Real-time monitoring and optimization control system for photoresist coating process

Abstract

The application discloses a photoresist coating process real-time monitoring and optimization control system and method, aiming at solving the problems of real-time monitoring absence, weak dynamic interference response and the like in the prior art. The system comprises a coating unit, an integrated detection module, a data processing and optimization unit, a real-time control unit and the like, multi-dimensional parameters are collected in real time through the integrated detection module, the data processing and optimization unit evaluates the quality and calculates the adjustment amount based on a machine learning model, and the real-time control unit dynamically adjusts process parameters, so that closed-loop optimization is realized. The method realizes coating control through the circulation of "collection-evaluation-calculation-adjustment". The application improves coating uniformity and stability, reduces the defective product rate, realizes data tracing, and is suitable for semiconductor lithography processes.

Description

Technical Field

[0001] This invention relates to the field of semiconductor manufacturing technology, and more specifically to a real-time monitoring and optimization control system for the photoresist coating process. Background Technology

[0002] In semiconductor chip manufacturing, photolithography is one of the key processes determining chip resolution and yield. The quality of photoresist coating directly affects the effectiveness of subsequent exposure and development processes, ultimately determining the chip's pattern accuracy and performance stability. Current photoresist coating technologies mostly employ an open-loop control method with preset fixed process parameters. This means that after determining the process parameters based on experience or offline experiments, these parameters are kept constant during the coating process. Furthermore, coating quality inspection is mostly offline, involving sampling and testing of the substrate after coating is completed using specialized equipment. This approach has the following significant drawbacks: Poor real-time performance: Offline detection cannot detect quality problems in time during the coating process. Once problems such as uneven thickness or film defects occur, the coated substrate will become a defective product, resulting in material waste and reduced production efficiency. Weak adaptability: It cannot cope with dynamic disturbances during the coating process, such as fluctuations in ambient temperature and humidity, changes in photoresist viscosity over time, and drops in the liquid level of the adhesive tank. These disturbances can cause the actual coating quality to deviate from the target range, and the open-loop control with fixed parameters cannot be adjusted in time. Parameter optimization lag: The adjustment of process parameters depends on the feedback of offline detection results, and multiple experiments are required to determine the optimal parameters, making it impossible to achieve online optimization of the coating process; Insufficient glue supply stability: Traditional glue supply systems have difficulty accurately maintaining the viscosity and level of the glue, and fluctuations in the properties of the glue will directly affect the coating effect. Summary of the Invention

[0003] To overcome the shortcomings of the prior art, the purpose of this invention is to provide a real-time monitoring and optimization control system for the photoresist coating process.

[0004] To achieve the aforementioned objective, the technical solution of the present invention is implemented as follows: (I) Real-time monitoring and optimization control system for photoresist coating process This system, through modular design, realizes parameter acquisition, data processing, real-time control, and closed-loop optimization of the coating process, specifically including the following core units and subsystems: The coating unit is the core actuator for performing the photoresist coating process, mainly consisting of an adjustable-speed spin coater. This spin coater employs a high-precision servo drive system, supporting dynamic adjustment of the speed curve. It can achieve precise speed control during acceleration, steady-state, and deceleration phases, with a speed adjustment range of 500-10000 r / min and a speed control accuracy of ±1 r / min. The stage of the spin coater uses a vacuum adsorption structure to ensure the substrate remains stable during high-speed rotation, avoiding uneven coating caused by substrate misalignment.

[0005] The integrated detection module is used to collect various process parameters and status data during the coating process in real time, providing data support for subsequent data processing and optimized control. This module achieves synchronous acquisition of multi-dimensional data through the integrated design of multiple high-precision sensors, with an acquisition frequency of no less than 100Hz, ensuring the real-time performance and integrity of the data. Specifically, it includes the following detection components: Non-contact infrared thermometer: Installed above the rotary coater, it uses infrared thermal imaging technology to measure the temperature distribution on the substrate surface in real time. The measurement range is 20-80℃, and the measurement accuracy is ±0.1℃. The non-contact design avoids interference with the coating process while enabling comprehensive detection of the substrate surface temperature. Online viscometer: Integrated into the photoresist supply pipeline, it adopts the vibration viscosity measurement principle to detect the dynamic viscosity of the photoresist in real time. The measurement range is 1-100 mPa·s, and the measurement accuracy is ±0.5%. The detection data of the online viscometer can directly reflect the flow characteristics of the photoresist, providing a basis for adjusting the photoresist supply parameters; Liquid level sensor: Installed inside the photoresist tank, it uses capacitive liquid level measurement technology to monitor the real-time liquid level of the photoresist in the tank. The measurement range is 0-500mm, and the measurement accuracy is ±1mm. Liquid level monitoring can promptly detect insufficient photoresist, avoiding interruptions in photoresist supply or unstable supply pressure due to low liquid level. Optical Interferometer / Spectral Reflectometer: Installed on the side of the spin coater, it uses the principles of optical interference or spectral reflection to measure the coating thickness and uniformity of photoresist on the substrate surface in real time. The measurement range is 100-5000 nm, with a measurement accuracy of ±1 nm. It can simultaneously measure multiple detection points on the substrate surface and generate thickness distribution maps. Ambient temperature and humidity sensor: Installed inside the coating process chamber, it collects real-time temperature and humidity data within the chamber. The temperature measurement range is 20-30℃ with an accuracy of ±0.1℃; the humidity measurement range is 30%-60%RH with an accuracy of ±1%RH. Stable ambient temperature and humidity are crucial for ensuring a stable evaporation rate of the photoresist solvent, and the data provides a basis for adjusting environmental parameters. Coating machine speed sensor: Integrated into the drive system of the rotary coating machine, it collects the real-time speed of the coating machine in real time. The measurement accuracy matches the speed control accuracy of the coating machine, providing feedback data for closed-loop adjustment of the speed parameters. The data processing and optimization unit is the "brain" of the system. Connected to the integrated detection module via signal lines, it receives various types of data collected in real time and, based on a preset process model and optimization algorithm, performs evaluation of coating quality, judgment of process deviations, and calculation of parameter adjustments. Its specific functions are as follows: Data preprocessing: The received real-time data is filtered, denoised, and standardized to remove outliers and ensure data reliability. For example, Kalman filtering is used to process thickness measurement data to reduce the impact of measurement noise on quality assessment. Process Model Construction and Application: The preset process model is a machine learning-based predictive model, trained using a large amount of historical process data and corresponding coating quality results. Historical data includes coating thickness, uniformity, and other quality indicators corresponding to different process parameters (rotation speed, adhesive flow rate, etc.), environmental parameters (temperature and humidity), and adhesive property parameters (viscosity). The model is constructed using random forest or neural network algorithms, establishing a non-linear mapping relationship between process parameters, environmental parameters, and coating quality indicators. It can accurately predict the coating quality at current and future moments based on real-time collected parameters. Quality assessment and deviation judgment: Based on real-time data and the output of the prediction model, the current coating quality status is assessed. Specifically, this includes comparing the real-time thickness distribution with the target thickness distribution, calculating thickness non-uniformity indicators (such as the difference between the maximum and minimum thickness values, thickness standard deviation, etc.), and determining that intervention is needed when the indicator exceeds a preset threshold, triggering optimization calculations. Optimization algorithm calculation: Parameter optimization is performed using either Model Predictive Control (MMC) or Adaptive PID Control (PID) algorithms. MMC algorithms input real-time data into a predictive model to predict the coating quality trend at multiple future time points, aiming to maximize coating thickness uniformity while considering process parameter adjustment constraints (such as rotation speed adjustment range and adhesive flow rate limits), and continuously calculate the optimal process parameter adjustment sequence. Adaptive PID control algorithms can automatically adjust PID parameters based on the dynamic characteristics of the coating process, improving the adaptability and stability of the control.

[0006] The real-time control unit is connected to the data processing and optimization unit and the coating unit via signal lines. It is responsible for converting the process parameter adjustments calculated by the data processing and optimization unit into specific control signals, dynamically adjusting relevant process parameters during the coating process to achieve online closed-loop control. The specific control functions are as follows: Rotary coating machine speed adjustment: During a single coating rotation, the acceleration, steady-state speed value, or deceleration curve of the rotation speed are dynamically adjusted based on real-time thickness measurement feedback. For example, when the substrate edge is detected to be too thick, the speed reduction rate during the deceleration phase is increased to promote the leveling of the adhesive at the edge and compensate for uneven thickness caused by adhesive leveling or solvent evaporation. Ambient temperature and humidity adjustment: Based on real-time data from ambient temperature and humidity sensors, control the temperature and humidity regulation devices (such as heaters, coolers, humidifiers, and dehumidifiers) in the process chamber to maintain the ambient temperature and humidity within the target range, ensuring a stable evaporation rate of the photoresist solvent. Adhesive supply parameter adjustment: Based on real-time data of adhesive tank level and viscosity, adjust the adhesive supply rate or trigger a replenishment operation. By controlling the metering pump and valves in the closed-loop adhesive supply subsystem, precisely regulate the adhesive flow rate and maintain the stability of the adhesive properties.

[0007] Auxiliary Units and Subsystems User Interface and Data Storage Unit: Provides a visual user interface to display real-time monitoring data (such as real-time values ​​of various parameters and thickness distribution maps), alarm information (such as excessive thickness non-uniformity and low glue tank level), and optimization suggestions (such as suggested speed adjustment and glue supply flow rate adjustment). Simultaneously, it stores a complete process data chain, including real-time collected parameter data, optimization adjustment records, and coating quality results. The data storage format supports common database formats (such as SQLite and MySQL), enabling data traceability and process analysis, facilitating subsequent process optimization and quality issue troubleshooting. The closed-loop adhesive supply subsystem includes precisely controllable components such as a metering pump, solenoid valves, adhesive tank, and piping. The metering pump is a servo-driven plunger pump with a flow rate adjustable from 0.1 to 10 mL / min and a control accuracy of ±0.01 mL / min. The solenoid valve controls the on / off state of the adhesive supply pipeline, with a response time of no more than 10 ms. The real-time control unit adjusts the adhesive supply rate of the metering pump based on real-time data of the adhesive tank level and viscosity, or triggers a replenishment operation (when the liquid level is below a preset lower limit, the solenoid valve is opened to replenish photoresist into the adhesive tank) to maintain the stability of the adhesive properties.

[0008] (II) Real-time monitoring and optimization control methods for photoresist coating process Based on the above system, the present invention also provides a method for real-time monitoring and optimization control of the photoresist coating process, realizing online closed-loop optimization control of the coating process, specifically including the following steps: S1: Start the coating process and collect parameter data in real time. The photoresist coating process is initiated, and simultaneously, the integrated detection module starts working to collect various process parameters and status data in real time during the coating process, including substrate surface temperature, ambient temperature and humidity, photoresist viscosity, resist tank level, real-time rotation speed of the coating machine, and coating thickness distribution. The collected data is transmitted in real time to the data processing and optimization unit via signal lines and simultaneously stored in the data storage unit.

[0009] S2: Assess the coating quality and determine if intervention is needed. The data processing and optimization unit preprocesses the received real-time data (filtering, noise reduction, and standardization), and then evaluates the current coating quality status based on a preset machine learning prediction model. The evaluation includes: comparing the real-time thickness distribution with the target thickness distribution, calculating thickness non-uniformity indices (such as thickness standard deviation and maximum / minimum thickness difference); and predicting the quality change trend of the subsequent coating process under the current parameters, combined with parameters such as photoresist viscosity and ambient temperature and humidity. If the thickness non-uniformity index exceeds a preset threshold, or if the predicted subsequent quality will deviate from the target range, intervention is deemed necessary; otherwise, no intervention is deemed required, and the process returns to step S1 to continue real-time data acquisition.

[0010] S3: If intervention is required, calculate the optimal process parameter adjustment amount. When step S2 determines that intervention is needed, the data processing and optimization unit initiates an optimization algorithm (model predictive control algorithm or adaptive PID control algorithm). Combining real-time data, the output of the predictive model, and process parameter adjustment constraints, it calculates the optimal process parameter adjustment amount. The adjustment amount includes, but is not limited to: the rotational speed adjustment value of the rotary coater (acceleration, steady-state speed, deceleration curve), the ambient temperature and humidity adjustment value, the adhesive supply flow rate adjustment value, and the adhesive replenishment trigger signal.

[0011] S4: Dynamically adjust process parameters to achieve real-time control. The real-time control unit receives process parameter adjustments from the data processing and optimization unit, converts them into corresponding control signals, and dynamically adjusts the process parameters of the coating unit before the coating process is completed. For example, it controls the servo drive system of the rotary coater based on the rotation speed adjustment value to adjust the rotation speed curve; it controls the temperature and humidity control device based on the ambient temperature and humidity adjustment value to maintain stable environmental parameters; and it controls the speed of the metering pump based on the glue supply flow rate adjustment value to adjust the glue supply rate or trigger a glue replenishment operation. The adjusted parameter status is fed back to the data processing and optimization unit in real time through the integrated detection module, forming a closed-loop control.

[0012] S5: Repeat the cycle until the coating process is complete. Repeat steps S1-S4 to continuously monitor, assess, optimize, and dynamically adjust the coating process in real time until the coating process is complete. After coating is completed, the system automatically saves the complete process data chain, including parameter acquisition data, optimization and adjustment records, and final coating quality results. At the same time, the system displays process completion information and the final quality assessment report on the user interface.

[0013] (iii) Computer-readable storage media This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned real-time monitoring and optimization control method for the photoresist coating process. The computer-readable storage medium can be a USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, optical disk, or any other medium capable of storing program code. When the program is executed by the processor, it can drive the data processing and optimization unit, real-time control unit, and other components in the system to perform data processing, optimization calculations, and real-time control functions, thereby achieving online closed-loop optimization control of the coating process.

[0014] The beneficial effects of this invention are reflected in: High real-time performance enables online closed-loop control: The integrated detection module collects multi-dimensional parameters in real time, the data processing and optimization unit evaluates the quality status in real time, and the real-time control unit dynamically adjusts the process parameters. Intervention can be carried out without waiting for coating to be completed, avoiding the generation of defective products and improving production yield. Stable coating quality and high uniformity: The machine learning-based prediction model can accurately establish the mapping relationship between parameters and quality, and the optimization algorithm can calculate the optimal adjustment parameters to dynamically compensate for thickness unevenness caused by factors such as adhesive leveling, solvent evaporation, and environmental fluctuations, thus significantly improving the uniformity of coating thickness. It is highly adaptable and has outstanding anti-interference capabilities: It can respond to dynamic interferences such as fluctuations in ambient temperature and humidity, changes in photoresist viscosity, and drop in the liquid level of the adhesive tank in real time. Through closed-loop control, it maintains the stability of process parameters and adhesive properties, ensuring the consistency of coating quality. Data traceability facilitates process optimization: The complete process data chain storage function supports data traceability and process analysis, and can further optimize process models and parameters based on historical data to continuously improve production efficiency and quality; High degree of automation, reducing human intervention: The entire process is automated for monitoring, evaluation, optimization and adjustment, reducing manual operation and judgment, reducing human error, and reducing the workload of operators. Attached Figure Description

[0015] In the attached diagram: Figure 1 This is a structural block diagram of the real-time monitoring and optimization control system for the photoresist coating process of the present invention; Figure 2 This is a flowchart illustrating the real-time monitoring and optimization control method for the photoresist coating process of the present invention. Detailed Implementation

[0016] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are only a part of the embodiments of the invention, and not all of them. Unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0017] Please refer to the instruction manual appendix. Figures 1-2 This invention provides a real-time monitoring and optimization control system for the photoresist coating process. I. System Hardware Configuration In this embodiment, the hardware configuration of the real-time monitoring and optimization control system for the photoresist coating process is as follows: Coating unit: The KW-4A adjustable speed rotary coater is used, with a speed range of 500-8000 r / min, a speed control accuracy of ±1 r / min, a stage diameter of 200 mm, and supports vacuum adsorption. Integrated detection module: Non-contact infrared thermometer: model Fluke Ti400+, measurement range 20-80℃, accuracy ±0.1℃, sampling frequency 100Hz; Online viscometer: Model SV-10, measuring range 1-100 mPa·s, accuracy ±0.5%, integrated into the adhesive supply line; Liquid level sensor: Model CY-30, measuring range 0-500mm, accuracy ±1mm, installed at the bottom of the glue tank; Optical interferometer: Model Filmetrics F20, measurement range 100-5000nm, accuracy ±1nm, sampling frequency 100Hz, with 4 detection points (substrate center and 20, 40, and 60mm from the center). Ambient temperature and humidity sensor: Model SHT30, temperature accuracy ±0.1℃, humidity accuracy ±1%RH, installed in the process chamber; Speed ​​sensor: integrated into the drive motor of the rotary coating machine, synchronized with the motor speed feedback signal, with an accuracy of ±1 r / min; Data processing and optimization unit: It adopts an industrial control computer, configured with an Intel Core i7-12700 processor, 32GB of memory, 1TB SSD hard drive, and Windows 10 IoT Enterprise operating system. It runs Python-based data analysis and optimization software and integrates random forest prediction model and model predictive control algorithm. Real-time control unit: It adopts a PLC of model S7-1500, equipped with analog input / output modules (AI 8×13bit, AO 4×13bit) and digital input / output modules (DI 16×24VDC, DO 16×24VDC). The PLC communicates with the industrial control computer via Profinet bus with a communication rate of 100Mbps. Closed-loop glue supply subsystem: metering pump model is PPS-C100, glue supply flow range is 0.1-10mL / min, accuracy is ±0.01mL / min; solenoid valve model is VZ300, response time is ≤10ms; glue tank volume is 5L, equipped with a stirring device; User interface and data storage unit: It adopts a 15-inch industrial touch screen to display real-time data, thickness distribution map, alarm information, etc.; data storage adopts a MySQL database and is stored on the SSD hard drive of the industrial control computer, supporting data query and export.

[0018] II. System Software Configuration The system software includes a data acquisition module, a data preprocessing module, a process model module, an optimization algorithm module, a real-time control module, a user interface module, and a data storage module. The functions of each module are as follows: Data acquisition module: Communicates with various sensors of the integrated detection module via OPC UA protocol, acquires data in real time and transmits it to the data preprocessing module, with an acquisition frequency of 100Hz; Data preprocessing module: Kalman filtering algorithm is used to reduce noise in thickness measurement data, and Z-score normalization method is used to normalize various parameter data and remove outlier data points; Process model module: A prediction model is built based on the random forest algorithm. The training data includes 1,000 sets of coating quality data corresponding to different process parameters and environmental parameters. The model prediction accuracy reaches more than 98%. Optimization algorithm module: The model predictive control algorithm is adopted, with a prediction step size of 5 steps, a control period of 100ms, and the objective function is to minimize the thickness non-uniformity (thickness standard deviation ≤ 5nm). Real-time control module: Converts the adjustment amount calculated by the optimization algorithm into a control signal that can be recognized by the PLC, and transmits it to the PLC through the Profinet bus; User interface module: Developed using Qt, it provides a visual interface and supports functions such as real-time data display, parameter setting, alarm prompts, and data query. Data storage module: Stores collected real-time data, optimization and adjustment records, quality assessment results, etc., in a MySQL database, with a data retention period of 1 year.

[0019] III. Implementation Process of Control Methods Based on the above hardware and software configuration, the specific implementation process of the real-time monitoring and optimization control method for the photoresist coating process is as follows: Preliminary preparation: Place the silicon substrate to be coated on the stage of the spin coater and start the vacuum adsorption device to fix the substrate; set the target coating thickness to 1000nm, the thickness non-uniformity threshold to 5nm (i.e., thickness standard deviation ≤ 5nm), the target ambient temperature to 25℃, the target ambient humidity to 45%RH, and the target photoresist viscosity to 20mPa·s in the user interface. S1: Start the coating process and collect data in real time: Start the coating process and the integrated detection module begins to collect data in real time, including substrate surface temperature (initial 24.8℃), ambient temperature (24.9℃), ambient humidity (44.8%RH), photoresist viscosity (20.2mPa·s), resist tank level (400mm), coating machine speed (initial acceleration phase, acceleration 500r / min², target steady-state speed 3000r / min), and coating thickness distribution (real-time collection of thickness data at 4 detection points). S2: Quality Assessment and Intervention Judgment: The data processing and optimization unit preprocesses the collected data and evaluates the current coating quality through a predictive model. After 10 seconds of coating, the thickness at the substrate edge (60mm from the center) is detected to be 1012nm, the center thickness is 998nm, and the standard deviation of the thickness is 6.2nm, exceeding the preset threshold of 5nm, indicating that intervention is required. S3: Calculate the optimal adjustment amount: The optimization algorithm module starts the model predictive control algorithm, and combines real-time data and the predictive model to calculate the optimal speed adjustment amount: the steady-state speed is adjusted from 3000 r / min to 3050 r / min, and the acceleration during the deceleration phase is adjusted from -500 r / min² to -600 r / min²; at the same time, the ambient temperature does not need to be adjusted, and the photoresist viscosity and liquid level are within the target range, so there is no need to adjust the photoresist supply parameters; S4: Dynamic adjustment of process parameters: The real-time control unit converts the speed adjustment amount into a control signal and transmits it to the PLC. The PLC controls the servo drive system of the rotary coating machine to adjust the speed curve. After adjustment, it is detected in real time that the edge thickness gradually decreases and the center thickness is basically stable. S5: Repeat the cycle until the process is complete: Continue to repeat steps S1-S4. After 20 seconds of coating, the standard deviation of the thickness is 4.8 nm, which is below the threshold and no intervention is required. During the subsequent process, the ambient humidity fluctuates slightly (45.5%RH). The system fine-tunes the humidity by using a humidifier to maintain the humidity at around 45%RH. After 60 seconds of coating, the process is complete. The system automatically saves the complete process data chain. The user interface displays that the final coating thickness is 1000±4 nm and the thickness non-uniformity index is 4.2 nm, which meets the target requirements.

[0020] IV. Implementation Results Verification The system and method of this embodiment were used to conduct photoresist coating experiments, and the results were compared with those of the traditional open-loop control method. The experimental results are shown in the table below: Control methods Target thickness (nm) Actual average thickness (nm) Thickness standard deviation (nm) Defect rate (%) Traditional open-loop control 1000 1002 8.5 12.3 This invention provides closed-loop control. 1000 1000 4.2 2.1 The experimental results show that the system and method of the present invention can significantly improve the uniformity of photoresist coating thickness and reduce the defect rate, which has obvious advantages over the traditional open-loop control method.

Claims

1. A real-time monitoring and optimization control system for the photoresist coating process, characterized in that, include: A coating unit for performing photoresist coating processes, including at least an adjustable speed spin coater; An integrated detection module is used to collect process parameters and status data in real time during the coating process. The data includes at least the substrate surface temperature, ambient temperature and humidity, photoresist viscosity, glue tank level, real-time rotation speed of the coating machine, and coating thickness distribution. The data processing and optimization unit is connected to the integrated detection module to receive the real-time data, analyze the current coating quality based on the preset process model and optimization algorithm, determine whether it deviates from the process window, and calculate the process parameter adjustment amount to bring the coating quality back to the target range. A real-time control unit, whose signal is connected to the data processing and optimization unit and the coating unit, is used to dynamically adjust one or more coating process parameters during the coating process according to the adjustment amount of the process parameters. The parameters include at least the rotation speed curve of the rotary coater, the ambient temperature and humidity, and the adhesive supply flow rate.

2. The real-time monitoring and optimization control system for the photoresist coating process according to claim 1, characterized in that, The integrated detection module includes: A non-contact infrared thermometer for measuring the surface temperature of a substrate; An online viscometer and a liquid level sensor are integrated into the photoresist supply line and the glue tank, respectively. Optical interferometer or spectral reflectometer is used to measure coating thickness and its uniformity in real time.

3. The real-time monitoring and optimization control system for the photoresist coating process according to claim 1, characterized in that, The preset process model in the data processing and optimization unit is a machine learning-based prediction model. This model is trained using historical process data and corresponding coating quality results to establish a nonlinear mapping relationship between process parameters, environmental parameters, and coating thickness and uniformity.

4. The real-time monitoring and optimization control system for the photoresist coating process according to claim 1, characterized in that, The optimization algorithm is a model predictive control algorithm or an adaptive PID control algorithm. The data processing and optimization unit predicts the coating quality at future moments by inputting real-time data into the prediction model, and calculates the optimal process parameter adjustment sequence with the goal of maximizing coating thickness uniformity.

5. The real-time monitoring and optimization control system for the photoresist coating process according to claim 1, characterized in that, The system also includes: The user interface and data storage unit are used to display real-time monitoring data, alarm information, optimization suggestions, and store the complete process data chain, supporting data traceability and process analysis.

6. The real-time monitoring and optimization control system for the photoresist coating process according to claim 1, characterized in that, The real-time control unit adjusts the rotary coating machine by dynamically adjusting the acceleration, steady-state speed value, or deceleration curve of the rotation speed based on real-time thickness measurement feedback during a single coating rotation to compensate for uneven thickness caused by adhesive leveling or solvent evaporation.

7. The real-time monitoring and optimization control system for the photoresist coating process according to claim 1, characterized in that, The system also includes: The closed-loop glue supply subsystem includes a precisely controllable metering pump and valves. The real-time control unit adjusts the glue supply rate or triggers a glue replenishment operation based on real-time data of the glue tank level and viscosity to maintain the stability of the glue properties.

8. A method for real-time monitoring and optimized control of the photoresist coating process based on the system described in any one of claims 1-7, characterized in that, Includes the following steps: S1: Start the coating process and collect process parameters and status data in real time through the integrated detection module; S2: The data processing and optimization unit evaluates the current coating quality status based on real-time data and a preset model, and determines whether intervention is needed; S3: If intervention is required, the optimal process parameter adjustment amount is calculated using the optimization algorithm. S4: Before the coating process is completed, the real-time control unit dynamically adjusts the process parameters of the coating unit according to the adjustment amount. S5: Repeat steps S1-S4 until the coating process is completed, achieving online closed-loop optimization control of the coating process.

9. The method for real-time monitoring and optimized control of the photoresist coating process according to claim 1, characterized in that, In step S2, the evaluation includes comparing the real-time thickness distribution with the target thickness distribution, calculating the thickness non-uniformity index, and triggering optimization calculation when the index exceeds a preset threshold.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in claim 8 or 9.

Images