Patent Publication Number: US-2022238300-A1

Title: Rating substrate support assemblies based on impedance circuit electron flow

Description:
TECHNICAL FIELD 
     Embodiments of the present disclosure relate, in general, to manufacturing systems and more particularly to rating substrate support assemblies based on impedance circuit electron flow. 
     BACKGROUND 
     Substrate support assemblies are configured to support substrates during a process at a manufacturing system. Electrical components of a substrate support assembly can be damaged during manufacturing of the substrate support assembly or can be otherwise ineffective. As a result, substrates can be damaged during a process performed at a manufacturing system using a substrate support assembly with such electrical components. 
     SUMMARY 
     Some of the embodiments described cover a system and method for rating a substrate support assembly based on impedance circuit electron flow. The method includes providing, as input to a trained machine learning model, data associated with an amount of radio frequency (RF) power flowed through an electrical component of a current substrate support assembly during a current testing process performed for the current substrate support assembly. The method further includes obtaining one or more outputs of the trained machine learning model. The method further includes extracting, from the one or more outputs, a measurement value for an electron flow across an impedance circuit of the current substrate support assembly. The method further includes determining whether the extracted measurement value for the electron flow satisfies an electron flow criterion. In response to a determination that the extracted measurement value satisfies the electron flow criterion, the method includes assigning a first quality rating to the current substrate support assembly. In response to a determination that the extracted measurement value does not satisfy the electron flow criterion, the method includes assigning a second quality rating to the current substrate support assembly. The first quality rating can correspond to a higher quality substrate support assembly than a respective substrate assembly associated with the second quality rating. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       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 references indicate similar 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. 
         FIG. 1  depicts an illustrative computer system architecture, according to aspects of the present disclosure. 
         FIG. 2  is a top schematic view of an example manufacturing system, according to aspects of the present disclosure. 
         FIG. 3  is a cross-sectional schematic side view of an example process chamber of the example manufacturing system, according to aspects of the present disclosure. 
         FIG. 4  is a flow chart of a method for rating a substrate support assembly based on impedance circuit electron flow, according to aspects of the present disclosure. 
         FIG. 5  illustrates example testing equipment coupled to components of a substrate support assembly, according to aspects of the present disclosure. 
         FIG. 6  illustrates an example graphical user interface (GUI) for rating a substrate support assembly based on impedance circuit electron flow, according to aspects of the present disclosure. 
         FIG. 7  is a flow chart of a method for training a machine learning model, according to aspects of the present disclosure. 
         FIG. 8  depicts a block diagram of an illustrative computer system operating in accordance with one or more aspects of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION OF EMBODIMENTS 
     Implementations described herein provide systems and methods for rating substrate support assemblies based on impedance circuit electron flow. A substrate support assembly of a process chamber can support a substrate during a process performed at the process chamber. The substrate support assembly can include electrical components that are configured to perform various functions during the process. For example, the substrate support assembly can include an electrostatic chuck configured to electrostatically secure the substrate to a surface of the substrate support assembly. The electrostatic chuck can include electrodes that generate an electrostatic force between the substrate support assembly and the substrate. In another example, the substrate support assembly can include an electrode configured to transfer RF power to a process gas within the process chamber. The transferred RF power can cause the process gas to become a process plasma (e.g., an etching plasma, etc.), in accordance with a process recipe for the substrate. In yet another example, the substrate support assembly can include heating elements that are configured to heat the substrate to a target temperature during a process at the process chamber. 
     As can be seen above, substrate support assemblies can include various electrical components configured to perform different functions during a process performed for a substrate at a process chamber. In some instances, one or more electrical components can be defective or otherwise ineffective. For example, an electrode of the electrostatic chuck can be defective, which prevents the electrostatic chuck from generating a target electrostatic force to secure the substrate to the surface of the substrate support assembly. As a result, the substrate can be damaged during the process performed at the process chamber. In another example, another electrode of the substrate support assembly can be defective, which prevents the electrode from effectively and/or efficiently transferring RF power to a process gas within the process chamber. As a result, plasma is not generated according to the process recipe. In some instances, significant changes are made to the process recipe during the substrate process, which can delay the overall process performed at the process chamber and can use a significant amount of system resources (e.g., computer processing resources, process gas resources, RF power resources, etc.). 
     In some instances, a substrate support assembly including defective electrical components can be unusable in a process chamber of a manufacturing system. In other instances, a substrate support assembly including defective electrical components cannot be used for substrates that are subject to strict process conditions (e.g., an actual process condition at the process chamber is to be within 99.999% of a target value), but can be used in processes that are subject to lenient process conditions (e.g., an actual process condition at the process chamber is to be within 90% of a target value). However, users of a manufacturing system (e.g., operators) are unable to easily and effectively identify substrate support assemblies that include defective electrical components after the substrate support assembly is manufactured. As a result, substrate support assemblies including defective electrical components can be installed in process chambers for the manufacturing system, which can cause damage to substrates processed at the process chamber and/or other components of the process chamber. In some instances, the defective electrical components prevent a substrate from being processed according to a target process recipe and significant changes are made to the process recipe to account for the defective electrical components. As mentioned above, changes made to the process recipe can use a significant amount of system resources (e.g., computer processing resources, process gas resources, RF power resources, etc.), which can delay the overall process performed at the process chamber. The use of system resources and delay of the overall process can result in a significant increase in overall system latency and a decrease in overall system efficiency. 
     Aspects of the present disclosure address the above noted and other deficiencies by providing systems and methods for rating substrate support assemblies based on impedance circuit electron flow. A processing device of a testing system can provide, as input to a trained machine learning model, data associated with an amount of RF power flowed through an electrical component (e.g., an electrode, a heating element, etc.) of a substrate support assembly during a testing process. In some embodiments, the data associated with the amount of RF power flowed through the electrical component can include one or more scattering parameter values (e.g., a parameter value indicating an amount of RF power loss) for the electrical component. In some embodiments, the data associated with an amount of RF power can be collected by a testing fixture of the testing system, where the testing fixture is configured to simulate RF power flowed from electrodes of a process chamber to the electrical components of the substrate support assembly. 
     The machine learning model can be trained to predict a measurement value for an electron flow (i.e., a current) across an impedance circuit coupled to the substrate support assembly. An impedance circuit can be a circuit coupled to an RF power supply that is configured to maintain a particular amount of RF power transmitted to the electrical component of the substrate support assembly. The measurement value for the electron flow across the impedance circuit corresponds to an amount of ion energy at the substrate during a process at a process chamber, which can indicate a quality or effectiveness of the electrical components of the substrate support assembly. In some embodiments, the impedance circuit can be a component of an auto capacitance tuner. 
     The processing device of the testing system can obtain one or more outputs of the trained machine learning model and can extract the measurement value for the electron flow across the impedance circuit from the obtained outputs. In some embodiments, the processing device can determine whether the electron flow measurement satisfies an electron flow criterion. In response to determining that the electron flow criterion is satisfied (e.g., the electron flow measurement exceeds a threshold electron flow measurement value), the processing device can assign a first quality rating to the substrate support assembly. In response to determining that the electron flow criterion is not satisfied (e.g., the electron flow measurement does not exceed the threshold electron flow measurement value), the processing device can assign a second quality rating to the substrate support assembly. The first quality rating can be associated with a higher quality substrate support assembly than a respective substrate support assembly associated with the second quality rating. 
     In some instances, the assigned quality rating can indicate whether a substrate support assembly should be installed at a process chamber. For example, it can be determined (e.g., by a user of a manufacturing system (e.g., an operator)) that substrate support assemblies associated with a first quality rating are to be installed at process chambers of the manufacturing system while substrate support assemblies associated with the second quality rating are not to be installed at process chambers. In another example, it can be determined that substrate support assemblies associated with a first quality ratings are to be installed at process chambers for processes subject to strict process conditions while substrate support assemblies associated with second quality ratings are to be installed at process chambers for processes subject to lenient process conditions. 
     Aspects of the present disclosure address deficiencies of the conventional technology by providing systems and methods for providing a rating for a substrate support assembly based on the effectiveness of electrical components of the substrate support assembly after the substrate support assembly is constructed. Using data associated with a prior testing process performed for a prior substrate support assembly, the machine learning model can be trained to predict a measurement value for an electron flow across an impedance circuit of a current substrate support assembly. The processing device of the testing system can determine, based on the predicted electron flow measurement value, whether an electron flow criterion is satisfied and assign a respective quality rating to the substrate support assembly based on the determination. 
     By assigning the quality rating based on the predicted electron flow measurement, a substrate support assembly that includes defective electrical components can be identified before the substrate support assembly is installed at a process chamber. Determining to not install a substrate support assembly including defective electrical components at a process chamber, can, in some instances, prevent substrates from being damaged by the defective substrate support assembly. In other or similar instances, determining not to install the defective substrate support assembly can prevent the waste of significant system resources (e.g., computer processing resources, process gas resources, RF power resources) used to modify a process recipe to account for a defective electrical component. As a result, an overall system latency decreases and an overall system efficiency increases. 
       FIG. 1  depicts an illustrative computer system architecture  100 , according to aspects of the present disclosure. Computer system architecture  100  can include a client device  120 , a predictive server  112  (e.g., to generate predictive data, to provide model adaptation, to use a knowledge base, etc.), and a data store  140 . The predictive server  112  can be part of a predictive system  110 . The predictive system  110  can further include server machines  170  and  180 . In some embodiments, computer system architecture  100  can be included as part of a manufacturing system for processing substrates, such as manufacturing system  200  of  FIG. 2 . In such embodiments, computer system architecture  100  can include manufacturing equipment  124  and/or testing equipment  122 . In other or similar embodiments, computer system architecture  100  can be included as part of a testing system. In such embodiments, computer system architecture  100  may not include manufacturing equipment  124  and instead may include testing equipment  122 . 
     Manufacturing equipment  124  can produce products, such as electronic devices, following a recipe or performing runs over a period of time. Manufacturing equipment  124  can include a process chamber, such as process chamber  300  described with respect to  FIG. 3 . Manufacturing equipment  124  can perform a process for a substrate (e.g., a wafer, etc.) at the process chamber. Examples of substrate processes include a deposition process to deposit a film on a surface of the substrate, an etch process to form a pattern on the surface of the substrate, etc. Manufacturing equipment  124  can perform each process according to a process recipe. A process recipe defines a particular set of operations to be performed for the substrate during the process and can include one or more settings associated with each operation. For example, a deposition process recipe can include a temperature setting for the process chamber, a pressure setting for the process chamber, a flow rate setting for a precursor for a material included in the film deposited on the substrate surface, etc. 
     Testing equipment  122  can provide testing data associated with one or more components of manufacturing equipment  124  (e.g., a substrate support assembly, etc.). It should be noted that although embodiments of the present disclosure refer to testing a substrate support assembly, embodiments of the present disclosure can be applied to any substrate support assembly. Testing data refers to data collected by testing equipment  122  during performance of one or more operations of a testing process for a substrate support assembly. In some embodiments, testing equipment  122  can include a processing device and a memory component. In some embodiments, the memory component of testing equipment  122  can be included at data store  140 . In other or similar embodiments, the memory component of testing equipment  122  can be separate from data store  140 . The memory component can be configured to store the one or more operations of the testing process for the substrate support assembly and the processing device of testing equipment  122  can be configured to execute the testing process operations. 
     In some embodiments, testing equipment  122  can further include one or more testing condition components  126 . The processing device of testing equipment  122  can cause one or more conditions to be applied during a testing process performed for the substrate support assembly. For example, a test condition component  126  can include a radio frequency (RF) power component that is configured to deliver RF power to one or more electrical components of the substrate support assembly during a testing process performed by testing equipment  122 . The processing device of testing equipment  122  can transmit instructions to the RF power component that cause the RF power component to provide a particular amount of RF power to the electrical components of the substrate support assembly during the testing process. 
     In some embodiments, testing equipment  122  can further include one or more testing data collection components  128 . A testing data collection component  128  can include sensors configured to collect the testing data for the substrate support assembly during the testing process and transmit the collected data to the processing device of testing equipment  122 . In accordance with the previous example, a testing data collection component  128  can include a RF power sensor configured to monitor an amount of power flowed through an electrical component of the substrate support assembly during the testing process performed by testing equipment  122 . The RF power sensor can detect the amount of RF power flowed through the electrical component during the testing process and transmit data associated with the detected amount of RF power to the processing device of testing equipment  122 . In response to receiving the testing data from the RF power sensor, the processing device of testing equipment  122  can store the testing data at the memory component for testing equipment  122 . 
     In some embodiments, test condition component  126  and testing data collection component  128  of testing equipment  122  can be included as part of a testing fixture, such as testing fixture  500  of  FIG. 5 , that is coupled to the processing device of testing equipment  122 . In other or similar embodiments, one or more testing condition component  126  and one or more testing data collection components  128  can be included as part of the testing fixture and one or more additional testing data collection components  128  can be separate from the testing fixture. For example, the testing fixture can include a RF power component configured to deliver RF power to a substrate support assembly and a RF power sensor configured to monitor the amount of RF power flowed through the component. Testing equipment  122  can further include an electron flow sensor (i.e., a current monitor) configured to monitor and generate a measurement value for an electron flow across an impedance circuit for the substrate support assembly during the testing process. The electron flow monitor can be separate from the testing fixture that includes the RF power component and the RF power sensor. Further details about testing fixtures are provided with respect to  FIG. 5 . 
     The client device  120  my include a computing device such as personal computers (PCs), laptops, mobile phones, smart phones, tablet computers, netbook computers, network connected televisions (“smart TVs”), network-connected media players (e.g., Blu-ray player), a set-top box, over-the-top (OTT) streaming devices, operator boxes, etc. In some embodiments, computer system architecture  100  can receive testing data for a substrate support assembly from client device  120 . For example, client device  120  can display a graphical user interface (GUI), where the GUI enables the user to provide, as input, testing data associated with a substrate support assembly. In some embodiments, the GUI of client device  120  can provide an indication of a quality rating for the substrate support assembly, in accordance with embodiments described herein. 
     Data store  140  can be a memory (e.g., random access memory), a drive (e.g., a hard drive, a flash drive), a database system, or another type of component or device capable of storing data. Data store  140  can include multiple storage components (e.g., multiple drives or multiple databases) that can span multiple computing devices (e.g., multiple server computers). The data store  140  can store data associated with testing a substrate support assembly. For example, data store  140  can store data collected by one or more testing data collection components  128  of testing equipment  122 . Testing data can refer to prior testing data (e.g.,) and/or current testing data (e.g., testing data generated for a current substrate support assembly tested at testing equipment  122 ). 
     Prior testing data refers to testing data generated for a prior substrate support assembly tested at testing equipment  122  according to a prior or prior testing process. For example, a prior substrate support assembly can be tested according to a prior testing process where a RF power component delivers power to an electrical component (e.g., an electrode, a heater) of the prior substrate support assembly. The RF power component can deliver various amounts of RF power to the component, or can deliver the RF power at various frequencies, in accordance with one or more operations of the prior testing process. A RF power sensor can be coupled to the electrical component and can monitor an amount of power flowed through the electrical component as RF power is delivered to the component. An electron flow sensor can be coupled to an impedance circuit for the prior substrate support assembly and can generate a measurement for the electron flow (i.e. the current) across the impedance circuit during the prior testing process. Data collected by the RF power sensor and the electron flow sensor can be stored at data store  140  as prior testing data. In some embodiments, data associated with the one or more operations of the prior testing process can also be stored at data store  140  as prior testing data. For example, an indication of each amount of RF power and/or a frequency of RF power delivered to the substrate support assembly component by the RF power component can be stored at data store  140  as prior testing data. 
     Current testing data refers to testing data generated for a current substrate support assembly that is tested according to a current testing process. In some embodiments, the current testing process for the current substrate support assembly can be performed at testing equipment  122 . In other or similar embodiments, the current testing process for the current electrical component can be performed at other testing equipment (i.e., testing equipment that is not part of computer system architecture  100 ). In such embodiments, the testing equipment may not include the same or similar test condition components or testing data collection components as included in testing equipment  122  of computing system  100 . For example, the testing equipment can include a testing fixture that includes a RF power component and a RF power sensor, but the testing equipment does not include an electron power sensor. In such example, a testing process can be performed for the electrical component of manufacturing equipment  122  (e.g., a substrate support assembly), as previously described. Data collected by the RF power sensor of the testing fixture can be stored at data store  140  as current testing data. In some embodiment, data associated with the one or more operations of the current testing process can also be stored at data store  140  as current testing data. For example, an indication of each amount of RF power and/or a frequency of RF power delivered to the current substrate support assembly component by the RF power component can be stored at data store  140  as current testing data. As the testing equipment does not include an electron flow sensor, the current testing data stored at data store  140  does not include a measurement value for an electron flow through an impedance circuit of the current substrate support assembly. 
     The data store  140  can also store contextual data associated with one or more substrate support assemblies that are tested in accordance with embodiment described herein. Contextual data can include an identifier for the substrate support assembly, an identifier for one or more electrical components of the substrate support assembly, an identifier for a recipe for constructing the substrate support assembly, an identifier for a testing process performed for the substrate support assembly, and so forth. Contextual data can refer to prior contextual data (e.g., contextual data associated with a prior testing process performed for a prior substrate support assembly) and/or current contextual data (e.g., contextual data associated with a current testing process performed for a current substrate support assembly). 
     In some embodiments, data store  140  can be configured to store data that is not accessible to a user of the manufacturing system. For example, testing data, contextual data, etc. for a substrate support assembly is not accessible to a user (e.g., an operator) of the manufacturing system and/or testing system. In some embodiments, all data stored at data store  140  can be inaccessible by the user of the system. In other or similar embodiments, a portion of data stored at data store  140  can be inaccessible by the user while another portion of data stored at data store  140  can be accessible by the user. In some embodiments, one or more portions of data stored at data store  140  can be encrypted using an encryption mechanism that is unknown to the user (e.g., data is encrypted using a private encryption key). In other or similar embodiments, data store  140  can include multiple data stores where data that is inaccessible to the user is stored in one or more first data stores and data that is accessible to the user is stored in one or more second data stores. 
     In some embodiments, predictive system  110  includes server machine  170  and server machine  180 . Server machine  170  includes a training set generator  172  that is capable of generating training data sets (e.g., a set of data inputs and a set of target outputs) to train, validate, and/or test a machine learning model  190 . Some operations of data set generator  172  are described in detail below with respect to  FIG. 7 . In some embodiments, the data set generator  172  can partition the training data into a training set, a validating set, and a testing set. In some embodiments, the predictive system  110  generates multiple sets of training data. 
     Server machine  180  can include a training engine  182 , a validation engine  184 , a selection engine  185 , and/or a testing engine  186 . An engine can refer to hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, processing device, etc.), software (such as instructions run on a processing device, a general purpose computer system, or a dedicated machine), firmware, microcode, or a combination thereof. Training engine  182  can be capable of training a machine learning model  190 . The machine learning model  190  can refer to the model artifact that is created by the training engine  182  using the training data that includes training inputs and corresponding target outputs (correct answers for respective training inputs). The training engine  182  can find patterns in the training data that map the training input to the target output (the answer to be predicted), and provide the machine learning model  190  that captures these patterns. The machine learning model  190  can use one or more of support vector machine (SVM), Radial Basis Function (RBF), clustering, supervised machine learning, semi-supervised machine learning, unsupervised machine learning, k-nearest neighbor algorithm (k-NN), linear regression, random forest, neural network (e.g., artificial neural network), etc. 
     The validation engine  184  can be capable of validating a trained machine learning model  190  using a corresponding set of features of a validation set from training set generator  172 . The validation engine  184  can determine an accuracy of each of the trained machine learning models  190  based on the corresponding sets of features of the validation set. The validation engine  184  can discard a trained machine learning model  190  that has an accuracy that does not meet a threshold accuracy. In some embodiments, the selection engine  185  can 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  can be capable of selecting the trained machine learning model  190  that has the highest accuracy of the trained machine learning models  190 . 
     The testing engine  186  can be capable of testing a trained machine learning model  190  using a corresponding set of features of a testing set from data set generator  172 . For example, a first trained machine learning model  190  that was trained using a first set of features of the training set can be tested using the first set of features of the testing set. The testing engine  186  can determine a trained machine learning model  190  that has the highest accuracy of all of the trained machine learning models based on the testing sets. 
     Predictive server  112  includes a predictive component  114  that is capable of providing a measurement value for an electron flow across an impedance circuit of a substrate support assembly. As described in detail below with respect to  FIG. 4 , in some embodiments, predictive component  114  is capable of providing testing data associated with a current testing process performed for a current substrate support assembly as and input to model  190  and obtain one or more outputs from model  190 . As described above, the data associated with the current testing process can include data indicating an amount of RF power flowed through an element (e.g., an electrical component) of a substrate support assembly during the current testing process. Predictive server  112  can extract a measurement value for an electron flow across an impedance circuit of the substrate support assembly form the one or more outputs obtained from model  190 . In response to determining that the extracted measurement value satisfies an electron flow criterion, predictive server  112  can assign a quality rating to the substrate support assembly that is associated with a high quality component. In response to determining that the extracted measurement value does not satisfy the electron flow criterion, predictive server  112  can assign a quality rating to the substrate support assembly that is associated with a low quality component. Predictive server  112  can store the quality rating of the substrate support assembly at data store  140 . 
     In some embodiments predictive server  112  can assign the quality rating to the substrate support assembly, as described above. In other embodiments, a component other than predictive server  112  can assign the quality rating to the substrate support assembly, based on the measurement value extracted from the output of model  190 . For example, predictive server  112  can extract the measurement value from the output of model  190 , in accordance with previously described embodiments, and can store the extracted measurement value at data store  140 . Another component of computer system architecture  100  (e.g., manufacturing equipment  124 , testing equipment  122 , client device  120 ) can assign the quality rating to the substrate support assembly and store the assigned quality rating at data store  140 , as previously described. 
     In some embodiments, predictive server  112  (or manufacturing equipment  124  or testing equipment  122 ) can transmit an indication of the assigned quality rating to client device  120 . Client device  120  can provide the indication of the assigned quality rating to a user of the manufacturing system and/or testing system (e.g., an operator) via the GUI of client device  120 . 
     The client device  120 , manufacturing equipment  124 , testing equipment  122 , predictive server  112 , data store  140 , server machine  170 , and server machine  180  can be coupled to each other via a network  130 . In some embodiments, network  130  is a public network that provides client device  120  with access to predictive server  112 , data store  140 , and other publically available computing devices. In some embodiments, network  130  is a private network that provides client device  120  access to manufacturing equipment  124 , testing equipment  122 , data store  140 , and other privately available computing devices. Network  130  can include one or more wide area networks (WANs), local area networks (LANs), wired networks (e.g., Ethernet network), wireless networks (e.g., an 802.11 network or a Wi-Fi network), cellular networks (e.g., a Long Term Evolution (LTE) network), routers, hubs, switches, server computers, cloud computing networks, and/or a combination thereof. 
     It should be noted that in some other implementations, the functions of server machines  170  and  180 , as well as predictive server  112 , can be provided by a fewer number of machines. For example, in some embodiments, server machines  170  and  180  can be integrated into a single machine, while in some other or similar embodiments, server machines  170  and  180 , as well as predictive server  112 , can be integrated into a single machine. 
     In general, functions described in one implementation as being performed by server machine  170 , server machine  180 , and/or predictive server  112  can also be performed on client device  120 . In addition, the functionality attributed to a particular component can be performed by different or multiple components operating together. 
     In embodiments, a “user” can be represented as a single individual. However, other embodiments of the disclosure encompass a “user” being an entity controlled by a plurality of users and/or an automated source. For example, a set of individual users federated as a group of administrators can be considered a “user.” 
       FIG. 2  is a top schematic view of an example manufacturing system  200 , according to aspects of the present disclosure. Manufacturing system  200  can perform one or more processes on a substrate  202 . Substrate  202  can be any suitably rigid, fixed-dimension, planar article, such as, e.g., a silicon-containing disc or wafer, a patterned wafer, a glass plate, or the like, suitable for fabricating electronic devices or circuit components thereon. 
     Manufacturing system  200  can include a process tool  204  and a factory interface  206  coupled to process tool  204 . Process tool  204  can include a housing  208  having a transfer chamber  210  therein. Transfer chamber  210  can include one or more process chambers (also referred to as processing chambers)  214 ,  216 ,  218  disposed therearound and coupled thereto. Process chambers  214 ,  216 ,  18  can be coupled to transfer chamber  210  through respective ports. Transfer chamber  210  can also include a transfer chamber robot  212  configured to transfer substrate  202  between process chambers  214 ,  216 ,  218 , load lock  220 , etc. 
     Process chambers  214 ,  216 ,  218  can be adapted to carry out any number of processes on substrates  202 . A same or different substrate process can take place in each processing chamber  214 ,  216 ,  218 . A substrate process can include atomic layer deposition (ALD), physical vapor deposition (PVD), chemical vapor deposition (CVD), etching, annealing, curing, pre-cleaning, metal or metal oxide removal, or the like. Other processes can be carried out on substrates therein. 
     A load lock  220  can also be coupled to housing  208  and transfer chamber  210 . Load lock  220  can be configured to interface with, and be coupled to, transfer chamber  210  on one side and factory interface  206 . Factory interface  206  can be any suitable enclosure, such as, e.g., an Equipment Front End Module (EFEM). Factory interface  206  can be configured to receive substrates  202  from substrate carriers  222  (e.g., Front Opening Unified Pods (FOUPs)) docked at various load ports  224  of factory interface  206 . A factory interface robot  226  (shown dotted) can be configured to transfer substrates  202  between carriers (also referred to as containers)  222  and load lock  220 . 
     Manufacturing system  200  can also include a system controller  228 . System controller  228  can be and/or include a computing device such as a personal computer, a server computer, a programmable logic controller (PLC), a microcontroller, and so on. System controller  228  can include one or more processing devices, which can be general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device can be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing device can also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. System controller  228  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. System controller  228  can execute instructions to perform any one or more of the methodologies and/or embodiments described herein. In some embodiments, system controller  228  can execute instructions to perform one or more operations at manufacturing system  200  in accordance with a process recipe. The instructions can be stored on a computer readable storage medium, which can include the main memory, static memory, secondary storage and/or processing device (during execution of the instructions). 
       FIG. 3  is a cross-sectional schematic side view of an example process chamber  300  of the example manufacturing system  200 , according to aspects of the present disclosure. In some embodiments, process chamber  300  can correspond to process chamber  214 ,  216 ,  218  described with respect to  FIG. 2 . Process chamber  300  can be used for processes in which a corrosive plasma environment is provided. For example, the process chamber  300  can be a chamber for a plasma etcher or plasma etch reactor, and so forth. In another example, process chamber can be a chamber for a deposition process, as previously described. In one embodiment, the process chamber  300  includes a chamber body  302  and a showerhead  330  that encloses an interior volume  306 . The showerhead  330  can include a showerhead base and a showerhead gas distribution plate. Alternatively, the showerhead  330  can be replaced by a lid and a nozzle in some embodiments, or by multiple pie shaped showerhead compartments and plasma generation units in other embodiments. The chamber body  302  can be fabricated from aluminum, stainless steel or other suitable material such as titanium (Ti). The chamber body  302  generally includes sidewalls  308  and a bottom  310 . An exhaust port  326  can be defined in the chamber body  302 , and can couple the interior volume  306  to a pump system  328 . The pump system  328  can include one or more pumps and throttle valves utilized to evacuate and regulate the pressure of the interior volume  306  of the process chamber  300 . 
     The showerhead  330  can be supported on the sidewall  308  of the chamber body  302 . The showerhead  320  (or lid) can be opened to allow access to the interior volume  306  of the process chamber  300 , and can provide a seal for the process chamber  300  while closed. A gas panel  358  can be coupled to the process chamber  300  to provide process and/or cleaning gases to the interior volume  306  through the showerhead  330  or lid and nozzle (e.g., through apertures of the showerhead or lid and nozzle). The showerhead  330  can include a gas distribution plate (GDP) and can have multiple gas delivery holes  332  (also referred to as channels) throughout the GDP. A substrate support assembly  348  is disposed in the interior volume  306  of the process chamber  300  below the showerhead  330 . The substrate support assembly  348  holds a substrate  202  during processing. 
     As described above, process chamber  300  can be used to perform a process in which plasma is generated (e.g., an etch process, a deposition process, etc.). Process chamber can include an electrode  322  that is configured to generate and/or maintain a plasma from gases provided by gas panel  358 . Electrode  322  can be coupled to a RF power source  324  that is configured to provide an RF power signal to electrode  322 . Electrode  322  can transmit the RF power signal from RF power source  324  to the interior volume  306  of process chamber  300  and generate a capacitively-coupled plasma therein. In some embodiments, electrode  322  can transmit the RF power signal to other components of process chamber  300 , such as substrate support assembly  348  and elements included in substrate support assembly  348 . As illustrated in  FIG. 3 , electrode  322  can be coupled to an exterior portion of process chamber  300  (e.g., embedded in a lid, etc.), in some embodiments. It should be noted that, in other or similar embodiments, electrode  322  can be included at any portion of process chamber  300  (e.g., embedded in the interior volume  306  of process chamber  300 , etc.). 
     The substrate support assembly  348  generally includes at least a substrate support  332 . The substrate support  332  may be a vacuum chuck, an electrostatic chuck, a susceptor, or other workpiece support surface. In some embodiments, the substrate support  332  can be an electrostatic chuck (referred to as electrostatic chuck  332  herein). The substrate support assembly  348  can additionally include heating elements  350  (e.g., in a heater assembly). The substrate support assembly  348  may also include a cooling base. The cooling base may alternately be separate from the substrate support assembly  348 . The substrate support assembly  348  may be removably coupled to a support pedestal  325 . The support pedestal  325 , which may include a pedestal base and a facility plate, is mounted to the chamber body  302 . The facility plate can be configured to accommodate electrical connections from the electrostatic chuck  332  and the heating elements  350 . The electrical connections from electrostatic chuck  332  and/or heating elements  350  can run externally or internally of substrate support assembly  348 . 
     Electrostatic chuck  332  has a mounting surface and a workpiece surface opposite the mounting surface. The electrostatic chuck  332  generally includes a chucking electrode  336  embedded in a dielectric body. The chucking electrode  336  can be configured as a mono polar or bipolar electrode, or other suitable arrangement. The chucking electrode  336  can be coupled through an RF filter to a chucking power source  338  which provides a radio frequency (RF) or direct current (DC) power to electrostatically secure the substrate  202  to the upper surface of the dielectric body. The RF filter prevents RF power utilized to form a plasma within the processing chamber  300  from damaging electrical equipment or presenting an electrical hazard outside the chamber. 
     In some embodiments, another electrode  340  can be embedded in substrate support assembly  348 . RF power can be provided to electrode  340  via an RF power supply  386 . RF power supply  386  can be coupled to electrode  340  via an impedance circuit  388 . Impedance circuit  388  can be configured to maintain a particular amount of RF power transmitted to an element of substrate support assembly  348  (e.g., chucking electrode  336 , heating elements  350 , etc.). In some embodiments, impedance circuit  388  can be a component of an auto capacitance tuner. RF power supply  386  can include one or more frequency generators configured to generate RF power at a particular frequency. In some embodiments, RF power supply  386  can include two or more frequency generators each configured to generate RF power at different frequencies (i.e., separately or simultaneously). 
     In some embodiments, impedance circuit  388  can enable the striking and sustaining of plasma in the interior volume  106  of process chamber  300 . In some embodiments, impedance circuit  388  can combine the RF power signals of various frequencies from RF power supply  386  and can transmit the combined RF power signal to electrode  340 . Electrode  340  can transmit the combined RF power signal to the process gas in interior volume  106  to generate and/or maintain a capacitively-coupled plasma therein. In some embodiments, electrode  322  and electrode  340  can both be configured to generate and maintain plasma in the interior volume  106 . In other or similar embodiments, electrode  340  can be configured to generate and maintain plasma in the interior volume  106  without electrode  322 . Chamber body  402  can be coupled to ground (not shown) and can provide an RF return path for facilitating generation of the plasma. 
     As described above, substrate support assembly  348  can include a heater assembly that includes heating elements  350 . Heating elements  350  can include one or more main resistive heating elements and/or multiple auxiliary heating elements embedded in a body (e.g., of the electrostatic chuck). The main resistive heating elements can be configured to elevate the temperature of the substrate support assembly  348  and the supported substrate  202  to a temperature specified in a process recipe. The auxiliary heating elements can be configured to provide localized adjustments to the temperature profile of the substrate support assembly  348  generated by the main resistive heating elements. Thus, the main resistive heating elements operate on a globalized macro scale while the auxiliary heating elements operate on a localized micro scale. In some embodiments, heating elements  350  can be coupled to a switching module (not shown) that includes one or more switching devices. The switching module can be coupled through an RF filter to a heater power source  356 . The switching devices in the switching module switch on and off the flow of power to the heating elements  350  based on signals received from a controller (e.g., system controller  238  of  FIG. 2 ). The power source  356  can provide up to 900 watts or more power to heating elements  350 . 
       FIG. 4  is a flow chart of a method for rating a substrate support assembly based on impedance circuit electron flow, according to aspects of the present disclosure. Method  400  is performed by processing logic that can include hardware (circuitry, dedicated logic, etc.), software (such as is run on a general purpose computer system or a dedicated machine), firmware, or some combination thereof. In one implementation, method  400  can 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  400  can be performed by one or more other machines not depicted in the figures. In some aspects, one or more operations of method  400  can be performed by predictive component  114  of predictive server  112 . 
     For simplicity of explanation, the methods are depicted and described as a series of acts. However, acts in accordance with this disclosure can occur in various orders and/or concurrently, and with other acts not presented and described herein. Furthermore, not all illustrated acts can be performed to implement the methods in accordance with the disclosed subject matter. In addition, those skilled in the art will understand and appreciate that the methods could alternatively be represented as a series of interrelated states via a state diagram or events. Additionally, it should be appreciated that the methods disclosed in this specification are capable of being stored on an article of manufacture to facilitate transporting and transferring such methods to computing devices. The term article of manufacture, as used herein, is intended to encompass a computer program accessible from any computer-readable device or storage media. 
     At block  410 , processing logic can optionally receive data associated with an amount of RF power flowed through an electrical component of a substrate support assembly from a testing fixture coupled to the substrate support assembly. In some embodiments, the testing fixture can be testing fixture  500  of  FIG. 5 . Testing fixture  500  can be configured to simulate the flow of RF power through electrical components (e.g., electrode  340 , electrode  336 , heating elements  350 , etc.) of a substrate support assembly during a process performed at a process chamber (e.g., process chamber  300 ). In some embodiments, testing fixture  500  can include a RF power test component  510 , one or more RF power input components  512 , and a RF power output component  514 . 
     RF power test component  510  can include a memory component configured to store one or more operations for a testing process to be performed for substrate support assembly  348  and a processing device configured to execute the one or more operations for the testing process. In some embodiments, RF power test component  510  can be or include components of a network analyzer device. RF power input component  512  can be configured to simulate one or more electrodes that flow RF power through process chamber  300 , as described above. For example, RF power input component  512  can include a RF power source and an electrode that is configured to simulate RF power source  324  and electrode  322  of  FIG. 3 , respectively. In some embodiments, RF power input component  512  can engage with a workpiece surface of electrostatic chuck  332  included in substrate support assembly  348  and can be configured to flow RF power through the electrical components (e.g., electrodes, heating elements, etc.) of substrate support assembly  348  in a similar fashion as provided by electrode  322 . 
     RF power output component  514  can include one or more sensors configured to collect data associated with RF power flowed through the electrical components of substrate support assembly  348 . For example, RF power output component  514  can include one or more sensors configured to detect an amount of RF power that is transmitted through each electrical component. In some embodiments, RF power output component  514  can be coupled directly to a respective electrical component that RF power output component  514  is configured to monitor. In other or similar embodiments, RF power output component  514  can be coupled to an electrical connection that is coupled to a respective electrical component. 
     As described previously, RF power input component  512  can flow RF power through the electrical components of substrate support assembly  348  in accordance with one or more operations of a testing process. As RF power input component  512  flows RF power through the electrical components, RF power output component  514  can collect data associated with the transmitted RF power and can transmit the collected data to RF power test component  510 . RF power test component  510  can receive the collected data from RF power output test component  514  and can generate testing data based on the collected data. In some embodiments, RF power test component  510  can generate one or more scattering parameter values (i.e., a parameter value corresponding to an amount of RF power loss) associated with a respective electrical component. 
     In some embodiments, the operations of the testing process for substrate support assembly  348  can include instructions to collect RF power testing data for the electrical components of substrate support assembly  348  for multiple different frequencies. RF power test component  510  can transmit instructions to RF power input component  512  to cause RF power to be flowed through the electrical components at each different frequency and can receive data collected by RF power output component  514  for each different frequency. A module of RF power test component  510  (e.g., a network analyzer) can generate scattering parameter values based on the received data, as previously described, and can generate a mapping between the scattering parameter values and an indication of the frequency applied to the electrical component when the RF power data associated with the scattering parameter values was collected. RF power test component  514  can store the generated mapping at the memory component of RF power test component and, in some embodiments, RF power test component  514  can transmit the generated mapping to another component (i.e., of testing equipment  122 ). 
     Referring back to  FIG. 4 , in some embodiments, processing logic may not receive data associated with the amount of RF power flowed through the electrical component of the substrate support assembly form the testing fixture and instead may receive the data from another component. For example, in some embodiments, testing data can be generated using components that are not included in computer system architecture  100  of  FIG. 1 . In such example, a user of a manufacturing system and/or a testing system can provide the testing data (e.g., scattering parameter values) to computer architecture  100  via a client device (e.g., client device  120 ) of computer architecture  100 , in accordance with previously described embodiments. 
     At block  412 , processing logic can provide the data associated with the amount of RF power flowed through the electrical component as input to a trained machine learning model. As described with respect to  FIG. 5 , the data associated with the amount of RF power flowed through the electrical component can include the one or more scattering parameter values generated by RF power test component  510  (e.g., by a network analyzer of RF power test component  510 ). In some embodiments, the data provided as input to the trained machine learning model can include the mapping between the scattering parameter values and the associated frequency, as previously described. 
     In some embodiments, the trained machine learning model can be model  190  described with respect to  FIG. 1 . Model  190  can be trained with an input-output mapping including an input and output. The input can be based on prior data associated with a prior amount of RF power flowed through a prior electrical component of a prior substrate support assembly during a prior testing process performed for the prior substrate support assembly. In some embodiments, the prior amount of RF power flowed through the prior electrical component can be detected by testing fixture  500  or a testing fixture similar to testing fixture  500 . The output can include an identification of a prior measurement value for an electron flow across a impedance circuit of the prior substrate support assembly as the RF power is flowed through the prior electrical component. In some embodiments, the prior measurement value can be generated by circuit test component  516  or a circuit test component similar to circuit test component  516 . Further details about circuit test component  516  are provided below. 
     At block  414 , processing logic can obtain one or more outputs of the trained machine learning model. In some embodiments, the one or more outputs can include a measurement value for a prior electron flow across a prior impedance circuit of a prior substrate support assembly during a prior testing process performed for the prior substrate support assembly. The one or more outputs can also include a level of confidence that the substrate support assembly being tested according to the current testing process at the manufacturing system is associated with the measurement value for the prior electron flow across the prior impedance circuit of the prior substrate support assembly. 
     At block  416 , the processing logic extracts the measurement value for the electron flow across the impedance circuit of the substrate support assembly from the one or more outputs. In some embodiments, the processing logic can extract the measurement value from the outputs of the trained machine learning model by determining that a level of confidence associated with a particular measurement value satisfies a confidence criterion. For example, the processing logic can determine that a particular measurement value is associated with a level of confidence that exceeds a threshold level of confidence and/or larger than a level of confidence associated with other measurement values of the one or more outputs. 
     At block  418 , processing logic can determine whether the extracted measurement value for the electron flow satisfies an electron flow criterion. In some embodiments, the extracted measurement value for the electron flow can satisfy the electron flow criterion if the extracted measurement value exceeds a threshold measurement value. In response to processing logic determining that the extracted measurement value satisfies the electron flow criterion, method  400  can continue to block  420 . In response to processing logic determining that the extracted measurement value does not satisfy the electron flow criterion, method  400  can continue to block  422 . 
     At block  420 , processing logic can assign a first quality rating to the substrate support assembly responsive to a determination that the extracted measurement value satisfies the electron flow criterion. At block  422 , processing logic can assign a second quality rating to the substrate support assembly responsive to a determination that the extracted measurement value satisfies the electron flow criterion. In some embodiments, the first quality rating can correspond to a higher quality substrate support assembly than a respective substrate support assembly associated with the second quality rating. 
     In some embodiments, processing logic can transmit an indication of the assigned first quality rating or the assigned second quality rating to a client device coupled to the testing system. The client device can be client device  120  of  FIG. 1 , in some embodiments. In response to receiving the quality rating assigned to the substrate support assembly, the client device can provide an indication of the quality rating to a user (e.g., an operator) of the testing system via a GUI of the client device. 
       FIG. 6  illustrates an example GUI  600  provided by a client device (e.g., client device  120 ) coupled to the testing system. GUI  600  includes a first portion configured to enable a user to provide power data for an electrical component of substrate support assembly  348  to the testing system. As illustrated in  FIG. 6 , the first portion of GUI  600  includes a field  610  that enables a user to provide a name of a file that includes the power data for an electrical component. In some embodiments, the file can include one or more scattering parameter values generated for the electrical component at various frequencies as RF power was flowed through the electrical component. The first portion of GUI  600  further includes an element  612  that enables the user to upload the file to the testing system. In some embodiments, in response to a user engaging with element  612 , the file including the power data for the electrical component is transmitted to predictive server  112 . One or more components of the testing system (e.g., predictive server  112 ) can provide the power data for the electrical component included in the file as an input to the trained machine learning model and can assign a quality rating to the substrate support assembly, in accordance with previously described embodiments. In some embodiments, the one or more components of the testing system does not assign the quality rating to the substrate support assembly and instead obtains the measurement for the electron flow through the impedance circuit of the substrate support assembly, as previously described. 
     In response to the one or more components of the testing system assigning a quality rating to the substrate support assembly, the one or more components can transmit the assigned quality rating and/or an indication of the electron flow measurement to the client device. In some embodiments, the client device receives the indication of the electron flow measurement and determines the quality rating based on the received indication. For example, the client device can determine that a first quality rating is to be assigned to the substrate support assembly in response to determining that the electron flow measurement exceeds a threshold measurement value. 
     The client device can update GUI  600  to provide information associated with the quality rating of the substrate support assembly. For example, GUI  600  can include a second portion that includes a field  614  that provides an identifier of the electrical component (e.g., “component  1 ”). GUI  600  can further include a field  616  that provides an indication of the electron flow measurement that was extracted from the one or more outputs of the trained machine learning model. In some embodiments, GUI  600  can further include a field  618  that provides an indication of a quality rating for the substrate support assembly based on the extracted electron flow measurement. In some embodiments, GUI  600  can include both fields  616  and  618 . In other or similar embodiments, GUI  600  can include field  616  or field  618 . 
     Referring back to  FIG. 4 , in some embodiments, processing logic can store the assigned quality rating for the substrate support assembly at a data store of the testing system (e.g., data store  140 ). In some embodiments, one or more components of computer architecture  100  can determine whether to include the respective substrate support assembly as part of manufacturing equipment  124  for a manufacturing system (e.g., manufacturing system  200 ) based on the assigned quality rating for the substrate support assembly. For example, a component of computer architecture  100  (e.g., a processing device of testing equipment  122 , manufacturing equipment  124 , etc.) can determine that a particular substrate support assembly assigned to a second quality rating is not to be installed at a process chamber for manufacturing system  200 . In another example, a component of computer system architecture  100  can determine that a substrate support assembly assigned to a first quality rating is to be installed at a process chamber for processes with strict process constraints and a substrate support assembly assigned to a second quality rating is to be installed at a process chamber for processes with less strict process constraints. 
       FIG. 7  is a flow chart of a method  700  for training a machine learning model, according to aspects of the present disclosure. Method  700  is performed by processing logic that can include hardware (circuitry, dedicated logic, etc.), software (such as is run on a general purpose computer system or a dedicated machine), firmware, or some combination thereof. In one implementation, method  700  can 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  can be performed by one or more other machines not depicted in the figures. In some aspects, one or more operations of method  700  can be performed by training set generator  172  of server machine  170 . 
     For simplicity of explanation, the methods are depicted and described as a series of acts. However, acts in accordance with this disclosure can occur in various orders and/or concurrently, and with other acts not presented and described herein. Furthermore, not all illustrated acts can be performed to implement the methods in accordance with the disclosed subject matter. In addition, those skilled in the art will understand and appreciate that the methods could alternatively be represented as a series of interrelated states via a state diagram or events. Additionally, it should be appreciated that the methods disclosed in this specification are capable of being stored on an article of manufacture to facilitate transporting and transferring such methods to computing devices. The term article of manufacture, as used herein, is intended to encompass a computer program accessible from any computer-readable device or storage media. 
     At block  710 , processing logic initializes a training set T to an empty set (e.g.,  0 ). At block  712 , processing logic obtains data associated with prior RF power flowed through a prior electrical component of a prior substrate support assembly. In some embodiments, processing logic can obtain data associated with the prior RF power using a testing fixture, such as testing fixture  500  of  FIG. 5 . Processing logic can obtain the data associated with prior RF power through other techniques, in other or similar embodiments. 
     At block  714 , processing logic obtains a measurement value for a prior electron flow across an impedance circuit of the prior substrate support assembly. In some embodiments, processing logic can obtain the measurement value from testing equipment coupled to the prior substrate support assembly during performance of the prior testing process. For example, a prior testing process can be performed for a prior substrate support assembly, as previously described. During the prior testing process, a testing fixture (e.g., testing fixture  500 ) generate testing data associated with the prior RF power flowed through the electrical components of the prior substrate support assembly. A circuit test component (e.g., circuit test component  516  of  FIG. 5 ) can be coupled to an impedance circuit  388  during the prior testing process. Circuit test component  516  can be configured to collect data associated with an electron flow across the impedance circuit during the prior testing process. In some embodiments, circuit test component  516  can be configured to generate a measurement for a current across the impedance circuit of the substrate support assembly. Circuit testing component  516  can collect the data associated with the electron flow and can transmit the collected data to a processing device of computer architecture  100 . The processing device (e.g., of testing equipment  122 ), can store the generated measurement at a data store (e.g., data store  140 ). 
     At block  716 , processing logic generates first training data based on the data associated with the prior RF power flowed through the prior electrical component. At block  718 , processing logic generates second training data based on the measurement value for the prior electron flow across the impedance circuit. At block  720 , processing logic generates a mapping between the first training data and the second training data. The mapping refers to the first training data that includes or is based on the data associated with the prior RF power flowed through the prior electrical component and the second training data that includes or is based on the measurement value for the prior electron flow across the impedance circuit, where the first training data is associated with (or mapped to) the second training data. At block  722 , processing logic adds the mapping to training set T. 
     At block  724 , processing logic determines whether the training set, T, includes a sufficient amount of training data to train a machine learning model. It should be noted that in some implementations, the sufficiency of training set T can be determined based simply on the number of mappings in the training set, while in some other implementations, the sufficiency of training set T can be determined based on one or more other criteria (e.g., a measure of diversity of the training examples, etc.) in addition to, or instead of, the number of input/output mappings. Responsive to determining the training set does not include a sufficient amount of training data to train the machine learning model, method  700  returns to block  712 . Responsive to determining the training set, T, includes a sufficient amount of training data to train the machine learning model, method  700  continues to block  726 . 
     At block  726 , processing logic provides training set T to train the machine learning model. In one implementation, the training set T is provided to training engine  182  of 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 the output nodes of the neural network. The connection weights in the neural network are then adjusted in accordance with a learning algorithm (e.g., backpropagation, etc.), and the procedure is repeated for the other input/output mappings in the training set T. After block  726 , machine learning model  190  can be used to predict a measurement value for an electron flow across an impedance circuit of a substrate support assembly, in accordance with embodiments described above. 
       FIG. 8  depicts a block diagram of an illustrative computer system operating in accordance with one or more aspects of the present disclosure. In alternative embodiments, the machine can be connected (e.g., networked) to other machines in a Local Area Network (LAN), an intranet, an extranet, or the Internet. The machine can operate in the capacity of a server or a client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine can be a personal computer (PC), a tablet computer, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a server, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines (e.g., computers) that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. In embodiments, computing device  800  can correspond to predictive server  112  of  FIG. 1  or another processing device of system  100 . 
     The example computing device  800  includes a processing device  802 , a main memory  804  (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM), etc.), a static memory  806  (e.g., flash memory, static random access memory (SRAM), etc.), and a secondary memory (e.g., a data storage device  828 ), which communicate with each other via a bus  808 . 
     Processing device  802  can represent one or more general-purpose processors such as a microprocessor, central processing unit, or the like. More particularly, the processing device  802  can be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, processor implementing other instruction sets, or processors implementing a combination of instruction sets. Processing device  802  can also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. Processing device  802  can also be or include a system on a chip (SoC), programmable logic controller (PLC), or other type of processing device. Processing device  802  is configured to execute the processing logic for performing operations and steps discussed herein. 
     The computing device  800  can further include a network interface device  822  for communicating with a network  864 . The computing device  800  also can include a video display unit  810  (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device  812  (e.g., a keyboard), a cursor control device  814  (e.g., a mouse), and a signal generation device  820  (e.g., a speaker). 
     The data storage device  828  can include a machine-readable storage medium (or more specifically a non-transitory computer-readable storage medium)  824  on which is stored one or more sets of instructions  826  embodying any one or more of the methodologies or functions described herein. Wherein a non-transitory storage medium refers to a storage medium other than a carrier wave. The instructions  826  can also reside, completely or at least partially, within the main memory  804  and/or within the processing device  802  during execution thereof by the computer device  800 , the main memory  804  and the processing device  802  also constituting computer-readable storage media. 
     The computer-readable storage medium  824  can also be used to store model  190  and data used to train model  190 . The computer readable storage medium  824  can also store a software library containing methods that call model  190 . While the computer-readable storage medium  824  is shown in an example embodiment to be a single medium, the term “computer-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “computer-readable storage medium” shall also be taken to include any medium that is capable of storing or encoding a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure. The term “computer-readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media. 
     The preceding description sets forth numerous specific details such as examples of specific systems, components, methods, and so forth in order to provide a good understanding of several embodiments of the present disclosure. It will be apparent to one skilled in the art, however, that at least some embodiments of the present disclosure can be practiced without these specific details. In other instances, well-known components or methods are not described in detail or are presented in simple block diagram format in order to avoid unnecessarily obscuring the present disclosure. Thus, the specific details set forth are merely exemplary. Particular implementations can vary from these exemplary details and still be contemplated to be within the scope of the present disclosure. 
     Reference throughout this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrase “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. In addition, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” When the term “about” or “approximately” is used herein, this is intended to mean that the nominal value presented is precise within ±10%. 
     Although the operations of the methods herein are shown and described in a particular order, the order of operations of each method can be altered so that certain operations can be performed in an inverse order so that certain operations can be performed, at least in part, concurrently with other operations. In another embodiment, instructions or sub-operations of distinct operations can be in an intermittent and/or alternating manner. 
     It is understood that the above description is intended to be illustrative, and not restrictive. Many other embodiments will be apparent to those of skill in the art upon reading and understanding the above description. The scope of the disclosure should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.