Wafer storage bin dynamic airflow management and contamination intelligent tracking and suppression system

Abstract

The embodiment of the specification provides a wafer storage bin dynamic air flow management and intelligent pollution tracking and inhibition system, by constructing an intelligent system integrated with multi-modal perception, digital twin prediction and feedforward control, dynamic, active and accurate control of the clean environment of the wafer storage bin is realized. The system can simulate and offset air flow disturbance in advance based on equipment operation instructions, weaken the diffusion conditions of pollutants from the source; through the fusion of multi-source sensing data, ultra-early abnormality identification and high-precision pollution tracing are realized, which significantly improves the response speed and positioning accuracy; further, the adaptive adjustment of the target inhibition strategy is adopted, the efficient closed-loop control of the pollution event is realized. At the same time, the system has the ability of continuous self-learning and evolution, can mine the law from the historical data, identify the potential risk of the equipment in advance, so as to change the environmental management from post-processing to prevention, and comprehensively improve the robustness, production yield and equipment operation efficiency of the clean environment.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of chip manufacturing technology, and in particular to a dynamic airflow management and intelligent contaminant tracking and suppression system for wafer memory silos. Background Technology

[0002] In the semiconductor manufacturing industry, the stability of the clean environment in wafer memory silos is crucial for ensuring product yield. Existing technologies typically rely on fixed laminar flow air systems and periodic environmental monitoring, resulting in static airflow management that lacks adaptability to the dynamic operation of internal equipment. When robotic arms perform wafer cassette access operations, their movement inevitably disrupts the established airflow pattern, generating localized eddies that can lead to the diffusion of trapped contaminants. Traditional monitoring methods suffer from response lag, often triggering alarms only after contaminant concentrations significantly exceed limits, making it difficult to quickly and accurately locate and immediately suppress contamination sources. Furthermore, the systems lack deep self-learning capabilities based on historical operational data, hindering the effective prediction and prevention of potential faults and limiting further improvements in clean environment management efficiency.

[0003] Therefore, a better solution is urgently needed. Summary of the Invention

[0004] In view of this, the embodiments of this specification provide a dynamic airflow management and intelligent contaminant tracking and suppression system for wafer storage bins to address the technical deficiencies existing in the prior art.

[0005] According to a first aspect of the embodiments of this specification, a wafer memory bay dynamic airflow management and intelligent contaminant tracking and suppression system is provided, comprising:

[0006] The central intelligent purification management hub is used for overall system control and decision-making.

[0007] The dynamic airflow field control module is connected to the central intelligent purification management center and is used to establish and adjust the airflow environment inside the chamber.

[0008] The multimodal environmental perception and baseline modeling module is connected to the central intelligent purification management hub and is used to collect environmental data and establish environmental baselines.

[0009] The digital twin prediction module is integrated into the central intelligent purification management hub and is used for operation simulation and airflow disturbance prediction.

[0010] An active pollutant suppression module, connected to the central intelligent purification management hub, is used to purify pollutants.

[0011] The storage equipment linkage interface connects to the central intelligent purification management hub to receive wafer box access instructions;

[0012] The multimodal environment perception and baseline modeling module collects environmental data through a sensor network and establishes a multimodal environmental baseline database. When a wafer cassette access command is received through the warehouse equipment linkage interface, the digital twin prediction module performs operation rehearsal and airflow disturbance prediction, and sends feedforward control commands to the dynamic airflow field control module through the central intelligent purification management center based on the prediction results. During the operation of the robotic arm, the multimodal environment perception and baseline modeling module performs real-time monitoring. If an anomaly is detected, the central intelligent purification management center activates the pollution source tracing algorithm to locate the pollution source. After locating the pollution source, the central intelligent purification management center instructs the dynamic airflow field control module and the active pollutant suppression module to implement targeted suppression measures. After the suppression measures are implemented, the multimodal environment perception and baseline modeling module continuously monitors environmental data, and the central intelligent purification management center evaluates the purification effect and adjusts the strategy. All process data is recorded and used for self-learning and predictive maintenance.

[0013] In one possible implementation, the multimodal environmental perception and baseline modeling module includes a distributed sensor network, which includes an optical particulate counter, a gas phase molecular contaminant monitor, a temperature sensor, a humidity sensor, a particulate charge monitor, a micro-vibration sensor, and an infrared thermal imager. The central intelligent purification management hub performs fusion analysis on the collected data to establish a multi-dimensional environmental fingerprint baseline, including particulate concentration, types and concentrations of molecular contaminants, temperature, humidity, micro-vibration spectrum characteristics, thermal distribution maps of equipment surfaces, and background particulate electrostatic characteristics.

[0014] In one possible implementation, the digital twin prediction module includes a virtual digital twin model that integrates a computational fluid dynamics simulation engine and a mechanical kinematics simulation engine. Based on the received wafer cell access commands, the module simulates the robot's trajectory, velocity changes, acceleration, and wafer cell pose changes in the virtual digital twin model. The computational fluid dynamics simulation engine predicts the eddy region, intensity, scale, and movement path of the robot and wafer cell motion on the laminar flow field. The feedforward control commands include adjusting the wind speed and angle of the fan filter unit upstream of the robot's motion path to form a pioneer sweep flow, and adjusting the wind direction of the fan filter units downstream of the path and above the target location to form a dynamic air wall.

[0015] In one possible implementation, when the central intelligent purification management center initiates the pollution source tracing algorithm, it retrieves the time series of reading changes of all sensors in the multimodal environmental perception and baseline modeling module and the spatial gradient distribution of pollutant concentrations. Combined with the dynamic airflow map provided by the digital twin prediction module and the real-time motion data of the robotic arm, the source of the pollution event is calculated through a multi-source information fusion algorithm, and a high-highlight warning is displayed on the three-dimensional visualization interface.

[0016] In one possible implementation, the dynamic airflow field control module includes multiple zoned fan filter units, each with independent vector control capabilities, enabling dynamic adjustment of wind speed and outlet angle. The active pollutant suppression module is located in the return air channel on the side wall of the chamber and includes an ultraviolet photocatalytic oxidation unit, a low-temperature plasma generator, and a chemical filter with dynamically switchable pathways. When implementing targeted suppression measures, the central intelligent purification management center instructs the dynamic airflow field control module to adjust the fan filter units in the pollution source area to form a centripetally converging dynamic cyclone funnel, while simultaneously instructing the active pollutant suppression module to select a purification unit based on the pollutant type to purify the return air.

[0017] In one possible implementation, the multimodal environmental perception and baseline modeling module continuously monitors changes in pollutant concentrations in the pollution source area and downstream area after the suppression measures are implemented; the central intelligent purification management center compares the sensor data before and after suppression, and if the pollutant concentration has not dropped to within the safe baseline, it automatically instructs the dynamic airflow field control module to expand the airflow blockade range or instructs the active pollutant suppression module to increase the power of the purification unit until the environmental data returns to safety.

[0018] In one possible implementation, the central intelligent purification management hub includes a data recording and self-learning unit. This unit records the source, type, concentration, suppression measures, treatment duration, and effects of pollution events in a panoramic event log. By performing machine learning and deep mining on long-term log data, it identifies the correlation between the movement patterns of the robotic arm and the release of particulate matter, and issues predictive maintenance warnings to the equipment maintenance system.

[0019] In one possible implementation, the multi-source information fusion algorithm includes a step of calculating the location of pollution sources, wherein the coordinates of the pollution source locations are derived through the following formula:

[0020] First, based on the timestamp sequence of pollution events detected by sensors and the spatial gradient of pollutant concentration, the possible areas of pollution sources are calculated;

[0021] Secondly, the weighted least squares method is used to optimize the pollution source coordinates, and the calculation formula is as follows:

[0022]

[0023] in, The number of sensors is determined by the deployment of the sensor network; It is a sensor The detected pollutant concentrations are derived from real-time monitoring data from the multimodal environmental perception and baseline modeling module; It is a sensor The Euclidean distance from the location of the pollution source is calculated as follows: ;

[0024] in( , , () is a sensor The known location coordinates are from sensor network configuration data, and (x, y, z) are the location coordinates of the pollution source to be determined. It is a sensor The timestamp of the detected pollution event; time-series data from the sensor. It is the estimated time of the pollution event, obtained by reverse engineering from the timestamp sequence; It is the pollutant release intensity coefficient, which is initially estimated by fitting the concentration gradient; It is the pollutant attenuation coefficient, derived from historical diffusion characteristics in a multimodal environmental baseline database; It is a sensor The weights are determined based on sensor accuracy and relative position to the pollution source, calculated using concentration reliability and the reciprocal of distance.

[0025] In one possible implementation, evaluating the purification effect includes the step of calculating the purification efficiency, wherein the purification efficiency index is derived through the following formula:

[0026] First, the rate of concentration decrease is calculated based on the time series data of pollutant concentrations after the implementation of the suppression measures;

[0027] Secondly, combining airflow velocity and purification unit parameters, the comprehensive purification efficiency index is calculated, as follows:

[0028]

[0029] in, The number of monitoring areas is determined by the partitioning of the multimodal environment perception and baseline modeling module; It is a region The initial pollutant concentrations before the implementation of containment measures were obtained from real-time monitoring data. It is a region The final pollutant concentration after the implementation of containment measures is derived from data from continuous monitoring. It is a region The airflow volumetric flow rate is calculated from the fan filter unit wind speed and area of ​​the region in the dynamic airflow field control module; It is a region The efficiency coefficient of the purification unit is derived from the unit performance data of the active pollutant suppression module. For the ultraviolet photocatalytic oxidation unit, it is the photolysis efficiency, and for the chemical filter, it is the filtration efficiency.

[0030] In one possible implementation, when the dynamic airflow field control module forms a dynamic cyclone funnel, the central intelligent purification management center coordinates and controls the outlet air vectors of multiple fan filter units around the pollution source, so that the airflow direction is consistent towards the nearest return air vent. The convergence angle and wind speed of the funnel are dynamically adjusted according to the real-time airflow sensor data in the multimodal environmental perception and baseline modeling module to ensure that pollutants are quickly captured and discharged.

[0031] This specification provides an embodiment of a dynamic airflow management and intelligent contaminant tracking and suppression system for wafer memory silos. By constructing an intelligent system integrating multimodal sensing, digital twin prediction, and feedforward control, it achieves dynamic, proactive, and precise control of the clean environment of the wafer memory silo. The system can pre-simulate and counteract airflow disturbances based on equipment operation commands, weakening contaminant diffusion conditions at the source. Through the fusion of multi-source sensor data for ultra-early anomaly identification and high-precision contamination source tracing, it significantly improves response speed and positioning accuracy. Furthermore, it employs an adaptive and adjustable targeted suppression strategy, achieving efficient closed-loop control of contamination events. Simultaneously, the system possesses continuous self-learning and evolution capabilities, enabling it to mine patterns from historical data and identify potential equipment risks in advance. This shifts environmental management from post-event treatment to pre-event prevention, comprehensively improving the robustness of the clean environment, production yield, and equipment operation and maintenance efficiency. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of a dynamic airflow management and intelligent contaminant tracking and suppression system for a wafer storage bin, provided in one embodiment of this specification. Detailed Implementation

[0033] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0034] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this specification. The singular forms “a” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0035] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0036] This specification provides a dynamic airflow management and intelligent contaminant tracking and suppression system for wafer storage bins, which will be described in detail in the following embodiments.

[0037] See Figure 1 , Figure 1 This diagram illustrates a system schematic of a dynamic airflow management and intelligent contaminant tracking and suppression system for a wafer storage silo according to an embodiment of this specification. Specifically, it includes a central intelligent purification management hub for overall system control and decision-making; a dynamic airflow field control module connected to the central intelligent purification management hub for establishing and adjusting the airflow environment within the silo; a multimodal environmental perception and baseline modeling module connected to the central intelligent purification management hub for collecting environmental data and establishing an environmental baseline; a digital twin prediction module integrated into the central intelligent purification management hub for operation simulation and airflow disturbance prediction; an active contaminant suppression module connected to the central intelligent purification management hub for purifying contaminants; and a silo equipment linkage interface connected to the central intelligent purification management hub for receiving wafer cassette access commands. The multimodal environmental perception and baseline modeling module utilizes a sensor network. The system collects environmental data and establishes a multimodal environmental baseline database. When a wafer retrieval command is received through the warehouse equipment linkage interface, the digital twin prediction module performs operation rehearsal and airflow disturbance prediction, and sends feedforward control commands to the dynamic airflow field control module through the central intelligent purification management center based on the prediction results. During the operation of the robotic arm, the multimodal environmental perception and baseline modeling module performs real-time monitoring. If an anomaly is detected, the central intelligent purification management center activates the pollution source tracing algorithm to locate the pollution source. After locating the pollution source, the central intelligent purification management center instructs the dynamic airflow field control module and the active pollutant suppression module to implement targeted suppression measures. After the suppression measures are implemented, the multimodal environmental perception and baseline modeling module continuously monitors environmental data, and the central intelligent purification management center evaluates the purification effect and adjusts the strategy. All process data is recorded and used for self-learning and predictive maintenance.

[0038] The central intelligent purification management hub refers to the core control unit of the system, used to coordinate the operation of various modules and make intelligent decisions. The dynamic airflow field control module refers to a device with vector control fan filter units to establish and dynamically adjust the airflow organization in the cleanroom. The multimodal environmental perception and baseline modeling module refers to a monitoring network integrating multiple types of sensors, used to collect environmental parameters and establish a baseline database. The digital twin prediction module refers to a virtual model containing a computational fluid dynamics simulation engine, used to predict the impact of mechanical motion on airflow. The active pollutant suppression module refers to a combination of purification equipment located in the return air duct, used to specifically treat air pollutants. The storage equipment linkage interface refers to the interface for communication with the upper-level material management system, used to receive equipment operation commands. The sensor network refers to a distributed collection of monitoring devices, used to acquire multi-dimensional environmental data in real time. The fan filter unit refers to an air supply terminal device with independent control capabilities to maintain a local clean environment. The feedforward control command refers to control signals generated based on prediction results, used to pre-adjust airflow parameters. The pollution source tracing algorithm refers to an analytical model that integrates multi-source data, used to locate the source of pollutant release. Targeted containment measures refer to precise control strategies implemented against pollution sources to quickly curb the spread of pollution. A comprehensive event log refers to a database that records data from the entire operational process, used for system analysis and optimization.

[0039] As a concrete example: The system is deployed in an ISO 5-level wafer storage warehouse, with 36 vector fan filter units and 24 monitoring points evenly distributed within the warehouse. During the initialization phase, all fan filter units operate at a wind speed of 0.45 m / s. The multimodal environmental perception and baseline modeling module establishes an environmental baseline through 48 hours of continuous monitoring, including a background concentration of 0.1 μm particles ≤ 0.5 particles / m³, a temperature of 22 ± 0.5℃, and a relative humidity of 45 ± 3%. When the warehouse equipment linkage interface receives a command to retrieve the wafer box in storage location A13, the digital twin prediction module completes the robot arm motion simulation within 200 ms, predicting that a vortex group with a flow velocity ≥ 0.8 m / s will be generated in the B7-B9 area. The central intelligent purification management center immediately instructs the fan filter units in the upstream B5-B6 area to adjust their tilt angle by 15° and increase the wind speed to 0.6 m / s, while simultaneously forming a vertical air curtain in the B8 area. During execution, the particulate charge monitor detected a sudden increase in the concentration of 0.3μm particles to 85 particles / m³ in area B8. Within 500ms, the system compared data streams from 12 sensors and traced the source to lubricant evaporation at the third joint of the robotic arm. Targeted suppression was then activated: the fan filter unit in areas B7-B9 formed a 25° tilted cyclone funnel, and the power of the UV photocatalytic unit in the return air duct was increased to 120% of its rated value. After three cycles, the particulate concentration in this area returned to baseline levels, and all parameters of this event were recorded in the log database. Based on 3000 event records accumulated over three months of operation, the system autonomously identified a 37% increase in lubricant release probability for a specific robotic arm model when acceleration exceeded 2m / s², triggering an early maintenance warning.

[0040] This invention achieves proactive maintenance and precise control of clean environments by constructing an intelligent predictive and closed-loop control system. The system can predict and compensate for airflow disturbances based on equipment operation, effectively suppressing eddy current generation and reducing pollution risks at the source. Multimodal sensing technology enables ultra-early pollution identification, and multi-source data fusion achieves precise pollution source location, significantly improving response speed and accuracy. A unique targeted suppression mechanism can quickly construct local control zones, achieving efficient capture and removal of pollutants and preventing diffusion. Full-process data recording and self-learning capabilities enable the system to continuously optimize, uncovering potential patterns from historical data and providing early warnings of abnormal equipment conditions. This proactive defense management strategy significantly improves environmental stability, extends equipment service life, and provides a more reliable clean environment guarantee for high-end manufacturing.

[0041] In one possible implementation, the multimodal environmental perception and baseline modeling module includes a distributed sensor network, which includes an optical particulate counter, a gas phase molecular contaminant monitor, a temperature sensor, a humidity sensor, a particulate charge monitor, a micro-vibration sensor, and an infrared thermal imager. The central intelligent purification management hub performs fusion analysis on the collected data to establish a multi-dimensional environmental fingerprint baseline, including particulate concentration, types and concentrations of molecular contaminants, temperature, humidity, micro-vibration spectrum characteristics, thermal distribution maps of equipment surfaces, and background particulate electrostatic characteristics.

[0042] Among these, optical particulate counters refer to detection devices based on the principle of light scattering, used to monitor the particle size and quantity distribution of suspended particles in the air. Gas phase molecular pollution monitors refer to analytical instruments using chromatography or mass spectrometry to identify and quantify the concentration of volatile organic compounds and acidic gases. Temperature sensors refer to sensing elements based on thermoelectric effects or resistance changes, used to collect environmental thermodynamic parameters. Humidity sensors refer to detection devices using capacitance or resistance principles to measure relative humidity. Particulate charge monitors refer to devices that detect the charge characteristics of particles through electrostatic induction, used to distinguish types of pollution sources. Micro-vibration sensors refer to high-sensitivity vibration pickups based on the piezoelectric principle to capture mechanical vibration signals from equipment. Infrared thermal imagers refer to imaging systems that generate thermal distribution maps by detecting infrared radiation, used to identify areas of abnormal temperature on equipment surfaces.

[0043] As a concrete example: In a Class 100 cleanroom wafer storage silo, a sensor network consisting of 28 monitoring points operates continuously. An optical particulate counter collects the concentration of particles in the 0.1-0.5μm diameter range every 30 seconds, while a gaseous molecular contamination monitor tracks 12 molecular contaminants, including isopropanol and ammonia, in real time. When the system detects that the concentration of 0.2μm particles near the C-zone shelf has increased from the baseline of 3 particles / m³ to 52 particles / m³, it simultaneously detects a 1250Hz characteristic frequency vibration detected by a micro-vibration sensor in that area, and an infrared thermal imager shows that the robotic arm's joint temperature is 4.2℃ higher than the surrounding area. The central intelligent purification management center immediately initiates multi-source data fusion. By comparing the charge polarity distribution displayed by the particle charge monitor with historical feature databases, it determines that the particles are metal particles generated by wear of mechanical transmission components. The system automatically labels the robotic arm with its number and triggers an early warning, while simultaneously adjusting the C-zone fan filter unit to create a local air curtain to prevent contamination spread. On-site inspection by maintenance personnel confirms that the robotic arm's guide rail has micron-level wear, completely consistent with the system's diagnostic results.

[0044] This invention achieves comprehensive capture and accurate identification of pollutants by constructing a multi-dimensional environmental sensing system. Employing a multi-parameter collaborative analysis mechanism, it effectively distinguishes the characteristic signals of different types of pollution sources, significantly improving the reliability of anomaly detection. By integrating physical parameter and chemical component data, a unique environmental fingerprint database is formed, providing richer judgment criteria for pollution source tracing. This comprehensive monitoring scheme can detect hidden equipment faults in advance, avoiding misjudgments and omissions caused by single-parameter monitoring, and providing more comprehensive data support for clean environment management. The system possesses cross-dimensional correlation analysis capabilities, enabling it to uncover potential risks from seemingly unrelated changes in environmental parameters, achieving true preventative maintenance.

[0045] In one possible implementation, the digital twin prediction module includes a virtual digital twin model that integrates a computational fluid dynamics simulation engine and a mechanical kinematics simulation engine. Based on the received wafer cell access commands, the module simulates the robot's trajectory, velocity changes, acceleration, and wafer cell pose changes in the virtual digital twin model. The computational fluid dynamics simulation engine predicts the eddy region, intensity, scale, and movement path of the robot and wafer cell motion on the laminar flow field. The feedforward control commands include adjusting the wind speed and angle of the fan filter unit upstream of the robot's motion path to form a pioneer sweep flow, and adjusting the wind direction of the fan filter units downstream of the path and above the target location to form a dynamic air wall.

[0046] Among these, a virtual digital twin model can refer to a digital mirror of a physical warehouse, used to simulate physical processes in a real environment. A computational fluid dynamics simulation engine can be an index simulation software core to predict airflow motion and pollutant diffusion behavior. A mechanical kinematics simulation engine can refer to a mechanical system motion simulator, used to calculate the trajectory and attitude changes of equipment components. Motion trajectory can refer to the spatial path of a robot's end effector to predict the area the equipment will traverse. Velocity change can refer to the rate change process of mechanical moving parts, used to analyze the dynamic characteristics of the motion state. Acceleration can refer to the change in velocity per unit time to assess the intensity of mechanical motion. Wafer cell pose change can refer to the spatial position and attitude changes of a vehicle, used to determine its impact on the airflow field. Vortex region can refer to the range of vortices formed after laminar flow is disrupted, used to identify the spatial distribution of airflow disturbances. Pioneer sweep can refer to upstream enhanced directional airflow, used to remove potential pollutants in advance. Dynamic airwall can refer to downstream airflow barriers to prevent pollutants from diffusing downstream.

[0047] As a concrete example: when the system receives an instruction to access the storage location at coordinates (X35, Y72), the digital twin prediction module completes a full-process pre-simulation within 150 milliseconds. The mechanical kinematics simulation engine accurately calculates the motion parameters of each joint of the robot arm's upper and lower arms, predicting that the robot arm will pass through the BC aisle at a maximum speed of 2.5 m / s and complete the box retrieval action at a height of 1500 mm on the Z-axis. Simultaneously, based on this motion data, the computational fluid dynamics simulation engine predicts that a vortex zone with a diameter of approximately 600 mm and an vortex intensity of 0.8 m / s will be generated at location G07 on the shelf. The system immediately generates feedforward control instructions: the wind speed of FFU units B12-B15 located upstream of the robot arm's movement path is increased from the standard 0.45 m / s to 0.65 m / s, and the tilt angle is adjusted by 8 degrees to form a pioneer sweep; at the same time, the wind direction of downstream FFU units C03-C06 is adjusted to form a dynamic air wall tilted at 30 degrees. Actual operation monitoring data shows that the pre-control measures successfully suppressed the eddy intensity in the target area to below 0.2 m / s, effectively avoiding particulate matter suspension caused by airflow disturbance.

[0048] This invention achieves advanced prediction and compensation for the impact of equipment operation by constructing a high-precision digital twin system. Employing dual simulation engines working in tandem, it can accurately predict the coupling relationship between mechanical motion and airflow disturbance, providing a reliable basis for feedforward control. Through the combined application of pioneer sweeping and dynamic air walls, a complete airflow protection system is formed, effectively suppressing the diffusion path of contaminants. This predictive airflow management strategy significantly reduces disturbance to the clean environment and avoids the time lag problem of traditional passive response modes. The system has real-time simulation capabilities, completing complex operating condition simulations within milliseconds, meeting the high real-time control requirements of semiconductor manufacturing. The deep integration of virtual and physical systems provides a digital solution for clean environment management, significantly improving adaptability to complex operating conditions and control accuracy.

[0049] In one possible implementation, when the central intelligent purification management center initiates the pollution source tracing algorithm, it retrieves the time series of reading changes of all sensors in the multimodal environmental perception and baseline modeling module and the spatial gradient distribution of pollutant concentrations. Combined with the dynamic airflow map provided by the digital twin prediction module and the real-time motion data of the robotic arm, the source of the pollution event is calculated through a multi-source information fusion algorithm, and a high-highlight warning is displayed on the three-dimensional visualization interface.

[0050] Among these, reading change time series refers to a sequence of sensor monitoring data arranged chronologically, used to analyze the dynamic patterns of parameter changes. Spatial gradient distribution of pollutant concentration refers to the rate of change of pollutant concentration in space, used to identify the direction and trend of pollutant diffusion. Dynamic airflow field diagrams refer to the spatiotemporal distribution visualization of airflow velocity and direction, used to display the instantaneous state of the flow field. Real-time motion data refers to the current position and attitude information of a mechanical system, providing precise spatiotemporal coordinates of equipment operation. Multi-source information fusion algorithms refer to calculation methods that integrate multiple data sources, used to improve the accuracy of pollution source location. A 3D visualization interface refers to a human-computer interaction platform that displays the environmental status in three dimensions, intuitively presenting the spatial relationships of pollution events. Highlighted alarms refer to the function of highlighting abnormal areas on the display interface, used to quickly attract the attention of operators.

[0051] As a specific example: When the particulate sensor installed on the shelf in Zone D detected a sudden increase in the concentration of 0.3μm particles from 5 particles / m³ to 120 particles / m³ within 2 seconds, the system immediately initiated the pollution source tracing process. The central intelligent purification management center retrieved the 180-second time series of readings from all 32 sensors and found that concentration peaks appeared sequentially at points E07, E08, and F06, forming a clear southeast-facing propagation gradient. Combined with the real-time airflow dynamic map provided by the digital twin prediction module, it showed that there was a dominant southeast-facing airflow with a speed of 0.6 m / s in the area. Simultaneously, the real-time motion data of the robotic arm showed that robotic arm No. 3 was performing storage and retrieval operations in Zone D. The multi-source information fusion algorithm, by analyzing the matching relationship between time difference, concentration gradient, and airflow vector, calculated within 300 milliseconds that the pollution source was located at the second joint of robotic arm No. 3, and marked the joint component as flashing red on the 3D visualization interface. Maintenance personnel conducted an on-site inspection based on the alarm information and confirmed that there was a minor damage to the joint's sealing ring, which perfectly matched the system's location result.

[0052] This invention achieves precise location and rapid identification of pollution sources through deep fusion and intelligent analysis of multi-dimensional data. Employing spatiotemporal correlation analysis technology, it effectively tracks the propagation paths and diffusion patterns of pollutants, significantly improving the reliability of source tracing. By combining dynamic airflow fields and equipment operating status, a complete pollution event reconstruction model is constructed, providing ample evidence for anomaly diagnosis. A three-dimensional visualization alarm mechanism allows operators to intuitively grasp the spatial distribution characteristics of pollution events, greatly improving emergency response efficiency. This comprehensive source tracing method overcomes the limitations of traditional single-parameter monitoring, achieving accurate analysis of complex pollution scenarios and providing an advanced decision support tool for clean environment maintenance.

[0053] In one possible implementation, the dynamic airflow field control module includes multiple zoned fan filter units, each with independent vector control capabilities, enabling dynamic adjustment of wind speed and outlet angle. The active pollutant suppression module is located in the return air channel on the side wall of the chamber and includes an ultraviolet photocatalytic oxidation unit, a low-temperature plasma generator, and a chemical filter with dynamically switchable pathways. When implementing targeted suppression measures, the central intelligent purification management center instructs the dynamic airflow field control module to adjust the fan filter units in the pollution source area to form a centripetally converging dynamic cyclone funnel, while simultaneously instructing the active pollutant suppression module to select a purification unit based on the pollutant type to purify the return air.

[0054] Among these, zoned fan filter units refer to air purification equipment divided into zones, used to achieve independent control of local environmental parameters. Independent vector control capability refers to the control characteristic of simultaneously adjusting wind speed and angle to achieve precise airflow guidance. Air outlet angle refers to the angle between the airflow direction and the vertical direction, used to control the airflow coverage area. Ultraviolet photocatalytic oxidation units refer to treatment devices that use ultraviolet light to activate catalysts to decompose organic gaseous pollutants. Low-temperature plasma generators refer to devices that generate plasma through discharge, used to degrade various molecular pollutants. Dynamically switchable chemical filters refer to filtration systems with multi-channel structures to adapt to the treatment needs of different pollutants. Dynamic cyclone funnels refer to concentrically converging airflow patterns used to limit and guide the movement of pollutants.

[0055] As a concrete example: When the system confirmed an amine contamination event in area F12, the central intelligent purification management center issued a suppression command within 0.5 seconds. The dynamic airflow control module immediately adjusted the eight fan filter units in areas F10-F14, uniformly tilting their outlet angles 25 degrees towards point F12, increasing the wind speed to 0.8 m / s, forming a dynamic cyclone funnel with a bottom diameter of approximately 1.2 meters. Simultaneously, the active contaminant suppression module activated the ultraviolet photocatalytic oxidation unit in return air channel No. 3, setting its power to 150% of the rated value, and shutting down other chemical filter paths to ensure that all return air underwent enhanced photocatalytic treatment. Monitoring data showed that the amine contaminant concentration in this area decreased from 85 ppb to 8 ppb within 30 seconds, and the airflow organization stabilized. Throughout the entire process, the cleanliness of the adjacent area G remained within the design standard and was unaffected.

[0056] This invention achieves highly efficient and precise suppression of contamination incidents through coordinated control of airflow organization and purification units. The use of dynamic cyclone funnel technology rapidly constructs localized control zones, effectively limiting the spread of pollutants and preventing impact on the entire clean environment. An intelligent switching mechanism for multiple purification technologies ensures optimal treatment effects for different types of pollutants, significantly improving the adaptability and processing efficiency of the purification system. This targeted suppression strategy minimizes energy consumption while ensuring suppression effectiveness, achieving the dual goals of energy saving and high efficiency. The modular design allows the system to flexibly adjust its response based on contamination characteristics, providing reliable assurance for maintaining clean environments under complex conditions.

[0057] In one possible implementation, the multimodal environmental perception and baseline modeling module continuously monitors changes in pollutant concentrations in the pollution source area and downstream area after the suppression measures are implemented; the central intelligent purification management center compares the sensor data before and after suppression, and if the pollutant concentration has not dropped to within the safe baseline, it automatically instructs the dynamic airflow field control module to expand the airflow blockade range or instructs the active pollutant suppression module to increase the power of the purification unit until the environmental data returns to safety.

[0058] The pollution source area refers to the spatial range where pollutant release was initially detected, used to determine the core location for key monitoring and treatment. The downstream area refers to the spatial range located along the airflow direction from the pollution source, used to monitor the extent of pollutant diffusion. Pollutant concentration change refers to the increase or decrease in pollutant content per unit volume, used to assess pollution trends and treatment effectiveness. Before and after suppression refers to the time boundary points of targeted measures implementation, used to compare and analyze the actual effectiveness of control measures. Sensor data refers to the raw readings collected by various monitoring devices, used to provide quantitative evidence of environmental status. The safety baseline refers to pre-set safety thresholds for environmental parameters, used to determine whether the environment has returned to a controllable state. The airflow blockade range refers to the spatial area covered by airflow control measures, used to restrict the movement path of pollutants. The purification unit power refers to the operating intensity parameters of the pollutant treatment equipment, used to adjust the treatment capacity of the purification system.

[0059] As a specific example: After the system implemented targeted suppression of isopropanol pollution in region K09, the multimodal environmental perception and baseline modeling module continuously monitored and showed that the concentration in this region decreased from the initial 125 ppb to 35 ppb. However, the concentration in the downstream region L02 rose to 28 ppb and continued to fluctuate. The central intelligent purification management center compared the data before and after suppression and determined that the initial cyclone funnel range was insufficient. It immediately instructed the dynamic airflow field control module to expand the airflow blockade range from the original three units K08-K10 to five units K07-L01, while simultaneously increasing the power of the ultraviolet photocatalytic oxidation unit from the standard operating level to the enhanced level. After 45 seconds of enhanced processing, monitoring data showed that the concentration in region K09 decreased to 5 ppb and in region L02 decreased to 3 ppb, both below the safe baseline of 8 ppb. The system then maintained the current parameters and continued to monitor.

[0060] This invention achieves real-time verification and dynamic optimization of environmental governance effectiveness through a closed-loop control mechanism. Employing continuous monitoring and intelligent judgment strategies, it accurately assesses the actual effectiveness of containment measures and promptly identifies areas of inadequacy. Adaptive adjustment ensures the system can respond quickly to changes in pollution conditions, effectively preventing secondary pollution caused by incomplete treatment. This data-driven decision-making mechanism significantly improves the accuracy and reliability of environmental governance, ensuring that every pollution incident is thoroughly resolved. The system possesses self-optimization capabilities, accumulating experience through multiple treatments to continuously improve its efficiency in handling complex pollution scenarios, providing a continuous and stable cleanliness guarantee for high-end manufacturing environments.

[0061] In one possible implementation, the central intelligent purification management hub includes a data recording and self-learning unit. This unit records the source, type, concentration, suppression measures, treatment duration, and effects of pollution events in a panoramic event log. By performing machine learning and deep mining on long-term log data, it identifies the correlation between the movement patterns of the robotic arm and the release of particulate matter, and issues predictive maintenance warnings to the equipment maintenance system.

[0062] Among these, the data recording and self-learning unit refers to a processing module with data storage and analysis functions, used to accumulate operational experience and optimize system performance. The panoramic event log refers to a database that records complete event information to preserve key parameters throughout the pollution treatment process. Machine learning refers to intelligent methods that use algorithms to mine data patterns, used to discover potential patterns and correlations. Deep mining refers to techniques for multi-level analysis of massive amounts of data to extract valuable, in-depth information. Robotic arm motion patterns refer to the trajectory and posture characteristics of the robotic arm, used to analyze equipment operating patterns. The correlation of particulate release refers to the causal relationship between equipment status and pollutant generation, used to establish a basis for early warning judgments. Predictive maintenance early warning refers to equipment risk alerts based on data analysis, used to guide preventative maintenance arrangements.

[0063] As a concrete example: After six months of operation, the system's data recording and self-learning unit had accumulated over two thousand contamination event records. Through in-depth analysis of the panoramic event logs, machine learning algorithms discovered that the MCR-200 robotic arm's particle release probability would increase to six times the normal value when the following conditions were met: acceleration exceeding 3.5 m / s², operating time exceeding four thousand hours, and ambient temperature below twenty degrees Celsius. The system then sent a predictive maintenance alert to the equipment maintenance system, specifically indicating the need to focus on inspecting the guide rail lubrication system and sealing components of this robotic arm model. Based on the alert information, maintenance personnel conducted advance inspections on three robotic arms matching the characteristics, discovering that two of them indeed had insufficient lubrication issues, effectively preventing potential mass contamination incidents.

[0064] This invention achieves a shift in operation and maintenance (O&M) mode from passive response to proactive prevention through systematic data accumulation and intelligent analysis. Utilizing machine learning technology to deeply mine the inherent patterns in equipment operating data enables the early identification of potential failure risks, providing a scientific basis for preventative maintenance. A comprehensive event log provides a rich data foundation for system optimization, making the decision-making process more accurate and reliable. This self-learning mechanism enables the system to continuously improve, enhancing overall O&M capabilities through the accumulation of experience. The introduction of predictive maintenance significantly improves equipment reliability, reduces unplanned downtime, and provides strong support for continuous production. By optimizing resource allocation through data-driven methods, effective control of O&M costs is achieved while ensuring a clean environment.

[0065] In one possible implementation, the multi-source information fusion algorithm includes a step of calculating the location of pollution sources, wherein the coordinates of the pollution source locations are derived through the following formula:

[0066] First, based on the timestamp sequence of pollution events detected by sensors and the spatial gradient of pollutant concentration, the possible areas of pollution sources are calculated;

[0067] Secondly, the weighted least squares method is used to optimize the pollution source coordinates, and the calculation formula is as follows:

[0068]

[0069] in, The number of sensors is determined by the deployment of the sensor network; It is a sensor The detected pollutant concentrations are derived from real-time monitoring data from the multimodal environmental perception and baseline modeling module; It is a sensor The Euclidean distance from the location of the pollution source is calculated as follows: ;

[0070] in( , , () is a sensor The known location coordinates are from sensor network configuration data, and (x, y, z) are the location coordinates of the pollution source to be determined. It is a sensor The timestamp of the detected pollution event; time-series data from the sensor. It is the estimated time of the pollution event, obtained by reverse engineering from the timestamp sequence; It is the pollutant release intensity coefficient, which is initially estimated by fitting the concentration gradient; It is the pollutant attenuation coefficient, derived from historical diffusion characteristics in a multimodal environmental baseline database; It is a sensor The weights are determined based on sensor accuracy and relative position to the pollution source, calculated using concentration reliability and the reciprocal of distance.

[0071] The time stamp sequence refers to data records ordered by occurrence time, used to determine the chronological order of events. The spatial gradient of pollutant concentration refers to the rate of change of concentration in space, indicating the direction of pollutant diffusion. Weighted least squares refers to an optimization algorithm that considers weighting factors, used to improve the accuracy of parameter estimation. The pollutant release intensity coefficient is a parameter characterizing the release capacity of a pollution source, quantifying its intensity characteristics. Euclidean distance refers to the straight-line distance between two points in space, used to calculate the geometric relationship between the sensor and the pollution source. The pollutant decay coefficient is a parameter characterizing the natural decay rate of pollutants, reflecting their persistence. Sensor weights are coefficients characterizing the reliability of sensor data, used to adjust the contribution of different sensors in the calculation.

[0072] As a specific example: When eight sensors in the chamber simultaneously detected abnormal particulate matter concentration, the system recorded a timestamp sequence of t1=0.0s, t2=0.3s, t3=0.5s, and t4=0.7s, corresponding to concentration values ​​c1=85, c2=150, c3=120, and c4=95 (unit: particles / cubic meter). Preliminary analysis determined that the pollution source was likely located near Zone C, with an initial estimated release intensity coefficient k=18000 and an attenuation coefficient λ=0.02 (from the historical database). The system assigned weights w1=0.9, w2=1.0, w3=0.8, and w4=0.7 to each sensor (based on sensor calibration accuracy and positional relationships). After three rounds of weighted least squares iterative calculations, the final pollution source coordinates were determined to be (x=3.45, y=2.18, z=1.62) meters, corresponding to the second joint of the No. 7 robotic arm. On-site inspection confirmed that there was indeed seal wear at this location, verifying the accuracy of the calculation results.

[0073] This invention significantly improves the accuracy and reliability of pollution source localization by introducing a weighted optimization algorithm. The multi-parameter fusion calculation model effectively overcomes the limitations of single data sources and enhances source tracing capabilities in complex environments. A dynamic weight allocation mechanism ensures a greater contribution from high-precision sensor data, thereby improving the confidence level of the overall calculation results. This scientific calculation method effectively distinguishes between real pollution sources and interference signals, reducing the probability of false alarms. The algorithm possesses adaptive optimization characteristics, continuously improving parameter estimation accuracy through repeated applications. This technology provides a powerful analytical tool for clean environment management, achieving a technological leap from coarse area identification to precise localization, and providing a reliable basis for precise governance.

[0074] In one possible implementation, evaluating the purification effect includes the step of calculating the purification efficiency, wherein the purification efficiency index is derived through the following formula:

[0075] First, the rate of concentration decrease is calculated based on the time series data of pollutant concentrations after the implementation of the suppression measures;

[0076] Secondly, combining airflow velocity and purification unit parameters, the comprehensive purification efficiency index is calculated, as follows:

[0077]

[0078] in, The number of monitoring areas is determined by the partitioning of the multimodal environment perception and baseline modeling module; It is a region The initial pollutant concentrations before the implementation of containment measures were obtained from real-time monitoring data. It is a region The final pollutant concentration after the implementation of containment measures is derived from data from continuous monitoring. It is a region The airflow volumetric flow rate is calculated from the fan filter unit wind speed and area of ​​the region in the dynamic airflow field control module; It is a region The efficiency coefficient of the purification unit is derived from the unit performance data of the active pollutant suppression module. For the ultraviolet photocatalytic oxidation unit, it is the photolysis efficiency, and for the chemical filter, it is the filtration efficiency.

[0079] Among these, the concentration decrease rate refers to the magnitude of the decrease in pollutant concentration per unit time, used to assess the immediate effect of purification measures. Airflow volumetric flow rate refers to the volume of air passing through a cross-section per unit time, reflecting airflow delivery capacity. The purification unit efficiency coefficient is a parameter characterizing the performance of purification equipment, used to quantify the actual effectiveness of different purification technologies. Photolysis efficiency refers to the conversion rate of pollutants decomposed by the ultraviolet photocatalytic oxidation unit, measuring the degree of completion of the photocatalytic reaction. Filtration efficiency refers to the proportion of pollutants retained by a chemical filter, used to assess the collection capacity of the filter media. The comprehensive purification efficiency index is an overall evaluation index considering multiple factors, comprehensively reflecting the overall performance of the purification system.

[0080] As a specific example: After the system completed pollution suppression in zone D, the monitoring data was divided into five zones for analysis. The initial concentration records were C1=280ppb, C2=190ppb, C3=150ppb, C4=110ppb, and C5=80ppb, respectively. After treatment, the final concentrations decreased to C1=15ppb, C2=12ppb, C3=10ppb, C4=8ppb, and C5=6ppb. Based on the operating parameters of the fan filter units in each zone, the calculated airflow volumetric flow rates were V1=2.8m³ / s, V2=2.5m³ / s, V3=2.2m³ / s, V4=2.0m³ / s, and V5=1.8m³ / s, respectively. The corresponding purification efficiency coefficients were determined based on the performance of the UV photocatalytic unit used: η1=0.92, η2=0.90, η3=0.88, η4=0.85, and η5=0.82. Substituting the values ​​into the calculation formula, the comprehensive purification efficiency index E=0.867 is obtained. The system determines that the purification effect is of excellent level. The current operating parameters are maintained and all data from this operation are recorded for subsequent analysis and optimization.

[0081] This invention establishes a scientific purification efficiency evaluation system, achieving precise quantification and objective evaluation of pollution control effectiveness. The multi-parameter fusion calculation method comprehensively reflects the purification status of different areas, avoiding the one-sidedness of single-indicator evaluation. By introducing key parameters such as airflow volumetric flow rate and purification efficiency coefficient, it ensures that the evaluation results truly reflect the system's actual processing capacity. This comprehensive evaluation mechanism provides a reliable basis for optimizing operating parameters, helping to achieve the optimal balance between energy efficiency and purification effect. The system has a self-verification function, automatically adjusting its operating strategy based on the efficiency evaluation results to continuously improve pollution control levels. This technology provides advanced performance monitoring tools for clean environment management, realizing technological progress from qualitative judgment to quantitative evaluation, and providing data support for continuous system optimization.

[0082] In one possible implementation, when the dynamic airflow field control module forms a dynamic cyclone funnel, the central intelligent purification management center coordinates and controls the outlet air vectors of multiple fan filter units around the pollution source, so that the airflow direction is consistent towards the nearest return air vent. The convergence angle and wind speed of the funnel are dynamically adjusted according to the real-time airflow sensor data in the multimodal environmental perception and baseline modeling module to ensure that pollutants are quickly captured and discharged.

[0083] The outlet air vector refers to the combination of direction and velocity parameters of the airflow delivered by the fan filter unit, used to precisely control the airflow trajectory. The nearest return air vent refers to the air recirculation device closest to the pollution source, providing the shortest path for pollutant discharge. Real-time airflow sensor data refers to the instantaneously collected measurements of airflow velocity and direction, reflecting the actual state of the current flow field. The convergence angle refers to the inclination of the sidewall of the dynamic cyclone funnel, controlling the rate of pollutant accumulation. Capture refers to the process of confining pollutants within the airflow range and guiding them to the return air vent for effective pollutant removal.

[0084] As a concrete example: When the system confirms molecular contamination in area H05, the central intelligent purification management center immediately coordinates and controls the six fan filter units surrounding that area. Initially, the outlet air vector is set to a velocity of 0.6 meters per second, with the direction uniformly towards the northwest return air vent 1.5 meters away, forming a dynamic cyclone funnel with an initial convergence angle of 25 degrees. Subsequently, based on real-time airflow sensor monitoring data showing insufficient flow at the bottom of the funnel, the system adjusts the outlet air vector of four units to a velocity of 0.75 meters per second and increases the convergence angle to 30 degrees within 2.3 seconds, while the other two units maintain their original parameters to form an auxiliary airflow. After adjustment, monitoring data shows that the pollutants are completely discharged through the return air vent within eight seconds, and the cleanliness of the area returns to within the safe baseline range.

[0085] This invention achieves highly efficient directional removal of pollutants by precisely coordinating the operating parameters of multiple fan filtration units. A real-time feedback adjustment mechanism ensures the dynamic cyclone funnel maintains its optimal shape, effectively improving pollutant capture efficiency. By optimizing the coordination of airflow guidance and return air path, the residence time of pollutants in the clean area is significantly shortened. This dynamic adjustment capability allows the system to adapt to contamination events with different characteristics at different locations, ensuring consistent treatment results. The intelligent airflow control strategy rapidly removes pollutants while minimizing the impact on the overall clean environment, providing a more stable and reliable cleanliness guarantee for high-end manufacturing.

[0086] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments in this specification are not limited to the described order of actions, because according to the embodiments in this specification, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments in this specification.

[0087] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0088] The preferred embodiments disclosed above are merely illustrative of this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments described herein. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A dynamic airflow management and intelligent contaminant tracking and suppression system for wafer memory cascades, characterized in that, include: The central intelligent purification management hub is used for overall system control and decision-making. The dynamic airflow field control module is connected to the central intelligent purification management center and is used to establish and adjust the airflow environment inside the chamber. The multimodal environmental perception and baseline modeling module is connected to the central intelligent purification management hub and is used to collect environmental data and establish environmental baselines. The digital twin prediction module is integrated into the central intelligent purification management hub and is used for operation simulation and airflow disturbance prediction. An active pollutant suppression module, connected to the central intelligent purification management hub, is used to purify pollutants. The storage equipment linkage interface is connected to the central intelligent purification management hub and is used to receive wafer box storage and retrieval instructions; The multimodal environment perception and baseline modeling module collects environmental data through a sensor network and establishes a multimodal environment baseline database. When a wafer cassette access command is received through the storage equipment linkage interface, the digital twin prediction module performs operation rehearsal and airflow disturbance prediction, and sends a feedforward control command to the dynamic airflow field control module through the central intelligent purification management center based on the prediction results. During the operation of the robotic arm, the multimodal environment perception and baseline modeling module performs real-time monitoring. If an anomaly is detected, the central intelligent purification management center activates a pollution source tracing algorithm to locate the pollution source. After locating the pollution source, the central intelligent purification management center controls the dynamic airflow field control module and the active pollutant suppression module to implement targeted suppression measures.

2. The system according to claim 1, characterized in that, The multimodal environmental perception and baseline modeling module includes a distributed sensor network, which includes an optical particulate counter, a gas phase molecular pollution monitor, a temperature sensor, a humidity sensor, a particulate charge monitor, a micro-vibration sensor, and an infrared thermal imager. The central intelligent purification management hub performs fusion analysis on the collected data to establish a multi-dimensional environmental fingerprint baseline, including particulate concentration, types and concentrations of molecular pollutants, temperature, humidity, micro-vibration spectrum characteristics, thermal distribution map of equipment surface, and background particulate electrostatic characteristics.

3. The system according to claim 1, characterized in that, The digital twin prediction module includes a virtual digital twin model, which integrates a computational fluid dynamics simulation engine and a mechanical kinematics simulation engine. Based on the received wafer cell access instructions, the module simulates the robot's motion trajectory, velocity changes, acceleration, and wafer cell pose changes in the virtual digital twin model. The computational fluid dynamics simulation engine predicts the eddy region, intensity, scale, and movement path of the robot arm and wafer cell motion on the laminar flow field; the feedforward control commands include adjusting the wind speed and angle of the fan filter unit upstream of the robot arm's motion path to form a pioneer sweep flow, and adjusting the wind direction of the fan filter unit downstream of the path and above the target cargo location to form a dynamic air wall.

4. The system according to claim 1, characterized in that, When the central intelligent purification management hub activates the pollution source tracing algorithm, it retrieves the time series of reading changes of all sensors in the multimodal environmental perception and baseline modeling module and the spatial gradient distribution of pollutant concentrations. Combined with the dynamic airflow map provided by the digital twin prediction module and the real-time motion data of the robotic arm, it calculates the source of the pollution event through a multi-source information fusion algorithm and highlights the alarm on the three-dimensional visualization interface.

5. The system according to claim 1, characterized in that, The dynamic airflow field control module includes multiple zoned fan filter units, each with independent vector control capabilities, enabling dynamic adjustment of wind speed and outlet angle. The active pollutant suppression module is located within the return air channel on the side wall of the chamber and includes an ultraviolet photocatalytic oxidation unit, a low-temperature plasma generator, and a chemical filter with dynamically switchable pathways. When implementing targeted suppression measures, the central intelligent purification management center controls the dynamic airflow field control module to adjust the fan filter units in the pollution source area to form a centripetally converging dynamic cyclone funnel. The active pollutant suppression module selects a purification unit based on the pollutant type to purify the return air.

6. The system according to claim 1, characterized in that, The multimodal environmental perception and baseline modeling module continuously monitors the changes in pollutant concentration in the pollution source area and downstream area after the suppression measures are implemented; the central intelligent purification management center compares the sensor data before and after suppression, and if the pollutant concentration has not dropped to within the safe baseline, it controls the dynamic airflow field regulation module to expand the airflow blockade range or controls the active pollutant suppression module to increase the purification unit power.

7. The system according to claim 1, characterized in that, The central intelligent purification management hub includes a data recording and self-learning unit. The data recording and self-learning unit records the source, type, concentration, suppression measures, treatment duration and effect of pollution events in a panoramic event log. By performing machine learning and deep mining on long-term log data, it identifies the correlation between the movement pattern of the robotic arm and the release of particulate matter, and issues predictive maintenance warnings to the equipment maintenance system.

8. The system according to claim 4, characterized in that, The multi-source information fusion algorithm includes a step of calculating the location of pollution sources, which includes: Based on the timestamp sequence of pollution events detected by sensors and the spatial gradient of pollutant concentration, the possible areas of pollution sources are calculated. The coordinates of the pollution source are determined using the weighted least squares method based on the possible regions.

9. The system according to claim 6, characterized in that, After the suppression measures are implemented, the process also includes a step of calculating the purification efficiency index: The rate of concentration decrease was calculated based on time-series data of pollutant concentrations after the implementation of suppression measures. The comprehensive purification efficiency index is calculated based on the concentration decrease rate combined with the airflow velocity and purification unit parameters.

10. The system according to claim 5, characterized in that, When the dynamic airflow field control module forms a dynamic cyclone funnel, the central intelligent purification management center coordinates and controls the airflow vectors of multiple fan filter units around the pollution source, so that the airflow direction is consistent towards the nearest return air inlet. The convergence angle and wind speed of the funnel are dynamically adjusted according to the real-time airflow sensor data in the multimodal environmental perception and baseline modeling module to ensure that pollutants are quickly captured and discharged.

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