METHODS AND MECHANISMS FOR COUPLING SENSORS TO TRANSFER CHAMBER ROBOT

An electronic device manufacturing system includes a transfer chamber, a tool station situated 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 comprising one or more sensors configured to take measurements inside the process chamber. The sensor tool is retrievable from the tool station by an end effector of the transfer chamber robot.

TECHNICAL FIELD

The present disclosure relates to electrical components, and, more particularly, to methods and mechanisms for coupling sensors to a transfer chamber robot.

BACKGROUND

An electronics manufacturing system generally includes multiple process chambers that are subject to vacuum during operation. During manufacturing of substrates, contaminants and residue deposits are introduced into various components of the process chambers. As such, the process chambers need to be periodically inspected and, based on the level of contamination or level of deposits, cleaned to remove contaminants and residue deposits from the walls and the gas distribution plate.

Traditionally, operators would periodically disengage 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 a cleaning is needed. This, however, is a time-consuming, costly, and ineffective process. Alternatively, some electronics manufacturing systems modify the process chamber walls to include sensors for detecting deposit buildup. However, these wall sensors introduce defects to the process chamber and affect plasma uniformity.

SUMMARY

In an aspect of the disclosure, an electronic device manufacturing system includes a transfer chamber, a tool station situated 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 comprising one or more sensors configured to take measurements inside the process chamber. The sensor tool is retrievable from the tool station by an end effector of the transfer chamber robot.

In another aspect of the disclosure,

In another aspect of the 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 obtaining, using the one or more sensors, sensor data associated with the process chamber. The method further includes removing the portion of the transfer chamber robot from the process chamber.

In another aspect of the disclosure, a method includes obtaining, by a sensor device coupled to a transfer chamber robot, a plurality of sensor values from a process chamber. The method further includes applying a machine-learning model to the plurality of sensor values, the machine-learning model trained based on historical sensor data of a sub-system of the process chamber and task data associated with the recipe for depositing the film. The method further includes generating an output of the machine-learning model, wherein the output is indicative of a type of failure of the sub-system. The method further includes determining the type of failure of the sub-system and generating a corrective action based on the type of failure.

DETAILED DESCRIPTION

Described herein are technologies directed to methods and mechanisms for coupling sensors to a transfer chamber robot. A film can be deposited on a surface of a substrate during a deposition process (e.g., a deposition (CVD) process, an atomic layer deposition (ALD) process, and so forth) performed at a process chamber of a manufacturing system. For example, in a CVD process, the substrate is exposed to one or more precursors, which react on the substrate surface to produce the desired deposit. The film can include one or more layers of materials that are formed during the deposition process, and each layer can include a particular thickness gradient (e.g., changes in the thickness along a layer of the deposited film). For example, a first layer can be formed directly on the surface of the substrate (referred to as a 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 completed and a final layer is formed for the film (referred to as the distal layer or distal end of the film). The film can include alternating layers of different materials. For example, the film can include alternating layers of oxide and nitride layers (oxide-nitride-oxide-nitride stack or ONON stack), alternating oxide and polysilicon layers (oxide-polysilicon-oxide-polysilicon stack or OPOP stack), and so forth. The film can then be subjected to, for example, an etch 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 manufacture the finished substrate.

A process chamber can have multiple sub-systems operating during each substrate manufacturing process (e.g., the deposition process, the etch process, the polishing process, etc.). A sub-system can be characterized as a set of sensors related with an operational parameter of the process chamber. An operational parameter can be a temperature, a flow rate, a pressure, and so forth. In an example, a pressure sub-system can be characterized by one or more sensors measuring the gas flow, the chamber pressure, the control valve angle, the foreline (vacuum line between pumps) pressure, the pump speed, and so forth. Accordingly, the process chamber can include a pressure sub-system, a flow sub-system, a temperature subsystem, and so forth. Each sub-system can experience deterioration and deviate from optimal performance conditions. For example, the pressure sub-system can generate reduced pressure due to one or more of pump issues, control valve issues, etc.

During the substrate manufacturing process, the process chamber can experience deteriorating conditions, such as a build-up of contaminant, erosion on certain components, etc. Failure to catch and repair these deteriorating conditions can cause defects in the substrates, leading to inferior products, reduced manufacturing yield, and significant downtime and repair time.

Existing systems may modify the process chamber walls to include sensors for detecting such deteriorating conditions. However, these intrusive wall sensors can introduce defects to the process chamber and affect plasma uniformity. This can cause a delay in achieving optimal process chamber pressure and flow rate of a process gas, which can result in deformations in the deposited and/or etched film. Further, these sensors may be difficult to install because the process chamber may be modified at customer site.

Aspects and implementations of the present disclosure address these and other shortcomings of the existing technology by enabling a transfer chamber robot to equip one or more sensors capable of performing measurements and retrieving data from inside the process chamber. In particular, an electronic device manufacturing system can employ a robot apparatus (e.g., a transfer chamber robot) in the transfer chamber that is configured to transport substrates between a load lock and the process chambers. The transfer chamber, process chambers, and load locks can operate under a vacuum at certain times. The transfer chamber robot can be configured to couple, to its end effector, one or more sensors used to characterize, take readings of, or take 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., to determine a height, a width or a length between two objects), a camera (e.g., a high resolution camera, a high speed camera, etc.), a capacitive sensor, a reflectometer, a pyrometer (e.g., a remote sensing thermometer, an infrared camera, etc.), an electronic throttle control, a laser inducted florescence spectroscope, fiber optics (e.g., a fiber optic probe), a surface acoustic sensor, an eddy current sensor, a borescope, a photodiode sensor, a photo multiplier tube, a solid state detector, a thermocouple, a voltage sensor, a current sensor, a resistance sensor, a light source, etc.

In some embodiments, the sensors can be attached to a sensor tool that can be coupled to the end effector. For example, the 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 transfer chamber robot's end effector. Each sensor tool can include one or more sensor. A tool station can be used to house, move, change and/or recharge the sensor tools. In some embodiments, the tool station can be an automatic tool changer capable of enabling the process chamber robot to select different sensor tools. The tool station can include an array of sensor tools stored on a magazine or other container (e.g., a drum magazine, a chain type magazine, etc.). Responsive to a selection of a sensor tool, the array of tools can reposition and the process chamber robot can select the target sensor tool from a predetermined position. In some embodiments, responsive to a selection of a sensor tool, the process chamber robot can position the end effector to the specified location of the sensor tool in the tool station. In some embodiments, the tool station can include one or more charging ports for electrically charging each sensor tool. The sensor tools can further include an electronics module capable of facilitating communication (e.g., send and receive data) with the manufacturing system.

In some embodiments, the sensors can be attached to a sensor disc. The sensor disc can be a substrate shaped device that includes one or more sensors. The transfer chamber robot can be configured to retrieve, using an end effector, the sensor disc from a load lock and use the sensor disc to take readings and measurements inside the process chambers. In some embodiments, the sensors disc is connected to a power-link and/or a data-link. The power-link can be any wired or wireless (e.g., inductive) connection capable of providing electrical power, from the process chamber robot, to the sensor(s) 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 disc (or other sensor tool) in order to provide power to the sensor tool, a data connection (also referred to as a data-link) to the sensor tool, and so on. In some embodiments, the power-link can be similar or the same system used to provide electrical power to other functions of the transfer chamber robot112(e.g., link movement functions, effector operating function, 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(s). For example, the data-link can be used to provide instructions to the sensors to take measurements or readings, send collected data to an interface (e.g., a user interface) or a data storage system, etc.

In some embodiments, a predictive system can train and apply a machine-learning model to current sensor values to generate an output, such as, one or more values indicative of a fault pattern (e.g., abnormal behavior) of a process chamber sub-system and/or predicative data indicative of the type of fault (e.g., issue, failure, etc.) that occurred. In some embodiments, the output is a value indicative of a difference between the expected behavior of the process chamber sub-system and the actual behavior of the process chamber sub-system. In some embodiments, the value is indicative of a fault pattern associated with the process chamber sub-system. The system can then compare the fault pattern to a library of known fault patterns to determine the type of failure experienced by the sub-system in some embodiments. In some embodiments, the system performs a corrective action to adjust one or more parameters of a 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 for a material included in the film deposited on the substrate surface, etc.) based on the fault pattern.

Aspects of the present disclosure result in technological advantages of significant reduction in time that it takes to perform inspection of a process chamber. The configuration allows for the transfer chamber robot to perform inspections and characterize the process chamber periodically or each time the transfer chamber robot places a substrate into the process chamber or retrieves the substrate from the process chamber. The inspections are performed while maintaining the vacuum environment, thus removing any procedure to disengage the vacuum system and remove components of the electronics manufacturing system (such as the process chamber door) associated with manual inspections. The configuration also eliminates the defects to the process chamber and plasma uniformity issues associated with installing sensors within the process chamber. Aspects of the present disclosure further result in technological advantages of significant reduction in time to detect issues or failures experienced by a chamber sub-system, as well as improvements in energy consumption, and so forth. The present disclosure can also result in generating diagnostic data and performing corrective actions to avoid inconsistent and abnormal products, and unscheduled user time or down time.

FIG.1depicts an illustrative computer system architecture100, according to aspects of the present disclosure. In some embodiments, computer system architecture100can be included as part of a manufacturing system for processing substrates, such as manufacturing system300ofFIG.3. Computer system architecture100includes a client device120, manufacturing equipment124, metrology equipment128, a predictive server112(e.g., to generate predictive data, to provide model adaptation, to use a knowledge base, etc.), and a data store140. The predictive server112can be part of a predictive system110. The predictive system110can further include server machines170and180. The manufacturing equipment124can include sensors126configured to capture data for a substrate being processed at the manufacturing system. In some embodiments, the manufacturing equipment124and sensors126can be part of a sensor system that includes a sensor server (e.g., field service server (FSS) at a manufacturing facility) and sensor identifier reader (e.g., front opening unified pod (FOUP) radio frequency identification (RFID) reader for sensor system). In some embodiments, metrology equipment128can be part of a metrology system that includes a metrology server (e.g., a metrology database, metrology folders, etc.) and metrology identifier reader (e.g., FOUP RFID reader for metrology system).

Manufacturing equipment124can produce products, such as electronic devices, following a recipe or performing runs over a period of time. Manufacturing equipment124can include a process chamber, such as process chamber400described with respect toFIG.4. Manufacturing equipment124can perform a process for a substrate (e.g., a wafer, etc.) at the process chamber. Examples of substrate processes include a deposition process to deposit one or more layers of film on a surface of the substrate, an etch process to form a pattern on the surface of the substrate, etc. Manufacturing equipment124can perform each process according to a process recipe. A process recipe defines a particular set of operations to be performed for the substrate during the process and can include one or more settings associated with each operation. For example, a deposition process recipe can include a temperature setting for the process chamber, a pressure setting for the process chamber, a flow rate setting for a precursor for a material included in the film deposited on the substrate surface, etc.

In some embodiments, manufacturing equipment124includes sensors126that are configured to generate data associated with a substrate processed at manufacturing system100. For example, a process chamber can include one or more sensors configured to generate spectral or non-spectral data associated with the substrate before, during, and/or after a process (e.g., a deposition process) is performed for the substrate. In some embodiments, one or more of the sensors can be coupled (e.g., removably coupled) to a transfer chamber robot. In particular, manufacturing equipment124can employ a robot apparatus (e.g., a transfer chamber robot) in a transfer chamber that is configured to transport substrates between a load lock and the process chambers. The process chamber robot is described in greater detail with respect toFIG.3. In an example, the sensors can be attached to an end effector of the transfer chamber robot that is used to support a substrate. In some embodiments, the sensors can be attached to a sensor tool that can be coupled to and decoupled from the end effector in an automated fashion without the manual intervention of a user. For example, the transfer chamber robot can 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 a sensor tool. Further details regarding the sensor tools are provided with respect toFIGS.5A-5B. In some embodiments, the sensors can be attached to a sensor disc. For example, the transfer chamber robot can be configured to retrieve a sensor disc from a load lock and use the sensor disc to take readings and measurements inside the process chambers. Further details regarding the sensor disc are provided with respect toFIGS.6A-6B.

In some embodiments, spectral data generated by sensors126can indicate a concentration of one or more materials deposited on a surface of a substrate. Sensors126configured to generate spectral data associated with a substrate can include reflectometry sensors, ellipsometry sensors, thermal spectra sensors, capacitive sensors, and so forth. Sensors126configured to generate non-spectral data associated with a substrate can include temperature sensors, pressure sensors, flow rate sensors, voltage sensors, etc. Further details regarding manufacturing equipment124are provided with respect toFIG.3andFIG.4.

In some embodiments, sensors126provide sensor data (e.g., sensor values, features, trace data) associated with manufacturing equipment124(e.g., associated with producing, by manufacturing equipment124, corresponding products, such as wafers). The manufacturing equipment124may produce products following a recipe or by performing runs over a period of time. Sensor data received over a period of time (e.g., corresponding to at least part of a recipe or run) may be referred to as trace data (e.g., historical trace data, current trace data, etc.) received from different sensors126over time. Sensor data can include a value of one or more of temperature (e.g., heater temperature), spacing (SP), pressure, high frequency radio frequency (HFRF), voltage of electrostatic chuck (ESC), electrical current, material flow, power, voltage, etc. Sensor data can be associated with or indicative of manufacturing parameters such as hardware parameters, such as settings or components (e.g., size, type, etc.) of the manufacturing equipment124, or process parameters of the manufacturing equipment124. The sensor data can be provided while the manufacturing equipment124is performing manufacturing processes (e.g., equipment readings when processing products). The sensor data can be different for each substrate.

Metrology equipment128can provide metrology data associated with substrates processed by manufacturing equipment124. The metrology data can include a value of film property data (e.g., wafer spatial film properties), dimensions (e.g., thickness, height, etc.), dielectric constant, dopant concentration, density, defects, etc. In some embodiments, the metrology data can further include a value of one or more surface profile property data (e.g., an etch rate, an etch rate uniformity, a critical dimension of one or more features included on a surface of the substrate, a critical dimension uniformity across the surface of the substrate, an edge placement error, etc.). The metrology data can be of a finished or semi-finished product. The metrology data can be different for each substrate. Metrology data can be generated using, for example, reflectometry techniques, ellipsometry techniques, TEM techniques, and so forth.

In some embodiments, metrology equipment128can be included as part of the manufacturing equipment124. For example, metrology equipment128can be included inside of or coupled to a process chamber and configured to generate metrology data for a 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 instances, metrology equipment128can be referred to as in-situ metrology equipment. In another example, metrology equipment128can be coupled to another station of manufacturing equipment124. For example, metrology equipment can be coupled to a transfer chamber, such as transfer chamber310ofFIG.3, a load lock, such as load lock320, or a factory interface, such as factory interface306. In such instances, metrology equipment128can be referred to as integrated metrology equipment. In other or similar embodiments, metrology equipment128is not coupled to a station of manufacturing equipment124. In such instances, metrology equipment128can be referred to as inline metrology equipment or external metrology equipment. In some embodiments, integrated metrology equipment and/or inline metrology equipment are configured to generate metrology data for a substrate before and/or after a process.

The client device120may include a computing device such as personal computers (PCs), laptops, mobile phones, smart phones, tablet computers, netbook computers, network connected televisions (“smart TVs”), network-connected media players (e.g., Blu-ray player), a set-top box, over-the-top (OTT) streaming devices, operator boxes, etc. In some embodiments, the metrology data can be received from the client device120. Client device120can display a graphical user interface (GUI), where the GUI enables the user to provide, as input, metrology measurement values for substrates processed at the manufacturing system. The client device120can include a corrective action component122. Corrective action component122can receive user input (e.g., via a Graphical User Interface (GUI) displayed via the client device120) of an indication associated with manufacturing equipment124. In some embodiments, the corrective action component122transmits the indication to the predictive system110, receives output (e.g., predictive data) from the predictive system110, determines a corrective action based on the output, and causes the corrective action to be implemented. In some embodiments, the corrective action component122receives an indication of a corrective action from the predictive system110and causes the corrective action to be implemented. Each client device120may include an operating system that allows users to one or more of generate, view, or edit data (e.g., indication associated with manufacturing equipment124, corrective actions associated with manufacturing equipment124, etc.).

Data store140can 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. Data store140can include multiple storage components (e.g., multiple drives or multiple databases) that can span multiple computing devices (e.g., multiple server computers). The data store140can store data associated with processing a substrate at manufacturing equipment124. For example, data store140can store data collected by sensors126at manufacturing equipment124before, during, or after a substrate process (referred to as process data). Process data can refer to historical process data (e.g., process data generated for a prior substrate processed at the manufacturing system) and/or current process data (e.g., process data generated for a current substrate processed at the manufacturing system). Data store can also store spectral data or non-spectral data associated with a portion of a substrate processed at manufacturing equipment124. Spectral data can include historical spectral data and/or current spectral data.

The data store140can also store contextual data associated with one or more substrates processed at the manufacturing system. Contextual data can include a recipe name, recipe step number, preventive maintenance indicator, operator, etc. Contextual data can refer to historical contextual data (e.g., contextual data associated with a prior process performed for a prior substrate) and/or current process data (e.g., contextual data associated with current process or a future process to be performed for a prior substrate). The contextual data can further include identify sensors that are associated with a particular sub-system of a process chamber.

The data store140can also store task data. Task data can include one or more sets of operations to be performed for the 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 a process chamber, a flow rate setting for a precursor for a material of a film deposited on a substrate, etc. In another example, task data can include controlling pressure at a defined pressure point for the flow value. Task data can refer to historical task data (e.g., task data associated with a prior process performed for a prior substrate) and/or current task data (e.g., task data associated with current process or a future process to be performed for a substrate).

In some embodiments, data store140can be configured to store data that is not accessible to a user of the manufacturing system. For example, process data, spectral data, contextual data, etc. obtained for a substrate being processed at the manufacturing system is not accessible to a user (e.g., an operator) of the manufacturing system. In some embodiments, all data stored at data store140can be inaccessible by the user of the manufacturing system. In other or similar embodiments, a portion of data stored at data store140can be inaccessible by the user while another portion of data stored at data store140can be accessible by the user. In some embodiments, one or more portions of data stored at data store140can be encrypted using an encryption mechanism that is unknown to the user (e.g., data is encrypted using a private encryption key). In other or similar embodiments, data store140can include multiple data stores where data that is inaccessible to the user is stored in one or more first data stores and data that is accessible to the user is stored in one or more second data stores.

In some embodiments, data store140can be configured to store data associated with known fault patterns. A fault pattern can be a one or more values (e.g., a vector, a scalar, etc.) associated with one or more issues or failures associated with a process chamber sub-system. In some embodiments, a fault pattern can be associated with a corrective action. For example, a fault pattern can include parameter adjustment steps to correct the issue or failure indicated by the fault pattern. For example, the predictive system can compare a determined fault pattern to a library of known fault patterns to determine the type of failure experienced by a sub-system, the cause of the failure, the recommended corrective action to correct the fault, and so forth.

In some embodiments, predictive system110includes predictive server112, server machine170and server machine180. The predictive server112, server machine170, and server machine180may each include one or more computing devices such as a rackmount server, a router computer, a server computer, a personal computer, a mainframe computer, a laptop computer, a tablet computer, a desktop computer, Graphics Processing Unit (GPU), accelerator Application-Specific Integrated Circuit (ASIC) (e.g., Tensor Processing Unit (TPU)), etc.

Server machine170includes a training set generator172that is capable of generating training data sets (e.g., a set of data inputs and a set of target outputs) to train, validate, and/or test a machine-learning model190. Machine-learning model190can be any algorithmic model capable of learning from data. Some operations of data set generator172is described in detail below with respect toFIG.2. In some embodiments, the data set generator172can partition the training data into a training set, a validating set, and a testing set. In some embodiments, the predictive system110generates multiple sets of training data.

Server machine180can include a training engine182, a validation engine184, a selection engine185, and/or a testing engine186. An engine can refer to hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, processing device, etc.), software (such as instructions run on a processing device, a general purpose computer system, or a dedicated machine), firmware, microcode, or a combination thereof. Training engine182can be capable of training one or more machine-learning models190. Machine-learning model190can refer to the model artifact that is created by the training engine182using the training data (also referred to herein as a training set) that includes training inputs and corresponding target outputs (correct answers for respective training inputs). The training engine182can find patterns in the training data that map the training input to the target output (the answer to be predicted), and provide the machine-learning model190that captures these patterns. The machine-learning model190can use one or more of a statistical modelling, support vector machine (SVM), Radial Basis Function (RBF), clustering, supervised machine-learning, semi-supervised machine-learning, unsupervised machine-learning, k-nearest neighbor algorithm (k-NN), linear regression, random forest, neural network (e.g., artificial neural network), etc.

One type of machine learning model that may 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 regression layers that map features to a desired output space. A convolutional neural network (CNN), for example, hosts multiple layers of convolutional filters. Pooling is performed, and non-linearities may be addressed, at lower layers, on top of which a multi-layer perceptron is commonly appended, mapping top layer features extracted by the convolutional layers to decisions (e.g. classification outputs). Deep learning is a class of machine learning algorithms that use 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 may learn in a supervised (e.g., classification) and/or unsupervised (e.g., pattern analysis) manner. Deep neural networks include a hierarchy of layers, where the different layers learn different levels of representations that correspond to different levels of abstraction. In deep learning, each level learns to transform its input data into a slightly more abstract and composite representation. In a plasma process tuning, for example, the raw input may be process result profiles (e.g., thickness profiles indicative of one or more thickness values across a surface of a substrate); the second layer may compose feature data associated with a status of one or more zones of controlled elements of a plasma process system (e.g., orientation of zones, plasma exposure duration, etc.); the third layer may include a starting recipe (e.g., a recipe used as a starting point for determining an updated process recipe the process a substrate to generate a process result the meets threshold criteria). Notably, a deep learning process can learn which features to optimally place in which level on its own. The “deep” in “deep learning” refers to the number of layers through which the data is transformed. More precisely, deep learning systems have a substantial credit assignment path (CAP) depth. The CAP is the chain of transformations from input to output. CAPs describe potentially causal connections between input and output. For a feedforward neural network, the depth of the CAPs may be that of the network and may be the number of hidden layers plus one. For recurrent neural networks, in which a signal may propagate through a layer more than once, the CAP depth is potentially unlimited.

In one embodiment, one or more machine learning model is a recurrent neural network (RNN). An RNN is a type of neural network that includes a memory to enable the neural network to capture temporal dependencies. An RNN is able to learn input-output mappings that depend on both a current input and past inputs. The RNN will address past and future flow rate measurements and make predictions based on this continuous metrology information. RNNs may be trained using a training dataset to generate a fixed number of outputs (e.g., to determine a set of substrate processing rates, determine modification to a substrate process recipe). One type of RNN that may be used is a long short term memory (LSTM) neural network.

Training of a neural network may be achieved in a supervised learning manner, which involves feeding a training dataset consisting of labeled inputs through the network, observing its outputs, defining an error (by measuring the difference between the outputs and the label values), and using techniques such as deep gradient descent and backpropagation to tune the weights of the network across all its layers and nodes such that the error is minimized. In many applications, repeating this process across the many labeled inputs in the training dataset yields a network that can produce correct output when presented with inputs that are different than the ones present in the training dataset.

A training dataset containing 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) may be used to form a training dataset.

To effectuate training, processing logic may input the training dataset(s) into one or more untrained machine learning models. Prior to inputting a first input into a machine learning model, the machine learning model may be initialized. Processing logic trains the untrained machine learning model(s) based on the training dataset(s) to generate one or more trained machine learning models that perform various operations as set forth above. Training may be performed by inputting one or more of the sensor data into the machine learning model one at a time.

The machine learning model processes the input to generate an output. An artificial neural network includes an input layer that consists of values in a data point. The next layer is called a hidden layer, and nodes at the hidden layer each receive one or more of the input values. Each node contains parameters (e.g., weights) to apply to the input values. Each node therefore essentially inputs the input values into a multivariate function (e.g., a non-linear mathematical transformation) to produce an output value. A next layer may be another hidden layer or an output layer. In either case, the nodes at the next layer receive the output values from the nodes at the previous layer, and each node applies weights to those values and then generates its own output value. This may be performed at each layer. A final layer is the output layer, where there is one node for each class, prediction and/or output that the machine learning model can produce.

Accordingly, the output may include one or more predictions or inferences. For example, an output prediction or inference may include one or more predictions of film buildup on chamber components, erosion of chamber components, predicted failure of chamber components, and so on. window). Processing logic determines an error (i.e., a classification error) based on the differences between the output (e.g., predictions or inferences) of the machine learning model and target labels associated with the input training data. Processing logic adjusts weights of one or more nodes in the machine learning model based on the error. An error term or delta may be determined for each node in the artificial neural network. Based on this error, the artificial neural network adjusts one or more of its parameters for one or more of its nodes (the weights for one or more inputs of a node). Parameters may be updated in a back propagation manner, such that nodes at a highest layer are updated first, followed by nodes at a next layer, and so on. An artificial neural network contains multiple layers of “neurons”, where each layer receives as input values from neurons at a previous layer. The parameters for each neuron include weights associated with the values that are received from each of the neurons at a previous layer. Accordingly, adjusting the parameters may include adjusting the weights assigned to each of the inputs for one or more neurons at one or more layers in the artificial neural network.

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

Once one or more trained machine learning models190are generated, they may be stored in predictive server112as predictive component114or as a component of predictive component114.

The validation engine184can be capable of validating machine-learning model190using a corresponding set of features of a validation set from training set generator172. Once the model parameters have been optimized, model validation may be performed to determine whether the model has improved and to determine a current accuracy of the deep learning model. The validation engine184can determine an accuracy of machine-learning model190based on the corresponding sets of features of the validation set. The validation engine184can discard a trained machine-learning model190that has an accuracy that does not meet a threshold accuracy. In some embodiments, the selection engine185can be capable of selecting a trained machine-learning model190that has an accuracy that meets a threshold accuracy. In some embodiments, the selection engine185can be capable of selecting the trained machine-learning model190that has the highest accuracy of the trained machine-learning models190.

The testing engine186can be capable of testing a trained machine-learning model190using a corresponding set of features of a testing set from data set generator172. For example, a first trained machine-learning model190that was trained using a first set of features of the training set can be tested using the first set of features of the testing set. The testing engine186can determine a trained machine-learning model190that has the highest accuracy of all of the trained machine-learning models based on the testing sets.

As described in detail below, predictive server112includes a predictive component114that is capable of providing data indicative of the expected behavior of each sub-system of a process chamber, and running trained machine-learning model190on the current sensor data input to obtain one or more outputs. The predictive server112can further provide data indicative of the health of the process chamber sub-system and diagnostics. This will be explained in further detail below.

The client device120, manufacturing equipment124, sensors126, metrology equipment128, predictive server112, data store140, server machine170, and server machine180can be coupled to each other via a network130. In some embodiments, network130is a public network that provides client device120with access to predictive server112, data store140, and other publically available computing devices. In some embodiments, network130is a private network that provides client device120access to manufacturing equipment124, metrology equipment128, data store140, and other privately available computing devices. Network130can include one or more wide area networks (WANs), local area networks (LANs), wired networks (e.g., Ethernet network), wireless networks (e.g., an 802.11 network or a Wi-Fi network), cellular networks (e.g., a Long Term Evolution (LTE) network), routers, hubs, switches, server computers, cloud computing networks, and/or a combination thereof.

It should be noted that in some other implementations, the functions of server machines170and180, as well as predictive server112, can be provided by a fewer number of machines. For example, in some embodiments, server machines170and180can be integrated into a single machine, while in some other or similar embodiments, server machines170and180, as well as predictive server112, can be integrated into a single machine.

In general, functions described in one implementation as being performed by server machine170, server machine180, and/or predictive server112can also be performed on client device120. In addition, the functionality attributed to a particular component can be performed by different or multiple components operating together.

In embodiments, a “user” can be represented as a single individual. However, other embodiments of the disclosure encompass a “user” being an entity controlled by a plurality of users and/or an automated source. For example, a set of individual users federated as a group of administrators can be considered a “user.”

FIG.2is a flow chart of a method200for training a machine-learning model, according to aspects of the present disclosure. Method200is performed by processing logic that can include hardware (circuitry, dedicated logic, etc.), software (such as is run on a general purpose computer system or a dedicated machine), firmware, or some combination thereof. In one implementation, method200can be performed by a computer system, such as computer system architecture100ofFIG.1. In other or similar implementations, one or more operations of method200can be performed by one or more other machines not depicted in the figures. In some aspects, one or more operations of method200can be performed by server machine170, server machine180, and/or predictive server112.

At block210, processing logic initializes a training set T to an empty set (e.g., { }).

At block212, processing logic obtains sensor data (e.g., sensor values, features, trace data) associated with a prior deposition process performed to deposit one or more layers of film on a surface of a prior substrate. The sensor data can be further associated with a sub-system of a process chamber. A sub-system can be characterized as a set of sensors related with an operational parameter of the process chamber. An operational parameter can be a temperature, a flow rate, a pressure, and so forth. For example, a pressure sub-system can be characterized by one or more sensors measuring the gas flow, the chamber pressure, the control valve angle, the foreline (vacuum line between pumps) pressure, the pump speed, and so forth. Each process chamber can include multiple different sub-systems, such as a pressure sub-system, a flow sub-system, a temperature sub-system, and so forth.

In some embodiments, the sensor data associated with the deposition process is historical data associated with one or more prior deposition settings for a prior deposition process previously performed for a prior substrate at a manufacturing system. For example, the historical data can be historical contextual data associated with the prior deposition process stored at data store140. In some embodiments, the one or more prior deposition settings can include at least one of a prior temperature setting for the prior deposition process, a prior pressure setting for the prior deposition setting, a prior flow rate setting for a precursor for one or more material of the prior film deposited on the surface of the prior substrate, or any other setting associated with the deposition process. A flow rate setting can refer to a flow rate setting for the precursor at an initial instance of the prior deposition process (referred to as an initial flow rate setting), a flow rate setting for the precursor at a final instance of the prior deposition process (referred to as a final flow rate setting), or a ramping rate for the flow rate of the precursor during the deposition process. In one example, the precursor for the prior film can include a boron-containing precursor or a silicon-containing precursor. In some embodiments, the sensor data can also be associated with a prior etching process performed on the prior substrate, or any other process performed in the process chamber.

At block214, processing logic obtains task data associated with a recipe for film deposited on the surface of the prior substrate. For example, the task data can a required temperature setting, a pressure setting, a flow rate setting for a precursor for a material of a film deposited on a substrate, etc. Task data can include historical task data for a prior film deposited on a surface of a prior substrate. In some embodiments, the historical task data for the prior film can correspond to a historical task value associated with a recipe for the prior film. Processing logic can obtain the task data from data store140, in accordance with previously described embodiments.

At block216, processing logic generates first training data based on the obtained sensor data associated with the prior deposition process performed for the prior substrate. At block218, processing logic generates second training data based on the task data associated with the recipe for film deposited on the surface of the prior substrate.

At block220, processing logic generates a mapping between the first training data and the second training data. The mapping refers to the first training data that includes or is based on data for the prior deposition process performed for the prior substrate and the second training data that includes or is based on task data associated with the recipe for film deposited on the surface of the prior substrate, where the first training data is associated with (or mapped to) the second training data. At block224, processing logic adds the mapping to the training set T.

At block226, processing logic determines whether the training set, T, includes a sufficient amount of training data to train a machine-learning model. It should be noted that in some implementations, the sufficiency of training set T can be determined based simply on the number of mappings in the training set, while in some other implementations, the sufficiency of training set T can be determined based on one or more other criteria (e.g., a measure of diversity of the training examples, etc.) in addition to, or instead of, the number of input/output mappings. Responsive to determining the training set does not include a sufficient amount of training data to train the machine-learning model, method200returns to block212. Responsive to determining the training set T includes a sufficient amount of training data to train the machine-learning model, method200continues to block228.

At block228, processing logic provides the training set T to train the machine-learning model. In one implementation, the training set T is provided to training engine182of server machine180to perform the 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 the output nodes of the neural network. The connection weights in the neural network are then adjusted in accordance with a learning algorithm (e.g., backpropagation, etc.), and the procedure is repeated for the other input/output mappings in the training set T.

In some embodiments, the processing logic can perform outlier detection methods to remove anomalies from the training set T prior to training the machine-learning model. Outlier detection methods can include techniques that identify values that differ significantly from the majority the training data. These values can be generated from errors, noise, etc.

At block230, processing logic perform a calibration process on the trained machine-learning model. In some embodiments, the processing logic can compare the expected behavior of the process chamber sub-system to current behavior of the process chamber sub-system based on the differences in values between the predictive behavior and the current behavior. For example, the processing logic can compare one or more values associated with the predictive data of the pressure sub-system, the flow sub-system, or the temperature sub-system to one or more values associated with the current measured behavior of the pressure sub-system, the flow sub-system, or the temperature sub-system, respectively.

After block230, the machine-learning model can be used to generate one or more values indicative of a fault pattern (e.g., abnormal behavior) of the process chamber sub-system, generate predicative data indicative of the type of fault (e.g., issue, failure, etc.), and/or perform a corrective action(s) to correct the suspected issue or failure. The predictive data can be generated by comparing the fault pattern to the library of known fault patterns.

In some embodiments, a manufacturing system can include more than one process chambers. For example, example manufacturing system300ofFIG.3illustrates multiple process chambers314,316,318. It should be noted that, in some embodiments, data obtained to train the machine-learning model and data collected to be provided as input to the machine-learning model can be associated with the same process chamber of the manufacturing system. In other or similar embodiments, data obtained to train the machine-learning model and data collected to be provided as input to the machine-learning model can be associated with different process chambers of the manufacturing system. In other or similar embodiments, data obtained to train the machine-learning model can be associated with a process chamber of a first manufacturing system and data collected to be provide as input to the machine-learning model can be associated with a process chamber of a second manufacturing system.

FIG.3is a top schematic view of an example manufacturing system300, according to aspects of the present disclosure. Manufacturing system300can perform one or more processes on a substrate302. Substrate302can be any suitably rigid, fixed-dimension, planar article, such as, e.g., a silicon-containing disc or wafer, a patterned wafer, a glass plate, or the like, suitable for fabricating electronic devices or circuit components thereon.

Manufacturing system300can include a process tool304and a factory interface306coupled to process tool304. Process tool304can include a housing308having a transfer chamber310therein. Transfer chamber310can include one or more process chambers (also referred to as processing chambers)314,316,318disposed therearound and coupled thereto. Process chambers314,316,318can be coupled to transfer chamber310through respective ports, such as slit valves or the like. Transfer chamber310can also include a transfer chamber robot312configured to transfer substrate302between process chambers314,316,318, load lock320, etc. Transfer chamber robot312can include one or multiple arms where each arm includes one or more end effectors at the end of each arm. The end effector can be configured to handle particular objects, such as wafers, sensor discs, sensor tools, etc. In some embodiments, the end effector can be configured to couple to one or more sensor tools from tool station340.

Tool station340can be a station used to house, move, change and/or recharge the sensor tools. In some embodiments, tool station340can be an automatic tool changer capable of enabling the process chamber robot to select different sensor tools. Tool station340can include an array of sensor tools stored on a magazine (e.g., a drum magazine, a chain type magazine, etc.) or other container. In some embodiments, responsive to a selection of a sensor tool, the array of tools can reposition and the process chamber robot312can select the desired sensor tool from a predetermined position. In some embodiments, responsive to a selection of a sensor tool, the process chamber robot312can position the end effector to the specified location of the sensor tool in the tool station. In some embodiments, tool station340can include one or more charging ports for electrically charging each sensor tool. For example, when a sensor tool is positioned within the tool station340(e.g., on the magazine), the sensor tool's charging component can be connected to (e.g., a wired connection) or within proximity (e.g., wireless connection such as inductive charging) of the charging port. Further detail regarding transfer chamber robot312and tool station340are provided with respect toFIG.5A-5B.

In some embodiments, the end effector can be configured to retrieve a sensor disc from a load lock. The sensor disc can be a substrate or any other device that includes one or more sensors. Further detail regarding transfer chamber robot312and the sensor disc are provided with respect toFIG.6A-B.

Process chambers314,316,318can be adapted to carry out any number of processes on substrates302. A same or different substrate process can take place in each processing chamber314,316,318. A substrate process can include atomic layer deposition (ALD), physical vapor deposition (PVD), chemical vapor deposition (CVD), etching, annealing, curing, pre-cleaning, metal or metal oxide removal, or the like. Other processes can be carried out on substrates therein. Process chambers314,316,318can each include one or more sensors configured to capture data for substrate302before, after, or during a substrate process. For example, the one or more sensors can be configured to capture spectral data and/or non-spectral data for a portion of substrate302during a substrate process. In other or similar embodiments, the one or more sensors can be configured to capture data associated with the environment within process chamber314,316,318before, after, or during the substrate process. For example, the one or more sensors can be configured to capture data associated with a temperature, a pressure, a gas concentration, etc. of the environment within process chamber314,316,318during the substrate process.

A load lock320can also be coupled to housing308and transfer chamber310. Load lock320can be configured to interface with, and be coupled to, transfer chamber310on one side and factory interface306. Load lock320can have an environmentally-controlled atmosphere that can be changed from a vacuum environment (wherein substrates can be transferred to and from transfer chamber310) to an at or near atmospheric-pressure inert-gas environment (wherein substrates can be transferred to and from factory interface306) in some embodiments. Factory interface306can be any suitable enclosure, such as, e.g., an Equipment Front End Module (EFEM). Factory interface306can be configured to receive substrates302from substrate carriers322(e.g., Front Opening Unified Pods (FOUPs)) docked at various load ports324of factory interface306. A factory interface robot326(shown dotted) can be configured to transfer substrates302between carriers (also referred to as containers)322and load lock320. Carriers322can be a substrate storage carrier or a replacement part storage carrier.

Manufacturing system300can also be connected to a client device (not shown) that is configured to provide information regarding manufacturing system300to a user (e.g., an operator). In some embodiments, the client device can provide information to a user of manufacturing system300via one or more graphical user interfaces (GUIs). For example, the client device can provide information regarding a target thickness profile for a film to be deposited on a surface of a substrate302during a deposition process performed at a process chamber314,316,318via a GUI. The client device can also provide information regarding a modification to a process recipe in view of a respective set of deposition settings predicted to correspond to the target profile, in accordance with embodiments described herein.

Manufacturing system300can also include a system controller328. System controller328can be and/or include a computing device such as a personal computer, a server computer, a programmable logic controller (PLC), a microcontroller, and so on. System controller328can include one or more processing devices, which can be general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device can be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing device can 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), network processor, or the like. System controller328can 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. System controller328can execute instructions to perform any one or more of the methodologies and/or embodiments described herein. In some embodiments, system controller328can execute instructions to perform one or more operations at manufacturing system300in accordance with a process recipe. The instructions can be stored on a computer readable storage medium, which can include the main memory, static memory, secondary storage and/or processing device (during execution of the instructions).

System controller328can receive data from sensors included on or within various portions of manufacturing system300(e.g., processing chambers314,316,318, transfer chamber310, load lock320, etc.). In some embodiments, data received by the system controller328can include spectral data and/or non-spectral data for a portion of substrate302. In other or similar embodiments, data received by the system controller328can include data associated with processing substrate302at processing chamber314,316,318, as described previously. For purposes of the present description, system controller328is described as receiving data from sensors included within process chambers314,316,318. However, system controller328can receive data from any portion of manufacturing system300and can use data received from the portion in accordance with embodiments described herein. In an illustrative example, system controller328can receive data from one or more sensors for process chamber314,316,318before, after, or during a substrate process at the process chamber314,316,318. Data received from sensors of the various portions of manufacturing system300can be stored in a data store350. Data store350can be included as a component within system controller328or can be a separate component from system controller328. In some embodiments, data store350can be data store140described with respect toFIG.1.

FIG.4is a cross-sectional schematic side view of a process chamber400, in accordance with embodiments of the present disclosure. In some embodiments, process chamber400can correspond to process chamber314,316,318, described with respect toFIG.3. Process chamber400can be used for processes in which a corrosive plasma environment is provided. For example, the process chamber400can be a chamber for a plasma etcher or plasma etch reactor, and so forth. In another example, process chamber can be a chamber for a deposition process, as previously described. In one embodiment, the process chamber400includes a chamber body402and a showerhead430that encloses an interior volume406. The showerhead430can include a showerhead base and a showerhead gas distribution plate. Alternatively, the showerhead430can be replaced by a lid and a nozzle in some embodiments, or by multiple pie shaped showerhead compartments and plasma generation units in other embodiments. The chamber body402can be fabricated from aluminum, stainless steel or other suitable material such as titanium (Ti). The chamber body402generally includes sidewalls408and a bottom410. An exhaust port426can be defined in the chamber body402, and can couple the interior volume406to a pump system428. The pump system428can include one or more pumps and throttle valves utilized to evacuate and regulate the pressure of the interior volume406of the process chamber400.

The showerhead430can be supported on the sidewall408of the chamber body402. The showerhead420(or lid) can be opened to allow access to the interior volume406of the process chamber400, and can provide a seal for the process chamber400while closed. A gas panel458can be coupled to the process chamber400to provide process and/or cleaning gases to the interior volume406through the showerhead430or lid and nozzle (e.g., through apertures of the showerhead or lid and nozzle). For example. gas panel458can provide precursors for materials of a film451deposited on a surface of a substrate302. In some embodiments, a precursor can include a silicon-based precursor or a boron-based precursor. The showerhead430can include a gas distribution plate (GDP) and can have multiple gas delivery holes432(also referred to as channels) throughout the GDP. A substrate support assembly448is disposed in the interior volume406of the process chamber400below the showerhead430. The substrate support assembly448holds a substrate302during processing (e.g., during a deposition process).

In some embodiments, processing chamber400can include metrology equipment (not shown) configured to generate in-situ metrology measurements during a process performed at process chamber400. The metrology equipment can be operatively coupled to the system controller (e.g., system controller328, as previously described). In some embodiments, the metrology equipment can be configured to generate a metrology measurement value (e.g., a thickness) for film451during particular instances of the deposition process. The system controller can generate a thickness profile for film451based on the received metrology measurement values from the metrology equipment. In other or similar embodiments, processing chamber400does not include metrology equipment. In such embodiments, the system controller can receive one or more metrology measurement values for film451after completion of the deposition process at process chamber400. System controller can determine a deposition rate based on the one or more metrology measurement values and can associate generate the thickness profile for film451based on the determined concentration gradient and the determined deposition rate of the deposition process.

FIG.5Ais a schematic view of end effector510, in accordance with embodiments of the present disclosure.FIG.5Bis a schematic view of end effector510coupled to sensor tool520, in accordance with embodiments of the present disclosure. End effector510can include tool connector512, substrate platform514, and robot connector516. In some embodiments, the end effector510can include one or more sensors. Sensor tool520can include end effector connector522and charging component526.

Robot connector516can be used to couple end effector510to transfer chamber robot (e.g., transfer chamber robot310). Substrate platform514can be used to handle particular objects, such as substrates (e.g., wafers). Tool connector512can be used to couple end effector510to sensor tool520. For example, the transfer chamber robot312can receive instructions to couple to a specific sensor tool (e.g., sensor tool520) housed in the tool station (e.g., tool station340), position the end effector510in a first position in proximity to the end effector connector522of sensor tool520(e.g., a pre-couple position), place the end effector510in a second position to couple the end effector510(via tool connector512) to the end effector connector522of sensor tool520(e.g., a couple position), and then withdraw the sensor tool520from the tool station340. Upon completion of use of the sensor tool520, the transfer chamber robot312can place the sensor tool520back into the tool station340. In some embodiments, tool station340can be configured to automatically couple a selected sensor tool to end effector510using, for example, a gripper.

The sensor tool520can include one or more sensors. The sensors can be used to characterize, take readings of, or take measurements of one or more aspects of the process chambers314,316,318. The sensors may include one or more of an accelerometer, a distance sensor (e.g., to determine a height, a width or a length between two objects), a camera (e.g., a high resolution camera, a high speed camera, etc.), a capacitive sensor, a reflectometer, a pyrometer (e.g., a remote sensing thermometer, an infrared camera, etc.), a laser inducted florescence spectroscope, fiber optics (e.g., a fiber optic probe), a surface acoustic sensor, an eddy current sensor, a borescope, a photodiode sensor, a photo multiplier tube, a solid state detector, a thermocouple, a voltage sensor, a current sensor, a resistance sensor, or any other type of sensor.

The accelerometer can be used to detect and correct (or calibrate) for vibration and position noise of the transfer chamber robot312. The distance sensor can be used to detect erosion and/or corrosion on the chuck (table), edge ring, shower head, walls, or any other component of the process chamber314,316,318. For example, during the etching process, an edge ring can be used to promote uniformity along a substrate surface. However, the etching may corrode the edge ring. Accordingly, the position sensor may be used to detect said corrosion by measuring the distance between the top plane of the edge ring and, for example, the top plane of the substrate. The camera can record sections of the process chamber for314,316,318for visual inspection by an operator. The capacitor sensor can be used to detect the position of the showerhead used for gas distribution inside the process chamber314,316,318, determine the leveling of the substrate, detect erosion, etc. The reflectometer can be used to probe the quality of a seasoning film on the walls of the process chamber314,316,318. For example, the reflectometer can generate a light onto the wall of the process chamber314,316,318, and record the refection index of the light reflected. The pyrometer can be used to detect temperature uniformity of the heater in the process chamber314,316,318, to detect hot spots inside the process chamber314,316,318, etc. The transfer chamber robot312can include any quantity or combination of the discussed or other sensors.

In some embodiments, the sensor tool520can include an electronics module capable of facilitating communication with system100(e.g., system controller328, predictive system110, client device120, etc.). The electronics module can include a microcontroller and a memory buffer coupled to the microcontroller. The memory buffer can be used to collect and store data obtained by the sensor tool520before transmitting the data to system100. In some embodiments, the data can be transmitted using a wireless communication circuit. In other embodiments, the data can be transmitted using a wired connection between the sensor tool520and the system100. For example, end effector connector522and/or tool connector512can include one or more contact pins, low particle connections, pogo pins, or any other type of connectors that can transfer data (or power) between the sensor tool520and the system100(via the transfer chamber robot312). In some embodiments, the data can first be stored (buffered) in the memory buffer prior to being transmitted to the system100. In other embodiments, the data can be transmitted to the system100as the data is collected, without being stored in the memory buffer. In some embodiments, the wireless or wired connection can be continuous. In other embodiments, the wireless or wired connection can be established periodically or upon completion of the inspection or some other triggering event (e.g., when the memory buffer is close to being full, when the sensor tool520is positioned on tool station340, etc.). For example, the sensor tool520can include a wired or wireless communication circuit for communicating with the tool station520. Sensor tool520can collect and store sensor data on the memory buffer, and transmit the sensor data once positioned on tool station340. In some embodiments, tool station340can include a wired or wireless connector capable of receiving data (e.g., sensor data) from the sensor tool520or transmitting data (e.g., instructions) to the sensor tool520.

The electronics module can further include a power element and a power-up circuit. For example, the power element can be a battery, a capacitor (such as an ultracapacitor or a supercapacitor), or any other power element capable of providing electrical power to the sensor and/or the electronics module (e.g., a power-link). In some embodiments, the power element can be rechargeable from the tool station340. For example, while the sensor tool520is housed and idle in the tool station340, the sensor tool520can be charged. In particular, the sensor tool520can be coupled, via the charging component526, to a charging port of the tool station340. In some embodiments, the charging component526can 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 component526can be positioned within proximity of the charging port and the sensor tool520can be charged via wireless charging (e.g., inductive charging).

FIGS.6A and6Bare schematic views of sensor disc610and end effector620, in accordance with embodiments of the present disclosure. End effector620can include electric connector622. Electric connector622can be any type of connector (e.g., contact pins, low particle connections, pogo pins) used to provide electrical power and/or transfer data to and from sensor disk610, which will be explained in more detail below.

The sensor disc610can be any device that includes one or more sensors (e.g. sensors612A-612E). The sensors612A-612E can be used to characterize, take readings of, or take measurements of one or more aspects of the process chambers314,316,318. The sensors612A-612E may include one or more of an accelerometer, a distance sensor (e.g., to determine a height, a width or a length between two objects), a camera (e.g., a high resolution camera, a high speed camera, etc.), a capacitive sensor, a reflectometer, a pyrometer (e.g., a remote sensing thermometer, an infrared camera, etc.), a laser inducted florescence spectroscope, fiber optics (e.g., a fiber optic probe), a surface acoustic sensor, an eddy current sensor, a borescope, a photodiode sensor, a photo multiplier tube, a solid state detector, a thermocouple, a voltage sensor, a current sensor, a resistance sensor, or any other type of sensor. While sensor disc610illustrates five sensors612A-612E, it should be understood that any amount of sensors can be coupled to sensor disc610.

In some embodiments, the sensor disc610(and/or the sensors612A-E) is connected to one or more of a power-link and/or a data-link. The power-link can be any wired (e.g., via electric connector622) or wireless (e.g., inductive) connection capable of providing electrical power to the sensor(s). In some embodiments, the power-link is similar or the same system used to provide electrical power to other function of the transfer chamber robot112(e.g., link movement functions, effector operating function, etc.). In some embodiments, the power-link is a system independent of another power-link used to provide electrical power to the other functions of the transfer chamber robot312. In some embodiments, the sensor disc610can include a power element and/or a power-up circuit. The data-link can be any wired (e.g., via electric connector622) or wireless (WiFi, Bluetooth, Internet-based, etc.) connection used to provide or retrieve data from the sensor(s). For example, the data-link can be used to provide instructions to the sensors to take measurements or readings, send collected data to an interface (e.g., a user interface) or a data storage system, etc. In some embodiments, the data-link is a system independent of another data-link used to provide instructions and communicate with the transfer chamber robot312to enable the transfer chamber robot312to transfer and position substrates between the transfer chambers314,316,318and the load locks320. In some embodiments, the sensor disc610can include an electronics module (e.g., a microcontroller and a memory buffer coupled to the microcontroller) capable of facilitating communication with system100(e.g., system controller328, predictive system110, client device120, etc.).

The sensor disc610can be handled by the end effector620. For example, the sensor disc can be housed in a FOUP, which may be docked at a load port324of factory interface306. Factory interface robot326can be configured to transfer sensor disc610between the FOUP and load lock320. Transfer chamber robot312can position end effector620(which is coupled to sensor disc620) within any process chamber314,316,318. End effector and/an arm of transfer chamber robot may include one or more connectors for connecting to the sensor tool or sensor disc620. Such connectors may provide a power connection and/or a data-link connection. Using sensor disc620, transfer chamber robot312can take readings and/or measurements of the process chambers. In some embodiments, the sensor(s) and or transfer chamber robot312includes a processing device, such as a central processing unit (CPU), microcontroller, a programmable logic controller (PLC), a system on a chip (SoC), a server computer, or other suitable type of computing device. The processing device can be configured to execute programming instructions related to the operation of the sensors. The processing device can receive feedback signals from the sensors device and compute the signals into sensor data (e.g., temperature, video data, position data, etc.). The processing device can further transmit control signals to sensors based on received instructions. In some embodiments, the processing device is configured for high-speed feedback processing, and can include, for example, an EPM. In some embodiments, the processing device is configured to transmit or send the feedback signals and/or the sensor data to an interface (e.g., a user interface), a data store, etc.

Process chamber robot312can further be configure to position sensor disc610back into the load lock320. In some embodiments, sensor disc610can be of a shape similar to that of a wafer. In other embodiments, sensor disc610can be of any shape, including but not limited to, circular, oval, square, rectangular, irregular, etc.

FIG.7is a flow chart of a method700for determining a failure type of a process chamber sub-system using a machine-learning model, according to aspects of the present disclosure. Method700is performed by processing logic that can include hardware (circuitry, dedicated logic, etc.), software (such as is run on a general purpose computer system or a dedicated machine), firmware, or some combination thereof. In one implementation, method700can be performed by a computer system, such as computer system architecture100ofFIG.1. In other or similar implementations, one or more operations of method700can be performed by one or more other machines not depicted in the figures. In some aspects, one or more operations of method600can be performed by server machine170, server machine180, and/or predictive server112.

At block710, processing logic obtains sensor data associated with an operation performed in a process chamber. In some embodiments, the operation can include a deposition process performed in a process chamber to deposit one or more layers of film on a surface of a substrate, an etch process performed on the one or more layers of film on the surface of the substrate, etc. The operation can be performed according to a recipe. The sensor data can include a value of one or more of temperature (e.g., heater temperature), spacing, pressure, high frequency radio frequency, voltage of electrostatic chuck, electrical current, material flow, power, voltage, etc. Sensor data can be associated with or indicative of manufacturing parameters such as hardware parameters, such as settings or components (e.g., size, type, etc.) of the manufacturing equipment124, or process parameters of the manufacturing equipment124.

At block712, processing logic applies a machine-learning model (e.g. model190) to the obtained sensor data. The machine-learning model can be used to generate one or more values associated with the expected behavior of the process chamber sub-system. For example, the machine-learning model can use an algorithm to generate predictive behavior of the process chamber sub-system using the training set T. In some embodiments, the machine-learning model is trained using historical sensor data of a sub-system of the process chamber and task data associated with the recipe used to perform the operation.

At block714, processing logic generates an output via the machine-learning model based on the sensor data. In some embodiments, the output can be a value indicative of a pattern (e.g., a fault pattern). In particular, the output can include predicative data of whether the current data is indicative of a failure being experience by the process chamber. In some embodiments, the output can be at least one value indicative of a difference between the expected behavior of the process chamber sub-system and the actual behavior of the process chamber sub-system. In particular, the value(s) can be indicative of a difference between actual values of a set of sensors associated with the sub-system and the expected values of the set of sensors. The failure can include a mechanism failure, high or low pressure, high or low gas flow, high or low temperature, etc.

At block716, the processing logic determines whether the process chamber sub-system is experiencing a failure. In some embodiments, a failure can include a mechanism failure, high or low pressure, high or low gas flow, high or low temperature, corrosion, erosion, deterioration, etc. In some embodiments, the processing logic can determine whether the process chamber sub-system is experiencing a failure by comparing the output to a predetermined threshold value. In some embodiments, the processing logic can determine whether the process chamber sub-system is experiencing a failure by determining that the output fails to match expected behavior. Responsive to the processing logic determining that the process chamber sub-system is not experiencing a failure (e.g., a value of the output does not exceed the predetermined threshold value), the processing logic can proceed to block710. Responsive to the processing logic determining that the process chamber sub-system is experiencing a failure (e.g., a value of the output exceeds the predetermined threshold value), the processing logic can proceed to block718.

At block718, the processing logic can identify the type of failure based on the output. In some embodiments, the processing logic can compare the fault pattern against the manufacturing data graph(s) and/or a library of known fault patterns to determine the type of failure based on a similarity of the fault pattern when compared to a known fault pattern or the manufacturing data graph(s). In some embodiments, the type of fault can be extracted from the manufacturing data graphs using natural language processing and then associated with the corresponding fault pattern. In some embodiments, the type of failure can be displayed (to a user) on a user interface.

At block720, the processing logic can perform (or suggest), based on the identified failure, a corrective action. In some embodiments, the corrective action can be determined based on data obtained from the fault library. In some embodiments, the corrective action can include generating an alert or an indication, to the client device120, of the determined problem. In some embodiments, the corrective action can include the processing logic indicating the type of fault or failure, the cause of the fault or failure, and/or a recommend corrective action. In some embodiments, the corrective action can include the processing logic adjusting one or more parameters of a 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 for a material included in the film deposited on the substrate surface, etc.) based on a desired property for the film. In some embodiments, the deposition process recipe can be adjusted before, during (e.g., in real time) or after the deposition process.

In a further aspect, the computer system800may include a processing device802, a volatile memory804(e.g., Random Access Memory (RAM)), a non-volatile memory806(e.g., Read-Only Memory (ROM) or Electrically-Erasable Programmable ROM (EEPROM)), and a data storage device816, which may communicate with each other via a bus808.

Computer system800may further include a network interface device822(e.g., coupled to network874). Computer system800also may include a video display unit810(e.g., an LCD), an alphanumeric input device812(e.g., a keyboard), a cursor control device814(e.g., a mouse), and a signal generation device820.

In some implementations, data storage device816may include a non-transitory computer-readable storage medium824on which may store instructions826encoding any one or more of the methods or functions described herein, including instructions encoding components ofFIG.1(e.g., corrective action component122, predictive component114, etc.) and for implementing methods described herein.

Instructions826may also reside, completely or partially, within volatile memory804and/or within processing device802during execution thereof by computer system800, hence, volatile memory804and processing device802may also constitute machine-readable storage media.

The methods and illustrative examples 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 methods described herein and/or each of their individual functions, routines, subroutines, or operations. Examples of the structure for a variety of these systems are set forth in the description above.