Systems and methods for process chamber health monitoring and diagnostics using virtual model

Abstract

To monitor the health status of a process chamber and provide diagnostics using a virtual model.SOLUTION: A method includes: obtaining, by a processor, multiple sensor values associated with a deposition process performed, according to a recipe, in a process chamber to deposit film on a surface of a substrate; and applying a machine learning model to the multiple sensor values. The machine learning model is trained on the basis of historical sensor data of a subsystem of the process chamber and task data associated with the recipe for depositing the film. The method further includes generating an output of the machine learning model, in which the output indicates a health status of the subsystem.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] FIELD OF THE DISCLOSURE The present disclosure relates to electrical components, and more particularly to monitoring the health of a process chamber and providing diagnostics using virtual models. [Background technology]

[0002] Products may be created by performing one or more manufacturing processes using manufacturing equipment. For example, semiconductor manufacturing equipment may be used to create semiconductor devices (e.g., substrates, wafers, etc.) through a semiconductor manufacturing process. The manufacturing equipment may deposit multiple layers of films on the surface of the substrate and may also perform etching processes to form complex patterns in the deposited films. For example, the manufacturing equipment may perform a chemical vapor deposition (CVD) process to deposit alternating layers on the substrate. Sensors may be used to determine manufacturing parameters of the manufacturing equipment during the manufacturing process, and metrology equipment may be used to determine property data of the products produced by the manufacturing equipment, such as the overall thickness of a layer on the substrate. Typically, manufacturing equipment can monitor individual sensors to detect problems during the deposition process. However, monitoring individual sensors does not provide an indication of the overall health of different subsystems of the manufacturing equipment, and adverse conditions may go undetected, leading to significant downtime and repair time. Therefore, a system capable of generating an indicator of the overall system health for each subsystem during the manufacturing process is desirable. Summary of the Invention

[0003] The following is a simplified summary of the disclosure to provide a basic understanding of some aspects of the disclosure. This summary is not an extensive overview of the disclosure. It is not intended to identify key or critical elements of the disclosure, nor to delineate the scope of any particular implementation of the disclosure or the scope of the claims. Its sole purpose is to present some concepts of the disclosure in a simplified form as a prelude to the more detailed description that is presented later.

[0004] In an aspect of the present disclosure, a method includes acquiring, by a processor, sensor data associated with a deposition process performed in a process chamber to deposit a film on a surface of a substrate. The sensor data includes sensor values ​​associated with a subsystem of the process chamber. The method further includes acquiring task data associated with a recipe for depositing the film. The method further includes training a machine learning model using a training set based on the sensor data and the task data. The machine learning model is trained to generate predictive data indicative of expected sensor values ​​of the subsystem.

[0005] In another aspect of the present disclosure, a method includes acquiring, by a processor, a plurality of sensor values ​​associated with a deposition process performed in a process chamber to deposit a film on a surface of a substrate. 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 the film. The method further includes generating an output of the machine learning model, the output being indicative of a health state of the subsystem.

[0006] In another aspect of the present disclosure, a system includes a memory and a processing device, the processing device operably coupled to the memory device for performing, by a processor, operations including acquiring a plurality of sensor values ​​associated with a deposition process performed in a process chamber to deposit a film on a surface of a substrate. Performing the operations 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 the film. Performing the operations further includes generating an output of the machine learning model, the output being indicative of a health state of the subsystem.

[0007] The present disclosure is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings in which like reference symbols indicate similar elements and in which: [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a block diagram illustrating an exemplary system architecture, according to certain embodiments. [Figure 2] FIG. 1 is a flow diagram of a method for training a machine learning model, according to certain embodiments. [Figure 3] 1 is a schematic top view of an example manufacturing system, in accordance with certain embodiments. [Figure 4] 1 is a cross-sectional schematic side view of an example process chamber of an example manufacturing system, in accordance with certain embodiments. [Figure 5] FIG. 1 is a flow diagram of a method for determining process chamber subsystem health metrics using machine learning models, in accordance with certain embodiments. [Figure 6] 1 is a graph illustrating an example sigmoid transformation, in accordance with certain embodiments. [Figure 7] FIG. 1 illustrates a flow diagram of a method for determining a fault classification of a process chamber subsystem using a machine learning model, in accordance with certain embodiments. [Figure 8]FIG. 1 is a block diagram illustrating a computer system, in accordance with certain embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0009] Described herein is technology directed to systems and methods for monitoring the health of a process chamber and providing diagnostics using virtual models. A film can be deposited on a surface of a substrate during a deposition process (e.g., a deposition (CVD) process, an atomic layer deposition (ALD) process, etc.) performed in a process chamber of a manufacturing system. For example, in a CVD process, the substrate is exposed to one or more precursors that react on the substrate surface to produce a desired deposition. The film can include one or more layers of material formed during the deposition process, and each layer can include 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 can have a first thickness. After the first layer is formed on the surface of the substrate, a second layer having a second thickness can be formed on the first layer. This process continues until the deposition process is completed and a final layer is formed for the film (referred to as the distal layer or distal end of the film). The film can include alternating layers of different materials. For example, the film may include alternating oxide and nitride layers (oxide-nitride-oxide-nitride stack or ONON stack), or 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.

[0010] A process chamber may have multiple subsystems that operate during each substrate manufacturing process (e.g., deposition process, etch process, polishing process, etc.). A subsystem may be characterized as a set of sensors associated with an operating parameter of the process chamber. The operating parameter may be temperature, flow rate, pressure, etc. In an example, a pressure subsystem may be characterized by one or more sensors that measure gas flow, chamber pressure, control valve angle, foreline (vacuum line between pumps) pressure, pump speed, etc. Thus, a process chamber may include a pressure subsystem, a flow subsystem, a temperature subsystem, etc. Each subsystem may experience degradation and deviation from an optimal performance state. For example, a pressure subsystem may generate reduced pressure due to one or more of a pump problem, a control valve problem, etc.

[0011] Existing systems use limit checks on a single sensor value to detect faults within a process chamber. For example, existing systems may monitor whether a sensor value is below or above a predetermined threshold (e.g., a temperature sensor exceeding a predetermined temperature threshold). However, a sensor value below or above a predetermined threshold does not indicate the overall health of the process chamber subsystem as a function of each of the subsystem's sensors and components. The health of a subsystem may be characterized as the subsystem's current behavior (current sensor value) compared to the subsystem's expected behavior (expected sensor value). As such, existing systems cannot efficiently monitor the health of each subsystem of the process chamber. Furthermore, existing systems cannot efficiently provide diagnosis of "unhealthy" subsystems (e.g., subsystems whose current behavior exceeds a threshold relative to expected behavior, subsystems experiencing multiple sensor failures, etc.).

[0012] Aspects and implementations of the present disclosure address these and other shortcomings of existing technologies by training machine learning models capable of monitoring and indicating the health of each subsystem of a process chamber and providing diagnostics. In some embodiments, a system of the present disclosure acquires sensor data associated with a previous deposition process performed in a process chamber to deposit a film on the surface of a substrate. The sensor data may include sensor values ​​associated with subsystems of the process chamber. The system then acquires task data associated with a recipe for depositing the film and maps the task data to the sensor data to generate a training set. The system may use the training set to train a machine learning model to generate prediction data indicative of expected sensor values ​​of the subsystems.

[0013] In some embodiments, the system applies a machine learning model to the current sensor values ​​to generate an output indicative of the health of the subsystem. In some embodiments, the output is a scalar value that indicates the difference between the expected behavior of the process chamber subsystem and the actual behavior of the process chamber subsystem. In some embodiments, the system uses a conversion function to convert the output to a representative value within a predetermined range. The representative value provides a user-friendly representation of the subsystem health. In some embodiments, the machine learning model generates a vectorized version of the scalar value indicative of a fault pattern associated with the process chamber subsystem. The system may then compare the fault pattern to a library of known fault patterns to determine the type of failure the subsystem is experiencing. In some embodiments, the system implements corrective action to adjust one or more parameters of a deposition process recipe (e.g., temperature settings for the process chamber, pressure settings for the process chamber, flow rate settings for precursors of materials included in a film to be deposited on the substrate surface, etc.) based on the fault pattern.

[0014] Aspects of the present disclosure provide technical advantages such as a significant reduction in the time required to detect unhealthy process chamber subsystems during the manufacturing of substrates, as well as improved energy consumption. The present disclosure may also result in the generation of diagnostic data and the implementation of corrective actions to avoid inconsistent, abnormal products and unplanned user time.

[0015] FIG. 1 depicts an exemplary computer system architecture 100 according to aspects of the present disclosure. In some embodiments, computer system architecture 100 may be included as part of a manufacturing system for processing substrates, such as manufacturing system 300 of FIG. 3 . Computer system architecture 100 includes client devices 120, manufacturing equipment 124, metrology equipment 128, a prediction server 112 (e.g., for generating prediction data, providing model adaptation, using a knowledge base, etc.), and a data store 140. Prediction server 112 may be part of a prediction system 110. Prediction system 110 may further include server machines 170 and 180. Manufacturing equipment 124 may include sensors 125 configured to capture data about substrates being processed in the manufacturing system. In some embodiments, manufacturing equipment 124 and sensors 126 may be part of a sensor system including a sensor server (e.g., a field service server (FSS) at a manufacturing facility) and a sensor 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).

[0016] The manufacturing equipment 124 may produce products, such as electronic devices, according to a recipe or by executing an operation over a period of time. The manufacturing equipment 124 may include a process chamber, such as the process chamber 400 described with reference to FIG. 4 . The manufacturing equipment 124 may perform a process for a substrate (e.g., a wafer) in the process chamber. Examples of substrate processes include a deposition process for depositing one or more layers of a film on the surface of the substrate, an etching process for forming a pattern on the surface of the substrate, etc. The manufacturing equipment 124 may perform each process according to a process recipe. A process recipe defines a specific set of operations to be performed on the substrate during the process and may include one or more settings associated with each operation. For example, a deposition process recipe may include a temperature setting for the process chamber, a pressure setting for the process chamber, flow rate settings for precursors of materials included in the film to be deposited on the substrate surface, etc.

[0017] In some embodiments, the fabrication equipment 124 includes sensors 126 configured to generate data associated with a substrate processed in the system 100. For example, a process chamber may include one or more sensors configured to generate spectral or non-spectral data associated with a substrate before, during, and / or after a process (e.g., a deposition process) is performed on the substrate. In some embodiments, the spectral data generated by the sensors 126 may indicate the concentration of one or more materials deposited on the surface of the substrate. The sensors 126 configured to generate spectral data associated with the substrate may include reflectometry sensors, ellipsometry sensors, thermal spectral sensors, capacitance sensors, etc. The sensors 126 configured to generate non-spectral data associated with the substrate may include temperature sensors, pressure sensors, flow sensors, voltage sensors, etc. Further details regarding the fabrication equipment 124 are provided with respect to FIGS. 3 and 4.

[0018] In some embodiments, the sensors 126 provide sensor data (e.g., sensor values, characteristics, trace data) associated with the manufacturing equipment 124 (e.g., associated with the 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 executing 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 run) may be referred to as trace data (e.g., historical trace data, current trace data, etc.) received over time from different sensors 126. 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 voltage (ESC), current, material flow, power, voltage, etc. The sensor data may be associated with or indicative of manufacturing parameters, such as hardware parameters, such as settings or components (e.g., size, type, etc.) of the manufacturing 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., equipment readings as the product is being processed). The sensor data may vary from substrate to substrate.

[0019] The metrology equipment 128 may provide metrology data associated with substrates processed by the fabrication equipment 124. The metrology data may 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 may 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 may be of a finished product or a semi-finished product. The metrology data may vary from substrate to substrate. The metrology data may be generated using, for example, reflectometry techniques, ellipsometry techniques, TEM techniques, etc.

[0020] In some embodiments, the metrology instrument 128 may be included as part of the fabrication equipment 124. For example, the metrology instrument 128 may be included inside 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 instrument 128 may be referred to as an in-situ metrology instrument. In another example, the metrology instrument 128 may be coupled to another station of the fabrication equipment 124. For example, the metrology instrument 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 instrument 128 may be referred to as an integrated metrology instrument. In other or similar embodiments, the metrology instrument 128 is not coupled to a station of the fabrication equipment 124. In such cases, the metrology instrument 128 may be referred to as an in-line metrology instrument or an external metrology instrument. 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 processing.

[0021] The client device 120 may include a computing device such as a personal computer (PC), laptop, mobile phone, smartphone, tablet computer, netbook computer, network-connected television ("smart TV"), network-connected media player (e.g., Blu-ray player), set-top box, over-the-top (OTT) streaming device, 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 the manufacturing system. The client device 120 may include a corrective action component 122. The corrective action component 122 may receive user input of indicators associated with manufacturing equipment 124 (e.g., via a graphical user interface (GUI) displayed by the client device 120). In some embodiments, the corrective action component 122 transmits the indicators to the forecasting system 110, receives output (e.g., forecast data) from the forecasting system 110, determines corrective actions based on the output, and causes the corrective actions to be implemented. In some embodiments, the corrective action component 122 receives an indication of a corrective action from the predictive system 110 and causes the corrective action to be implemented. Each client device 120 may include an operating system that enables a user to one or more of generate, view, or edit data (e.g., indications associated with manufacturing equipment 124, corrective actions associated with manufacturing equipment 124, etc.).

[0022] 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 be spread across multiple computing devices (e.g., multiple server computers). The data store 140 may store data associated with processing substrates on the manufacturing equipment 124. For example, the data store 140 may store data (referred to as process data) collected by the sensors 126 on the manufacturing equipment 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 being processed in the manufacturing system). The data store may also store spectral or non-spectral data associated with a portion of a substrate being processed on the manufacturing equipment 124. The spectral data may include historical spectral data and / or current spectral data.

[0023] 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 preventive maintenance indicator, an operator, etc. The context data may refer to historical context data (e.g., context data associated with a previous process performed on a previous substrate) and / or current process data (e.g., context data associated with a current or future process to be performed on a previous substrate). The context data may further include and identify sensors associated with a particular subsystem of a process chamber.

[0024] The data store 140 may also store task data. The task data may include one or more sets of operations to be performed on a substrate during a deposition process and may include one or more settings associated with each operation. For example, task data for a deposition process may include temperature settings for a process chamber, pressure settings for the process chamber, flow rate settings for precursors of a film material to be deposited on the substrate, etc. In another example, the task data may include control pressures at predetermined pressure points relative to flow rate values. Task data may refer to historical task data (e.g., task data associated with a previous process performed on a previous substrate) and / or current task data (e.g., task data associated with a current or future process to be performed on a substrate).

[0025] In some embodiments, data store 140 may be configured to store data that is inaccessible to users of the manufacturing system. For example, process data, spectral data, contextual data, etc. acquired for substrates being processed in the manufacturing system may not be accessible to users (e.g., operators) of the manufacturing system. In some embodiments, all data stored in data store 140 may be inaccessible to users of the manufacturing system. In other or similar embodiments, a portion of the data stored in data store 140 may be inaccessible to users, while another portion of the data stored in data store 140 may be accessible to 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, with data that is inaccessible to users stored in one or more first data stores and data that is accessible to users stored in one or more second data stores.

[0026] In some embodiments, the data store 140 may be configured to store data associated with known fault patterns. A fault pattern may be a vector value associated with one or more problems or failures associated with a process chamber subsystem. In some embodiments, a fault pattern may be associated with a corrective action. For example, a fault pattern may include a parameter adjustment step to correct the problem or failure indicated by the fault pattern. Fault patterns are described in more detail in FIG. 7 below.

[0027] 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.

[0028] Server machine 170 includes a training set generator 172 capable of generating training datasets (e.g., a set of data inputs and a set of target outputs) for training, validating, and / or testing machine learning model 190. Machine learning model 190 may be any algorithmic model capable of learning from data. Some operations of dataset generator 172 are described in detail below with respect to FIG. 2. In some embodiments, dataset generator 172 may divide training data into a training set, a validation set, and a test set. In some embodiments, prediction system 110 generates multiple sets of training data.

[0029] 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 running on a processing device, a general-purpose computer system, or a dedicated machine, etc.), 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 discover patterns in the training data that map training inputs to target outputs (expected answers) and provide a machine learning model 190 that captures these patterns. The machine learning model 190 may use one or more of statistical modeling, support vector machines (SVMs), radial basis functions (RBFs), clustering, supervised machine learning, semi-supervised machine learning, unsupervised machine learning, k-nearest neighbor algorithms (k-NNs), linear regression, random forests, neural networks (e.g., artificial neural networks), and the like.

[0030] The validation engine 184 may be capable of validating the machine learning model 190 using a corresponding set of validation set features from the training set generator 172. 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 with accuracies that do not meet a threshold accuracy. In some embodiments, the selection engine 185 may be capable of selecting a trained machine learning model 190 with an accuracy that meets the threshold accuracy. In some embodiments, the selection engine 185 may be capable of selecting the trained machine learning model 190 with the highest accuracy of the trained machine learning models 190.

[0031] The testing engine 186 may be capable of testing 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 the trained machine learning models based on the test set.

[0032] As described in more detail below, the prediction server 112 provides data indicative of the expected behavior of each subsystem of the process chamber and includes a prediction component 114 capable of executing trained machine learning models 190 against current sensor data inputs to obtain one or more outputs. The prediction server 112 may further provide data indicative of the health and diagnostics of the process chamber subsystems, as described in more detail below.

[0033] Client devices 120, manufacturing equipment 124, sensors 126, measurement equipment 128, prediction server 112, data store 140, server machine 170, and server machine 180 may be coupled to each other via network 130. In some embodiments, network 130 is a public network that provides client 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, measurement 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.

[0034] 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 are combined into a single machine, while in some other or similar embodiments, server machines 170 and 180 and prediction server 112 may be combined into a single machine.

[0035] In general, functionality described in one embodiment as being performed by server machine 170, server machine 180, and / or prediction server 112 may also be performed on client device 120. Additionally, functionality attributed to a particular component may be implemented by different or multiple components operating together.

[0036] In embodiments, a "user" may be represented as a single individual. However, other embodiments of the present disclosure encompass a "user" being an entity controlled by multiple users and / or automated sources. For example, a collection of individual users federated as a group of administrators may be considered a "user."

[0037] 2 is a flowchart 200 of a method for training a machine learning model according to an aspect of the present disclosure. Method 200 is implemented by processing logic, which may include hardware (circuitry, dedicated logic, etc.), software (e.g., running on a general-purpose computer system or a dedicated machine), firmware, or some combination thereof. In one implementation, method 200 may be performed by a computer system such as the computer system architecture 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 depicted 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.

[0038] For ease of explanation, the methods are depicted 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. Furthermore, not all illustrated acts may be performed to implement a method in accordance with the disclosed subject matter. In addition, those skilled in the art will understand that the methods may alternatively be represented as a series of interrelated states via a state diagram or events. Additionally, it should be understood that the methods disclosed herein can be stored on an article of manufacture to facilitate transporting and transferring such methods to a computing device. The term article of manufacture, as used herein, is intended to encompass a computer program accessible from any computer-readable device or storage medium.

[0039] At block 210, processing logic initializes the training set T to an empty set (e.g., {}).

[0040] At block 212, processing logic acquires sensor data (e.g., sensor values, characteristics, trace data) associated with a previous deposition process performed to deposit one or more layers of a film on the surface of a previous substrate. The sensor data may be further associated with subsystems of the process chamber. A subsystem may be characterized as a set of sensors associated with 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 rate, 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.

[0041] In some embodiments, the sensor data associated with the deposition process is historical data associated with one or more previous deposition settings for a previous deposition process previously performed on a previous substrate in the manufacturing system. For example, the historical data may be historical context data associated with the previous deposition process stored in the data store 140. In some embodiments, the one or more previous deposition settings may 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 precursors of one or more materials of a previous film deposited on the surface of the previous substrate, or any other setting associated with the deposition process. The flow rate setting may 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 of the flow rate of a precursor during the deposition process. In one example, the precursor of the previous film may include a boron-containing precursor or a silicon-containing precursor. In some embodiments, the sensor data may also be associated with a previous etching process performed on a previous substrate or any other process performed in the process chamber.

[0042] At block 214, processing logic obtains 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 of 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 obtain the task data from data store 140 according to previously described embodiments.

[0043] At block 216, processing logic generates first training data based on acquired sensor data associated with a previous deposition process performed on a previous substrate, and at block 218, processing logic generates second training data based on task data associated with a recipe for a film deposited on the surface of the previous substrate.

[0044] At block 220, processing logic generates a mapping between first training data and 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 being associated with (or mapped to) the second training data. At block 224, processing logic adds the mapping to the training set T.

[0045] At block 226, processing logic determines whether training set T includes a sufficient amount of training data for training the machine learning model. Note that in some embodiments, the sufficiency of training set T may be determined simply based on the number of mappings in the training set, while in some other embodiments, the sufficiency of training set T may be determined based on one or more other criteria (e.g., a measure of diversity of training examples, etc.) in addition to or instead of the number of input / output mappings. In response to determining that the training set does not include a sufficient amount of training data for training the machine learning model, method 200 returns to block 212. In response to determining that training set T includes a sufficient amount of training data for training the machine learning model, method 200 proceeds to block 228.

[0046] At block 228, processing logic provides a training set T for training the machine learning model. In one embodiment, the training set T is provided to the training engine 182 of the server machine 180 to perform the training. In the case of a neural network, for example, the input values ​​of a given input / output mapping are inputs to the neural network, and the output values ​​of the input / output mapping are stored at the 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 this procedure is repeated for other input / output mappings in the training set T.

[0047] In some embodiments, processing logic may perform outlier detection methods to remove anomalies from the training set T before training the machine learning model. Outlier detection methods may include techniques to identify values ​​that are significantly different from the majority of the training data. These values ​​may be generated from error, noise, etc.

[0048] As an illustrative example, the machine learning model 190 may use a k-NN algorithm to generate predictive data (e.g., expected behavior under ideal or near-ideal operating parameters) for the process chamber subsystem using the training set T. In particular, the k-NN algorithm may cause processing logic to determine a decision boundary in the multidimensional space defined by the training set. An example of a k-NN algorithm is described in U.S. Pat. No. 9,910,430, which is incorporated by reference in its entirety. The decision boundary may then be used to compare any current (actual or measured) behavior sensor data of the process chamber subsystem with expected behavior values ​​to generate predictive data.

[0049] At block 230, processing logic performs a calibration process on the trained machine learning model. In some embodiments, processing logic may compare a predicted behavior of the process chamber subsystem to a current behavior of the process chamber subsystem based on a difference in values ​​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, the flow subsystem, or the temperature subsystem to one or more values ​​associated with the current measured behavior of the pressure subsystem, the flow subsystem, or the temperature subsystem, respectively. After block 230, the machine learning model may be used to generate one or more values ​​associated with a predicted behavior of the process chamber subsystem. The one or more values ​​associated with the predicted behavior may be compared to the current (actual) behavior of the process chamber subsystem to generate predicted data. The predicted data may include data indicative of the health state of the process chamber subsystem. This is described in more detail in FIG. 5 below.

[0050] In some embodiments, a manufacturing system may include two or more process chambers. For example, the example manufacturing system 300 of FIG. 3 illustrates 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.

[0051] 3 is a top-view schematic diagram of an example manufacturing system 300 according to an embodiment of the present disclosure. The manufacturing system 300 may perform 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.

[0052] The manufacturing system 300 may include a process tool 304 and a factory interface 306 coupled to the process tool 304. The process tool 304 may include a housing 308 having a transfer chamber 310 therein. The transfer chamber 310 may include one or more process chambers (also referred to as processing chambers) 314, 316, 318 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 or the like. 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 end effectors at the end of each arm. The end effectors may be configured to handle specific objects, such as wafers.

[0053] 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. The 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, or the like. 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 associated with the temperature, pressure, gas concentrations, etc. of the environment within the process chambers 314, 316, 318 during substrate processing.

[0054] 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 can be changed from a vacuum environment (where substrates may be transferred to or from the transfer chamber 310) to a near atmospheric inert gas environment (where substrates may be transferred to or from the factory interface 306). The factory interface 306 may be any suitable storage device, such as, for example, an Equipment Front End Module (EFEM). The factory interface 306 may be configured to receive substrates 302 from substrate carriers 322 (e.g., Front Opening Unified Pods (FOUPs)) that are docked to various load ports 324 of the factory interface 306. A factory interface robot 326 (shown in dotted lines) may be configured to transfer substrates 302 between carriers (also referred to as containers) 322 and load locks 320. Carriers 322 may be substrate storage carriers or replacement part storage carriers.

[0055] The fabrication system 300 may also be connected to a client device (not shown) configured to provide information regarding the fabrication system 300 to a user (e.g., an operator). In some embodiments, the client device may provide information to a user of the fabrication system 300 via one or more graphical user interfaces (GUIs). For example, the client device may provide, via the GUI, information regarding 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. The client device may also provide information regarding modifications to the process recipe taking into account respective sets of deposition settings that are predicted to correspond to the target profile, according to embodiments described herein.

[0056] The manufacturing system 300 may also include a system controller 328. The system controller 328 may be and / or include a computing device, such as, for example, a personal computer, a server computer, a programmable logic controller (PLC), a microcontroller, or the like. The system controller 328 may include one or more processing devices, which may be general-purpose processing devices, such as a microprocessor, a central processing unit, or the like. More specifically, the processing devices may be complex instruction set computing (CISC) microprocessors, reduced instruction set computing (RISC) microprocessors, very long instruction word (VLIW) microprocessors, or processors implementing other instruction sets or combinations of instruction sets. The processing devices may also be one or more special-purpose processing devices, such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), network processors, or the like. The system controller 328 may include data storage devices (e.g., one or more disk drives and / or solid-state drives), main memory, static memory, network interfaces, and / or other components. The system controller 328 may execute instructions to implement any one or more of the methodologies and / or embodiments described herein. In some embodiments, the system controller 328 may execute instructions to perform one or more operations in the manufacturing system 300 according to a process recipe. The instructions may be stored in a computer-readable storage medium, which may include a main memory, a static memory, a secondary storage, and / or a processing device (during execution of the instructions).

[0057] The system controller 328 may receive data from sensors contained on or within various portions of the manufacturing system 300 (e.g., processing chambers 314, 316, 318, transfer chamber 310, load lock 320, etc.). In some embodiments, the data received by the system controller 328 may include spectral and / or non-spectral data about a portion of the substrate 302. In other or similar embodiments, the data received by the system controller 328 may include data associated with processing the substrate 302 in the processing chambers 314, 316, 318, as previously described. For purposes of this description, the system controller 328 will be described as receiving data from sensors contained within the process chambers 314, 316, 318. However, the system controller 328 may receive data from any portion of the manufacturing system 300 and may use the data received from that portion in accordance with the embodiments described herein. In an illustrative example, the system controller 328 may receive data from one or more sensors for the process chambers 314, 316, 318 before, after, or during substrate processing in the process chambers 314, 316, 318. Data received from 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. In some embodiments, the data store 350 may be the data store 140 described with respect to FIG. 1 .

[0058] 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 may correspond to the process chambers 314, 316, and 318 described with respect to FIG. 3 . The process chamber 400 may be used for a process in which a corrosive plasma environment is provided. For example, the process chamber 400 may be a chamber for a plasma etcher or a plasma etching reactor. In another example, the process chamber may be a chamber for a deposition process, as previously described. In one embodiment, the process chamber 400 includes a chamber body 402 and a showerhead 430 surrounding an 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 a nozzle in some embodiments, or by multiple fan-shaped showerhead sections and a plasma generator in other embodiments. The chamber body 402 may be fabricated from other suitable materials, such as aluminum, stainless steel, or titanium (Ti). The chamber body 402 generally includes a sidewall 408 and a bottom 410. An exhaust port 426 may be defined within the chamber body 402 and may couple the interior volume 406 to a pumping system 428. The pumping system 428 may include one or more pumps and a throttle valve utilized to evacuate and adjust the pressure of the interior volume 406 of the process chamber 400.

[0059] The showerhead 430 may be supported on the sidewall 408 of the chamber body 402. The showerhead 420 (or lid) may be opened to allow access to the interior volume 406 of the process chamber 400 and may be closed to provide a seal for the process chamber 400. A gas panel 458 may be coupled to the process chamber 400 to provide process and / or cleaning gases to the interior volume 406 through the showerhead 430 or the lid and nozzles (e.g., through apertures in the showerhead or lid and nozzles). For example, the gas panel 458 may provide precursors of the material of the film 451 to be deposited on the surface of the substrate 302. In some embodiments, the precursors may include a silicon-based precursor or a boron-based precursor. The showerhead 430 may include a gas distribution plate (GDP) having multiple gas delivery holes 432 (also referred to as 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 (eg, during a deposition process).

[0060] In some embodiments, the processing chamber 400 may include a metrology instrument (not shown) configured to generate in-situ metrology measurements during a process performed in the process chamber 400. The metrology instrument may be operably coupled to a system controller (e.g., system controller 328 as previously described). In some embodiments, the metrology instrument 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 concentration profile for the film 451 based on the metrology measurements received from the metrology instrument. In other or similar embodiments, the processing chamber 400 does not include a metrology instrument. In such embodiments, the system controller may receive one or more metrology measurements for the film 451 after completion of a deposition process in the process chamber 400. The system controller may determine a deposition rate based on the one or more metrology measurements and correlate and generate a thickness profile for the film 451 based on the determined concentration gradient and the determined deposition rate of the deposition process.

[0061] 5 is a flow diagram of a method 500 for determining process chamber subsystem health metrics using a machine learning model, according to an embodiment of the present disclosure. Method 500 is implemented by processing logic, which may include hardware (circuitry, dedicated logic, etc.), software (e.g., running on a general-purpose computer system or a dedicated machine), firmware, or some combination thereof. In one embodiment, method 500 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 500 may be performed by one or more other machines not depicted in the figure. In some embodiments, one or more operations of method 500 may be performed by server machine 170, server machine 180, and / or prediction server 112.

[0062] At block 510, processing logic acquires sensor data associated with operations performed in the process chamber. In some embodiments, the operations may include a deposition process performed in the process chamber to deposit one or more layers of a film on the surface of a substrate, an etching process performed on one or more layers of a film on the surface of a substrate, or any other process performed in the process chamber. The operations may be performed according to a recipe. The sensor data may include one or more values ​​of one or more 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 associated with 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.

[0063] At block 512, 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 ​​associated with expected behavior of the process chamber subsystem. For example, the machine learning model may use a k-NN algorithm to generate the 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 operation.

[0064] At block 514, processing logic generates output via the machine learning model based on the sensor data. In some embodiments, the output may be at least one scalar value indicative of a difference between a predicted behavior of the process chamber subsystem and an actual behavior of the process chamber subsystem. In particular, the scalar value may be indicative of a difference between actual values ​​of a set of sensors associated with the subsystem and predicted values ​​of the set of sensors.

[0065] At block 516, processing logic converts the output to a representative value within a predetermined range. The representative value may be used to indicate the health of the process chamber subsystem (e.g., current behavior compared to expected behavior). In some embodiments, processing logic may generate the representative value by applying a linear or nonlinear conversion function to the output value (e.g., a scalar value) to scale the output value within a predetermined range. In some embodiments, the conversion function may include a linear function, a logit (log-odds) function, a sigmoid function, an exponential function, etc. The particular conversion function used may be based on the desired sensitivity of the current behavior of the process chamber subsystem. For example, a linear function may be sensitive to each deviation of the current behavior of the process chamber subsystem from expected behavior, while a sigmoid function is not sensitive to initial changes in the current behavior of the process chamber subsystem. In some embodiments, a user may change the sensitivity (e.g., apply a different conversion function) using the client device 120.

[0066] FIG. 6 is a graph illustrating an example sigmoid transform according to an embodiment of the present disclosure. As illustrated, the x-axis may represent a scalar value (e.g., output) from a machine learning model. The y-axis may represent a representative value within a predetermined range, e.g., 0-1, where a representative value of "0" indicates that the actual behavior of the process chamber subsystem is similar or the same as the expected behavior of the process chamber subsystem, and a representative value of "1" indicates that the current behavior of the process chamber subsystem deviates significantly (e.g., greater than a predetermined threshold) from the expected behavior of the process chamber subsystem. As shown in FIG. 6, increasing x values ​​initially result in a relatively small change in the value of y (before x=-4), then accelerate as the x value increases beyond x=-4, and then decelerate as the x value increases beyond x=4. As such, using a sigmoid transform, the prediction component 114 is less sensitive to initial deviations of the current behavior from the expected behavior, or to deviations when the actual behavior already deviates significantly from the expected behavior.

[0067] Returning to FIG. 5 , at block 518, processing logic displays the representative values ​​on a client device (e.g., client device 120). In some embodiments, different representative values ​​may be associated with different health indicators (e.g., healthy, declining, critical, failed, etc.), and the health indicators may be displayed on the client device. Representative values ​​may be associated with different health indicators based on the representative values ​​exceeding or below a threshold. For example, using the graph in FIG. 6 as an illustrative example, a representative value between 0 and 0.01 may indicate a healthy process chamber subsystem, a representative value between 0.01 and 0.5 may indicate a declining process chamber subsystem, a representative value between 0.5 and 0.99 may indicate a critical process chamber subsystem, and a representative value between 0.99 and 1.0 may indicate that a failure has occurred. The described set of health indicators is merely exemplary, and any indicator may be used.

[0068] 7 is a flow diagram of a method 700 for determining a fault classification of a process chamber subsystem using a machine learning model, according to an embodiment of the present disclosure. Method 700 is performed by processing logic, which may include hardware (circuitry, dedicated logic, etc.), software (e.g., running on a general-purpose computer system or a dedicated machine), firmware, or some combination thereof. In one implementation, method 700 may be performed by a computer system, such as computer system architecture 100 of FIG. 1. In other or similar implementations, one or more operations of method 700 may be performed by one or more other machines not depicted in the figure. In some embodiments, one or more operations of method 600 may be performed by server machine 170, server machine 180, and / or prediction server 112.

[0069] At block 710, processing logic acquires sensor data associated with operations performed in the process chamber. In some embodiments, the operations may include a deposition process performed in the process chamber to deposit one or more layers of a film on the surface of a substrate, an etching process performed on one or more layers of a film on the surface of the substrate, etc. The operations may be performed according to a recipe. The sensor data may include values ​​for one or more 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 associated with 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.

[0070] At block 712, 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 ​​associated with expected behavior of the process chamber subsystem. For example, the machine learning model may use a k-NN algorithm to generate the 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 operation.

[0071] At block 714, the processing logic generates an output via the machine learning model based on the sensor data. In some embodiments, the output may include a vectorized version of one or more scalar values ​​generated by the machine learning model. For example, the output may be at least one vector value indicating a pattern (e.g., a fault pattern) that describes the contribution of each sensor to the one or more scalar values ​​generated by the machine learning model.

[0072] At block 716, 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, 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 does not conform to expected behavior. In response to processing logic determining that the process chamber subsystem is not experiencing a fault (e.g., the scalar value of the output does not exceed the predetermined threshold), processing logic may proceed to block 710. In response to processing logic determining that the process chamber subsystem is experiencing a fault (e.g., the scalar value of the output exceeds the predetermined threshold), processing logic may proceed to block 718.

[0073] At block 718, processing logic may identify the type of fault based on the output. In some embodiments, processing logic may use a classification algorithm, such as a radial basis function (RBF) network, a neural network, or any other statistical or machine learning based model. An example of an RBF network is described in U.S. Pat. No. 9,852,371, which is incorporated by reference in its entirety. In particular, processing logic may compare the fault pattern to a library of known fault patterns and determine the type of fault based on the similarity of the fault pattern as compared to the known fault patterns. In some embodiments, prediction system 110 may generate the classification algorithm. In some embodiments, the classification algorithm may learn new fault patterns, for example, using a feedback mechanism. In some embodiments, the classification algorithm may also generate a confidence value. The confidence value may indicate a confidence level of the prediction. The confidence value may be generated by processing logic based on the similarity of the fault pattern to known fault patterns.

[0074] At block 720, processing logic may execute corrective action based on the identified fault. In some embodiments, the corrective action may include generating an alert or an indication to client device 120 of the determined problem. 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 of materials included in the film to be deposited on the substrate surface, etc.) based on the desired properties 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.

[0075] FIG. 8 is a block diagram illustrating a computer system 800, according to certain embodiments. In some embodiments, computer system 800 may be connected to other computer systems (e.g., via a network such as a local area network (LAN), an intranet, an extranet, or the Internet). Computer system 800 may operate in the capacity of a server or a client computer in a client-server environment, or as a peer computer in a peer-to-peer or distributed network environment. Computer system 800 may be provided by a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile phone, a web appliance, a server, a network router, switch, or bridge, or any device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that device. Furthermore, the term “computer” is intended to include any collection of computers that individually or collectively execute a set (or sets) of instructions to perform any one or more of the methodologies described herein.

[0076] In a further aspect, computer system 800 may include a processing device 802, a volatile memory 804 (e.g., random access memory (RAM)), a non-volatile memory 806 (e.g., read-only memory (ROM) or electrically erasable programmable ROM (EEPROM)), and a data storage device 816, which may communicate with each other via a bus 808.

[0077] The processing device 802 may be provided by one or more processors, such as a general-purpose processor (e.g., a Complex Instruction Set Computing (CISC) microprocessor, a Reduced Instruction Set Computing (RISC) microprocessor, a Very Long Instruction Word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of instruction set types, etc.), 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, etc.).

[0078] Computer system 800 may further include a network interface device 822 (e.g., coupled to a network 874). Computing device 800 may also include a video display device 810 (e.g., an LCD), an alphanumeric input device 812 (e.g., a keyboard), a cursor control device 814 (e.g., a mouse), and a signal generating device 820.

[0079] In some embodiments, the data storage device 816 may include a non-transitory computer-readable storage medium 824 that may store instructions 826 encoding 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 instructions for performing the methods described herein.

[0080] The instructions 826 may also reside, completely or partially, within the volatile memory 804 and / or within the processing device 802 during execution thereof by the computer system 800; thus, the volatile memory 804 and the processing device 802 may also constitute machine-readable storage media.

[0081] Although the computer-readable storage medium 824 is shown as a single medium in the illustrative example, the term "computer-readable storage medium" is intended to include a single medium or multiple media (e.g., a centralized or distributed database, 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 capable of storing and encoding a set of instructions for execution by a computer that cause the computer to perform any one or more of the methodologies described herein. The term "computer-readable storage medium" is intended to include, but is not limited to, solid-state memory, optical media, and magnetic media.

[0082] The methods, components, and features described herein may be implemented by separate hardware components or integrated into the functionality of other hardware components, such as ASICS, FPGAs, DSPs, or similar devices. In addition, the methods, components, and features may be implemented by firmware modules or functional circuits within a hardware device. Furthermore, the methods, components, and features may be implemented in any combination of hardware devices and computer program components, or in a computer program.

[0083] Unless specifically stated otherwise, terms such as "receive," "perform," "provide," "acquire," "cause," "access," "determine," "add," "use," "train," or the like refer to acts and processes performed or implemented by a computer system that manipulate and convert data represented as physical (electronic) quantities in computer system registers and memory 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, terms such as "first," "second," "third," "fourth," etc., when used herein, are intended as labels to distinguish between different elements and may not have a sequential meaning according to their numerical designation.

[0084] The examples described herein also relate to apparatus for performing the methods described herein. This apparatus may be specially constructed to perform the methods described herein, or it may comprise a general-purpose computer system that is selectively programmed by a computer program stored in the computer system. Such a computer program may be stored on a computer-readable tangible storage medium.

[0085] The methods and illustrative examples described herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems can be used in accordance with the techniques 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.

[0086] The above description is illustrative and not restrictive. While the present disclosure has been described with reference to particular illustrative examples and implementations, it should be recognized that the present disclosure is not limited to the described examples and implementations. The scope of the present disclosure should be determined with reference to the following claims, along with the full scope of equivalents to which such claims are entitled.

Claims

1. acquiring, by a processor, a plurality of sensor values ​​associated with a deposition process performed according to a recipe in a process chamber to deposit a film on a surface of a substrate; applying a machine learning model to the plurality of sensor values, the machine learning model being trained based on historical sensor data of a subsystem of the process chamber and task data associated with the recipe for depositing the film; generating an output of the machine learning model, the output being indicative of a health state of the subsystem.

2. The method of claim 1 , wherein the output comprises a scalar value indicating the difference between the measured values ​​of a set of sensors associated with the subsystem and the expected values ​​of the set of sensors.

3. The method of claim 1 , further comprising using a conversion function to convert the output to a representative value within a predetermined range.

4. The method of claim 3 , wherein the transformation function comprises at least one of a linear function, a logit function, a sigmoid function, or an exponential function.

5. The method of claim 1 , wherein the output comprises a vector value indicative of a fault pattern.

6. The method of claim 5 , further comprising using a classification algorithm to determine the type of failure suffered by the subsystem based on the failure pattern.

7. The method of claim 6 , wherein the classification algorithm compares the fault pattern to a library of known fault patterns.

8. The method of claim 6 , wherein the classification algorithm comprises a radial basis function (RBF) network or a neural network.

9. The method of claim 5 further comprising: performing a corrective action based on the failure pattern.

10. 1. A system comprising: Memory and a processing device, wherein the processing device acquiring a plurality of sensor values ​​associated with a deposition process performed according to a recipe in a process chamber to deposit a film on a surface of a substrate; applying a machine learning model to the plurality of sensor values, the machine learning model being trained based on historical sensor data of a subsystem of the process chamber and task data associated with the recipe for depositing the film; generating an output of the machine learning model, the output being indicative of a health state of the subsystem; and a system operatively coupled to the memory device to perform operations including:

11. The system of claim 10 , wherein the output comprises a scalar value indicating a difference between measurements of a set of sensors associated with the subsystem and expected values ​​of the set of sensors.

12. The system of claim 10 , wherein the processing device is to perform further operations including converting the output to a representative value within a predetermined range using a conversion function.

13. The system of claim 10 , wherein the output comprises a vector value indicative of a fault pattern.

14. 14. The system of claim 13, wherein the processing device is to perform further operations including using a classification algorithm to determine a type of failure suffered by the subsystem based on the failure pattern.

15. The system of claim 13 , wherein the processing device is to perform further operations including performing corrective actions based on the fault patterns.

16. acquiring, by a processor, sensor data associated with a deposition process performed in a process chamber to deposit a film on a surface of a substrate, the sensor data including sensor values ​​associated with subsystems of the process chamber; acquiring task data associated with a recipe for depositing the film; training a machine learning model using a training set based on the sensor data and the task data, the machine learning model being trained to generate predictive data indicative of expected sensor values ​​of the subsystem.

17. The method of claim 16 , further comprising performing an outlier detection technique to remove one or more anomalies from the training set.

18. The method of claim 16, wherein the machine learning model comprises a k-nearest neighbor (k-NN) algorithm.

19. 17. The method of claim 16, wherein the subsystem comprises a set of sensors for monitoring operating parameters of the process chamber.

20. 20. The method of claim 19, wherein the operating parameter comprises a pressure associated with the process chamber, a flow rate associated with the process chamber, or a temperature associated with the process chamber.

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