Method and system for manufacturing spectral library at system

Abstract

Spectral data associated with one or more regions of a surface of a substrate is identified. A substrate has been processed according to one or more first operations of a process recipe unknown to a system controller of a manufacturing system. The spectral data is provided as input to a machine learning model that is trained to predict a respective process recipe associated with the substrate and one or more operations of the respective process recipe that have been performed based on a given spectral data. Based on one or more outputs of the machine learning model, it is determined that the substrate is associated with the process recipe and one or more second operations are still to be performed. A second operation is caused to process the substrate according to one or more of the process formulations.

Description

Technical Field

[0001] Embodiments of the present disclosure relate generally to manufacturing systems, and more particularly to methods and systems for use with spectral libraries at manufacturing systems. Background Art

[0002] Substrate processing may include a series of processes performed in one or more process chambers of a manufacturing system. For example, a substrate may be processed according to a deposition process, an etching process, etc. In some cases, a process chamber may stop operating during the performance of a substrate process. For example, a manufacturing system including a process chamber performing a substrate process may lose power and the substrate process may unexpectedly terminate at the process chamber. When power is restored to the manufacturing system, a system controller of the manufacturing system may not be aware of the specific substrate at the process chamber and / or the substrate process being performed on the substrate when the process tool lost power.

[0003] In some cases, conditioning operations may be performed at a process chamber to bring the process chamber to a condition suitable for a substrate process. For example, performing a substrate process may cause wear to process chamber components of the process chamber and / or to interior surfaces of the process chamber. Substrate processing may be temporarily paused at a process chamber for a period of time so that a maintenance process (e.g., a preventive maintenance process, a corrective maintenance process, etc.) may be performed to correct and / or mitigate wear or damage to process chamber components and / or interior of the process chamber. One or more conditioning operations may be performed as part of the maintenance process. Once the maintenance process is completed and the process chamber is restored to a condition suitable for a substrate process, the process chamber may continue substrate processing. Summary of the invention

[0004] Some of the embodiments encompass methods and systems for preventing misprocessing of a substrate at a manufacturing system. The method includes identifying spectral data associated with a substrate processed at a manufacturing system according to one or more first operations of a process recipe. The process recipe is unknown to a system controller of the manufacturing system. The method further includes determining a process recipe associated with a substrate based on the identified spectral data. The method further includes identifying one or more second operations of the process recipe for the substrate. The method further includes causing the substrate to be processed according to the one or more second operations of the process recipe.

[0005] Some of the embodiments encompass methods and systems for monitoring a conditioning process at a process chamber of a manufacturing system. The method includes identifying spectral data associated with a first substrate at the manufacturing system. The first substrate has been processed at a process chamber of the manufacturing system according to one or more first operations of a chamber conditioning process for the process chamber. The method further includes determining a condition of the process chamber after execution of the one or more first operations based on the identified spectral data. The method further includes determining whether the condition of the process chamber satisfies one or more chamber condition criteria. The method further includes updating one or more second operations of the chamber conditioning process to cause the condition of the process chamber to satisfy the one or more chamber condition criteria in response to determining that the condition of the process chamber fails to satisfy the one or more chamber condition criteria. The method further includes causing a second substrate to be processed at the process chamber according to the updated one or more second operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] The present disclosure is illustrated by way of example and not limitation in the figures of the accompanying drawings in which like reference numerals indicate similar elements. It should be noted that different references to "one" or "an" embodiment in the present disclosure do not necessarily represent the same embodiment, and such references mean at least one.

[0007] Figure 1 An illustrative computer system architecture is depicted in accordance with aspects of the present disclosure.

[0008] Figure 2 is a top view schematic diagram of an example manufacturing system according to aspects of the present disclosure.

[0009] Figure 3 is a cross-sectional schematic side view of a substrate measurement subsystem according to aspects of the present disclosure.

[0010] Figure 4 An example process identifier engine is depicted in accordance with aspects of the present disclosure.

[0011] Figure 5 is a flow chart of an example method for preventing mishandling of substrates according to aspects of the present disclosure.

[0012] Figure 6 Flowchart of an example method for training a machine learning model according to aspects of the present disclosure.

[0013] Figure 7 An example chamber regulation engine is depicted according to aspects of the present disclosure.

[0014] Figure 8is a flow chart of an example method for monitoring one or more conditioning operations at a process chamber according to aspects of the present disclosure.

[0015] Fig. 9 Flowchart of another example method for training a machine learning model according to aspects of the present disclosure.

[0016] Fig.10 A block diagram depicts an illustrative computer system that operates in accordance with one or more aspects of the present disclosure. DETAILED DESCRIPTION

[0017] Embodiments described herein provide methods and systems for a spectral library at a manufacturing system. In particular, embodiments described herein provide methods and systems for preventing mishandling of substrates at a manufacturing system. Embodiments described herein additionally or alternatively provide methods and systems for monitoring a conditioning process at a process chamber of a manufacturing system.

[0018] Substrate processing may include a series of processes for producing circuits in semiconductors (e.g., silicon wafers, etc.) according to circuit designs. Such substrate processes may be performed using substrates placed in one or more process chambers at a process tool of a manufacturing system. Substrate processes may include deposition processes, etching processes, and the like. Deposition processes involve depositing one or more material layers on the surface of a substrate placed in a process chamber. Etching processes involve transferring the pattern of a mask material to one or more material layers on the surface of a substrate. For example, after placing a substrate in a process chamber, an etching plasma may flow into the process chamber and may remove (e.g., etch) portions of the material layer on the surface of the substrate that is not covered by the mask material. The portion of the layer that remains after the etching process may have a pattern that is the same as or similar to the pattern of the mask material. As mentioned herein, production substrates include any substrates processed according to a substrate process (e.g., an etching process, a deposition process, etc.).

[0019] In some cases, a system controller of a manufacturing system may lose track of one or more substrates being processed at the manufacturing system. For example, while a substrate process is being performed on a substrate at a process chamber, the manufacturing system may lose power (e.g., due to a weather event, a system failure, etc.). Prior to the power outage, the state of the substrates and / or process chambers at the manufacturing system may not have been stored in a memory for the system controller. Accordingly, when power is restored to the manufacturing system, the system controller may not know which substrate is located at each process chamber and / or which operations of the substrate process were performed on the substrates prior to the power outage. In another example, while one or more substrates are being processed at the manufacturing system, a computing system of the system controller may be reset or restarted (e.g., due to a failure at the computing system, to perform an emergency update of software or firmware running at the computing system, etc.). After resetting or restarting the computing system, the system controller may not know the state of the substrates and / or process chambers, as indicated above.

[0020] In some manufacturing systems, a substrate may include one or more identification marks that are unique for each respective substrate. For example, a unique serial number may be printed or otherwise formed on the surface of a substrate to distinguish the substrate from other substrates at the manufacturing system. Such an identification mark is referred to herein as a substrate identifier (ID). After the system controller loses track of one or more substrates, as described above, the substrate at the process chamber may be transferred to a substrate identifier station that scans the substrate surface to obtain a substrate ID. After identifying the substrate ID, the substrate identifier (ID) station may transmit an indication of the substrate ID to the system controller. The system controller may identify a process recipe for the substrate based on the substrate ID, and may determine whether to continue processing the substrate according to the process recipe (e.g., by performing one or more operations to be performed if the system controller does not lose track of the substrate) or terminate the processing of the substrate and start a substrate process for another substrate (e.g., replacing the substrate).

[0021] The internal environment of the process chamber and / or other stations of the process tool may be under vacuum and / or may be highly controlled to prevent oxygen, moisture, etc. from affecting the quality of the substrate processed at the chamber. The substrate ID station may be outside the process chamber and / or the process tool, and accordingly, when being transferred to the substrate ID station, the substrate is removed from the internal environment of the process tool. In some cases, exposing the substrate to the external environment of the process tool before each operation of the substrate process is completed will damage the substrate. The substrate may become unavailable, and the system controller may start a substrate process for one or more other substrates to replace the damaged substrate. In other or similar cases, the substrate ID station may be or otherwise include a substrate ID scanner device. The operator of the manufacturing system may open the lid of the process chamber including the substrate and use the substrate ID scanner device to scan the substrate ID on the substrate surface. By opening the lid to the process chamber, the substrate and the internal environment of the process chamber are exposed to the external environment of the process tool, which may damage the substrate and / or introduce undesirable particles (e.g., oxygen, moisture, etc.) into the process chamber. The substrate may become unavailable, as described above. In some cases, one or more conditioning operations may be performed at the process chamber (e.g., after removing the substrate) to return the process chamber to conditions suitable for processing the substrate. Performing substrate processes on additional substrates (e.g., to replace a damaged substrate) and / or performing process chamber conditioning operations may reduce the overall throughput and efficiency of the manufacturing system and may increase the overall latency of the manufacturing system.

[0022] In some embodiments, chamber conditioning operations may be performed at a process chamber as part of an initialization process and / or a maintenance process for the process chamber. An initialization process represents a process for preparing a process chamber to perform a particular substrate process (e.g., an etching process, a deposition process, etc.). An initialization process may be performed when a process chamber is used for the first time at a process tool and / or when a process chamber was previously used for a particular substrate process (e.g., a deposition process) and is now to be used for a different substrate process (e.g., an etching process). A maintenance process may be performed periodically to correct and / or reduce wear or damage to process chamber components and / or an interior of the process chamber. The maintenance process may be a preventative maintenance (PM) process and / or a corrective maintenance (CM) process. A PM process represents a maintenance process performed according to a routine maintenance schedule to maintain one or more components of a process chamber and / or reduce wear on an interior of a process chamber. A CM process represents a maintenance process used to correct or mitigate failures of one or more process chamber components detected before, during, or after completion of a substrate process (eg, between executions of a scheduled PM process).

[0023] The chamber conditioning operation may involve etching one or more substrates (e.g., blank silicon wafers or dummy wafers) to bring the interior of the process chamber to conditions suitable for substrate processing. The substrate processed during the chamber conditioning operation is referred to herein as a chamber conditioning substrate or simply a conditioning substrate. The substrate processed during the substrate process (e.g., for producing an electrical device, etc.) is referred to herein as a production substrate. In some systems, metrology is used to determine whether the interior of the process chamber is in a condition suitable for production substrate processing. For example, after the chamber conditioning operation is completed, the conditioning substrate is transferred from the process chamber to a metrology device. The metrology device may be located outside the process tool including the process chamber, and the conditioning substrate is removed from the vacuum environment of the process tool during transfer to the metrology device. The metrology device performs metrology measurements (e.g., critical dimension (CD) measurements, etch rate measurements, etc.) of the conditioning substrate, which may be used (e.g., by a system controller) to determine whether the chamber conditions are met (e.g., whether the obtained metrology measurements correspond to the target metrology measurements of the chamber). Subsequent conditioning operations may be performed at the process chamber until chamber conditions are met and / or a threshold number of conditioning substrates are processed. Once chamber conditions are met and / or a threshold number of conditioning substrates are processed, it is determined that the process chamber is to be restored to conditions suitable for substrate processing and the process chamber may be used for production substrate processing.

[0024] The amount of time between taking a process chamber offline to perform a conditioning operation and bringing the process chamber back online for production substrate processing is referred to as the green-to-green (G2G) time. As the amount of G2G time for a process chamber increases, the total number of production substrates processed by the process chamber decreases, which reduces overall efficiency and throughput, and increases overall latency for the process chamber and manufacturing system. As indicated above, for each conditioning operation performed in a process chamber, a conditioning substrate is removed from the process chamber and transferred to a metrology device for measurement, which may be external to the process tool. Transferring the conditioning substrate to the metrology device and obtaining metrology measurements of the conditioning substrate may take a significant amount of time, which increases the G2G time for the process chamber. In some systems, additional conditioning operations may be performed at the process chamber for subsequent conditioning substrates while an initial conditioning substrate is being transferred from the process chamber and measured by an external metrology device. After the conditioning operation for the initial conditioning substrate, the process chamber may have been restored to conditions suitable for processing production substrates, and therefore additional conditioning operations initiated while transferring and measuring the initial conditioning substrate may be unnecessary. The additional conditioning operations may thus increase the G2G time of the process chamber and unnecessarily consume resources (eg, processing resources, computing resources, etc.) of the manufacturing system, which may further reduce efficiency and throughput, and increase overall latency of the manufacturing system.

[0025] Aspects of the present disclosure address the above and other shortcomings by providing methods and systems for a spectral library at a manufacturing system. Spectral data of a substrate (e.g., a production substrate, a conditioning substrate, etc.) may be collected before, during, or after a process is performed at a process chamber. In some embodiments, spectral data may be collected by a substrate measurement subsystem, such as, Figure 3 The spectral data may include or otherwise correspond to the intensity (i.e., the strength or amount of energy) of each wavelength of the detected energy waves reflected from the substrate surface. According to embodiments described herein, the spectral data may be used to prevent mishandling of substrates at a manufacturing system and / or to monitor a conditioning process at a process chamber of a manufacturing system.

[0026] In some embodiments, the production substrate may be transferred to a process chamber for processing according to a substrate process (e.g., an etching process, a deposition process, etc.). Before, during, or after performing the substrate process, the system controller of the manufacturing system may lose track of the production substrate and may become unaware of the type of production substrate, the process recipe associated with the production substrate, and / or one or more operations of the process recipe that have been performed and / or will be performed for the production substrate. In an illustrative example, the manufacturing system may be powered off before, during, or after the substrate process. In another example, the system controller may be reset or restarted (e.g., by an operator of the manufacturing system) before, during, or after the substrate process. In some embodiments, the substrate measurement subsystem may collect spectral data of one or more regions of the substrate surface. In some embodiments, the substrate measurement subsystem may be configured to collect spectral data of the substrate while the substrate is located at the process chamber. In other or similar embodiments, the substrate measurement subsystem may be included in or otherwise connected to an area of ​​a process tool (e.g., a transfer chamber) having an internal environment that is in the same or similar conditions as the process chamber (e.g., under vacuum, with minimal oxygen, moisture, etc.). The substrate may be transferred from the process chamber to the substrate metrology subsystem for measurement. In such an embodiment, the substrate may be measured in the controlled environment of the process tool and is not removed from the process tool and exposed to the external environment for measurement.

[0027] The substrate measurement subsystem may collect spectral data of a production substrate and may transmit the collected spectral data (e.g., via a network) to a system controller or another computing system of a manufacturing system. In some embodiments, the system controller may determine a process recipe associated with a production substrate based on the collected spectral data and the spectral data of a spectral library. The spectral library may include spectral data collected for a previous substrate processed according to one or more previous substrate processes at the manufacturing system. In some embodiments, the system controller may compare the spectral data collected for the production substrate with the spectral data of the spectral library, and may identify the previous spectral data corresponding to the collected spectral data. After identifying the previous spectral data corresponding to the collected spectral data, the system controller may determine a previous substrate for which the previous spectral data was collected. In some embodiments, the spectral library may indicate one or more operations of a process recipe of a process performed on a previous substrate and / or a process performed on a previous substrate and / or yet to be performed. In other or similar embodiments, the spectral library may indicate an identifier associated with a previous substrate. In such an embodiment, the system controller may determine the operation of a process performed on a previous substrate and / or a process recipe for the process based on data stored at a memory for the manufacturing system. In response to determining a process recipe associated with producing a substrate and / or one or more operations to be performed and / or yet to be performed on the substrate based on the collected spectral data, the system controller may cause the substrate to be processed according to the one or more operations yet to be performed.

[0028] The spectral library may include spectral data collected for a large number of previous substrates processed at a manufacturing system (e.g., thousands, tens of thousands, hundreds of thousands, etc.). In some embodiments, the data of the spectral library may be used to train a machine learning model to predict a process recipe associated with a production substrate and / or one or more operations that have been performed and / or are yet to be performed for a production substrate based on given spectral data collected for the substrate. Additional details regarding the spectral library and the machine learning model are provided herein.

[0029] In additional or alternative embodiments, one or more conditioning operations may be performed at a process chamber of a manufacturing system using one or more conditioning substrates. In illustrative examples, in some embodiments, the conditioning operation(s) may be performed by etching the conditioning substrate at the process chamber. The substrate measurement system may collect spectral data associated with one or more regions of the conditioning substrate(s) before, during, and / or after the performance of the conditioning operation(s). As described above, the substrate measurement subsystem may be configured to collect spectral data of the substrate while the substrate is disposed in the process chamber. In such embodiments, the substrate measurement subsystem may collect spectral data of the substrate while the substrate is at the process chamber (e.g., before, during, or after the performance of the conditioning operation(s). In other or similar embodiments, after the conditioning operation is performed at the process chamber, the substrate may be transferred (e.g., by one or more robotic arms) to the substrate measurement subsystem for measurement. In any embodiment, the substrate measurement subsystem may transmit the spectral data collected for the conditioning substrate to a system controller or another computing system of the manufacturing system.

[0030] A system controller or another computing system may determine a condition of a process chamber before, during, or after the execution of (several) adjustment operations based on spectral data collected for the substrate. The spectral data may indicate a condition or state of the substrate before, during, or after the execution of (several) adjustment operations. The condition or state of the substrate may correspond to a condition or state of the process chamber before, during, or after the (several) operations are performed. Accordingly, the spectral data of the substrate may indicate a condition of the chamber before, during, or after the execution of (several) adjustment operations. In some embodiments, the system controller may determine the condition of the process chamber based on the collected spectral data and the spectral data of the spectral library. In some embodiments, the spectral library may include spectral data collected for previously adjusted substrates processed according to one or more previous adjustment operations at a process chamber of a manufacturing system. In other or similar embodiments, the spectral library may be the same or similar to the library described above. The system controller may compare the spectral data collected for (several) adjustment substrates with previous spectral data collected for previously adjusted substrates processed according to one or more previous adjustment operations at a manufacturing system, and may identify previous spectral data corresponding to the collected spectral data. After identifying the previous spectral data corresponding to the collected spectral data, the system controller may determine a previous conditioning substrate for which the previous spectral data was collected. In some embodiments, the spectral library may indicate the conditions of the previous conditioning substrate and / or the conditions of the process chamber in which the previous conditioning substrate was processed. In some embodiments, the system controller may determine the conditions of the process chamber used to process the current conditioning substrate based on the conditions of the previous conditioning substrate and / or the conditions of the process chamber in which the previous conditioning substrate was processed.

[0031] After determining the condition of the process chamber based on the spectral data collected for (several) adjustment substrates processed at the process chamber, the system controller may determine whether the condition of the process chamber satisfies one or more chamber condition criteria (e.g., whether the process chamber is under conditions suitable for processing production substrates). If the system controller determines that the one or more chamber condition criteria are satisfied, the system controller may determine that the initialization process and / or maintenance process for the process chamber is completed, and the system controller may start processing of the production substrate at the process chamber. If the system controller determines that the criteria are not satisfied, the system controller may update the initialization process and / or maintenance process based on the determined conditions of the chamber. In one example, the system controller may determine that one or more additional adjustment operations are to be performed for the process chamber based on the collected spectral data. In another example, the system controller may determine that the settings of one or more additional adjustment operations are to be modified to cause the condition of the chamber to correspond to the target condition based on the collected spectral data. In some embodiments, the system controller may cause the operation of the updated initialization process and / or maintenance process to be performed at the process chamber.

[0032] As indicated above, the spectral library may include spectral data collected for a large number of conditioning substrates processed at a manufacturing system (e.g., thousands, tens of thousands, hundreds of thousands, etc.). In some embodiments, the data from the spectral library may be used to train a machine learning model to predict the condition of the process chamber before, during, or after a conditioning operation for the conditioning substrate based on given spectral data collected for the conditioning substrate. In some embodiments, the system controller may feed (e.g., continuously) the spectral data collected for the conditioning substrate processed at the process chamber as input to the trained machine learning model. Once the system controller determines that the condition of the process chamber satisfies one or more chamber condition criteria based on one or more outputs of the trained model, the system controller may determine that the initialization process and / or maintenance process for the process chamber is complete, as described above. Additional details regarding the spectral library and the machine learning model are provided herein.

[0033] Aspects of the present disclosure address shortcomings of conventional techniques by providing techniques for preventing mishandling of substrates at a manufacturing system and monitoring a conditioning process at a process chamber of a manufacturing system. A system controller (or another computing system) that has lost track of one or more substrates (e.g., production substrates) at a manufacturing system may use spectral data collected by one or more substrates (e.g., by a substrate measurement subsystem) to determine a process recipe associated with (several) substrates and / or one or more operations that have been performed and / or are yet to be performed for (several) substrates. Spectral data may be collected by a substrate measurement subsystem without removing the substrate from a process tool and exposing it to the external environment of the process tool. The system controller may determine the correct process recipe for (several) substrates and continue the substrate process without damaging the (several) substrates, and perform fewer substrate processes on additional substrates at the manufacturing system (e.g., to replace damaged substrates). Accordingly, the overall efficiency and throughput of the manufacturing system are increased, and the overall delay of the manufacturing system is reduced.

[0034] According to additional aspects of the present disclosure, a system controller (or another computing system) may use spectral data collected for a substrate (e.g., to condition a substrate) to determine a condition of a process chamber during an initialization process and / or a maintenance process. The system controller may further determine, based on the determined conditions, whether the initialization process and / or the maintenance process is complete or whether operations of such processes are to be updated to cause the condition of the process chamber to correspond to a target condition. As indicated above, spectral data may be collected by a substrate measurement subsystem without removing the substrate from a process tool (e.g., for measurement by an external metrology device). By using spectral data to determine the condition of the process chamber, the system controller may determine the condition of the process chamber in a shorter time and / or after performing fewer conditioning operations. Accordingly, the G2G time of the process chamber is reduced, which increases overall efficiency and reduces overall latency of the manufacturing system.

[0035] Figure 1 An illustrative computer system architecture 100 is depicted in accordance with aspects of the present disclosure. In some embodiments, the computer system architecture 100 may be included as part of a manufacturing system for processing a substrate, such as, Figure 2The computer system architecture 100 includes a client device 120, a manufacturing device 124, a metrology device 128, a server machine 130, a prediction server 112 (e.g., for generating prediction data, providing model adaptation, using a knowledge base, etc.), and / or a data storage 140. The prediction server 112 may be part of the prediction system 110. The prediction system 110 may further include server machines 170 and 180. The manufacturing device 124 may include a sensor 125, which is configured to capture data of a substrate being processed at the manufacturing system. In some embodiments, the manufacturing device 124 and the sensor 126 may be part of a sensor system, which includes 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, a metrology database, a metrology folder, etc.) and a metrology identifier reader (eg, a FOUP RFID reader for the metrology system).

[0036] The manufacturing equipment 124 may follow a recipe or perform operations within a time period to produce a product (e.g., an electrical device). In some embodiments, the manufacturing equipment 124 may include one or more process chambers that process substrates (e.g., production substrates) according to a process recipe. In other or similar embodiments, the process chambers of the manufacturing equipment 124 may perform an initialization process and / or a maintenance process that involves performing one or more adjustment operations (e.g., using one or more adjustment substrates) to bring the process chamber to a condition suitable for processing a production substrate. The manufacturing equipment 124 may include a substrate measurement subsystem that includes one or more sensors 126 configured to generate spectral data and / or position data of a substrate embedded in the substrate measurement subsystem. The sensor 126 configured to generate spectral data (referred to herein as a spectral sensing component) may include a reflectance sensor, an ellipsometric sensor, a thermal spectral sensor, a capacitive sensor, etc. In some embodiments, the spectral sensing component may be included in the substrate measurement subsystem or in another part of the manufacturing system. One or more sensors 126 (eg, eddy current sensors, etc.) may also be configured to generate non-spectral data of the substrate. Figure 2 and Figure 3 Additional details are provided regarding the fabrication equipment 124 and the substrate measurement subsystem.

[0037] In some embodiments, the sensor 126 may provide sensor data associated with the manufacturing equipment 124. The sensor data may include values ​​of one or more of the following: temperature (e.g., heater temperature), spacing (SP), pressure, high frequency radio frequency (HFRF), voltage of an electrostatic chuck (ESC), current, flow rate, 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 collected while the manufacturing equipment 124 is performing a manufacturing process (e.g., equipment readings while processing a product). The sensor data may be different for each substrate.

[0038] The metrology equipment 128 may provide metrology data associated with substrates (e.g., wafers, etc.) processed by the manufacturing equipment 124. The metrology data may include values ​​of one or more of film property data (e.g., wafer-space film properties), dimensions (e.g., thickness, height, etc.), dielectric constants, dopant concentrations, densities, 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 included on the substrate surface, critical dimension uniformity across the substrate surface, edge placement error, etc.). The metrology data may be for finished or semi-finished products. The metrology data may be different for each substrate.

[0039] The client device 120 may include a computing device such as a personal computer (PC), a laptop, a mobile phone, a smart phone, a tablet, a netbook computer, a network-connected television ("Smart TV"), a network-connected media player (e.g., a Blu-ray player), a set-top box, an over-the-top (OTT) streaming device, an operator box, etc. In some embodiments, metrology data may be received from the client device 120. The client device 120 may display a graphical user interface (GUI), wherein the GUI enables a user to provide, as input, metrology measurements of substrates processed at the manufacturing system.

[0040] The data storage 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 storage 140 may include multiple storage components (e.g., multiple drives or multiple databases) that may span multiple computing devices (e.g., multiple server computers). The data storage 140 may store spectral data, non-spectral data, metrology data, and predictive data. The spectral data may include historical spectral data (e.g., spectral data generated for a previous substrate processed at the manufacturing system) and / or current spectral data (spectral data generated for a current substrate being processed at the manufacturing system). The current spectral data may be data for which predictive data is generated. Although embodiments of the present disclosure refer to spectral data for training a machine learning model, it should be noted that embodiments of the present disclosure may also include non-spectral data for training a machine learning model. In some embodiments, metrology data may include historical metrology data (e.g., metrology measurements of previous substrates processed at the manufacturing system). The data storage 140 may also store context data associated with substrates processed in the manufacturing system (eg, recipe name, recipe step number, preventive maintenance indicators, operators, etc.).

[0041] In some embodiments, the data storage 140 may be configured to store data that is not accessible to users of the manufacturing system. For example, spectral data, non-spectral data, and / or position data obtained for a substrate processed at the manufacturing system may be inaccessible to users of the manufacturing system. In some embodiments, all data stored at the data storage 140 may be inaccessible to users of the manufacturing system (e.g., operators). In other or similar embodiments, a portion of the data stored at the data storage 140 may be inaccessible to the user, while another portion of the data stored at the data storage 140 may be accessible to the user. In some embodiments, one or more portions of the data stored at the data storage 140 may be encrypted using an encryption mechanism unknown to the user (e.g., using a private key to encrypt the data). In other or similar embodiments, the data storage 140 may include multiple data storages, wherein data that is not accessible to the user is stored in one or more first data storages, and data that is accessible to the user is stored in one or more second data storages.

[0042] like Figure 1, in some embodiments, the data storage 140 may store data associated with a spectral library 142. The spectral library 142 may include one or more sets of spectral data collected for a substrate before, during, or after one or more operations are performed on the substrate at the manufacturing equipment 124. In some embodiments, as described herein, the spectral data may be collected for the substrate by the substrate measurement subsystem and / or by other sensors 126 of the manufacturing equipment 124. In an illustrative example, the spectral library 142 may include one or more sets of spectral data collected before, during, or after a substrate process is performed on a substrate (e.g., a production substrate) at one or more process chambers of the manufacturing equipment 124. In another illustrative example, the spectral library 142 may include one or more sets of spectral data collected before, during, or after an initialization process and / or a maintenance process (e.g., a PM process, a CM process, etc.) is performed on a process chamber of the manufacturing equipment 124. In some embodiments, the spectral library 142 may include additional data associated with the substrates (for which the spectral data was collected) and / or the process recipes and / or manufacturing equipment 124 used to process such substrates. For example, for a corresponding set of spectral data collected for a substrate, the spectral library 142 may include an indication of an identifier associated with the substrate, a type associated with the substrate, a process recipe associated with the substrate, one or more operations of the process recipe performed on the substrate, an identifier of a process chamber in which the substrate was processed (e.g., according to the one or more operations), a type associated with the process chamber, one or more settings associated with the process chamber (e.g., before, after, or during the performance of the one or more operations), a date and / or time when the process recipe was performed on the substrate, metrology data collected for the substrate after the one or more operations were performed, conditions of the process chamber before, during, or after the performance of the one or more operations, etc. In other or similar embodiments, the additional data associated with the substrate may be stored at another area of ​​the data storage 140 (e.g., separate from the spectral library 142).

[0043] In some embodiments, the system 100 may include one or more server machines 130, which include a spectral data engine 132. In some embodiments, a substrate measurement subsystem that collects spectral data of a substrate may transmit the spectral data to the spectral data engine 132 (e.g., via the network 108). As described above, in some embodiments, the spectral data engine 132 may generate a mapping between the spectral data collected for a respective substrate and additional data associated with the substrate and / or process recipe and / or manufacturing equipment 124. The spectral data engine 132 may store an indication of the mapping at a data storage 140 (e.g., with a spectral library 142 and / or at another area of ​​the data storage 140). In some embodiments, the spectral data engine 132 may provide the spectral data received from the substrate measurement subsystem to the prediction component 114 (e.g., via the network 108). In some embodiments, the prediction component 114 may provide the spectral data as input to a trained machine learning model 190, as described herein. It should be noted that although Figure 1 The spectral data engine 132 is illustrated as residing at the server machine 130, but the spectral data engine 132 may reside at any computing system or component of the system 100. For example, in some embodiments, one or more portions of the spectral data engine 132 may be included within the prediction component 114. In another example, one or more portions of the spectral data engine 132 may be included in a system controller (e.g., a system controller of a manufacturing system for the system 100) Figure 2 In yet another example, one or more portions of the spectral data engine 132 may reside at the client device 120.

[0044] In some embodiments, the prediction system 110 includes a server machine 170 and a server machine 180. The server machine 170 includes a training set generator 172, which is capable of generating a training data set (e.g., a set of data inputs and a set of target outputs) to train, validate and / or test the machine learning model 190. In some embodiments, the training set generator 172 may divide the training data into a training set, a validation set, and a test set. In some embodiments, the prediction system 110 generates multiple sets of training data. For example, a first set of training data may correspond to a first type of spectral data (e.g., reflectance spectral data), and a second set of training data may correspond to a second type of spectral data (ellipsometry spectral data). In some embodiments, according to embodiments described herein, the training set generator 172 may generate training data based on data from the spectral library 142.

[0045] 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 represent hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, a processing device, etc.), software (such as instructions running on a processing device, a general purpose computer system, or a dedicated machine), firmware, microcode, or a combination of the above. The training engine 182 may be capable of training a machine learning model 190. The machine learning model 190 may represent a model artifact created by the training engine 182 using training data, the training data including training inputs and corresponding target outputs (correct answers for the respective training inputs). The training engine 182 may find patterns in the training data that map the training inputs to the target outputs (the answers to be predicted), and provide a machine learning model 190 that captures these patterns. The machine learning model 190 may use one or more of a support vector machine (SVM), a radial basis function (RBF), clustering, supervised machine learning, semi-supervised machine learning, unsupervised machine learning, a k-nearest neighbor algorithm (k-NN), linear regression, random forest, a neural network (e.g., an artificial neural network), etc.

[0046] The validation engine 184 may be capable of validating the trained machine learning models 190 using the corresponding feature set of the validation set from the training set generator 172. The validation engine 184 may determine the accuracy of each of the trained machine learning models 190 based on the corresponding feature set of the validation set. The validation engine 184 may discard trained machine learning models 190 having an accuracy that does not meet a threshold accuracy. In some embodiments, the selection engine 185 may be capable of selecting a trained machine learning model 190 having an accuracy that meets a threshold accuracy. In some embodiments, the selection engine 185 may be capable of selecting a trained machine learning model 190 having a highest accuracy of the trained machine learning model 190.

[0047] The testing engine 186 may be able to test the trained machine learning model 190 using a corresponding feature set of a test set from the training set generator 172. For example, a first feature set of the test set may be used to test a first trained machine learning model 190 that has been trained using a first feature set of the training set. The testing engine 186 may determine a trained machine learning model 190 with the highest accuracy among all trained machine learning models based on the test set.

[0048] The prediction server 112 includes a prediction component 114 that is capable of providing spectral data and / or non-spectral data of a portion of a current substrate being processed at a manufacturing system as input to a trained machine learning model 190, and running the trained machine learning model 190 on the input to obtain one or more outputs. In some embodiments, the machine learning model 190 may be trained to predict a corresponding process recipe associated with one or more substrates at the manufacturing system, and one or more operations of the corresponding process recipe that have been performed for the one or more substrates based on given spectral data. In such an embodiment, the prediction component 114 may provide spectral data associated with the substrate as input to the trained model 190, and obtain one or more outputs of the model 190. The one or more outputs may indicate a process recipe associated with the substrate and one or more operations of the process recipe that have been performed for the substrate. In additional or alternative embodiments, the machine learning model 190 may be trained to predict a condition of a corresponding process chamber of the manufacturing system that processed the substrate based on given spectral data of the corresponding substrate (e.g., a conditioning substrate). In such an embodiment, the prediction component 114 can provide the spectral data collected for the conditioning substrate as input to the trained model 190 and obtain one or more outputs of the model 190. The one or more outputs can indicate the conditions of the process chamber in which the conditioning substrate was processed. Additional details on training and using the model 190 are provided herein.

[0049] Client devices 120, manufacturing equipment 124, sensors 126, metrology equipment 128, prediction server 112, server machine(s) 130, data storage 140, server machine 170, and server machine 180 may be coupled to one another via network 108. In some embodiments, network 108 is a public network that provides client devices 120 with access to prediction server 112, data storage 140, and other publicly available computing devices. In some embodiments, network 108 is a private network that provides client devices 120 with access to manufacturing equipment 124, metrology equipment 128, data storage 140, and other privately available computing devices. The network 108 may include one or more wide area networks (WAN), local area networks (LAN), wired networks (e.g., Ethernet), 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 of the foregoing.

[0050] It should be noted that in some other implementations, the functionality of server machines 130, 170, and 180 and prediction server 112 may be provided by a fewer number of machines. For example, in some implementations, server machines 130, 170, and 180 may be integrated into a single machine, while in some other or similar implementations, server machines 130, 170, and 180 and prediction server 112 may be integrated into a single machine.

[0051] In general, the functions described as being performed by server machine 130, server machine 170, server machine 180, and / or prediction server 112 in one implementation may also be performed on client device 120. Additionally, the functions attributed to a particular component may be performed by different or multiple components operating together.

[0052] In embodiments, a "user" may be represented as a single individual. However, other embodiments of the present disclosure encompass a "user" that is an entity controlled by multiple users and / or automation sources. For example, a group of individual users united into a group of administrators may be considered a "user."

[0053] Figure 2 2 is a top view schematic diagram of an example manufacturing system 200 according to aspects of the present disclosure. The manufacturing system 200 can perform one or more processes on a substrate 202. The substrate 202 can be any appropriately rigid, fixed-size planar article suitable for manufacturing electronic devices or circuit components on the substrate 202, such as a silicon-containing disc or wafer, a patterned wafer, a glass plate, or the like. In some embodiments, the substrate 202 can be a production substrate (e.g., a substrate used in the production of a product such as an electronic device), a conditioning substrate (e.g., a substrate used during the performance of one or more conditioning operations, such as an initialization process and / or a maintenance process), and / or any other type of substrate.

[0054] The manufacturing system 200 may include a process tool 204 and a factory interface 206 coupled to the process tool 204. The process tool 204 may include a housing 208 having a transfer chamber 210 therein. The transfer chamber 210 may include one or more process chambers (also referred to as process chambers) 214, 216, 218 disposed about and coupled to the transfer chamber 210. The process chambers 214, 216, 218 may be coupled to the transfer chamber 210 via respective ports, such as slit valves or the like. The transfer chamber 210 may also include a transfer chamber robot 212 configured to transfer the substrate 202 between the process chambers 214, 216, 218, a load lock 220, and the like. The transfer chamber robot 212 may include one or more arms, wherein each arm includes one or more end effectors at an end of each arm. The end effector may be configured to handle a specific object, such as a wafer.

[0055] The process chambers 214, 216, 218 may be adapted to perform any number of processes on the substrate 202. The same or different substrate processes may occur in each process chamber 214, 216, 218. In some embodiments, the process chambers 214, 216, 218 may perform substrate processes for one or more substrates 202. 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. In some embodiments, the substrate processes may include a combination of two or more of 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 substrates in the process chambers 214, 216, 218. For example, an initialization process may be performed at one or more of the process chambers 214, 216, 218 to prepare the process chambers 214, 216, 218 for substrate processing. In another example, a maintenance process (e.g., a PM process, a CM process, etc.) may be performed to mitigate and / or correct wear or damage to components and / or the interior of the process chambers 214, 216, 218. The process chambers 214, 216, 218 may each include one or more sensors configured to capture data of the substrate 202 and / or the environment within the process chambers 214, 216, 218 before, after, or during a substrate process. In some embodiments, the one or more sensors may be configured to capture spectral data and / or non-spectral data of a portion of the substrate 202.

[0056] The load lock 220 may also be coupled to the housing 208 and the transfer chamber 210. The load lock 220 may be configured to interface with and couple with the transfer chamber 210 and the factory interface 206 on one side. In some embodiments, the load lock 220 may have an environmentally controlled atmosphere that may be changed from a vacuum environment (in which substrates may be transferred to and from the transfer chamber 210) to an atmospheric or near atmospheric inert gas environment (in which substrates may be transferred to and from the factory interface 206).

[0057] The factory interface 206 may be any suitable enclosure, such as an Equipment Front End Module (EFEM). The factory interface 206 may be configured to receive substrates 202 from substrate carriers 222 (e.g., front opening unit (FOUP)) docked at respective load ports of the factory interface 206. A factory interface robot 226 (shown in phantom) may be configured to transfer substrates 202 between substrate carriers (also referred to as containers) 222 and load locks 220. In other and / or similar embodiments, the factory interface 206 may be configured to receive replacement parts from replacement parts storage containers 222.

[0058] The manufacturing system 200 may also be connected to a client device (not shown) that is configured to provide information to a user (e.g., an operator) regarding the manufacturing system 200. In some embodiments, the client device may provide information to a user of the manufacturing system 200 via one or more graphical user interfaces (GUIs). For example, the client device may provide information regarding one or more modifications to be made to a process recipe for the substrate 202 via the GUI.

[0059] The manufacturing system 200 may also include a system controller 228. The system controller 228 may be and / or include a computing device, such as a personal computer, a server computer, a programmable logic controller (PLC), a microcontroller, and the like. The system controller 228 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 device may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or a processor that implements other instruction sets or a processor that implements a combination of instruction sets. The processing device may also be one or more special-purpose processing devices, such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a network processor, or the like. The system controller 228 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 228 may execute instructions to perform any one or more of the methods and / or embodiments described herein. In some embodiments, the system controller 228 may execute instructions to perform one or more operations at the manufacturing system 200 according to a process recipe. The instructions may be stored on a computer-readable storage medium, which may include main memory, static memory, auxiliary storage, and / or a processing device (during execution of the instructions).

[0060] The system controller 228 may receive data from sensors included on or within various portions of the manufacturing system 200 (e.g., process chambers 214, 216, 218, transfer chamber 210, load lock 220, etc.). The data received by the system controller 228 may include spectral data and / or non-spectral data of a portion of the substrate 202. For the purposes of this description, the system controller 228 is described as receiving data from sensors included within the process chambers 214, 216, 218. However, the system controller 228 may receive data from any portion of the manufacturing system 200, and the data received from the portion may be used according to the embodiments described herein. In an illustrative example, the system controller 228 may receive spectral data from one or more sensors for the process chambers 214, 216, 218 before, after, or during a substrate process at the process chambers 214, 216, 218. The data received from the sensors of various portions of the manufacturing system 200 may be stored in the data storage 250. The data storage 250 may be included as a component within the system controller 228, or may be a separate component from the system controller 228. In some embodiments, the data storage 250 may be a component related to Figure 1 The data storage device 140.

[0061] The manufacturing system 200 can further include a substrate measurement subsystem 240. The substrate measurement subsystem 240 can obtain spectral measurements of one or more portions of the substrate 202 before, during, or after processing the substrate 202 at the manufacturing system 200. In some embodiments, the substrate measurement subsystem 240 can obtain spectral measurements of one or more portions of the substrate 202 in response to receiving a request for spectral measurements from the system controller 228. The substrate measurement subsystem 240 can be integrated within a portion of the manufacturing system 200. In some embodiments, the substrate measurement subsystem 240 can be integrated with the transfer chamber 210, such as Figure 2. In other or similar embodiments, the substrate measurement subsystem 240 may be integrated within the factory interface 206. In additional or similar embodiments, the substrate measurement subsystem 240 may be integrated within one or more of the process chambers 214, 216, and / or 218. For example, one or more components of the substrate measurement subsystem 240 may be included within the internal environment of the process chambers 214, 216, and / or 218. In another example, the process chambers 214, 216, 218 may include a window (e.g., in a lid of the process chamber, in a sidewall of the process chamber, etc.) that optically exposes the internal environment of the process chambers 214, 216, 218 to the external environment of the process chambers 214, 216, 218. The substrate measurement subsystem 240 may be disposed external to the process chambers 214, 216, 218 and configured to collect spectral data of the substrate while the substrate is within the process chambers 214, 216, 218. In additional or similar embodiments, the substrate measurement subsystem 240 may not be integrated with any portion of the manufacturing system 200, but may instead be a stand-alone component. In such embodiments, the substrate 202 measured at the system measurement subsystem 240 may be transferred to or from a portion of the manufacturing system 200 before or after the substrate 202 is processed at the manufacturing system 200.

[0062] The substrate measurement subsystem 240 may obtain spectral measurements of a portion of the substrate 202 by generating spectral data and / or non-spectral data of the portion of the substrate 202. In some embodiments, the substrate measurement subsystem 240 is configured to generate spectral data, non-spectral data, position data, and other substrate property data of the substrate 202 (e.g., thickness of the substrate 202, width of the substrate 202, etc.). After generating the data of the substrate 202, the substrate measurement subsystem 240 may transmit the generated data to the system controller 228. In response to receiving the data from the substrate measurement subsystem 240, the system controller 228 may store the data at the data storage 250. In other or similar embodiments, the substrate measurement subsystem 240 and / or the system controller 228 may provide the data to the spectral data engine 132. As described above, in some embodiments, one or more portions of the spectral data engine 132 may reside at the system controller 228.

[0063] Figure 3 FIG. 1 is a schematic side view of a cross-sectional view of a substrate measurement subsystem 300 according to aspects of the present disclosure. The substrate measurement subsystem 300 may be used with Figure 2 The substrate measurement subsystem 240 is the same as or may otherwise correspond to Figure 2The substrate measurement subsystem 300 may be configured to obtain a substrate (such as, for example, a substrate 202) before, during, or after processing the substrate 202 at a process chamber (eg, process chambers 214, 216, 218). Figure 2 The substrate measurement subsystem 300 may be configured to generate spectral data, non-spectral data, position data, and / or other property data associated with the substrate 202. The substrate measurement subsystem 300 may include a controller 330 configured to execute one or more instructions for generating data associated with a portion of the substrate 202.

[0064] The substrate measurement subsystem 300 may detect that the substrate 202 has been transferred to the substrate measurement subsystem 300. In response to detecting that the substrate 202 has been transferred to the substrate measurement subsystem 300, the substrate measurement subsystem 300 may determine a position and / or orientation of the substrate 202. The position and / or orientation of the substrate 202 may be determined based on an identification of a reference position of the substrate 202. The reference position may be a portion of the substrate 202 that includes an identification feature associated with a particular portion of the substrate 202. The controller 228 may determine the identification feature associated with the particular portion of the substrate 202 based on the determined identification information of the substrate 202.

[0065] The controller 330 may identify a reference position of the substrate 202 using one or more camera components 350 configured to capture image data of the substrate 202. The camera component 350 may generate image data about one or more portions of the substrate 202 and transmit the image data to the controller 330. The controller 330 may analyze the image data to identify identification features associated with the reference position of the substrate 202. The controller 330 may further determine the position and / or orientation of the substrate 202 as depicted in the image data based on the identified identification features of the substrate 202. The controller 330 may determine the position and / or orientation of the substrate 202 based on the identified identification features of the substrate 202 and the determined position and / or orientation of the substrate 202 as depicted in the image data. In response to determining the position and / or orientation of the substrate 202, the controller 330 may generate position data associated with one or more portions of the substrate 202. In some embodiments, the position data may include one or more coordinates (eg, Cartesian coordinates, polar coordinates, etc.) each associated with a portion of substrate 202 , wherein each coordinate is determined based on a distance from a reference location of substrate 202 .

[0066] The substrate measurement subsystem 300 may include one or more measurement components for measuring the substrate 202. In some embodiments, the substrate measurement subsystem 300 may include one or more spectral sensing components 320 configured to generate spectral data of one or more portions of the substrate 202. As previously discussed, the spectral data may correspond to detected energy wave intensities (i.e., intensities or amounts of energy) for each wavelength of the detected waves.

[0067] The spectral sensing component 320 may be configured to detect energy waves reflected from a portion of the substrate 202 and generate spectral data associated with the detected waves. The spectral sensing component 320 may include a wave generator 322 and a reflected wave receiver 324. In some embodiments, the wave generator 322 may be an optical wave generator configured to generate a light beam toward a portion of the substrate 202. In such an embodiment, the reflected wave receiver 324 may be configured to receive a reflected light beam from the portion of the substrate 202. The wave generator 322 may be configured to generate an energy stream 326 (e.g., a light beam) and transmit the energy stream 326 to a portion of the substrate 202. The reflected energy wave 328 may be reflected from the portion of the substrate 202 and received by the reflected wave receiver 324.

[0068] In response to the reflected wave receiver 324 receiving the reflected energy wave 328 from the portion of the substrate 202, the spectrum sensing component 320 may measure the wavelength of each wave included in the reflected energy wave 328. The spectrum sensing component 320 may further measure the intensity of each measured wavelength. In response to measuring each wavelength and each wavelength intensity, the spectrum sensing component 320 may generate spectrum data of the portion of the substrate 202. The spectrum sensing component 320 may transmit the generated spectrum data to the controller 330. The controller 330 may generate a mapping between the received spectrum data and the position data of the measured portion of the substrate 202 in response to receiving the generated spectrum data.

[0069] The substrate measurement subsystem 300 may be configured to generate a particular type of spectral data based on the type of measurement to be obtained at the substrate measurement subsystem 300. In some embodiments, the spectral sensing component 420 may be a first spectral sensing component that is configured to generate one type of spectral data. For example, the spectral sensing component 320 may be configured to generate reflectance spectral data, ellipsometry spectral data, hyperspectral imaging data, chemical imaging data, thermal spectral data, or conductive spectral data. In such embodiments, the first spectral sensing component may be removed from the substrate measurement subsystem 300 and replaced with a second spectral sensing component that is configured to generate a different type of spectral data (e.g., reflectance spectral data, ellipsometry spectral data, hyperspectral imaging data, chemical imaging data, eddy current spectral data, thermal spectral data, or conductive spectral data).

[0070] In some embodiments, the substrate measurement subsystem 300 may include a substrate holder 340. The substrate holder 340 may be a chuck, such as a vacuum chuck, an electrostatic chuck, a magnetic chuck, a mechanical chuck (e.g., a four-jaw chuck, a three-jaw chuck, an edge / ring clamp chuck, etc.), or other types of chucks. The substrate holder 340 may also be or include a plate or other surface with a substrate-shaped recess and / or a set of pins or other features (e.g., three pins) that surround the substrate and prevent the substrate from shifting relative to the substrate holder 340 during movement of the substrate holder 340. The substrate holder 340 may secure the substrate 202. In one embodiment, the substrate holder 340 includes an edge clamp that clamps the substrate from the edge. In one embodiment, the substrate holder 340 is a vacuum chuck. In other embodiments, the substrate holder 340 may be a different type of chuck, such as an electrostatic chuck, a mechanical chuck, a magnetic chuck, or the like.

[0071] The substrate measurement subsystem may include one or more position components configured to modify the position and / or positioning of the substrate 202 with respect to the spectral sensing component 320. In some embodiments, the position component may include a first actuator 352 that may move the substrate holder 340 about a first axis (e.g., a rotational axis) and / or a second axis (e.g., a vertical axis). In some embodiments, the first actuator 352 may be controlled by a servo controller and / or servo motor, which may allow for precise control of the rotational position, linear position, velocity, and / or acceleration of the first actuator 352 and, therefore, the substrate holder 340. The substrate holder 340 may have a mass between 1.0 kilograms (kg) and 2.0 kg, which allows for a 6000 degree / second rotational speed. 2 With 14000 degrees / second 2In some embodiments, the second actuator 354 may be controlled by a servo controller and / or servo motor, which may allow for precise control of the linear position, velocity, and / or acceleration of the second actuator 354 and, therefore, the substrate holder 340.

[0072] When the spectral sensing component 320 generates spectral data of one or more portions of the substrate 202, the one or more position components may modify the position and / or orientation of the substrate 202 according to the one or more determined portions of the substrate 202 to be measured. For example, before the spectral sensing component 320 generates spectral data of the substrate 202, the position component(s) may position the substrate 202 at Cartesian coordinates (0,0), and the spectral sensing component 320 may generate first spectral data of the substrate 202 at Cartesian coordinates (0,0). In response to the spectral sensing component 320 generating the first spectral data of the substrate 202 at Cartesian coordinates (0,0), the second actuator 354 may translate the substrate 202 along the horizontal axis so that the spectral sensing component 320 is configured to generate second spectral data of the substrate 202 at Cartesian coordinates (0,1). In response to the spectral sensing component 320 generating the second spectral data of the substrate 202 at the Cartesian coordinate (0, 1), the first actuator 352 can rotate the substrate 202 along the first axis so that the spectral sensing component 320 is configured to generate the third spectral data of the substrate 202 at the Cartesian coordinate (1, 1). This process can occur multiple times until spectral data is generated for each determined portion of the substrate 202.

[0073] In some embodiments, one or more layers 312 of material may be included on the surface of the substrate 202. The one or more layers 312 may include etching materials, photoresist materials, mask materials, deposited materials, etc. In some embodiments, the one or more layers 312 may include etching materials to be etched according to an etching process performed at a process chamber. In such embodiments, according to previously disclosed embodiments, spectral data may be collected for one or more portions of the unetched etching material of the layer 312 deposited on the substrate 202. In other or similar embodiments, the one or more layers 132 may include etching materials that have been etched according to an etching process at a process chamber. In such embodiments, one or more structural features (e.g., lines, columns, openings, etc.) may be etched into the one or more layers 312 of the substrate 202. In such embodiments, spectral data may be collected for the one or more structural features etched into the one or more layers 312 of the substrate 202.

[0074] In response to receiving at least one of spectral data, positional data, or property data of substrate 202 , controller 330 may transmit the received data to system controller 228 for processing and analysis according to embodiments described herein.

[0075] As indicated above, one or more components of the substrate measurement subsystem 300 may be included in the process chambers 214, 216, 218 and / or otherwise configured to collect spectral data of substrates within the process chambers 214, 216, 218. In one illustrative example, a process chamber (e.g., process chamber 214) may include a substrate support assembly configured to support the substrate 202 during a process (e.g., substrate process, etc.). The camera assembly 350 and / or the spectral sensing assembly 320 may be included within the process chamber 214 and may generate spectral data and / or non-spectral data of the substrate 202 before, during, or after the process, as described herein. The camera assembly 350 and / or the spectral sensing assembly 320 may transmit the generated measurement results to the controller 330, which may be located outside the process chamber. The controller 330 may transmit the generated measurement results to the system controller 228 and / or the spectral data engine 132, as described herein. In another illustrative example, the transparent window may be embedded in one or more surfaces (e.g., a lid, an outer wall, etc.) of the process chamber 214. In some embodiments, the camera component 350 and / or the spectral sensing component 320 may be configured to generate spectral data by detecting a spectrum from outside the process chamber 214 through the transparent window. It should be noted that according to embodiments described herein, the camera component 350, the spectral sensing component 320, and / or any other component of the substrate measurement subsystem 300 may be configured to generate spectral data and / or non-spectral data of the substrate 202 within or outside the process chamber 214, 216, 218 according to any orientation.

[0076] Return to reference Figure 1In some embodiments, the spectral data engine 132 may include or may be otherwise connected to a process identifier engine and / or a chamber conditioning engine. In some embodiments, the system controller 228 may lose track of one or more substrates (e.g., production substrates) at the process tool 204. The process identifier engine may be configured to determine a process recipe associated with a substrate and / or one or more operations of the process recipe that have been performed on the substrate based on spectral data collected for the substrate (e.g., by the substrate measurement subsystem 300). In additional or alternative embodiments, chamber conditioning operations of an initialization process and / or a maintenance process (e.g., a PM process, a CM process, etc.) may be performed at the process chambers 214, 216, 218 using one or more chamber conditioning substrates. The chamber conditioning engine may determine whether the conditions of the process chambers 214, 216, 218 meet one or more chamber condition criteria based on the spectral data collected for the chamber conditioning substrate(s). If so, the system controller 228 may start one or more substrate processes at the process chambers 214, 216, 218 using one or more production substrates. If not, the system controller may update the initialization process and / or the maintenance process to cause the chambers in the process chambers 214, 216, 218 to meet the chamber condition criteria. Figures 4 to 9 To describe additional details about the process marker engine and the chamber adjustment engine.

[0077] Figure 4 An example process identifier engine 410 is depicted in accordance with aspects of the present disclosure. As described above, in some embodiments, the process identifier engine 410 can be part of the spectral data engine 132. In other or similar embodiments, the process identifier engine 410 can reside at a computing system that is different from the computing system that runs the spectral data engine 132. In such embodiments, the process identifier engine 410 can be communicatively coupled to the spectral data engine 132 and / or the system controller 228 (e.g., via the network 108, via a bus, etc.). Figure 4 , the process identifier engine 410 may include a spectral data component 412, a process recipe component 414, and / or an operation component 416. The process identifier engine 410, the spectral data engine 132, and / or the system controller 228 may be coupled to a memory 450 (e.g., via the network 108, via a bus, etc.). In some embodiments, the memory 450 may include one or more areas of the data storage 140 and / or any other memory of the system 100.

[0078] Figure 5Flowchart of an example method 500 for preventing erroneous processing of a substrate according to aspects of the present disclosure. The method 500 is performed by processing logic, which may include hardware (circuitry, dedicated logic, etc.), software (such as running on a general-purpose computer system or a dedicated machine), firmware, or some combination of the above. In one implementation, the method 500 may be performed by a computer system, such as, Figure 1 In other or similar implementations, one or more operations of method 500 may be performed by one or more other machines not depicted in the figures. In some aspects, one or more operations of method 500 may be performed by process identifier engine 410. In other or similar aspects, one or more operations of method 500 may be performed by system controller 228.

[0079] At block 510, the process logic identifies spectral data associated with the substrate at the manufacturing system. In some embodiments, as described herein, the substrate 202 may be a production substrate. The substrate 202 may have been processed at the process chambers 214, 216, 218 according to one or more operations of a process recipe (e.g., for a substrate process). For purposes of explanation and illustration only, embodiments and examples of the present disclosure may be described with respect to the process chamber 214. However, embodiments and examples of the present disclosure may be applied to any of the process chambers 214, 216, 218 and / or any other process chamber of the manufacturing system 200 and / or another manufacturing system (e.g., a process system that does not include the process chambers 214, 216, 218). In the illustrative example, the substrate 202 may be processed at the process chamber 214 according to one or more first operations of the process recipe. Before, during, or after performing the one or more first operations, the system controller 228 may lose track of the substrate 202. For example, the process tool 204 and / or the system controller 228 may be powered off before, during, or after performing the one or more first operations, and the state of the substrate 202 and / or the process chamber 214 prior to the power off may not be stored to a memory (e.g., a non-volatile memory). Accordingly, the process recipe associated with the substrate 202 and / or the one or more first operations performed on the substrate 202 may be unknown to the system controller 228.

[0080] In some embodiments, the system controller 228 may determine that the process recipe and / or the one or more first operations performed on the substrate 202 are unknown to the system controller 228 (e.g., by determining that there is no data or information in the memory 450 indicating the association of the process recipe with the substrate 202). After determining that the process recipe and / or the first operation(s) are unknown, the system controller 228 may cause the substrate measurement subsystem 300 to generate spectral data of one or more regions of the substrate 202. As described above, in some embodiments, the substrate measurement subsystem 300 may be included at or otherwise connected to the transfer chamber 210 (e.g., within a vacuum environment). In such embodiments, the system controller 228 may transmit a signal to a motion controller of the transfer chamber robot 212 to cause the transfer chamber robot 212 to transfer the substrate 202 from the process chamber 214 to the substrate measurement subsystem 300. In other or similar embodiments, the substrate measurement subsystem 300 may be included at or otherwise connected to the factory interface 206 (e.g., outside of a vacuum environment). In such an embodiment, the system controller 228 may transmit a first signal to a motion controller of the transfer chamber robot 212 to cause the transfer chamber robot 212 to transfer the substrate 202 from the process chamber 214 to the load lock 220, and transmit a second signal to the motion controller factory interface robot 226 to cause the factory interface robot 226 to transfer the substrate 202 from the load lock 220 to the substrate metrology subsystem 300. In additional or similar embodiments, one or more components of the substrate metrology subsystem 300 may be included at the process chamber 214 or otherwise configured to collect spectral data and / or non-spectral data of the substrate 202 while the substrate 202 is at the process chamber 214. In such an embodiment, the substrate 202 remains in the process chamber 214 for measurement.

[0081] In some embodiments, the system controller 228 and / or the process identifier engine 410 may transmit a signal to the controller 330 to cause the substrate measurement subsystem 300 to generate spectral measurements of one or more regions of the surface of the substrate 202. In some embodiments, the signal may include an indication of the region of the surface of the substrate 202 to be measured. In other or similar embodiments, the region of the surface of the substrate 202 may be provided to the controller 330 (e.g., by an operator or engineer of the manufacturing system 200, etc.) prior to initialization of the process chamber 214 and / or the process tool 204. In some embodiments, the controller 330 may cause one or more position components to move (e.g., rotate, translate) the substrate 202 along one or more axes to a target position. In some embodiments, after the substrate 202 is moved to the target position, the spectral sensing component(s) 320 and / or the camera component(s) 350 may collect spectral data and / or non-spectral data of the substrate 202.

[0082] After the spectral sensing component(s) 320 and / or the camera component(s) 350 collect spectral data and / or non-spectral data of the substrate 202, the controller 330 can transmit the collected data to the process identifier engine 410 (e.g., via the network 108, via a bus, etc.). In some embodiments, the spectral data component 412 can store the spectral data collected for the substrate 202 at the memory 450 (e.g., as spectral data 452). In some embodiments, the spectral data 452 associated with the substrate 202 can be identified from a signal provided by the controller 330 and / or from the memory 450.

[0083] Return to reference Figure 5At block 520, the process logic determines a process recipe associated with the substrate based on the identified spectral data. In some embodiments, the process recipe component 414 of the process identifier engine 410 may determine the process recipe associated with the substrate 202 by comparing the spectral data 452 collected for the substrate 202 with spectral data collected for other substrates and stored at the spectral library 142. The spectral data 452 may indicate a state of the substrate 202 at the time the spectral data 254 was collected (e.g., after performing the first operation(s) of the process recipe). The process recipe component 414 may compare the spectral data 452 with the spectral data of the spectral library 142 and may identify a set of spectral data that corresponds to (e.g., matches or substantially matches) the spectral data 452. In response to identifying a set of spectral data corresponding to the spectral data 452, the process recipe component 414 may determine a substrate associated with the identified set of spectral data and / or a process recipe associated with a substrate for which the set of spectral data has been generated (e.g., based on additional data associated with the substrate and / or process recipe stored at the spectral library 142). As described herein, the identified set of spectral data may correspond to a previous substrate processed at a process chamber (e.g., process chambers 214, 216, 218, etc.) according to one or more operations of a previous substrate process. The set of spectral data collected for the previous substrate may indicate a state of the previous substrate after performing one or more operations of a process recipe used to process the previous substrate. The process recipe component 414 may store an indication of the process recipe at the memory 450 as process recipe data 454.

[0084] In some embodiments, data from the spectral library 142 may be used to train a machine learning model (e.g., model 190) to predict a process recipe associated with a substrate and one or more operations of the process recipe that have been performed on the substrate based on given spectral data collected for the substrate. Figure 6Details about the training model 190 are provided. In some embodiments, the process recipe component 414 may provide the spectral data 452 collected for the substrate 202 as input to the trained machine learning model 190 and obtain one or more outputs of the machine learning model 190. The one or more outputs of the model 190 may include an indication of one or more process recipes and, for each of the one or more process recipes, an indication of a confidence level that the corresponding process recipe is associated with the substrate 202 based on the spectral data 452. In additional or alternative embodiments, the one or more outputs of the model 190 may include an indication of one or more operations of the corresponding process recipe and, for each of the one or more operations, a confidence level that the operation has been performed on the substrate 202. The process recipe component 414 may determine the process recipe associated with the substrate 202 by identifying the process recipe of the one or more outputs of the model 190, the process recipe of the one or more outputs of the model 190 having a confidence level that satisfies one or more confidence criteria (e.g., exceeds a threshold confidence level, is greater than other confidence levels associated with other process recipes, etc.).

[0085] Return to reference Figure 5 , at block 530, the process logic identifies one or more second operations of the process recipe that are yet to be performed on the substrate. As described above, in some embodiments, the process recipe component 414 may determine the process recipe associated with the substrate 202 by comparing the spectral data 452 with the spectral data of the spectral library 142. Also as indicated above, after performing one or more operations of a process recipe (e.g., a process recipe associated with the substrate 202), spectral data of the spectral library 142 corresponding to the spectral data 452 may be generated for a previous substrate. In some embodiments, the spectral library 142 may include a mapping between the set of spectral data and an indication of one or more operations of the process recipe that have been performed on the previous substrate. In some embodiments, the operation component 416 may determine the one or more operations that have been performed on the substrate 202 based on the one or more operations performed on the previous substrate.

[0086] In other or similar embodiments, one or more outputs of the model 190 may include an indication of one or more operations of the process recipe, and, for each of the one or more operations, an indication of a confidence level that the corresponding operation has been performed on the substrate 202, given the spectral data 452 provided as input to the model 190. The operation component 416 may determine that the one or more operations have been performed on the substrate 202 by identifying the one or more operations having a confidence level that satisfies one or more confidence criteria (e.g., exceeds a threshold confidence level, is greater than other confidence levels associated with other operations, etc.).

[0087] In some embodiments, the process recipe associated with the substrate 202 may include one or more operations. The operations component 416 may identify (e.g., from the spectral library 142, from a library of process recipes at the data storage 140, etc.) a set of operations associated with the process recipe and determine which operations remain to be performed on the substrate 202 based on the determined operations performed on the substrate 202.

[0088] Return to reference Figure 5 , at block 540, the process logic causes the substrate to be processed according to one or more second operations of the process recipe. In some embodiments, the process identifier engine 410 may provide a notification to the system controller 228 (e.g., via the network 108, via a bus, etc.) indicating the process recipe associated with the substrate 202 and one or more operations of the process recipe that are yet to be performed. According to embodiments described herein, after receiving the notification, the system controller 228 may initiate the execution of the one or more operations. As described above, in some embodiments, the substrate 202 may have been transferred to the substrate measurement subsystem 300 for collection of the spectral data 452. In such embodiments, the system controller 228 may transmit one or more signals to the motion controller (e.g., for the FI robot 226, for the transfer chamber robot 212) to cause the substrate 202 to be transferred to the process chamber 214 and / or another process chamber (e.g., process chambers 216, 218, another process chamber of another process tool) for execution of the one or more second operations.

[0089] Figure 6 6 is a flow chart of an example method 600 for training a machine learning model according to aspects of the present disclosure. The method 600 is performed by processing logic, which may include hardware (circuitry, dedicated logic, etc.), software (such as running on a general-purpose computer system or a dedicated machine), firmware, or some combination of the above items. In one implementation, the method 600 may be performed by a computer system, such as, Figure 1 In other or similar implementations, one or more operations of method 500 may be performed by one or more other machines not depicted in the figures. In some aspects, one or more operations of method 600 may be performed by training set generator 172.

[0090] At block 610, the process logic initializes the training set T to an empty set (e.g., {}). At block 612, the process logic identifies spectral data associated with one or more substrates processed at the manufacturing system. In some embodiments, according to the above embodiments, spectral data may be generated by the substrate measurement subsystem 300 for one or more substrates (e.g., substrate 202) processed according to operations of a process recipe at one or more of the process chambers 214, 216, 218. In some embodiments, components of the substrate measurement subsystem 300 may collect spectral data while a substrate process is performed at a corresponding process chamber (e.g., process chamber 214). Accordingly, the spectral data collected for the substrate 202 may indicate a state of the substrate 202 during each operation of the substrate process. In other or similar embodiments, as described above, after one or more operations of the process recipe are completed (e.g., before all operations of the process recipe are performed), the one or more substrates 202 may be transferred from the process chamber 214 to the substrate measurement subsystem 300. The spectral data collected by the substrate measurement subsystem 300 may indicate the state of the substrate(s) 202 after completing the one or more operations and / or before completing all operations of the process recipe. In some embodiments, the controller 330 and / or the system controller 228 may store the spectral data collected for the one or more substrates 202 at the data storage 140 (e.g., at the spectral library 142).

[0091] At block 614, the process logic determines one or more operations of the process recipe performed on the one or more substrates. The system controller 228 and / or another component of the system 110 may store an indication of the process recipe performed on the substrate(s) 202 at the data storage 140, as described herein. As described above, in some embodiments, the system controller 228 may transfer the substrate 202 to the substrate measurement subsystem 300 for measurement after completing one or more operations of the process recipe. In such embodiments, as described above, the system controller 228 and / or another component of the system 110 may store an indication of the one or more operations of the completed process recipe at the data storage 140. In additional or alternative embodiments, components of the substrate measurement subsystem 300 may collect spectral data during each operation of the process recipe (e.g., without removing the substrate 202 from the process chamber 214). In such embodiments, the system controller 228 and / or another component of the system 100 may determine a start time period and an end time period for each operation of the process recipe based on the process recipe data and / or performance logs for the substrate process. The system controller 228 and / or other components may identify a portion of the spectral data collected between each start time period and each end time period, and may associate the identified portion of the spectral data with a corresponding operation of the process recipe (e.g., by updating a mapping between the portion of the spectral data and an identifier of the corresponding operation). As described above, the system controller 228 and / or other components may store this association and / or the mapping at the data storage 140.

[0092] At block 616, the process logic generates an input / output map, the input being based on the spectral data and the output identifying one or more operations. In some embodiments, the training set generator 172 may identify (e.g., at the data storage 140, at the spectral library 142, etc.) spectral data collected for a substrate (e.g., as identified at block 610) and an indication of one or more operations performed on a substrate for which spectral data has been collected (e.g., as identified at block 612), and generate the input / output map. In some embodiments, the generated input / output map may correspond to a mapping between the portion of the spectral data and an identifier of a corresponding operation generated by the system controller 228 and / or another component of the system 100, as described above. At block 618, the process logic adds the input / output map to the training set T. At block 620, the process logic determines whether the set T is sufficient for training. In response to the process logic determining that the training set T is insufficient for training, the method 600 returns to block 612. In response to the process logic determining that the training set T is sufficient for training, the method 600 proceeds to block 622. At block 622, the process logic provides the training set T to train a machine learning model (eg, model 190).

[0093] Figure 7 An example chamber adjustment engine 710 is depicted in accordance with aspects of the present disclosure. As described above, in some embodiments, the chamber adjustment engine 710 can be part of the spectral data engine 132. In other or similar embodiments, the chamber adjustment engine 710 can reside at a computing system that is different from the computing system that runs the spectral data engine 132. In such embodiments, the chamber adjustment engine 710 can be communicatively coupled to the spectral data engine 132 and / or the system controller 228 (e.g., via the network 108, via a bus, etc.). Figure 7 , the chamber adjustment engine 710 may include a spectral data component 712, a chamber condition component 714, and / or a process updater component 716. The chamber adjustment engine 710, the spectral data engine 132, and / or the system controller 228 may be coupled to a memory 750 (e.g., via the network 108, via a bus, etc.). In some embodiments, the memory 750 may include one or more areas of the data storage 140 and / or any other memory of the system 100.

[0094] Figure 8 800 is a flow chart of an example method 800 for monitoring one or more conditioning operations performed at a process chamber according to aspects of the present disclosure. The method 800 is performed by processing logic, which may include hardware (circuitry, dedicated logic, etc.), software (such as running on a general purpose computer system or a dedicated machine), firmware, or some combination of the above. In one implementation, the method 800 may be performed by a computer system, such as, Figure 1 In other or similar implementations, one or more operations of method 800 may be performed by one or more other machines not depicted in the figures. In some aspects, one or more operations of method 800 may be performed by chamber adjustment engine 710.

[0095] At block 810, the process logic identifies spectral data associated with a first substrate processed at a process chamber of the manufacturing system. In some embodiments, the substrate 202 may be a conditioning substrate, as described herein. According to previously described embodiments, the substrate measurement subsystem 300 may collect spectral data of the substrate 202 before, during, or after performance of one or more operations of a chamber conditioning process. In some embodiments, according to previously described embodiments, the controller 330 of the substrate measurement subsystem 300 may provide the spectral data to the system controller 228 and / or the chamber conditioning engine 710. The spectral data component 712 of the chamber conditioning engine 710 may store the spectral data at the memory 750 as spectral data 752. In some embodiments, the spectral data component 712 may store the spectral data at the spectral library 142.

[0096] At block 812, the process logic determines the condition of the process chamber after the performance of the first operation(s) based on the identified spectral data. As indicated above, the spectral data 752 may indicate a state of the substrate 202 before, during, or after the performance of one or more operations (e.g., conditioning operations) of a process performed at the process chamber 214 for the substrate. The state of the substrate 202 may depend on and / or otherwise be related to the condition of the process chamber 214 before, during, and / or after the performance of the one or more operations. Accordingly, as indicated by the spectral data 752, the state of the substrate 202 may correspond to the condition of the process chamber 214. As described above, the spectral library 142 may include one or more sets of spectral data collected for a previous substrate processed according to one or more previous conditioning operations (e.g., of a previous initialization process and / or a previous maintenance process) at a process chamber (e.g., process chambers 214, 216, 218, and / or another process chamber). Each set of spectral data collected for a respective previous substrate may indicate a state of the respective substrate before, during, and / or after performance of one or more prior conditioning operations. Accordingly, a set of respective spectral data may indicate a condition of a process chamber in which the one or more conditioning operations were performed on the respective substrate.

[0097] In some embodiments, the chamber condition component 714 can compare the spectral data 752 with one or more sets of spectral data of the spectral library 142, and can identify a set of spectral data that corresponds to (e.g., matches or substantially matches) the spectral data 752. The identified set of spectral data can indicate a state of a previous substrate before, during, and / or after the performance of one or more previous conditioning operations, which can correspond to a state of a process chamber that processed the previous substrate before, during, and / or after the performance of the previous conditioning operation(s). Accordingly, the chamber condition component 714 can determine a condition of the process chamber 214 that processed the substrate 202 based on the identified set of spectral data of the spectral library 142. In some embodiments, the chamber condition component 714 can store an indication of the process chamber condition at the memory 750 as the chamber condition data 754.

[0098] In some embodiments, data from the spectral library 142 may be used to train a machine learning model (e.g., model 190) to predict the conditions of a corresponding process chamber in which a substrate (e.g., a conditioning substrate) was processed based on given spectral data collected for the substrate. Fig. 9Details about the training model 190 are provided. In some embodiments, the chamber condition component 714 may provide the spectral data 752 collected for the substrate 202 as input to the trained model 190 and obtain one or more outputs of the machine learning model. The one or more outputs of the model 190 may include an indication of one or more process chamber conditions and, for each of the one or more process chamber conditions, an indication of a confidence level that the respective process chamber condition is associated with the process chamber 214 that processed the substrate 202. The chamber condition component 714 may determine the chamber condition associated with the process chamber 214 by identifying a process chamber condition from the one or more process chamber conditions of the output(s) of the model 190 that has a confidence level that satisfies one or more confidence criteria (e.g., exceeds a threshold confidence level, is greater than other confidence levels associated with other process chamber conditions). As described above, the chamber condition component 714 may store chamber conditions associated with the process chamber 214 (eg, determined based on outputs of the model 190 ) at the memory 750 as chamber condition data 754 .

[0099] Return to reference Figure 8 At block 814, the process logic determines whether the condition of the process chamber satisfies one or more chamber condition criteria. In some embodiments, the condition of the process chamber 214 may satisfy the chamber condition criteria if the condition of the process chamber 214 corresponds to (e.g., matches or substantially matches) a target condition of the process chamber 214 after execution of one or more conditioning operations performed at the process chamber 214. In some embodiments, the target condition of the process chamber 214 may be determined based on historical data and / or experimental data collected for one or more substrates and / or process chambers of the manufacturing system 200 (and / or other manufacturing systems). In other or similar embodiments, the target condition of the process chamber may be provided by an operator or engineer of the manufacturing system 200 (e.g., via the client device 120).

[0100] The chamber condition component 714 may compare the determined condition of the process chamber 214 with the target condition of the process chamber, and determine whether the determined condition corresponds to the target condition. If so, the chamber condition component 714 may determine that the chamber condition criterion is met. If not, the chamber condition component 714 may determine that the chamber condition criterion is not met. In the illustrative example, one or more first adjustment operations may be performed for the substrate 202 at the process chamber 214. As described above, the chamber condition component 714 may determine the condition of the process chamber 214 based on the spectral data 752 collected for the substrate 202, and compare the determined condition with the target condition of the process chamber 714 after performing the one or more first adjustment operations. After determining that the condition of the process chamber 214 after performing the one or more first adjustment operations corresponds to the target condition of the process chamber 214 after performing the first adjustment operation(s), the chamber condition component 714 may determine that the condition of the process chamber 214 meets the chamber condition criterion.

[0101] In response to the process logic determining that the chamber condition criteria are met, the method 600 proceeds to block 816. In response to the process logic determining that the chamber condition criteria are not met, the method 600 proceeds to block 820. At block 816, the process logic determines whether the initialization process and / or the maintenance process are complete. In some embodiments, the chamber condition component 714 may determine whether the initialization process and / or the maintenance process at the process chamber 214 is complete by determining whether the condition of the process chamber 214 corresponds to the target condition of the process chamber that has completed the initialization process and / or the maintenance process. As described above, the target condition of the process chamber that has completed the initialization process and / or the maintenance process may be determined based on historical and / or experimental data and / or provided by an operator or engineer of the manufacturing system 200 (e.g., via the client device 120). After determining that the condition of the process chamber 214 corresponds to the target condition of the process chamber that has completed the initialization process and / or the maintenance process, the chamber condition component 714 may determine that the initialization process and / or the maintenance process is complete.

[0102] In response to the process logic determining that the conditioning process is complete, the method 600 proceeds to block 818. At block 818, the process logic causes the production substrate to be processed at the process chamber. In response to the process logic determining that the conditioning process is not complete, the method 600 proceeds to block 822 described below. In some embodiments, the chamber conditioning engine 710 may transmit a notification to the system controller 228 indicating that the conditions of the process chamber 214 are suitable for processing the production substrate. In some embodiments, the system controller 228 may begin processing the production substrate in response to receiving the notification. In additional or alternative embodiments, the chamber conditioning engine 710 and / or the system controller 228 may transmit a notification to the client device 120 indicating that the conditions of the process chamber 214 are suitable for processing the production substrate. The client device 120 may provide the notification to a user of the client device (e.g., an operator, an engineer, etc.) via a graphical user interface (GUI). An operator and / or engineer may initiate processing of a production substrate based on the notification (e.g., via interaction with a GUI) and / or may cause one or more conditioning substrates at the process tool 204 to be transferred to additional testing equipment (e.g., metrology equipment 128) for further testing and / or validation.

[0103] Referring back to block 814, in response to the process logic determining that the chamber condition criteria are not satisfied, the method 600 proceeds to block 820. At block 820, the process logic updates one or more second operations of the chamber conditioning process (e.g., the initialization process and / or the maintenance process). In some embodiments, the process updater component 716 may determine that one or more updates are to be made to the process recipe associated with the initialization process and / or the maintenance process based on the determined conditions of the process chamber 214. In an illustrative example, the spectral data 752 may indicate an etch rate of the substrate 202, which depends on one or more conditions of the process chamber 214. If the indicated etch rate does not correspond to a target etch rate for the target conditions of the process chamber 214, the process updater component may determine that one or more settings of the process recipe are to be modified to cause the etch rate to correspond to the target etch rate. In some embodiments, the process updater component 716 may store an indication of the updated settings at the memory 750 as process recipe data 756. In additional or alternative embodiments, the process updater component 716 may transmit a notification to the client device 120 indicating that the chamber conditions of the process chamber 214 do not correspond to the target conditions. The client device 120 may provide the notification to an operator and / or engineer of the manufacturing system 200 (e.g., via a GUI of the client device 120). The operator and / or engineer may provide an indication of one or more updates to be made to the settings of the process recipe to cause the conditions of the process chamber 214 to correspond to the target conditions. The client device 120 may transmit a notification indicating the updated settings to the system controller 228 and / or the process updater component 716. In some embodiments, the updated settings may be stored at the memory 750 as process recipe data 756.

[0104] At block 822, the process logic causes one or more second operations of the chamber conditioning process to be performed. The process logic may cause a second substrate (e.g., a conditioning substrate) to be processed according to the one or more second operations. In some embodiments, the process updater component 716 may update one or more settings of a process recipe for an initialization process and / or a maintenance process to cause the condition of the process chamber 214 to correspond to a target condition of the process chamber 214, as described with respect to block 820. In such embodiments, the system controller 228 may initiate one or more additional conditioning operations performed on the one or more additional conditioning substrates according to the updated settings. After performing the additional conditioning operations, the substrate measurement subsystem 300 may collect spectral data associated with the conditioning substrate 202, and the chamber condition engine 710 may determine the condition of the process chamber 214, as described above.

[0105] As described with respect to block 816, in response to the process logic determining that the conditioning process is not complete, the method 600 proceeds to block 822. In such an embodiment, the system controller 228 may perform additional conditioning operations at the process chamber 214 according to the original (e.g., unupdated) settings for the conditioning operations. After performing the additional conditioning operations, the substrate measurement subsystem 300 may collect spectral data associated with the conditioned substrate 202, and the chamber condition engine 710 may determine the condition of the process chamber 214, as described above.

[0106] Fig. 9 900 is a flowchart of an example method for training a machine learning model according to aspects of the present disclosure. Method 900 is performed by processing logic, which may include hardware (circuitry, dedicated logic, etc.), software (such as running on a general-purpose computer system or a dedicated machine), firmware, or some combination of the above items. In one implementation, method 900 may be performed by a computer system, such as, Figure 1 In other or similar implementations, one or more operations of method 900 may be performed by one or more other machines not depicted in the figures. In some aspects, one or more operations of method 900 may be performed by training set generator 172.

[0107] At block 910, the process logic initializes the training set T to an empty set (e.g., {}). At block 912, the process logic identifies spectral data associated with one or more substrates processed according to a chamber conditioning process at the manufacturing system. In some embodiments, according to the above embodiments, spectral data may be generated by the substrate measurement subsystem 300 for one or more substrates (e.g., substrate 202) processed according to conditioning operations of an initialization process and / or a maintenance process at one or more of the process chambers 214, 216, 218. In some embodiments, components of the substrate measurement subsystem 300 may collect spectral data while conditioning operations are performed at the corresponding process chambers (e.g., process chamber 214). Accordingly, the spectral data collected for the substrate 202 may indicate a state of the substrate(s) 202 and / or a condition of the process chamber 214 during the conditioning operation. In other or similar embodiments, as described above, after the conditioning operation is completed, the one or more substrates 202 may be transferred from the process chamber 214 to the substrate measurement subsystem 300.

[0108] At block 914, the process logic obtains metrology data indicating metrology measurements of one or more substrates. In some embodiments, the system controller 228 may cause one or more robots of the manufacturing system 200 to transfer the substrate 202 out of the process chamber 214 to the metrology device 128. In some embodiments, the metrology device 128 may be included at or otherwise coupled to the process tool 204. In such embodiments, the robot may transfer the substrate 202 from the process chamber 214 to the metrology device 128. In other or similar embodiments, the metrology device 128 may be external to the process tool 204. In such embodiments, the robot may transfer the substrate 202 to the factory interface 206. The substrate 202 may be transferred from the factory interface 206 to the metrology device 128 (e.g., manually transferred by an operator or engineer of the manufacturing system 200). The metrology device 128 may collect metrology data of the substrate 202 and may provide the collected metrology data to the system controller 228 and / or another computing system of the system 100, as described herein.

[0109] At block 916, the process logic determines whether the chamber conditions of the process chamber meet one or more criteria based on the obtained metrology data. In some embodiments, the process logic determines whether the chamber conditions of the process chamber meet the criteria by determining whether the collected metrology data of the substrate corresponds to (e.g., matches or substantially matches) target metrology data collected for substrates processed in the chamber that meet the chamber conditions. The target metrology data may be determined based on historical or experimental data and / or provided by an operator and / or engineer of the manufacturing system 200 (e.g., via the client device 120). If the collected metrology data corresponds to the target metrology data, the process logic determines that the criteria are met. If the collected metrology data does not correspond to the target metrology data, the process logic determines that the criteria are not met. At block 918, the process logic generates an input / output mapping, the input being based on the spectral data, and the output indicating whether the chamber conditions of the process chamber are met. In an illustrative example, the input / output map may include an indication of spectral data collected for a substrate 202 that is mapped to an indication of whether conditions of a chamber processing the substrate 202 satisfy one or more criteria.

[0110] At block 920, the process logic adds the input / output mapping to the training set T. At block 922, the process logic determines whether the set T is sufficient for training. In response to the process logic determining that the training set T is insufficient for training, the method 900 returns to block 912. In response to the process logic determining that the training set T is sufficient for training, the method 900 proceeds to block 924. At block 924, the process logic provides the training set T to train the machine learning model (e.g., model 190).

[0111] For simplicity of explanation, methods 500, 600, 800 and 900 are depicted and described as a series of actions. However, actions according to the present disclosure may occur in various orders and / or simultaneously, and have other actions not presented and described herein. In addition, it may not be necessary to perform all illustrated actions to implement the method according to the disclosed subject matter. In addition, those skilled in the art will understand and appreciate that the method may alternatively be represented as a series of interrelated states via a state diagram or event. In addition, it should be understood that the methods disclosed in this specification can be stored on an article of manufacture so that such methods can be transported and transmitted to a computing device. As used herein, the term article of manufacture is intended to encompass a computer program accessible from any computer-readable device or storage medium.

[0112] Fig.10 A block diagram depicting an illustrative computer system 1000 operating in accordance with one or more aspects of the present disclosure. In alternative embodiments, the machine may be connected (e.g., networked) to other machines in a local area network (LAN), an intranet, an extranet, or the Internet. The machine may operate as 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 may be a personal computer (PC), a tablet computer, a set-top box (STB), a personal digital assistant (PDA), a cellular phone, a web appliance, a server, a network router, a switch or a bridge, or any machine capable of executing a set of instructions (sequentially or otherwise) specifying actions to be taken by this machine. In addition, although 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, the computing device 1000 may correspond to Figure 1 The prediction component 114 and / or the spectral data engine 132, Figure 2 System controller 228 or Figure 3 Controller 330.

[0113] The example computing device 1000 includes a processing device 1002, a main memory 1004 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) (such as synchronous DRAM (static random access memory; SDRAM)), etc.), a static memory 1006 (e.g., flash memory, static random access memory (SRAM)), etc.), and an auxiliary memory (e.g., a data storage device 1028), which communicate with each other via a bus 1008.

[0114] The processing device 1002 may represent one or more general-purpose processors, such as a microprocessor, a central processing unit, or the like. More specifically, the processing device 1002 may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor implementing other instruction sets, or a processor implementing a combination of instruction sets. The processing device 1002 may also be one or more special-purpose processing devices, such as an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a network processor, or the like. The processing device 1002 may also be or include a system on a chip (SoC), a programmable logic controller (PLC), or other types of processing devices. The processing device 1002 is configured to execute processing logic for performing the operations and steps discussed herein.

[0115] The computing device 1000 may further include a network interface device 1022 for communicating with a network 1064. The computing device 1000 may also include a video display unit 1010 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device 1012 (e.g., a keyboard), a cursor control device 1014 (e.g., a mouse), and a signal generating device 1020 (e.g., a speaker).

[0116] The data storage device 1028 may include a machine-readable storage medium (or more specifically, a non-transitory computer-readable storage medium) 1024 on which one or more sets of instructions 1026 are stored, the instructions embodying any one or more of the methods or functions described herein. The non-transitory storage medium represents a storage medium other than a carrier wave. During the execution of the instructions 1026 by the computer device 1000, the instructions 1026 may also reside in the main memory 1004 and / or the processing device 1002 in whole or in part, and the main memory 1004 and the processing device 1002 also constitute computer-readable storage media.

[0117] The computer-readable storage medium 1024 may also be used to store the model 190 and data used to train the model 190. The computer-readable storage medium 1024 may also store a software library containing methods for calling the model 190. Although the computer-readable storage medium 1024 is shown as a single medium in the example embodiment, the term "computer-readable storage medium" should be deemed 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 instructions. The term "computer-readable storage medium" should also be deemed to include any medium that is capable of storing or encoding a set of instructions for execution by a machine and causing the machine to perform any one or more of the methods of the present disclosure. The term "computer-readable storage medium" should accordingly be deemed to include, but is not limited to, solid-state memory, and optical and magnetic media.

[0118] The previous description has set forth many specific details, such as, examples of specific systems, components, methods, etc., in order to provide a good understanding of several embodiments of the present disclosure. However, it will be apparent to those skilled in the art that at least some embodiments of the present disclosure can be practiced without these specific details. In other cases, well-known components or methods are not described in detail or presented in the form of simple block diagrams in order to avoid making the present disclosure unnecessarily obscure. Therefore, the specific details set forth are only exemplary. A specific implementation may be different from these exemplary details, and is still expected to be within the scope of the present disclosure.

[0119] References throughout this specification to "one embodiment" or "an embodiment" mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the phrases "in one embodiment" or "in an embodiment" appearing in various places throughout this specification do not necessarily all refer to the same embodiment. Additionally, 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 accurate to within ±10%.

[0120] Although the operations of the methods are shown and described herein in a particular order, the order of operations of each method may be changed so that certain operations may be performed in a reverse order, so that certain operations may be performed at least partially simultaneously with other operations. In another embodiment, instructions or sub-operations of different operations may be performed in an intermittent and / or alternating manner.

[0121] It should be understood that the above description is intended to be illustrative, not restrictive. After reading and understanding the above description, many other embodiments will be apparent to those skilled in the art. Therefore, the scope of the present disclosure should be determined with reference to the attached claims together with the entire scope of equivalents given by such claims.

[0122] The following section includes examples of implementations of the present disclosure. Such examples are provided for illustrative purposes only and are not intended to be limiting.

[0123] Example 1 is a method comprising: identifying spectral data associated with a substrate at a manufacturing system, wherein the substrate has been processed according to one or more first operations of a process recipe and wherein the process recipe is unknown to a system controller of the manufacturing system; determining the process recipe associated with the substrate based on the identified spectral data; identifying one or more second operations of the process recipe yet to be performed on the substrate; and causing the substrate to be processed according to the one or more second operations of the process recipe.

[0124] Example 2 is a method of Example 1, wherein determining the process recipe associated with the substrate based on the identified spectral data includes: providing the identified spectral data as input to a machine learning model, wherein the machine learning model is trained to predict a corresponding process recipe associated with one or more substrates at the manufacturing system and one or more operations of the corresponding process recipe that have been performed on the one or more substrates based on given spectral data; obtaining one or more outputs of the machine learning model; and extracting data from the one or more outputs of the machine learning model, the data indicating the process recipe associated with the substrate and the one or more first operations of the process recipe that have been performed on the substrate.

[0125] Example 3 is the method of Example 2, wherein identifying the one or more second operations of the process recipe yet to be performed on the substrate includes: identifying a set of operations associated with the process recipe, wherein the set of operations includes the one or more first operations and the one or more second operations; and extracting the one or more second operations from the identified set of operations associated with the process recipe.

[0126] Example 4 is the method of Example 2, wherein the machine learning model is trained using historical spectral data, the historical spectral data collected during previous substrate processes performed at the manufacturing system for one or more previous substrates processed according to at least one operation associated with the process recipe.

[0127] Example 5 is the method of example 1, wherein the spectral data is generated based on one or more measurements of a substrate metrology subsystem for a processing tool including a process chamber for processing the substrate.

[0128] Example 6 is the method of Example 5, wherein the substrate measurement subsystem is configured to collect the spectral data associated with the substrate while the substrate is at the process chamber.

[0129] Example 7 is the method of Example 1, wherein causing the substrate to be processed according to the one or more second operations of the process recipe includes: transmitting a notification to the system controller of the manufacturing system, the notification including an indication of the one or more second operations of the process recipe.

[0130] Example 8 is the method of Example 1, wherein causing the substrate to be processed according to the one or more second operations of the process recipe includes: transmitting a signal to at least one component of the manufacturing system through the system controller to cause the component to begin execution of the one or more second operations of the process recipe.

[0131] Example 9 is the method of example 1, further comprising, before identifying the spectral data associated with the substrate at the manufacturing system, detecting that one or more components of the manufacturing system are inoperable for a time period.

[0132] Example 10 is a system comprising: a memory; and a processing device coupled to the memory, wherein the processing device is used to perform operations including the following: identifying spectral data associated with a substrate at a manufacturing system, wherein the substrate has been processed according to one or more first operations of a process recipe, and wherein the process recipe is unknown to a system controller of the manufacturing system; determining the process recipe associated with the substrate based on the identified spectral data; identifying one or more second operations of the process recipe yet to be performed on the substrate; and causing the substrate to be processed according to the one or more second operations of the process recipe.

[0133] Example 11 is the system of Example 10, wherein determining the process recipe associated with the substrate based on the identified spectral data includes: providing the identified spectral data as input to a machine learning model, wherein the machine learning model is trained to predict a corresponding process recipe associated with one or more substrates at the manufacturing system and one or more operations of the corresponding process recipe that have been performed on the one or more substrates based on given spectral data; obtaining one or more outputs of the machine learning model; and extracting data from the one or more outputs of the machine learning model, the data indicating the process recipe associated with the substrate and the one or more first operations of the process recipe that have been performed on the substrate.

[0134] Example 12 is the system of Example 11, wherein identifying the one or more second operations of the process recipe yet to be performed on the substrate includes: identifying a set of operations associated with the process recipe, wherein the set of operations includes the one or more first operations and the one or more second operations; and extracting the one or more second operations from the identified set of operations associated with the process recipe.

[0135] Example 13 is the system of Example 11, wherein the machine learning model is trained using historical spectral data, the historical spectral data collected during previous substrate processes performed at the manufacturing system for one or more previous substrates processed according to at least one operation associated with the process recipe.

[0136] Example 14 is the system of Example 10, wherein the spectral data is generated based on one or more measurements of a substrate metrology subsystem for a processing tool including a process chamber for processing the substrate.

[0137] Example 15 is the system of Example 14, wherein the substrate measurement subsystem is configured to collect the spectral data associated with the substrate while the substrate is at a process chamber of a manufacturing system.

[0138] Example 16 is the system of Example 10, wherein causing the substrate to be processed according to the one or more second operations of the process recipe includes: transmitting a notification to the system controller of the manufacturing system, the notification including an indication of the one or more second operations of the process recipe.

[0139] Example 17 is a non-transitory computer-readable medium comprising instructions that, when executed by a processing device, cause the processing device to perform operations including: identifying spectral data associated with a substrate at a manufacturing system, wherein the substrate has been processed according to one or more first operations of a process recipe and wherein the process recipe is unknown to a system controller of the manufacturing system; determining the process recipe associated with the substrate based on the identified spectral data; identifying one or more second operations of the process recipe yet to be performed on the substrate; and causing the substrate to be processed according to the one or more second operations of the process recipe.

[0140] Example 18 is a non-transitory computer-readable medium of Example 17, wherein determining the process recipe associated with the substrate based on the identified spectral data includes: providing the identified spectral data as input to a machine learning model, wherein the machine learning model is trained to predict a corresponding process recipe associated with one or more substrates at the manufacturing system and one or more operations of the corresponding process recipe that have been performed on the one or more substrates based on given spectral data; obtaining one or more outputs of the machine learning model; and extracting data from the one or more outputs of the machine learning model, the data indicating the process recipe associated with the substrate and the one or more first operations of the process recipe that have been performed on the substrate.

[0141] Example 19 is a non-transitory computer-readable medium of Example 18, wherein identifying the one or more second operations of the process recipe yet to be performed on the substrate includes: identifying a set of operations associated with the process recipe, wherein the set of operations includes the one or more first operations and the one or more second operations; and extracting the one or more second operations from the identified set of operations associated with the process recipe.

[0142] Example 20 is a non-transitory computer-readable medium of Example 18, wherein the machine learning model is trained using historical spectral data, the historical spectral data collected during previous substrate processes performed at the manufacturing system for one or more previous substrates processed according to at least one operation associated with the process recipe.

[0143] Example 21 is a method comprising: identifying spectral data associated with a first substrate at a manufacturing system, wherein the first substrate has been processed at a process chamber of the manufacturing system according to one or more first operations of a chamber conditioning process for the process chamber; determining a condition of the process chamber after performance of the one or more first operations based on the identified spectral data; determining whether the condition of the process chamber satisfies one or more chamber condition criteria; in response to determining that the condition of the process chamber fails to satisfy the one or more chamber condition criteria, updating one or more second operations of the chamber conditioning process to cause the condition of the process chamber to satisfy the one or more chamber condition criteria; and causing a second substrate to be processed at the process chamber according to the updated one or more second operations.

[0144] Example 22 is the method of Example 21, wherein determining the condition of the process chamber after performance of one or more first operations based on the identified spectral data includes: providing the identified spectral data as input to a machine learning model, wherein the machine learning model is trained to predict the condition of a corresponding process chamber of a manufacturing system that processes the substrate based on given spectral data of the corresponding substrate; obtaining one or more outputs of the machine learning model; and extracting data from the one or more outputs of the machine learning model, the data indicating the condition of the process chamber that processed the first substrate according to one or more first operations of a chamber adjustment process.

[0145] Example 23 is the method of Example 22, wherein the data indicating the condition of a process chamber in which a first substrate was processed according to one or more first operations includes an indication of an amount of drift of spectral data associated with the first substrate from target spectral data associated with one or more previous substrates processed according to the one or more first operations of the chamber condition process, wherein the corresponding process chamber that processed the one or more previous substrates satisfied the one or more chamber condition criteria.

[0146] Example 24 is the method of Example 23, wherein determining whether a condition of the process chamber satisfies one or more chamber condition criteria includes determining whether the drift amount of spectral data associated with the first substrate from the target spectral data exceeds a threshold drift amount.

[0147] Example 25 is the method of Example 22, wherein the machine learning model is trained using historical spectral data collected for one or more previous substrates processed according to at least one operation associated with a chamber conditioning process performed at a manufacturing system.

[0148] Example 26 is the method of Example 21, further comprising: in response to determining that the conditions of the process chamber satisfy one or more chamber condition criteria, determining whether the chamber conditioning process is completed based on the conditions of the process chamber; and in response to determining that the chamber conditioning process is not completed, causing a second substrate to be processed at the process chamber according to one or more second operations.

[0149] Example 27 is the method of Example 21, wherein the spectral data is generated based on one or more measurements of a substrate metrology subsystem for a processing tool including a process chamber.

[0150] Example 28 is the method of Example 27, wherein the substrate measurement subsystem is configured to collect the spectral data associated with the substrate while the substrate is at the process chamber.

[0151] Example 29 is a system comprising: a memory; and a processing device coupled to the memory, wherein the processing device is used to perform operations including the following: identifying spectral data associated with a first substrate at a manufacturing system, wherein the first substrate has been processed at a process chamber of the manufacturing system according to one or more first operations of a chamber conditioning process for the process chamber; determining a condition of the process chamber after execution of the one or more first operations based on the identified spectral data; determining whether the condition of the process chamber satisfies one or more chamber condition criteria; in response to determining that the condition of the process chamber fails to satisfy the one or more chamber condition criteria, updating one or more second operations of the chamber conditioning process to cause the condition of the process chamber to satisfy the one or more chamber condition criteria; and causing a second substrate to be processed at the process chamber according to the updated one or more second operations.

[0152] Example 30 is the system of Example 29, wherein determining the condition of the process chamber after performance of one or more first operations based on the identified spectral data includes: providing the identified spectral data as input to a machine learning model, wherein the machine learning model is trained to predict the condition of a corresponding process chamber of a manufacturing system that processed the substrate based on given spectral data of the corresponding substrate; obtaining one or more outputs of the machine learning model; and extracting data from the one or more outputs of the machine learning model, the data indicating the condition of the process chamber that processed the first substrate according to the one or more first operations of the chamber conditioning process.

[0153] Example 31 is the system of Example 30, wherein the data indicating the condition of a process chamber that processed a first substrate according to one or more first operations includes an indication of an amount of drift of spectral data associated with the first substrate from target spectral data, the target spectral data being associated with one or more previous substrates processed according to the one or more first operations of the chamber condition process, wherein the corresponding process chamber that processed the one or more previous substrates satisfied the one or more chamber condition criteria.

[0154] Example 32 is the system of example 31, wherein determining whether a condition of the process chamber satisfies one or more chamber condition criteria includes determining whether the drift amount of spectral data associated with the first substrate from the target spectral data exceeds a threshold drift amount.

[0155] Example 33 is the system of Example 30, wherein the machine learning model is trained using historical spectral data collected for one or more previous substrates processed according to at least one operation associated with a chamber conditioning process performed at the manufacturing system.

[0156] Example 34 is the system of Example 29, wherein the operations further include: in response to determining that the conditions of the process chamber satisfy one or more chamber condition criteria, determining whether the chamber conditioning process is complete based on the conditions of the process chamber; and in response to determining that the chamber conditioning process is not complete, causing a second substrate to be processed at the process chamber according to one or more second operations.

[0157] Example 35 is the system of Example 29, wherein the spectral data is generated based on one or more measurements of a substrate metrology subsystem for a processing tool including a process chamber.

[0158] Example 36 is a non-transitory computer-readable medium comprising instructions that, when executed by a processing device, cause the processing device to perform operations including: identifying spectral data associated with a first substrate at a manufacturing system, wherein the first substrate has been processed at a process chamber of the manufacturing system according to one or more first operations of a chamber conditioning process for the process chamber; determining a condition of the process chamber after execution of the one or more first operations based on the identified spectral data; determining whether the condition of the process chamber satisfies one or more chamber condition criteria; in response to determining that the condition of the process chamber fails to satisfy the one or more chamber condition criteria, updating one or more second operations of the chamber conditioning process to cause the condition of the process chamber to satisfy the one or more chamber condition criteria; and causing a second substrate to be processed at the process chamber according to the updated one or more second operations.

[0159] Example 37 is a non-transitory computer-readable medium of Example 36, wherein determining the condition of the process chamber after performance of one or more first operations based on the identified spectral data includes: providing the identified spectral data as input to a machine learning model, wherein the machine learning model is trained to predict the condition of a corresponding process chamber of a manufacturing system that processed the substrate based on given spectral data of the corresponding substrate; obtaining one or more outputs of the machine learning model; and extracting data from the one or more outputs of the machine learning model, the data indicating the condition of the process chamber that processed the first substrate according to one or more first operations of a chamber adjustment process.

[0160] Example 38 is the non-transitory computer-readable medium of Example 37, wherein the data indicating the condition of a process chamber that processed a first substrate according to one or more first operations includes an indication of an amount of drift of spectral data associated with the first substrate from target spectral data, the target spectral data being associated with one or more previous substrates processed according to the one or more first operations of the chamber condition process, wherein the corresponding process chamber that processed the one or more previous substrates satisfied the one or more chamber condition criteria.

[0161] Example 39 is the non-transitory computer-readable medium of Example 38, wherein determining whether a condition of the process chamber satisfies one or more chamber condition criteria includes determining whether an amount by which spectral data associated with the first substrate drifts from target spectral data exceeds a threshold drift amount.

[0162] Example 40 is the non-transitory computer-readable medium of Example 37, wherein the machine learning model is trained using historical spectral data collected for one or more previous substrates processed according to at least one operation associated with a chamber conditioning process performed at a manufacturing system.

Claims

1. A method comprising the following steps: identifying spectral data associated with a substrate processed at a manufacturing system according to one or more first operations of a process recipe, and wherein the process recipe is unknown to a system controller of the manufacturing system; determining the process recipe associated with the substrate based on the identified spectral data; identifying one or more second operations of the process recipe for the substrate; and The substrate is caused to be processed according to the one or more second operations of the process recipe.

2. The method of claim 1 , wherein the step of determining the process recipe associated with the substrate based on the identified spectral data comprises the following steps: providing the identified spectral data as input to a machine learning model, wherein the machine learning model is trained to predict, based on given spectral data, a corresponding process recipe associated with one or more substrates at the manufacturing system and one or more operations of the corresponding process recipe that have been performed on the one or more substrates; receiving one or more outputs of the machine learning model; and Data is extracted from the one or more outputs of the machine learning model, the data indicative of the process recipe associated with the substrate and the one or more first operations of the process recipe that have been performed on the substrate.

3. The method of claim 2, wherein the step of identifying the one or more second operations of the process recipe for the substrate comprises the steps of: identifying a set of operations associated with the process recipe, wherein the set of operations includes the one or more first operations and the one or more second operations; and The one or more second operations are extracted from the identified set of operations associated with the process recipe.

4. The method of claim 2, wherein the machine learning model is trained using historical spectral data collected during previous substrate processes performed at the manufacturing system for one or more previous substrates processed according to at least one operation associated with the process recipe.

5. The method of claim 1, wherein the spectral data is generated based on one or more measurements of a substrate metrology subsystem for a processing tool including a process chamber for processing the substrate.

6. The method of claim 5, wherein the substrate measurement subsystem is configured to collect the spectral data associated with the substrate while the substrate is at the process chamber.

7. The method of claim 1 , wherein causing the substrate to be processed according to the one or more second operations of the process recipe comprises the steps of: A notification is transmitted to the system controller of the manufacturing system, the notification including an indication of the one or more second operations of the process recipe.

8. The method of claim 1 , wherein causing the substrate to be processed according to the one or more second operations of the process recipe comprises the steps of: A signal is transmitted by the system controller to at least one component of the manufacturing system to cause the component to initiate execution of the one or more second operations of the process recipe.

9. The method of claim 1, further comprising the steps of: Prior to identifying the spectral data associated with the substrate at the manufacturing system, detecting that one or more components of the manufacturing system are inoperable for a period of time.

10. A system comprising: Memory; and A processing device coupled to the memory, wherein the processing device is configured to perform operations comprising the following steps: identifying spectral data associated with a substrate processed at a manufacturing system according to one or more first operations of a process recipe, and wherein the process recipe is unknown to a system controller of the manufacturing system; determining the process recipe associated with the substrate based on the identified spectral data; identifying one or more second operations of the process recipe for the substrate; and The substrate is caused to be processed according to the one or more second operations of the process recipe.

11. The system of claim 10, wherein the step of determining the process recipe associated with the substrate based on the identified spectral data comprises the following steps: providing the identified spectral data as input to a machine learning model, wherein the machine learning model is trained to predict, based on given spectral data, a corresponding process recipe associated with one or more substrates at the manufacturing system and one or more operations of the corresponding process recipe that have been performed on the one or more substrates; obtaining one or more outputs of the machine learning model; and Data is extracted from one or more outputs of the machine learning model, the data indicative of the process recipe associated with the substrate and the one or more first operations of the process recipe that have been performed on the substrate.

12. The system of claim 11, wherein the step of identifying the one or more second operations of the process recipe for the substrate comprises the steps of: identifying a set of operations associated with the process recipe, wherein the set of operations includes the one or more first operations and the one or more second operations; and The one or more second operations are extracted from the identified set of operations associated with the process recipe.

13. The system of claim 11, wherein the machine learning model is trained using historical spectral data collected during previous substrate processes performed at the manufacturing system for one or more previous substrates processed according to at least one operation associated with the process recipe.

14. The system of claim 10, wherein the spectral data is generated based on one or more measurements of a substrate metrology subsystem for a processing tool including a process chamber for processing the substrate.

15. The system of claim 14, wherein the substrate measurement subsystem is configured to collect the spectral data associated with the substrate while the substrate is at a process chamber of the manufacturing system.

16. The system of claim 10, wherein the step of causing the substrate to be processed according to the one or more second operations of the process recipe comprises the steps of: A notification is transmitted to the system controller for the manufacturing system, the notification including an indication of the one or more second operations of the process recipe.

17. A non-transitory computer readable medium comprising instructions which, when executed by a processing device, cause the processing device to perform operations comprising: identifying spectral data associated with a substrate processed at a manufacturing system according to one or more first operations of a process recipe, and wherein the process recipe is unknown to a system controller of the manufacturing system; determining the process recipe associated with the substrate based on the identified spectral data; identifying one or more second operations of the process recipe for the substrate; and The substrate is caused to be processed according to the one or more second operations of the process recipe.

18. The non-transitory computer readable medium of claim 17, wherein the step of determining the process recipe associated with the substrate based on the identified spectral data comprises the steps of: providing the identified spectral data as input to a machine learning model, wherein the machine learning model is trained to predict, based on given spectral data, a corresponding process recipe associated with one or more substrates at the manufacturing system and one or more operations of the corresponding process recipe that have been performed on the one or more substrates; obtaining one or more outputs of the machine learning model; and Data is extracted from one or more outputs of the machine learning model, the data indicative of the process recipe associated with the substrate and the one or more first operations of the process recipe that have been performed on the substrate.

19. The non-transitory computer readable medium of claim 18, wherein the step of identifying the one or more second operations of the process recipe for the substrate comprises the steps of: identifying a set of operations associated with the process recipe, wherein the set of operations includes the one or more first operations and the one or more second operations; and The one or more second operations are extracted from the identified set of operations associated with the process recipe.

20. The non-transitory computer-readable medium of claim 18, wherein the machine learning model is trained using historical spectral data collected during previous substrate processes performed at the manufacturing system for one or more previous substrates processed according to at least one operation associated with the process recipe.

21. A method comprising: identifying spectral data associated with a first substrate at a manufacturing system, wherein the first substrate has been processed at a process chamber of the manufacturing system according to one or more first operations of a chamber conditioning process for the process chamber; determining a condition of the process chamber after performance of the one or more first operations based on the identified spectral data; determining whether the condition of the process chamber satisfies one or more chamber condition criteria; In response to determining that the condition of the process chamber fails to satisfy the one or more chamber condition criteria, updating one or more second operations of the chamber conditioning process to cause the condition of the process chamber to satisfy the one or more chamber condition criteria; and Processing of a second substrate at the process chamber is caused according to the updated one or more second operations.

22. The method of claim 21 , wherein determining the condition of the process chamber after performance of the one or more first operations based on the identified spectral data comprises: providing the identified spectral data as input to a machine learning model, wherein the machine learning model is trained to predict a condition of a corresponding process chamber of the manufacturing system processing the substrate based on given spectral data of the corresponding substrate; obtaining one or more outputs of the machine learning model; and Data is extracted from the one or more outputs of the machine learning model, the data indicative of the condition of the process chamber processing the first substrate according to the one or more first operations of the chamber conditioning process.

23. The method of claim 22, wherein the data indicative of the condition of the process chamber in which the first substrate was processed according to the one or more first operations comprises an indication of an amount by which the spectral data associated with the first substrate drifts from target spectral data associated with one or more previous substrates processed according to the one or more first operations of the chamber conditioning process, wherein the corresponding process chambers that processed the one or more previous substrates satisfied the one or more chamber condition criteria.

24. The method of claim 23, wherein determining whether the condition of the process chamber satisfies the one or more chamber condition criteria comprises: A determination is made as to whether the drift amount of the spectral data associated with the first substrate from the target spectral data exceeds a threshold drift amount.

25. The method of claim 22, wherein the machine learning model is trained using historical spectral data collected for one or more previous substrates processed according to at least one operation associated with the chamber conditioning process performed at the manufacturing system.

26. The method of claim 21, further comprising: In response to determining that the condition of the process chamber satisfies the one or more chamber condition criteria, determining whether the chamber conditioning process is complete based on the condition of the process chamber; and In response to determining that the chamber conditioning process is not complete, processing the second substrate at the process chamber according to the one or more second operations is caused.

27. The method of claim 21, wherein the spectral data is generated based on one or more measurements of a substrate metrology subsystem for a processing tool including the process chamber.

28. The method of claim 27, wherein the substrate measurement subsystem is configured to collect the spectral data associated with the substrate while the substrate is at the process chamber.

29. A system comprising: Memory; and A processing device coupled to the memory, wherein the processing device is configured to perform operations comprising the steps of: identifying spectral data associated with a first substrate at a manufacturing system, wherein the first substrate has been processed at a process chamber of the manufacturing system according to one or more first operations of a chamber conditioning process for the process chamber; determining a condition of the process chamber after performance of the one or more first operations based on the identified spectral data; determining whether the condition of the process chamber satisfies one or more chamber condition criteria; In response to determining that the condition of the process chamber fails to satisfy the one or more chamber condition criteria, updating one or more second operations of the chamber conditioning process to cause the condition of the process chamber to satisfy the one or more chamber condition criteria; and Processing of a second substrate at the process chamber is caused according to the updated one or more second operations.

30. The system of claim 29, wherein determining the condition of the process chamber after performance of the one or more first operations based on the identified spectral data comprises: providing the identified spectral data as input to a machine learning model, wherein the machine learning model is trained to predict a condition of a corresponding process chamber of the manufacturing system processing the substrate based on given spectral data of the corresponding substrate; obtaining one or more outputs of the machine learning model; and Data is extracted from the one or more outputs of the machine learning model, the data indicative of the condition of the process chamber processing the first substrate according to the one or more first operations of the chamber conditioning process.

31. The system of claim 30, wherein the data indicative of the condition of the process chamber in which the first substrate is processed according to the one or more first operations comprises an indication of an amount by which the spectral data associated with the first substrate drifts from target spectral data associated with one or more previous substrates processed according to the one or more first operations of the chamber conditioning process, wherein the corresponding process chambers that processed the one or more previous substrates satisfied the one or more chamber condition criteria.

32. The system of claim 31 , wherein determining whether the condition of the process chamber satisfies the one or more chamber condition criteria comprises: A determination is made as to whether the drift amount of the spectral data associated with the first substrate from the target spectral data exceeds a threshold drift amount.

33. The system of claim 30, wherein the machine learning model is trained using historical spectral data collected for one or more previous substrates processed according to at least one operation associated with the chamber conditioning process performed at the manufacturing system.

34. The system of claim 29, wherein the operations further comprise: In response to determining that the condition of the process chamber satisfies the one or more chamber condition criteria, determining whether the chamber conditioning process is complete based on the condition of the process chamber; and In response to determining that the chamber conditioning process is not complete, processing the second substrate at the process chamber according to the one or more second operations is caused.

35. The system of claim 29, wherein the spectral data is generated based on one or more measurements of a substrate metrology subsystem for a processing tool including the process chamber.

36. A non-transitory computer readable medium comprising instructions which, when executed by a processing device, cause the processing device to perform operations comprising: identifying spectral data associated with a first substrate at a manufacturing system, wherein the first substrate has been processed at a process chamber of the manufacturing system according to one or more first operations of a chamber conditioning process for the process chamber; determining a condition of the process chamber after performance of the one or more first operations based on the identified spectral data; determining whether the condition of the process chamber satisfies one or more chamber condition criteria; In response to determining that the condition of the process chamber fails to satisfy the one or more chamber condition criteria, updating one or more second operations of the chamber conditioning process to cause the condition of the process chamber to satisfy the one or more chamber condition criteria; and Processing of a second substrate at the process chamber is caused according to the updated one or more second operations.

37. The non-transitory computer readable medium of claim 36, wherein determining the condition of the process chamber after performance of the one or more first operations based on the identified spectral data comprises: providing the identified spectral data as input to a machine learning model, wherein the machine learning model is trained to predict a condition of a corresponding process chamber of the manufacturing system processing the substrate based on given spectral data of the corresponding substrate; obtaining one or more outputs of the machine learning model; and Data is extracted from the one or more outputs of the machine learning model, the data indicative of the condition of the process chamber processing the first substrate according to the one or more first operations of the chamber conditioning process.

38. The non-transitory computer-readable medium of claim 37, wherein the data indicative of the condition of the process chamber in which the first substrate is processed according to the one or more first operations comprises an indication of an amount by which the spectral data associated with the first substrate drifts from target spectral data, the target spectral data being associated with one or more previous substrates processed according to the one or more first operations of the chamber conditioning process, wherein the corresponding process chambers that processed the one or more previous substrates satisfied the one or more chamber condition criteria.

39. The non-transitory computer readable medium of claim 38, wherein determining whether the condition of the process chamber satisfies the one or more chamber condition criteria comprises: A determination is made as to whether an amount by which the spectral data associated with the first substrate drifts from the target spectral data exceeds a threshold drift amount.

40. The non-transitory computer readable medium of claim 37, wherein the machine learning model is trained using historical spectral data collected for one or more previous substrates processed according to at least one operation associated with the chamber conditioning process performed at the manufacturing system.

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