Semiconductor processing device, method and system

Abstract

The present disclosure provides a plurality of semiconductor processing devices, methods and systems to predict or determine irregular processing parameters during processing of a semiconductor wafer in a semiconductor processing device (e.g., an etching device). The semiconductor processing device includes a loading port configured to receive a semiconductor wafer. A process chamber is coupled to the loading port, and a fan is configured to selectively change a fluid flow in the process chamber. One or more sensors are provided in the process chamber and configured to sense one or more processing parameters in the process chamber. A controller is coupled to the fan and the one or more sensors, and the controller is configured to control the fan to change the fluid flow in the process chamber based on the sensed one or more processing parameters.

Description

Technical Field

[0001] The present disclosure relates to a semiconductor processing device, method, and system, and more particularly to a semiconductor processing flow field control device, method, and system for controlling processing parameters in a process chamber based on sensing data. Background Art

[0002] During the manufacture of semiconductor devices, semiconductor wafers are processed by various processing tools or devices. For example, during an etching process, an etching device may be used to process the wafer. The conditions within the etching device may vary depending on a variety of factors, such as the specific recipe used during the etching process, the length of time the etching is performed, humidity or water vapor introduced or developed during the etching process, temperature, etc.

[0003] Outgassing may occur on the surface of a wafer undergoing an etching process, and in some cases, outgassing may be a result of conditions within the etching apparatus. Summary of the invention

[0004] One aspect of the present disclosure provides a semiconductor processing device, comprising: a loading port, a process chamber, a fan, one or more sensors, and a controller. The loading port is configured to receive a semiconductor wafer; the process chamber is coupled to the loading port; the fan is configured to selectively change a fluid flow in the process chamber; the one or more sensors are configured to sense one or more processing parameters in the process chamber; and the controller is coupled to the one or more sensors and the fan, and is configured to control the fan to change the fluid flow in the process chamber based on the sensed one or more processing parameters.

[0005] Another aspect of the present disclosure provides a semiconductor processing method, comprising: sensing one or more processing parameters in a process chamber of a semiconductor processing device through at least one sensor; and controlling a fan in the process chamber to change a fluid flow in the process chamber based on the sensed one or more processing parameters.

[0006] Another aspect of the present disclosure provides a semiconductor processing system, comprising: multiple loading ports, multiple process chambers, multiple fans, multiple sensors, and a controller; wherein each loading port is configured to receive a semiconductor wafer; each process chamber is respectively coupled to these loading ports; these fans are respectively located in these process chambers, and each fan is configured to selectively change the fluid flow in each process chamber; these sensors are respectively located in these process chambers, and they are configured to sense one or more processing parameters in each process chamber; and the controller is coupled to these sensors and fans, and is configured to control these fans to change the fluid flow in these process chambers based on the sensed one or more processing parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The present disclosure is better understood by reading the following detailed description in conjunction with the accompanying drawings. It should be noted that, as is standard practice in the industry, many features are not drawn to scale. In fact, for clarity of discussion, the dimensions of many features may be scaled arbitrarily.

[0008] Figure 1 is a schematic diagram of a semiconductor processing device according to some embodiments;

[0009] Figure 2 is a block diagram of a semiconductor processing flow field control system according to some embodiments;

[0010] Figure 3 is a schematic diagram of a semiconductor processing device according to some embodiments;

[0011] Figure 4 is a flow chart of a method for controlling a process chamber fan according to some embodiments.

[0012]

Explanation of symbols

[0013] 10, 110, 210: Semiconductor processing equipment

[0014] 100: Flow field control system

[0015] 101: Communication Network

[0016] 120: Signal processing circuit

[0017] 130: Flow field control circuit

[0018] 140: Machine Learning Circuits

[0019] 142: Processing parameter database

[0020] 12: Wafer seat

[0021] 14: Wafer

[0022] 16: Process chamber

[0023] 18: Outer cover

[0024] 20, 220a-220e: Loading port

[0025] 201a-201e: Chamber

[0026] 22: Front Opening Wafer Transfer and (FOUP)

[0027] 24, 24: Controller

[0028] 26: Processor

[0029] 28: Memory

[0030] 30: Fan

[0031] 32: Fins

[0032] 34, 34a-34d: Sensor

[0033] 400: Methods

[0034] 402, 404: Action DETAILED DESCRIPTION

[0035] The following disclosure provides many different embodiments or examples to implement different features of the subject matter of the application. The specific examples of components and arrangements described below are intended to simplify the disclosure. These are of course only examples and are not intended to be limiting. For example, the process description of a first feature formed on or above a second feature includes embodiments in which the first feature and the second feature are in direct contact, and also includes embodiments in which other features are formed between the first feature and the second feature, so that the first feature and the second feature are not in direct contact. In addition, the disclosure repeats reference numbers and / or letters in various examples. The purpose of this repetition is to simplify and clarify the description and does not indicate a relationship between the various discussed embodiments and / or configurations.

[0036] Furthermore, spatially relative terms, such as "beneath," "below," "lower," "above," "upper," etc., are used to facilitate description of the relationship of an element or feature depicted in the drawings to other elements or features. Spatially relative terms encompass different orientations of the device in use or operation in addition to the orientation depicted in the drawings. The device may be otherwise oriented (rotated 90 degrees or at other orientations), and the spatially relative descriptors used herein may be interpreted so.

[0037] In various embodiments, the present disclosure provides systems, apparatus, and methods for controlling fluid flow in a process chamber based on parameters sensed in the process chamber during operation. The fluid flow can be controlled at least in part by a fan or a fan fin in the process chamber to adjust the fluid flow in the process chamber.

[0038] Embodiments provided herein include systems, apparatus, and methods for determining irregular process parameters in a semiconductor processing apparatus based on one or more process parameters sensed by a sensor in a process chamber of the apparatus. In some embodiments, the sensed process parameters may be analyzed using machine learning techniques based at least in part on historical process parameters associated with irregular process conditions stored in a process parameter database.

[0039] Figure 1 FIG. 1 is a schematic diagram of a semiconductor processing device 10 according to one or more embodiments of the present disclosure. Figure 1 The semiconductor processing device 10 shown in the figure can be, for example, a semiconductor processing tool related to etching, such as a dry etching or plasma etching tool; however, the embodiments of the present disclosure are not limited thereto. In various embodiments, the semiconductor processing device 10 can be any device or processing tool used for processing semiconductor devices.

[0040] The semiconductor processing device 10 includes a wafer base 12 on which a semiconductor wafer 14 is placed during a semiconductor manufacturing process (such as an etching process). The wafer 14 may be a semiconductor wafer, and in some embodiments, it may include a front side (such as a Figure 1 In some embodiments, wafer 14 may be a single crystal silicon (Si) wafer, a polycrystalline silicon wafer, a gallium arsenide (GaAs) wafer, or any other semiconductor wafer.

[0041] In one example where the semiconductor processing apparatus 10 is a plasma etching tool, the semiconductor processing apparatus 10 may be configured to generate a high velocity flow of a plasma (e.g., charged ions) of a desired gas mixture and direct the plasma flow toward the wafer 14. The plasma produces volatile etching products at room temperature from chemical reactions between elements of the material being etched (e.g., the surface of the wafer 14) and reactive species generated by the plasma. Depending on the type of plasma and the type of material on the wafer 14, collisions between the charged particles of the plasma and the material of the wafer 14 displace that material, effectively removing it from the surface of the wafer 14. Other types of plasma etching may be used in accordance with the principles described herein.

[0042] The wafer bed 12 is located in a process chamber 16 of the semiconductor processing device 10. The process chamber 16 may be at least partially surrounded by an outer cover 18. For example, the process chamber 16 may be enclosed in the outer cover 18.

[0043] In various embodiments, additional components may be placed within the process chamber 16 depending on the type of semiconductor processing performed by the semiconductor processing apparatus 10. For example, in some embodiments, one or more electrodes are located within the process chamber 16 and are configured to generate an electromagnetic field that generates a plasma from a precursor gas injected into the process chamber 16. In such embodiments, a lower electrode may be placed below the semiconductor wafer 14 and an upper electrode may be placed above the semiconductor wafer 14.

[0044] The process chamber 16 may include one or more fluid inlets or outlets for receiving or exhausting fluids. For example, the process chamber 16 may be in fluid communication with a gas source that provides a variety of precursor gases to the process chamber 16 for various manufacturing processes performed by the semiconductor processing device 10. In some embodiments, the process chamber 16 may be a vacuum chamber, and the process chamber 16 may be in fluid communication with a negative pressure or vacuum to remove gases from the chamber.

[0045] As previously described herein, the semiconductor processing apparatus 10 may be any apparatus or any processing tool used in semiconductor device processing. Similarly, the process chamber 16 is an example of a process chamber that may be used in the semiconductor processing apparatus 10; however, the embodiments provided herein are not limited to such a process chamber. Figure 1 Process chamber 16 is shown. In various embodiments, any components or features that may be used in any of a variety of semiconductor processing tools may be located within or interconnected with process chamber 16.

[0046] The semiconductor processing apparatus 10 includes a load port 20 configured to receive a plurality of wafers. For example, in some embodiments, the load port 20 is configured to receive a front opening universal pod 22 (FOUP) carrying a plurality of wafers. The FOUP 22 is configured to transfer wafers between various devices used during semiconductor device manufacturing. In some embodiments, the FOUP 22 includes fins or rails protruding from the inner sidewalls, and these fins or rails can support and fix the wafers. In some embodiments, the FOUP 22 has the ability to accommodate 25 wafers at a time. In some embodiments, the FOUP 22 has the ability to accommodate more or less than 25 wafers at a time. The FOUP 22 is configured to facilitate the insertion and removal of wafers through the front opening. In some embodiments, a robotic handling system is used to insert and remove wafers from the FOUP 22. In some embodiments, the internal environment of the FOUP 22 can be controlled to help maintain a low moisture content and reduce the risk of contaminant particles adsorbing onto the wafers stored in the FOUP 22.

[0047] During use, the wafers in the FOUP 22 may be introduced into the process chamber 16 by, for example, a robotic handling system, a robotic arm, or the like. In some embodiments, the wafers may remain in the FOUP 22 and may be in fluid communication with the process chamber 16 so that the wafers in the FOUP 22 may be processed, for example, by etching, while the wafers are in the FOUP 22. In other embodiments, multiple wafers may be moved into the process chamber one at a time, for example, on the wafer bed 12 to be processed by the semiconductor processing device 10.

[0048] Wafer susceptor 12 is configured to support semiconductor wafer 14 during processing of semiconductor wafer 14 in semiconductor processing apparatus 10. Wafer susceptor 12 may be any structure that supports, holds, or secures wafer 14 during processing.

[0049] In some embodiments, the semiconductor wafer 14 may be placed on the wafer bed 12 by a mobile wafer handling structure such as a robot arm, or any mobile robotic structure capable of fixing and transferring the semiconductor wafer 14 to the wafer bed 12. After the semiconductor wafer 14 is processed in the process chamber 16, the mobile wafer handling structure (such as a robot arm) may similarly move the semiconductor wafer 14 and transfer the wafer 14 to another location (such as a buffer chamber), another process chamber, a transfer chamber, or other similar chambers.

[0050] In some embodiments, the semiconductor processing apparatus 10 includes a controller or control circuit 24 (herein referred to as a control system 24) that controls one or more operations of the semiconductor processing apparatus 10. For example, in various embodiments, the control system 24 may control the generation of plasma, the flow of fluids into or out of the process chamber 16, the application of electrical energy to one or more electrodes within the process chamber 16, other heating or environmental parameters of the process chamber 16, or other similar operations. In some embodiments, the control system 24 may control the operation of one or more robotic mechanisms, such as a robotic arm or a mobile robotic structure that can be operated to move the semiconductor wafer 14 onto or off the wafer pedestal 12.

[0051] The control system 24 may include a processor 26 and a computer readable memory 28. The memory 28 may include machine readable instructions that, when executed by the processor 26, cause the control system 24 to send command signals to one or more components of the semiconductor processing apparatus 10, such as a plasma generating component, a gas inlet or outlet mechanism, one or more electrodes, or the like.

[0052] The semiconductor processing apparatus 10 includes a fan 30, which may be located within the process chamber 16 or in fluid communication with the process chamber 16. The fan 30 is configured to provide a fluid flow (e.g., air or any fluid present within the process chamber 16), which can be used to maintain or control a flow field (e.g., a flow field of the fluid flow) to remove outgassing on the surface of the wafer 14. The fan 30 may include fins 32 mechanically coupled to the fan 30, and the fins 32 guide the fluid flow provided by the fan 30. In some embodiments, the fins 32 may be variable fins, for example, the position or orientation (e.g., angle) of the fins can be selectively changed in a desired manner to guide the fluid flow. The positioning of the fins (including the angle of the fins) can be controlled by a motor or actuator, which is coupled to the fan and is operable to move the fins to a desired position. Thus, the fluid flow can be directed as desired, which can be used to control or remove outgassing on the surface of the wafer 14. In some embodiments, the fluid flow provided by the fan 30 may be directed toward the surface of the wafer 14 (eg, Figure 1 The direction and speed of the fluid flow can be selectively changed by the fan 30 and the fins 32.

[0053] Furthermore, if Figure 1 As shown, the semiconductor processing device 10 may include one or more sensors 34, which are configured to sense one or more parameters of the semiconductor process performed by the semiconductor processing device 10. The one or more sensors 34 may be located at a suitable location within the semiconductor processing device 10 to sense or measure various processing parameters of the semiconductor processing device 10. In some embodiments, the one or more sensors 34 may be located within the process chamber 16. For example, the one or more sensors 34 may be suspended by one or more supports at any suitable or desired location within the process chamber 16, and the supports may extend from, for example, a sidewall, an upper surface, a lower surface, or any suitable surface of the process chamber 16. In some embodiments, the one or more sensors 34 may be located above the wafer 14; however, this is not limited to this embodiment.

[0054] In some embodiments, the one or more sensors 34 may include at least one of a flow meter, a hygrometer, a thermometer, and a gas detector. For example, in some embodiments, the one or more sensors 34 may include a flow meter configured to sense or measure a linear or nonlinear, mass or volume flow rate of a fluid or gas, such as the flow rate of the fluid flow provided by the fan 30. In some embodiments, the one or more sensors 34 may thus sense the speed of the fluid flow provided by the fan 30, and the sensed speed may be used, for example, by the controller 24 to adjust the speed of the fluid flow provided by the fan 30 to a desired speed.

[0055] In some embodiments, the one or more sensors 34 may include a hygrometer configured to sense or measure the amount of humidity or water vapor in an environment, such as within the process chamber 16. The amount of humidity or water vapor may be monitored by the one or more sensors 34 and may be controlled based on the sensed or measured amount of humidity or water vapor by, for example, the controller 24. For example, to adjust or control the amount of humidity or water vapor within the process chamber 16, the controller 24 may adjust one or more chemicals, gases, or the like introduced into the process chamber 16.

[0056] In some embodiments, the one or more sensors 34 may include a thermometer configured to sense or measure a temperature or temperature gradient in an environment, such as within the process chamber 16. The temperature may be monitored by the one or more sensors 34 and may be controlled by, for example, the controller 24 based on the sensed or measured temperature. For example, the controller 24 may control a heating element based on the sensed or measured temperature to increase or decrease the temperature in the process chamber 16. In some embodiments, the controller 24 may adjust the temperature in the process chamber 16 by controlling or adjusting the flow of a fluid within the process chamber 16.

[0057] In some embodiments, the one or more sensors 34 may include a gas detector configured to sense or measure the presence of a gas in an environment, such as within the process chamber 16. The gas within the process chamber 16 may be monitored by the one or more sensors 34, and may be controlled by, for example, the controller 24 based on the sensed or measured gas. For example, to adjust or control the type or amount of gas in the process chamber 16, the controller 24 adjusts one or more gas flows introduced into the process chamber 16 based on the gas detected by the one or more sensors 34. In some embodiments, the controller 24 may adjust or control the gas in the process chamber 16 by adjusting the flow field within the process chamber 16, which may, for example, adjust or control outgassing at the surface of the wafer 14.

[0058] Any additional features or components may be included in the semiconductor processing apparatus 10 , which may include any additional features or components of semiconductor processing apparatuses (eg, etching apparatuses) known to those skilled in the art of semiconductor processing tools or apparatuses.

[0059] As discussed in greater detail below, various parameters of semiconductor processing performed by the semiconductor processing apparatus 10 sensed or measured by the one or more sensors 34 may be analyzed to determine processing conditions (e.g., within the process chamber 16), which may be used to maintain desired processing conditions during processing by the semiconductor processing apparatus 10 to reduce or eliminate irregular or undesirable conditions. Irregular or abnormal processing conditions may result in undesirable results of wafer 14 processing, such as various defects caused by outgassing on the surface of the wafer 14.

[0060] By sensing or measuring various semiconductor processing apparatus 10 process parameters with one or more sensors 34 and analyzing the sensed or measured parameters, irregularities in the semiconductor processing apparatus 10 process parameters may be detected, which may help correct the irregular or undesirable parameters to prevent or reduce the occurrence of defects in the wafers 14, such as due to outgassing on the surface of the wafers 14. The process parameters may be adjusted based on the sensed or measured process parameters, such as by adjusting the fluid flow in the process chamber 16, such as by adjusting the speed of the fan 30, the angle of the fins 32, or the like. In some embodiments, the controller 24 may be used to control the speed of one or more fans 30, the direction of the fans (e.g., by adjusting the angle or orientation of the fans 32), the frequency of the fans 30, or the opening angle of the load port door of the load port 20 to adjust the amount of fluid flow directed into or otherwise received by the FOUP 22.

[0061] Figure 2 FIG. 1 is a block diagram of a semiconductor processing flow control system 100 according to an embodiment of the present disclosure. The flow control system 100 may be used to connect to a semiconductor processing device 110 including one or more features or functions. The semiconductor processing device 110 may be, for example, Figure 1 The semiconductor processing device 10 is shown. However, the embodiments provided by the present disclosure are not limited thereto. In various embodiments, the semiconductor processing device 110 can be any device used during the semiconductor device manufacturing process, including, for example, devices or tools for performing chemical vapor deposition (CVD), physical vapor deposition (PVD), etching, optical lithography, or any other semiconductor processing. In some embodiments, the semiconductor processing device 110 includes a portion of the flow control system 100. During the processing of one or more wafers by the semiconductor processing device 110, the flow control system 100 can detect irregular or unwanted processing conditions based on one or more processing parameters sent by one or more sensors 34.

[0062] like Figure 2As shown, the semiconductor processing device 110 may include a first sensor 34a, a second sensor 34b, a third sensor 34c, and a third sensor 34d. However, the disclosed embodiments are not limited thereto, and in various embodiments, the semiconductor processing device 110 may include more or fewer sensors.

[0063] The sensors 34a-34d may be located at any suitable location in the semiconductor processing device 110 and configured to sense a process parameter of the semiconductor processing device 110. In various embodiments, the sensors 34a-34d may be the sensors previously described for Figure 1 Any of the one or more sensors 34 may be a flow meter, a hygrometer, a thermometer, or a gas detector. In some embodiments, the first sensor 34a may be a flow meter; the second sensor 34b may be a hygrometer; the third sensor 34c may be a thermometer; and the fourth sensor 34d may be a gas detector.

[0064] like Figure 2 As shown, the flow field control circuit system 100 includes a signal processing circuit 120 and a flow field control circuit 130 .

[0065] The sensors 34a-34d are communicatively coupled to the signal processing circuit 120 such that the signal processing circuit 120 receives signals output by the sensors 34a-34d that are indicative of parameters sensed or measured by the semiconductor processing device 110, such as a sensed or measured parameter related to the velocity or rate of fluid flow, the amount of humidity or water vapor, temperature, or gases present in the processing environment. The sensors 34a-34d may be communicatively coupled to the signal processing circuit 120 via any suitable communication network 101. The communication network 101 may utilize one or more protocols to communicate via one or more physical networks, including local area networks, wireless networks, dedicated lines, intranets, and the like.

[0066] In some embodiments, the communication network 101 includes one or more wires communicatively coupled to the sensors 34a-34d and the signal processing circuit 120. In some embodiments, the communication network 101 may include a wireless communication network to communicate a signal from any of the sensors 34a-34d to the signal processing circuit 120. The signal processing circuit 120 and any of the sensors 34a-34d may include wireless communication circuits that facilitate wireless communication between the sensors 34a-34d and the signal processing circuit 120.

[0067] The signal processing circuit 120 may be or may include circuits configured to perform any signal processing technique on the processed signals from the sensors 34a-34d to produce a suitable output for analysis by the flow field control circuit 130. In some embodiments, the signal processing circuit 120 may include or be executed by a computer processor, microprocessor, microcontroller, or similar component, which is configured to perform the various functions and operations described herein for the signal processing circuit 120. For example, the signal processing circuit 120 may be executed by a computer processor that is selectively activated or reconfigured by a stored computer program, or may be a computing platform specifically configured to perform the features and operations described herein. In some embodiments, the signal processing circuit 120 can be configured to execute software instructions stored in any computer-readable storage medium, such as a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive, an optical storage device, a magnetic storage device, an electronically erasable programmable read-only memory (EEPROM), an organic storage medium, or a similar storage medium.

[0068] The signal processing circuit 120 receives and processes the signals output by the sensors 34a-34d. In some embodiments, the signal processing circuit 120 includes an analog-to-digital converter (ADC) that can convert analog signals (e.g., signals received from the sensors 34a-34d) to digital signals. The digital signals (e.g., output by the analog-to-digital converter) can be processed separately by the signal processing circuit 120, so that the signals from the sensors 34a-34d are processed separately for analysis suitability by the flow field control circuit 130.

[0069] In some embodiments, the signal processing circuit 120 may combine the signals received from the sensors 34a-34d into a single marker of the process condition. For example, the signals received from the sensors 34a-34d may be combined so that the output of the signal processing circuit 120 indicates parameters related to the velocity of each fluid flow, the amount of humidity or water vapor, the temperature, and the gases present in the process environment.

[0070] like Figure 2As shown, the signal processing circuit 120 is communicatively coupled to the flow field control circuit 130. The flow field control circuit 130 may include or be executed by a computer processor, which is configured to perform the various functions and operations described herein. For example, the flow field control circuit 130 may be executed by a computer processor that is selectively activated or reconfigured by a stored computer program, or may be a computing platform specially constructed for performing the features and operations described herein.

[0071] In some embodiments, the flow field control circuit 130 includes a memory storing one or more instructions for performing the features and operations described herein, and the flow field control circuit 130 is operable to execute the stored instructions, such as the memory used to perform the functions of the flow field control circuit 130. The memory can be or include any computer-readable storage medium, including, for example, read-only memory, random access memory, flash memory, hard disk drive, optical storage device, magnetic storage device, electronically erasable programmable read-only memory, organic storage medium, or similar storage medium.

[0072] The flow control circuit 130 may receive signals related to process parameters of the semiconductor processing device 110, for example, from the signal processing circuit 120. The flow control circuit 130 analyzes the received signals to determine the current state of the process performed by the semiconductor processing device 110 (e.g., the current process parameters in the process chamber), and in some embodiments, the flow control circuit 130 may predict or determine irregularities in the process parameters of the semiconductor processing device 110, for example, based on a comparison of the signals received from the sensors 34a-34d with past data, or based on an analysis of the received signals by a machine learning model, wherein the machine learning model is trained with past data indicating irregular process parameters of the semiconductor processing device 110 (e.g., past sensor signals associated with specific process parameters).

[0073] In some embodiments, the flow control circuit 130 may employ one or more artificial intelligence or machine learning techniques to predict or determine irregular processing parameters of the semiconductor processing device 110, which in some embodiments may be implemented in part by the machine learning circuit 140. Some or all of the analyses, decisions, or similar actions described herein performed by the flow control circuit 130 may be performed automatically by the flow control circuit 130, for example, in response to a received signal from the signal processing circuit 120. The machine learning circuit 140 may be included as part of the flow control circuit 130 (not shown), or may be remotely disposed and communicatively coupled to the flow control circuit 130. The machine learning circuit 140 may use past data (e.g., the machine learning circuit 140 may be trained based on the past data) to predict or determine irregular or unwanted processing parameters of the semiconductor processing device 110, where the past data indicates processing parameters that are known to be irregular (e.g., the velocity of a fluid flow, the amount of humidity or water vapor, the temperature, or the gas present in the processing environment), and the machine learning circuit 140 may compare the signals received from the sensors 34a-34d with the past data to predict or determine the irregular processing parameters based on similarity and deviation signals from the past data or from a trained model, where the trained model is contained within the machine learning circuit 140, managed by the machine learning circuit 140, or otherwise accessed by the machine learning circuit 140.

[0074] As used herein, “artificial intelligence” broadly describes any computational intelligence system and method that can learn knowledge (e.g., based on training data) and use the learned knowledge to adapt its method to solve one or more problems, such as making inferences based on received inputs, such as signals received from sensors 34a-34d related to process parameters of semiconductor processing device 110. Machine learning generally refers to a subfield or category of artificial intelligence and is used herein to broadly describe any algorithm, mathematical model, statistical model, or the like implemented in one or more computer systems or circuits (e.g., processing circuits) that constructs one or more models based on sample data (or training data) in order to make predictions or decisions.

[0075] The flow control circuit 130 or the machine learning circuit 140 may use, for example, neural networks, deep learning, convolutional neural networks, Bayesian program learning, support vector machines, and pattern recognition techniques to solve problems, such as predicting or determining irregular processing signals of semiconductor processing devices. Furthermore, the flow control circuit 130 or the machine learning circuit 140 may be implemented in any of the following computational algorithms or techniques or a combination thereof: classification, regression, supervised learning, unsupervised learning, feature learning, clustering, decision trees, or similar techniques.

[0076] For example, an artificial neural network may be used by the flow field control circuit 130 or the machine learning circuit 140 to develop, train, or update one or more machine learning models that can be used to predict or determine irregular process parameters. The illustrated artificial neural network may include a plurality of "neurons" that are interconnected and exchange information with each other. These connections have numerical weights that can be adjusted based on experience, so that the neural network can adapt to the input and has the ability to learn. The "neurons" may be included in a plurality of separate layers that are interconnected, such as an input layer, a hidden layer, and an output layer. The neural network may be trained by providing training data (e.g., past data or past signals that indicate irregular process parameters) to the input layer. Through training, the neural network may generate and / or modify hidden layers that represent weighted connections that map the training data provided by the input layer to known output information on the output layer (e.g., the classification of the received sensor data as a representative of the irregular process parameter, such as irregular fluid flow velocity, humidity or water vapor quantity, temperature, or gas present in the process environment). The relationship between neurons in the input layer, hidden layer, and output layer formed through the training process (which may include weight connection relationships) can be stored as, for example, one or more machine learning models or another machine learning model that can be accessed by the machine learning circuit 140.

[0077] Once the neural network has been sufficiently trained, untrained data (e.g., new sensed or measured data received from sensors 34a-34d during operation of semiconductor processing device 110) may be provided to the neural network at an input layer. Using knowledge of the irregular process parameters (e.g., as stored in a machine learning model, which may include, for example, weighted connections between neurons of the neural network), the neural network may make decisions at an output layer regarding the received sensed or measured data. For example, the neural network may predict or determine the irregular process parameters of semiconductor processing device 110.

[0078] By employing one or more computational intelligence and / or machine learning techniques, the flow field control circuit 130 may learn (eg, by developing and / or updating a machine learning algorithm or model based on training data) to predict or determine irregular process parameters.

[0079] The machine learning circuitry 140 may be implemented in one or more processors that can access instructions, which may be stored in any computer-readable storage medium and executed by the machine learning circuitry 140 to perform any operations or functions described herein.

[0080] In some embodiments, the machine learning circuit 140 is communicatively coupled to a process parameter database 142, which may be stored, for example, in any computer-readable storage medium. The process parameter database 142 may include information correlating sensed parameters (e.g., sensed by the sensors 34a-34d) to irregular process parameters of the semiconductor processing device 110. In some embodiments, the process parameter database 142 stores a plurality of historical (past) sensor data having known results or otherwise representing known irregular process parameters or conditions of the semiconductor processing device 110.

[0081] In some embodiments, the machine learning circuit 140 may be trained based on the historical processing parameters stored in the processing parameter database 142. Therefore, the historical processing parameter data may be provided as training data for training the machine learning circuit 140, and an algorithm or machine learning model included in or accessible to the machine learning circuit 140 may be updated or modified based on the historical processing parameters stored in the processing parameter database 142, so that the trained machine learning circuit 140 may predict or determine irregular processing parameters of the semiconductor processing device 110.

[0082] In some embodiments, the training data (e.g., historical processing parameters stored in the processing parameter database 142) may be or may include labeled training data, and the machine learning circuit 140 or the flow field control circuit 130 may learn from the labeled training data to predict or determine the irregular processing parameters of the semiconductor processing device 110. The labeled training data may include a plurality of labels indicating that one or more processing parameters stored in the processing parameter database 142 represent, for example, irregular processing parameters of the semiconductor processing device 110.

[0083] During use of the semiconductor processing device 110, various processing parameters sensed by the signal sensors 34a-34d are processed by the signal processing circuit 120, and then the processed signals are analyzed by the flow field control circuit 130 or the machine learning circuit 140 to determine the current processing parameters and / or irregular processing parameters of the semiconductor processing device 110. The flow field control circuit 130 or the machine learning circuit 140 can analyze the received signals, for example, by comparing the received signals with historical signals related to historical processing parameters stored in the processing parameter database 142, which are known to be related to irregular processing parameters or similar information. In some embodiments, the flow field control circuit 130 or the machine learning circuit 140 can use a trained machine learning model (such as a neural network or similar method) to analyze the received processing parameter sensor data.

[0084] In some embodiments, the flow field control circuit 130 or the machine learning circuit 140 may include or access multiple machine learning models that are trained based on sensor data of a specific type (e.g., the velocity of a fluid flow, the value of humidity or water vapor, temperature, or gas present in the processing environment).

[0085] In some embodiments, the flow field control circuit 130 or the machine learning circuit 140 may analyze sensor data received from a plurality of different sensors of the semiconductor processing device 110 in a combined manner. For example, sensed process parameter data may be received by each sensor 34a-34d in the semiconductor processing device 110. Each of the sensed different process parameter data may be processed by, for example, the machine learning circuit 140 (which may be a neural network in some embodiments) according to a specific weight or value. The weighted sensor data may be combined into a single signal that simultaneously represents the sensor data from all separate sensors 34a-34d, and the combined sensor signal may be compared with a machine learning model to predict or determine the irregular process parameters of the semiconductor processing device 110.

[0086] In some embodiments, the flow control circuit 130 may provide an output to the controller 24 of the semiconductor processing device 110 based on the predicted or determined irregular processing parameters. The output of the flow control circuit 130 may be used by the controller 24 to reduce or eliminate the predicted or determined irregular processing parameters. For example, to reduce or eliminate the predicted or determined irregular processing parameters, the controller 24 may be operable to control the fan 30 or the fins 32 to adjust the fluid flow based on the output of the flow control circuit 130. For example, during the processing of wafers in the semiconductor processing device 110, in order to correct or otherwise eliminate the undesirable effects of irregular temperature, humidity, or flow rate, the controller 24 may control the speed of the fan 30, the direction of the fan 30 (e.g., the angle or orientation of the fins 32), the frequency of the fan 30, or the opening angle of the load port door of the load port 20 based on the predicted or determined irregular temperature, humidity, or flow rate (as provided and output by the flow control circuit 130).

[0087] In some embodiments, during processing of wafers in the semiconductor processing device 110, in order to correct or otherwise eliminate the unwanted effects of irregular temperature, humidity, or flow rate, the flow field control circuit 130 or the machine learning circuit 140 may be trained based on training data that indicates recommendations for adjustment of the speed of the fan 30, the direction of the fan 30 (e.g., the angle or orientation of the fins 32), the frequency of the fan 30, or the opening angle of the loading port door of the loading port 20.

[0088] In some embodiments, the flow control circuit 130 or the machine learning circuit 140 may receive recipe information associated with a specific semiconductor process recipe used to process a wafer in the semiconductor processing device 110. The semiconductor processing device 110 may use this recipe information (e.g., gas molecular composition, temperature information, humidity information, or flow rate information) to perform a specific process. In some embodiments, the recipe information may be associated with specific process parameters, and historical recipe information associated with historical irregular process parameters is stored in, for example, a process parameter database 142. During use, a specific recipe for a semiconductor processing device 110 to perform a specific process may be received by the flow control circuit 130 or the machine learning circuit 140, and parameters sensed during operation of the semiconductor processing device 110 (e.g., sensed by sensors 34a-34d) may be compared or otherwise analyzed by the flow control circuit 130 or the machine learning circuit 140 against historical process parameters associated with the same recipe stored in the process parameter database 142.

[0089] In some embodiments, in order to correct or otherwise eliminate the unwanted effects of irregular temperature, humidity, or flow rate during processing of wafers in the semiconductor processing device 110, recommended adjustments to the speed of the fan 30, the direction of the fan 30 (e.g., the angle or orientation of the fins 32), the frequency of the fan 30, or the opening angle of the loading port door of the loading port 20 may be provided for a specific recipe implemented in the semiconductor processing device 110.

[0090] Figure 3 FIG. 2 is a schematic diagram of a semiconductor processing device 210 according to one or more embodiments of the present disclosure. Figure 3 The semiconductor processing device 210 shown in the figure can be, for example, an etching-related semiconductor processing tool, such as a dry etching or plasma etching tool; however, the embodiments of the present disclosure are not limited thereto. In various embodiments, the semiconductor processing device 210 can be any device or processing tool used in semiconductor device processing.

[0091] Figure 3 The semiconductor processing device 210 shown includes a plurality of process chambers 201a-201e. In some embodiments, the process chambers may be etching chambers. Each of the process chambers 201a-201e may include the aforementioned Figure 1 Some or all features of the semiconductor processing apparatus 10 are described and illustrated.

[0092] Each process chamber 201a-201e may include a respective load port 220a-220e. The load ports 220a-220e are configured to receive semiconductor wafers carried by, for example, a FOUP. In some embodiments, the FOUP transfers wafers between the various process chambers 201a-201e so that the wafers can be processed in one or more process chambers 201a-201e.

[0093] Each process chamber 201a-201e may include a fan 30 and fins 32, which are substantially the same as described herein before. In some embodiments, the fan 30 and fins 32 may be provided above the load ports 220a-220e, whereby the fan 30 and fins 32 may provide and control fluid flow through the load ports 220a-220e. In some embodiments, the fluid flow is provided to the wafers in the load ports 220a-220e (e.g., a FOUP loaded into the load ports). In some embodiments, each process chamber 201a-201e may include a wafer holder, or other structure on which the wafers may be placed in the process chamber 201a-201e during wafer processing. The fan 30 and fins 32 provide and control fluid flow over the wafers placed in the process chamber 201a-201e.

[0094] Each of the process chambers 201a-201e may include at least one sensor 34, which may be at least one of a flow meter, a humidity meter, a thermometer, or a gas detector, as described above.

[0095] The semiconductor processing device 210 can be used for Figure 2 In the semiconductor processing flow field control system 100 shown and described, for example, the semiconductor processing device 210 may include Figure 2 The semiconductor processing device 110 is shown. In operation, various process parameters are sensed by the sensor 34, and the flow field control circuit 130 and the machine learning circuit 140 can be used to predict or determine the irregular process parameters of the semiconductor processing device 210 based on the sensed parameters.

[0096] In some embodiments, the flow control circuit 130 or the machine learning circuit 140 may receive recipe information associated with a specific semiconductor process recipe used to process wafers in each process chamber 201a-201e of the semiconductor processing device 210. The recipe information used to process wafers in each chamber 201a-201e of the semiconductor processing device 210 may include, for example, gas molecular composition, temperature information, humidity information, or flow rate information. During use, the flow control circuit 130 or the machine learning circuit 140 may receive a specific recipe for a specific process performed by the chambers 201a-201e of the semiconductor processing device 210, and the parameters sensed during operation of each chamber 201a-201e of the semiconductor processing device 210 (e.g., sensed by the sensor 34) may be compared or otherwise analyzed by the flow control circuit 130 or the machine learning circuit 140 against historical parameters associated with the same recipe stored in the process parameter database 142.

[0097] In some embodiments, during processing of wafers in the semiconductor processing apparatus 210, in order to correct or otherwise eliminate the undesirable effects of irregular temperature, humidity, or flow rate, a recommended adjustment of the speed of the fan 30, the direction of the fan 30 (e.g., the angle or orientation of the fins 32), the frequency of the fan 30, or the opening angle of the load port door of the load port 220a-220b may be provided for a particular recipe implemented in the semiconductor processing apparatus 210. In some embodiments, the flow field control circuit 130 or the machine learning circuit 140 also receives information indicating the number of wafers in the FOUP to be processed by the chambers 201a-201e of the semiconductor processing apparatus 210. The processing parameter associated with the irregular processing parameter (e.g., by the flow field control circuit 130 or the machine learning circuit 140) is associated with a particular number of wafers in the FOUP, and the determination or prediction of the irregular processing parameter may be based at least in part on the number of wafers processed in the FOUP in a particular chamber 201a-201e.

[0098] Figure 4 FIG. 4 is a flow chart of a process chamber fan control method 400 according to one or more embodiments of the present disclosure. The method may be at least partially implemented, for example, for Figure 1 The semiconductor processing apparatus 10 shown and described is directed to Figure 2 The process flow control system 100 shown and described, or for Figure 3 The semiconductor processing apparatus 210 is shown and described as an implementation.

[0099] In operation 402, the method includes sensing one or more process parameters in a process chamber of a semiconductor processing device using at least one sensor. The process parameters are sensed by, for example, one or more sensors 34, which may be one or more of a flow meter, a humidity meter, a thermometer, or a gas detector.

[0100] In operation 404, the method includes controlling a fan in the process chamber to change the fluid flow in the process chamber based on the sensed one or more process parameters. The fan may be controlled by, for example, the controller 24. In some embodiments, the controller may control at least one of the speed of the fan, the direction of the fluid flow in the process chamber, and the frequency of the fan. In some embodiments, the method may include determining an irregular process parameter in the process chamber based on the sensed one or more process parameters, and the fan may be controlled to change the fluid flow in the process chamber to correct the irregular process parameter.

[0101] The various embodiments disclosed herein provide several advantages and provide a number of technical solutions to technical problems that arise in the field of, for example, semiconductor processing devices, systems, or methods. For example, the various embodiments disclosed herein may be operated to control the flow of fluid in a process chamber based on parameters sensed in the process chamber. The embodiments disclosed herein provide significant advantages over known systems that do not control the flow field based on any parameters sensed by the semiconductor device, which may result in defects due to outgassing or other irregular parameters in the process chamber during processing. Because defective wafers may be scrapped or require further processing to eliminate defects caused by irregular processing parameters, these defects will further lead to increased costs and reduced revenues. However, by controlling the fluid in the process chamber based on parameters sensed in the process chamber, the embodiments disclosed herein can avoid or reduce these defects, thereby reducing or eliminating defects caused by processing conditions, outgassing, or similar factors.

[0102] One aspect of the present disclosure provides a semiconductor processing device, comprising: a loading port, a process chamber, a fan, one or more sensors, and a controller. The loading port is configured to receive a semiconductor wafer; the process chamber is coupled to the loading port; the fan is configured to selectively change the fluid flow in the process chamber; the one or more sensors are configured to sense one or more processing parameters in the process chamber; and the controller is coupled to the one or more sensors and the fan, and is configured to control the fan to change the fluid flow in the process chamber based on the sensed one or more processing parameters. In some embodiments, the one or more sensors include at least one of a flow meter, a hygrometer, a thermometer, or a gas detector. In some embodiments, the semiconductor processing device further includes a plurality of fins mechanically coupled to the fan, the fins having adjustable angles, and configured to adjust the direction of the fluid flow in the process chamber. In some embodiments, the controller is configured to control at least one of the speed of the fan, the direction of the fluid flow in the process chamber, the frequency of the fan, or the opening angle of the loading port door of the loading port. In some embodiments, the semiconductor processing device further comprises a flow field control circuit communicatively coupled to the one or more sensors, and configured to determine a plurality of irregular processing parameters of the semiconductor processing device based on the sensed one or more processing parameters. In some embodiments, the semiconductor processing device further comprises a processing parameter database communicatively coupled to the flow field control circuit, which stores information related to the irregular processing parameters of the semiconductor processing device; wherein the flow field control circuit is configured to determine the irregular processing parameters of the semiconductor processing device based on the sensed one or more processing parameters and the information stored in the processing parameter database. In some embodiments, the flow field control circuit is configured to receive recipe information related to a process performed by the semiconductor processing device, and the information related to the irregular processing parameters stored in the processing parameter database is related to the received recipe information. In some embodiments, the flow field control circuit is configured to output a recommended adjustment to at least one of a speed of a fan, a direction of fluid flow, a frequency of a fan, and an opening angle of a load port door of a load port, and the controller is configured to control the fan based on the recommended adjustment. In some embodiments, the load port is disposed between the fan and the one or more sensors.

[0103] Another aspect of the present disclosure provides a semiconductor processing method, comprising: sensing one or more processing parameters in a process chamber of a semiconductor processing device by at least one sensor; and controlling a fan in the process chamber to change a fluid flow in the process chamber based on the sensed one or more processing parameters. In some embodiments, the operation of sensing the one or more processing parameters includes: sensing at least one of flow field velocity, humidity or water vapor, temperature, and gas in the process chamber. In some embodiments, the operation of controlling the fan to change the fluid flow in the process chamber includes: controlling at least one of the speed of the fan, the direction of the fluid flow in the process chamber, and the frequency of the fan. In some embodiments, the semiconductor processing method determines an irregular processing parameter in the process chamber based on the sensed one or more processing parameters, wherein the operation of controlling the fan to change the fluid flow in the process chamber includes: controlling the fan to change the fluid flow in the process chamber to correct the irregular processing parameter. In some embodiments, the operation of determining the irregular processing parameter in the process chamber includes: comparing the sensed one or more processing parameters with a plurality of irregular processing parameters stored in a database. In some embodiments, determining an irregular process parameter in a process chamber includes using a machine learning circuit to determine the irregular process parameter based on one or more sensed process parameters and based on recipe information associated with a particular process performed by a semiconductor processing apparatus.

[0104] Another aspect of the present disclosure provides a semiconductor processing system, comprising: a plurality of loading ports, a plurality of process chambers, a plurality of fans, a plurality of sensors, and a controller; wherein each loading port is configured to receive a semiconductor wafer; each process chamber is respectively coupled to the loading ports; the fans are respectively located in the process chambers, and each fan is configured to selectively change the fluid flow in each process chamber; the sensors are respectively located in the process chambers, and are configured to sense one or more processing parameters in each process chamber; and the controller is coupled to the sensors and the fans, and is configured to control the fans to change the fluid flow in the process chambers based on the sensed one or more processing parameters. In some embodiments, the semiconductor processing system further comprises a flow field control circuit communicatively coupled to the sensors, and is configured to determine a plurality of irregular processing parameters of the process chambers based on the sensed one or more processing parameters. In some embodiments, the semiconductor processing system further comprises a processing parameter database communicatively coupled to the flow control circuitry, storing information related to the irregular processing parameters of the process chambers, wherein the flow control circuitry is configured to determine the irregular processing parameters of the process chambers based on the sensed one or more processing parameters and the information stored in the database. In some embodiments, the flow control circuitry is configured to receive recipe information related to a plurality of processes performed in each of the process chambers, and the information related to the irregular processing parameters stored in the processing parameter database is related to the received recipe information. In some embodiments, the sensors comprise at least one of a flow meter, a hygrometer, a thermometer, or a gas detector.

[0105] The above summary summarizes the features of many embodiments, so that those with ordinary knowledge in the art can better understand the aspects of the present disclosure. Those with ordinary knowledge in the art should understand that other processes and structures can be designed or modified based on the present disclosure to achieve the same purpose and / or achieve the same advantages as the embodiments described. Those with ordinary knowledge in the art should also understand that equivalent structures do not deviate from the spirit and scope of the present disclosure, and various changes, exchanges and substitutions can be made without departing from the spirit and scope of the present disclosure.

[0106] The various embodiments described above may be combined to provide further embodiments. These and other variations herein may be embodiments inspired by the above detailed description. In general, the words used in the appended claims should not be construed to limit the claims to the specific embodiments disclosed in the specification and claims, but should be construed to include all possible embodiments within the full range of equivalents of the claims to which the rights are granted. Accordingly, the claims are not limited to the present disclosure.

Claims

1. A semiconductor processing device, characterized in that: Include: a load port configured to receive a semiconductor wafer; a process chamber coupled to the load port; a fan configured to selectively change a fluid flow in the process chamber; one or more sensors configured to sense one or more processing parameters in the process chamber; as well as A controller is coupled to the one or more sensors and the fan, and is configured to control the fan to change the fluid flow in the process chamber based on the sensed one or more processing parameters and to change an opening angle of a loading port door of the loading port based on the sensed one or more processing parameters.

2. The semiconductor processing device according to claim 1, wherein: The one or more sensors include at least one of a flow meter, a hygrometer, a thermometer, or a gas detector.

3. The semiconductor processing device according to claim 1, wherein: Also included are a plurality of fins mechanically coupled to the fan, the plurality of fins having an adjustable angle configured to adjust a direction of the fluid flow in the process chamber.

4. The semiconductor processing device according to claim 3, characterized in that The controller is configured to control at least one of a speed of the fan, the direction of the fluid flow in the process chamber, and a frequency of the fan.

5. The semiconductor processing device according to claim 1, wherein: Also includes: A flow control circuit is communicatively coupled to the one or more sensors, and is configured to determine a plurality of irregular processing parameters of the semiconductor processing device based on the one or more sensed processing parameters.

6. The semiconductor processing device according to claim 5, characterized in that Also includes: a processing parameter database, communicatively coupled to the flow field control circuit, the processing parameter database storing information related to the plurality of irregular processing parameters of the semiconductor processing device; The flow field control circuit is configured to determine the plurality of irregular processing parameters of the semiconductor processing device based on the sensed one or more processing parameters and information stored in the processing parameter database.

7. A semiconductor processing device according to claim 6, wherein the flow field control circuit is configured to receive recipe information related to a process performed by the semiconductor processing device, and the information related to the multiple irregular processing parameters stored in the processing parameter database is related to the received recipe information.

8. The semiconductor processing device according to claim 5, characterized in that The flow field control circuit is configured to output a recommended adjustment to at least one of a speed of the fan, a direction of the fluid flow, a frequency of the fan, and the opening angle of the loading port door of the loading port, and the controller is configured to control the fan based on the recommended adjustment.

9. The semiconductor processing device according to claim 1, wherein: The loading port is positioned between the fan and the one or more sensors.

10. A semiconductor processing method, characterized in that: Include: sensing one or more processing parameters in a process chamber of a semiconductor processing apparatus by at least one sensor, the process chamber being coupled to a load port configured to receive a semiconductor wafer; controlling a fan in the process chamber to vary a fluid flow in the process chamber based on the sensed one or more process parameters; as well as An opening angle of a loadport door of the loadport is changed based on the sensed one or more process parameters.

11. The method according to claim 10, characterized in that The operation of sensing the one or more processing parameters includes sensing at least one of flow velocity, humidity or water vapor, temperature, and gas in the process chamber.

12. The method according to claim 10, characterized in that The operation of controlling the fan to change the fluid flow in the process chamber includes controlling at least one of a speed of the fan, a direction of the fluid flow in the process chamber, and a frequency of the fan.

13. The method according to claim 10, characterized in that Also includes: determining an irregular process parameter in the process chamber based on the sensed one or more process parameters, The operation of controlling the fan to change the fluid flow in the process chamber includes: controlling the fan to change the fluid flow in the process chamber to correct the irregular processing parameter.

14. The method according to claim 13, characterized in that The operation of determining the irregular process parameters in the process chamber includes comparing the sensed one or more process parameters to a plurality of irregular process parameters stored in a database.

15. The method according to claim 13, characterized in that The operation of determining the irregular process parameter in the process chamber includes: using machine learning circuitry to determine the irregular process parameter based on the sensed one or more process parameters and based on recipe information associated with a specific process performed by the semiconductor processing device.

16. A semiconductor processing system, characterized in that: Include: a plurality of load ports, each load port being configured to receive a semiconductor wafer; a plurality of process chambers, respectively coupled to the plurality of loading ports; A plurality of fans, respectively located in the plurality of process chambers, each of the fans being configured to selectively change a fluid flow in a respective process chamber; a plurality of sensors, respectively located in the plurality of process chambers, the plurality of sensors being configured to sense one or more processing parameters in the respective process chambers; as well as A controller is coupled to the multiple sensors and the multiple fans, and the controller is configured to control the multiple fans to change the fluid flow in the multiple process chambers based on the sensed one or more processing parameters and to change an opening angle of at least one loading port door in the multiple loading ports based on the sensed one or more processing parameters.

17. The semiconductor processing system according to claim 16, wherein: Also includes: A flow control circuit is communicatively coupled to the plurality of sensors, the flow control circuit being configured to determine a plurality of irregular processing parameters of the plurality of process chambers based on the sensed one or more processing parameters.

18. The semiconductor processing system according to claim 17, wherein: Also includes: a processing parameter database, communicatively coupled to the flow field control circuit, the processing parameter database storing information related to the plurality of irregular processing parameters of the plurality of process chambers, The flow field control circuit is configured to determine the plurality of irregular processing parameters of the plurality of process chambers based on the sensed one or more processing parameters and the information stored in the database.

19. The semiconductor processing system according to claim 18, wherein: The flow field control circuit is configured to receive recipe information associated with a plurality of processes performed in each of the process chambers, and the information associated with the plurality of irregular process parameters stored in the process parameter database is associated with the received recipe information.

20. The semiconductor processing system of claim 16, wherein: The plurality of sensors include at least one of a flow meter, a hygrometer, a thermometer, or a gas detector.

Images