Method and mechanism for coupling a sensor to a transfer chamber robot

Abstract

To provide an electronic device manufacturing system and method for coupling a sensor tool to a transfer chamber robot in place of a wall sensor that affects plasma uniformity in a process chamber.SOLUTION: Electronic device manufacturing system 300 includes a transfer chamber 310, a tool station 340 located within the transfer chamber, process chambers 314, 316, 318 coupled to the transfer chamber, and a transfer chamber robot 312. The transfer chamber robot transfers substrates 302 to and from the process chambers. The transfer chamber robot is also coupled to a sensor tool comprising one or more sensors that capture measurements in the process chamber. The sensor tool is retrievable from the tool station by the end effector of the transfer chamber robot.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] FIELD OF THE DISCLOSURE The present disclosure relates to electrical components, and more particularly to methods and mechanisms for coupling sensors to a transfer chamber robot. [Background technology]

[0002] Electronics manufacturing systems typically include multiple process chambers that are exposed to a vacuum during operation. During substrate manufacturing, contaminants and residual deposits are introduced into various components of the process chambers. Therefore, the process chambers must be periodically inspected and cleaned based on the level of contamination or deposits to remove contaminants and residual deposits from the walls and gas distribution plates.

[0003] Traditionally, operators periodically disconnect the vacuum system and remove components of the electronics manufacturing system (such as the process chamber door) to inspect the process chamber and determine whether cleaning is required. However, this is a time-consuming, expensive, and ineffective process. Alternatively, some electronics manufacturing systems have modified the process chamber walls to include sensors to detect deposit buildup. However, these wall sensors introduce defects into the process chamber, affecting plasma uniformity. Summary of the Invention

[0004] The following is a simplified summary of the disclosure to provide a basic understanding of some aspects of the disclosure. This summary is not an extensive overview of the disclosure. It is not intended to identify key or critical elements of the disclosure, nor is it intended to delineate the scope or claims of particular embodiments of the disclosure. Its sole purpose is to present some concepts of the disclosure in a simplified form as a prelude to the more detailed description that is presented later.

[0005] In one aspect of the 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 substrates 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 capture measurements within the process chamber. The sensor tool is retrievable from the tool station by an end effector of the transfer chamber robot.

[0006] In another aspect of the present disclosure,

[0007] In another aspect of the present disclosure, a method includes positioning, by a processor, a portion of a transfer chamber robot coupled to a sensor device within a process chamber, the sensor device comprising one or more sensors. The method further includes acquiring sensor data associated with the process chamber using the one or more sensors. The method further includes removing the portion of the transfer chamber robot from the process chamber.

[0008] In another aspect of the present disclosure, a method includes acquiring a plurality of sensor values ​​from a process chamber by a sensor device coupled to a transfer chamber robot. The method further includes 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 recipe for depositing a film. The method further includes generating an output of the machine learning model, the output indicating a type of fault of the subsystem. The method further includes determining a type of fault of the subsystem and generating a corrective action based on the type of fault.

[0009] The present disclosure is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a block diagram illustrating an exemplary system architecture, according to certain embodiments. [Figure 2] FIG. 1 is a flow diagram of a method for training a machine learning model, according to certain embodiments. [Figure 3] FIG. 1 is a schematic top view of an exemplary manufacturing system, in accordance with certain embodiments. [Figure 4] 1 is a cross-sectional schematic side view of an exemplary process chamber of an exemplary manufacturing system, in accordance with certain embodiments. [Figure 5A] FIG. 1 is a schematic diagram of an end effector according to an embodiment of the present disclosure. [Figure 5B] FIG. 1 is a schematic diagram of an end effector coupled to a sensor tool, according to an embodiment of the present disclosure. [Figure 6A] FIG. 1 is a schematic diagram of a sensor disk and an end effector according to an embodiment of the present disclosure. [Figure 6B] FIG. 1 is a schematic diagram of a sensor disk and an end effector according to an embodiment of the present disclosure. [Figure 7] FIG. 1 is a flow diagram of a method for determining a type of fault in a process chamber subsystem using a machine learning model, in accordance with certain embodiments. [Figure 8] FIG. 1 is a block diagram illustrating a computer system, in accordance with certain embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0011] Described herein is technology directed to methods and mechanisms for coupling sensors to a transfer chamber robot. Films can be deposited on the surface of a substrate during a deposition process (e.g., a deposition (CVD) process, an atomic layer deposition (ALD) process, etc.) performed in a process chamber of a manufacturing system. For example, in a CVD process, the substrate is exposed to one or more precursors that react on the substrate surface to produce a desired deposit. The film can include one or more layers of material formed during the deposition process, with each layer including a particular thickness gradient (e.g., a thickness variation along the layer of the deposited film). For example, a first layer can be formed directly on the surface of the substrate (referred to as the proximal layer or proximal end of the film) and have a first thickness. After the first layer is formed on the surface of the substrate, a second layer having a second thickness can be formed on the first layer. This process continues until the deposition process is complete and the final layer of the film (referred to as the distal layer or distal end of the film) is formed. The film can include alternating layers of different materials. For example, the film may include alternating layers of oxide and nitride (oxide-nitride-oxide-nitride stack or ONON stack), alternating layers of oxide and polysilicon (oxide-polysilicon-oxide-polysilicon stack or OPOP stack), etc. The film may then be subjected to, for example, an etching process to form a pattern on the surface of the substrate, a chemical mechanical polishing (CMP) process to smooth the surface of the film, or any other process necessary to produce a finished substrate.

[0012] A process chamber may have multiple subsystems that operate during each substrate manufacturing process (e.g., deposition process, etching process, polishing process, etc.). A subsystem may be characterized as a set of sensors related to an operating parameter of the process chamber. The operating parameter may be temperature, flow rate, pressure, etc. In one example, a pressure subsystem may be characterized by one or more sensors that measure gas flow, chamber pressure, control valve angle, foreline (vacuum line between pumps) pressure, pump speed, etc. Thus, a process chamber may include a pressure subsystem, a flow rate subsystem, a temperature subsystem, etc. Each subsystem may degrade and deviate from optimal performance conditions. For example, a pressure subsystem may experience a drop in pressure due to one or more of a pump problem, a control valve problem, etc.

[0013] During the substrate manufacturing process, process chambers can experience degradation conditions such as contaminant buildup, corrosion of certain components, etc. If these degradation conditions are not detected and repaired, they can result in defects in the substrate, leading to inferior products, reduced manufacturing yields, and significant downtime and repair time.

[0014] Existing systems sometimes modify process chamber walls to include sensors to detect such degradation conditions. However, these intrusive wall sensors can introduce defects into the process chamber and affect plasma uniformity. This can cause delays in achieving optimal process chamber pressure and process gas flow rates, resulting in distortions in the deposited and / or etched films. Furthermore, because process chambers are often modified at customer sites, these sensors can be difficult to install.

[0015] Aspects and embodiments of the present disclosure address these and other shortcomings of existing technology by enabling a transfer chamber robot to be equipped with one or more sensors capable of performing measurements and retrieving data from the interior of a process chamber. In particular, an electronic device manufacturing system can employ a robotic apparatus (e.g., a transfer chamber robot) in a transfer chamber configured to transport substrates between a load lock and a process chamber. The transfer chamber, process chamber, and load lock can operate under vacuum at certain times. The transfer chamber robot can be configured to couple one or more sensors to its end effector that are used to characterize, capture readings of, or capture measurements of one or more aspects of the process chamber. The sensors may include one or more of an accelerometer, a distance or position sensor (e.g., for determining the height, width, or length between two objects), a camera (e.g., a high-resolution camera, a high-speed camera, etc.), a capacitance sensor, a reflectometer, a pyrometer (e.g., a remote-sensing thermometer, an infrared camera, etc.), an electronic throttle control, a laser-induced fluorescence spectrometer, an optical fiber (e.g., an optical fiber probe), a surface acoustic sensor, an eddy current sensor, a borescope, a photodiode sensor, a photomultiplier tube, a solid-state detector, a thermocouple, a voltage sensor, a current sensor, a resistance sensor, a light source, etc.

[0016] In some embodiments, the sensors can be attached to a sensor tool that can be coupled to the end effector. For example, a transfer chamber robot can 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 the transfer chamber robot. Each sensor tool can include one or more sensors. A tool station can be used to store, move, replace, and / or recharge the sensor tools. In some embodiments, the tool station can be an automated tool changer that can enable the process chamber robot to select different sensor tools. The tool station can include a series of sensor tools stored in a magazine or other container (e.g., a drum magazine, a chain-type magazine, etc.). In response to selecting a sensor tool, the series of tools can be repositioned, and the process chamber robot can select a target sensor tool from a predetermined location. In some embodiments, in response to selecting a sensor tool, the process chamber robot can position the end effector at a designated location of the sensor tool within the tool station. In some embodiments, the tool station can include one or more charging ports for charging each sensor tool. The sensor tool can further include an electronics module that can facilitate communication with a manufacturing system (e.g., sending and receiving data).

[0017] In some embodiments, the sensors can be attached to a sensor disk. The sensor disk can be a substrate-shaped device containing one or more sensors. The transfer chamber robot can be configured to use an end effector to retrieve the sensor disk from the load lock and use the sensor disk to capture readings and measurements within the process chamber. In some embodiments, the sensor disk is connected to a power link and / or a data link. The power link can be any wired or wireless (e.g., inductive) connection that can provide power from the process chamber robot to the sensor using any type of connector (e.g., contact pins, low-particle connections, pogo pins). For example, the robot arm or end effector may include one or more types of connectors that can connect to the sensor disk (or other sensor tool) to provide power to the sensor tool, a data connection (also referred to as a data link) to the sensor tool, etc. In some embodiments, the power link can be a similar or identical system used to provide power to other functions of the transfer chamber robot 312 (e.g., link movement functions, effector operation functions, etc.). The data link can be any wired or wireless (WiFi, Bluetooth, internet-based, etc.) connection used to provide or retrieve data from the sensor. For example, the data link can be used to provide instructions to the sensor to take measurements or readings, transmit collected data to an interface (e.g., a user interface) or data storage system, etc.

[0018] In some embodiments, the predictive system may train and apply machine learning models to current sensor values ​​to generate output, such as one or more values ​​indicative of a failure pattern (e.g., anomalous behavior) of the process chamber subsystem and / or predicted data indicative of a type of failure (e.g., problem, fault, etc.) that has occurred. In some embodiments, the output is a value indicative of a difference between an expected behavior of the process chamber subsystem and an actual behavior of the process chamber subsystem. In some embodiments, the value is indicative of a failure pattern associated with the process chamber subsystem. The system may then, in some embodiments, compare the failure pattern to a library of known failure patterns to determine the type of failure that has occurred in the subsystem. In some embodiments, the system performs corrective action to adjust one or more parameters of the deposition process recipe (e.g., a temperature setting for the process chamber, a pressure setting for the process chamber, a flow rate setting for a precursor of a material included in a film deposited on the substrate surface, etc.) based on the failure pattern.

[0019] Aspects of the present disclosure provide a technical advantage of significantly reducing the time required to perform process chamber inspections. This configuration allows the transfer chamber robot to perform inspections and characterize the process chamber periodically, or each time the transfer chamber robot places a substrate in or removes a substrate from the process chamber. The inspections are performed while maintaining a vacuum environment, eliminating the steps of disconnecting the vacuum system and removing electronics manufacturing system components (such as the process chamber door) associated with manual inspections. This configuration also eliminates process chamber defects and plasma uniformity issues associated with installing sensors in the process chamber. Aspects of the present disclosure further provide technical advantages, such as significantly reduced time to detect problems or failures in chamber subsystems and improved energy consumption. The present disclosure also enables diagnostic data to be generated and corrective actions to be taken, avoiding inconsistent and abnormal products and unplanned user time or downtime.

[0020] FIG. 1 illustrates an exemplary computer system architecture 100 according to aspects of the present disclosure. In some embodiments, the computer system architecture 100 may be included as part of a manufacturing system for processing substrates, such as the manufacturing system 300 of FIG. 3 . The computer system architecture 100 includes a client device 120, a manufacturing tool 124, a metrology tool 128, a prediction server 112 (e.g., for generating prediction data, providing model adaptation, using a knowledge base, etc.), and a data store 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 tool 124 may include a sensor 126 configured to capture data on substrates being processed in the manufacturing system. In some embodiments, the manufacturing tool 124 and the sensor 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 identifier reader (e.g., a radio frequency identification (RFID) reader for a front-opening unified pod (FOUP) for the sensor system). In some embodiments, the metrology device 128 may be part of a metrology system that includes a metrology server (eg, metrology database, metrology folder, etc.) and a metrology identifier reader (eg, a FOUP RFID reader for the metrology system).

[0021] The manufacturing tool 124 may operate according to a recipe or over a period of time to produce products such as electronic devices. The manufacturing tool 124 may include a process chamber, such as the process chamber 400 described with respect to FIG. 4. The manufacturing tool 124 may perform processes on substrates (e.g., wafers) in the process chamber. Examples of substrate processes include deposition processes that deposit one or more layers on the surface of the substrate and etching processes that form patterns on the surface of the substrate. The manufacturing tool 124 may perform each process according to a process recipe. A process recipe defines a specific series 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 a temperature setting for the process chamber, a pressure setting for the process chamber, flow rates for precursors of materials included in a film to be deposited on the substrate surface, etc.

[0022] In some embodiments, the manufacturing tool 124 includes a sensor 126 configured to generate data associated with a substrate processed in the manufacturing system 100. For example, a process chamber may include one or more sensors configured to generate spectral or non-spectral data associated with a substrate before, during, and / or after a process (e.g., a deposition process) is performed on the substrate. In some embodiments, one or more of the sensors may be coupled (e.g., removably coupled) to a transfer chamber robot. In particular, the manufacturing tool 124 may employ a robotic device (e.g., a transfer chamber robot) in a transfer chamber configured to transport substrates between a load lock and a process chamber. Process chamber robots are described in more detail with respect to FIG. 3. In one example, the sensor may be attached to an end effector of the transfer chamber robot used to support the substrate. In some embodiments, the sensor may be attached to a sensor tool that can be coupled to and detached from the end effector in an automated manner without manual user intervention. For example, the transfer chamber robot may be configured to attach different sensor tools to its end effector based on positioning the end effector in a manner that engages the end effector with the sensor tool. Further details regarding the sensor tool are provided with respect to Figures 5A-5B. In some embodiments, the sensors can be attached to a sensor disk. For example, a transfer chamber robot can be configured to retrieve the sensor disk from the load lock and use the sensor disk to take readings and measurements within the process chamber. Further details regarding the sensor disk are provided with respect to Figures 6A-6B.

[0023] In some embodiments, the spectral data generated by the sensor 126 may indicate the concentration of one or more materials deposited on the surface of the substrate. The sensor 126 configured to generate spectral data associated with the substrate may include a reflectance sensor, an ellipsometry sensor, a thermal spectral sensor, a capacitive sensor, etc. The sensor 126 configured to generate non-spectral data associated with the substrate may include a temperature sensor, a pressure sensor, a flow sensor, a voltage sensor, etc. Further details regarding the manufacturing tool 124 are provided with respect to FIGS. 3 and 4.

[0024] In some embodiments, the sensors 126 provide sensor data (e.g., sensor values, characteristics, trace data) associated with the manufacturing equipment 124 (e.g., associated with the manufacturing equipment 124 producing a corresponding product, such as a wafer). The manufacturing equipment 124 can produce a product according to a recipe or by performing an operation over a period of time. Sensor data received over a period of time (e.g., corresponding to at least a portion of a recipe or operation) may be referred to as trace data (e.g., historical trace data, current trace data, etc.) received from different sensors 126 over time. The sensor data may include one or more values ​​of temperature (e.g., heater temperature), spacing (SP), pressure, high frequency radio frequency (HFRF), electrostatic chuck (ESC) voltage, current, material flow, power, voltage, etc. The sensor data may be associated with or indicative of hardware parameters, such as settings or components (e.g., size, type, etc.) of the manufacturing equipment 124, or manufacturing parameters, such as process parameters of the manufacturing equipment 124. The sensor data may be provided while the manufacturing equipment 124 is performing a manufacturing process (e.g., equipment readings as the product is processed). The sensor data may vary from board to board.

[0025] The metrology tool 128 can provide metrology data associated with substrates processed by the fabrication tool 124. The metrology data can include values ​​of film property data (e.g., wafer-space film properties), dimensions (e.g., thickness, height, etc.), dielectric constant, dopant concentration, density, defects, etc. In some embodiments, the metrology data can further include values ​​of one or more surface profile property data (e.g., etch rate, etch rate uniformity, critical dimensions of one or more features included in the surface of the substrate, critical dimension uniformity across the surface of the substrate, edge placement error, etc.). The metrology data can be of finished or semi-finished products. The metrology data can vary from substrate to substrate. The metrology data can be generated using, for example, reflectometry techniques, ellipsometry techniques, TEM techniques, etc.

[0026] In some embodiments, the metrology tool 128 may be included as part of the fabrication tool 124. For example, the metrology tool 128 may be included within or coupled to a process chamber and configured to generate metrology data for the substrate before, during, and / or after a process (e.g., a deposition process, an etch process, etc.) while the substrate remains in the process chamber. In such an example, the metrology tool 128 may be referred to as an in-situ metrology tool. In another example, the metrology tool 128 may be coupled to another station of the fabrication tool 124. For example, the metrology tool may be coupled to a transfer chamber, such as the transfer chamber 310 in FIG. 3 , a load lock, such as the load lock 320, or a factory interface, such as the factory interface 306. In such a case, the metrology tool 128 may be referred to as an integrated metrology tool. In other or similar embodiments, the metrology tool 128 is not coupled to a station of the fabrication tool 124. In such a case, the metrology tool 128 may be referred to as an in-line metrology tool or an external metrology tool. In some embodiments, the integrated metrology tool and / or the in-line metrology tool is configured to generate metrology data for the substrate before and / or after a process.

[0027] The client device 120 may include a computing device such as a personal computer (PC), a laptop, a mobile phone, a smartphone, a tablet computer, a netbook computer, a network-connected television ("smart TV"), a network-connected media player (e.g., a Blu-ray player), a set-top box, an over-the-top (OTT) streaming device, an operator box, etc. In some embodiments, metrology data can be received from the client device 120. The client device 120 can display a graphical user interface (GUI) through which a user can provide as input metrology measurements of substrates processed in the manufacturing system. The client device 120 can include a corrective action component 122. The corrective action component 122 can receive user input of instructions associated with the manufacturing equipment 124 (e.g., via a graphical user interface (GUI) displayed via the client device 120). In some embodiments, the corrective action component 122 sends instructions to the prediction system 110, receives output (e.g., prediction data) from the prediction system 110, determines corrective actions based on the output, and causes the corrective actions to be implemented. In some embodiments, the corrective action component 122 receives corrective action instructions from the predictive system 110 and causes the corrective action to be implemented. Each client device 120 may include an operating system that enables a user to one or more of create, view, or edit data (e.g., instructions associated with a manufacturing equipment 124, corrective actions associated with a manufacturing equipment 124, etc.).

[0028] The data store 140 can be a memory (e.g., random access memory), a drive (e.g., a hard drive, a flash drive), a database system, or another type of component or device capable of storing data. The data store 140 can include multiple storage components (e.g., multiple drives or multiple databases) that can span multiple computing devices (e.g., multiple server computers). The data store 140 can store data associated with the processing of substrates on the manufacturing tool 124. For example, the data store 140 can store data (referred to as process data) collected by the sensors 126 of the manufacturing tool 124 before, during, or after the substrate process. The process data can refer to historical process data (e.g., process data generated for a previous substrate processed on the manufacturing system) and / or current process data (e.g., process data generated for a current substrate processed on the manufacturing system). The data store can also store spectral or non-spectral data associated with some of the substrates processed on the manufacturing tool 124. The spectral data can include historical spectral data and / or current spectral data.

[0029] The data store 140 may also store context data associated with one or more substrates processed in the manufacturing system. The context data may include a recipe name, a recipe step number, a preventative maintenance indicator, an operator, etc. The context data may refer to historical context data (e.g., context data associated with a previous process performed on a previous substrate) and / or current process data (e.g., context data associated with a current or future process performed on a previous substrate). The context data may further include identifying sensors associated with a particular subsystem of a process chamber.

[0030] The data store 140 can also store task data. The task data can include one or more sets of operations to be performed on a substrate during a deposition process and can include one or more settings associated with each operation. For example, task data for a deposition process can include a temperature setting for a process chamber, a pressure setting for the process chamber, a flow rate setting for a precursor of a material of a film to be deposited on the substrate, etc. In another example, the task data can include controlling pressure at a pressure point defined relative to a flow rate value. The task data can refer to past 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 a current or future process to be performed on a substrate).

[0031] In some embodiments, data store 140 can be configured to store data that is inaccessible to users of the manufacturing system. For example, process data, spectral data, contextual data, etc. acquired for substrates being processed in the manufacturing system are inaccessible to users (e.g., operators) of the manufacturing system. In some embodiments, all data stored in data store 140 can be inaccessible to users of the manufacturing system. In other or similar embodiments, some of the data stored in data store 140 can be inaccessible to users, while other portions of the data stored in data store 140 can be accessible to users. In some embodiments, one or more portions of the data stored in data store 140 can be encrypted using an encryption mechanism unknown to the users (e.g., the data is encrypted using a private encryption key). In other or similar embodiments, data store 140 can include multiple data stores, where data that is inaccessible to users is stored in one or more first data stores and data that is accessible to users is stored in one or more second data stores.

[0032] In some embodiments, the data store 140 may be configured to store data associated with known failure patterns. A failure pattern may be one or more values ​​(e.g., a vector, a scalar, etc.) associated with one or more problems or faults associated with a process chamber subsystem. In some embodiments, a failure pattern may be associated with a corrective action. For example, a failure pattern may include a parameter adjustment step to correct the problem or fault indicated by the failure pattern. For example, a predictive system may compare a determined failure pattern to a library of known failure patterns to determine the type of failure that occurred in the subsystem, the cause of the failure, a recommended corrective action to correct the failure, etc.

[0033] In some embodiments, prediction system 110 includes prediction server 112, server machine 170, and server machine 180. Prediction server 112, server machine 170, and server machine 180 may each include one or more computing devices, such as a rack-mounted server, a router computer, a server computer, a personal computer, a mainframe computer, a laptop computer, a tablet computer, a desktop computer, a graphics processing unit (GPU), an accelerator application-specific integrated circuit (ASIC) (e.g., a tensor processing unit (TPU)), etc.

[0034] The server machine 170 includes a training set generator 172 that can generate training datasets (e.g., a set of data inputs and a set of target outputs) for training, validating, and / or testing the machine learning model 190. The machine learning model 190 can be any algorithmic model capable of learning from data. Some operations of the dataset generator 172 are described in more detail below with respect to FIG. 2. 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 training datasets.

[0035] The server machine 180 may include a training engine 182, a validation engine 184, a selection engine 185, and / or a test engine 186. An engine may refer to hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, a processing unit, etc.), software (e.g., instructions executing on a processing unit, a general-purpose computer system, or a dedicated machine), firmware, microcode, or a combination thereof. The training engine 182 may train one or more machine learning models 190. A machine learning model 190 may refer to a model artifact created by the training engine 182 using training data (also referred to herein as a training set) that includes training inputs and corresponding target outputs (correct answers for each training input). The training engine 182 may find patterns in the training data that map the training inputs to the target outputs (predicted answers) and provide a machine learning model 190 that captures these patterns. The machine learning model 190 may use one or more of statistical modeling, support vector machines (SVM), radial basis functions (RBF), clustering, supervised machine learning, semi-supervised machine learning, unsupervised machine learning, k-nearest neighbor algorithms (k-NN), linear regression, random forests, neural networks (e.g., artificial neural networks), and the like.

[0036] One type of machine learning model that can be used to perform some or all of the above tasks is an artificial neural network, such as a deep neural network. Artificial neural networks generally include a feature representation component with a classifier or recurrent layer that maps features to a desired output space. Convolutional neural networks (CNNs), for example, host multiple layers of convolutional filters. Pooling may be performed and nonlinearities may be addressed in lower layers, on top of which a multilayer perceptron is typically added to map the top-layer 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 be trained in a supervised (e.g., classification) and / or unsupervised (e.g., pattern analysis) manner. Deep neural networks include a hierarchy of layers, with different layers learning different levels of representation corresponding to different levels of abstraction. In deep learning, each level learns to transform its input data into a slightly more abstract and complex representation. In plasma process tuning, for example, the raw input may be a process result profile (e.g., a thickness profile showing one or more thickness values ​​across the surface of a substrate), a second layer may comprise feature data associated with the state of one or more zones of a plasma processing system's control elements (e.g., zone orientation, plasma exposure time, etc.), and a third layer may contain an initiation strategy (e.g., a strategy used as a starting point to determine an updated process strategy for processing the substrate to produce a process result that meets a threshold criterion). Notably, the deep learning process can teach itself which features to optimally place at which level. The "deep" in "deep learning" refers to the number of layers through which data is transformed. More precisely, deep learning systems have a significant confidence assignment path (CAP) depth. A CAP is a chain of transformations from input to output.CAP describes the potential causal relationships between inputs and outputs. For feedforward neural networks, the depth of the CAP may be the depth of the network, or the number of hidden layers + 1. For recurrent neural networks, where a signal may propagate through a layer more than once, the depth of the CAP is potentially unlimited.

[0037] In one embodiment, the one or more machine learning models are recurrent neural networks (RNNs). RNNs are a type of neural network that includes memory, allowing the neural network to capture temporal dependencies. RNNs can learn input-output mappings that depend on both current and past inputs. RNNs account for past and future flow measurements and make predictions based on this continuous measurement information. RNNs can be trained using a training data set and generate a fixed number of outputs (e.g., determining a set of substrate processing rates, determining modifications to a substrate process recipe). One type of RNN that can be used is a long short-term memory (LSTM) neural network.

[0038] Training a neural network may be accomplished in a supervised learning fashion, which involves feeding a training data set consisting of labeled inputs through the network, observing its outputs, defining an error (by measuring the difference between the output and the label values), and using techniques such as deep gradient descent and backpropagation to adjust the network weights across all layers and nodes of the network so that the error is minimized. In many applications, repeating this process across many labeled inputs in the training data set results in a network that can generate correct outputs even when presented with inputs that differ from those present in the training data set.

[0039] A training data set including hundreds, thousands, tens of thousands, hundreds of thousands, or more sensor data and / or process result data (e.g., metrology data such as one or more thickness profiles associated with the sensor data) can be used to form the training data set.

[0040] To accomplish the training, the processing logic may input the training dataset to one or more untrained machine learning models. Before inputting the first input to the machine learning models, the machine learning models may be initialized. The processing logic may train the untrained machine learning models based on the training dataset to generate one or more trained machine learning models that perform various operations as described above. The training may be performed by inputting one or more of the sensor data to the machine learning models one at a time.

[0041] A machine learning model processes inputs to generate outputs. An artificial neural network includes an input layer made up of data point values. The next layer is called the hidden layer, and each node in the hidden layer receives one or more input values. Each node includes parameters (e.g., weights) that it applies to the input values. Thus, each node essentially inputs the input values ​​into a multivariate function (e.g., a nonlinear mathematical transformation) to generate an output value. The next layer may be another hidden layer or an output layer. In either case, the nodes in the next layer receive output values ​​from the nodes in the previous layer, and each node applies weights to these values ​​and then generates its own output value. This can occur at each layer. The final layer is the output layer, with one node for each class, prediction, and / or output that the machine learning model can generate.

[0042] Thus, the output may include one or more predictions or inferences. For example, the output prediction or inference may include one or more predictions of film buildup on a chamber component, corrosion of a chamber component, predicted failure of a chamber component, etc. Processing logic determines an error (i.e., classification error) based on the difference between the output (e.g., prediction or inference) of the machine learning model and a target label associated with the input training data. Processing logic adjusts the weights of one or more nodes of the machine learning model based on the error. An error term or delta may be determined for each node of the artificial neural network. Based on this error, the artificial neural network adjusts one or more of its parameters (weights of one or more inputs of the node) of one or more of its nodes. Parameters may be updated in a back-propagation fashion, with nodes in the top layer updated first, followed by nodes in the next layer, and so on. An artificial neural network includes multiple layers of "neurons," each layer receiving values ​​from neurons in the previous layer as inputs. The parameters of each neuron include weights associated with values ​​received from each of the neurons in the previous layer. Thus, adjusting the parameters may include adjusting weights assigned to each of the inputs of one or more neurons in one or more layers within the artificial neural network.

[0043] After one or more training rounds, the processing logic can determine whether a stopping criterion has been met. The stopping criterion may be a target level of accuracy, a target number of processed images from the training dataset, a target amount of change to a parameter over one or more previous data points, combinations 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 may be, for example, 70%, 80%, or 90% accuracy. In one embodiment, the stopping criterion is met when the accuracy of the machine learning model stops improving. If the stopping criterion is not met, further training is performed. If the stopping criterion is met, training can be completed. Once the machine learning model is trained, the model can be tested using a reserved portion of the training dataset.

[0044] Once one or more trained machine learning models 190 are generated, these models may be stored in the prediction server 112 as a prediction component 114 or as a component of the prediction component 114.

[0045] The validation engine 184 may be capable of validating the machine learning model 190 using the corresponding set of features of the validation set from the training set generator 172. Once the model parameters are optimized, model validation may be performed to determine whether the model has been improved and to determine the current accuracy of the deep learning model. The validation engine 184 may determine the accuracy of the machine learning model 190 based on the corresponding set of features of the validation set. The validation engine 184 may discard trained machine learning models 190 with accuracies that do not meet a threshold accuracy. In some embodiments, the selection engine 185 may be capable of selecting a trained machine learning model 190 with an accuracy that meets the threshold accuracy. In some embodiments, the selection engine 185 may be capable of selecting the trained machine learning model 190 with the highest accuracy among the trained machine learning models 190.

[0046] The testing engine 186 may be able to test the trained machine learning model 190 using a corresponding set of features of a test set from the dataset generator 172. For example, a first trained machine learning model 190 trained using a first set of features of the training set may be tested using a first set of features of the test set. The testing engine 186 may determine the trained machine learning model 190 with the highest accuracy of all the trained machine learning models based on the test set.

[0047] As described in more detail below, the prediction server 112 provides data indicative of the expected behavior of each subsystem of the process chamber and includes a prediction component 114 that can run a trained machine learning model 190 against current sensor data inputs to obtain one or more outputs. The prediction server 112 can further provide data indicative of the health and diagnostics of the process chamber subsystems, as described in more detail below.

[0048] Client devices 120, manufacturing equipment 124, sensors 126, metrology equipment 128, prediction server 112, data store 140, server machine 170, and server machine 180 can be coupled to each other via network 130. In some embodiments, network 130 is a public network that provides client devices 120 with access to prediction server 112, data store 140, and other publicly available computing devices. In some embodiments, network 130 is a private network that provides client devices 120 with access to manufacturing equipment 124, metrology equipment 128, data store 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 networks), wireless networks (e.g., 802.11 networks or Wi-Fi networks), cellular networks (e.g., Long Term Evolution (LTE) networks), routers, hubs, switches, server computers, cloud computing networks, and / or combinations thereof.

[0049] It should be noted that in some other implementations, the functionality of server machines 170 and 180 and prediction server 112 may be provided by fewer machines. For example, in some embodiments, server machines 170 and 180 may be combined into a single machine, while in some other or similar embodiments, server machines 170 and 180 and prediction server 112 may be combined into a single machine.

[0050] In general, functionality described in one embodiment as being performed by server machine 170, server machine 180, and / or prediction server 112 may also be performed on client device 120. Additionally, functionality attributed to a particular component may be performed by a different component or multiple components working together.

[0051] In embodiments, a "user" may be represented as a single individual. However, other embodiments of the present disclosure encompass a "user" being an entity controlled by multiple users and / or automated sources. For example, a collection of individual users federated as a group of administrators may be considered a "user."

[0052] 2 is a flow diagram of a method 200 for training a machine learning model according to an aspect of the present disclosure. Method 200 is performed by processing logic, which may include hardware (e.g., circuitry, dedicated logic), software (e.g., running on a general-purpose computer system or a dedicated machine), firmware, or some combination thereof. In one implementation, method 200 may be performed by a computer system such as computer system architecture 100 of FIG. 1. In other or similar implementations, one or more operations of method 200 may be performed by one or more other machines not shown in the figure. In some aspects, one or more operations of method 200 may be performed by server machine 170, server machine 180, and / or prediction server 112.

[0053] For ease of explanation, the methods are shown and described as a series of acts. However, acts according to the present disclosure may occur in various orders and / or simultaneously, as well as with other acts not shown and described herein. Moreover, not all illustrated acts may be performed to implement a method in accordance with the disclosed subject matter. Moreover, those skilled in the art will understand and appreciate that a method may alternatively be represented as a series of interrelated states, via a state diagram or events. Furthermore, it will be appreciated that the methods disclosed herein may be stored on an article of manufacture to facilitate transport and transfer of such methodologies to a computing device. As used herein, the term article of manufacture is intended to encompass a computer program accessible from any computer-readable device or storage medium.

[0054] At block 210, processing logic initializes a training set T to an empty set (e.g., {}).

[0055] At block 212, processing logic acquires sensor data (e.g., sensor values, characteristics, trace data) associated with a previous deposition process performed to deposit one or more layers of a film on the surface of a previous substrate. The sensor data may be further associated with subsystems of the process chamber. A subsystem may be characterized as a set of sensors related to an operating parameter of the process chamber. The operating parameter may be temperature, flow rate, pressure, etc. For example, a pressure subsystem may be characterized by one or more sensors measuring gas flow, chamber pressure, control valve angle, foreline (vacuum line 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.

[0056] In some embodiments, the sensor data associated with the deposition process is historical data associated with one or more previous deposition settings for a previous deposition process previously performed on a previous substrate in the manufacturing system. For example, the historical data can be historical context data associated with a previous deposition process stored in the data store 140. In some embodiments, the one or more previous deposition settings can include at least one of a previous temperature setting for the previous deposition process, a previous pressure setting for the previous deposition setting, a previous flow rate setting for a precursor for one or more materials of a previous film deposited on the surface of the previous substrate, or any other setting associated with the deposition process. A flow rate setting can refer to a flow rate setting for a precursor in an initial stage of the previous deposition process (referred to as an initial flow rate setting), a flow rate setting for a precursor in a final stage of the previous deposition process (referred to as a final flow rate setting), or a ramp rate of a precursor flow rate during the deposition process. In one example, the precursor for the previous film can include a boron-containing precursor or a silicon-containing precursor. In some embodiments, the sensor data can also be associated with a previous etching process performed on a previous substrate or any other process performed in the process chamber.

[0057] At block 214, processing logic retrieves task data associated with a recipe for a film deposited on the surface of a previous substrate. For example, the task data may be temperature settings, pressure settings, flow rate settings, etc. required for precursors of the material of the film to be deposited on the substrate. The task data may include past task data for a previous film deposited on the surface of the previous substrate. In some embodiments, the past task data for a previous film may correspond to past task values ​​associated with the recipe for the previous film. Processing logic may retrieve the task data from data store 140 according to the above-described embodiments.

[0058] At block 216, processing logic generates first training data based on acquired sensor data associated with a previous deposition process performed on a previous substrate, and at block 218, processing logic generates second training data based on task data associated with a recipe for a film deposited on the surface of the previous substrate.

[0059] At block 220, processing logic generates a mapping between the first training data and the second training data, where the first training data includes or is based on data of a previous deposition process performed on a previous substrate, and the second training data includes or is based on task data associated with a recipe for a film deposited on the surface of the previous substrate, and the first training data is associated with (or mapped to) the second training data. At block 224, processing logic adds the mapping to the training set T.

[0060] At block 226, processing logic determines whether training set T includes a sufficient amount of training data to train the machine learning model. Note that in some implementations, the sufficiency of training set T may be determined solely based on the number of mappings in the training set, while in some other implementations, the sufficiency of training set T may be determined based on one or more other criteria (e.g., a measure of diversity of training examples, etc.) in addition to or instead of the number of input / output mappings. In response to determining that the training set does not include a sufficient amount of training data to train the machine learning model, method 200 returns to block 212. In response to determining that training set T includes a sufficient amount of training data to train the machine learning model, method 200 continues to block 228.

[0061] At block 228, processing logic provides a training set T for training the machine learning model. In one embodiment, the training set T is provided to the training engine 182 of the server machine 180 for training. In the case of a neural network, for example, input values ​​of a given input / output mapping are input to the neural network, and output values ​​of the input / output mapping are stored in output nodes of the neural network. The connection weights in the neural network are then adjusted according to a learning algorithm (e.g., backpropagation), and this procedure is repeated for other input / output mappings in the training set T.

[0062] In some embodiments, processing logic may perform outlier detection methods to remove anomalies from the training set T before training the machine learning model. Outlier detection methods may include techniques to identify values ​​that differ significantly from the majority of the training data. These values ​​may be generated from error, noise, etc.

[0063] At block 230, processing logic performs a calibration process on the trained machine learning model. In some embodiments, processing logic may compare the expected behavior of the process chamber subsystem to the current behavior of the process chamber subsystem based on a value difference between the predicted behavior and the current behavior. For example, processing logic may compare one or more values ​​associated with the predicted data for the pressure subsystem, the flow subsystem, or the temperature subsystem to one or more values ​​associated with the current measured behavior of the pressure subsystem, the flow subsystem, or the temperature subsystem, respectively.

[0064] After block 230, the machine learning model may be used to generate one or more values ​​indicative of a failure pattern (e.g., anomalous behavior) of the process chamber subsystem, generate predictive data indicative of a type of failure (e.g., a problem, a fault, etc.), and / or take corrective action to fix the suspected problem or fault. The predictive data may be generated by comparing the failure pattern to a library of known failure patterns.

[0065] In some embodiments, a manufacturing system may include two or more process chambers. For example, the exemplary manufacturing system 300 of FIG. 3 shows multiple process chambers 314, 316, and 318. Note that in some embodiments, the data acquired to train the machine learning model and the data collected to provide as input to the machine learning model may be associated with the same process chamber of the manufacturing system. In other or similar embodiments, the data acquired to train the machine learning model and the data collected to provide as input to the machine learning model may be associated with different process chambers of the manufacturing system. In other or similar embodiments, the data acquired to train the machine learning model may be associated with a process chamber of a first manufacturing system, and the data collected to provide as input to the machine learning model may be associated with a process chamber of a second manufacturing system.

[0066] 3 is a top schematic view of an exemplary manufacturing system 300 according to an embodiment of the present disclosure. The manufacturing system 300 can perform one or more processes on a substrate 302. The substrate 302 can be any suitable rigid, fixed-dimension, planar article suitable for manufacturing electronic devices or circuit components, such as, for example, a silicon-containing disk or wafer, a patterned wafer, a glass plate, etc.

[0067] The manufacturing system 300 may include a process tool 304 and a factory interface 306 coupled to the process tool 304. The process tool 304 may include a housing 308 having a transfer chamber 310 therein. The transfer chamber 310 may include one or more process chambers (also referred to as processing chambers) 314, 316, and 318 arranged around and coupled to it. The process chambers 314, 316, and 318 may be coupled to the transfer chamber 310 through respective ports, such as slit valves. The transfer chamber 310 may also include a transfer chamber robot 312 configured to transfer substrates 302 between the process chambers 314, 316, and 318, load locks 320, and the like. The transfer chamber robot 312 may include one or more arms, each including one or more end effectors at the end of each arm. The end effectors may be configured to handle specific objects, such as wafers, sensor disks, and sensor tools. In some embodiments, the end effector can be configured to couple to one or more sensor tools from tool station 340.

[0068] The tool station 340 can be a station used to store, move, replace, and / or recharge sensor tools. In some embodiments, the tool station 340 can be an automated tool changer that allows the process chamber robot to select different sensor tools. The tool station 340 can include a series of sensor tools stored in a magazine (e.g., a drum magazine, a chain magazine, etc.) or other container. In some embodiments, in response to a sensor tool selection, the series of tools can be repositioned, and the process chamber robot 312 can select a desired sensor tool from a predetermined location. In some embodiments, in response to a sensor tool selection, the process chamber robot 312 can position an end effector at a designated position of the sensor tool within the tool station. In some embodiments, the tool station 340 can include one or more charging ports for charging each sensor tool. For example, when a sensor tool is positioned within the tool station 340 (e.g., on a magazine), the charging components of the sensor tool can be connected to the charging port (e.g., a wired connection) or within its vicinity (e.g., a wireless connection such as inductive charging). Further details regarding the transfer chamber robot 312 and tool station 340 are provided with respect to Figures 5A-5B.

[0069] In some embodiments, the end effector can be configured to remove a sensor disk from a load lock. The sensor disk can be a substrate or any other device containing one or more sensors. Further details regarding the transfer chamber robot 312 and the sensor disk are provided with respect to Figures 6A-6B.

[0070] The process chambers 314, 316, 318 can be adapted to perform any number of processes on the substrate 302. The same or different substrate processes can be performed in each processing chamber 314, 316, 318. The substrate processes can 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 in the processing chamber. The process chambers 314, 316, 318 can each include one or more sensors configured to capture data on the substrate 302 before, after, or during substrate processing. For example, the one or more sensors can be configured to capture spectral and / or non-spectral data of a portion of the substrate 302 during substrate processing. In other or similar embodiments, the one or more sensors can be configured to capture data associated with the environment within the process chambers 314, 316, 318 before, after, or during substrate processing. For example, one or more sensors can be configured to capture data related to the temperature, pressure, gas concentrations, etc. of the environment within the process chambers 314, 316, 318 during substrate processing.

[0071] A load lock 320 may also be coupled to the housing 308 and the transfer chamber 310. The load lock 320 may be configured to interface with and couple to the transfer chamber 310 on one side and the factory interface 306. The load lock 320, in some embodiments, may have an environmentally controlled atmosphere that can be varied from a vacuum environment (where substrates may be transferred to and from the transfer chamber 310) to an inert gas environment at or near atmospheric pressure (where substrates may be transferred to and from the factory interface 306). The factory interface 306 may be any suitable enclosure, such as, for example, an Equipment Front End Module (EFEM). The factory interface 306 may be configured to receive substrates 302 from substrate carriers 322 (e.g., front-opening unified pods (FOUPs)) docked to various load ports 324 of the factory interface 306. A factory interface robot 326 (shown in dotted lines) may be configured to transfer substrates 302 between the carriers (also called containers) 322 and the load lock 320. Carrier 322 may be a substrate storage carrier or a replacement part storage carrier.

[0072] The manufacturing system 300 may also be connected to a client device (not shown) configured to provide information about the manufacturing system 300 to a user (e.g., an operator). In some embodiments, the client device may provide information to a user of the manufacturing system 300 via one or more graphical user interfaces (GUIs). For example, the client device may provide information via the GUI regarding a target thickness profile of a film to be deposited on the surface of the substrate 302 during a deposition process performed in the process chambers 314, 316, 318. The client device may also provide information regarding modifications to the process recipe given respective sets of deposition settings that are predicted to correspond to the target profile, according to embodiments described herein.

[0073] The manufacturing system 300 may also include a system controller 328. The system controller 328 may be and / or include a computing device such as a personal computer, a server computer, a programmable logic controller (PLC), a microcontroller, etc. The system controller 328 may include one or more processing devices, which may be a general-purpose processing device such as a microprocessor, a central processing unit, 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 combinations of instruction sets. The processing device may also be one or more special-purpose processing devices, such as an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), a network processor, etc. The system controller 328 may include a data storage device (e.g., one or more disk drives and / or solid-state drives), a main memory, a static memory, a network interface, and / or other components. The system controller 328 may execute instructions to perform any one or more of the methods and / or embodiments described herein. In some embodiments, the system controller 328 may execute instructions to perform one or more operations in the manufacturing system 300 in accordance with the process recipe. The instructions may be stored in a computer-readable storage medium, which may include (during execution of the instructions) a main memory, a static memory, a secondary storage device, and / or a processor.

[0074] The system controller 328 may receive data from sensors contained on or within various portions of the manufacturing system 300 (e.g., processing chambers 314, 316, 318, transfer chamber 310, load lock 320, etc.). In some embodiments, the data received by the system controller 328 may include spectral and / or non-spectral data about portions of the substrate 302. In other or similar embodiments, the data received by the system controller 328 may include data associated with processing the substrate 302 in the processing chambers 314, 316, 318, as previously described. For purposes of this description, the system controller 328 will be described as receiving data from sensors contained within the process chambers 314, 316, 318. However, the system controller 328 may receive data from any portion of the manufacturing system 300 and use the data received from that portion in accordance with the embodiments described herein. In an illustrative example, the system controller 328 may receive data from one or more sensors in the process chambers 314, 316, 318 before, after, or during substrate processing in the process chambers 314, 316, 318. The data received from the sensors in various portions of the manufacturing system 300 may be stored in a data store 350. The data store 350 may be included as a component within the system controller 328 or may be a component separate from the system controller 328. In some embodiments, the data store 350 may be the data store 140 described with respect to FIG. 1 .

[0075] FIG. 4 is a cross-sectional schematic side view of a process chamber 400 according to an embodiment of the present disclosure. In some embodiments, the process chamber 400 can correspond to the process chambers 314, 316, and 318 described with respect to FIG. 3. The process chamber 400 can be used for processes in which a corrosive plasma environment is provided. For example, the process chamber 400 can be a chamber for a plasma etcher, a plasma etch reactor, or the like. In another example, the process chamber can 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 surrounding an interior volume 406. The showerhead 430 can include a showerhead base and a showerhead gas distribution plate. Alternatively, the showerhead 430 can be replaced by a lid and a nozzle in some embodiments, or by multiple pie-shaped showerhead sections and plasma generation units in other embodiments. The chamber body 402 can be fabricated from other suitable materials, such as aluminum, stainless steel, or titanium (Ti). The chamber body 402 generally includes a sidewall 408 and a bottom 410. An exhaust port 426 may be defined in the chamber body 402 and may couple the interior volume 406 to a pumping system 428. The pumping system 428 may include one or more pumps and a throttle valve utilized to evacuate and regulate the pressure of the interior volume 406 of the process chamber 400.

[0076] The showerhead 430 can be supported on the sidewall 408 of the chamber body 402. The showerhead 420 (or lid) can open to allow access to the interior volume 406 of the process chamber 400 and, when closed, can provide a seal for the process chamber 400. A gas panel 458 can be coupled to the process chamber 400 to supply process gases and / or cleaning gases to the interior volume 406 through the showerhead 430 or the lid and nozzles (e.g., through apertures in the showerhead or lid and nozzles). For example, the gas panel 458 can provide precursors of the material of the film 451 to be deposited on the surface of the substrate 302. In some embodiments, the precursors can include silicon-based precursors or boron-based precursors. The showerhead 430 can include a gas distribution plate (GDP) having multiple gas supply holes 432 (also called channels) throughout the GDP. A substrate support assembly 448 is disposed within the interior volume 406 of the process chamber 400 below the showerhead 430. The substrate support assembly 448 holds the substrate 302 during processing (eg, during a deposition process).

[0077] In some embodiments, the processing chamber 400 may include a metrology device (not shown) configured to generate in situ metrology measurements during a process performed in the processing chamber 400. The metrology device may be operably coupled to a system controller (e.g., system controller 328 as described above). In some embodiments, the metrology device may be configured to generate metrology measurements (e.g., thickness) of the film 451 during a particular instance of the deposition process. The system controller may generate a thickness profile of the film 451 based on the metrology measurements received from the metrology device. In other or similar embodiments, the processing chamber 400 does not include a metrology device. In such embodiments, the system controller may receive one or more metrology measurements of the film 451 after completion of a deposition process in the process chamber 400. The system controller may determine a deposition rate based on the one or more metrology measurements and may generate an associated thickness profile of the film 451 based on the determined concentration gradient and the determined deposition rate of the deposition process.

[0078] 5A is a schematic diagram of an end effector 510 according to an embodiment of the present disclosure. FIG. 5B is a schematic diagram of the end effector 510 coupled to a sensor tool 520 according to an embodiment of the present disclosure. The end effector 510 can include a tool connector 512, a substrate platform 514, and a robot connector 516. In some embodiments, the end effector 510 can include one or more sensors. The sensor tool 520 can include an end effector connector 522 and a charging component 526.

[0079] The robot connector 516 can be used to couple the end effector 510 to a transfer chamber robot (e.g., transfer chamber robot 310). The substrate platform 514 can be used to handle a particular object, such as a substrate (e.g., a wafer). The tool connector 512 can be used to couple the end effector 510 to a sensor tool 520. For example, the transfer chamber robot 312 can receive instructions to couple to a particular sensor tool (e.g., sensor tool 520) housed within a tool station (e.g., tool station 340), position the end effector 510 in a first position (e.g., a pre-mating position) proximate the end effector connector 522 of the sensor tool 520, place the end effector 510 in a second position (e.g., a mating position) that couples the end effector 510 (via the tool connector 512) to the end effector connector 522 of the sensor tool 520, and then withdraw the sensor tool 520 from the tool station 340. Once use of the sensor tool 520 is complete, 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 a selected sensor tool to the end effector 510, for example, using a gripper.

[0080] The sensor tool 520 can include one or more sensors. The sensors can be used to characterize, capture readings of, or capture measurements of one or more aspects of the process chambers 314, 316, 318. The sensors can include one or more of an accelerometer, a distance sensor (e.g., to determine the height, width, or length between two objects), a camera (e.g., a high-resolution camera, a high-speed camera, etc.), a capacitance sensor, a reflectometer, a pyrometer (e.g., a remote-sensing thermometer, an infrared camera, etc.), a laser-induced fluorescence spectrometer, an optical fiber (e.g., an optical fiber probe), a surface acoustic sensor, an eddy current sensor, a borescope, a photodiode sensor, a photomultiplier tube, a solid-state detector, a thermocouple, a voltage sensor, a current sensor, a resistance sensor, or any other type of sensor.

[0081] Accelerometers can be used to detect and compensate for (or calibrate) vibration and position noise of the transfer chamber robot 312. Distance sensors can be used to detect erosion and / or corrosion of the chuck (table), edge ring, showerhead, walls, or any other component 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 erode the edge ring. Therefore, position sensors can be used to detect such corrosion by measuring the distance between the top surface of the edge ring and, for example, the top surface of the substrate. Cameras can record sections of the process chambers 314, 316, 318 for visual inspection by an operator. Capacitance sensors can be used to detect the position of showerheads used for gas distribution within the process chambers 314, 316, 318, determine substrate levelness, detect erosion, etc. Reflectometers can be used to examine the quality of seasoning films on the walls of the process chambers 314, 316, 318. For example, a reflectometer can generate light on the walls of the process chambers 314, 316, 318 and record the reflectivity of the reflected light. A pyrometer can be used to detect the temperature uniformity of heaters within the process chambers 314, 316, 318, to detect hot spots within the process chambers 314, 316, 318, etc. The transfer chamber robot 312 can include any quantity or combination of the discussed sensors or other sensors.

[0082] In some embodiments, the sensor tool 520 may include an electronics module that can facilitate communication with the system 100 (e.g., the system controller 328, the prediction system 110, the client device 120, etc.). The electronics module may include a microcontroller and a memory buffer coupled to the microcontroller. The memory buffer may be used to collect and store data acquired by the sensor tool 520 before transmitting the data to the 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 the sensor tool 520 and the system 100. For example, the end effector connector 522 and / or the tool connector 512 may include one or more contact pins, low-particle connections, pogo pins, or any other type of connector that can transfer data (or power) between the sensor tool 520 and the system 100 (via the transfer chamber robot 312). In some embodiments, the data may be first stored (buffered) in a memory buffer before being transmitted to the system 100. In other embodiments, the data may be transmitted to the system 100 as the data is collected without being stored in a memory buffer. In some embodiments, the wireless or wired connection may be continuous. In other embodiments, the wireless or wired connection may be established periodically or upon completion of an inspection or some other trigger event (e.g., when a memory buffer is nearly full, when the sensor tool 520 is placed on the tool station 340, etc.). For example, the sensor tool 520 may include wired or wireless communication circuitry for communicating with the tool station 340. The sensor tool 520 may collect and store sensor data in a memory buffer and transmit the sensor data when placed on the tool station 340. In some embodiments, the tool station 340 may include a wired or wireless connector that may receive data (e.g., sensor data) from or transmit data (e.g., instructions) to the sensor tool 520.

[0083] The electronics module may further include a power element and power-on circuitry. For example, the power element may be a battery, a capacitor (such as an ultracapacitor or supercapacitor), or any other power element (e.g., a power link) capable of providing power to the sensor and / or electronics module. In some embodiments, the power element may be rechargeable from the tool station 340. For example, the sensor tool 520 may be charged while the sensor tool 520 is stored and idle within the tool station 340. In particular, the sensor tool 520 may be coupled to a charging port of the tool station 340 via a charging component 526. In some embodiments, the charging component 526 may be coupled to the charging port using one or more connectors (e.g., contact pins, low-particle connections, pogo pins). In other embodiments, the charging component 526 may be located within the vicinity of the charging port, and the sensor tool 520 may be charged via wireless charging (e.g., inductive charging).

[0084] 6A and 6B are schematic diagrams of a sensor disk 610 and an end effector 620 according to an embodiment 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., contact pins, low particle connections, pogo pins) used to provide power and / or transfer data to and from the sensor disk 610, as described in more detail below.

[0085] The sensor disk 610 can be any instrument that includes one or more sensors (e.g., sensors 612A-612E). The sensors 612A-612E can be used to characterize, capture readings of, or capture measurements of one or more aspects of the process chambers 314, 316, 318. The sensors 612A-612E can include one or more of an accelerometer, a distance sensor (e.g., for determining the height, width, or length between two objects), a camera (e.g., a high-resolution camera, a high-speed camera, etc.), a capacitance sensor, a reflectometer, a pyrometer (e.g., a remote-sensing thermometer, an infrared camera, etc.), a laser-induced fluorescence spectrometer, an optical fiber (e.g., an optical fiber probe), a surface acoustic sensor, an eddy current sensor, a borescope, a photodiode sensor, a photomultiplier tube, a solid-state detector, a thermocouple, a voltage sensor, a current sensor, a resistance sensor, or any other type of sensor. Although five sensors 612A-612E are shown on sensor disk 610, it should be understood that any number of sensors may be coupled to sensor disk 610.

[0086] In some embodiments, the sensor disk 610 (and / or sensors 612A-612E) are connected to one or more of a power link and / or a data link. The power link can be any wired (e.g., 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 same system used to provide power to other functions of the transfer chamber robot 312 (e.g., link movement functions, effector operation functions, etc.). In some embodiments, the power link is a system independent of another power link used to provide power to other functions of the transfer chamber robot 312. In some embodiments, the sensor disk 610 can include power elements and / or power-on circuitry. The data link can be any wired (e.g., via electrical connector 622) or wireless (WiFi, Bluetooth, internet-based, etc.) connection used to provide data to or retrieve data from the sensors. For example, the data link can be used to provide instructions to the sensors to take measurements or readings and transmit the collected data to an interface (e.g., a user interface) or a data storage system, etc. In some embodiments, this data link is a separate system from another data link used to provide and communicate instructions to the transfer chamber robot 312 so that the transfer chamber robot 312 can transfer and position substrates between the transfer chambers 314, 316, 318 and the load lock 320. In some embodiments, the sensor disk 610 can include an electronics module (e.g., a microcontroller and a memory buffer coupled to the microcontroller) that can facilitate communication with the system 100 (e.g., the system controller 328, the prediction system 110, the client device 120, etc.).

[0087] The sensor disk 610 can be handled by the end effector 620. For example, the sensor disk can be stored in a FOUP, which may be docked to a load 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 load lock 320. The transfer chamber robot 312 can position the end effector 620 (coupled to the sensor disk 610) within any of the process chambers 314, 316, 318. The end effector and / or the arm of the transfer chamber robot can include one or more connectors for connecting to a sensor tool or the sensor disk 610. Such connectors can provide power and / or data link connections. Using the sensor disk 610, the transfer chamber robot 312 can take process chamber readings and / or measurements. In some embodiments, the sensors and / or transfer chamber robot 312 include a processing unit, such as a central processing unit (CPU), microcontroller, programmable logic controller (PLC), system-on-chip (SoC), server computer, or other suitable type of computing device. The processing unit can be configured to execute programming instructions related to the operation of the sensors. The processing unit can receive feedback signals from the sensor devices and convert the signals into sensor data (e.g., temperature, video data, position data, etc.). The processing unit can further send control signals to the sensors based on the received instructions. In some embodiments, the processing unit is configured for high-speed feedback processing and can include, for example, an EPM. In some embodiments, the processing unit is configured to transmit or route the feedback signals and / or sensor data to an interface (e.g., a user interface), a data store, etc.

[0088] The process chamber robot 312 can be further configured to return the sensor disk 610 to the load lock 320. In some embodiments, the sensor disk 610 can be shaped similar to the shape of the wafer. In other embodiments, the sensor disk 610 can be any shape, including, but not limited to, circular, oval, square, rectangular, irregular, etc.

[0089] 7 is a flow diagram of a method 700 for determining a fault type of a process chamber subsystem using a machine learning model, according to an embodiment of the present disclosure. Method 700 is performed by processing logic, which may include hardware (circuitry, dedicated logic, etc.), software (such as running on a general-purpose computer system or a dedicated machine), firmware, or some combination thereof. In one implementation, method 700 may be performed by a computer system, such as computer system architecture 100 of FIG. 1. In other or similar implementations, one or more operations of method 700 may be performed by one or more other machines not shown in the figure. In some embodiments, one or more operations of method 600 may be performed by server machine 170, server machine 180, and / or prediction server 112.

[0090] At block 710, processing logic acquires sensor data associated with operations performed in the process chamber. In some embodiments, the operations may include a deposition process performed in the process chamber to deposit one or more layers of a film on the surface of a substrate, an etching process performed on one or more layers of a film on the surface of the substrate, etc. The operations may be performed according to a recipe. The sensor data may include one or more values ​​of temperature (e.g., heater temperature), spacing, pressure, radio frequency radio frequency, electrostatic chuck voltage, current, material flow, power, voltage, etc. The sensor data may be associated with or indicative of hardware parameters such as settings or components (e.g., size, type, etc.) of the manufacturing tool 124, or manufacturing parameters such as process parameters of the manufacturing tool 124.

[0091] At block 712, 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 expected behavior of the process chamber subsystem. For example, the machine learning model can employ 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 a strategy used to perform the operation.

[0092] At block 714, processing logic generates an output via the machine learning model based on the sensor data. In some embodiments, the output may be a value indicative of a pattern (e.g., a failure pattern). In particular, the output may include predictive data on whether current data indicates a fault occurring in the process chamber. In some embodiments, the output may be at least one value indicative of a difference between an expected behavior of the process chamber subsystem and an actual behavior of the process chamber subsystem. In particular, the value may indicate a difference between actual values ​​of a set of sensors associated with the subsystem and expected values ​​of the set of sensors. Faults may include mechanism failures, high or low pressure, high or low gas flow, high or low temperature, etc.

[0093] At block 716, processing logic determines whether a fault has occurred in the process chamber subsystem. In some embodiments, the fault may include a mechanism failure, high or low pressure, high or low gas flow, high or low temperature, corrosion, erosion, degradation, etc. In some embodiments, processing logic may determine whether a fault has occurred in the process chamber subsystem by comparing the output to a predetermined threshold. In some embodiments, processing logic may determine whether a fault has occurred in the process chamber subsystem by determining that the output does not match expected behavior. In response to processing logic determining that a fault has not occurred in the process chamber subsystem (e.g., the value of the output does not exceed the predetermined threshold), processing logic may proceed to block 710. In response to processing logic determining that a fault has occurred in the process chamber subsystem (e.g., the value of the output exceeds the predetermined threshold), processing logic may proceed to block 718.

[0094] At block 718, processing logic may identify a type of fault based on the output. In some embodiments, processing logic may compare the failure pattern to a manufacturing data graph and / or a library of known failure patterns to determine the type of fault based on the similarity of the failure pattern when compared to the known failure patterns or the manufacturing data graph. In some embodiments, the type of fault may be extracted from the manufacturing data graph using natural language processing and then associated with the corresponding failure pattern. In some embodiments, the type of fault may be displayed (to the user) on a user interface.

[0095] At block 720, processing logic may perform (or suggest) a corrective action based on the identified fault. In some embodiments, the corrective action may be determined based on data obtained from a fault library. In some embodiments, the corrective action may include generating an alert or indication of the determined problem to client device 120. In some embodiments, the corrective action may include processing logic indicating the type of fault or failure, the cause of the fault or failure, and / or a recommended corrective action. In some embodiments, the corrective action may include processing logic adjusting one or more parameters of the deposition process recipe (e.g., a temperature setting for the process chamber, a pressure setting for the process chamber, a flow rate setting for a precursor of a material included in the film to be deposited on the substrate surface, etc.) based on desired characteristics of the film. In some embodiments, the deposition process recipe may be adjusted before, during (e.g., in real time), or after the deposition process.

[0096] 8 is a block diagram illustrating a computer system 800 according to certain embodiments. In some embodiments, computer system 800 may be connected to other computer systems (e.g., via a network such as a local area network (LAN), an intranet, an extranet, or the Internet). Computer system 800 may operate in the capacity of a server or a client computer in a client-server environment, or as a peer computer in a peer-to-peer or distributed network environment. Computer system 800 may be provided by a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile phone, a web appliance, a server, a network router, switch, or bridge, or any device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that device. Furthermore, the term "computer" is intended to include any collection of computers that individually or jointly execute a set (or sets) of instructions to perform any one or more of the methodologies described herein.

[0097] In a further aspect, computer system 800 may include a processing unit 802, a volatile memory 804 (e.g., random access memory (RAM)), a non-volatile memory 806 (e.g., read-only memory (ROM) or electrically erasable programmable ROM (EEPROM)), and a data storage device 816, which may communicate with each other via a bus 808.

[0098] The processing unit 802 may be provided by one or more processors, such as a general-purpose processor (e.g., a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of multiple types of instruction sets) or a special-purpose processor (e.g., an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).

[0099] Computer system 800 may further include a network interface device 822 (e.g., coupled to a network 874). Computer system 800 may also include a video display unit 810 (e.g., an LCD), an alphanumeric input device 812 (e.g., a keyboard), a cursor control device 814 (e.g., a mouse), and a signal generating device 820.

[0100] In some implementations, the data storage device 816 may include a non-transitory computer-readable storage medium 824 capable of storing instructions 826 encoding any one or more of the methods or functions described herein, including instructions for encoding the components of FIG. 1 (e.g., the corrective action component 122, the prediction component 114, etc.) and implementing the methods described herein.

[0101] The instructions 826 may also reside, completely or partially, within the volatile memory 804 and / or within the processing unit 802 during execution thereof by the computer system 800; thus, the volatile memory 804 and the processing unit 802 may also constitute machine-readable storage media.

[0102] Although the computer-readable storage medium 824 is shown as a single medium in the illustrative example, the term "computer-readable storage medium" is intended to include a single medium or multiple media (e.g., centralized or distributed databases, and / or associated caches and servers) that store one or more sets of executable instructions. The term "computer-readable storage medium" is also intended to include any tangible medium that is capable of storing or encoding a set of instructions for execution by a computer that cause the computer to perform any one or more of the methodologies described herein. The term "computer-readable storage medium" is intended to include, but is not limited to, solid-state memory, optical media, and magnetic media.

[0103] The methods, components, and features described herein may be implemented by discrete hardware components or integrated into the functionality of other hardware components, such as an ASIC, FPGA, DSP, or similar device. Furthermore, the methods, components, and features may be implemented by firmware modules or functional circuits within a hardware device. Furthermore, the methods, components, and features may be implemented in any combination of hardware devices and computer program components, or in a computer program.

[0104] Unless otherwise specified, terms such as "receive," "execute," "provide," "obtain," "cause," "access," "determine," "add," "use," "train," and the like refer to computer system-performed or implemented actions and processes 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. Also, as used herein, terms such as "first," "second," "third," "fourth," and the like are intended as labels to distinguish between different elements and may not have an ordinal meaning due to their numerical designations.

[0105] The examples described herein also relate to apparatus for performing the methods described herein. The apparatus may be specially constructed to perform the methods described herein, or may comprise a general-purpose computer system selectively programmed by a computer program stored on the computer system. Such a computer program may be stored on a computer-readable tangible storage medium.

[0106] The methods and illustrative embodiments described herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems may be used in accordance with the teachings described herein, or it may prove convenient to construct more specialized apparatus to perform the methods described herein and / or each of their individual functions, routines, subroutines, or operations. Examples of configurations for a variety of these systems are set forth in the description above.

[0107] The above description is intended to be illustrative, not limiting. While the present disclosure has been described with reference to certain exemplary examples and embodiments, it will be recognized that the disclosure is not limited to the described examples and embodiments. The scope of the present disclosure should be determined with reference to the following claims, along with the full scope of equivalents to which such claims are entitled.

Claims

1. a transfer chamber; a tool station located within the transfer chamber; a process chamber coupled to the transfer chamber; a transfer chamber robot configured to transfer substrates to and from the process chamber, the transfer chamber robot configured to be coupled to a sensor tool comprising one or more sensors configured to capture measurements within the process chamber, the sensor tool being removable from the tool station by an end effector of the transfer chamber robot; and An electronic device manufacturing system comprising:

2. The electronic device manufacturing system of claim 1 , wherein the sensor tool further comprises an electronics module capable of facilitating wireless communication over a network.

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

4. The electronic device manufacturing system of claim 3 , further comprising a power-on circuit that enables the sensor tool to charge the power element through a charging port of the tool station.

5. The electronic device manufacturing system of claim 1 , wherein the end effector comprises 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 comprises one or more electrical connectors for receiving at least one of data or power from the transfer chamber robot.

7. 10. The electronic device manufacturing system of claim 1, wherein the one or more sensors comprise at least one of an accelerometer, a distance sensor, a camera, a capacitance sensor, a reflectometer, a pyrometer, a laser induced fluorescence spectrometer, a fiber optic probe, a surface acoustic sensor, an eddy current sensor, a borescope, 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 comprises a connector element configured to couple the sensor tool to the end effector.

9. A load lock and a transfer chamber coupled to the load lock; a process chamber coupled to the transfer chamber; a transfer chamber robot configured to transfer substrates to and from the process chamber, the transfer chamber robot configured to be coupled to a sensor disk comprising one or more sensors configured to take measurements within the process chamber, the sensor disk being removable from the load lock by an end effector of the transfer chamber robot; and An electronic device manufacturing system comprising:

10. The electronic device manufacturing system of claim 9 , wherein the end effector comprises 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 comprises one or more electrical connectors for receiving at least one of data or power from the transfer chamber robot.

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

13. 10. The electronic device manufacturing system of claim 9, wherein the one or more sensors comprise at least one of an accelerometer, a distance sensor, a camera, a capacitance sensor, a reflectometer, a pyrometer, a laser induced fluorescence spectrometer, a fiber optic probe, a surface acoustic sensor, an eddy current sensor, a borescope, 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 electronics module capable of facilitating wireless communication over a network.

15. positioning, by a processor, a portion of a transfer chamber robot coupled to a sensor device within a process chamber, the sensor device comprising one or more sensors; acquiring sensor data associated with the process chamber using the one or more sensors; removing the portion of the transfer chamber robot from the process chamber; A method comprising:

16. Positioning the sensor device in a load lock 16. The method of claim 15, further comprising:

17. removing the sensor device from a tool station located within the transfer chamber before positioning the portion of the transfer chamber robot within a process chamber; placing the sensor device in the tool station after removing the portion of the transfer chamber robot from the process chamber; 16. The method of claim 15, further comprising:

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

19. acquiring a plurality of sensor values ​​from the process chamber with a sensor device coupled to the 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 recipe for depositing the film; generating an output of the machine learning model, the output indicating a type of fault in the subsystem; determining the type of the fault in the subsystem; generating a corrective action based on the type of fault; A method comprising:

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

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