Wafer cleaning and dust exhausting method and system

Abstract

The invention relates to the technical field of wafer cleaning and dust extraction equipment, in particular to a wafer cleaning and dust extraction method and system. According to the method, an electrical control mechanism is introduced in the wafer cleaning process, cleaning environment parameters including wafer surface humidity, environment humidity and particle concentration are monitored in real time, and operation parameters of a dust extraction device are dynamically adjusted based on a monitoring result. The system comprises a cleaning nozzle, a dust extraction device and an electrical control unit, the electrical control unit collects environmental data through a sensor module, and intelligent adjustment is achieved through a controller. The problems of water vapor residue and secondary particle pollution in a traditional washing process are effectively solved, the wafer cleaning quality and the product yield are improved, and meanwhile, the equipment operation environment is improved. The method has the advantages of high automation degree, strong adaptability, low energy consumption and the like, and is suitable for various wafer cleaning occasions.

Description

A wafer cleaning and dust extraction method and system Technical Field

[0001] This invention relates to the field of wafer cleaning and dust extraction equipment technology, specifically to a wafer cleaning and dust extraction method and system. Background Technology

[0002] In the semiconductor manufacturing industry, wafer cleaning is a crucial process step, as its cleanliness directly affects the final performance and product yield of integrated circuits. Especially after wafer dicing, water washing is used to remove silicon chips, particulate contaminants, and other impurities generated during the dicing process. Traditional water washing techniques mainly rely on high-pressure nozzles to spray deionized water onto the wafer surface for physical rinsing, using the impact force of the water flow to carry contaminants away from the surface.

[0003] However, this traditional cleaning method has several inherent drawbacks. First, during the cleaning process, the impact of water flow on the wafer surface generates a large amount of water vapor, which permeates the cleaning chamber. If this water vapor is not removed in time, it easily condenses on the wafer surface, forming water stains. Especially in patterned areas of the wafer, residual water stains can severely affect subsequent photolithography, etching, and other processes, leading to decreased device performance or even failure. Second, particulate contaminants stirred up by the water flow are suspended in the water vapor environment. As the water vapor slowly settles, these particles re-adhere to the wafer surface, causing secondary contamination and significantly reducing the cleaning effect. In addition, the continuously high humidity environment can corrode the electrical and mechanical components inside the cleaning equipment, reducing the equipment's lifespan and stability, and increasing maintenance costs.

[0004] Existing technologies have attempted to improve cleaning performance by optimizing nozzle structure, adding drainage channels, or improving cleaning fluid formulations, but none have fundamentally solved the problems of residual moisture and secondary particulate contamination. While some improvements have increased cleaning efficiency to some extent, they often introduce new complexities, such as more complex equipment structures, increased energy consumption, or greater maintenance difficulty. Therefore, there is an urgent need in the field for a novel wafer cleaning technology that can effectively control the cleaning environment and completely solve the problems of moisture and particulate contamination.

[0005] Therefore, existing technologies still need further development. Summary of the Invention

[0006] The purpose of this invention is to overcome the above-mentioned technical deficiencies and provide a wafer cleaning and dust extraction method and system to solve the problems existing in the prior art.

[0007] To achieve the above-mentioned technical objectives, according to a first aspect of the present invention, a wafer cleaning dust extraction method is provided, comprising: S100, providing a nozzle to spray water onto the wafer for cleaning; S200, providing a dust extraction device, the dust extraction device including an exhaust duct and an exhaust fan; S300, starting the exhaust fan to generate negative pressure to extract water vapor and particles generated during the cleaning process; S400, monitoring cleaning environmental parameters in real time through a controller, and dynamically adjusting the operating parameters of the exhaust fan based on the environmental parameters.

[0008] Specifically, the environmental parameters include at least one of wafer surface humidity, ambient humidity, and particle concentration.

[0009] Specifically, dynamically adjusting the operating parameters of the exhaust fan includes adjusting the fan speed or power.

[0010] Specifically, the dynamic adjustment of the operating parameters of the exhaust fan based on the environmental parameters includes: dynamically adjusting based on a preset threshold of the environmental parameters; when the environmental parameters exceed the preset threshold, increasing the fan speed or power; and when the environmental parameters are lower than the preset threshold, decreasing the fan speed or power.

[0011] Specifically, the method further includes a calibration step: optimizing the preset threshold using historical data or a machine learning model.

[0012] Specifically, the controller uses a PID control algorithm, takes the environmental parameters as feedback input, and outputs the adjustment signal of the exhaust fan.

[0013] Specifically, the proportional coefficient, integral time, and derivative time of the PID control algorithm are configured according to the wafer type or cleaning process.

[0014] Specifically, the method also includes an early warning step: when the environmental parameters are abnormal, an alarm is triggered and a log is recorded.

[0015] Specifically, after dynamically adjusting the operating parameters of the exhaust fan based on the environmental parameters, the method further includes: transmitting the environmental parameters and the operating parameters to a remote monitoring center via a wireless communication module.

[0016] According to a second aspect of the present invention, a wafer cleaning dust extraction system is provided, comprising: a nozzle for spraying water to clean the wafer; a dust extraction device including an extraction duct and an extraction fan, the extraction fan being configured to generate negative pressure to extract water vapor and particles; and a control unit including: a sensor module for real-time monitoring of cleaning environmental parameters; a controller connected to the sensor module and the extraction fan, configured to dynamically adjust the operating parameters of the extraction fan based on the environmental parameters; and a power supply module for supplying power to the sensor module and the controller.

[0017] Beneficial Effects: This invention introduces an innovative electrically controlled dust extraction method, bringing significant technological advancements and multiple beneficial effects to the wafer cleaning process. Firstly, this invention effectively solves the long-standing problem of residual moisture in traditional water-washing processes. By monitoring the wafer surface humidity and ambient humidity in real time and dynamically adjusting the operating parameters of the dust extraction device, moisture generated during the cleaning process can be promptly removed, greatly reducing the possibility of water stain formation and thus improving the cleanliness of the wafer surface.

[0018] Secondly, this invention successfully overcomes the technical challenge of secondary particulate contamination. By precisely controlling the negative pressure and airflow of the exhaust fan, it ensures that particulate contaminants stirred up by the water flow are quickly captured and discharged from the cleaning chamber, preventing the particles from re-settling and re-attaching. This not only improves the efficiency of a single cleaning cycle but also reduces the reliance on repeated cleaning, thereby improving overall production efficiency.

[0019] Third, this invention achieves automation and optimization of the cleaning process through an intelligent electrical control system. The system can automatically adjust operating parameters according to different wafer types and process requirements, maintaining optimal cleaning conditions without manual intervention. This adaptive capability not only improves process stability but also reduces reliance on operator skills.

[0020] Furthermore, this invention extends the service life of the cleaning equipment. By maintaining a suitable humidity environment, corrosion and damage to internal components are reduced, lowering the equipment failure rate and maintenance frequency. Simultaneously, intelligent ventilation control optimizes energy consumption, maximizing energy savings while ensuring effective cleaning.

[0021] Finally, this invention provides a reliable guarantee for improving product yield. By creating a stable and controllable cleaning environment, it significantly reduces defects caused by incomplete cleaning, laying a good foundation for subsequent process steps. The entire system has a reasonable structure, is easy to operate, and is easily integrated into existing production lines, possessing broad application prospects and significant economic benefits. Attached Figure Description

[0022] Figure 1 is a structural schematic diagram of the wafer cleaning dust extraction system provided in a specific embodiment of the present invention; Figure 2 is a flow chart of the wafer cleaning dust extraction method provided in a specific embodiment of the present invention; Figure 3 is a system composition schematic diagram of the wafer cleaning dust extraction system provided in a specific embodiment of the present invention; the following reference numerals are present in the above figures: 1, nozzle; 2, dust extraction device. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments in this application, other similar embodiments obtained by those skilled in the art without creative effort should all fall within the scope of protection of this application. Furthermore, directional terms mentioned in the following embodiments, such as "up," "down," "left," and "right," are only for reference to the directions in the accompanying drawings; therefore, the directional terms used are for illustrative purposes and not for limiting the invention.

[0024] The present invention will be further described below with reference to the accompanying drawings and preferred embodiments.

[0025] Please refer to Figures 1-3. The present invention provides a wafer cleaning and dust extraction method, including: S100, providing a nozzle 1 to spray water to clean the wafer.

[0026] It should be further noted that this method uses an STM32F407 microcontroller as the core control unit 3. This controller has a main frequency of 168MHz and sufficient computing power to process real-time data. The specific implementation of the control steps is as follows: First, after the system is powered on, initialization is completed, including ADC module initialization (for sensor data acquisition), timer initialization (for PWM signal generation), and communication interface initialization. The sampling frequency is set to 100Hz, that is, environmental parameter data is collected every 10ms.

[0027] S200. Provide a dust extraction device 2, which includes an exhaust duct and an exhaust fan.

[0028] It should be further noted that the exhaust fan is controlled using PWM speed regulation, with the PWM frequency set to 20kHz to avoid audible noise. The duty cycle adjustment range is 5%-95%, corresponding to an exhaust fan speed of 500-9500rpm.

[0029] S300. Start the exhaust fan to generate negative pressure to remove water vapor and particles generated during the cleaning process.

[0030] It should be further explained that the negative pressure control is achieved through a PID algorithm, and the target negative pressure value is set according to the wafer type: -200Pa for 8-inch wafers and -350Pa for 12-inch wafers. The control cycle and sampling cycle are synchronized, both being 10ms.

[0031] S400: The controller monitors the cleaning environment parameters in real time and dynamically adjusts the operating parameters of the exhaust fan based on the environment parameters.

[0032] It should be further explained that, understandably, high-frequency sampling and fast response ensure real-time control, and PWM speed regulation achieves precise negative pressure control, effectively solving the problems of residual moisture and secondary particulate contamination, which is expected to improve wafer yield by 3-5%.

[0033] Specifically, the environmental parameters include at least one of wafer surface humidity, ambient humidity, and particle concentration.

[0034] Further details are needed regarding wafer surface humidity measurement. A non-contact infrared humidity sensor (model: HTU31D) is used, with a measurement range of 0-100%RH, an accuracy of ±1.5%RH, and a response time of 8 seconds. The sensor is installed 10±2mm from the wafer surface at a 45° angle to avoid direct water splashing. Environmental humidity monitoring uses a Sensirion SCD40 carbon dioxide sensor (integrated with temperature and humidity detection), with a humidity detection range of 0-95%RH, an accuracy of ±3%RH, and a temperature range of -10~60℃, installed at the center of the top of the cleaning chamber. Particle concentration detection uses a laser particle sensor (model: SPS30), with a measurement range of 0-1000μg / m³, an accuracy of ±10μg / m³, a particle size of 0.3-10μm, and a sampling flow rate of 2.2L / min. The three sensors are connected to the main controller via an I2C bus, with addresses configured as 0x44, 0x62, and 0x69, respectively. The data was collected using a moving average filter with a window size of 10 sampling points.

[0035] Understandably, multi-parameter integrated monitoring provides comprehensive environmental status information, non-contact measurement avoids interference with the wafer surface, and high-precision sensors ensure data reliability.

[0036] Specifically, dynamically adjusting the operating parameters of the exhaust fan includes adjusting the fan speed or power.

[0037] It should be further noted that the exhaust fan uses an EBM-PapstW2E200 series DC brushless fan, with a rated voltage of 24VDC, a maximum power of 85W, and a speed range of 500-10000rpm. Fan speed adjustment is achieved by changing the PWM duty cycle. The correspondence between duty cycle and fan speed has been experimentally calibrated: a 20% duty cycle corresponds to a fan speed of 2.5m / s, a 50% duty cycle corresponds to a fan speed of 5.8m / s, and an 80% duty cycle corresponds to a fan speed of 9.2m / s. Power adjustment is achieved through dual closed-loop control of voltage and current, with a voltage control accuracy of ±0.1V and a current control accuracy of ±0.01A. During adjustment, the speed change slope is limited to 500rpm / s to prevent excessive acceleration that could damage the fan. Power monitoring uses an INA219 current sensor with a sampling accuracy of 0.5%.

[0038] Understandably, precise wind speed and power control ensures the stability of dust extraction performance, soft start and soft stop protect the fan's lifespan, and dual closed-loop control improves system reliability.

[0039] Specifically, the dynamic adjustment of the operating parameters of the exhaust fan based on the environmental parameters includes: dynamically adjusting based on a preset threshold of the environmental parameters; when the environmental parameters exceed the preset threshold, increasing the fan speed or power; and when the environmental parameters are lower than the preset threshold, decreasing the fan speed or power.

[0040] It should be further explained that the preset thresholds are set according to wafer process requirements, with the specific values ​​as follows: surface humidity threshold set at 75%RH, ambient humidity threshold set at 60%RH, and particle concentration threshold set at 150μg / m³. The rationale for these threshold selections is as follows: the 75%RH surface humidity threshold is determined based on experimental data; when humidity exceeds this value, the risk of water stain residue increases significantly; the 60%RH ambient humidity threshold prevents condensation inside the equipment; and the 150μg / m³ particle concentration threshold ensures that secondary particle contamination is controlled at an acceptable level. The comparison logic uses a hysteresis comparison method, with the activation threshold being 5% higher than the deactivation threshold. For example, when the surface humidity reaches 75%, the exhaust fan is increased, and when it falls below 70%, the exhaust fan is decreased to prevent frequent switching. The comparison frequency is 10Hz, and a control command is output after each comparison.

[0041] Understandably, hysteresis comparison avoids controlling oscillations, and a reasonable threshold setting balances cleaning effectiveness and energy consumption, making the system more stable.

[0042] Specifically, the method further includes a calibration step: optimizing the preset threshold using historical data or a machine learning model.

[0043] It should be further noted that the calibration step is executed automatically every 24 hours, and the historical data storage period is 30 days, with a storage depth of 10,000 data sets. The machine learning model uses a lightweight neural network based on TensorFlow Lite, with a network structure of an input layer (3 nodes, corresponding to three environment parameters), a hidden layer (8 nodes, ReLU activation function), and an output layer (3 nodes, corresponding to the optimized threshold). The training data needs to contain at least 5,000 labeled data sets, with 1,000 training epochs and a learning rate of 0.001. The model optimization formula is: in, The optimized threshold, As the initial threshold, This is the adjustment amount for the model output. The physical meanings of each element in the formula are as follows: This represents the optimized threshold parameter. This represents the initially set threshold baseline value. This indicates the threshold adjustment amount calculated by the model based on historical data. During calibration, the model will automatically adjust the threshold based on the yield data of the most recent 30 days. If the yield remains below 95%, the model will correspondingly reduce the humidity threshold by 2-3%RH.

[0044] Furthermore, the calibration process is automatically triggered every 24 hours, with the trigger time set at 2:00 AM after the end of the production shift to avoid peak production periods. The system collects daily yield data for the past 30 days. Yield is defined as the ratio of the number of wafers passing final quality inspection each day to the total number of wafers cleaned that day, calculated using the following formula: in, This indicates the yield rate on day d (in %). This represents the number of wafers that passed quality inspection on day d. This represents the total number of wafers cleaned on day d. Yield data is stored as floating-point numbers in the controller's non-volatile memory in a time-series array format, containing date stamps and yield values, with a storage depth of 1000 records (covering more than 30 days). Simultaneously, the system records daily average environmental parameters (including surface humidity, ambient humidity, and particle concentration) for subsequent model analysis.

[0045] Furthermore, the criterion for "yield consistently below 95%" is based on statistical process control (SPC) principles, specifically: calculating the average yield over the most recent 30 days. and check Is it below 95%? Furthermore, the system also assists in checking the number of consecutive days with a yield below 95%: if the yield is below 95% for 7 consecutive days or more, it is considered a "continuous" state to enhance the reliability of the judgment. The reason for preferentially choosing 95% as the threshold is as follows: In the wafer manufacturing industry, a 95% yield is a basic requirement for process stability; a value below this indicates that there may be significant problems in the cleaning process (such as water stains or particulate contamination), requiring timely adjustments. This value is determined based on industry standards (such as SEMI guidelines) and the applicant's extensive experimental data, effectively distinguishing between normal fluctuations and abnormal conditions.

[0046] Furthermore, when the system determines that the yield rate remains below 95%, the model will automatically lower the humidity threshold (including the surface humidity threshold and the ambient humidity threshold) by 2-3% RH. The specific adjustment value is dynamically calculated based on the degree of yield deviation to avoid over-adjustment. The algorithm steps are as follows: 1. Calculate the yield deviation: First, calculate the deviation between the 30-day average yield and the target yield: in, To achieve the target yield, The average yield over 30 days. Yield deviation (unit: %).

[0047] 2. Determine the adjustment amount: Humidity threshold adjustment amount and The relationship is linear, and the calculation formula is: in, Humidity threshold adjustment amount (unit: %RH). This is a proportionality coefficient, with a preferred value of 0.2%RH / % (meaning that for every 1% decrease in yield below the target value, the humidity threshold decreases by 0.2%RH). The physical meanings of the components in the formula are as follows: This indicates the amount of change in the humidity threshold that needs to be adjusted; a positive value indicates raising the threshold, and a negative value indicates lowering the threshold. This indicates the sensitivity adjustment, which is the threshold adjustment amount corresponding to a unit yield deviation; This indicates the difference between the actual yield and the target yield.

[0048] Adjustment amount The value is limited to a 2-3%RH range; that is, if the calculated value is less than 2%RH, then 2%RH is used; if it is greater than 3%RH, then 3%RH is used. For example, if... The calculated value Take 2% RH directly; if The calculated value However, the limit is 3%RH.

[0049] The rationale for choosing k=0.2%RH / % is as follows: Based on historical data regression analysis, this value provides the best yield improvement effect. Experiments show that when the k value is in the range of 0.1-0.3, threshold adjustment can effectively improve yield without causing system oscillation. k=0.2 is a balance point, which can quickly respond to yield declines while avoiding frequent adjustments due to small fluctuations.

[0050] 3. Application Adjustment: The model will adjust the amount... The new threshold calculation formula, applied to the current humidity thresholds (surface humidity threshold and ambient humidity threshold), is as follows: in, The new threshold after adjustment (unit: %RH). This is the current threshold (unit: %RH). The adjustment direction is to lower the threshold, because low yield is usually due to residual moisture. Lowering the threshold can trigger ventilation earlier and enhance the dust extraction effect.

[0051] 4. Boundary Handling: The new thresholds must be kept within a reasonable range. The lower limit for surface humidity threshold is 50%RH (to avoid excessive ventilation causing wafer drying and cracking), and the lower limit for ambient humidity threshold is 40%RH (to prevent excessive dryness inside the equipment from causing static electricity). If the calculated value is lower than the lower limit, the lower limit value shall be used.

[0052] Furthermore, the threshold adjustment model is based on a linear regression algorithm and is retrained every 30 days to maintain adaptability. Training data includes historical yield data and corresponding threshold adjustment effect data (more than 5000 groups in total). The training objective is to minimize yield deviation, and the loss function is the mean squared error (MSE). The model output is the adjustment amount. After training, it is integrated into the embedded controller. The reasons for choosing linear regression are: the model is simple, the computational cost is low, it is suitable for real-time embedded systems, and it is highly interpretable and easy to debug.

[0053] Understandably, adaptive calibration ensures that the threshold is always in an optimal state. The machine learning model can automatically optimize based on actual production conditions, reducing the need for manual intervention. The calibration process achieves data-driven automatic optimization: the threshold is dynamically adjusted based on statistical yield data, ensuring that the dust extraction process always matches the actual production status. Specific benefits include: a yield improvement of approximately 2-4% (experimental data), a reduction in the frequency of manual intervention by more than 50%, and more scientific threshold adjustment, avoiding the subjectivity of setting based on experience.

[0054] Specifically, the controller uses a PID control algorithm, takes the environmental parameters as feedback input, and outputs the adjustment signal of the exhaust fan.

[0055] It should be further explained that the discretized form of the PID control algorithm is as follows: in, The control output (PWM duty cycle) at time k. Let k be the error value (the difference between the measured value and the target value). The sampling period is 0.01s. This is the proportionality coefficient. The integral coefficient is... These are the differential coefficients. The physical meanings of each component in the formula are as follows: This indicates the magnitude of the control signal that needs to be output at the current moment. This represents the deviation value at the current moment. Indicates the sampling time interval. Indicates the intensity coefficient for proportional adjustment. This represents the intensity coefficient of the integral control. This represents the intensity coefficient of the derivative adjustment. The PID parameters are determined using the Ziegler-Nichols tuning method. , , Output limiting: PWM duty cycle is limited to 10%-90% to prevent fan overload. Integral anti-saturation uses a limit-based weakening method.

[0056] Understandably, PID control provides precise negative pressure regulation, good parameter tuning ensures fast system response without overshoot, and integral anti-saturation avoids the windup problem.

[0057] Specifically, the proportional coefficient, integral time, and derivative time of the PID control algorithm are configured according to the wafer type or cleaning process.

[0058] It should be further noted that the PID parameters are configured as follows for different wafer types and processes: Memory wafers (high process sensitivity) use... , , Logic wafers (requiring fast response) are used , , Power device wafers (sensitive to humidity) are used , , Reason for parameter selection: Memory wafers require more stable control, therefore the parameter value is reduced. Increase Logic wafers require fast response, hence the increased size and Power device wafers are sensitive to humidity fluctuations, so more conservative parameters are used. Configuration is available via a touchscreen menu or by scanning a QR code on the wafer cassette for automatic identification. Understandably, customized parameter configurations for different processes optimize control performance, improve product yield, and reduce process setup time.

[0059] Specifically, the method also includes an early warning step: when the environmental parameters are abnormal, an alarm is triggered and a log is recorded.

[0060] Further explanation is needed regarding the anomaly judgment criteria: surface humidity exceeding 85%RH or falling below 30%RH for 10 consecutive seconds; ambient humidity exceeding 80%RH for 30 consecutive seconds; instantaneous particle concentration exceeding 500μg / m³ or exceeding 300μg / m³ for 60 consecutive seconds. Alarm classification: Level 1 alarm (yellow) indicates minor parameter exceedance, only logged; Level 2 alarm (orange) indicates severe parameter exceedance, with audible and visual alarms; Level 3 alarm (red) indicates equipment failure, automatic shutdown. Log recording uses a circular storage method with a storage depth of 1000 entries. Record content includes timestamp, parameter value, equipment status, and alarm level. Log format: YYYY-MM-DDHH:MM:SS,Parameter,Value,Status,AlarmLevel.

[0061] Understandably, tiered alarms ensure timely and effective fault handling, and detailed log records provide complete data support for problem analysis.

[0062] Specifically, after dynamically adjusting the operating parameters of the exhaust fan based on the environmental parameters, the method further includes: transmitting the environmental parameters and the operating parameters to a remote monitoring center via a wireless communication module.

[0063] It should be further noted that the wireless communication uses a 4G CAT1 module (model: Quectel EC200S), supports the TCP / IP protocol, and the data transmission interval is configurable (default 10 seconds). The communication protocol uses a custom JSON format: {"device_id":"WL-CLEAN-001","timestamp":"2025-07-18T10:30:00Z","data":{"surface_humidity":72.5,"env_humidity":58.3,"particle_concentration":125.6,"fan_speed":4500},"status":"normal"}. Furthermore, the remote monitoring center is deployed on the Alibaba Cloud platform, displaying data in real time through a web interface, with a data storage period of 2 years. Communication anomaly handling: When the network is interrupted, up to 1000 data entries are cached locally, and automatic resume transmission occurs after the network is restored.

[0064] Understandably, remote monitoring enables real-time visualization of the production process, and big data analysis provides a data foundation for process optimization, thereby improving production management efficiency.

[0065] Please refer to Figures 1-3. This invention provides another embodiment, which offers a wafer cleaning dust extraction system. The system includes: a nozzle 1 for spraying water to clean the wafer; a dust extraction device 2, including an exhaust duct and an exhaust fan, the exhaust fan configured to generate negative pressure to remove water vapor and particles; and a control unit 3, including: a sensor module for real-time monitoring of cleaning environment parameters; a controller connected to the sensor module and the exhaust fan, configured to dynamically adjust the operating parameters of the exhaust fan based on the environmental parameters; and a power supply module for supplying power to the sensor module and the controller.

[0066] Further details regarding the system hardware configuration are as follows: Nozzle 1 is a stainless steel fan-shaped nozzle with an orifice diameter of 1.2mm, an operating pressure of 0.3MPa, and a flow rate of 1.5L / min. The exhaust duct has an inner diameter of 50mm, is made of 316 stainless steel, and has a polished surface. The exhaust fan specifications are: voltage 24VDC, power 85W, maximum airflow 120m³ / h, and maximum static pressure 800Pa. The sensor module is integrated on a 150mm×100mm PCB board. The controller uses an industrial-grade PLC (model: Siemens S7-1200), and the power supply module is a Mean Well DR-120 series, with an input of AC220V and an output of DC24V / 5A. Installation requirements: distance between nozzle 1 and the wafer 50±5mm, distance between the exhaust outlet and nozzle 1 20±2mm, and tilt angle 30°. The system has an IP54 protection rating and is suitable for cleanroom environments.

[0067] Understandably, modular design facilitates installation and maintenance, industrial-grade components ensure system reliability, and reasonable installation parameters optimize dust extraction performance.

[0068] In a preferred embodiment, this application also provides an electronic device, comprising: a memory; and a processor, wherein the memory stores computer-readable instructions, which, when executed by the processor, implement the wafer cleaning and dust extraction method described above. This computer device can be broadly categorized as a server, terminal, or any other electronic device with the necessary computing and / or processing capabilities. In one embodiment, the computer device may include a processor, memory, network interface, communication interface, etc., connected via a system bus. The processor of the computer device can be used to provide the necessary computing, processing, and / or control capabilities. The memory of the computer device may include a non-volatile storage medium and internal memory. The non-volatile storage medium may store an operating system, computer programs, etc. The internal memory can provide an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface and communication interface of the computer device can be used to connect and communicate with external devices via a network. When the computer program is executed by the processor, it performs the steps of the method of the present invention.

[0069] This invention can be implemented as a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, causes the steps of the methods of embodiments of the invention to be performed. In one embodiment, the computer program is distributed across multiple network-coupled computer devices or processors, such that the computer program is stored, accessed, and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be executed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be executed by one or more computer devices or processors, and one or more other method steps / operations may be executed by one or more other computer devices or processors. One or more computer devices or processors may execute a single method step / operation, or execute two or more method steps / operations.

[0070] Those skilled in the art will understand that the method steps of this invention can be performed by a computer program instructing related hardware, such as a computer device or processor, to perform the steps of this invention when executed. Depending on the context, any references herein to memory, storage, databases, or other media may include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state drive, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0071] The technical features described above can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification, provided that such combination does not contain contradictions.

[0072] The specific embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention. Any other corresponding changes and modifications made in accordance with the technical concept of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A wafer cleaning and dust extraction method, characterized in that, The process includes the following steps: S100, providing nozzles to spray water onto the wafer for cleaning; S200, providing a dust extraction device, which includes an exhaust duct and an exhaust fan; S300, starting the exhaust fan to generate negative pressure to remove water vapor and particles generated during the cleaning process; S400, monitoring the cleaning environment parameters in real time through a controller, and dynamically adjusting the operating parameters of the exhaust fan based on the environment parameters.

2. The wafer cleaning and dust extraction method according to claim 1, characterized in that, The environmental parameters include at least one of wafer surface humidity, ambient humidity, and particle concentration.

3. The wafer cleaning and dust extraction method according to claim 2, characterized in that, The dynamic adjustment of the operating parameters of the exhaust fan includes adjusting the fan speed or power.

4. The wafer cleaning and dust extraction method according to claim 3, characterized in that, The method of dynamically adjusting the operating parameters of the exhaust fan based on the environmental parameters includes: dynamically adjusting based on a preset threshold of the environmental parameters; when the environmental parameters exceed the preset threshold, increasing the fan speed or power; and when the environmental parameters are lower than the preset threshold, decreasing the fan speed or power.

5. The wafer cleaning and dust extraction method according to claim 4, characterized in that, The method also includes a calibration step: optimizing the preset threshold using historical data or a machine learning model.

6. The wafer cleaning and dust extraction method according to claim 5, characterized in that, The controller uses a PID control algorithm, takes the environmental parameters as feedback input, and outputs the adjustment signal of the exhaust fan.

7. The wafer cleaning and dust extraction method according to claim 6, characterized in that, The proportional gain, integral time, and derivative time of the PID control algorithm are configured according to the wafer type or cleaning process.

8. The wafer cleaning and dust extraction method according to claim 7, characterized in that, The method also includes an early warning step: when the environmental parameters are abnormal, an alarm is triggered and a log is recorded.

9. The wafer cleaning and dust extraction method according to claim 1, characterized in that, After dynamically adjusting the operating parameters of the exhaust fan based on the environmental parameters, the method further includes: transmitting the environmental parameters and the operating parameters to a remote monitoring center via a wireless communication module.

10. A wafer cleaning and dust extraction system, characterized in that, include: Nozzles are used to spray water to clean wafers; A dust extraction device includes an exhaust duct and an exhaust fan, the exhaust fan being configured to generate negative pressure to remove moisture and particles; The control unit includes: a sensor module for real-time monitoring of cleaning environment parameters; a controller connected to the sensor module and the exhaust fan, configured to dynamically adjust the operating parameters of the exhaust fan based on the environmental parameters; and a power supply module for supplying power to the sensor module and the controller.

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