Method and mechanism for non-contact characterization of a process chamber - Patent Application 20070122997

Abstract

Disclosed herein are embodiments of a transfer chamber robot and a method of using the transfer chamber robot. In one embodiment, a process tool for an electronic device manufacturing system comprises a 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 and comprises a sensor configured to take measurements within the process chamber.

Description

[Technical Field]

[0001] FIELD OF THE DISCLOSURE Embodiments of the present disclosure generally relate to methods and mechanisms for contact-free characterization of semiconductor process chambers. [Background technology]

[0002]

[0003] Electronics manufacturing systems generally include multiple process chambers that are subjected to a vacuum during processing. During the manufacturing of substrates, contaminants and residual deposits are introduced into various components of the process chambers. Therefore, the process chambers need to be periodically inspected and, based on the level of contamination or deposits, cleaned to remove contaminants and residual deposits from the walls and gas distribution plates.

[0003] Traditionally, operators periodically release the vacuum system and release 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, costly, and ineffective process. Alternatively, some electronics manufacturing systems modify 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] Some embodiments of the present disclosure relate to a process tool for an electronic device manufacturing system, the process tool including a transfer chamber, a process chamber coupled to the transfer chamber, and a transfer chamber robot configured to transfer substrates to and from the process chamber, the transfer chamber robot including a sensor configured to perform measurements within the process chamber.

[0005] Another aspect of the present disclosure relates to an electronic device manufacturing system comprising a load lock and a process tool, the process tool comprising a transfer chamber, a process chamber coupled to the transfer chamber, and a transfer chamber robot configured to transfer substrates to and from the process chamber, the transfer chamber robot comprising a sensor configured to take measurements within the process chamber.

[0006] Another aspect of the present disclosure relates to a transfer chamber robot comprising an arm assembly including a plurality of links, an end effector coupled to the arm assembly, the end effector configured to transfer substrates to and from the process chamber, and a sensor coupled to the arm assembly and configured to take measurements within the process chamber.

[0007] Another aspect of the present disclosure relates to a method that includes positioning, by a processor, a portion of a transfer chamber robot within a process chamber, the portion comprising at least one sensor; acquiring sensor data related to the process chamber with the one or more sensors; and removing the portion of the transfer chamber robot from the process chamber.

[0008] Another aspect of the present disclosure includes a method including acquiring, by a processor, a plurality of sensor values ​​generated by a sensor device in a process chamber. 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 failure of the subsystem. The method further includes determining a type of failure of the subsystem and generating a corrective action based on the type of failure.

[0009] The present disclosure is illustrated by way of example, and not limitation, in the figures of the accompanying drawings, in which like reference numerals indicate like elements. It should be noted that different references to "an" or "one" embodiment in this disclosure are not necessarily to the same embodiment, and such references mean at least one. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a block diagram illustrating an exemplary system architecture, according to some embodiments. [Figure 2] 1 is a flow diagram of a method for training a machine learning model, according to some embodiments. [Figure 3] 1 is a top schematic view of an exemplary manufacturing system, according to some embodiments. [Figure 4] 1 is a cross-sectional schematic side view of an exemplary process chamber of an exemplary manufacturing system, in accordance with some embodiments. [Figure 5] 1 is a schematic top view of a process tool according to an embodiment of the present disclosure. [Figure 6] FIG. 1 is a schematic top view of a transfer chamber robot link assembly according to an embodiment of the present disclosure. [Figure 7]10 is a flowchart of a method for controlling a transfer chamber robot to take measurements using sensors, according to an aspect of the present disclosure. [Figure 8] 1 is a flow diagram of a method for determining a fault type of a process chamber subsystem using a machine learning model, according to some embodiments. [Figure 9] FIG. 1 is a block diagram illustrating a computer system according to some embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0011] Techniques directed to methods and mechanisms for non-contact characterization of semiconductor process chambers of a manufacturing system are described herein. A film can be deposited on a surface of a substrate during a deposition process (e.g., a chemical vapor deposition (CVD) process, an atomic layer deposition (ALD) process, etc.) performed in the process chamber of the manufacturing system. For example, in a CVD process, the substrate is exposed to one or more precursors, which react on the substrate surface to create 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 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 may include alternating 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), 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] During deposition and etching processes, process chambers can experience deterioration, such as buildup of contaminants, erosion on some components, etc. Failure to catch and repair these deteriorations can cause defects in the substrate, leading to rejected parts, reduced manufacturing yields, and significant downtime and repair time.

[0013] Existing systems may modify the process chamber walls to include sensors to detect such condition deterioration. However, these intrusive wall sensors 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, which can result in film distortion. Furthermore, installing these sensors can be difficult because the process chamber may need to be modified at the customer site.

[0014] Aspects and implementations of the present disclosure address these and other shortcomings of existing technologies by equipping a transfer chamber robot with one or more sensors capable of performing measurements and acquiring data from within 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 some time. The transfer chamber robot can be equipped with one or more sensors used to characterize, read, or measure one or more aspects of the process chamber. The sensors 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 capacitive sensor, a reflectometer, a pyrometer (e.g., a remote sensing thermometer, an infrared camera, etc.), a laser-induced fluorescence spectrometer, an optical fiber, etc.

[0015] In some embodiments, the sensor may be coupled to an end effector of the transfer chamber robot or to one or more joints of the transfer chamber robot. In other embodiments, the transfer chamber robot may include additional links and / or more degrees of freedom that may be used to operate the sensor. In particular, the transfer chamber robot may include one or more additional links for coupling to the sensor and / or one or more additional degrees of freedom for translating, rotating, and / or positioning the sensor within the process chamber. Data retrieved by the sensor may be sent to a user interface or database for processing and analysis. Based on the data, an operator of the manufacturing system can decide whether to stop operation and perform maintenance or continue fabricating substrates.

[0016] In some embodiments, the predictive system can train a machine learning model and apply the machine learning model to current sensor values ​​to generate an output, such as one or more values ​​indicative of a fault pattern (e.g., anomalous behavior) of the process chamber subsystem and / or predictive data indicative of a type of fault (e.g., a problem, a failure, 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 fault pattern associated with the process chamber subsystem. The system can then, in some embodiments, compare the fault pattern to a library of known fault patterns to determine a type of fault experienced by the subsystem. In some embodiments, the system implements 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 a film deposited on a substrate surface, etc.) based on the fault pattern.

[0017] Aspects of the present disclosure yield a technical advantage of a significant reduction in the time required to perform an inspection of a process chamber. This configuration allows the transfer chamber robot to perform an inspection and evaluate the characteristics of the process chamber each time the transfer chamber robot places a substrate in or removes a substrate from the process chamber. The inspection is performed while maintaining a vacuum environment, thus eliminating the need to disengage the vacuum system or remove components of the electronics manufacturing system (such as the process chamber door) associated with manual inspection. This configuration also eliminates process chamber defects and plasma uniformity issues associated with placing sensors in the process chamber.

[0018] 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 about 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 front-opening unified pod (FOUP) radio frequency identification (RFID) reader 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).

[0019] The manufacturing tool 124 can perform operations according to a recipe or over a period of time to create products such as electronic devices. The manufacturing tool 124 can include a process chamber, such as the process chamber 400 described with respect to FIG. 4. The manufacturing tool 124 can perform processes on substrates (e.g., wafers) in the process chamber. Examples of substrate processes include deposition processes for depositing one or more layers of a film on the surface of the substrate, etching processes for forming a pattern on the surface of the substrate, etc. The manufacturing tool 124 can perform each process according to a process recipe. The process recipe defines a specific sequence of operations to be performed on the substrate during the process and can include one or more settings associated with each operation. For example, a deposition process recipe can include temperature settings for the process chamber, pressure settings for the process chamber, flow rate settings for precursors for materials included in the film deposited on the substrate surface, etc.

[0020] In some embodiments, the manufacturing tool 124 includes a sensor 126 configured to generate data related to 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 related to 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 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. The process chamber robot is described in more detail with respect to FIG. 3. In one example, the sensor may be mounted on an end effector of the transfer chamber robot used to support the substrate. Further details regarding the mounted sensors are provided with respect to FIGS. 5 and 6.

[0021] In some embodiments, the spectral data generated by the sensor 126 can indicate the concentration of one or more materials deposited on the surface of the substrate. The sensor 126 configured to generate spectral data related to the substrate can include a reflectometry sensor, an ellipsometry sensor, a thermal spectral sensor, a capacitive sensor, etc. The sensor 126 configured to generate non-spectral data related to the substrate can 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.

[0022] In some embodiments, the sensors 126 provide sensor data (e.g., sensor values, characteristics, trace data) related to the manufacturing equipment 124 (e.g., related to the production of a corresponding product, such as a wafer, by the manufacturing equipment 124). The manufacturing equipment 124 may 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 received from different sensors 126 over time (e.g., historical trace data, current trace data, etc.). 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 related to or indicative of manufacturing parameters, such as hardware parameters such as settings or components (e.g., size, type, etc.) of the manufacturing equipment 124 or process parameters of the manufacturing equipment 124. The sensor data may be provided while the manufacturing equipment 124 is performing the manufacturing process (e.g., readings of the equipment as it processes the product). The sensor data may be different for each substrate.

[0023] The metrology tool 128 can provide metrology data related to 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 contained on the surface of the substrate, critical dimension uniformity across the surface of the substrate, edge placement error, etc.). The metrology data can be for finished or semi-finished products. The metrology data can be different for each substrate. The metrology data can be generated using, for example, reflectometry techniques, ellipsometry techniques, TEM techniques, etc.

[0024] 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 about 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 cases, 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 cases, 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 cases, 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 about the substrate before and / or after a process.

[0025] 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 may be received from the client device 120. The client device 120 may display a graphical user interface (GUI) that allows a user to provide as input metrology measurements for substrates processed in a manufacturing system. The client device 120 may include a corrective action component 122. The corrective action component 122 may receive user input (e.g., via a graphical user interface (GUI) displayed via the client device 120) of instructions related to a manufacturing tool 124. 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 a corrective action based on the output, and causes the corrective action to be performed. In some embodiments, the corrective action component 122 receives corrective action instructions from the predictive system 110 and causes the corrective action to be performed. 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 related to the manufacturing equipment 124, corrective actions related to the manufacturing equipment 124, etc.).

[0026] The data store 140 may 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 may include multiple storage components (e.g., multiple drives or multiple databases) that may span multiple computing devices (e.g., multiple server computers). The data store 140 may store data related to processing substrates in the manufacturing tool 124. For example, the data store 140 may store data (referred to as process data) collected by the sensors 126 in the manufacturing tool 124 before, during, or after the substrate process. Process data may refer to historical process data (e.g., process data generated for a previous substrate processed in the manufacturing system) and / or current process data (e.g., process data generated for a current substrate processed in the manufacturing system). The data store may also store spectral or non-spectral data related to a portion of a substrate processed in the manufacturing tool 124. The spectral data may include historical spectral data and / or current spectral data.

[0027] 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 context data (e.g., context data associated with a current or future process to be performed on a previous substrate). The context data may further include sensors associated with a particular subsystem of a process chamber.

[0028] The data store 140 can also store task data. The task data can include one or more sets of treatments to be performed on a substrate during a deposition process and can include one or more settings associated with each treatment. For example, task data for a deposition process can include temperature settings for a process chamber, pressure settings for the process chamber, flow rate settings for precursors for a material of a film to be deposited on the substrate, etc. In another example, the task data can include control pressures at defined pressure points for flow rate values. Task data can refer to historical task data (e.g., task data associated with a previous process performed on a previous substrate) and / or current task data (e.g., task data associated with a current or future process to be performed on a substrate).

[0029] In some embodiments, the data store 140 can store an expected profile, a thickness profile, and a corrections profile. The expected profile can include one or more data points related to a desired film profile expected to be produced by a process recipe. In some embodiments, the expected profile can include a desired thickness of the film. The thickness profile can include one or more data points related to a current film profile generated by the fabrication tool 124. The thickness profile can be measured using the metrology tools 127, 128. The corrections profile can include one or more adjustments or offsets to be applied to parameters of the process chamber or process recipe. For example, the corrections profile can include adjustments to 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, a power supplied to the process chamber, a ratio of two or more settings, etc. The corrections profile can be generated by comparing the expected profile (e.g., a thickness profile expected to be produced by the process recipe) and using a library of known fault patterns and / or algorithms to determine adjustments to be applied to parameters of the process recipe to achieve the expected profile. The corrective profile may be applied to steps related to deposition processes, etching processes, and the like.

[0030] In some embodiments, data store 140 may be configured to store data that is not accessible 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 not accessible to users (e.g., operators) of the manufacturing system. In some embodiments, all data stored in data store 140 may be inaccessible by users of the manufacturing system. In other or similar embodiments, some portions of the data stored in data store 140 may be inaccessible by users, while other portions of the data stored in data store 140 may be accessible by users. In some embodiments, one or more portions of the data stored in data store 140 may 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 may 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.

[0031] In some embodiments, the data store 140 may be configured to store data related to known failure patterns. A failure pattern may be one or more values ​​(e.g., a vector, a scalar, etc.) related to one or more issues or failures 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 parameter adjustment steps to correct the issues or failures indicated by the failure pattern. For example, the predictive system may compare the determined failure pattern to a library of known failure patterns to determine the type of failure experienced by the subsystem, the cause of the failure, a recommended corrective action to correct the failure, etc.

[0032] 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.

[0033] The server machine 170 includes a training set generator 172 capable of generating a training dataset (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 may be any algorithmic model capable of learning from data. Some operations of the training set generator 172 are described in detail below with respect to FIG. 2. In some embodiments, the dataset generator 172 may partition the training data into a training set, a validation set, and a test set. In some embodiments, the prediction system 110 generates multiple sets of training data.

[0034] 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 device, etc.), software (e.g., instructions executing on a processing device, a general-purpose computer system, or a dedicated machine), firmware, microcode, or a combination thereof. The training engine 182 may be capable of training 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 and provide a machine learning model 190 that captures these patterns, mapping the training inputs to target outputs (answers to be predicted). 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.

[0035] 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. An artificial neural network generally includes a feature representation component with a classifier or recurrent layer that maps features to a desired output space. A convolutional neural network (CNN), for example, hosts multiple layers of convolutional filters. In lower layers, pooling may be performed to address nonlinearities, and a multilayer perceptron is typically added above the lower layers 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. A deep neural network includes a hierarchy of layers, with different layers learning different representation levels 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 related to the status of one or more zones of a controlled element of a plasma processing system (e.g., zone orientation, plasma exposure duration, etc.), and a third layer may include a starting recipe (e.g., a recipe used as a starting point for determining an updated process recipe for processing a substrate to produce a process result that meets a threshold criterion). In particular, a deep learning process can independently learn which features should optimally be placed at which level. The "deep" in "deep learning" refers to the number of layers through which data is transformed. More precisely, a deep learning system has a significant credit assignment path (CAP) depth. A CAP is a chain of transformations from input to output. A CAP potentially represents a causal connection between the input and output.For feedforward neural networks, the depth of the CAP may be the depth of the network, which may be the number of hidden layers plus 1. For recurrent neural networks, where a signal can propagate through a layer more than once, the CAP depth is potentially unlimited.

[0036] 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 to enable the neural network to capture time dependencies. RNNs are capable of learning input-output mappings that depend on both current and past inputs. The RNN accounts for past and future flow measurements and makes predictions based on this continuous metrology information. The RNN can be trained using a training data set to generate a fixed number of outputs (e.g., to determine a set of substrate processing rates or to determine modifications to a substrate process recipe). One type of RNN that can be used is a long-short-term memory (LSTM) neural network.

[0037] Training a neural network can be accomplished in a supervised learning fashion, 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 output and the label value), and using techniques such as deep gradient descent and backpropagation to adjust the network's weights across all its layers and nodes so that the error is minimized. In many applications, repeating this process across many labeled inputs in the training dataset results in a network that can produce correct outputs when presented with inputs that differ from those present in the training dataset.

[0038] A training data set containing hundreds, thousands, tens of thousands, hundreds of thousands, or more pieces of 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 the training data set.

[0039] To effectively perform training, the processing logic may input the training dataset(s) to one or more untrained machine learning models. Prior to inputting the first input to the machine learning model, the machine learning model may be initialized. The 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 described above. The training may be performed by inputting one or more of the sensor data to the machine learning model one by one.

[0040] A machine learning model processes inputs and generates outputs. An artificial neural network includes an input layer consisting of values ​​at data points. The next layer is called a hidden layer, and nodes in the hidden layer each receive one or more of the input values. Each node includes parameters (e.g., weights) to apply to the input values. Thus, each node essentially inputs the input values ​​into a multivariate function (e.g., a nonlinear mathematical transformation) to produce an output value. The next layer can be another hidden layer or an output layer. In either case, nodes in the next layer receive output values ​​from nodes in the previous layer, and each node applies weights to those values ​​and then generates its own output value. This can be performed in 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 produce.

[0041] 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, erosion 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 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 (weights for one or more inputs of the node) for one or more of its nodes. Parameters may be updated in a backpropagation manner, such that nodes in the top layer are updated first, followed by nodes in the next layer, and so on. The artificial neural network includes multiple layers of "neurons," each layer receiving values ​​as inputs from neurons in the previous layer. The parameters for 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 for one or more neurons in one or more layers in the artificial neural network.

[0042] After one or more rounds of training, the processing logic can determine whether a stopping criterion has been met. The stopping criterion can be a target level of accuracy, a target number of processed images from the training dataset, a target amount of change to a parameter over one or more previous data points, a combination thereof, and / or other criteria. In one embodiment, the stopping criterion is met when at least a minimum number of data points have been processed and at least a threshold accuracy has been achieved. The threshold accuracy can be, for example, 70%, 80%, or 90% accuracy. In one embodiment, the stopping criterion is met 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, a reserved portion of the training dataset can be used to test the model.

[0043] Once one or more trained machine learning models 190 are generated, they may be stored in the prediction server 112 as a prediction component 114 or as a component of the prediction component 114.

[0044] The validation engine 184 may be capable of validating the machine learning model 190 using the corresponding set of validation set features from the training set generator 172. Once the model parameters are optimized, model validation may be performed to determine whether the model has 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 validation set features. The validation engine 184 may discard trained machine learning models 190 that have an accuracy that does not meet a threshold accuracy. In some embodiments, the selection engine 185 may be capable of selecting a trained machine learning model 190 that has an accuracy that meets a 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.

[0045] The testing engine 186 may be able to test the trained machine learning model 190 using a corresponding set of test set features from the dataset generator 172. For example, a first trained machine learning model 190 trained using a first set of training set features may be tested using a first set of test set features. The testing engine 186 may determine the trained machine learning model 190 with the highest accuracy of all of the trained machine learning models based on the test set.

[0046] As described in more detail below, the prediction server 112 includes a prediction component 114 that can provide data indicative of the expected behavior of each subsystem of the process chamber and 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.

[0047] Client devices 120, manufacturing equipment 124, sensors 126, metrology equipment 128, prediction server 112, data store 140, server machine 170, and server machine 180 may be coupled to one another 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.

[0048] 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, and in some other or similar embodiments, server machines 170 and 180 and prediction server 112 may be combined into a single machine.

[0049] Generally, 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 different or multiple components operating together.

[0050] 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 set of individual users federated as a group of administrators may be considered a "user."

[0051] 2 is a flowchart of a method 200 for training a machine learning model according to aspects of the present disclosure. Method 200 is implemented by processing logic, which may include hardware (circuitry, dedicated logic, etc.), software (such as that running on a general-purpose computer system or a dedicated machine), firmware, or some combination thereof. In one embodiment, method 200 may be performed by a computer system, such as computer system architecture 100 of FIG. 1. In other or similar embodiments, one or more processes of method 200 may be performed by one or more other machines not shown in the figure. In some aspects, one or more processes of method 200 may be performed by server machine 170, server machine 180, and / or prediction server 112.

[0052] For ease of explanation, methods are shown and described as a series of acts. However, acts in accordance with the present disclosure may occur in various orders and / or simultaneously, as well as with other acts not presented 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 transporting and transferring 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.

[0053] At block 210, processing logic initializes the training set T to an empty set (e.g., {}).

[0054] At block 212, processing logic acquires sensor data (e.g., sensor values, features, 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 further relate to 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.

[0055] In some embodiments, the sensor data related to the deposition process is historical data related to 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 related to 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 process, 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 related to the deposition process. A flow rate setting can refer to a flow rate setting for a precursor in an initial instance of the previous deposition process (referred to as an initial flow rate setting), a flow rate setting for a precursor in a final instance of the previous deposition process (referred to as a final flow rate setting), or a ramping rate for 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 relate to a previous etching process performed on a previous substrate or any other process performed in the process chamber.

[0056] 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 required temperature settings, pressure settings, flow rate settings for precursors for the material of the film to be deposited on the substrate, etc. The task data may include historical task data for a previous film deposited on the surface of the previous substrate. In some embodiments, the historical task data for the previous film may correspond to historical task values ​​associated with the recipe for the previous film. Processing logic may retrieve the task data from data store 140 according to previously described embodiments.

[0057] 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. 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.

[0058] At block 220, processing logic generates a mapping between the first training data and the second training data. The mapping refers to the first training data including or based on data for a previous deposition process performed on a previous substrate, and the second training data including or based on task data associated with a recipe for a film deposited on the surface of the previous substrate, and the first training data may be related to (or mapped to) the second training data. At block 224, processing logic adds the mapping to the training set T.

[0059] At block 226, processing logic determines whether the training set T includes a sufficient amount of training data for training the machine learning model. Note that in some implementations, the sufficiency of the 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 the training set T may 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. In response to determining that the training set does not include a sufficient amount of training data for training the machine learning model, method 200 returns to block 212. In response to determining that the training set T includes a sufficient amount of training data for training the machine learning model, method 200 continues at block 228.

[0060] At block 228, processing logic provides a training set T for training the machine learning model. In one implementation, the training set T is provided to the training engine 182 of the server machine 180 to 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 output nodes of the neural network. The connection weights in the neural network are then adjusted according to a learning algorithm (e.g., backpropagation, etc.), and the procedure is repeated for other input / output mappings in the training set T.

[0061] In some embodiments, processing logic may perform outlier detection methods to remove anomalies from the training set T prior to training the machine learning model. Outlier detection methods may include techniques to identify values ​​that are significantly different from the majority of the training data. These values ​​may be generated from error, noise, etc.

[0062] At block 230, processing logic performs a calibration process on the trained machine learning model. In some embodiments, processing logic may compare the predicted 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 predicted data for the pressure subsystem, flow subsystem, or temperature subsystem, respectively, to one or more values ​​associated with the current measured behavior of the pressure subsystem, flow subsystem, or temperature subsystem.

[0063] After block 230, the machine learning model may be used to generate one or more values ​​indicative of a fault pattern (e.g., anomalous behavior) of the process chamber subsystem, generate descriptive data indicative of a type of fault (e.g., a problem, a failure, etc.), and / or implement corrective action(s) to correct the suspected problem or failure. Predictive data may be generated by comparing the fault pattern to a library of known fault patterns.

[0064] 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.

[0065] 3 is a top-view schematic diagram of an exemplary manufacturing system 300 according to an embodiment of the present disclosure. The manufacturing system 300 is capable of performing one or more processes on a substrate 302. The substrate 302 may be any suitably rigid, planar article of fixed dimensions, such as, for example, a silicon-containing disk or wafer, a patterned wafer, a glass plate, or the like, suitable for fabricating electronic devices or circuit components thereon.

[0066] 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 314, 316, 318 (also referred to as processing chambers) arranged around and coupled thereto. The process chambers 314, 316, 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, 318, load locks 320, etc. The transfer chamber robot 312 may include one or more arms, each including one or more links and one or more end effectors at the end of each arm. The end effectors may be configured to handle specific objects, such as wafers. Alternatively or additionally, the end effector is configured to handle objects such as process kit rings, etc. In some embodiments, the transfer chamber robot 312 is a SCARA robot, such as a two-link selective compliance assembly robot arm (SCARA) robot, a three-link SCARA robot, a four-link SCARA robot, etc.

[0067] The transfer chamber robot 312 can further include one or more sensors. The sensors can be used to characterize, read, or measure 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, or any other type of sensor.

[0068] Accelerometers can be used to detect and correct (or calibrate) vibration and position noise of the transfer chamber robot 312. Distance sensors can be used to detect erosion and / or corrosion on 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 may be used to promote uniformity along the substrate surface. However, the etching may erode the edge ring. Therefore, position sensors can be used to detect such corrosion by measuring the distance between the top plane of the edge ring and, for example, the top plane of the substrate. Cameras can record sections of the process chambers 314, 316, 318 for visual inspection by an operator. Capacitor sensors can be used to detect the position of showerheads used for gas distribution within the process chambers 314, 316, 318, determine substrate leveling, detect erosion, etc. Reflectometers can be used to probe 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 in 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 described sensors or other sensors.

[0069] The one or more sensors may be coupled to an end effector of the transfer chamber robot 312, to one or more links of the transfer chamber robot 312, or to any other section of the transfer chamber robot 312. In some embodiments, the transfer chamber robot 312 may include additional links and / or more degrees of freedom than are needed to transfer substrates. The additional links and / or degrees of freedom may be used to operate sensors. In particular, the transfer chamber robot 312 may include one or more additional links for coupling to sensors, and the link(s) (or the transfer chamber robot 312) may include one or more additional degrees of freedom for translating, rotating, and / or positioning the sensor within the process chambers 314, 316, 318. For example, the transfer chamber robot 312 may include an additional link coupled to a high-resolution camera capable of rotating the camera within the process chambers 314, 316, 318.

[0070] In some embodiments, the sensors are coupled to one or more of a power link and / or a data link. The power link can be any wired or wireless (e.g., inductive) connection capable of providing power to the sensor(s). 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. 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, the data link is a separate system from another data link used to provide instructions and communicate with the transfer chamber robot 312 to enable the transfer chamber robot 312 to transfer and place substrates between the transfer chambers 314, 316, 318 and the load lock 320.

[0071] The process chambers 314, 316, 318 may be adapted to perform any number of processes on the substrate 302. The same or different substrate processes may occur in each processing chamber 314, 316, 318. Substrate processes may include atomic layer deposition (ALD), physical vapor deposition (PVD), chemical vapor deposition (CVD), etching, annealing, curing, pre-cleaning, metal or metal oxide removal, etc. Other processes may be performed on the substrate therein. The process chambers 314, 316, 318 may each include one or more sensors configured to capture data about the substrate 302 before, after, or during substrate processing. For example, the one or more sensors may be configured to capture spectral and / or non-spectral data about a portion of the substrate 302 during substrate processing. In other or similar embodiments, the one or more sensors may be configured to capture data related to the environment within the process chambers 314, 316, 318 before, after, or during substrate processing. For example, one or more sensors may 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.

[0072] 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 be coupled to the transfer chamber 310 on one side and the factory interface 306 on the other. The load lock 320, in some embodiments, may have an environmentally controlled atmosphere that may be changed 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, a front-end equipment 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 at 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 carriers 322 (also called containers) and load locks 320. Carriers 322 may be substrate storage carriers or replacement part storage carriers.

[0073] 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 about a target thickness profile for a film to be deposited on the surface of the substrate 302 during a deposition process performed in the process chambers 314, 316, 318 via the GUI. The client device may also provide information about modifications to the process recipe in view of respective sets of deposition settings that are predicted to correspond to the target profile, according to embodiments described herein.

[0074] The manufacturing system 300 can also include a system controller 328. The system controller 328 can 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 can include one or more processing devices, which can be general-purpose processing devices such as a microprocessor, a central processing unit, etc. More specifically, the processing device can be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or a processor 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), a network processor, etc. The system controller 328 can 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 can execute instructions to implement any one or more of the methodologies and / or embodiments described herein. In some embodiments, the system controller 328 can execute instructions to perform one or more operations in the manufacturing system 300 according to a process recipe. The instructions can be stored in a computer-readable storage medium, which can include a main memory, a static memory, a secondary storage, and / or a processing device (during execution of the instructions).

[0075] The system controller 328 can 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 can include spectral and / or non-spectral data about a portion of the substrate 302. In other or similar embodiments, the data received by the system controller 328 can include data related to processing the substrate 302 in the processing chambers 314, 316, 318, as previously described. The system controller 328 is described herein as receiving data from sensors contained within the process chambers 314, 316, 318. However, the system controller 328 can receive data from any portion of the manufacturing system 300 and use the data received from that portion, according to embodiments described herein. In an illustrative example, the system controller 328 may receive data from one or more sensors for 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 separate component from the system controller 328.

[0076] 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 a process in which a corrosive plasma environment is provided. For example, the process chamber 400 can be a chamber for a plasma etcher or a plasma etch reactor, etc. In another example, the process chamber can be a chamber for a deposition process, as previously described. In some embodiments, the process chamber 400 can be any chamber used in an electronic device manufacturing system. The transfer chamber robot 312 can enter the processing chamber 400 and perform readings, measurements, and collect data using one or more sensors, as described herein.

[0077] In one embodiment, the process chamber 400 includes a chamber body 402 and a showerhead 430 enclosing the interior volume 406. The showerhead 430 may include a showerhead base and a showerhead gas distribution plate. Alternatively, the showerhead 430 may be replaced by a lid and nozzle in some embodiments, or by multiple pie-shaped showerhead sections and plasma generation units in other embodiments. The chamber body 402 may be fabricated from aluminum, stainless steel, or other suitable materials, such as 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 flow controller 428. The flow controller 428 may include one or more pumps and a throttle valve utilized to evacuate and adjust the pressure of the interior volume 406 of the process chamber 400.

[0078] The showerhead 430 may be supported on the sidewall 408 of the chamber body 402. The showerhead 430 (or lid) may be opened to allow access to the interior volume 406 of the process chamber 400 and may provide a seal to the process chamber 400 when closed. A gas panel 458 may be coupled to the process chamber 400 to provide process and / or cleaning gases via supply lines 412 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 may provide precursors for the material of the film 451 to be deposited on the surface of the substrate 302. In some embodiments, the precursors may include silicon-based precursors or boron-based precursors. The showerhead 430 may include a gas distribution plate (GDP) having multiple gas delivery holes 432 (also called channels) throughout the GDP. A substrate support assembly 448 is disposed in the interior volume 406 of the process chamber 400 below the showerhead 430. The substrate support assembly 448 holds the substrate 302 during processing (e.g., during a deposition process).

[0079] 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 process chamber 400. The metrology device may be operably coupled to a system controller (e.g., the previously described system controller 328). In some embodiments, the metrology device may be configured to generate metrology measurements (e.g., thickness) for the film 451 during a particular instance of the deposition process. The system controller may generate a thickness profile for 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 for the film 451 after completion of the 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 a thickness profile for the film 451 based on the determined concentration gradient and the determined deposition rate of the deposition process.

[0080] 5 is a schematic top view of a process tool 500 according to an embodiment of the present disclosure. In some embodiments, the process tool 500 can correspond to the process tool 304 described with respect to FIG. 3. The process tool 500 can include process chambers 510A-510F, a transfer chamber 515, and a transfer chamber robot 520. The transfer chamber robot 520 can include end effectors 525A and 525B. The end effector 525A can include sensors 530A and 530B. The sensors 530A, 530B can include one or more of an accelerometer, a distance sensor, a camera, a capacitive sensor, a reflectometer, a pyrometer, a laser-induced fluorescence spectrometer, an optical fiber, etc.

[0081] The transfer chamber robot 520 can position end effectors 525A, 525B in any of the process chambers 510A-510F. As an illustrative example, FIG. 5 shows end effector 525A positioned in process chamber 510C. Using sensors 530A and 530B, the transfer chamber robot 520 can read and / or measure the process chamber 510C. In some embodiments, the sensor(s) and / or the transfer chamber robot 520 include a processing device, 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 device can be configured to execute programming instructions related to the operation of the sensors. The processing device can receive feedback signals from the sensor devices and calculate the signals into sensor data (e.g., temperature, video data, position data, etc.). The processing device can further send control signals to the sensors based on the received instructions. In some embodiments, the processing device is configured for fast feedback processing and can include, for example, an EPM. In some embodiments, the processing device is configured to transmit or send the feedback signal and / or the sensor data to an interface (e.g., a user interface), a data store, or the like.

[0082] 6 is a schematic top view of a transfer chamber robot link assembly 600 in accordance with an embodiment of the present disclosure. Link assembly 600 may include a first link 610, a second link 615, and a sensor 620. Sensor 620 may be coupled to second link 615. Sensor 620 may include one or more of an accelerometer, a distance sensor, a camera, a capacitive sensor, a reflectometer, a pyrometer, a laser-induced fluorescence spectrometer, an optical fiber, or the like. As an illustrative example, second link 615 may be coupled to first link 610 via a pin, which allows second link 615 (and sensor 620) to move in an arc motion 625. First link 610 may be coupled to the transfer chamber robot via another link of the transfer chamber robot, such as to an end effector of the transfer chamber robot.

[0083] 7 is a flowchart of a method 700 for controlling a transfer chamber robot to take measurements using sensors, according to aspects 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 embodiment, method 700 may be performed by a computer system, such as system controller 328 of FIG. 3. In other or similar embodiments, one or more operations of method 700 may be performed by one or more other machines not shown in the figure.

[0084] At block 710, processing logic positions one or more sensors within the process chamber. The one or more sensors may be coupled to the transfer chamber robot. For example, processing logic may position an end effector of the transfer chamber robot to which the sensor is attached, a link of the transfer chamber robot to which the sensor is attached, or any combination thereof. Processing logic may position the sensor in response to user input (e.g., via a user interface), in response to a predetermined command (e.g., a scheduled command), etc. In some embodiments, processing logic may use a database or library containing data related to the location of sensors within the process chamber based on the type of sensor and / or process chamber.

[0085] At block 720, processing logic acquires sensor data from one or more sensors of the transfer chamber robot. For example, the one or more sensors can capture spectral and / or non-spectral data of any part or component associated with the process chamber. Additionally, the one or more sensors may be configured to capture data related to the environment within the process chamber. For example, the one or more sensors may be configured to capture data related to the temperature, pressure, gas concentration, etc. of the environment within the process chamber. In some embodiments, once positioned, processing logic can move the sensor (by moving a link, end effector, etc.) within the process chamber to collect sensor data. Movement can be automatic (e.g., predetermined movement) or manual movement based on user input via a user interface, controller (e.g., joystick, touch screen), etc. For example, in one embodiment, the sensor is a camera, a user can point the camera at different locations within the process chamber to record video and / or audio data. The sensor data can be sent to a user interface, a database structure, etc.

[0086] At block 730, processing logic removes the sensor from the process chamber. For example, processing logic can place the sensor in the transfer chamber or in a different process chamber.

[0087] 8 is a flowchart of a method 800 for determining a fault type of a process chamber subsystem using a machine learning model, according to aspects of the present disclosure. Method 800 is performed by processing logic, which may include hardware (circuitry, dedicated logic, etc.), software (such as that running on a general-purpose computer system or a dedicated machine), firmware, or some combination thereof. In one embodiment, method 800 may be performed by a computer system, such as computer system architecture 100 of FIG. 1. In other or similar embodiments, one or more operations of method 800 may be performed by one or more other machines not shown in the figure. In some aspects, one or more operations of method 800 may be performed by server machine 170, server machine 180, and / or prediction server 112.

[0088] At block 810, processing logic acquires sensor data related to a process performed in the process chamber. In some embodiments, the process 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 process may be performed according to a recipe. The sensor data may include one or more values ​​of temperature (e.g., heater temperature), spacing, pressure, high frequency radio frequency, electrostatic chuck voltage, current, material flow, power, voltage, etc. The sensor data may be related to or indicative of manufacturing parameters, such as hardware parameters such as settings or components (e.g., size, type, etc.) of the manufacturing tool 124 or process parameters of the manufacturing tool 124. The sensor data may be acquired using the sensors 126.

[0089] At block 812, processing logic applies a machine learning model (e.g., model 190) to the acquired sensor data. The machine learning model may be used to generate one or more values ​​related to expected behavior of the process chamber subsystem. For example, the machine learning model may 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 recipe used to perform the process.

[0090] At block 814, 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 fault pattern). In particular, the output may include descriptive data on whether the current data indicates a fault being experienced by 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(s) may indicate a difference between an actual value of a set of sensors associated with the subsystem and an expected value of the set of sensors. The fault may include a mechanism failure, high or low pressure, high or low gas flow, high or low temperature, etc.

[0091] At block 816, processing logic determines whether the process chamber subsystem is experiencing a fault. 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 the process chamber subsystem is experiencing a fault by comparing the output to a predetermined threshold. In some embodiments, processing logic may determine whether the process chamber subsystem is experiencing a fault by determining that the output fails to conform to expected behavior. In response to processing logic determining that the process chamber subsystem is not experiencing a fault (e.g., the value of the output does not exceed the predetermined threshold), processing logic may proceed to block 810. In response to processing logic determining that the process chamber subsystem is experiencing a fault (e.g., the value of the output exceeds the predetermined threshold), processing logic may proceed to block 818.

[0092] At block 818, processing logic may identify a type of fault based on the output. In some embodiments, processing logic may compare the fault pattern to the manufacturing data graph(s) and / or a library of known fault patterns to determine the type of fault based on the similarity of the fault pattern when compared to the known fault pattern or the manufacturing data graph(s). 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 fault pattern. In some embodiments, the type of fault may be displayed (to the user) on a user interface.

[0093] At block 820, 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 retrieved from a fault library. In some embodiments, the corrective action may include generating an alert or indication to the client device 120 of the determined problem. In some embodiments, the corrective action may include processing logic indicating the type of fault or fault, the cause of the fault or fault, and / or the 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., temperature settings for the process chamber, pressure settings for the process chamber, flow rate settings for precursors for materials included in the film deposited on the substrate surface, etc.) based on desired characteristics for the film. In some embodiments, the deposition process recipe may be adjusted before, during (e.g., in real time), or after the deposition process.

[0094] 9 is a block diagram illustrating a computer system 900, according to some embodiments. In some embodiments, computer system 900 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 900 may operate in the capacity of a server computer 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 900 may be provided by a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a cellular 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 together execute a set (or sets) of instructions to perform any one or more of the methodologies described herein.

[0095] In a further aspect, the computer system 900 may include a processing device 902, a volatile memory 904 (e.g., random access memory (RAM)), a non-volatile memory 906 (e.g., read-only memory (ROM) or electrically erasable programmable ROM (EEPROM)), and a data storage device 918, which may communicate with each other via a bus 908.

[0096] The processing device 902 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 that executes other types of instruction sets, or a microprocessor that executes a combination of 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).

[0097] Computer system 900 may further include a network interface device 922 (e.g., coupled to network 974). Computer system 900 may also include a video display unit 910 (e.g., LCD), an alphanumeric input device 912 (e.g., keyboard), a cursor control device 914 (e.g., mouse), and a signal generating device 920.

[0098] In some embodiments, the data storage device 916 may include a non-transitory computer-readable storage medium 924 that may store instructions 926 encoding any one or more of the methods or functions described herein, including instructions encoding the components of FIG. 1 (e.g., the corrective action component 122, the prediction component 114, etc.) and for performing the methods described herein.

[0099] The instructions 926 may also reside, completely or partially, within the volatile memory 904 and / or within the processing device 902 during execution thereof by the computer system 900; thus, the volatile memory 904 and the processing device 902 may also constitute machine-readable storage media.

[0100] Although the computer-readable storage medium 924 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, causing 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.

[0101] The methods, components, and functions described herein may be performed by discrete hardware components or may be integrated into the functionality of other hardware components, such as an ASIC, FPGA, DSP, or similar device. Furthermore, the methods, components, and functions may be performed by firmware modules or functional circuits within a hardware device. Furthermore, the methods, components, and functions may be performed by any combination of hardware devices and computer program components, or may be performed by a computer program.

[0102] Unless otherwise specified, terms such as "receiving," "performing," "providing," "obtaining," "causing," "accessing," "determining," "adding," "using," "training," and the like refer to actions and processes performed or implemented by a computer system that manipulate data represented as physical (electronic) quantities in computer system registers and memory and transform that data into other data similarly represented as physical quantities in the computer system memory or registers, or other such information storage, transmission, or display device. Also, as used herein, terms such as "first," "second," "third," "fourth," and the like are meant as labels to distinguish between different elements and may not have any ordering meaning due to their numerical designation.

[0103] 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 the apparatus may comprise a general-purpose computer system that is 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.

[0104] 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 the methods described herein and / or each of their individual functions, routines, subroutines, or operations. Example structures for a variety of these systems are set forth in the description above.

[0105] The above description is illustrative, and not limiting. While the present disclosure has been described with reference to particular illustrative examples and embodiments, it will be recognized that the present 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.

[0106] The preceding description sets forth numerous specific details, such as examples of particular systems, components, and methods, to provide a thorough understanding of some embodiments of the present disclosure. However, it will be apparent to those skilled in the art that at least some embodiments of the present disclosure may be practiced without these specific details. In other instances, well-known components or methods have not been described in detail or have been presented in a simple block diagram format to avoid unnecessarily obscuring the present disclosure. Thus, the specific details described are by way of example only. It is contemplated that particular embodiments may vary from these illustrative details and still fall within the scope of the present disclosure.

[0107] References throughout this specification to "one embodiment" or "an embodiment" mean that a particular feature, structure, or property described with respect to that embodiment is included in at least one embodiment. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." When the terms "about" or "approximately" are used herein, this is intended to mean that the stated nominal value is accurate to within ±10%.

Claims

1. 1. A process tool for an electronic device manufacturing system, the process tool comprising: a transfer chamber; a process chamber coupled to the transfer chamber; a transfer chamber robot having an end effector and configured to transfer substrates to and from the process chamber, the transfer chamber robot connected to a sensor, the sensor configured to take measurements within the process chamber, the sensor being positionable with an additional degree of freedom independent of the end effector; A process tool comprising:

2. The process tool of claim 1 , wherein the sensor is coupled to an end effector of the transfer chamber robot.

3. The process tool of claim 1 , wherein the sensor is coupled to a link of the transfer chamber robot.

4. The process tool of claim 1 , wherein the sensor comprises at least one of an accelerometer, a distance sensor, a camera, a capacitive sensor, a reflectometer, a pyrometer, a laser-induced fluorescence spectrometer, or an optical fiber.

5. The process tool of claim 1 , wherein the sensor is coupled to a power link capable of providing power to the sensor.

6. The process tool of claim 1 , wherein the sensor is coupled to a data link capable of transmitting the measured values ​​to a user interface or a data storage system.

7. 1. An electronic device manufacturing system, comprising: a load lock and a process tool, the process tool comprising: a transfer chamber; a process chamber coupled to the transfer chamber; a transfer chamber robot having an end effector and configured to transfer substrates to and from the process chamber, the transfer chamber robot connected to a sensor, the sensor configured to take measurements within the process chamber, the sensor being positionable with an additional degree of freedom independent of the end effector; An electronic device manufacturing system comprising:

8. The electronic device manufacturing system of claim 7 , wherein the sensor is coupled to an end effector of the transfer chamber robot.

9. The electronic device manufacturing system of claim 7 , wherein the sensor is coupled to a link of the transfer chamber robot.

10. The electronic device manufacturing system of claim 7 , wherein the sensor comprises at least one of an accelerometer, a distance sensor, a camera, a capacitive sensor, a reflectometer, a pyrometer, a laser-induced fluorescence spectrometer, or an optical fiber.

11. The electronic device manufacturing system of claim 7 , wherein the sensor is coupled to a power link capable of providing power to the sensor.

12. The electronic device manufacturing system of claim 7 , wherein the sensor is coupled to a data link capable of transmitting the measured values ​​to a user interface or a data storage system.

13. an arm assembly comprising a plurality of links; an end effector coupled to the arm assembly, the end effector configured to transfer substrates to and from a process chamber; a sensor coupled to the arm assembly and configured to take measurements within the process chamber, the sensor being positionable with an additional degree of freedom independent of the end effector; and A transfer chamber robot comprising:

14. The transfer chamber robot of claim 13 , wherein the sensor is coupled to the end effector of the transfer chamber robot.

15. The transfer chamber robot of claim 13 , wherein the sensor is coupled to any of the plurality of links.

16. The transfer chamber robot of claim 13 , wherein the sensor comprises at least one of an accelerometer, a distance sensor, a camera, a capacitive sensor, a reflectometer, a pyrometer, a laser induced fluorescence spectrometer, or an optical fiber.

17. positioning, by a processor, a portion of a transfer chamber robot within a process chamber, the portion comprising at least one sensor, the portion being positionable with an additional degree of freedom independent of an end effector connected to the transfer chamber; acquiring sensor data associated with the process chamber using one or more of the sensors; removing the portion of the transfer chamber robot from the process chamber; A method comprising:

18. acquiring, by a processor, a plurality of sensor values ​​generated by a sensor device in a process chamber, the sensor device being connected to a transfer chamber robot, the sensor device being positionable with an additional degree of freedom independent of an end effector associated with 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 characterized as a sensor associated with an operating parameter of the process chamber and task data associated with a process recipe for depositing a film on a surface of a previous substrate in the process chamber; generating an output of the machine learning model, the output indicating a type of fault associated with a failure pattern of the subsystem; determining the type of failure of the subsystem; generating a corrective action based on the type of fault to adjust one or more parameters of the process recipe; A method comprising:

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