Methods and electronic device manufacturing systems for coupling sensors to transfer chamber robot

Abstract

An electronic device manufacturing system includes: a transfer chamber; a tool station located within the transfer chamber; a process chamber coupled to the transfer chamber; and a transfer chamber robot. The transfer chamber robot is configured to transfer a substrate to and from the process chamber. The transfer chamber robot is further configured to be coupled to a sensor tool including one or more sensors configured to perform measurements inside the process chamber. The sensor tool can be retrieved from the tool station via an end effector of the transfer chamber robot.

Description

Technical Field

[0001] This disclosure relates to electrical components, and more specifically to methods and mechanisms for coupling sensors to a transmission chamber robot. Prior Technology

[0002] Electronic component manufacturing systems typically comprise multiple process chambers that are subjected to vacuum during operation. During substrate fabrication, contaminants and residual deposits are introduced into various components within these process chambers. Therefore, it is necessary to periodically inspect these process chambers and clean them based on contamination or deposit levels to remove contaminants and residual deposits from the walls and gas distribution panels.

[0003] Traditionally, operators periodically disassemble vacuum systems and remove components from electronic component manufacturing systems (such as process chamber doors) to inspect the process chambers and determine if cleaning is necessary. However, this is a time-consuming, labor-intensive, and inefficient process. Additionally, some electronic component manufacturing systems have modified process chamber walls to include sensors for detecting deposit buildup. However, these wall-mounted sensors can introduce defects into the process chambers and affect plasma uniformity. Summary of the Invention

[0004] The following is a simplified summary of this disclosure to provide a basic understanding of some aspects of it. This summary is not a comprehensive overview of this disclosure. It is not intended to identify key or essential elements of this disclosure, nor is it intended to define any scope of any particular implementation of this disclosure or any claim. Its sole purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that follows.

[0005] In this disclosure, an electronic device manufacturing system includes: a transfer chamber; a tool station located within the transfer chamber; a process chamber coupled to the transfer chamber; and a transfer chamber robot. The transfer chamber robot is configured to transfer a substrate to and from the process chamber. The transfer chamber robot is further configured to be coupled to a sensor tool including one or more sensors configured to perform measurements inside the process chamber. The sensor tool can be retrieved from the tool station via an end effector of the transfer chamber robot.

[0006] In another form of this disclosure,

[0007] In another embodiment of this disclosure, a method includes the following steps: positioning a portion of a transfer chamber robot coupled to a sensing device, including one or more sensors, within a process chamber using a processor. The method further includes the step of obtaining sensor data associated with the process chamber using the one or more sensors. The method further includes the step of removing the portion of the transfer chamber robot from the process chamber.

[0008] In another embodiment of this disclosure, a method includes the steps of: obtaining a plurality of sensor values ​​from a process chamber via a sensing device coupled to a transfer chamber robot. The method further includes the steps of: applying a machine learning model to the plurality of sensor values, the machine learning model being trained based on historical sensor data of a subsystem of the process chamber and task data associated with the formulation used to deposit the thin film. The method further includes the steps of: generating an output of the machine learning model, wherein the output indicates a fault type of the subsystem. The method further includes the steps of: determining the fault type of the subsystem; and generating corrective actions based on the fault type. Simple Explanation of the Diagram

[0009] The contents of this disclosure are illustrated in the accompanying drawings by way of example rather than limitation.

[0010] Figure 1 is a block diagram illustrating an exemplary system architecture according to certain embodiments.

[0011] Figure 2 is a flowchart of a method for training a machine learning model according to certain embodiments.

[0012] Figure 3 is a top view of an example manufacturing system according to certain embodiments.

[0013] Figure 4 is a schematic cross-sectional side view of an example process chamber of an example manufacturing system according to certain embodiments.

[0014] Figure 5A is a schematic diagram of an end effector according to an embodiment of the present disclosure.

[0015] Figure 5B is a schematic diagram of an end effector coupled to a sensor tool according to an embodiment of the present disclosure.

[0016] Figures 6A and 6B are schematic diagrams of a sensor disk and an end effector according to embodiments of the present disclosure.

[0017] Figure 7 is a flowchart of a method for using a machine learning model to determine the fault type of a process chamber subsystem, according to certain embodiments.

[0018] Figure 8 is a block diagram illustrating a computer system according to certain embodiments. Implementation

[0019] This document describes techniques relating to methods and mechanisms for coupling sensors to a transmission chamber robot. During deposition processes (e.g., chemical vapor deposition (CVD), atomic layer deposition (ALD), etc.) performed in a process chamber of a manufacturing system, thin films can be deposited on the surface of a substrate. For example, in a CVD process, the substrate is exposed to one or more precursors that react on the substrate surface to produce the desired deposition. The thin film may include one or more layers of material formed during the deposition process, each layer including a specific thickness gradient (e.g., a variation in thickness along the layers of the deposited thin film). For example, a first layer may be formed directly on the surface of the substrate (referred to as the proximal layer or proximal end of the thin film) and has a first thickness. After the first layer is formed on the surface of the substrate, a second layer with a second thickness may be formed on the first layer. This process continues until the deposition process is complete and the final layer of the thin film is formed (referred to as the distal layer or distal end of the thin film). The thin film may include alternating layers of different materials. For example, the thin film may include alternating oxide and nitride layers (oxide-nitride-oxide-nitride stacks or ONON stacks), alternating oxide and polycrystalline silicon layers (oxide-polycrystalline silicon-oxide-polycrystalline silicon stacks or OPOP stacks), and so on. For example, the thin film may then be etched to form a pattern on the surface of the substrate, chemical-mechanical polishing (CMP) may be performed to smooth the surface of the thin film, or any other processes required to manufacture the finished substrate may be performed.

[0020] A process chamber can have multiple subsystems operating during each substrate manufacturing process (e.g., deposition, etching, polishing, etc.). A subsystem can be characterized as a set of sensors related to the operating parameters of the process chamber. Operating parameters can be temperature, flow rate, pressure, etc. In one example, a pressure subsystem can be characterized by one or more sensors measuring gas flow rate, chamber pressure, control valve angle, foreground piping (vacuum piping between pumps) pressure, pump speed, etc. Therefore, a process chamber can include a pressure subsystem, a flow rate subsystem, a temperature subsystem, etc. Each subsystem may experience degradation and deviate from optimal performance conditions. For example, the pressure subsystem may experience reduced pressure due to one or more of the following: pump problems, control valve problems, etc.

[0021] During the substrate manufacturing process, the process chamber may experience deteriorating conditions, such as contaminant buildup and corrosion on certain components. If these deteriorating conditions are not detected and repaired, they will cause defects in the substrate, resulting in inferior products, reduced manufacturing yield, and significant downtime and repair time.

[0022] Existing systems allow modification of the process chamber walls to include sensors for detecting such deteriorating conditions. However, these invasive wall-mounted sensors can introduce defects into the process chamber and affect plasma uniformity. This can lead to delays in achieving optimal process chamber pressure and process gas flow rates, potentially causing deformation of the deposited and / or etched films. Furthermore, these sensors can be difficult to install because the process chamber may be modified at the customer's site.

[0023] The present disclosure addresses these and other drawbacks of the prior art by enabling a transfer chamber robot to be equipped with one or more sensors capable of performing measurements and acquiring data from within the process chamber. Specifically, an electronic device manufacturing system can employ a robotic device (e.g., a transfer chamber robot) within a transfer chamber, configured to transport substrates between a loading gate and a process chamber. The transfer chamber, process chamber, and loading gate can, in some cases, operate under vacuum. The transfer chamber robot can be configured to couple one or more sensors to its end effector, these sensors being used to characterize one or more states of the process chamber, capture readings of one or more states of the process chamber, or measure one or more states of the process chamber. These sensors may include one or more of the following: accelerometers, distance or position sensors (which, for example, are used to determine the height, width, or length between two objects), cameras (e.g., high-resolution cameras, high-speed cameras, etc.), capacitive sensors, reflectometers, pyrometers (e.g., remote sensing thermometers, infrared cameras, etc.), electronic throttling controls, laser-induced fluorescence spectrometers, optical fibers (e.g., fiber optic probes), surface acoustic sensors, eddy current sensors, pipe mirrors, photodiode sensors, photomultiplier tubes, solid-state detectors, thermocouples, voltage sensors, current sensors, resistance sensors, light sources, etc.

[0024] In some embodiments, these sensors may be attached to sensor tools that can be coupled to an end effector. For example, a transfer chamber robot may be configured to attach different sensor tools to its end effector. Alternatively, one or more sensor tools may be permanently attached to the end effector of a transfer chamber robot. Each sensor tool may include one or more sensors. A tool station may be used to house, move, change, and / or recharge sensor tools. In some embodiments, the tool station may be an automated tool changer that enables the process chamber robot to select different sensor tools. The tool station may include an array of sensor tools stored in a cassette or other container (e.g., a drum cassette, a chain cassette, etc.). In response to the selection of a sensor tool, the tool array may be repositioned, and the process chamber robot may select a target sensor tool from a predetermined location. In some embodiments, in response to the selection of a sensor tool, the process chamber robot may position an end effector to a designated location of the sensor tool in the tool station. In some embodiments, the tool station may include one or more charging ports for charging each sensor tool. The sensor tool may further include electronic component modules that enable communication with the manufacturing system (e.g., sending and receiving data).

[0025] In some embodiments, sensors may be attached to a sensor disk. The sensor disk may be a substrate-shaped device comprising one or more sensors. The transfer chamber robot may be configured to retrieve the sensor disk from a loading gate using an end effector and to use the sensor disk to intercept readings and perform measurements inside the process chamber. In some embodiments, the sensor disk is connected to a power link and / or a data link. The power link may be any wired or wireless (e.g., inductive) connection capable of supplying power from the process chamber robot to the sensors using any type of connector (e.g., pins, low-particle connections, pogo pins). For example, the robotic arm or end effector may include one or more types of connectors that can be connected to the sensor disk (or other sensor tool) to supply power to the sensor tool, provide a data connection (also referred to as a data link) to the sensor tool, and so on. In some embodiments, the power link may be a similar or identical system for supplying power to other functions of the transfer chamber robot 112 (e.g., coupled mobility functions, effector operation functions, etc.). A data link can be any wired or wireless (WiFi, Bluetooth, Internet-based, etc.) connection used to provide or retrieve data from a sensor. For example, a data link can be used to provide instructions to a sensor to perform a measurement or capture a reading, to send collected data to an interface (e.g., a user interface) or a data storage system, and so on.

[0026] In some embodiments, the prediction system may train a machine learning model and apply the model to current sensor values ​​to produce outputs, such as one or more values ​​indicating a fault pattern (e.g., anomalous behavior) of the process chamber subsystem and / or predictive data indicating the type of fault that occurred (e.g., problem, failure, etc.). In some embodiments, the output is a value indicating the difference between the expected behavior of the process chamber subsystem and its actual behavior. In some embodiments, the value indicates a fault pattern associated with the process chamber subsystem. Then, in some embodiments, the system may compare the fault pattern with a known fault pattern library to determine the type of fault experienced by the subsystem. In some embodiments, the system performs corrective actions to adjust one or more parameters of the deposition process formulation (e.g., process chamber temperature setting, process chamber pressure setting, flow rate setting of precursors for materials included in the thin film deposited on the substrate surface, etc.) based on the fault pattern.

[0027] The technical advantages of the various embodiments of this disclosure include a significant reduction in the time required to perform inspections on the process chamber. This configuration allows the transfer chamber robot to perform inspections and characterizations of the process chamber periodically or each time it places or retrieves a substrate from the process chamber. Inspections are performed while maintaining a vacuum environment, thereby eliminating any procedures associated with manual inspection, such as removing components from the vacuum system and removing parts of the electronic component manufacturing system (e.g., process chamber doors). This configuration also eliminates process chamber defects and plasma uniformity issues associated with mounting sensors within the process chamber. Further technical advantages of the various embodiments of this disclosure include a significant reduction in the time required to detect problems or failures experienced by the chamber subsystem, as well as improved energy consumption, among other benefits. This disclosure can also lead to the generation of diagnostic data and the execution of corrective actions to avoid inconsistent and abnormal products, as well as unplanned user time or downtime.

[0028] Figure 1 illustrates an illustrative computer system architecture 100 according to the present disclosure. In some embodiments, the computer system architecture 100 may be included as part of a manufacturing system for processing substrates (e.g., manufacturing system 300 of Figure 3). The computer system architecture 100 includes a client device 120, a manufacturing apparatus 124, a metering device 128, a prediction server 112 (which is used, for example, to generate prediction data, provide model adaptation, use a knowledge base, etc.), and a data storage 140. The prediction server 112 may be part of a prediction system 110. The prediction system 110 may further include server machines 170 and 180. The manufacturing apparatus 124 may include sensors 126 configured to capture data of the substrates being processed in the manufacturing system. In some embodiments, the manufacturing apparatus 124 and the sensors 126 may be part of a sensor system, including a sensor server (e.g., a field service server (FSS) at a manufacturing facility) and a sensor identification code reader (e.g., a front-open wafer transfer box (FOUP) radio frequency identification (RFID) reader for a sensor system). In some embodiments, the metering device 128 may be part of a metering system, including a metering server (e.g., a metering database, metering folder, etc.) and a metering identification code reader (e.g., a FOUP RFID reader for the metering system).

[0029] Manufacturing equipment 124 can produce products, such as electronic devices, according to a recipe or by executing processes over a period of time. Manufacturing equipment 124 may include process chambers, such as process chamber 400 as described with respect to FIG. 4. Manufacturing equipment 124 can perform processes on substrates (e.g., wafers, etc.) at the process chambers. Examples of substrate processes include deposition processes that deposit one or more layers of thin films on the surface of a substrate, etching processes that form patterns on the surface of a substrate, etc. Manufacturing equipment 124 can execute each process according to a process recipe. A process recipe defines a specific set of operations to be performed on the substrate during the process and may include one or more settings associated with each operation. For example, a deposition process recipe may include temperature settings for the process chamber, pressure settings for the process chamber, flow rate settings for precursors of materials included in the thin film deposited on the substrate surface, etc.

[0030] In some embodiments, manufacturing apparatus 124 includes sensors 126 configured to generate data associated with a substrate processed at manufacturing system 100. For example, a process chamber may include one or more sensors configured to generate spectral or non-spectral data associated with the substrate before, during, and / or after performing a process (e.g., deposition) on the substrate. In some embodiments, one or more sensors may be coupled (e.g., removably coupled) to a transfer chamber robot. In particular, manufacturing apparatus 124 may employ a robotic device (e.g., a transfer chamber robot) in a transfer chamber configured to transport a substrate between a loading gate and the process chamber. The process chamber is described in more detail with reference to FIG3. In one example, a sensor may be attached to an end effector of a transfer chamber robot for supporting the substrate. In some embodiments, a sensor may be attached to a sensor tool that can be coupled and decoupled from the end effector in an automated manner without manual user intervention. For example, a transfer chamber robot can be configured to attach different sensor tools to the end effector of the transfer chamber robot based on positioning the end effector in a manner that engages the end effector with a sensor tool. Further details regarding the sensor tools will be provided with reference to Figures 5A-5B. In some embodiments, the sensor can be attached to a sensor disk. For example, the transfer chamber robot can be configured to retrieve the sensor disk from a loading gate and use the sensor disk to intercept readings and perform measurements inside the process chamber. Further details regarding the sensor disk will be provided with reference to Figures 6A-6B.

[0031] In some embodiments, the spectral data generated by sensor 126 can indicate the concentration of one or more materials deposited on the surface of a substrate. Sensor 126 configured to generate spectral data associated with the substrate may include a reflectance measurement sensor, an elliptic measurement sensor, a thermal spectral sensor, a capacitive sensor, etc. Sensor 126 configured to generate non-spectral data associated with the substrate may include a temperature sensor, a pressure sensor, a flow rate sensor, a voltage sensor, etc. Further details regarding the manufacturing apparatus 124 will be provided with reference to Figures 3 and 4.

[0032] In some embodiments, sensor 126 provides sensor data (e.g., sensor values, characteristics, tracking data) associated with manufacturing equipment 124 (e.g., associated with the production of a corresponding product (e.g., a wafer) by manufacturing equipment 124). Manufacturing equipment 124 may produce products according to a recipe or by performing operations over a period of time. Sensor data received over a period of time (which, for example, corresponds to at least a portion of a recipe or operation) may be referred to as tracking data received from different sensors 126 over time (e.g., historical tracking data, current tracking data, etc.). Sensor data may include values ​​of one or more of the following: temperature (e.g., heater temperature), interval (SP), pressure, high-frequency radio frequency (HFRF), voltage of electrostatic chuck (ESC), current, material flow rate, power, voltage, etc. Sensor data may be associated with or indicate manufacturing parameters, such as hardware parameters, such as settings or components of manufacturing equipment 124 (e.g., size, type, etc.), or process parameters of manufacturing equipment 124. Sensor data may be provided while manufacturing equipment 124 is performing a manufacturing process (e.g., equipment readings while processing a product). The sensor data for each substrate can be different.

[0033] The metrology equipment 128 can provide metrological data associated with the substrate processed by the manufacturing equipment 124. The metrological data may include values ​​for thin film properties (e.g., wafer space thin film properties), dimensions (e.g., thickness, height, etc.), dielectric constant, dopant concentration, density, defects, etc. In some embodiments, the metrological data may further include values ​​for one or more surface profile properties (e.g., etching rate, etching rate uniformity, critical dimensions of one or more features on the surface of the substrate, critical dimension uniformity across the entire substrate surface, edge placement error, etc.). The metrological data may be data for a finished product or a semi-finished product. The metrological data may be different for each substrate. The metrological data may be generated using techniques such as reflectance measurement, elliptic measurement, TEM, etc.

[0034] In some embodiments, the metering device 128 may be included as part of the manufacturing apparatus 124. For example, the metering device 128 may be included inside or coupled to a process chamber and configured to generate metering data for the substrate before, during, and / or after a process (e.g., deposition, etching, etc.) while the substrate is held in the process chamber. In this case, the metering device 128 may be referred to as an in-situ metering device. In another example, the metering device 128 may be coupled to another station of the manufacturing apparatus 124. For example, the metering device may be coupled to a transfer chamber (e.g., transfer chamber 310 as shown in FIG. 3), a loading gate (e.g., loading gate 320), or a factory interface (e.g., factory interface 306). In this case, the metering device 128 may be referred to as an integrated metering device. In other or similar embodiments, the metering device 128 is not coupled to a station of the manufacturing apparatus 124. In this case, the metering device 128 may be referred to as an inline metering device or an external metering device. In some embodiments, integrated metering devices and / or inline metering devices are configured to generate metering data for the substrate before and / or after the process.

[0035] Client device 120 may include computing devices such as personal computers (PCs), laptops, mobile phones, smartphones, tablets, portable eNet computers, network-connected televisions ("smart TVs"), network-connected media players (e.g., Blu-ray players), set-top boxes, over-the-top (OTT) streaming devices, operator boxes, etc. In some embodiments, measurement data may be received from client device 120. Client device 120 may display a graphical user interface (GUI) that allows a user to provide measurement values ​​of a substrate processed at the manufacturing system as input. Client device 120 may include a correction action component 122. Correction action component 122 may receive user input (e.g., via the graphical user interface (GUI) displayed on client device 120) indicating a command associated with manufacturing equipment 124. In some embodiments, correction action component 122 transmits an instruction to prediction system 110, receives output (e.g., prediction data) from prediction system 110, determines a correction action based on the output, and causes the correction action to be implemented. In some embodiments, the correction action component 122 receives an instruction for a correction action from the prediction system 110 and causes the correction action to be performed. Each client device 120 includes an operating system that allows a user to perform one or more operations such as generating, viewing, or editing data (e.g., instructions associated with manufacturing equipment 124, correction actions associated with manufacturing equipment 124, etc.).

[0036] Data storage 140 may be memory (e.g., random access memory), a drive (e.g., a hard drive, flash drive), a database system, or another type of component or device capable of storing data. Data storage 140 may include multiple storage components (e.g., multiple drives or multiple databases) that can span multiple computing devices (e.g., multiple server computers). Data storage 140 may store data associated with processing a substrate at manufacturing equipment 124. For example, data storage 140 may store data (referred to as process data) collected by sensors 126 at manufacturing equipment 124 before, during, or after the substrate processing. Process data may refer to historical process data (e.g., process data generated for a previous substrate processed at the manufacturing system) and / or current process data (e.g., process data generated for the current substrate processed at the manufacturing system). The data storage may also store spectral or non-spectral data associated with a portion of the substrate processed at manufacturing equipment 124. Spectral data may include historical spectral data and / or current spectral data.

[0037] Data storage 140 may also store contextual information associated with one or more substrates processed at the manufacturing system. Contextual information may include recipe names, recipe step numbers, preventative maintenance instructions, operators, etc. Contextual information may refer to historical contextual information (e.g., contextual information associated with a previous process performed on a previous substrate) and / or current process information (e.g., contextual information associated with the current process or a future process to be performed on a previous substrate). Contextual information may include identification sensors associated with specific subsystems of the process chamber.

[0038] Data storage 140 may also store task data. Task data may include one or more sets of operations performed on the substrate during the deposition process, and may include one or more settings associated with each operation. For example, task data for the deposition process may include temperature settings for the process chamber, pressure settings for the process chamber, flow rate settings for precursors of thin film material deposited on the substrate, etc. In another example, task data may include controlling the pressure at a pressure point defined by the flow rate value. Task data may refer to historical task data (e.g., task data associated with a previous process performed on a previous substrate) and / or current task data (e.g., task data associated with the current process or a future process to be performed on the substrate).

[0039] In some embodiments, data storage 140 may be configured to store data inaccessible to users of the manufacturing system. For example, users of the manufacturing system (e.g., operators) may not have access to process data, spectral data, contextual data, etc., obtained for a substrate being processed at the manufacturing system. In some embodiments, all data stored in data storage 140 may be inaccessible to users of the manufacturing system. In other or similar embodiments, a portion of the data stored in data storage 140 may be inaccessible to users, while another portion of the data stored in data storage 140 may be accessible to users. In some embodiments, one or more portions of the data stored in data storage 140 may be encrypted using an encryption mechanism unknown to the user (e.g., the data is encrypted using a private encryption key). In other or similar embodiments, data storage 140 may include multiple data storage units, wherein user-inaccessible data is stored in one or more first data storage units, while user-accessible data is stored in one or more second data storage units.

[0040] In some embodiments, data storage 140 may be configured to store data associated with known error patterns. An error pattern may be one or more values ​​(e.g., vectors, scalars, etc.) associated with one or more problems or faults related to a process chamber subsystem. In some embodiments, an error pattern may be associated with corrective actions. For example, an error pattern may include parameter adjustment steps to correct the problem or fault indicated by the error pattern. For example, a prediction system may compare a determined error pattern with a known library of error patterns to determine the type of fault experienced by the subsystem, the cause of the fault, recommended corrective actions for correcting the error, etc.

[0041] In some embodiments, the prediction system 110 includes a prediction server 112, a server machine 170, and a server machine 180. The prediction server 112, server machine 170, and server machine 180 may each include one or more computing devices, such as rack-mount servers, router computers, server computers, personal computers, mainframe computers, laptops, tablets, desktop computers, graphics processing units (GPUs), accelerator application-specific integrated circuits (ASICs) (e.g., tensor processing units (TPUs)), etc.

[0042] Server machine 170 includes a training set generator 172, which generates training datasets (e.g., a set of input data and a set of target output data) to train, validate, and / or test machine learning model 190. Machine learning model 190 can be any algorithmic model capable of learning from data. Some operations of the training set generator 172 are described in detail below with reference to FIG2. In some embodiments, the dataset generator 172 can divide the training data into a training set, a validation set, and a test set. In some embodiments, the prediction system 110 generates multiple sets of training data.

[0043] Server machine 180 may include training engine 182, validation engine 184, selection engine 185, and / or testing engine 186. An engine may refer to hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, processing equipment, etc.), software (e.g., instructions running on a processing equipment, general-purpose computer system, or dedicated machine), firmware, microcode, or a combination of the above. Training engine 182 may be capable of training one or more machine learning models 190. Machine learning model 190 may refer to model artifacts generated by training engine 182 using training data (also referred to herein as a training set), which includes data inputs and corresponding target outputs (the correct answers to the corresponding training inputs). Training engine 182 may search for patterns in the training data that map training inputs to target outputs (the answers to be predicted) and provide machine learning models 190 that capture these patterns. Machine learning models 190 can use one or more of the following: statistical modeling, support vector machine (SVM), radial basis function (RBF), clustering, supervised machine learning, semi-supervised machine learning, unsupervised machine learning, k-nearest neighbor algorithm (k-NN), linear regression, random forest, neural network (e.g., artificial neural network), etc.

[0044] One type of machine learning model that can be used to perform some or all of the tasks described above is an artificial neural network, such as a deep neural network. Artificial neural networks typically include feature representation components with classifier or regression layers that map features to a desired output space. For example, a convolutional neural network (CNN) contains multiple layers of convolutional filters. At lower layers, pooling is performed, and nonlinear problems can be solved; these lower layers are typically supplemented with multiple perceptrons that map the top-level features extracted by the convolutional layers to a decision (e.g., classification output). Deep learning is a class of machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. Deep neural networks can learn in a supervised (e.g., classification) and / or unsupervised (e.g., pattern analysis) manner. Deep neural networks consist of a hierarchical structure of layers, with different layers learning different levels of representation corresponding to different levels of abstraction. In deep learning, each layer learns to transform its input data into a slightly more abstract and comprehensive representation. For example, in plasma process tuning, the initial input can be a distribution of process results (e.g., a thickness distribution indicating one or more thickness values ​​across the entire substrate surface); the second layer can consist of feature data associated with the state of one or more regions of the control elements of the plasma process system (e.g., region orientation, plasma exposure duration, etc.); the third layer can include a starting recipe (e.g., a recipe used as a starting point to determine an updated process recipe for processing the substrate to produce process results that meet threshold criteria). It is noteworthy that deep learning processes can learn on their own which features should be optimally placed in which layers. The "depth" in "deep learning" refers to the number of layers through which the data is transformed. More precisely, deep learning systems have a considerable credit allocation path (CAP) depth. A CAP is a transformation chain from input to output. CAP describes the underlying causal relationship between the input and output. For feedforward neural networks, the CAP depth can be the depth of the network and can be the number of hidden layers plus one. For recurrent neural networks where a signal can propagate more than once through a layer, the CAP depth can be infinite.

[0045] In one embodiment, one or more machine learning models are recurrent neural networks (RNNs). An RNN is a neural network that includes memory, enabling it to capture temporal dependencies. An RNN can learn an input-output mapping that depends on both current and past inputs. An RNN will process past and future flow rate measurements and make predictions based on this continuous measurement information. An RNN can be trained using a training dataset to produce a fixed number of outputs (e.g., determining a set of substrate processing rates, deciding on modifications to the substrate process formulation). One type of RNN that can be used is a long short-term memory (LSTM) neural network.

[0046] Training neural networks can be achieved through supervised learning, which involves feeding the network a training dataset consisting of labeled inputs, observing its outputs, defining the error (by measuring the difference between the output and the label value), and using techniques such as deep gradient descent and backpropagation to adjust the weights of all layers and nodes in the network to minimize the error. In many applications, repeating this process with many labeled inputs in the training dataset produces a network that can produce the correct output when an input different from the inputs present in the training dataset appears.

[0047] Training datasets containing hundreds, thousands, tens of thousands, hundreds of thousands or more sensor data and / or process outcome data (e.g., metrological data, such as one or more thickness distributions associated with sensor data) can be used to form training datasets.

[0048] To perform training, the processing logic can input a training dataset into one or more untrained machine learning models. The machine learning models can be initialized before the first input is fed into them. The processing logic trains the untrained machine learning models based on the training dataset to produce one or more trained machine learning models that perform the various operations described above. Training can be performed by feeding one or more sensor data points into the machine learning models one at a time.

[0049] Machine learning models process inputs to produce outputs. Artificial neural networks consist of an input layer, which comprises values ​​from data points. The next layer is called a hidden layer, where nodes each receive one or more input values. Each node contains parameters (e.g., weights) applied to the input values. Therefore, each node essentially feeds the input values ​​into a multivariate function (e.g., a nonlinear mathematical transformation) to produce an output value. The next layer could be another hidden layer or an output layer. In both cases, nodes in the next layer receive output values ​​from nodes in the previous layer, each applying weights to those values ​​and then producing its own output value. This can be performed at each layer. The final layer is the output layer, where there is a node for each category, prediction, and / or output that the machine learning model can produce.

[0050] Therefore, the output can include one or more predictions or inferences. For example, the output predictions or inferences can include one or more predictions about film buildup on a chamber component, erosion of a chamber component, predicted failure of a chamber component, etc. (window). The processing logic determines the error (i.e., classification error) based on the difference between the output (e.g., prediction or inference) of the machine learning model and the target label associated with the input training data. The processing logic adjusts the weights of one or more nodes in the machine learning model based on the error. An error term or delta can be determined for each node in the artificial neural network. Based on this error, the artificial neural network adjusts one or more parameters (weights of one or more inputs of the node) of one or more of its nodes. The parameters can be updated in a backpropagation manner, such that the nodes at the highest layer are updated first, then the nodes at the next layer, and so on. The artificial neural network contains multiple layers of "neurons", where each layer receives input values ​​from the neurons in the layer above. The parameters of each neuron include weights associated with the values ​​received from each neuron in the layer above. Therefore, adjusting the parameters can include adjusting the weights of each input of one or more neurons in one or more layers of the artificial neural network.

[0051] After one or more rounds of training, the processing logic can determine whether a stopping criterion has been met. The stopping criterion can be a target level of accuracy, a target number of processed images from the training dataset, a target amount of change in parameters relative to one or more previous data points, a combination thereof, and / or other criteria. In one embodiment, the stopping criterion is met when at least a minimum number of data points have been processed and at least a threshold accuracy has been achieved. The threshold accuracy can be, for example, 70%, 80%, or 90% accuracy. In one embodiment, the stopping criterion is met if the accuracy of the machine learning model has stopped improving. If the stopping criterion is not met, further training is performed. If the stopping criterion is met, training may be complete. Once the machine learning model has been trained, a reserved portion of the training dataset can be used to test the model.

[0052] Once one or more trained machine learning models 190 are generated, they can be stored in the prediction server 112 as prediction component 114 or components of prediction component 114.

[0053] The validation engine 184 can validate the machine learning model 190 using the corresponding feature set of the validation set from the training set generator 172. Once the model parameters are optimized, model validation can be performed to determine whether the model has improved and to determine the current accuracy of the deep learning model. The validation engine 184 can determine the accuracy of the machine learning model 190 based on the corresponding feature set of the validation set. The validation engine 184 can discard trained machine learning models 190 whose accuracy does not meet a threshold accuracy. In some embodiments, the selection engine 185 can select trained machine learning models 190 whose accuracy meets the threshold accuracy. In some embodiments, the selection engine 185 can select the trained machine learning model 190 with the highest accuracy among the trained machine learning models 190.

[0054] The testing engine 186 can test the trained machine learning model 190 using the corresponding feature set from the test set generated by the data set generator 172. For example, a first trained machine learning model 190 trained using the first feature set of the training set can be tested using the first feature set of the training set. The testing engine 186 can determine the trained machine learning model 190 with the highest accuracy among all trained machine learning models based on the test set.

[0055] As detailed below, the prediction server 112 includes a prediction component 114 that provides data indicating the expected behavior of each subsystem in the process chamber and runs a trained machine learning model 190 on current sensor data input to obtain one or more outputs. The prediction server 112 can further provide data and diagnostics indicating the health status of the process chamber subsystems. This will be explained in more detail below.

[0056] Client device 120, manufacturing equipment 124, sensor 126, metering device 128, prediction server 112, data storage 140, server machine 170, and server machine 180 may be coupled to each other via network 130. In some embodiments, network 130 is a public network that provides client device 120 with access to prediction server 112, data storage 140, and other publicly available computing devices. In some embodiments, network 130 is a private network that provides client device 120 with access to manufacturing equipment 124, metering device 128, data storage 140, and other privately available computing devices. Network 130 may include one or more wide area networks (WANs), local area networks (LANs), wired networks (e.g., Ethernet), wireless networks (e.g., 802.11 networks or Wi-Fi networks), cellular networks (e.g., LTE networks), routers, hubs, switches, server computers, cloud computing networks, and / or combinations of the above.

[0057] It should be noted that in some other embodiments, the functionality of server machines 170 and 180, as well as the prediction server 112, can be provided by a smaller number of machines. For example, in some embodiments, server machines 170 and 180 can be consolidated into a single machine, while in some other or similar embodiments, server machines 170 and 180, as well as the prediction server 112, can be consolidated into a single machine.

[0058] Generally, functions described in one embodiment as being performed by server machine 170, server machine 180, and / or prediction server 112 can also be performed on client device 120. Furthermore, functionality attributable to specific components can also be performed by different or multiple components operating together.

[0059] In this embodiment, a "user" may be referred to as a single individual. However, other embodiments of this disclosure include "users" as entities controlled by a plurality of users and / or automated sources. For example, a collection of individual users collectively acting as an administrator group can be considered a "user".

[0060] Figure 2 is a flowchart of a method 200 for training a machine learning model according to the present disclosure. Method 200 is executed by processing logic, which may include hardware (circuit systems, special-purpose logic, etc.), software (e.g., running on a general-purpose computer system or a special-purpose machine), firmware, or some combination thereof. In one embodiment, method 200 may be executed by a computer system (e.g., computer system architecture 100 of Figure 1). In other or similar embodiments, one or more operations of method 200 may be executed by one or more other machines not depicted in the figures. In some embodiments, one or more operations of method 200 may be executed by server machine 170, server machine 180, and / or prediction server 112.

[0061] For ease of explanation, these methods are depicted and described as a series of actions. However, actions according to this disclosure can occur in various sequences and / or in parallel, and together with other actions not presented or described herein. Furthermore, the methods according to the disclosed subject matter can be implemented without performing all the shown actions. Moreover, those skilled in the art will understand that these methods can alternatively be represented as a series of interrelated states via state diagrams or events. Furthermore, it should be understood that the methods disclosed in this specification can be stored on an article of manufacture to facilitate the transport and transfer of such methods to a computing device. The term "article of manufacture" as used herein is intended to include computer programs accessible from any computer-readable device or storage medium.

[0062] At block 210, the processing logic initializes the training set T to an empty set (e.g., {}).

[0063] At block 212, the processing logic obtains sensor data (e.g., sensor values, features, tracking data) associated with a previous deposition process performed to deposit one or more layers of a thin film on the surface of a previous substrate. The sensor data can be further associated with subsystems of the process chamber. A subsystem can be characterized as a set of sensors related to operating parameters of the process chamber. Operating parameters can be temperature, flow rate, pressure, etc. For example, a pressure subsystem can be characterized by one or more sensors measuring gas flow rate, chamber pressure, control valve angle, foreground piping (vacuum piping between pumps) pressure, pump speed, etc. Each process chamber may include multiple different subsystems, such as a pressure subsystem, a flow rate subsystem, a temperature subsystem, etc.

[0064] In some embodiments, the sensor data associated with the deposition process is historical data associated with one or more previous deposition settings of a previous deposition process previously performed at the manufacturing system for a previous substrate. For example, the historical data may be historical context data associated with a previous deposition process stored at data storage 140. In some embodiments, the one or more previous deposition settings may include at least one of a previous temperature setting of the previous deposition process, a previous pressure setting of the previous deposition setting, a previous flow rate setting of a precursor for one or more materials of a previous thin film deposited on the surface of the previous substrate, or any other setting associated with the deposition process. The flow rate setting may refer to the flow rate setting of the precursor in an initial instance of the previous deposition process (referred to as the initial flow rate setting), the flow rate setting of the precursor in a final instance of the previous deposition process (referred to as the final flow rate setting), or the rate of increase of the precursor flow rate during the deposition process. In one example, the precursor of the previous thin film may include a boron-containing precursor or a silicon-containing precursor. In some embodiments, the sensor data may also be associated with a previous etching process performed on the previous substrate or any other process performed in the process chamber.

[0065] At block 214, the processing logic obtains task data associated with the formulation of the thin film deposited on the surface of the previous substrate. For example, the task data may be the required temperature setting, pressure setting, flow rate setting of the precursor material for the thin film deposited on the substrate, etc. The task data may include historical task data of the previous thin film deposited on the surface of the previous substrate. In some embodiments, the historical task data of the previous thin film may correspond to historical task values ​​associated with the formulation of the previous thin film. According to the previously described embodiment, the processing logic may obtain the task data from data storage 140.

[0066] At block 216, the processing logic generates first training data based on the obtained sensor data associated with the previous deposition process performed on the previous substrate. At block 218, the processing logic generates second training data based on task data associated with the formulation of the thin film deposited on the surface of the previous substrate.

[0067] At block 220, the processing logic generates a mapping between first training data and second training data. This mapping refers to first training data that includes or is based on data from a previous deposition process performed on a previous substrate, and second training data that includes or is based on task data associated with the formulation of a thin film deposited on the surface of the previous substrate, wherein the first training data is associated with (or mapped to) the second training data. At block 224, the processing logic adds this mapping to the training set T.

[0068] At block 226, the processing logic determines whether the training set T includes sufficient training data to train the machine learning model. It should be noted that in some implementations, the sufficiency of the training set T can be determined solely based on the number of mappings in the training set, while in other implementations, the sufficiency of the training set T can be determined based on one or more other criteria (e.g., a measure of the diversity of training examples, etc.) in addition to or instead of the number of input / output mappings. In response to the determination that the training set does not include sufficient training data to train the machine learning model, method 200 returns to block 212. In response to the determination that the training set (T) includes sufficient training data to train the machine learning model, method 200 continues to block 228.

[0069] At block 228, the processing logic provides a training set T to train a machine learning model. In one implementation, the training set T is provided to the training engine 182 of the server machine 180 to perform training. In the case of a neural network, for example, given the input values ​​of an input / output mapping, the input values ​​of the input / output mapping are input into the neural network, and the output values ​​of the input / output mapping are stored in the output nodes of the neural network. Then, the connection weights in the neural network are adjusted according to a learning algorithm (e.g., backpropagation, etc.), and the procedure is repeated for other input / output mappings in the training set T.

[0070] In some embodiments, the processing logic may execute an outlier detection method to remove outliers from the training set T before training the machine learning model. The outlier detection method may include techniques for identifying values ​​that differ significantly from the majority of the training data. These values ​​may be generated by errors, noise, etc.

[0071] At block 230, the processing logic performs a calibration process on the trained machine learning model. In some embodiments, the processing logic may compare the expected behavior of the process chamber subsystem with the current behavior of the process chamber subsystem based on the numerical difference between the predicted behavior and the current behavior. For example, the processing logic may compare one or more values ​​associated with predicted data for the pressure subsystem, flow subsystem, or temperature subsystem with one or more values ​​associated with the currently measured behavior of the pressure subsystem, flow subsystem, or temperature subsystem, respectively.

[0072] Following block 230, the machine learning model can be used to generate one or more values ​​indicating error patterns (e.g., anomalous behavior) of the process chamber subsystem, generate predictive data indicating error types (e.g., problems, failures, etc.), and / or execute corrective actions to correct suspected problems or failures. Predictive data can be generated by comparing error patterns with a known library of error patterns.

[0073] In some embodiments, a manufacturing system may include more than one process chamber. For example, the example manufacturing system 300 of FIG3 illustrates multiple process chambers 314, 316, and 318. It should be noted that in some embodiments, data obtained for training a machine learning model and data collected to provide input to the machine learning model may be associated with the same process chamber of the manufacturing system. In other or similar embodiments, data obtained for training a machine learning model and data collected to provide input to the machine learning model may be associated with different process chambers of the manufacturing system. In other or similar embodiments, data obtained for training a machine learning model may be associated with a process chamber of a first manufacturing system, and data collected to provide input to the machine learning model may be associated with a process chamber of a second manufacturing system.

[0074] Figure 3 is a top view of an example manufacturing system 300 according to the present disclosure. The manufacturing system 300 can perform one or more processes on a substrate 302. The substrate 302 can be any planar article with suitable rigidity, fixed size, such as a silicon-containing disk or wafer, a patterned wafer, a glass plate, etc., which is suitable for manufacturing electronic devices or circuit components thereon.

[0075] Manufacturing system 300 may include process tool 304 and a factory interface 306 coupled to process tool 304. Process tool 304 may include housing 308 having a transfer chamber 310. Transfer chamber 310 may include one or more process chambers (also called handling chambers) 314, 316, 318 disposed around and coupled to the transfer chamber. Process chambers 314, 316, 318 may be coupled to transfer chamber 310 via corresponding ports (e.g., slit valves, etc.). Transfer chamber 310 may also include transfer chamber robot 312 configured to transfer substrate 302 between process chambers 314, 316, 318, loading gate 320, etc. Transfer chamber robot 312 may include one or more arms, each arm including one or more end effectors at each end of each arm. End effectors may be configured to handle specific objects, such as wafers, sensor disks, sensor tools, etc. In some embodiments, the end effector may be configured to couple with one or more sensor tools from tool station 340.

[0076] Tool station 340 may be a station for accommodating, moving, changing, and / or recharging sensor tools. In some embodiments, tool station 340 may be an automated tool changer, enabling a process chamber robot to select different sensor tools. Tool station 340 may include an array of sensor tools stored in a cassette (e.g., a drum cassette, chain cassette, etc.) or other container. In some embodiments, in response to the selection of a sensor tool, the tool array may be repositioned, and process chamber robot 312 may select a desired sensor tool from a predetermined location. In some embodiments, in response to the selection of a sensor tool, process chamber robot 312 may position an end effector to a designated location of the sensor tool within the tool station. In some embodiments, tool station 340 may include one or more charging ports for charging each sensor tool. For example, when a sensor tool is positioned within tool station 340 (e.g., on a cassette), the charging component of the sensor tool may be connected (e.g., wired) to or near a charging port (e.g., wirelessly, such as inductively charged). Further details regarding the transfer chamber robot 312 and the tool station 340 will be provided in Figures 5A-5B.

[0077] In some embodiments, the end effector can be configured to retrieve a sensor disk from a loading gate. The sensor disk can be a substrate comprising one or more sensors or any other device. Further details regarding the transfer chamber robot 312 and the sensor disk will be provided with reference to Figures 6A-B.

[0078] Process chambers 314, 316, and 318 can be adapted to perform any number of processes on substrate 302. The same or different substrate processes can be performed in each process chamber 314, 316, and 318. Substrate processes may include atomic layer deposition (ALD), physical vapor deposition (PVD), chemical vapor deposition (CVD), etching, annealing, curing, pre-cleaning, metal or metal oxide removal, etc. Other processes can be performed on the substrate within these chambers. Process chambers 314, 316, and 318 may each include one or more sensors configured to capture data about substrate 302 before, after, or during substrate processing. For example, the one or more sensors may be configured to capture spectral and / or non-spectral data of a portion of substrate 302 during substrate processing. In other or similar embodiments, the one or more sensors may be configured to capture data associated with the environment within process chambers 314, 316, and 318 before, after, or during substrate processing. For example, the one or more sensors can be configured to capture data related to temperature, pressure, gas concentration, etc., of the environment within the process chambers 314, 316, 318 during the substrate manufacturing process.

[0079] Load gate 320 may also be coupled to housing 308 and transfer chamber 310. Load gate 320 may be configured to interface and couple to transfer chamber 310 on one side and to fab interface 306 on the other. In some embodiments, load gate 320 may have an environmentally controlled atmosphere that can change from a vacuum environment (where substrates can be transferred to or from transfer chamber 310) to atmospheric pressure or an inert gas environment near atmospheric pressure (where substrates can be transferred to and from fab interface 306). Fab interface 306 may be any suitable housing, such as a device front-end module (EFEM). Fab interface 306 may be configured to receive substrate 302 from substrate carrier 322 (e.g., front-open wafer transfer box (FOUP)) mated at various loading ports 324 of fab interface 306. Fab interface robot 326 (shown in dashed lines) may be configured to transfer substrate 302 between carrier (also called container) 322 and load gate 320. The carrier 322 can be a substrate storage carrier or a replacement parts storage carrier.

[0080] Manufacturing system 300 can also be connected to a client device (not shown) configured to provide information about manufacturing system 300 to a user (e.g., an operator). In some embodiments, the client device can provide information to a user of manufacturing system 300 via one or more graphical user interfaces (GUIs). For example, the client device can provide information via the GUI about a target thickness distribution of a thin film to be deposited on the surface of substrate 302 during deposition processes performed in process chambers 314, 316, 318. According to the embodiments described herein, the client device can also provide information about modifications to the process formulation based on a corresponding set of deposition settings predicted to correspond to the target distribution.

[0081] Manufacturing system 300 may also include system controller 328. System controller 328 may be and / or may include computing devices such as personal computers, server computers, programmable logic controllers (PLCs), microcontrollers, etc. System controller 328 may include one or more processing devices, which may be general-purpose processing devices such as microprocessors, central processing units, etc. More specifically, the processing device may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or a combination of instruction sets. The processing device may also be one or more special-purpose processing devices such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), network processors, etc. System controller 328 may include data storage devices (e.g., one or more disk drives and / or solid-state drives), main memory, static memory, network interfaces, and / or other components. System controller 328 may execute instructions to perform any or more of the methods and / or embodiments described herein. In some embodiments, system controller 328 may execute instructions to perform one or more operations at manufacturing system 300 in accordance with a process recipe. The instructions may be stored on a computer-readable storage medium, which may include main memory, static memory, secondary storage, and / or processing devices (during execution of the instructions).

[0082] System controller 328 may receive data from sensors included on or within various parts of manufacturing system 300, such as processing chambers 314, 316, 318, transfer chamber 310, loading gate 320, etc. In some embodiments, the data received by system controller 328 may include spectral and non-spectral data of a portion of substrate 302. In other or similar embodiments, the data received by system controller 328 may include data associated with processing substrate 302 at processing chambers 314, 316, 318, as previously described. For the purposes of this description, system controller 328 is described as receiving data from sensors included within process chambers 314, 316, 318. However, system controller 328 may also receive data from any part of manufacturing system 300 and may use data received from that part according to the embodiments described herein. In an illustrative example, system controller 328 may receive data from one or more sensors in process chambers 314, 316, 318 before, after, or during substrate processing in process chambers 314, 316, 318. Data received from sensors in various parts of manufacturing system 300 may be stored in data storage 350. Data storage 350 may be included as a component within system controller 328, or it may be a component separate from system controller 328. In some embodiments, data storage 350 may be data storage 140 as described in FIG. 1.

[0083] Figure 4 is a schematic cross-sectional side view of a process chamber 400 according to an embodiment of the present disclosure. In some embodiments, the process chamber 400 may correspond to process chambers 314, 316, and 318 as described in Figure 3. The process chamber 400 may be used for processes that provide a corrosive plasma environment. For example, the process chamber 400 may be a chamber for a plasma etcher or plasma etching reactor, etc. In another example, the process chamber may be a chamber for a deposition process, as previously described. In one embodiment, the process chamber 400 includes a chamber body 402 and a showerhead 430 that surround an internal volume 406. The showerhead 430 may include a showerhead base and a showerhead gas distribution plate. Alternatively, the showerhead 430 may be replaced by a cover and nozzle in some embodiments, or by a plurality of pie-shaped showerhead compartments and a plasma generation unit in other embodiments. The chamber body 402 may be made of aluminum, stainless steel, or other suitable materials (e.g., titanium (Ti)). The chamber body 402 generally includes sidewalls 408 and a bottom 410. An exhaust port 426 may be defined within the chamber body 402 and may couple the internal volume 406 to a pump system 428. The pump system 428 may include one or more pumps and throttle valves used to evacuate and regulate the pressure of the internal volume 406 of the process chamber 400.

[0084] A showerhead 430 may be supported on a sidewall 408 of the chamber body 402. A showerhead 420 (or cover) may be opened to allow access to the internal volume 406 of the process chamber 400 and may provide a seal to the process chamber 400 when closed. A gas panel 458 may be coupled to the process chamber 400 to supply process and / or cleaning gases to the internal volume 406 via the showerhead 430 or the cover and nozzle (e.g., via orifices in the showerhead or cover and nozzle). For example, the gas panel 458 may provide a precursor for the material of the thin film 451 deposited on the surface of the substrate 302. In some embodiments, the precursor may include a silicon-based precursor or a boron-based precursor. The showerhead 430 may include a gas distribution plate (GDP) and may have a plurality of gas delivery holes 432 (also referred to as channels) throughout the GDP. A substrate support assembly 448 is disposed in the internal volume 406 of the process chamber 400 below the showerhead 430. The substrate support assembly 448 holds the substrate 302 during processing (e.g., during the deposition process).

[0085] In some embodiments, the processing chamber 400 may include a metering device (not shown) configured to generate in-situ metering measurements during a process performed at the processing chamber 400. The metering device may be operatively coupled to a system controller (e.g., system controller 328, as previously described). In some embodiments, the metering device may be configured to generate metering measurements (e.g., thickness) for the thin film 451 during a specific instance of the deposition process. The system controller may generate a thickness distribution of the thin film 451 based on the metering measurements received from the metering device. In other or similar embodiments, the processing chamber 400 does not include a metering device. In such embodiments, the system controller may receive one or more metering measurements of the thin film 451 after the deposition process in the processing chamber 400 has completed. The system controller may determine a deposition rate based on the one or more metering measurements and may correlate the generated thickness distribution of the thin film 451 based on the determined concentration gradient and the determined deposition rate of the deposition process.

[0086] Figure 5A is a schematic diagram of an end effector 510 according to an embodiment of the present disclosure. Figure 5B is a schematic diagram of an end effector 510 coupled to a sensor tool 520 according to an embodiment of the present disclosure. The end effector 510 may include a tool connector 512, a substrate platform 514, and a robot connector 516. In some embodiments, the end effector 510 may include one or more sensors. The sensor tool 520 may include an end effector connector 522 and a charging component 526.

[0087] Robot connector 516 can be used to couple end effector 510 to a transfer chamber robot (e.g., transfer chamber robot 310). Substrate platform 514 can be used to handle specific objects, such as substrates (e.g., wafers). Tool connector 512 can be used to couple end effector 510 to sensor tool 520. For example, transfer chamber robot 312 can receive instructions to couple to a specific sensor tool (e.g., sensor tool 520) housed in a tool station (e.g., tool station 340), positioning end effector 510 in a first position (e.g., pre-coupling position) near end effector connector 522 of sensor tool 520, placing end effector 510 in a second position to couple end effector 510 to end effector connector 522 of sensor tool 520 (e.g., coupling position), and then withdrawing sensor tool 520 from tool station 340. After using the sensor tool 520, the transfer chamber robot 312 can return the sensor tool 520 to the tool station 340. In some embodiments, the tool station 340 can be configured to automatically couple the selected sensor tool to the end effector 510 using, for example, a gripper.

[0088] Sensor tool 520 may include one or more sensors. These sensors may be used to characterize one or more states of process chambers 314, 316, 318, capture readings of the one or more states, or measure the one or more states. These sensors may include one or more of the following: accelerometers, distance sensors (for example, used to determine the height, width, or length between two objects), cameras (e.g., high-resolution cameras, high-speed cameras, etc.), capacitive sensors, reflectometers, pyrometers (e.g., remote sensing thermometers, infrared cameras, etc.), laser-induced fluorescence spectrometers, optical fibers (such as fiber optic probes), surface acoustic sensors, eddy current sensors, pipe mirrors, photodiode sensors, photomultiplier tubes, solid-state detectors, thermocouples, voltage sensors, current sensors, resistance sensors, or any other type of sensor.

[0089] Accelerometers can be used to detect and correct (or calibrate) vibration and position noise in the transmission chamber robot 312. Distance sensors can be used to detect erosion and / or corrosion of the chuck (table), edge rings, showerheads, walls, or any other components of the process chambers 314, 316, 318. For example, during an etching process, an edge ring can be used to promote uniformity along the substrate surface. However, etching can corrode the edge ring. Therefore, position sensors can be used to detect said corrosion by measuring the distance between the top plane of the edge ring and, for example, the top plane of the substrate. Cameras can record sections of the process chambers 314, 316, 318 for operator visual inspection. Capacitor sensors can be used to detect the position of the showerheads used for gas distribution inside the process chambers 314, 316, 318, determine substrate leveling, detect erosion, etc. A reflectometer can be used to detect the quality of the seasoning film on the walls of process chambers 314, 316, and 318. For example, the reflectometer can generate a beam of light onto the walls of process chambers 314, 316, and 318 and record the reflectivity of the reflected light. A pyrometer can be used to detect the temperature uniformity of the heaters in process chambers 314, 316, and 318, detect hot spots inside process chambers 314, 316, and 318, etc. The transfer chamber robot 312 may include any number of the discussed or other sensors, or any combination of the discussed or other sensors.

[0090] In some embodiments, sensor tool 520 may include an electronic component module capable of facilitating communication with system 100 (e.g., system controller 328, prediction system 110, client device 120, etc.). The electronic component module may include a microcontroller and a memory cache coupled to the microcontroller. The memory cache may be used to collect and store data acquired by sensor tool 520 before transmitting the data to system 100. In some embodiments, the data may be transmitted using wireless communication circuitry. In other embodiments, the data may be transmitted using a wired connection between sensor tool 520 and system 100. For example, end effector connector 522 and / or tool connector 512 may include one or more pins, low-particle connectors, spring pins, or any other type of connector that can transmit data (or power) between sensor tool 520 and system 100 (via transfer chamber robot 312). In some embodiments, data may be stored (cached) in the memory cache before being transmitted to system 100. In other embodiments, data can be transmitted to system 100 upon collection without needing to be stored in a memory cache. In some embodiments, the wireless or wired connection can be continuous. In other embodiments, the wireless or wired connection can be established periodically or after a verification completion or some other triggering event (e.g., when the memory cache is nearing full, when sensor tool 520 is positioned on tool station 340, etc.). For example, sensor tool 520 may include wired or wireless communication circuitry for communicating with tool station 520. Sensor tool 520 can collect sensor data and store it in a memory cache, and transmit the sensor data once positioned on tool station 340. In some embodiments, tool station 340 may include a wired or wireless connector capable of receiving data (e.g., sensor data) from sensor tool 520 or transmitting data (e.g., instructions) to sensor tool 520.

[0091] The electronic component module may further include power supply elements and power-on circuitry. For example, the power supply element may be a battery, a capacitor (such as an ultracapacitor or supercapacitor), or any other power supply element (e.g., a power link) capable of providing power to the sensor and / or electronic component module. In some embodiments, the power supply element can be recharged from tool station 340. For example, the sensor tool 520 can be charged when it is housed in tool station 340 and idle therein. In particular, the sensor tool 520 may be coupled to a charging port of tool station 340 via charging component 526. In some embodiments, charging component 526 may be coupled to the charging port using one or more connectors (e.g., pins, low-particle connectors, spring pins). In other embodiments, charging component 526 may be positioned near the charging port, and the sensor tool 520 may be charged via wireless charging (e.g., inductive charging).

[0092] Figures 6A and 6B are schematic diagrams of a sensor disk 610 and an end effector 620 according to embodiments of the present disclosure. The end effector 620 may include an electrical connector 622. The electrical connector 622 may be any type of connector (e.g., pins, low-particle connectors, spring pins) for supplying power to and / or transmitting data to and from the sensor disk 610, as will be explained in detail below.

[0093] The sensor disk 610 can be any device that includes one or more sensors (e.g., sensors 612A-612E). Sensors 612A-612E can be used to characterize one or more states of process chambers 314, 316, 318, capture readings of the one or more states, or measure the one or more states. Sensors 612A-612E can include one or more of the following: accelerometers, distance sensors (e.g., used to determine the height, width, or length between two objects), cameras (e.g., high-resolution cameras, high-speed cameras, etc.), capacitive sensors, reflectometers, pyrometers (e.g., remote sensing thermometers, infrared cameras, etc.), laser-induced fluorescence spectrometers, optical fibers (e.g., fiber optic probes), surface acoustic sensors, eddy current sensors, pipe mirrors, photodiode sensors, photomultiplier tubes, solid-state detectors, thermocouples, voltage sensors, current sensors, resistance sensors, or any other type of sensor. Although sensor disk 610 describes five sensors 612A-612E, it should be understood that any number of sensors can be coupled to sensor disk 610.

[0094] In some embodiments, sensor disk 610 (and / or sensors 612A-E) is connected to one or more of a power link and / or a data link. The power link can be any wired connection (e.g., connected via electrical connector 622) or wireless (e.g., inductive) connection capable of providing power to the sensors. In some embodiments, the power link is a similar or identical system for providing power to other functions of the transmission chamber robot 112 (e.g., connection mobility functions, effector operation functions, etc.). In some embodiments, the power link is a system independent of another power link for providing power to other functions of the transmission chamber robot 312. In some embodiments, sensor disk 610 may include power supply elements and / or power-on circuitry. The data link can be any wired connection (e.g., connected via electrical connector 622) or wireless (WiFi, Bluetooth, Internet-based, etc.) connection for providing or retrieving data from the sensors. For example, the data link can be used to provide instructions to the sensors to perform measurements or capture readings, send collected data to an interface (e.g., a user interface) or a data storage system, etc. In some embodiments, the data link is a system independent of another data link, used to provide instructions and communicate with the transfer chamber robot 312 to enable the transfer chamber robot 312 to transfer and position substrates between transfer chambers 314, 316, 318 and loading gate 320. In some embodiments, the sensor disk 610 may include electronic component modules (e.g., a microcontroller and a memory cache coupled to the microcontroller) capable of facilitating communication with system 100 (e.g., system controller 328, prediction system 110, client device 120, etc.).

[0095] The sensor disk 610 can be transported by an end effector 620. For example, the sensor disk can be housed in a FOUP, which can dock at a loading port 324 of the factory interface 306. The factory interface robot 326 can be configured to transfer the sensor disk 610 between the FOUP and the loading gate 320. A transfer chamber robot 312 can position the end effector 620 (coupled to the sensor disk 620) within any of the process chambers 314, 316, 318. The end effector and / or arm of the transfer chamber robot can include one or more connectors for connecting to the sensor tool or the sensor disk 620. Such connectors can provide power and / or data link connections. Using the sensor disk 620, the transfer chamber robot 312 can intercept readings and / or measure the process chambers. In some embodiments, the sensor and / or transmission chamber robot 312 includes a processing device, such as a central processing unit (CPU), microcontroller, programmable logic controller (PLC), system-on-a-chip (SoC), server computer, or other suitable type of computing device. The processing device may be configured to execute programmed instructions relating to the operation of the sensor. The processing device may receive feedback signals from the sensor device and calculate these signals into sensor data (e.g., temperature, video data, position data, etc.). The processing device may further transmit control signals to the sensor based on the received instructions. In some embodiments, the processing device is configured for high-speed feedback processing and may include, for example, an EPM. In some embodiments, the processing device is configured to transmit or send feedback signals and / or sensor data to an interface (e.g., a user interface), data storage, etc.

[0096] The process chamber robot 312 can be further configured to position the sensor disk 610 back into the loading gate 320. In some embodiments, the sensor disk 610 may be wafer-like in shape. In other embodiments, the sensor disk 610 may be any shape, including but not limited to circular, elliptical, square, rectangular, irregular, etc.

[0097] Figure 7 is a flowchart of a method 700 for determining the fault type of a process chamber subsystem using a machine learning model, according to a pattern of this disclosure. Method 700 is executed by processing logic, which may include hardware (circuit systems, dedicated logic, etc.), software (e.g., running on a general-purpose computer system or a dedicated machine), firmware, or some combination thereof. In one embodiment, method 700 may be executed by a computer system (e.g., computer system architecture 100 of Figure 1). In other or similar embodiments, one or more operations of method 700 may be executed by one or more other machines not depicted in the figures. In some patterns, one or more operations of method 700 may be executed by server machine 170, server machine 180, and / or prediction server 112.

[0098] At block 710, the processing logic obtains sensor data associated with an operation performed in the process chamber. In some embodiments, the operation may include a deposition process performed in the process chamber to deposit one or more layers of a thin film on the surface of a substrate, an etching process performed on the one or more layers of the thin film on the surface of the substrate, and so on. The operation may be performed according to a formulation. The sensor data may include values ​​for one or more of the following: temperature (e.g., heater temperature), spacing, pressure, high-frequency radio frequency, voltage of the electrostatic chuck, current, material flow rate, power, voltage, etc. The sensor data may be associated with or indicate manufacturing parameters, such as hardware parameters, such as settings or components (e.g., size, type, etc.) of manufacturing equipment 124, or process parameters of manufacturing equipment 124.

[0099] At block 712, the processing logic applies a machine learning model (e.g., model 190) to the acquired sensor data. The machine learning model can be used to generate one or more values ​​associated with the expected behavior of the process chamber subsystem. For example, the machine learning model can use an algorithm to generate predicted behavior of the process chamber subsystem using a training set T. In some embodiments, the machine learning model is trained using historical sensor data of the process chamber subsystem and task data associated with the recipe used to perform the operation.

[0100] At block 714, the processing logic generates an output based on sensor data via a machine learning model. In some embodiments, this output may be a value indicating a pattern (e.g., a failure pattern). In particular, the output may include predictive data indicating whether current data indicates that the process chamber is experiencing a failure. In some embodiments, the output may be at least one value indicating the difference between the expected behavior of the process chamber subsystem and the actual behavior of the process chamber subsystem. In particular, this value may indicate the difference between the actual values ​​of the sensor set associated with the subsystem and the expected values ​​of the sensor set. Failures may include mechanical failures, high or low pressure, high or low gas flow rates, high or low temperatures, etc.

[0101] At block 716, the processing logic determines whether the process chamber subsystem is experiencing a fault. In some embodiments, a fault may include mechanical failure, high or low pressure, high or low gas flow rate, high or low temperature, corrosion, erosion, deterioration, etc. In some embodiments, the processing logic may determine whether the process chamber subsystem is experiencing a fault by comparing the output with a predetermined threshold. In some embodiments, the processing logic may determine whether the process chamber subsystem is experiencing a fault by determining that the output fails to match the expected behavior. In response to the processing logic determining that the process chamber subsystem is not experiencing a fault (e.g., the output value does not exceed a predetermined threshold), the processing logic may proceed to block 710. In response to the processing logic determining that the process chamber subsystem is experiencing a fault (e.g., the output value exceeds a predetermined threshold), the processing logic may proceed to block 718.

[0102] At block 718, the processing logic can identify the type of fault based on the output. In some embodiments, the processing logic can compare the error pattern with a manufacturing data map and / or a library of known error patterns to determine the fault type based on the similarity between the error pattern and the known error patterns or the manufacturing data map. In some embodiments, natural language processing can be used to extract the error type from the manufacturing data map, and then the error type is associated with the corresponding error pattern. In some embodiments, the fault type can be displayed on the user interface (shown to the user).

[0103] At block 720, the processing logic can perform (or suggest) corrective actions based on identified faults. In some embodiments, the corrective action can be determined based on data obtained from a fault database. In some embodiments, the corrective action can include generating an alarm or indication to the client device 120 regarding the identified problem. In some embodiments, the corrective action can include the processing logic indicating the type of fault, the cause of the fault, and / or the recommended corrective action. In some embodiments, the corrective action can include the processing logic adjusting one or more parameters of the deposition process formulation based on the desired properties of the thin film (e.g., temperature setting of the process chamber, pressure setting of the process chamber, flow rate setting of the precursor of the material included in the thin film deposited on the substrate surface, etc.). In some embodiments, the deposition process formulation can be adjusted before, during (e.g., in real time), or after the deposition process.

[0104] Figure 8 is a block diagram illustrating a computer system 800 according to certain embodiments. In some embodiments, the computer system 800 may be connected to other computer systems (e.g., via a network connection, such as a local area network (LAN), intranet, extranet, or internet). The computer system 800 may operate as a server or client computer in a client-server environment, or as a peer computer in a peer-to-peer or distributed network environment. The computer system 800 may be provided by a personal computer (PC), tablet PC, set-top box (STB), personal digital assistant (PDA), cellular phone, web appliance, server, network router, switch, or bridge, or any device capable of executing a set of instructions (executed sequentially or otherwise) specifying actions to be taken by that device. Further, the term "computer" should include any collection of computers that individually or jointly execute one or more sets of instructions to perform any or more of the methods described herein.

[0105] In another embodiment, the computer system 800 may include a processing device 802, volatile memory 804 (e.g., random access memory (RAM)), non-volatile memory 806 (e.g., read-only memory (ROM) or electrically erasable programmable ROM (EEPROM)), and data storage device 816, which may communicate with each other via bus 808.

[0106] The processing device 802 may be provided by one or more processors such as a general-purpose processor (for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor that implements other types of instruction sets, or a microprocessor that implements a combination of multiple types of instruction sets) or a special-purpose processor (for example, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).

[0107] The computer system 800 may further include a network interface device 822 (which is coupled to, for example, a network 874). The computer system 800 may also include a video display unit 810 (e.g., an LCD), a digit input device 812 (e.g., a keyboard), a cursor control device 814 (e.g., a mouse), and a signal generating device 820.

[0108] In some embodiments, data storage device 816 may include a non-transitory computer-readable storage medium 824 on which instructions 826 may be stored, which encode any or more of the methods or functions described herein, including encoding components of FIG1 (e.g., correction action component 122, prediction component 114, etc.) and instructions for implementing the methods described herein.

[0109] Instruction 826 may also reside wholly or partially within volatile memory 804 and / or processing device 802 during its execution by computer system 800, thus volatile memory 804 and processing device 802 may also constitute machine-readable storage media.

[0110] Although computer-readable storage medium 824 is shown as a single medium in the illustrative examples, the term "computer-readable storage medium" should also include single or multiple media (e.g., centralized or distributed databases and / or associated caches and servers) storing the one or more executable instruction sets. The term "computer-readable storage medium" should also include any media capable of storing or encoding instruction sets for execution by a computer, causing the computer to perform any or more of the methods described herein. The term "computer-readable storage medium" should include, but is not limited to, solid-state memory, optical media, and magnetic media.

[0111] The methods, components, and features described herein can be implemented by discrete hardware components or integrated into the functionality of other hardware components such as ASICs, FPGAs, DSPs, or similar devices. Furthermore, the methods, components, and features can also be implemented by firmware modules or functional circuitry systems within hardware devices. Further, the methods, components, and features can be implemented as any combination of hardware devices and computer programming components or as a computer program.

[0112] Unless otherwise specifically stated, terms such as “receive,” “execute,” “provide,” “obtain,” “cause,” “access,” “determine,” “add,” “use,” and “train” refer to actions and processes performed or implemented by a computer system that manipulate and transform data represented as physical (electronic) quantities in the computer system's registers and memory into other data similarly represented as physical quantities in the computer system's memory or registers or other such information storage, transmission, or display devices. Furthermore, the terms “first,” “second,” “third,” “fourth,” etc., as used herein are labels to distinguish different elements and may not have a sequential meaning based on their numerical designation.

[0113] The examples described herein also relate to an apparatus for performing the methods described herein. This apparatus may be specifically configured to perform the methods described herein, or it may comprise a general-purpose computer system selectively programmed by a computer program stored in a computer system. Such a computer program may be stored in a computer-readable physical storage medium.

[0114] The methods and illustrative examples described herein are not inherently related to any particular computer or other device. Various general-purpose systems can be used in accordance with the teachings described herein, or it may prove appropriate to construct more specialized devices to perform each of the individual functions, norms, sub-norms, or operations of the methods described herein. Examples of structures for various such systems are illustrated in the description above.

[0115] The above description is intended to be illustrative and not restrictive. While this disclosure has been described with reference to specific illustrative examples and embodiments, it will be understood that this disclosure is not limited to the examples and embodiments described. The scope of this disclosure will be determined by referring to the following claims and the full scope of their equivalents.

[0116] 100: Computer System Architecture 110: Prediction System 112: Prediction Server 114: Predicted Components 120: Client device 122: Correcting the motion components 124: Manufacturing equipment 126: Sensor 128: Measuring equipment 130: Internet 140: Data storage 170: Server Machine 172: Training Set Generator 180: Server Machine 182: Training Engine 184: Verification Engine 185: Select Engine 186: Test Engine 190: Machine Learning Model 200: Method 210: Square 212: Square 214: Square 216: Square 218: Square 220: Square 222: Square 226: Square 228: Square 230: Square 300: Manufacturing System 302:Substrate 304: Process Tools 306: Factory Interface 310: Transmission Chamber 312: Transfer Chamber Robot 314: Process Chamber 316: Process Chamber 318: Process Chamber 320: Loading gate 322: Carrier 324: Loading port 326: Factory Interface Robot 328: System Controller 340: Tool Station 350: Data storage 400: Process Chamber 402: Main body of the chamber 406: Internal volume 408: Sidewall 410: Bottom 426: Exhaust Port 428: Pump System 430: Showerhead 432: Gas delivery port 448: Substrate support assembly 451:Thin film 458: Gas Panel 510: End effector 512: Tool Connector 514: Substrate Platform 516: Robot Connector 520: Sensor Tools 526: Charging components 610: Sensor disk 620: End effector 700: Method 710: Square 712: Square 714: Square 716: Square 718: Square 720: Square 800: Computer System 802: Processing equipment 804: Volatile Memory 806: Non-volatile memory 808: Busbar 810: Video display unit 812: Digital input device 814: Vernier control equipment 818: Data storage equipment 820: Signal generating equipment 822: Network Interface Device 824: Computer-readable storage media 826: Instruction 874: Network 612A: Sensor 612B: Sensor 612C: Sensor 612D: Sensor 612E: Sensor

[0117] Domestic storage information (please note in order of storage institution, date, and number) none

[0118] Overseas storage information (please note in the order of storage country, institution, date, and number) none

Claims

1. An electronic device manufacturing system, comprising: One transmission chamber; A tool station is located within the transmission chamber; A process chamber, coupled to the transmission chamber; And a transfer chamber robot configured to transfer a substrate to and from the process chamber, wherein the transfer chamber robot is configured to be coupled to a sensor tool including one or more sensors configured to perform measurements inside the process chamber, wherein the sensor tool can be retrieved from the tool station by means of an end effector of the transfer chamber robot.

2. The electronic device manufacturing system as claimed in claim 1, wherein the sensor tool further comprises: An electronic component module that enables wireless communication via a network.

3. The electronic device manufacturing system of claim 1, wherein the sensor tool further comprises: A power supply element capable of providing power to the one or more sensors.

4. The electronic device manufacturing system as claimed in claim 3, wherein the sensor tool further comprises: A power supply circuit is provided to charge the power supply element via a charging port on the tool station.

5. The electronic device manufacturing system of claim 1, wherein the end effector includes one or more electrical connectors for providing at least one of data or power to the sensor tool.

6. The electronic device manufacturing system of claim 1, wherein the sensor tool includes one or more electrical connectors for receiving at least one of data or power from the transmission chamber robot.

7. The electronic device manufacturing system of claim 1, wherein the one or more sensors include at least one of the following: an accelerometer, a distance sensor, a camera, a capacitive sensor, a reflectometer, a pyrometer, a laser-induced fluorescence spectrometer, a fiber optic probe, a surface acoustic sensor, an eddy current sensor, a pipe mirror, a photodiode sensor, a photomultiplier tube, a solid-state detector, a thermocouple, a voltage sensor, a current sensor, or a resistance sensor.

8. The electronic device manufacturing system of claim 1, wherein the sensor tool includes a connector element configured to couple the sensor tool to the end effector.

9. An electronic device manufacturing system, comprising: One loading gate; A transmission chamber coupled to the loading gate; A process chamber, coupled to the transmission chamber; And a transfer chamber robot configured to transfer a substrate to and from the process chamber, wherein the transfer chamber robot is configured to be coupled to a sensor disk including one or more sensors configured to perform measurements inside the process chamber, wherein the sensor disk can be retrieved from the loading gate by an end effector of the transfer chamber robot.

10. The electronic device manufacturing system of claim 9, wherein the end effector includes one or more electrical connectors for providing at least one of data or power to the sensor disk.

11. The electronic device manufacturing system of claim 9, wherein the sensor disk includes one or more electrical connectors for receiving at least one of data or power from the transmission chamber robot.

12. The electronic device manufacturing system of claim 9, wherein the sensor disk includes a data link capable of transmitting the measurements to a user interface or a data storage system.

13. The electronic device manufacturing system of claim 9, wherein the one or more sensors include at least one of the following: an accelerometer, a distance sensor, a camera, a capacitive sensor, a reflectometer, a pyrometer, a laser-induced fluorescence spectrometer, a fiber optic probe, a surface acoustic sensor, an eddy current sensor, a pipe mirror, a photodiode sensor, a photomultiplier tube, a solid-state detector, a thermocouple, a voltage sensor, a current sensor, or a resistance sensor.

14. The electronic device manufacturing system of claim 9, wherein the sensor disk further comprises: An electronic component module that enables wireless communication via a network.

15. A method of manufacturing an electronic device, comprising the steps of: positioning a portion of a transfer chamber robot coupled to a sensor device, the sensor device including one or more sensors, within a process chamber by means of a processor; obtaining sensor data associated with the process chamber using the one or more sensors; and removing the portion of the transfer chamber robot from the process chamber.

16. The method of claim 15 further includes the step of: positioning the sensor device in a loading gate.

17. The method of claim 15 further comprises the steps of: retrieving the sensor device from a tool station located within a transfer chamber before positioning the portion of the transfer chamber robot in the process chamber; and placing the sensor device in the tool station after removing the portion of the transfer chamber robot from the process chamber.

18. The method of claim 15, wherein the sensing device includes at least one of a sensing tool or a sensing disk.

19. A method for manufacturing an electronic device, comprising the steps of: obtaining a plurality of sensor values ​​from a process chamber via a sensor device coupled to a transfer chamber robot; applying a machine learning model to the plurality of sensor values, the machine learning model being trained based on historical sensor data of a subsystem of the process chamber and task data associated with a formulation for depositing a thin film; generating an output of the machine learning model, wherein the output indicates a fault type of the subsystem; determining the fault type of the subsystem; and generating a corrective action based on the fault type.

20. The method of claim 19, wherein the sensing device includes at least one of a sensing tool or a sensing disk.

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