Semiconductor wet cleaning process optimization method based on PLC programming

Abstract

The invention relates to the technical field of wet cleaning in a semiconductor manufacturing process, and discloses a semiconductor wet cleaning process optimization method based on PLC programming, and the method specifically comprises the following operation steps: constructing a PLC programming dissolution rate matching system, designing and integrating a set of PLC-based dissolution rate matching system, and optimizing the PLC-based dissolution rate matching system. The system needs to have the functions of data acquisition, processing and feedback, a dissolution rate model is established according to cleaning liquid components and wafer material characteristics, an algorithm is preset to be matched with the actual dissolution process, and sensors are deployed in cleaning equipment and used for monitoring key parameters such as the temperature and concentration of cleaning liquid and the dissolution rate of the surface of a wafer in real time. According to the semiconductor wet cleaning process optimization method based on PLC programming, the rotating speed and cleaning liquid distribution in the cleaning process are accurately controlled, the problem of flowing particle defects caused by the poor dissolving speed in traditional wet cleaning is effectively solved, and the cleanliness and the yield of semiconductor wafers are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wet cleaning in semiconductor manufacturing processes, and in particular to a semiconductor wet cleaning process optimization method based on PLC programming. Background Art

[0002] In semiconductor manufacturing, wet cleaning is the core link to ensure the cleanliness of the chip surface. This step uses a combination of chemical solutions and physical flushing to effectively remove impurities such as particles, metal ions, organic matter, etc. remaining on the chip surface. Wet cleaning achieves fine cleaning of the chip surface by precisely controlling parameters such as the cleaning solution composition, temperature, concentration, and cleaning time, thereby avoiding impurities from having adverse effects on subsequent processes such as photolithography and etching. This process is not only related to the performance and reliability of semiconductor devices, but also directly affects the overall manufacturing yield and cost. Therefore, the continuous optimization and innovation of wet cleaning technology is of great significance to promoting the high-quality development of the semiconductor industry.

[0003] The main technical problem faced by traditional wet cleaning is uneven cleaning, which is caused by the difference in the dissolution rate of the cleaning liquid on the chip surface, especially in the edge and center areas, resulting in the formation of fluidity particle defects. These defects, as a source of pollution, directly threaten the performance stability and finished product yield of semiconductor devices. Although rotating wafer technology has been introduced to improve cleaning uniformity, existing solutions are mostly limited by the lack of precise control strategies and real-time monitoring systems. The lack of precise control makes it difficult to optimize key parameters such as rotation speed and cleaning liquid distribution, and the lack of monitoring mechanism makes it impossible to respond to environmental changes during the cleaning process in a timely manner. Summary of the invention

[0004] The purpose of the present invention is to effectively solve the problem of fluidity particle defects caused by differences in dissolution rates in traditional wet cleaning by accurately controlling the rotation speed and distribution of the cleaning liquid during the cleaning process, thereby improving the cleanliness and yield of semiconductor chips, and proposing a semiconductor wet cleaning process optimization method based on PLC programming.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: The semiconductor wet cleaning process optimization method based on PLC programming specifically includes the following steps: S10. Build a PLC-programmed dissolution rate matching system. Design and integrate a PLC-based dissolution rate matching system. The system needs to have the functions of data collection, processing and feedback. According to the composition of the cleaning liquid and the characteristics of the wafer material, a dissolution rate model is established, and an algorithm is preset to match the actual dissolution process. Sensors are deployed in the cleaning equipment to monitor key parameters such as cleaning liquid temperature, concentration and wafer surface dissolution rate in real time; S20, realizing dynamic rotation speed control, the PLC system receives real-time data from the sensor, analyzes the change of dissolution speed of each component in the cleaning solution, and automatically formulates a strategy for adjusting the rotation speed of the wafer according to the dissolution speed data. For areas with faster dissolution speed, the rotation speed is reduced; for areas with slower dissolution speed, the rotation speed is increased. The control signal is output through the PLC to drive the cleaning equipment to adjust the rotation speed of the wafer to realize dynamic control; S30, implement precise cleaning liquid distribution control, use the wafer surface real-time image acquisition system or cleaning liquid distribution sensor to obtain detailed information on the cleaning liquid distribution on the wafer surface, dynamically adjust the nozzle opening time, spray angle and spray pressure through PLC programming according to the acquired data, ensure that the cleaning liquid accurately covers the wafer surface, regularly check the cleaning liquid distribution effect, and fine-tune the parameters as needed to achieve the best cleaning effect; S40. Establish a real-time monitoring and intelligent feedback mechanism, integrate high-sensitivity sensors, monitor key parameters such as cleaning liquid temperature, concentration, dissolution rate, and wafer rotation speed in real time, and transmit the monitoring data to the PLC control system in real time. The system has a built-in intelligent algorithm to quickly process and analyze the data. According to the processing results, the intelligent algorithm automatically adjusts the control strategy and sends adjustment instructions to the cleaning equipment to realize intelligent management of the cleaning process; S50, data recording and analysis. During the entire cleaning process, record the changes and adjustment records of all key parameters to form a cleaning process database. Use data analysis tools to conduct in-depth analysis of the recorded data, evaluate the cleaning effect, identify potential problems, and continuously optimize the cleaning process based on the data analysis results to improve cleaning efficiency and product quality.

[0006] Based on the above technical solution, the present invention can also be improved as follows.

[0007] Furthermore, the step S10 specifically includes the following operation steps: S101, System design and planning, clarify the functions that the system needs to achieve, including data acquisition, real-time processing, model matching, control signal output and feedback mechanism, etc., design the overall architecture of the system, including PLC, sensors, actuators, etc., control algorithms, data processing programs, etc. and interfaces for monitoring and debugging; S102, PLC selection and programming, select the appropriate PLC model according to system requirements, ensure that it has sufficient input and output points, processing speed and scalability, use PLC-specific programming languages ​​such as ladder diagrams, function block diagrams or structured text methods to write control programs, and the program must include data acquisition modules, data processing modules, model matching modules and control signal output modules; S103, establishing a dissolution rate model, collecting experimental data on the composition of the cleaning solution, the properties of the wafer material and their interactions, and establishing a dissolution rate model based on the experimental data using regression analysis. The model should be able to accurately predict the dissolution rate of the cleaning solution on the wafer surface under different conditions. The model is embedded in the PLC control program, and an algorithm is preset to match the actual dissolution process in real time. The algorithm must be able to receive real-time data, calculate the current dissolution rate, and output a corresponding control signal; S104, sensor deployment and integration, select appropriate sensors according to monitoring requirements, such as temperature sensors, concentration sensors and dissolution rate sensors, deploy sensors at key locations of the cleaning equipment to ensure accurate monitoring of the temperature and concentration of the cleaning solution and the dissolution rate of the wafer surface, connect the sensor signal to the input port of the PLC, and ensure that the PLC can read the sensor data in real time; S105, system integration and testing, complete the physical connection of PLC, sensors, actuators and other hardware devices, test the PLC program in a simulated or actual environment to ensure accurate data collection, timely processing, effective model matching and correct control signal output, optimize and adjust the system according to the test results to ensure stable operation of the system and meet the design requirements.

[0008] Furthermore, the temperature sensor in step S104 is mainly installed at the cleaning liquid inlet, cleaning liquid circulation pipeline and near the chip of the cleaning equipment, and the concentration sensor is mainly installed at the cleaning liquid storage tank, cleaning liquid outlet and mixing area. In order to indirectly monitor the dissolution rate, the dissolution rate sensor that can reflect the changes in the dissolution process can be deployed in the cleaning liquid on the chip surface or near the chip, which is an optical sensor and a surface tension sensor.

[0009] Furthermore, the step S20 specifically includes the following steps: S201, data reception and analysis. The PLC system receives data from sensors deployed on the cleaning equipment in real time through predefined communication protocols and interfaces. These data include but are not limited to the temperature and concentration of the cleaning liquid and dissolution rate-related parameters that may be obtained indirectly. After receiving these raw data, the data processing module inside the PLC performs necessary analysis and preprocessing, including noise removal, data filtering, unit conversion and other operations to ensure the accuracy and reliability of the data for subsequent processing; S202, dissolution rate evaluation, using a pre-established dissolution rate model or an empirical formula based on real-time data to calculate the parsed data and evaluate the dissolution rate of each component in the current cleaning solution. This step may require a comprehensive calculation based on parameters such as temperature and concentration. According to the structure of the cleaning equipment and the layout of the wafer, the cleaning area is divided into several sub-areas, each of which may have different dissolution rate characteristics; S203, formulation of adjustment strategy. Based on the evaluation result of the dissolution speed, the adjustment strategy logic built into the PLC system will formulate an adjustment strategy for the wafer rotation speed for each sub-area according to a preset rule or algorithm. For example, for an area with a faster dissolution speed, the strategy may require reducing the rotation speed to reduce excessive dissolution; while for an area with a slower dissolution speed, the strategy may require increasing the rotation speed to promote dissolution. When there are multiple sub-areas that need to be adjusted, the system may also need to determine the order of adjustment according to a preset priority rule. S204, control signal output. According to the formulated adjustment strategy, the PLC system generates corresponding control signals. These signals are usually electrical signals or digital signals, which are used to drive the motor or other actuators in the cleaning equipment. The control signal is sent to the control system of the cleaning equipment through the output port of the PLC to drive the motor to adjust the rotation speed of the wafer. This process may require certain amplification, conversion or isolation processing to ensure that the signal can accurately drive the actuator; S205, feedback and adjustment. During the adjustment process, the PLC system continues to receive real-time data from the sensor and monitors the changes in the wafer rotation speed and its impact on the dissolution rate. Based on the feedback data, the PLC system can adjust the control strategy and control signal in real time to achieve more precise dynamic control.

[0010] Furthermore, the step S202 selects or establishes a suitable dissolution rate model according to the specific components of the cleaning solution such as acid, alkali, solvent, etc. and the properties of the wafer material such as metal, semiconductor, etc. This model may be a theoretical model based on physical and chemical principles, such as the Arrhenius equation or its modified form, or it may be an empirical model obtained by fitting experimental data. The data obtained from the sensor in real time includes key parameters such as temperature T, concentration C, pressure P, etc. These data need to be preprocessed before entering the model, including noise elimination, unit conversion, data filtering, etc., to ensure the accuracy and reliability of the data. The specific formula is: [ v = A \cdot C^n \cdot \exp\left(-\frac{E_a}{RT}\right) \] Where v is the dissolution rate, C is the concentration, and n represents the degree of influence of concentration on the dissolution rate. If there is no ready-made theoretical model available, an empirical formula can also be obtained based on experimental data fitting, where the formula is: [ v = a \cdot T^b \cdot C^c \cdot \exp(d \cdot P) \] Among them, a, b, c, d are coefficients obtained by fitting experimental data, T is temperature, C is concentration, and P is pressure. According to the structure of the cleaning equipment and the layout of the wafer, the cleaning area is divided into several sub-areas. Each sub-area may have different dissolution rate characteristics due to factors such as temperature distribution, concentration gradient, stirring effect, etc. In each sub-area, the dissolution rate can be calculated using the above formula, and adjustments can be made considering the specific conditions in the area. After calculating the dissolution rate of each sub-area, other factors such as cleaning efficiency and wafer protection need to be considered comprehensively to make necessary adjustments. Since the conditions during the cleaning process may change at any time, such as temperature fluctuations and concentration changes, the dissolution rate evaluation also needs to be carried out in real time. The PLC system should be able to continuously receive sensor data and re-evaluate and adjust the dissolution rate based on the latest data. At the same time, the system should also have a feedback mechanism so that it can issue an alarm or take other emergency measures in time when an abnormal situation is found.

[0011] Furthermore, the step S30 specifically includes the following steps: S301, collecting cleaning liquid distribution data, using a high-definition camera or a dedicated image acquisition system installed on the cleaning equipment to take real-time photos of the wafer surface. These systems can capture subtle changes on the wafer surface during the cleaning process, including the distribution, flow, and possible residues of the cleaning liquid. In addition to image acquisition, special cleaning liquid distribution sensors can also be used to directly measure the distribution of the cleaning liquid on the wafer surface. These sensors may be based on optical, electrical, or chemical principles and can provide real-time feedback on the coverage and concentration information of the cleaning liquid. S302, data analysis and evaluation, the data obtained by the image acquisition system are analyzed by image processing software, which includes steps such as noise removal, contrast enhancement, edge detection, etc., so as to more clearly identify the distribution area and form of the cleaning liquid. The data of the cleaning liquid distribution sensor are directly analyzed and converted to obtain quantitative indicators of the cleaning liquid distribution, such as coverage, uniformity, etc. According to the preset evaluation standards, such as the cleaning liquid coverage must reach more than 95%, and the distribution is uniform without obvious blank areas, the distribution effect of the cleaning liquid is evaluated; S303, dynamically adjusting the nozzle parameters. Based on the results of data analysis, the nozzle parameters are dynamically adjusted through PLC programming. These parameters include the nozzle opening time to control the spray volume of the cleaning liquid, the spray angle to affect the distribution of the cleaning liquid on the wafer surface, and the spray pressure to determine the impact force and penetration ability of the cleaning liquid. The high-precision control capability of the PLC is used to achieve fine adjustments to the nozzle parameters. This requires the PLC system to have fast response capabilities and high stability to ensure that the nozzle parameters can be adjusted in real time and accurately during the cleaning process. S304, regular inspection and parameter fine-tuning. During the cleaning process, the cleaning liquid distribution effect is regularly inspected. This can be done by using the image acquisition system or sensor to collect data again and comparing and analyzing with previous data. According to the inspection results, if it is found that the cleaning liquid distribution effect is not optimal, the nozzle parameters need to be fine-tuned, including adjusting the length of the opening time, the size of the spray angle, and the level of the spray pressure, so as to ensure that the cleaning liquid can accurately cover the wafer surface. S305. Continuous optimization and improvement: Summarize and analyze the data from previous cleaning processes to find out the key factors and rules that affect the distribution of the cleaning liquid. Based on these analysis results, continuously optimize and improve the cleaning process and nozzle parameters, establish an effective feedback mechanism, and promptly deliver the cleaning results and user feedback to relevant personnel.

[0012] Furthermore, the cleaning liquid distribution sensor in step S302 usually directly outputs electrical signals or digital signals, and these signals need to be analyzed and converted into quantitative indicators of the cleaning liquid distribution. According to the type and working principle of the sensor, a corresponding data analysis algorithm is designed to convert the original signal into useful information such as cleaning liquid coverage and concentration distribution. The sensor data is calibrated to eliminate the influence of system errors and external interference on the measurement results. The accuracy and reliability of the sensor data are ensured by comparing and verifying with data of standard samples or known cleaning effects. Similar to the data analysis of the image acquisition system, the data obtained by the cleaning liquid distribution sensor is quantitatively evaluated to calculate indicators such as coverage and uniformity.

[0013] Furthermore, the step S40 specifically includes the following operation steps: S401. Integration of highly sensitive sensors, such as thermistors, thermocouples or infrared temperature sensors installed at key positions of the cleaning liquid circulation system, to monitor the temperature change of the cleaning liquid in real time, conductivity sensors, refractometers or sensors based on spectral analysis to measure the concentration of the cleaning liquid, and indirectly estimate it by monitoring the signals generated when the cleaning liquid interacts with the wafer surface, such as electrochemical signals, optical signals, etc., and installing photoelectric encoders or Hall effect sensors on the wafer rotating device to monitor the rotation speed of the wafer in real time; S402, data transmission and PLC control system integration, each sensor is connected to the data acquisition module through a dedicated interface or communication protocol such as RS-485, Modbus, etc., and the real-time monitored data is transmitted to the PLC control system. The data acquisition module should have the characteristics of multi-channel, high precision and high reliability, and establish a stable and reliable data transmission network to ensure that the monitoring data can be transmitted to the PLC control system in real time and accurately, which includes the selection and configuration of wired and wireless networks. The PLC control system, as the core of the real-time monitoring and intelligent feedback mechanism, is responsible for receiving and processing the monitoring data from each sensor. The system has a built-in high-performance processor and large-capacity memory to ensure rapid processing and analysis of data; S403, intelligent algorithm and control strategy: Integrate fuzzy control algorithm in PLC control system, quickly process and analyze monitoring data, and automatically adjust the control strategy of cleaning equipment according to the processing result of intelligent algorithm. PLC control system sends specific adjustment instructions to cleaning equipment according to the adjusted control strategy. These instructions may include changing the circulation speed of cleaning liquid, adjusting the working state of nozzle, adjusting the rotation speed of wafer, etc. S404. Real-time monitoring and alarm function. Develop a real-time monitoring interface such as a touch screen interface or remote monitoring software to show the operator the real-time status of the cleaning process and the changing trends of key parameters. The interface should be intuitive and easy to understand, so that the operator can quickly understand the cleaning status. When an abnormal situation is detected, the PLC control system should be able to immediately trigger the alarm function. The alarm method may include sound and light alarm, SMS notification or remote alarm, so that the operator can take timely measures to solve the problem.

[0014] Furthermore, the step S50 specifically includes the following operation steps: S501, data recording, real-time recording of water temperature or solution temperature at each stage of the cleaning process, especially for cleaning steps that require specific temperature conditions, such as pre-rinsing, detergent dissolution and final rinse, etc., to ensure that the temperature is controlled within the preset range, accurately record the duration of each cleaning step, including pre-rinsing, detergent action time, final rinse, etc., to ensure that the time parameters meet the process requirements, regularly test and record the concentration of the detergent in the cleaning solution, to ensure that the concentration is within the effective range, to avoid poor cleaning effect due to too low concentration or increased difficulty and cost of subsequent rinsing due to too high concentration, and to record in detail the action mode of the cleaning process, such as immersion, washing, impact or turbulence, etc., as well as the parameter settings of these modes, such as stirring speed, flow rate of the spray device, etc., use automated data acquisition systems, such as sensors, PLCs, etc., to collect and store the above key parameter data in real time, design data recording tables or database templates, to ensure the standardization and traceability of data records; S502, data analysis, cleaning the collected raw data, removing outliers, duplicate values, etc., ensuring the accuracy and consistency of the data, converting the data into a format suitable for analysis, such as organizing time series data into a time series table, converting concentration data into percentages or concentration units, etc., using data analysis software to conduct in-depth analysis of the cleaned data, applying statistical analysis methods and data mining techniques to mine patterns and trends in the data, and evaluating the effectiveness of the cleaning process in removing pollutants, residues, etc. by comparing the data before and after cleaning. Based on the data analysis results, identify the key factors that affect the cleaning effect, such as temperature, time, concentration, etc., and put forward optimization suggestions. Through methods such as outlier detection and trend analysis, identify potential problems that may occur in the cleaning process, such as fluctuations in detergent concentration, insufficient cleaning time, etc., and use charts, graphs and other visual means to display data analysis results, such as temperature-time curves, concentration change line graphs, etc., to make the analysis results more intuitive and easy to understand; S503. Continuous optimization. Based on the data analysis results, the cleaning process is continuously optimized, for example, the detergent concentration is adjusted, the cleaning time or temperature is changed, etc., to improve the cleaning efficiency and product quality. For the potential problems identified, corresponding improvement measures are formulated and implemented. A data-driven feedback mechanism is established to promptly feed back the data analysis results to the production department and the technical department so that the cleaning process can be adjusted and optimized in time. The data records and analysis results of each cleaning process are organized into a knowledge base to provide reference and reference for the subsequent optimization and improvement of the cleaning process.

[0015] Compared with the prior art, the technical solution of this application has the following beneficial technical effects: The present invention designs and integrates a PLC-based dissolution rate matching system. The system can establish an accurate dissolution rate model for the cleaning liquid composition and the wafer material characteristics, and preset an algorithm to match the actual dissolution process. This feature effectively solves the problem of uneven cleaning caused by dissolution rate differences in traditional methods. Through real-time monitoring and adjustment, the dissolution rate of the cleaning liquid on the wafer surface is ensured to be more consistent, thereby reducing the formation of fluidity particle defects. The PLC system automatically formulates an adjustment strategy for the wafer rotation speed based on the dissolution rate data fed back by the sensor in real time. For areas with faster dissolution rates, the rotation speed is reduced to reduce excessive flushing of the cleaning liquid; for areas with slower dissolution rates, the rotation speed is increased to enhance the cleaning effect. This dynamic control strategy significantly improves the uniformity of cleaning and further reduces cleaning defects caused by uneven dissolution rates. By using a real-time image acquisition system on the wafer surface or a cleaning liquid distribution sensor, PLC programming can dynamically adjust the opening time, spray angle and spray pressure of the nozzle to ensure that the cleaning liquid accurately covers the wafer surface. This precise control not only improves the cleaning efficiency, but also avoids waste and unnecessary pollution of the cleaning liquid, further improving the cleaning effect and product quality. The integrated high-sensitivity The sensitivity sensor monitors the key parameters of the cleaning process in real time, and quickly processes and analyzes the data through the intelligent algorithm built into the PLC control system. The intelligent algorithm can automatically adjust the control strategy according to the monitoring results and send adjustment instructions to the cleaning equipment, realizing the intelligent management of the cleaning process. This mechanism greatly improves the stability and reliability of the cleaning process, reduces human intervention and errors, and ensures the consistency of the cleaning effect. During the entire cleaning process, the changes and adjustment records of all key parameters are recorded to form a cleaning process database. Through in-depth analysis of the recorded data, the cleaning effect can be evaluated, potential problems can be identified, and the cleaning process can be continuously optimized based on the data analysis results. This data-driven optimization method not only improves the cleaning efficiency and product quality, but also provides strong support for subsequent process improvements. In summary, by building a PLC-programmed dissolution speed matching system, realizing dynamic rotation speed control, implementing precise cleaning liquid distribution control, establishing a real-time monitoring and intelligent feedback mechanism, and data recording and analysis, the technical defects in traditional wet cleaning are effectively solved, and the cleaning uniformity, stability and intelligence level are significantly improved, which is of great significance to promoting the high-quality development of the semiconductor industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The present invention is a method flow chart of the semiconductor wet cleaning process optimization method based on PLC programming. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. 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.

[0018] Combination Figure 1 As shown, the semiconductor wet cleaning process optimization method based on PLC programming of the present invention specifically includes the following operating steps: S10. Build a PLC-programmed dissolution rate matching system. Design and integrate a PLC-based dissolution rate matching system. The system needs to have the functions of data collection, processing and feedback. According to the composition of the cleaning liquid and the characteristics of the wafer material, a dissolution rate model is established, and an algorithm is preset to match the actual dissolution process. Sensors are deployed in the cleaning equipment to monitor key parameters such as cleaning liquid temperature, concentration and wafer surface dissolution rate in real time; Step S10 specifically includes the following steps: S101, System design and planning, clarify the functions that the system needs to achieve, including data acquisition, real-time processing, model matching, control signal output and feedback mechanism, etc., design the overall architecture of the system, including PLC, sensors, actuators, etc., control algorithms, data processing programs, etc. and interfaces for monitoring and debugging; S102, PLC selection and programming, select the appropriate PLC model according to system requirements, ensure that it has sufficient input and output points, processing speed and scalability, use PLC-specific programming languages ​​such as ladder diagrams, function block diagrams or structured text methods to write control programs, and the program must include data acquisition modules, data processing modules, model matching modules and control signal output modules; S103, establishing a dissolution rate model, collecting experimental data on the composition of the cleaning solution, the properties of the wafer material and their interactions, and establishing a dissolution rate model based on the experimental data using regression analysis. The model should be able to accurately predict the dissolution rate of the cleaning solution on the wafer surface under different conditions. The model is embedded in the PLC control program, and an algorithm is preset to match the actual dissolution process in real time. The algorithm must be able to receive real-time data, calculate the current dissolution rate, and output a corresponding control signal; S104, sensor deployment and integration, select appropriate sensors according to monitoring requirements, such as temperature sensors, concentration sensors and dissolution rate sensors, deploy sensors at key locations of the cleaning equipment to ensure accurate monitoring of the temperature and concentration of the cleaning solution and the dissolution rate of the wafer surface, connect the sensor signal to the input port of the PLC, and ensure that the PLC can read the sensor data in real time; S105, system integration and testing, complete the physical connection of PLC, sensors, actuators and other hardware devices, test the PLC program in a simulated or actual environment to ensure accurate data collection, timely processing, effective model matching and correct control signal output, optimize and adjust the system according to the test results to ensure stable operation of the system and meet the design requirements.

[0019] In S10, the system design and planning stage clearly defined the functions that the system needed to achieve, including data acquisition, real-time processing, model matching, control signal output and feedback mechanism, which provided a clear blueprint for the subsequent system construction. The overall architecture of the system was designed, including the selection and control algorithms of hardware devices such as PLC, sensors, actuators, the compilation of data processing programs, and the development of monitoring and debugging interfaces, ensuring the comprehensiveness and operability of the system. In the PLC selection and programming stage, the appropriate PLC model was carefully selected according to the system requirements to ensure that its input and output points, processing speed and scalability met the system requirements. The control program was written in a programming language dedicated to PLC, covering modules such as data acquisition, processing, model matching and control signal output, providing a solid foundation for the precise control of the system. In the dissolution rate model establishment stage, by collecting experimental data on the composition of the cleaning solution, the material properties of the wafer and their interactions, an accurate dissolution rate model was established using scientific methods such as regression analysis. The model can accurately predict the dissolution rate of the cleaning solution on the wafer surface under different conditions, providing a scientific basis for real-time matching of the actual dissolution process. The model was embedded into the PLC control program, and the algorithm was preset to receive sensor data in real time, calculate the current dissolution rate and output the corresponding control signal, thus realizing the dynamic adjustment of the cleaning process. In the sensor deployment and integration stage, suitable sensors were selected according to the monitoring requirements, and deployed at key positions of the cleaning equipment to ensure accurate monitoring of the temperature, concentration and dissolution rate of the cleaning liquid on the surface of the wafer. The sensor signal was connected to the input port of the PLC to realize the real-time transmission and processing of data, providing reliable data support for the precise control of the system. In the system integration and testing stage, the physical connection of hardware devices such as PLC, sensors, and actuators was completed, and the PLC program was fully tested in a simulated or actual environment. The accuracy of data acquisition, the timeliness of processing, the effectiveness of model matching, and the correctness of control signal output were verified through testing. The system was optimized and adjusted according to the test results to ensure the stable operation of the system and meet the design requirements.

[0020] Step S104: The temperature sensor is mainly installed at the cleaning liquid inlet, cleaning liquid circulation pipeline and wafer of the cleaning equipment. The initial temperature of the cleaning liquid entering the cleaning tank is monitored at the cleaning liquid inlet to ensure that the cleaning process is carried out within an appropriate temperature range. A temperature sensor is set in the cleaning liquid circulation system to monitor the temperature change of the cleaning liquid during the circulation process in real time to prevent overheating or overcooling from affecting the cleaning effect. The temperature sensor is deployed in the cleaning liquid on or near the wafer surface to directly monitor the cleaning liquid temperature around the wafer, which is crucial for accurately controlling the dissolution rate. The concentration sensor is mainly installed at the cleaning liquid storage tank, cleaning liquid outlet and mixing area. The cleaning liquid storage tank mainly monitors the initial concentration of the cleaning liquid to ensure that the temperature of the cleaning liquid is maintained at the temperature of the wafer before adding the cleaning liquid. The concentration before the cleaning tank meets the requirements. A concentration sensor is set at the position where the cleaning liquid flows out of the cleaning tank to evaluate the change in concentration during the cleaning process. If the cleaning liquid requires a mixture of multiple components, a concentration sensor is set in the mixing area to monitor the concentration after mixing in real time. In order to indirectly monitor the dissolution rate, the dissolution rate sensors that can reflect the changes in the dissolution process can be deployed in the cleaning liquid on the surface of the chip or nearby. They are optical sensors and surface tension sensors. Optical sensors use optical principles to monitor changes in light scattering, absorption or reflection characteristics on the surface of the chip or in the cleaning liquid. These changes are related to the dissolution rate. The dissolution process will change the surface tension of the cleaning liquid. The surface tension sensor can indirectly estimate the dissolution rate by monitoring the changes in surface tension.

[0021] In terms of temperature sensor deployment, it is pointed out that temperature sensors are mainly installed at the cleaning liquid inlet, cleaning liquid circulation pipeline and near the wafer of the cleaning equipment. This layout ensures all-round monitoring from the initial temperature of the cleaning liquid entering the cleaning tank, to the temperature change during the circulation process, and then to the local temperature around the wafer. Monitoring the initial temperature at the cleaning liquid inlet ensures that the cleaning process starts within the preset suitable temperature range; setting a temperature sensor in the circulation pipeline can timely detect and correct temperature fluctuations to prevent overheating or overcooling from adversely affecting the cleaning effect; deploying temperature sensors on or near the wafer surface is directly related to the control of dissolution rate, because temperature is one of the key factors affecting the dissolution rate. Accurately controlling the temperature around the wafer is crucial to improving cleaning uniformity and efficiency. Secondly, the installation location of the concentration sensor is also carefully planned. Monitoring the initial concentration at the cleaning liquid storage tank ensures that the cleaning liquid has reached the predetermined standard before being added to the cleaning tank, avoiding differences in cleaning effects caused by inconsistent concentrations. Setting a concentration sensor at the cleaning liquid outlet can evaluate the dynamic changes in concentration during the cleaning process, providing a basis for adjusting the cleaning liquid formula or the number of cycles. For cleaning fluids that require a mixture of multiple components, the concentration sensor in the mixing area plays a key role in real-time monitoring of the mixing effect, ensuring that the concentration of the mixed fluid is accurately controllable. Finally, in order to indirectly monitor the dissolution rate, it is also mentioned that optical sensors and surface tension sensors are deployed on or near the surface of the chip. Optical sensors use optical principles to monitor changes in light scattering, absorption or reflection characteristics on the surface of the chip or in the cleaning fluid. These changes are closely related to the dissolution rate and can indirectly reflect the dynamics of the dissolution process. The surface tension sensor estimates the dissolution rate by monitoring changes in the surface tension of the cleaning fluid, because the dissolution process changes the physical and chemical properties of the cleaning fluid, including the surface tension. This change provides another effective means for real-time monitoring of the dissolution rate.

[0022] S20, realizing dynamic rotation speed control, the PLC system receives real-time data from the sensor, analyzes the change of dissolution speed of each component in the cleaning solution, and automatically formulates a strategy for adjusting the rotation speed of the wafer according to the dissolution speed data. For areas with faster dissolution speed, the rotation speed is reduced; for areas with slower dissolution speed, the rotation speed is increased. The control signal is output through the PLC to drive the cleaning equipment to adjust the rotation speed of the wafer to realize dynamic control; Step S20 specifically includes the following steps: S201 Data reception and analysis: The PLC system receives data from sensors deployed on the cleaning equipment in real time through predefined communication protocols and interfaces. These data include but are not limited to the temperature and concentration of the cleaning liquid and parameters related to the dissolution rate that may be obtained indirectly. After receiving these raw data, the data processing module inside the PLC performs necessary analysis and preprocessing, including noise removal, data filtering, unit conversion and other operations to ensure the accuracy and reliability of the data for subsequent processing; S202, dissolution rate evaluation, using a pre-established dissolution rate model or an empirical formula based on real-time data to calculate the parsed data and evaluate the dissolution rate of each component in the current cleaning solution. This step may require a comprehensive calculation based on parameters such as temperature and concentration. According to the structure of the cleaning equipment and the layout of the wafer, the cleaning area is divided into several sub-areas, each of which may have different dissolution rate characteristics; S203, formulation of adjustment strategy. Based on the evaluation result of the dissolution speed, the adjustment strategy logic built into the PLC system will formulate an adjustment strategy for the wafer rotation speed for each sub-area according to a preset rule or algorithm. For example, for an area with a faster dissolution speed, the strategy may require reducing the rotation speed to reduce excessive dissolution; while for an area with a slower dissolution speed, the strategy may require increasing the rotation speed to promote dissolution. When there are multiple sub-areas that need to be adjusted, the system may also need to determine the order of adjustment according to a preset priority rule. S204, control signal output. According to the formulated adjustment strategy, the PLC system generates corresponding control signals. These signals are usually electrical signals or digital signals, which are used to drive the motor or other actuators in the cleaning equipment. The control signal is sent to the control system of the cleaning equipment through the output port of the PLC to drive the motor to adjust the rotation speed of the wafer. This process may require certain amplification, conversion or isolation processing to ensure that the signal can accurately drive the actuator; S205, feedback and adjustment. During the adjustment process, the PLC system continues to receive real-time data from the sensor and monitors the changes in the wafer rotation speed and its impact on the dissolution rate. Based on the feedback data, the PLC system can adjust the control strategy and control signal in real time to achieve more precise dynamic control.

[0023] In the S201 data reception and analysis stage, the PLC system receives data from sensors on the cleaning equipment in real time through predefined communication protocols and interfaces. These data not only cover direct parameters such as temperature and concentration of the cleaning solution, but also include dissolution rate-related parameters that may be obtained indirectly. Through the analysis and preprocessing of the data processing module, such as noise removal, data filtering, unit conversion and other operations, the accuracy and reliability of the data for subsequent processing are ensured. This step provides a solid foundation for the subsequent dissolution rate evaluation and adjustment strategy formulation. Secondly, in the S202 dissolution rate evaluation stage, the pre-established dissolution rate model or experience based on real-time data is used to evaluate the dissolution rate. Formula is used to calculate the parsed data and evaluate the dissolution rate of each component in the current cleaning solution. This step combines key parameters such as temperature and concentration to achieve accurate evaluation of the dissolution rate. At the same time, according to the structure of the cleaning equipment and the layout of the wafer, the cleaning area is divided into several sub-areas. Each sub-area may have different dissolution rate characteristics. This division makes the subsequent adjustment strategy more refined and targeted. Next, in the S203 adjustment strategy formulation stage, the built-in adjustment strategy logic of the PLC system will formulate an adjustment strategy for the wafer rotation speed for each sub-area based on the preset rules or algorithms. This step fully considers the evaluation results of the dissolution rate. , by dynamically adjusting the rotation speed of the wafer, the aim is to achieve uniform dissolution during the cleaning process. For areas with faster dissolution, the strategy may require reducing the rotation speed to reduce excessive dissolution; while for areas with slower dissolution, it may require increasing the rotation speed to promote dissolution. When there are multiple sub-areas that need to be adjusted, the system can also determine the order of adjustment according to the preset priority rules, ensuring the order and efficiency of the adjustment process. Then, in the S204 control signal output stage, the PLC system generates corresponding control signals according to the formulated adjustment strategy. These signals are used to drive the motor or other actuators in the cleaning equipment. Through the output port of the PLC, the control The signal is sent to the control system of the cleaning equipment, which drives the motor to adjust the rotation speed of the chip. In order to ensure that the signal drives the actuator accurately, it may need to undergo certain amplification, conversion or isolation processing. Finally, in the S205 feedback and adjustment stage, the PLC system continues to receive real-time data from the sensor and monitors the changes in the rotation speed of the chip and its impact on the dissolution rate. According to the feedback data, the PLC system can adjust the control strategy and control signal in real time to achieve more precise dynamic control. This closed-loop control mechanism ensures that the system can respond quickly and make corresponding adjustments when faced with various changes, thereby ensuring the stability and reliability of the cleaning process.

[0024] Step S202 selects or establishes a suitable dissolution rate model according to the specific components of the cleaning solution such as acid, alkali, solvent, etc. and the properties of the wafer material such as metal, semiconductor, etc. This model may be a theoretical model based on physical and chemical principles, such as the Arrhenius equation or its modified form, or it may be an empirical model obtained by fitting experimental data. The data obtained from the sensor in real time includes key parameters such as temperature T, concentration C, pressure P, etc. These data need to be preprocessed before entering the model, including noise elimination, unit conversion, data filtering, etc., to ensure the accuracy and reliability of the data. The specific formula is: [ v = A \cdot C^n \cdot \exp\left(-\frac{E_a}{RT}\right) \] Where v is the dissolution rate, C is the concentration, and n represents the degree of influence of concentration on the dissolution rate. If there is no ready-made theoretical model available, an empirical formula can also be obtained based on experimental data fitting, where the formula is: [ v = a \cdot T^b \cdot C^c \cdot \exp(d \cdot P) \] Among them, a, b, c, d are coefficients obtained by fitting experimental data, T is temperature, C is concentration, and P is pressure. According to the structure of the cleaning equipment and the layout of the wafer, the cleaning area is divided into several sub-areas. Each sub-area may have different dissolution rate characteristics due to factors such as temperature distribution, concentration gradient, stirring effect, etc. In each sub-area, the dissolution rate can be calculated using the above formula, and adjustments can be made considering the specific conditions in the area. After calculating the dissolution rate of each sub-area, other factors such as cleaning efficiency and wafer protection need to be considered comprehensively to make necessary adjustments. Since the conditions during the cleaning process may change at any time, such as temperature fluctuations and concentration changes, the dissolution rate evaluation also needs to be carried out in real time. The PLC system should be able to continuously receive sensor data and re-evaluate and adjust the dissolution rate based on the latest data. At the same time, the system should also have a feedback mechanism so that it can issue an alarm or take other emergency measures in time when an abnormal situation is found.

[0025] According to the specific components of the cleaning solution (such as acid, alkali, solvent, etc.) and the properties of the chip material (such as metal, semiconductor, etc.), a suitable dissolution rate model is selected or established. This approach fully considers the influence of different components and properties on the dissolution rate, making the model closer to the actual cleaning process. Whether it is a theoretical model based on physical and chemical principles (such as the Arrhenius equation or its modified form) or an empirical model obtained by fitting experimental data, it can provide strong support for the evaluation of the dissolution rate. The flexibility of this model selection ensures the accuracy and reliability of the evaluation results. Secondly, in terms of data preprocessing, it is required to perform noise elimination, unit conversion, data filtering and other processing on the data obtained from the sensor in real time to ensure the accuracy and reliability of the data. This step is crucial for the subsequent calculation of the dissolution rate, because any slight data error may lead to deviations in the evaluation results. Through preprocessing, the influence of data noise and error on the evaluation results can be minimized, and the accuracy and stability of the evaluation can be improved. In addition, a strategy is proposed to divide the cleaning area into several sub-areas and use the dissolution rate model to calculate and adjust in each sub-area. This area division method fully considers the influence of the cleaning equipment structure and chip layout on the dissolution rate, making the evaluation results more refined and targeted. In each sub-area, the dissolution rate can be calculated and adjusted according to the specific temperature distribution, concentration gradient, stirring effect and other factors, so as to achieve the uniformity of dissolution during the cleaning process. Finally, the real-time and dynamic nature of the dissolution rate evaluation is emphasized. Since the conditions during the cleaning process may change at any time (such as temperature fluctuations, concentration changes, etc.), the dissolution rate evaluation also needs to be carried out in real time. The PLC system should be able to continuously receive sensor data and re-evaluate and adjust the dissolution rate based on the latest data. At the same time, the system should also have a feedback mechanism so that it can issue an alarm or take other emergency measures in time when an abnormal situation is found. This real-time and dynamic evaluation mechanism ensures the stability and reliability of the cleaning process and improves the cleaning efficiency and product quality.

[0026] S30, implement precise cleaning liquid distribution control, use the wafer surface real-time image acquisition system or cleaning liquid distribution sensor to obtain detailed information on the cleaning liquid distribution on the wafer surface, dynamically adjust the nozzle opening time, spray angle and spray pressure through PLC programming according to the acquired data, ensure that the cleaning liquid accurately covers the wafer surface, regularly check the cleaning liquid distribution effect, and fine-tune the parameters as needed to achieve the best cleaning effect; Step S30 specifically includes the following steps: S301, collecting cleaning liquid distribution data, using a high-definition camera or a dedicated image acquisition system installed on the cleaning equipment to take real-time photos of the wafer surface. These systems can capture subtle changes on the wafer surface during the cleaning process, including the distribution, flow, and possible residues of the cleaning liquid. In addition to image acquisition, special cleaning liquid distribution sensors can also be used to directly measure the distribution of the cleaning liquid on the wafer surface. These sensors may be based on optical, electrical, or chemical principles and can provide real-time feedback on the coverage and concentration information of the cleaning liquid. S302, data analysis and evaluation, the data obtained by the image acquisition system are analyzed by image processing software, which includes steps such as noise removal, contrast enhancement, edge detection, etc., so as to more clearly identify the distribution area and form of the cleaning liquid. The data of the cleaning liquid distribution sensor are directly analyzed and converted to obtain quantitative indicators of the cleaning liquid distribution, such as coverage, uniformity, etc. According to the preset evaluation standards, such as the cleaning liquid coverage must reach more than 95%, and the distribution is uniform without obvious blank areas, the distribution effect of the cleaning liquid is evaluated; S303, dynamically adjusting the nozzle parameters. Based on the results of data analysis, the nozzle parameters are dynamically adjusted through PLC programming. These parameters include the nozzle opening time to control the spray volume of the cleaning liquid, the spray angle to affect the distribution of the cleaning liquid on the wafer surface, and the spray pressure to determine the impact force and penetration ability of the cleaning liquid. The high-precision control capability of the PLC is used to achieve fine adjustments to the nozzle parameters. This requires the PLC system to have fast response capabilities and high stability to ensure that the nozzle parameters can be adjusted in real time and accurately during the cleaning process. S304, regular inspection and parameter fine-tuning. During the cleaning process, the cleaning liquid distribution effect is regularly inspected. This can be done by using the image acquisition system or sensor to collect data again and comparing and analyzing with previous data. According to the inspection results, if it is found that the cleaning liquid distribution effect is not in the optimal state, the nozzle parameters need to be fine-tuned, including adjusting the length of the opening time, the size of the spray angle, and the level of the spray pressure, so as to ensure that the cleaning liquid can accurately cover the wafer surface. S305. Continuous optimization and improvement: Summarize and analyze the data from previous cleaning processes to find out the key factors and rules that affect the distribution of the cleaning liquid. Based on these analysis results, continuously optimize and improve the cleaning process and nozzle parameters, establish an effective feedback mechanism, and promptly deliver the cleaning results and user feedback to relevant personnel.

[0027] In step S301, by using high-definition cameras or dedicated image acquisition systems and cleaning liquid distribution sensors, real-time and precise monitoring of the distribution of cleaning liquid on the wafer surface is achieved. These advanced devices can capture subtle changes on the wafer surface during the cleaning process, including the distribution, flow state and possible residues of the cleaning liquid, providing a rich source of information for subsequent data analysis and evaluation. This real-time monitoring capability is more intuitive and accurate than traditional methods, and can promptly discover and solve the problem of uneven distribution of cleaning liquid. Secondly, in step S302, the collected images and sensor data are deeply analyzed through image processing software and special data analysis methods to extract quantitative indicators of cleaning liquid distribution, such as coverage, uniformity, etc. These quantitative indicators not only provide objective standards for the evaluation of cleaning effects, but also make the evaluation of cleaning liquid distribution effects more accurate and scientific. At the same time, according to the preset evaluation standards, it can quickly determine whether the cleaning liquid distribution meets the requirements, providing a basis for subsequent adjustments and optimizations. Then, in step S303, based on the results of data analysis, dynamic adjustment of nozzle parameters is achieved through PLC programming. This step makes full use of the high-precision control capability and rapid response of the PLC system. The dynamic adjustment mechanism is more flexible and efficient than the traditional method, and can quickly adapt to various changes in the cleaning process to ensure the uniformity and stability of the cleaning liquid distribution. In addition, in step S304, the uniformity of the cleaning liquid distribution is further consolidated through regular inspection and parameter fine-tuning mechanism. The cleaning liquid distribution effect is regularly inspected and compared with previous data for analysis, which can timely discover and correct problems in the cleaning process. The nozzle parameters are fine-tuned according to the inspection results to ensure that the cleaning liquid can always accurately cover the wafer surface, thereby improving the cleaning effect and quality. Finally, in step S305, the optimization level of the cleaning process and nozzle parameters is continuously improved through continuous optimization and improvement mechanism. The data of previous cleaning processes are summarized and analyzed to find out the key factors and laws affecting the cleaning liquid distribution effect, which provides strong support for subsequent process improvement and optimization. At the same time, an effective feedback mechanism is established to timely transmit the cleaning effect and user feedback to relevant personnel, which promotes continuous improvement and continuous improvement of the process.

[0028] Step S302: The cleaning liquid distribution sensor usually directly outputs electrical signals or digital signals. These signals need to be analyzed and converted into quantitative indicators of the cleaning liquid distribution. According to the type and working principle of the sensor, a corresponding data analysis algorithm is designed to convert the original signal into useful information such as cleaning liquid coverage and concentration distribution. The sensor data is calibrated to eliminate the influence of system errors and external interference on the measurement results. The accuracy and reliability of the sensor data are ensured by comparing and verifying with standard samples or data of known cleaning effects. Similar to the data analysis of the image acquisition system, the data obtained by the cleaning liquid distribution sensor is quantitatively evaluated to calculate indicators such as coverage and uniformity.

[0029] The cleaning liquid distribution sensor can directly output electrical signals or digital signals. These signals are converted into quantitative indicators of cleaning liquid distribution, such as coverage, concentration distribution and other useful information through the designed data analysis algorithm. This process realizes the accurate conversion from original signal to cleaning liquid distribution characteristics, overcoming the limitation that traditional methods cannot directly quantify and evaluate the distribution of cleaning liquid. Through quantitative evaluation, we can more intuitively understand the distribution of cleaning liquid on the wafer surface, providing reliable data support for subsequent adjustment and optimization. Secondly, calibrating sensor data is an important step to ensure the accuracy and reliability of measurement results. By eliminating the influence of system errors and external interference on the measurement results, the accuracy and credibility of the data can be significantly improved. This calibration process not only takes into account the performance characteristics of the sensor itself, but also combines the characteristics of the actual cleaning process to ensure the reality of the measurement results. Applicability and effectiveness. In addition, by comparing and verifying with standard samples or data of known cleaning effects, the accuracy and reliability of sensor data can be further ensured. This verification method not only verifies the accuracy of the sensor and analytical algorithm, but also verifies the reliability of the entire monitoring and evaluation system. This double verification mechanism provides a strong guarantee for the quantitative evaluation of the cleaning fluid distribution. Finally, similar to the data analysis of the image acquisition system, the data obtained by the cleaning fluid distribution sensor is quantitatively evaluated, and indicators such as coverage and uniformity are calculated, which provides a more comprehensive and objective basis for the evaluation of the cleaning effect. These quantitative indicators not only reflect the uniformity of the cleaning fluid distribution, but also reflect the quality of the cleaning effect. By comparing the quantitative indicators under different cleaning conditions, the key factors and laws affecting the cleaning effect can be found, which provides strong support for the optimization and improvement of the cleaning process.

[0030] S40. Establish a real-time monitoring and intelligent feedback mechanism, integrate high-sensitivity sensors, monitor key parameters such as cleaning liquid temperature, concentration, dissolution rate, and wafer rotation speed in real time, and transmit the monitoring data to the PLC control system in real time. The system has a built-in intelligent algorithm to quickly process and analyze the data. According to the processing results, the intelligent algorithm automatically adjusts the control strategy and sends adjustment instructions to the cleaning equipment to realize intelligent management of the cleaning process; Step S40 specifically includes the following steps: S401. Integration of highly sensitive sensors, such as thermistors, thermocouples or infrared temperature sensors installed at key positions of the cleaning liquid circulation system, to monitor the temperature change of the cleaning liquid in real time, conductivity sensors, refractometers or sensors based on spectral analysis to measure the concentration of the cleaning liquid, and indirectly estimate it by monitoring the signals generated when the cleaning liquid interacts with the wafer surface, such as electrochemical signals, optical signals, etc., and installing photoelectric encoders or Hall effect sensors on the wafer rotating device to monitor the rotation speed of the wafer in real time; S402, data transmission and PLC control system integration, each sensor is connected to the data acquisition module through a dedicated interface or communication protocol such as RS-485, Modbus, etc., and the real-time monitored data is transmitted to the PLC control system. The data acquisition module should have the characteristics of multi-channel, high precision and high reliability, and establish a stable and reliable data transmission network to ensure that the monitoring data can be transmitted to the PLC control system in real time and accurately, which includes the selection and configuration of wired and wireless networks. The PLC control system, as the core of the real-time monitoring and intelligent feedback mechanism, is responsible for receiving and processing the monitoring data from each sensor. The system has a built-in high-performance processor and large-capacity memory to ensure rapid processing and analysis of data; S403, intelligent algorithm and control strategy: Integrate fuzzy control algorithm in PLC control system, quickly process and analyze monitoring data, and automatically adjust the control strategy of cleaning equipment according to the processing result of intelligent algorithm. For example, when the temperature of cleaning liquid is too high, intelligent algorithm may trigger the start of cooling system; when the dissolution speed is too slow, the injection pressure of cleaning liquid may be increased or the nozzle angle may be adjusted to improve the dissolution efficiency. PLC control system sends specific adjustment instructions to cleaning equipment according to the adjusted control strategy. These instructions may include changing the circulation speed of cleaning liquid, adjusting the working state of nozzle, adjusting the rotation speed of wafer, etc. S404. Real-time monitoring and alarm function. Develop a real-time monitoring interface such as a touch screen interface or remote monitoring software to show the operator the real-time status of the cleaning process and the changing trends of key parameters. The interface should be intuitive and easy to understand, so that the operator can quickly understand the cleaning status. When an abnormal situation is detected (such as the cleaning liquid temperature is too high, the concentration is too low, the dissolution rate is abnormal, etc.), the PLC control system should be able to immediately trigger the alarm function. The alarm method may include sound and light alarm, SMS notification or remote alarm, so that the operator can take timely measures to solve the problem.

[0031] In step S401, by integrating highly sensitive sensors, real-time monitoring of the temperature, concentration and rotation speed of the cleaning liquid is achieved. These sensors include thermistors, thermocouples, infrared temperature sensors, conductivity sensors, refractometers, spectral analysis sensors, photoelectric encoders or Hall effect sensors, etc., which can accurately capture the changes in key parameters in the cleaning process and provide an accurate data basis for subsequent intelligent control and adjustment. Secondly, in step S402, by building a stable and reliable data transmission network and integrating a high-performance PLC control system, real-time, accurate transmission and rapid processing of monitoring data are achieved. Each sensor is connected to the data acquisition module through a dedicated interface or communication protocol. The data acquisition module has the characteristics of multi-channel, high precision and high reliability, ensuring the integrity and accuracy of the monitoring data. As the core of the real-time monitoring and intelligent feedback mechanism, the PLC control system has a built-in high-performance processor and a large-capacity memory, which can quickly receive and process the monitoring data from each sensor, providing strong support for the subsequent intelligent algorithm analysis and control strategy adjustment. Furthermore, in step S403, by integrating intelligent algorithms such as fuzzy control algorithms, intelligent control and strategy adjustment of cleaning equipment are achieved. The intelligent algorithm can quickly process and analyze the monitoring data, and automatically adjust the control strategy of the cleaning equipment according to the processing results. For example, when the temperature of the cleaning liquid is too high, the intelligent algorithm will trigger the cooling system to start to reduce the temperature; when the dissolution rate is too slow, the spray pressure of the cleaning liquid will be increased or the nozzle angle will be adjusted to improve the dissolution efficiency. Finally, in step S404, by developing a real-time monitoring interface and alarm function, comprehensive monitoring of the cleaning process and timely response to abnormal situations are achieved. Real-time monitoring interfaces such as touch screen interfaces or remote monitoring software can intuitively display the real-time status of the cleaning process and the changing trends of various key parameters to the operator, so that the operator can quickly understand the cleaning status and make corresponding adjustments. When an abnormal situation is detected, the PLC control system can immediately trigger the alarm function, and remind the operator to take timely measures to solve the problem through sound and light alarms, SMS notifications or remote alarms. This real-time monitoring and alarm function greatly improves the safety and stability of the cleaning process.

[0032] S50, data recording and analysis. During the entire cleaning process, record the changes and adjustment records of all key parameters to form a cleaning process database. Use data analysis tools to conduct in-depth analysis of the recorded data, evaluate the cleaning effect, identify potential problems, and continuously optimize the cleaning process based on the data analysis results to improve cleaning efficiency and product quality.

[0033] Step S50 specifically includes the following steps: S501, data recording, real-time recording of water temperature or solution temperature at each stage of the cleaning process, especially for cleaning steps that require specific temperature conditions, such as pre-rinsing, detergent dissolution and final rinse, etc., to ensure that the temperature is controlled within the preset range, accurately record the duration of each cleaning step, including pre-rinsing, detergent action time, final rinse, etc., to ensure that the time parameters meet the process requirements, regularly test and record the concentration of the detergent in the cleaning solution, to ensure that the concentration is within the effective range, to avoid poor cleaning effect due to too low concentration or increased difficulty and cost of subsequent rinsing due to too high concentration, and to record in detail the action mode of the cleaning process, such as immersion, washing, impact or turbulence, etc., as well as the parameter settings of these modes, such as stirring speed, flow rate of the spray device, etc., use automated data acquisition systems, such as sensors, PLCs, etc., to collect and store the above key parameter data in real time, design data recording tables or database templates, to ensure the standardization and traceability of data records; S502, data analysis, cleaning the collected raw data, removing outliers, duplicate values, etc., ensuring the accuracy and consistency of the data, converting the data into a format suitable for analysis, such as organizing time series data into a time series table, converting concentration data into percentages or concentration units, etc., using data analysis software to conduct in-depth analysis of the cleaned data, applying statistical analysis methods and data mining techniques to mine patterns and trends in the data, and evaluating the effectiveness of the cleaning process in removing pollutants, residues, etc. by comparing the data before and after cleaning. Based on the data analysis results, identify the key factors that affect the cleaning effect, such as temperature, time, concentration, etc., and put forward optimization suggestions. Through methods such as outlier detection and trend analysis, identify potential problems that may occur in the cleaning process, such as fluctuations in detergent concentration, insufficient cleaning time, etc., and use charts, graphs and other visual means to display data analysis results, such as temperature-time curves, concentration change line graphs, etc., to make the analysis results more intuitive and easy to understand; S503. Continuous optimization. Based on the data analysis results, the cleaning process is continuously optimized, for example, the detergent concentration is adjusted, the cleaning time or temperature is changed, etc., to improve the cleaning efficiency and product quality. For the potential problems identified, corresponding improvement measures are formulated and implemented. A data-driven feedback mechanism is established to promptly feed back the data analysis results to the production department and the technical department so that the cleaning process can be adjusted and optimized in time. The data records and analysis results of each cleaning process are organized into a knowledge base to provide reference and reference for the subsequent optimization and improvement of the cleaning process.

[0034] In the S501 data recording link, the comprehensiveness and accuracy of the data are ensured by real-time recording of key parameters at each stage of the cleaning process, such as water temperature, solution temperature, duration of cleaning steps, detergent concentration, and mode of action. This not only meets the process's requirements for precise control of parameters such as temperature, time, and concentration, but also improves the efficiency and reliability of data recording by adopting an automated data acquisition system. In addition, the designed data recording form or database template further ensures the standardization and traceability of data recording, providing a solid foundation for subsequent data analysis and optimization. Secondly, in the S502 data analysis link, the collected raw data is systematically cleaned and sorted to ensure the accuracy and consistency of the data. Through the conversion of data formats and the application of data analysis software, the patterns and trends in the data are deeply excavated, and the effectiveness of the cleaning process in removing pollutants, residues, etc. is evaluated. In this process, not only the key factors affecting the cleaning effect are identified, but also potential problems that may exist in the cleaning process are timely discovered through methods such as outlier detection and trend analysis. These analysis results are displayed through visualization methods such as charts and graphs, making the analysis results more intuitive and easy to understand, providing strong support for optimizing the cleaning process. Finally, in the S503 continuous optimization phase, the cleaning process was continuously optimized based on the data analysis results. The cleaning efficiency and product quality were improved by adjusting the detergent concentration, changing the cleaning time or temperature, and other measures. At the same time, corresponding improvement measures were formulated and implemented for the potential problems identified, and a data-driven feedback mechanism was established. This continuous optimization mechanism not only improves the adaptability and flexibility of the cleaning process, but also forms a knowledge base by continuously accumulating and organizing the data records and analysis results of the cleaning process, providing valuable reference and reference for the subsequent optimization and improvement of the cleaning process.

[0035] The method first constructs a PLC-programmed dissolution rate matching system, which integrates the functions of data acquisition, processing and feedback. It can establish a dissolution rate model based on the composition of the cleaning liquid and the material characteristics of the chip, and monitor the key parameters such as the cleaning liquid temperature, concentration and chip surface dissolution rate in real time through sensors deployed in the cleaning equipment. These real-time data provide a basis for subsequent dynamic control and optimization. In the dynamic rotation speed control stage, the PLC system receives real-time data from the sensor and analyzes the changes in the dissolution rate of each component in the cleaning liquid. Based on these data, the PLC automatically formulates an adjustment strategy for the chip rotation speed, and matches the dissolution rate of different areas by reducing or increasing the rotation speed, thereby ensuring the uniformity and efficiency of the cleaning process. This dynamic control process directly drives the cleaning equipment to adjust the rotation speed of the chip through the PLC output control signal. In order to achieve precise cleaning liquid distribution control, this method uses the chip surface real-time image acquisition system or cleaning liquid distribution sensor to obtain detailed information on the cleaning liquid distribution on the chip surface. Based on these data, the PLC programming dynamically adjusts the nozzle opening time, spray angle and spray pressure to ensure that the cleaning liquid can accurately cover the chip. The surface is cleaned regularly to avoid waste and insufficient cleaning. In addition, the cleaning liquid distribution effect is checked regularly, and parameters are fine-tuned as needed to achieve the best cleaning effect. In order to further improve the intelligence level of the cleaning process, this method establishes a real-time monitoring and intelligent feedback mechanism. By integrating high-sensitivity sensors, key parameters such as cleaning liquid temperature, concentration, dissolution rate, and wafer rotation speed are monitored in real time, and the monitoring data are transmitted to the PLC control system in real time. The system's built-in intelligent algorithm quickly processes and analyzes these data, automatically adjusts the control strategy according to the processing results, and sends adjustment instructions to the cleaning equipment. This intelligent management not only improves the stability and reliability of the cleaning process, but also reduces dependence on manual intervention. Finally, in the data recording and analysis stage, this method records the changes and adjustment records of all key parameters in the entire cleaning process, forming a cleaning process database. Using data analysis tools to conduct in-depth analysis of the recorded data, the cleaning effect can be evaluated, potential problems can be identified, and the cleaning process can be continuously optimized based on the data analysis results. This continuous optimization mechanism ensures that the cleaning process can continuously adapt to new needs and challenges, and improves cleaning efficiency and product quality.

[0036] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0037] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A semiconductor wet cleaning process optimization method based on PLC programming, characterized in that: The specific steps include the following: S10. Build a PLC-programmed dissolution rate matching system. Design and integrate a PLC-based dissolution rate matching system. The system needs to have the functions of data collection, processing and feedback. According to the composition of the cleaning liquid and the characteristics of the wafer material, a dissolution rate model is established, and an algorithm is preset to match the actual dissolution process. Sensors are deployed in the cleaning equipment to monitor key parameters such as cleaning liquid temperature, concentration and wafer surface dissolution rate in real time; S20, realizing dynamic rotation speed control, the PLC system receives real-time data from the sensor, analyzes the change of dissolution speed of each component in the cleaning solution, and automatically formulates a strategy for adjusting the rotation speed of the wafer according to the dissolution speed data. For areas with faster dissolution speed, the rotation speed is reduced; for areas with slower dissolution speed, the rotation speed is increased. The control signal is output through the PLC to drive the cleaning equipment to adjust the rotation speed of the wafer to realize dynamic control; S30, implement precise cleaning liquid distribution control, use the wafer surface real-time image acquisition system or cleaning liquid distribution sensor to obtain detailed information on the cleaning liquid distribution on the wafer surface, dynamically adjust the nozzle opening time, spray angle and spray pressure through PLC programming according to the acquired data, ensure that the cleaning liquid accurately covers the wafer surface, regularly check the cleaning liquid distribution effect, and fine-tune the parameters as needed to achieve the best cleaning effect; S40. Establish a real-time monitoring and intelligent feedback mechanism, integrate high-sensitivity sensors, monitor key parameters such as cleaning liquid temperature, concentration, dissolution rate, and wafer rotation speed in real time, and transmit the monitoring data to the PLC control system in real time. The system has a built-in intelligent algorithm to quickly process and analyze the data. According to the processing results, the intelligent algorithm automatically adjusts the control strategy and sends adjustment instructions to the cleaning equipment to realize intelligent management of the cleaning process; S50, data recording and analysis. During the entire cleaning process, record the changes and adjustment records of all key parameters to form a cleaning process database. Use data analysis tools to conduct in-depth analysis of the recorded data, evaluate the cleaning effect, identify potential problems, and continuously optimize the cleaning process based on the data analysis results to improve cleaning efficiency and product quality.

2. The semiconductor wet cleaning process optimization method based on PLC programming according to claim 1, characterized in that: The step S10 specifically includes the following steps: S101, System design and planning, clarify the functions that the system needs to achieve, including data acquisition, real-time processing, model matching, control signal output and feedback mechanism, etc., design the overall architecture of the system, including PLC, sensors, actuators, etc., control algorithms, data processing programs, etc. and interfaces for monitoring and debugging; S102, PLC selection and programming, select the appropriate PLC model according to system requirements, ensure that it has sufficient input and output points, processing speed and scalability, use PLC-specific programming languages ​​such as ladder diagrams, function block diagrams or structured text methods to write control programs, and the program must include data acquisition modules, data processing modules, model matching modules and control signal output modules; S103, establishing a dissolution rate model, collecting experimental data on the composition of the cleaning solution, the properties of the wafer material and their interactions, and establishing a dissolution rate model based on the experimental data using regression analysis. The model should be able to accurately predict the dissolution rate of the cleaning solution on the wafer surface under different conditions. The model is embedded in the PLC control program, and an algorithm is preset to match the actual dissolution process in real time. The algorithm must be able to receive real-time data, calculate the current dissolution rate, and output a corresponding control signal; S104, sensor deployment and integration, select appropriate sensors according to monitoring requirements, such as temperature sensors, concentration sensors and dissolution rate sensors, deploy sensors at key locations of the cleaning equipment to ensure accurate monitoring of the temperature and concentration of the cleaning solution and the dissolution rate of the wafer surface, connect the sensor signal to the input port of the PLC, and ensure that the PLC can read the sensor data in real time; S105, system integration and testing, complete the physical connection of PLC, sensors, actuators and other hardware devices, test the PLC program in a simulated or actual environment to ensure accurate data collection, timely processing, effective model matching and correct control signal output, optimize and adjust the system according to the test results to ensure stable operation of the system and meet the design requirements.

3. The semiconductor wet cleaning process optimization method based on PLC programming according to claim 1, characterized in that: The temperature sensor in step S104 is mainly installed in the cleaning liquid inlet, cleaning liquid circulation pipeline and near the chip of the cleaning equipment, and the concentration sensor is mainly installed in the cleaning liquid storage tank, cleaning liquid outlet and mixing area. In order to indirectly monitor the dissolution rate, the dissolution rate sensor that can reflect the changes in the dissolution process can be deployed in the cleaning liquid on the surface of the chip or near the chip, which is an optical sensor and a surface tension sensor.

4. The semiconductor wet cleaning process optimization method based on PLC programming according to claim 2 is characterized in that: The step S20 specifically includes the following steps: S201 Data reception and analysis: The PLC system receives data from sensors deployed on the cleaning equipment in real time through predefined communication protocols and interfaces. These data include but are not limited to the temperature and concentration of the cleaning liquid and parameters related to the dissolution rate that may be obtained indirectly. After receiving these raw data, the data processing module inside the PLC performs necessary analysis and preprocessing, including noise removal, data filtering, unit conversion and other operations to ensure the accuracy and reliability of the data for subsequent processing; S202, dissolution rate evaluation, using a pre-established dissolution rate model or an empirical formula based on real-time data to calculate the parsed data and evaluate the dissolution rate of each component in the current cleaning solution. This step may require a comprehensive calculation based on parameters such as temperature and concentration. According to the structure of the cleaning equipment and the layout of the wafer, the cleaning area is divided into several sub-areas, each of which may have different dissolution rate characteristics; S203, formulation of adjustment strategy. Based on the evaluation result of the dissolution speed, the adjustment strategy logic built into the PLC system will formulate an adjustment strategy for the wafer rotation speed for each sub-area according to a preset rule or algorithm. For example, for an area with a faster dissolution speed, the strategy may require reducing the rotation speed to reduce excessive dissolution; while for an area with a slower dissolution speed, the strategy may require increasing the rotation speed to promote dissolution. When there are multiple sub-areas that need to be adjusted, the system may also need to determine the order of adjustment according to a preset priority rule. S204, control signal output. According to the formulated adjustment strategy, the PLC system generates corresponding control signals. These signals are usually electrical signals or digital signals, which are used to drive the motor or other actuators in the cleaning equipment. The control signal is sent to the control system of the cleaning equipment through the output port of the PLC to drive the motor to adjust the rotation speed of the wafer. This process may require certain amplification, conversion or isolation processing to ensure that the signal can accurately drive the actuator; S205, feedback and adjustment. During the adjustment process, the PLC system continues to receive real-time data from the sensor and monitors the changes in the wafer rotation speed and its impact on the dissolution rate. Based on the feedback data, the PLC system can adjust the control strategy and control signal in real time to achieve more precise dynamic control.

5. The semiconductor wet cleaning process optimization method based on PLC programming according to claim 4 is characterized in that: The step S202 selects or establishes a suitable dissolution rate model according to the specific components of the cleaning solution, such as acid, alkali, solvent, etc., and the properties of the wafer material, such as metal, semiconductor, etc. This model may be a theoretical model based on physical and chemical principles, such as the Arrhenius equation or its modified form, or it may be an empirical model obtained by fitting experimental data. The data obtained from the sensor in real time include key parameters such as temperature T, concentration C, pressure P, etc. These data need to be preprocessed before entering the model, including noise elimination, unit conversion, data filtering, etc., to ensure the accuracy and reliability of the data. The specific formula is: [ v = A \cdot C^n \cdot \exp\left(-\frac{E_a}{RT}\right) \] Where v is the dissolution rate, C is the concentration, and n represents the degree of influence of concentration on the dissolution rate. If there is no ready-made theoretical model available, an empirical formula can also be obtained based on experimental data fitting, where the formula is: [ v = a \cdot T^b \cdot C^c \cdot \exp(d \cdot P) \] Among them, a, b, c, d are coefficients obtained by fitting experimental data, T is temperature, C is concentration, and P is pressure. According to the structure of the cleaning equipment and the layout of the wafer, the cleaning area is divided into several sub-areas. Each sub-area may have different dissolution rate characteristics due to factors such as temperature distribution, concentration gradient, stirring effect, etc. In each sub-area, the dissolution rate can be calculated using the above formula, and adjustments can be made considering the specific conditions in the area. After calculating the dissolution rate of each sub-area, other factors such as cleaning efficiency and wafer protection need to be considered comprehensively to make necessary adjustments. Since the conditions during the cleaning process may change at any time, such as temperature fluctuations and concentration changes, the dissolution rate evaluation also needs to be carried out in real time. The PLC system should be able to continuously receive sensor data and re-evaluate and adjust the dissolution rate based on the latest data. At the same time, the system should also have a feedback mechanism so that it can issue an alarm or take other emergency measures in time when an abnormal situation is found.

6. The semiconductor wet cleaning process optimization method based on PLC programming according to claim 1, characterized in that: The step S30 specifically includes the following steps: S301, collecting cleaning liquid distribution data, using a high-definition camera or a dedicated image acquisition system installed on the cleaning equipment to take real-time photos of the wafer surface. These systems can capture subtle changes on the wafer surface during the cleaning process, including the distribution, flow, and possible residues of the cleaning liquid. In addition to image acquisition, special cleaning liquid distribution sensors can also be used to directly measure the distribution of the cleaning liquid on the wafer surface. These sensors may be based on optical, electrical, or chemical principles and can provide real-time feedback on the coverage and concentration information of the cleaning liquid. S302, data analysis and evaluation, the data obtained by the image acquisition system are analyzed by image processing software, which includes steps such as noise removal, contrast enhancement, edge detection, etc., so as to more clearly identify the distribution area and form of the cleaning liquid. The data of the cleaning liquid distribution sensor are directly analyzed and converted to obtain quantitative indicators of the cleaning liquid distribution, such as coverage, uniformity, etc. According to the preset evaluation standards, such as the cleaning liquid coverage must reach more than 95%, and the distribution is uniform without obvious blank areas, the distribution effect of the cleaning liquid is evaluated; S303, dynamically adjusting the nozzle parameters. Based on the results of data analysis, the nozzle parameters are dynamically adjusted through PLC programming. These parameters include the nozzle opening time to control the spray volume of the cleaning liquid, the spray angle to affect the distribution of the cleaning liquid on the wafer surface, and the spray pressure to determine the impact force and penetration ability of the cleaning liquid. The high-precision control capability of the PLC is used to achieve fine adjustments to the nozzle parameters. This requires the PLC system to have fast response capabilities and high stability to ensure that the nozzle parameters can be adjusted in real time and accurately during the cleaning process. S304, regular inspection and parameter fine-tuning. During the cleaning process, the cleaning liquid distribution effect is regularly inspected. This can be done by using the image acquisition system or sensor to collect data again and comparing and analyzing with previous data. According to the inspection results, if it is found that the cleaning liquid distribution effect is not in the optimal state, the nozzle parameters need to be fine-tuned, including adjusting the length of the opening time, the size of the spray angle, and the level of the spray pressure, so as to ensure that the cleaning liquid can accurately cover the wafer surface. S305. Continuous optimization and improvement: Summarize and analyze the data from previous cleaning processes to identify the key factors and rules that affect the distribution of the cleaning fluid. Based on these analysis results, continuously optimize and improve the cleaning process and nozzle parameters, establish an effective feedback mechanism, and promptly deliver the cleaning results and user feedback to relevant personnel.

7. The semiconductor wet cleaning process optimization method based on PLC programming according to claim 6, characterized in that: The cleaning liquid distribution sensor in step S302 usually directly outputs electrical signals or digital signals. These signals need to be analyzed and converted into quantitative indicators of the cleaning liquid distribution. According to the type and working principle of the sensor, a corresponding data analysis algorithm is designed to convert the original signal into useful information such as cleaning liquid coverage and concentration distribution. The sensor data is calibrated to eliminate the influence of system errors and external interference on the measurement results. The accuracy and reliability of the sensor data are ensured by comparing and verifying with data of standard samples or known cleaning effects. Similar to the data analysis of the image acquisition system, the data obtained by the cleaning liquid distribution sensor is quantitatively evaluated to calculate indicators such as coverage and uniformity.

8. The semiconductor wet cleaning process optimization method based on PLC programming according to claim 1, characterized in that: The step S40 specifically includes the following steps: S401. Integration of highly sensitive sensors, such as thermistors, thermocouples or infrared temperature sensors installed at key positions of the cleaning liquid circulation system, to monitor the temperature change of the cleaning liquid in real time, conductivity sensors, refractometers or sensors based on spectral analysis to measure the concentration of the cleaning liquid, and indirectly estimate it by monitoring the signals generated when the cleaning liquid interacts with the wafer surface, such as electrochemical signals, optical signals, etc., and installing photoelectric encoders or Hall effect sensors on the wafer rotating device to monitor the rotation speed of the wafer in real time; S402, data transmission and PLC control system integration, each sensor is connected to the data acquisition module through a dedicated interface or communication protocol such as RS-485, Modbus, etc., and the real-time monitored data is transmitted to the PLC control system. The data acquisition module should have the characteristics of multi-channel, high precision and high reliability, and establish a stable and reliable data transmission network to ensure that the monitoring data can be transmitted to the PLC control system in real time and accurately, which includes the selection and configuration of wired and wireless networks. The PLC control system, as the core of the real-time monitoring and intelligent feedback mechanism, is responsible for receiving and processing the monitoring data from each sensor. The system has a built-in high-performance processor and large-capacity memory to ensure rapid processing and analysis of data; S403, intelligent algorithm and control strategy: Integrate fuzzy control algorithm in PLC control system, quickly process and analyze monitoring data, and automatically adjust the control strategy of cleaning equipment according to the processing result of intelligent algorithm. PLC control system sends specific adjustment instructions to cleaning equipment according to the adjusted control strategy. These instructions may include changing the circulation speed of cleaning liquid, adjusting the working state of nozzle, adjusting the rotation speed of wafer, etc. S404. Real-time monitoring and alarm function. Develop a real-time monitoring interface such as a touch screen interface or remote monitoring software to show the operator the real-time status of the cleaning process and the changing trends of key parameters. The interface should be intuitive and easy to understand, so that the operator can quickly understand the cleaning status. When an abnormal situation is detected, the PLC control system should be able to immediately trigger the alarm function. The alarm method may include sound and light alarm, SMS notification or remote alarm, so that the operator can take timely measures to solve the problem.

9. The semiconductor wet cleaning process optimization method based on PLC programming according to claim 1, characterized in that: The step S50 specifically includes the following steps: S501, data recording, real-time recording of water temperature or solution temperature at each stage of the cleaning process, especially for cleaning steps that require specific temperature conditions, such as pre-rinsing, detergent dissolution and final rinse, etc., to ensure that the temperature is controlled within the preset range, accurately record the duration of each cleaning step, including pre-rinsing, detergent action time, final rinse, etc., to ensure that the time parameters meet the process requirements, regularly test and record the concentration of the detergent in the cleaning solution, to ensure that the concentration is within the effective range, to avoid poor cleaning effect due to too low concentration or increased difficulty and cost of subsequent rinsing due to too high concentration, and to record in detail the action mode of the cleaning process, such as immersion, washing, impact or turbulence, etc., as well as the parameter settings of these modes, such as stirring speed, flow rate of the spray device, etc., use automated data acquisition systems, such as sensors, PLCs, etc., to collect and store the above key parameter data in real time, design data recording tables or database templates, to ensure the standardization and traceability of data records; S502, data analysis, cleaning the collected raw data, removing outliers, duplicate values, etc., ensuring the accuracy and consistency of the data, converting the data into a format suitable for analysis, such as organizing time series data into a time series table, converting concentration data into percentages or concentration units, etc., using data analysis software to conduct in-depth analysis of the cleaned data, applying statistical analysis methods and data mining techniques to mine patterns and trends in the data, and evaluating the effectiveness of the cleaning process in removing pollutants, residues, etc. by comparing the data before and after cleaning. Based on the data analysis results, identify the key factors that affect the cleaning effect, such as temperature, time, concentration, etc., and put forward optimization suggestions. Through methods such as outlier detection and trend analysis, identify potential problems that may occur in the cleaning process, such as fluctuations in detergent concentration, insufficient cleaning time, etc., and use charts, graphs and other visual means to display data analysis results, such as temperature-time curves, concentration change line graphs, etc., to make the analysis results more intuitive and easy to understand; S503. Continuous optimization. Based on the data analysis results, the cleaning process is continuously optimized, for example, the detergent concentration is adjusted, the cleaning time or temperature is changed, etc., to improve the cleaning efficiency and product quality. For the potential problems identified, corresponding improvement measures are formulated and implemented. A data-driven feedback mechanism is established to promptly feed back the data analysis results to the production department and the technical department so that the cleaning process can be adjusted and optimized in time. The data records and analysis results of each cleaning process are organized into a knowledge base to provide reference and reference for the subsequent optimization and improvement of the cleaning process.

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