fan structure parameters and the refrigerant flow and the wafer temperature uniformity, the refrigeration system energy efficiency, forming a rapid evaluation tool for wafer cooling system design and selection, and formulating a fan layout scheme and a refrigerant temperature and flow regulation strategy according to different wafer material properties and processing temperature requirements.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to an optimization method and an optimization system for a wafer cleaning and cooling system. Background Art

[0002] Cooling cleaning is a crucial step in the production of semiconductor wafers. This process involves spraying coolant onto the wafer surface through a nozzle to achieve rapid cooling and effective cleaning.

[0003] However, there is a significant technical contradiction in the current nozzle structure design, namely the trade-off between cooling uniformity and cooling efficiency. In order to achieve highly uniform cooling of the wafer surface, it is necessary to rationally design the nozzle structure so that the coolant is ideally distributed on the wafer surface, which requires precise control of the nozzle's spray angle. Improving cooling efficiency requires that the coolant be able to quickly remove heat from the wafer surface, which places higher demands on the spray speed and droplet size. High-speed, fine droplets are conducive to cooling, but may affect the uniform distribution of droplets. How to balance and optimize the nozzle structure design, improve cooling efficiency while meeting uniformity requirements, and find a suitable combination of spray angle, speed, and droplet size parameters is a challenging technical problem. This requires in-depth research on the intrinsic relationship between nozzle structure and spray performance, and through a large number of experiments and simulation optimization, to obtain the optimal nozzle design scheme that takes into account both cooling uniformity and efficiency. Summary of the Invention

[0004] The present invention provides an optimization method for a wafer cleaning cooling system, which mainly includes the following steps:

[0005] Based on wafer size specifications and material properties, the wafer thermal conductivity and specific heat capacity are determined, and wafer thickness uniformity and surface roughness data are obtained. Based on the wafer defect density distribution, a wafer temperature field distribution model is established. Based on the temperature field distribution model and the principle of heat flux density uniformity, the ideal coolant injection area of ​​the wafer is divided, and the coolant flow rate required for each area is calculated to obtain a coolant flow distribution plan;

[0006] Using computational fluid dynamics (CFD) methods, a three-dimensional air duct model consisting of a fan, baffles, and nozzles was constructed based on the coolant injection area and flow distribution scheme obtained in the previous step. The flow and heat transfer characteristics of the refrigerant were simulated in this duct model to determine the refrigerant temperature adjustment range and flow control threshold. Through parametric design and a multi-objective optimization algorithm, the fan speed control strategy and refrigerant flow control scheme with the optimal energy efficiency for the cooling system were searched while satisfying the wafer temperature uniformity constraint.

[0007] According to the optimized fan blade geometry and refrigerant control scheme, a large eddy simulation method is used to perform fine numerical simulation of fan airflow pulsation, analyze the influence of airflow pulsation frequency and amplitude on the dynamic temperature distribution of the wafer surface, evaluate the wafer cooling uniformity control effect of the fan structure design, and optimize the refrigeration system operation mode according to the evaluation result, so as to obtain the ideal transient temperature distribution of the wafer surface;

[0008] According to the numerical simulation results, different fan blade structures and refrigerant nozzle samples are made, a refrigeration system performance test platform is built, a high-speed thermocouple array and an infrared thermal imager are used to test the wafer surface temperature distribution characteristics under different wafer processing temperatures, the optimized wafer cooling uniformity control effect is verified, and the numerical model is corrected and improved;

[0009] According to the numerical simulation and experimental test results, a quantitative correlation model of fan structure parameters and refrigerant flow and wafer temperature uniformity and refrigeration system energy efficiency is established, a rapid evaluation tool for wafer cooling system design and selection is formed, and fan layout schemes and refrigerant temperature and flow regulation strategies are formulated according to different wafer material properties and processing temperature requirements;

[0010] The rapid evaluation tool is verified, the accuracy and application range of the evaluation tool are evaluated by comparing with the experimental data, the evaluation tool is optimized and corrected according to the verification result, and the reliability is improved, the optimized rapid evaluation tool is used for preliminary selection of the refrigeration system, the fan structure and refrigerant control scheme most suitable for wafer cooling requirements are determined, and a wafer cooling system prototype is built according to the selection result, the optimized fan and refrigerant nozzle are integrated, and wafer cooling performance evaluation tests are carried out, the wafer temperature uniformity control effect of the refrigeration system is tested under different wafer defect density distributions and surface roughness conditions, and the fan noise level and vibration characteristics are optimized;

[0011] The prototype test results are compared and analyzed with the previous model prediction, the design method is corrected and improved, the wafer cooling system design and operation optimization specification is formed, and is applied in actual wafer cleaning equipment, the wafer surface temperature distribution and the refrigeration system energy efficiency are monitored in real time, the operation data are collected, the fan speed and refrigerant control mode are adjusted at fixed time intervals (such as every hour or every processing batch) according to the data analysis result, the adjustment basis includes wafer surface temperature uniformity deviation, refrigeration system energy consumption index and processing process requirement change, the refrigeration system performance is continuously improved through this regular adjustment mechanism, and the wafer cooling uniformity and refrigeration efficiency are long-term stable.

[0012] The present application provides an optimization system of a wafer cleaning and cooling system, which is established by an optimization method of the wafer cleaning and cooling system, and mainly includes:

[0013] Wafer temperature field distribution model building module, used to analyze wafer temperature distribution and cooling requirements;

[0014] Refrigerant flow and heat transfer characteristics simulation module, used to optimize coolant injection area and flow distribution;

[0015] Fan structure and refrigerant control scheme optimization module to improve wafer cooling uniformity and system energy efficiency;

[0016] Wafer cooling system performance testing and evaluation module, used to verify and correct numerical models;

[0017] A rapid evaluation tool development module for wafer cooling system design and selection to develop the optimal cooling strategy;

[0018] Module for practical application and continuous optimization of wafer cooling systems to ensure long-term stable cooling effects.

[0019] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0020] The optimization method of the wafer cleaning cooling system of the present invention first establishes a wafer temperature field distribution model, divides the coolant injection area and calculates the flow distribution. Then, a three-dimensional air duct model is constructed to simulate the refrigerant flow and heat transfer characteristics, and optimize the fan speed and refrigerant flow control scheme. Large eddy simulation is used to analyze the impact of airflow pulsation on the wafer temperature distribution, and the fan and refrigerant parameters are adjusted to obtain the ideal temperature distribution. The numerical model is verified by experiment, and a correlation model between fan parameters, refrigerant flow and wafer temperature uniformity is established to form a rapid evaluation tool. The tool is used to perform system selection and prototype testing to optimize noise and vibration characteristics. In actual applications, the operating parameters are regularly adjusted according to real-time monitoring data to continuously improve performance. The present invention achieves precise and uniform control of the wafer surface temperature, improves the energy efficiency of the refrigeration system, provides customized cooling solutions for different wafer materials and processing technologies, and effectively guarantees the quality and yield of wafer manufacturing. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Fig. 1 The figure is a flow chart of the method for optimizing the wafer cleaning and cooling system of the present invention.

[0022] Fig. 2 It is a structural schematic diagram of the optimized system of the wafer cleaning and cooling system of the present invention. DETAILED DESCRIPTION

[0023] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] like Figs. 1-2The optimization system of the wafer cleaning and cooling system can specifically include the following steps:

[0025] In step S101, the thermal conductivity and specific heat capacity of the wafer are determined according to the size and material properties of the wafer, the thickness uniformity and surface roughness of the wafer are obtained, and a wafer temperature field distribution model is established according to the wafer defect density distribution. Based on the temperature field distribution model, the wafer ideal coolant injection area is divided by using the heat flux homogenization principle, and the required coolant flow rate of each area is calculated to obtain a coolant flow rate distribution scheme.

[0026] In step S101, the thermal conductivity and specific heat capacity of the wafer are determined according to the size and material properties of the wafer, the thickness uniformity and surface roughness of the wafer are obtained, and a wafer temperature field distribution model is established according to the wafer defect density distribution. Based on the temperature field distribution model, the wafer ideal coolant injection area is divided by using the heat flux homogenization principle, and the required coolant flow rate of each area is calculated to obtain a coolant flow rate distribution scheme.

[0027] Specifically, wafer thermal management is crucial in semiconductor wafer manufacturing. First, the thermal conductivity and specific heat capacity of a wafer are fundamental physical parameters that determine its thermal properties. For example, the thermal conductivity of a silicon wafer is approximately 148 W / m·K, while the specific heat capacity is approximately 0.7 J / g·K. These parameters, which can be obtained from materials science handbooks, are crucial for understanding how heat energy is transferred during wafer heating. Furthermore, measurements of wafer thickness uniformity and surface roughness are equally important for modeling heat transfer. Consider a wafer with a slight thickness difference between the center and the edge. This will affect heat distribution within the wafer. For example, if the center is thicker than the edge, heat from the center will have a harder time dissipating, resulting in uneven temperatures. Surface roughness also affects heat transfer efficiency; wafers with rougher surfaces may have better heat dissipation due to their increased surface area. With this basic data, finite element analysis can be used to develop a numerical model of the wafer's temperature distribution. This model can help predict wafer temperature distribution under various process conditions, thereby optimizing heating and cooling processes. For example, simulations can reveal that certain areas of the wafer may overheat during heating, necessitating adjustments to the heater layout or power output. Based on the principle of uniform heat flux, the wafer can be divided into several ideal coolant injection zones. The cooling requirements for each zone may vary, depending on its heat flux and area. For example, a high heat flux in a zone will require more coolant. Calculations can determine the required coolant flow rate for each zone, leading to a coolant flow distribution plan for the entire wafer. Finally, this flow distribution plan is input into the cooling system control module, enabling precise control of wafer temperature. This precise control not only prevents wafer damage from overheating but also ensures wafer quality and performance throughout the production process. For example, precise control of coolant flow ensures uniform wafer temperature throughout the production process, thereby improving product consistency and reliability. Through this series of steps, from acquiring basic physical parameters to precisely controlling coolant flow, wafer temperature can be effectively managed during production, which is crucial for improving the performance and reliability of semiconductor equipment.

[0028] In step S102, computational fluid dynamics (CFD) methods are used to construct a three-dimensional duct model consisting of fans, baffles, and nozzles based on the coolant injection area and flow distribution scheme obtained in the previous step. The refrigerant's flow and heat transfer characteristics are simulated in this duct model to determine the refrigerant temperature adjustment range and flow control threshold. Through parametric design and a multi-objective optimization algorithm, the fan speed control strategy and refrigerant flow control scheme with the highest energy efficiency for the cooling system are searched for while satisfying wafer temperature uniformity constraints.

[0029] According to the physical properties of the refrigerant and the structural dimensions of the duct, a three-dimensional duct model is established and a grid is divided; initial values ​​of the fan speed and refrigerant flow rate are set as boundary conditions for the simulation calculation; the computational fluid dynamics method is used to solve the fluid flow and heat transfer equations inside the duct to obtain the velocity field, pressure field and temperature field distribution inside the duct; according to the temperature distribution on the wafer surface, it is judged whether the wafer temperature meets the uniformity requirement, and if not, the fan speed and refrigerant flow rate are adjusted and the simulation calculation is performed again; through parametric design, multiple groups of design schemes are automatically generated within the value range of the fan speed and refrigerant flow rate; a multi-objective optimization algorithm is used, with the energy efficiency of the refrigeration system and the wafer temperature uniformity as the optimization goals, the optimal solution is searched in the design scheme, and the fan speed control strategy and the refrigerant flow control scheme are obtained; the optimized fan speed and refrigerant flow rate are set as the control parameters of the refrigeration system to achieve automatic optimization of the refrigerant temperature adjustment range and the flow control threshold.

[0030] Specifically, precise temperature control is crucial during wafer cooling. To achieve precise wafer temperature control, the refrigeration system must be designed and optimized. First, a three-dimensional duct model must be constructed based on the refrigerant's physical properties, such as density, specific heat capacity, thermal conductivity, and duct structure dimensions. For example, SolidWorks, CATIA, or CAD software can be used to create the 3D duct model. Then, the duct model must be imported into meshing software, such as ICEM CFD or ANSYS Mesh, for meshing. The mesh density affects the accuracy of the calculation results and should be selected based on actual conditions. Next, initial values ​​for the fan speed and refrigerant flow rate are set as boundary conditions for the simulation. For example, the fan speed can be set to 3000 RPM and the refrigerant flow rate to 0.5 kg / s. These initial values ​​can be determined based on experience or preliminary calculations. Computational fluid dynamics (CFD) methods, such as Fluent or CFX, are used to solve the fluid flow and heat transfer equations within the duct, determining the velocity, pressure, and temperature distributions within the duct. For example, a simulation can determine a velocity of 10 m / s, a pressure of 101325 Pa, and a temperature of 20°C at the center of the duct. Then, based on the temperature distribution across the wafer surface, the system determines whether the wafer temperature uniformity meets the requirements. For example, if the standard deviation of the wafer surface temperature is less than 1°C, the uniformity requirement is met. If not, the fan speed and refrigerant flow rate need to be adjusted and the simulation repeated. For example, if the wafer edge temperature is too high, the fan speed can be increased to improve cooling effectiveness; if the wafer center temperature is too low, the refrigerant flow rate can be reduced to reduce cooling intensity. To find the optimal fan speed and refrigerant flow rate combination, a parametric design approach can be used. Multiple design solutions are automatically generated within a range of fan speed and refrigerant flow rates. For example, the fan speed range can be set from 2000 RPM to 4000 RPM, and the refrigerant flow rate range from 0.3 kg / s to 0.7 kg / s, with multiple combinations generated at a specific step size. A multi-objective optimization algorithm is used to search for the optimal solution among the design solutions, optimizing cooling system energy efficiency and wafer temperature uniformity. For example, optimization algorithms such as genetic algorithms and particle swarm optimization can be used to find the fan speed and refrigerant flow combination that maximizes cooling system energy efficiency and wafer temperature uniformity. Assume the optimal combination results in a fan speed of 3500 RPM and a refrigerant flow of 0.6 kg / s. Finally, the optimized fan speed and refrigerant flow are set as the cooling system's control parameters, automatically optimizing the refrigerant temperature adjustment range and flow control threshold. This improves cooling system efficiency, reduces energy consumption, ensures wafer temperature uniformity, and enhances product quality.For example, the optimization results can be written into the control program of the refrigeration system, so that the refrigeration system can adjust the fan speed and refrigerant flow in real time according to the temperature of the wafer, thereby achieving precise temperature control.

[0031] In step S103, large eddy simulation (LES) is used to perform detailed numerical simulation of fan airflow pulsation based on the optimized fan blade geometry and refrigerant control scheme. The effects of airflow pulsation frequency and amplitude on the dynamic temperature distribution on the wafer surface are analyzed, and the effectiveness of the fan structure design in controlling wafer cooling uniformity is evaluated. Based on the evaluation results, the cooling system operating mode is optimized by adjusting the fan speed control strategy and refrigerant injection angle to achieve the ideal transient temperature distribution on the wafer surface.

[0032] Based on the optimized fan blade geometry and refrigerant control scheme, a large eddy simulation method is used to establish a numerical model of fan airflow pulsation; dynamic temperature distribution data of the wafer surface under different airflow pulsation frequencies and amplitudes are obtained through numerical simulation; based on the temperature distribution data, the temperature uniformity index of the wafer surface is calculated to evaluate the control effect of the fan structure design on the wafer cooling uniformity; if the temperature uniformity index does not reach a preset threshold, the fan speed control strategy and refrigerant injection angle parameters are optimized using a machine learning algorithm based on the correlation between the airflow pulsation characteristics and the wafer surface temperature distribution; the optimized fan speed control strategy and refrigerant injection angle parameters are input into the numerical model, and the optimized transient temperature distribution of the wafer surface is obtained through simulation; it is determined whether the optimized wafer surface temperature uniformity index meets the preset conditions. If so, the optimized refrigeration system operation mode is determined, otherwise the optimization step of the machine learning algorithm is returned to continue optimization; based on the determined optimal operation mode of the refrigeration system, dynamic control curves of fan speed and refrigerant injection are generated to guide fan structure manufacturing and refrigeration system control parameter setting to obtain ideal cooling uniformity on the wafer surface.

[0033] Specifically, temperature uniformity during wafer manufacturing is crucial to final product quality. To achieve ideal cooling uniformity, precise control of the fan and refrigerant injection is required. The following provides a detailed analysis and example of an optimization solution. First, large eddy simulation (LES) is used to model fan airflow pulsations. Compared to traditional Reynolds-averaged Navier-Stokes (RANS) methods, LES more accurately captures the transient characteristics of turbulence, particularly at high Reynolds numbers, and can more accurately simulate the complex airflow pulsations generated by the fan. For example, assuming a fan with 12 blades and a rotational speed of 5000 RPM, LES can simulate the vortex structure and airflow separation generated by the rotating blades, as well as airflow pulsations of varying frequencies and amplitudes. LES numerical simulations can generate dynamic temperature distribution data on the wafer surface. For example, with a time step of 0.001 seconds, the temperature value of each point on the wafer surface can be recorded at each time step, forming a time series of temperature field data. Assuming a wafer diameter of 300 mm, the wafer surface can be divided into a 100x100 grid, with temperature data recorded at each grid point. Based on the acquired temperature distribution data, the temperature uniformity index (TUI) across the wafer surface can be calculated. Common metrics include maximum temperature difference, standard deviation, and root mean square error (RMS). For example, the difference between the highest and lowest temperatures on the wafer surface can be calculated as the maximum temperature difference, or the standard deviation of all temperature values ​​can be calculated to assess temperature dispersion. Assume the average wafer surface temperature is 25°C, the maximum temperature difference is 2°C, and the standard deviation is 0.5°C. The calculated TUI is compared with a preset threshold. If the metric falls below the preset threshold, for example, if the maximum temperature difference exceeds 1°C or the standard deviation exceeds 0.3°C, further optimization is required. The preset threshold is determined based on specific process requirements. For example, some processes have very high temperature uniformity requirements, and the maximum temperature difference cannot exceed 0.5°C. Machine learning algorithms can be used to optimize fan speed control strategies and refrigerant injection angles. For example, data from different fan speeds, refrigerant injection angles, and wafer surface TUI can be collected to construct a dataset. Next, a model is trained using machine learning algorithms such as support vector machines and neural networks to learn the correlation between airflow pulsation characteristics and wafer surface temperature distribution. This trained model can predict wafer surface temperature uniformity under different parameter combinations and identify the optimal parameter combination. For example, the machine learning model may discover that optimal temperature uniformity is achieved when the fan speed is adjusted to 4500 RPM and the refrigerant injection angle is adjusted to 30 degrees. The optimized parameters are input into the numerical model, and the LES simulation is repeated to obtain the optimized transient temperature distribution on the wafer surface. For example, a simulation using the optimized fan speed and refrigerant injection angle generates new temperature distribution data. The optimized wafer surface temperature uniformity index is recalculated to determine whether it meets the preset conditions.If the preset condition is met, the optimized refrigeration system operation mode is determined. For example, if the optimized maximum temperature difference is less than 1°C and the standard deviation is less than 0.3°C, it is considered that the preset condition is met. According to the determined optimal operation mode, dynamic control curves of fan speed and refrigerant injection are generated. For example, the fan speed and the refrigerant injection angle can be adjusted in real time according to the change of wafer temperature, so as to maintain the temperature uniformity of the wafer surface. These control curves can be used to guide the fan structure manufacturing and refrigeration system control parameter setting. For example, a frequency converter can be designed to control the fan speed according to the control curve, or a servo motor can be designed to control the angle of the refrigerant nozzle.

[0034] According to the correlation between the air flow pulsation characteristics and the wafer surface temperature distribution, the support vector machine algorithm is used to optimize the fan speed regulation strategy and the refrigerant injection angle parameter, the optimized wafer surface transient temperature distribution is obtained through simulation, it is judged whether the optimized wafer surface temperature uniformity index meets the preset condition, if it meets, the optimized refrigeration system operation mode is determined, otherwise, the machine learning algorithm optimization step is returned to continue optimization, and the dynamic control curves of fan speed and refrigerant injection are obtained.

[0035] The air flow pulsation characteristic data and the wafer surface temperature distribution data are obtained; the air flow pulsation characteristic data includes air flow velocity and pressure parameters; the wafer surface temperature distribution data is obtained by an infrared thermal imager. The air flow pulsation characteristic data and the wafer surface temperature distribution data are analyzed by using a multiple linear regression algorithm, and a correlation model between the air flow pulsation characteristics and the wafer surface temperature distribution is established. The initial values of the fan speed parameter and the refrigerant injection angle parameter are set. Based on the established correlation model, the initial value of the fan speed parameter and the initial value of the refrigerant injection angle parameter, the wafer surface temperature distribution is predicted by using a support vector machine algorithm, and the fan speed parameter and the refrigerant injection angle parameter are adjusted according to the prediction result, to obtain a first fan speed parameter and a first refrigerant injection angle parameter. According to the first fan speed parameter and the first refrigerant injection angle parameter, the wafer surface transient temperature distribution data is obtained by using a computational fluid dynamics software for simulation. The wafer surface temperature uniformity index is calculated. The dynamic control curves of the fan speed and the dynamic control curves of the refrigerant injection angle are fitted by using a Gaussian process regression algorithm, to generate smooth dynamic control curves.

[0036] Specifically, a wide variety of sensors are used to acquire data on airflow pulsation characteristics and wafer surface temperature distribution. For example, hot-wire anemometers or pitot tubes can measure airflow velocity, and pressure sensors can measure airflow pressure. Data from these sensors can be used to construct an airflow pulsation characteristic dataset. Furthermore, infrared thermal imaging cameras can capture the temperature distribution on the wafer surface and construct a temperature distribution dataset. For example, for a wafer with a diameter of 300 mm, an infrared thermal imaging camera can divide its surface into a 100x100 grid, recording temperature data at each grid point, such as 25°C or 26°C. A multivariate linear regression algorithm can be used to analyze the correlation between airflow pulsation characteristics and wafer surface temperature distribution. For example, higher fan speeds and faster airflow velocities are likely to result in lower average wafer surface temperature. Through multivariate linear regression analysis, a model can be constructed, such as: temperature = a*velocity + b*pressure + c, where a, b, and c are regression coefficients. This model can be used to predict wafer surface temperature distribution under different airflow pulsation characteristics. Before using the support vector machine algorithm to optimize fan speed and refrigerant injection angle, initial values ​​must be set. For example, the fan speed can be initially set to 5000 RPM, and the refrigerant injection angle can be initially set to 45 degrees. These initial values ​​can be determined based on experience or prior experimental results. The support vector machine algorithm can predict the wafer surface temperature distribution based on the established correlation model and initial parameters and adjust the fan speed and refrigerant injection angle parameters. For example, if the predicted wafer edge temperature is too high, you can try increasing the refrigerant injection angle or fan speed to enhance edge cooling. The goal of the support vector machine algorithm is to find the optimal parameter combination that achieves optimal temperature uniformity across the wafer surface. Computational fluid dynamics (CFD) software can be used to simulate the transient temperature distribution on the wafer surface. For example, the optimized fan speed and refrigerant injection angle parameters can be input into the CFD software to simulate the temperature distribution on the wafer surface at different time points. Assuming a time step of 0.001 seconds, the temperature of each point on the wafer surface can be recorded at each time step. For example, at 0.001 seconds, the temperature of a point is 25.5°C, and at 0.002 seconds, the temperature of the same point is 25.6°C. The wafer surface temperature uniformity index can be calculated in various ways, such as maximum temperature difference and standard deviation. For example, the maximum temperature difference can be calculated as the difference between the highest and lowest temperatures on the wafer surface. Assuming the highest wafer surface temperature is 26°C and the lowest is 24°C, the maximum temperature difference is 2°C. The calculated temperature uniformity index is compared with a preset threshold. If the index value is less than the preset threshold, for example, the maximum temperature difference is less than 1°C, the preset condition is considered met. The Gaussian process regression algorithm can further optimize the fan speed and refrigerant injection angle parameters optimized by the support vector machine algorithm. For example, assume that the fan speed and refrigerant injection angle obtained by the support vector machine algorithm are 4800 RPM and 48 degrees, respectively.The Gaussian process regression algorithm can fit these parameters to generate a smoother dynamic control curve. For example, the fan speed can increase smoothly from 4750 RPM to 4850 RPM over time, rather than suddenly. This improves control accuracy and stability, preventing system oscillation or overshoot.

[0037] In step S104, based on the numerical simulation results, prototypes of different fan blade structures and refrigerant nozzles were fabricated, and a refrigeration system performance test platform was constructed. Using a high-speed thermocouple array and infrared thermal imager, the wafer surface temperature distribution characteristics at different wafer processing temperatures were tested to verify the effectiveness of the optimized design for wafer cooling uniformity control and to refine and improve the numerical model.

[0038] Step S104 also includes the following steps: numerically simulating the fan blades and nozzle using CFD software based on the fan blade structural parameters and the nozzle dimensions to obtain flow field and temperature field distribution data. Based on the obtained wafer surface temperature distribution data, calculating the wafer surface temperature uniformity. If the wafer cooling uniformity does not meet a preset threshold, then modifying the parameters of the numerical model based on the test results, including the fan speed and nozzle flow rate.

[0039] Step S105 : By integrating numerical simulation and experimental test results, a quantitative correlation model is established between fan structural parameters and refrigerant flow rate, wafer temperature uniformity, and cooling system energy efficiency. This provides a rapid evaluation tool for wafer cooling system design and selection, and formulates fan layout plans and refrigerant temperature and flow rate control strategies based on different wafer material properties and processing temperature requirements.

[0040] Based on the preprocessed standardized data set, a quantitative relationship model is constructed between the fan structural parameters, the refrigerant flow rate, the wafer temperature uniformity, and the refrigeration system energy efficiency. Based on this quantitative relationship model, a rapid evaluation tool for wafer cooling system design and selection is developed. The wafer material properties and processing temperature requirements are obtained. Using a genetic algorithm or a particle swarm algorithm, with wafer temperature uniformity and refrigeration system energy efficiency as the objective functions, an optimized fan layout solution and refrigerant flow rate control strategy are derived.

[0041] Specifically, during the wafer cooling system design process, experimental test data and simulation data must first be collected. These data cover wafer temperature distribution and system energy consumption under different fan structural parameters and refrigerant flow conditions. For example, by setting different fan speeds and nozzle sizes in the laboratory, the temperature changes during wafer processing are recorded. Simultaneously, software is used to simulate the temperature distribution under the same conditions to compare the actual and theoretical differences. Data preprocessing is a critical step, eliminating outliers and erroneous data caused by equipment failure or operational errors. For example, if the temperature recorded in a particular experiment suddenly jumps to an unreasonably high value, this is likely due to a thermocouple failure, and such data should be removed from the dataset. Next, the data is cleaned and normalized to ensure that data from different sources and batches are compared under the same standards, thus forming a standardized dataset. Quantitative relationship models are constructed by analyzing the processed data. Multiple regression algorithms or support vector machine algorithms can be used to predict wafer temperature uniformity and system energy efficiency based on fan structural parameters and refrigerant flow. For example, by inputting specific fan speeds and nozzle flow rates, the model can output the expected wafer temperature uniformity and system energy consumption. Based on these models, a rapid evaluation tool for wafer cooling system design and selection can be developed. This tool allows users to input wafer material properties and processing temperature requirements, as well as fan configuration parameters and refrigerant flow rates, to quickly generate predicted wafer temperature uniformity and system energy efficiency. The development of such a tool can significantly improve design efficiency and reduce the need for experimental testing. In real-world applications, wafer material properties and processing temperature requirements are determined based on specific production requirements. For example, the material properties and temperature requirements of silicon wafers processed at high temperatures will differ significantly from those of glass wafers processed at room temperature. Optimizing fan layout and refrigerant flow control strategies is achieved using genetic algorithms or particle swarm optimization. These algorithms mimic the survival of the fittest principle of natural selection, iteratively finding the optimal fan layout and refrigerant flow configuration. For example, if the wafer temperature uniformity of the initial design does not meet a preset threshold, the algorithm automatically adjusts the fan placement and number until a new design that meets the temperature uniformity requirements is found. Ultimately, the resulting optimization results directly impact the performance of the wafer cooling system. The obtained fan layout scheme and refrigerant flow control strategy will be used in actual system design and manufacturing to ensure that the wafers can be maintained at optimal temperature conditions during processing, thereby ensuring product quality and production efficiency.

[0042] Step S106, the rapid evaluation tool is verified by comparing with experimental data to evaluate the accuracy and scope of the evaluation tool. According to the verification result, the evaluation tool is optimized and corrected to improve its reliability. The optimized rapid evaluation tool is used to preliminarily select the refrigeration system to determine the fan structure and refrigerant control scheme most suitable for wafer cooling demand. According to the selection result, a wafer cooling system prototype is built, and the optimized fan and refrigerant nozzle are integrated. The wafer cooling performance evaluation test is carried out, and under the conditions of different wafer defect density distribution and surface roughness, the wafer temperature uniformity control effect of the refrigeration system is tested, and the fan noise level and vibration characteristics are optimized.

[0043] Step S107, the prototype test results are compared and analyzed with the previous model prediction, the design method is corrected and improved, and the wafer cooling system design and operation optimization specification is formed. In the actual wafer cleaning equipment, the wafer surface temperature distribution and refrigeration system energy efficiency are monitored in real time, and the operation data are collected. According to the data analysis result, the fan speed and refrigerant control mode are adjusted every fixed time interval (such as every hour or every processing batch). The adjustment basis includes wafer surface temperature uniformity deviation, refrigeration system energy consumption index and change of processing requirements. Through this regular adjustment mechanism, the performance of the refrigeration system is continuously improved, and the wafer cooling uniformity and refrigeration efficiency are ensured to be stable for a long time.

[0044] Obtain the wafer surface temperature distribution data; calculate the temperature uniformity deviation value according to the wafer surface temperature distribution data; obtain the refrigeration system energy consumption index data and the processing process requirement parameters; if the temperature uniformity deviation value or the refrigeration system energy consumption index exceeds the preset threshold value, then according to the temperature uniformity deviation value, the refrigeration system energy consumption index and the processing process requirement parameters, a fuzzy control algorithm is used to obtain the fan speed adjustment amount and the refrigerant control mode adjustment amount; according to the fan speed adjustment amount, a PID control algorithm is used to obtain the fan speed control signal; according to the historical operation data and the current process parameters, a machine learning algorithm is used to establish the wafer temperature prediction model; according to the prediction result of the wafer temperature prediction model, the fan speed and the refrigerant control mode are adjusted.

[0045] Specifically, temperature control is crucial in wafer manufacturing. The uniformity of temperature directly affects the yield and performance of chips. The following explains how to build an accurate wafer temperature control system. First, we need to obtain the temperature distribution data of the wafer surface. We can use a high-precision platinum resistance temperature sensor array, which is evenly distributed on the wafer surface at different positions. For example, on a 300mm diameter wafer, we can arrange 100 sensors to form a 10x10 matrix. Each sensor collects temperature data in real time and transmits it to the central control system to form a real-time temperature distribution map. The reason for choosing platinum resistance sensors is that they have high accuracy, good stability, and are suitable for high-temperature environments. Then, according to the collected temperature data, we calculate the wafer temperature uniformity deviation. For example, the highest temperature measured is 205°C, the lowest temperature is 200°C, and the average temperature is 202°C. The uniformity deviation is (205-200) / 202=0.025, which is 2.5%. The smaller the uniformity deviation, the more uniform the temperature distribution. At the same time, we also need to monitor the energy consumption indicators of the refrigeration system. Use a power meter to monitor the power consumption of the refrigeration system in real time, for example, the current power consumption is 10kW. This indicator is used to evaluate the energy efficiency of the system and provide a basis for subsequent optimization. Reducing energy consumption not only saves costs, but also reduces the impact on the environment. In addition, we need to obtain the process requirement parameters of the current processing batch from the process database. For example, the target temperature of the current batch is 200°C, and the temperature uniformity tolerance is ±1%. These parameters are set according to the specific process flow and chip type, and are the key to ensuring product quality. Next, based on the data analysis results, adjust the fan speed and refrigerant control mode. If the wafer temperature uniformity deviation exceeds the preset threshold (for example, 2%) or the refrigeration system energy consumption indicator exceeds the preset threshold (for example, 12kW), the fan speed and refrigerant control mode need to be adjusted. The control strategy can use a fuzzy control algorithm. For example, when the uniformity deviation is large, the fan speed can be increased to enhance air circulation and improve temperature uniformity. When energy consumption is too high, the refrigerant flow can be reduced to reduce energy consumption. The fuzzy control algorithm can flexibly adjust the control parameters according to the actual situation to achieve more precise control. If the fan speed needs to be adjusted, use a PID control algorithm to adjust it. The PID control algorithm can automatically adjust the fan speed based on the deviation between the set target speed and the actual speed, so that it stabilizes at the target value. For example, the fan speed needs to be increased from 1000rpm to 1200rpm, and the PID controller will calculate the appropriate control signal according to the difference between the current speed and 1200rpm to drive the fan motor to reach the target speed. Finally, to achieve more advanced control, we can use a prediction model to predict the wafer temperature. Using machine learning algorithms such as neural networks, we can predict the wafer temperature in the future based on historical operating data and current process parameters.For example, the wafer temperature for the next hour can be predicted based on the past week's temperature data, fan speed, refrigerant flow rate, and other data, along with current process parameters. Based on the prediction results, fan speed and refrigerant control mode can be adjusted in advance to achieve predictive control, avoid temperature fluctuations, and further improve temperature control accuracy and efficiency. Regularly training and updating the model with the latest operating data can improve prediction accuracy and adapt to process changes.

[0046] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for optimizing a wafer cleaning cooling system, characterized in that: The method comprises the following steps: Step S101: Determine the wafer thermal conductivity and specific heat capacity values ​​based on wafer size specifications and material properties, obtain wafer thickness uniformity and surface roughness data, establish a wafer temperature field distribution model based on wafer defect density distribution, and divide the wafer into ideal coolant injection areas based on the temperature field distribution model and the heat flux density uniformity principle. Calculate the coolant flow rate required for each area and obtain a coolant flow distribution plan. Step S102: Using computational fluid dynamics (CFD) methods, a three-dimensional air duct model including a fan, a guide plate, and a nozzle is constructed based on the coolant injection area and flow distribution scheme obtained in the previous step. The flow and heat transfer characteristics of the refrigerant are simulated in the air duct model to obtain the refrigerant temperature adjustment range and flow control threshold. Through parametric design and a multi-objective optimization algorithm, a fan speed control strategy and refrigerant flow control scheme with optimal energy efficiency for the cooling system are searched while satisfying the wafer temperature uniformity constraint. Step S103: Based on the optimized fan blade geometry and refrigerant control scheme, a large eddy simulation method is used to perform a detailed numerical simulation of the fan airflow pulsation, analyze the impact of the airflow pulsation frequency and amplitude on the dynamic temperature distribution on the wafer surface, evaluate the wafer cooling uniformity control effect of the fan structure design, and based on the evaluation results, optimize the refrigeration system operation mode by adjusting the fan speed control strategy and refrigerant injection angle to obtain the ideal transient temperature distribution on the wafer surface; Step S104: Based on the numerical simulation results, different fan blade structures and refrigerant nozzle samples are manufactured, and a refrigeration system performance test platform is built. A high-speed thermocouple array and an infrared thermal imager are used to test the wafer surface temperature distribution characteristics at different wafer processing temperatures, verify the wafer cooling uniformity control effect of the optimized design, and revise and improve the numerical model; Step S105 , integrating numerical simulation and experimental test results, establishing a quantitative correlation model between fan structural parameters and refrigerant flow rate, wafer temperature uniformity, and refrigeration system energy efficiency, forming a rapid evaluation tool for wafer cooling system design and selection, and formulating fan layout plans and refrigerant temperature and flow rate control strategies based on different wafer material properties and processing temperature requirements; Step S106: Verify the rapid evaluation tool by comparing it with experimental data to evaluate its accuracy and applicability. Based on the verification results, optimize and modify the evaluation tool to improve its reliability. Use the optimized rapid evaluation tool to conduct a preliminary selection of the cooling system to determine the fan structure and refrigerant control scheme that best suits the wafer cooling requirements. Based on the selection results, build a wafer cooling system prototype, integrate the optimized fan and refrigerant nozzle, and conduct wafer cooling performance evaluation tests. Under different wafer defect density distributions and surface roughness conditions, test the cooling system's wafer temperature uniformity control effect and optimize the fan noise level and vibration characteristics. In step S107, the prototype test results are compared and analyzed with the previous model predictions, the design method is revised and improved, and the wafer cooling system design and operation optimization specifications are formed for application in actual wafer cleaning equipment. By real-time monitoring of the wafer surface temperature distribution and the energy efficiency of the refrigeration system, operation data is collected. According to the data analysis results, the fan speed and refrigerant control mode are adjusted at fixed time intervals. The adjustment is based on the changes in the wafer surface temperature uniformity deviation, the refrigeration system energy consumption index and the processing technology requirements. Through this regular adjustment mechanism, the refrigeration system performance is continuously improved to ensure the long-term stability of the wafer cooling uniformity and refrigeration efficiency.

2. The method for optimizing the wafer cleaning and cooling system according to claim 1, wherein: The step S101 further comprises the following steps: Obtain the thermal conductivity and specific heat capacity values ​​of the wafer, and use the measurement data of wafer thickness uniformity and surface roughness as input parameters for the temperature field distribution model; Based on the wafer defect density distribution data, a finite element analysis method is used to establish a numerical model of the wafer temperature field distribution; According to the heat flux uniformity principle, the wafer is divided into a plurality of ideal coolant spraying areas; For each of the injection areas, the required coolant flow rate is calculated according to the area and heat flux density of the injection area; The coolant flow distribution plan for the entire wafer is obtained by integrating the coolant flow requirements of each injection area; The flow distribution scheme is input into a cooling system control module to achieve precise temperature control of the wafer.

3. The method for optimizing the wafer cleaning and cooling system according to claim 1, wherein: The step S102 further includes: According to the physical properties of the refrigerant and the dimensions of the duct structure, a three-dimensional duct model is established and the grid is divided; Set the initial values ​​of fan speed and refrigerant flow rate as boundary conditions for simulation calculations; Computational fluid dynamics (CFD) methods are used to solve the fluid flow and heat transfer equations inside the air duct to obtain the velocity field, pressure field, and temperature field distribution inside the air duct; According to the temperature distribution on the wafer surface, determining whether the wafer temperature meets the uniformity requirement; if not, adjusting the fan speed and refrigerant flow rate and re-performing the simulation calculation; Through parametric design, multiple sets of design solutions are automatically generated within the value range of the fan speed and refrigerant flow rate; A multi-objective optimization algorithm was used to optimize the cooling system energy efficiency and wafer temperature uniformity. The optimal solution was searched among the design schemes, and a fan speed control strategy and a refrigerant flow control scheme were obtained. The fan speed and refrigerant flow rate obtained by optimization are set as control parameters of the refrigeration system to achieve automatic optimization of the refrigerant temperature adjustment range and the flow control threshold.

4. The method for optimizing a wafer cleaning and cooling system according to claim 1, wherein: The step S103 further includes: Based on the optimized fan blade geometry and refrigerant control scheme, a numerical model of fan airflow pulsation was established using the large eddy simulation method. The dynamic temperature distribution data of the wafer surface under different airflow pulsation frequencies and amplitudes are obtained through numerical simulation; Calculating the temperature uniformity index of the wafer surface based on the temperature distribution data and evaluating the control effect of the fan structure design on the wafer cooling uniformity; If the temperature uniformity index does not reach the preset threshold, the support vector machine algorithm is used to optimize the fan speed control strategy and the refrigerant injection angle parameters according to the correlation law between the airflow pulsation characteristics and the wafer surface temperature distribution. The optimized transient temperature distribution of the wafer surface is obtained through simulation, and it is judged whether the optimized wafer surface temperature uniformity index meets the preset conditions. If so, the optimized refrigeration system operation mode is determined. Otherwise, the optimization step of the machine learning algorithm is returned to continue the optimization to obtain the dynamic control curve of the fan speed and the refrigerant injection. The optimized fan speed control strategy and refrigerant injection angle parameters are input into the numerical model, and the optimized transient temperature distribution on the wafer surface is obtained through simulation; Determine whether the optimized wafer surface temperature uniformity index meets the preset conditions. If so, determine the optimized cooling system operation mode. Otherwise, return to the machine learning algorithm optimization step to continue optimization. Based on the determined optimal operating mode of the cooling system, dynamic control curves for fan speed and refrigerant injection are generated to guide fan structure manufacturing and cooling system control parameter settings to achieve ideal cooling uniformity on the wafer surface.

5. The method for optimizing a wafer cleaning and cooling system according to claim 4, wherein: According to the correlation law between the airflow pulsation characteristics and the wafer surface temperature distribution, the support vector machine algorithm is used to optimize the fan speed control strategy and the refrigerant injection angle parameters, the optimized wafer surface transient temperature distribution is obtained through simulation, and it is judged whether the optimized wafer surface temperature uniformity index meets the preset conditions. If so, the optimized refrigeration system operation mode is determined, otherwise the machine learning algorithm optimization step is returned to continue optimization to obtain the dynamic control curve of the fan speed and the refrigerant injection, including the following steps: Acquiring the airflow pulsation characteristic data and the wafer surface temperature distribution data; The airflow pulsation characteristic data includes airflow velocity and pressure parameters; The wafer surface temperature distribution data is obtained by an infrared thermal imager; Using a multiple linear regression algorithm to analyze the airflow pulsation characteristic data and the wafer surface temperature distribution data, and establish a correlation model between the airflow pulsation characteristic and the wafer surface temperature distribution; Setting initial values ​​of the fan speed parameter and the refrigerant injection angle parameter; Using a support vector machine algorithm, based on the established association model, the initial value of the fan speed parameter, and the initial value of the refrigerant injection angle parameter, predict the wafer surface temperature distribution, and adjust the fan speed parameter and the refrigerant injection angle parameter according to the prediction result to obtain a first fan speed parameter and a first refrigerant injection angle parameter; According to the first fan speed parameter and the first refrigerant injection angle parameter, a simulation is performed using computational fluid dynamics software to obtain transient temperature distribution data on the wafer surface; Calculating the wafer surface temperature uniformity index; A Gaussian process regression algorithm is used to fit the dynamic control curve of the fan speed and the dynamic control curve of the refrigerant injection angle to generate a smooth dynamic control curve.

6. The method for optimizing a wafer cleaning and cooling system according to any one of claims 1 to 5, characterized in that: The step S104 further comprises the following steps: According to the fan blade structural parameters and the nozzle size, CFD software is used to perform numerical simulation on the fan blade and the nozzle to obtain flow field and temperature field distribution data; Calculating the temperature uniformity of the wafer surface based on the acquired wafer surface temperature distribution data; If the wafer cooling uniformity does not reach a preset threshold, the parameters of the numerical model are corrected according to the test results, and the parameters include the fan speed and the nozzle flow rate.

7. The optimization system of the wafer cleaning and cooling system is characterized in that: The optimization system is established by the optimization method of the wafer cleaning and cooling system according to any one of claims 1 to 6, and the system comprises: Wafer temperature field distribution model building module, used to analyze wafer temperature distribution and cooling requirements; Refrigerant flow and heat transfer characteristics simulation module, used to optimize coolant injection area and flow distribution; Fan structure and refrigerant control scheme optimization module to improve wafer cooling uniformity and system energy efficiency; Wafer cooling system performance testing and evaluation module, used to verify and correct numerical models; A rapid evaluation tool development module for wafer cooling system design and selection to develop the optimal cooling strategy; Module for practical application and continuous optimization of wafer cooling systems to ensure long-term stable cooling effects.

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