Abstract: A system includes a processing device, operatively coupled to the memory device, to perform operations comprising obtaining a plurality of sensor values associated with a deposition process performed, according to a recipe, in a process chamber to deposit film on a surface of a substrate. A machine-learning model is applied to the plurality of sensor values. The machine-learning model is trained based on historical sensor data of a sub-system of the process chamber and task data associated with the recipe for depositing the film. An output of the machine-learning model is generated that is indicative of a suspected failure of the sub-system and a corrective action is generated based on the suspected failure of the sub-system.

Abstract: An identification feature of a substrate in a substrate measurement subsystem is identified. The identification feature is located at a predefined location relative to a reference location of the substrate. A position of the substrate within the substrate measurement subsystem is determined based on the identified identification feature. Based on the determined position of the substrate, one or more sensing components of the substrate measurement subsystem is caused to capture spectral data representing features of at least one of the reference location of the substrate or another location of the substrate. A determination is made of whether to update a process recipe associated with the substrate based on the captured spectral data.

Abstract: A method for a substrate measurement subsystem is provided. An indication is received that a substrate being processed at a manufacturing system has been loaded into a substrate measurement subsystem. First positional data of the substrate within the substrate measurement subsystem is determined. One or more portions of the substrate to be measured by one or more sensing components of the substrate measurement subsystem are determined based on the first positional data of the substrate and a process recipe for the substrate. Measurements of each of the determined portions of the substrate are obtained by one or more sensing components of the substrate measurement subsystem. The obtained measurements of each of the determined portions of the substrate are transmitted to a system controller.

Abstract: A process recipe associated with a substrate at a manufacturing system is identified. A first set of measurements for the substrate is obtained from a substrate measurement subsystem. A second set of measurements for the substrate is obtained from one or more sensors of a chamber of the manufacturing system. A determination is made based on the obtained first set of measurements and the obtained second set of measurements of whether to modify the process recipe by at least one of modifying an operation of the process recipe or generating an instruction to prevent completion of execution of one or more operations of the process recipe.

Abstract: A process recipe associated with a substrate at a manufacturing system is identified. A first set of measurements for the substrate is obtained from a substrate measurement subsystem. A second set of measurements for the substrate is obtained from one or more sensors of a chamber of the manufacturing system. A determination is made based on the obtained first set of measurements and the obtained second set of measurements of whether to modify the process recipe by at least one of modifying an operation of the process recipe or generating an instruction to prevent completion of execution of one or more operations of the process recipe.

Abstract: A system includes a processing device, operatively coupled to the memory device, to perform operations comprising obtaining a plurality of sensor values associated with a deposition process performed, according to a recipe, in a process chamber to deposit film on a surface of a substrate. A machine-learning model is applied to the plurality of sensor values. The machine-learning model is trained based on historical sensor data of a sub-system of the process chamber and task data associated with the recipe for depositing the film. An output of the machine-learning model is generated that is indicative of a suspected failure of the sub-system and a corrective action is generated based on the suspected failure of the sub-system.

Abstract: A system includes a processing device, operatively coupled to the memory device, to perform operations comprising obtaining a plurality of sensor values associated with a deposition process performed, according to a recipe, in a process chamber to deposit film on a surface of a substrate; generating a manufacturing data graph based on the plurality of sensor values; receiving, via a user interface, a selection of a data point on the manufacturing graph; receiving failure data associated with the data point; and storing, in a data structure, the failure data to be accessible via the user interface presenting the manufacturing data graph.

Abstract: A process recipe associated with a substrate at a manufacturing system is identified. A first set of measurements for the substrate is obtained from a substrate measurement subsystem. A second set of measurements for the substrate is obtained from one or more sensors of a chamber of the manufacturing system. A determination is made based on the obtained first set of measurements and the obtained second set of measurements of whether to modify the process recipe by at least one of modifying an operation of the process recipe or generating an instruction to prevent completion of execution of one or more operations of the process recipe.

Abstract: A method for determining whether to modify a manufacturing process recipe is provided. A substrate to be processed at a manufacturing system according to the first process recipe is identified. An instruction to transfer the substrate to a substrate measurement subsystem to obtain a first set of measurements for the substrate is generated. The first set of measurements for the substrate is received from the substrate measurement subsystem. An instruction to transfer the substrate from the substrate measurement subsystem to a processing chamber is generated. A second set of measurements for the substrate is received from one or more sensors of the processing chamber. A first mapping between the first set of measurements and the second set of measurements for the substrate is generated. The first set of measurements mapped to the second set of measurements for the substrate is stored.

Abstract: A system includes a processing device, operatively coupled to the memory device, to perform operations comprising obtaining a plurality of sensor values associated with a deposition process performed, according to a recipe, in a process chamber to deposit film on a surface of a substrate; generating a manufacturing data graph based on the plurality of sensor values; receiving, via a user interface, a selection of a data point on the manufacturing graph; receiving failure data associated with the data point; and storing, in a data structure, the failure data to be accessible via the user interface presenting the manufacturing data graph.

Abstract: Spectral data associated with a first prior substrate and/or a second prior substrate is obtained. A metrology measurement value associated with the first portion of the first prior substrate is determined based on one or more metrology measurement values measured for at least one of a second portion of the first prior substrate or a third portion of a second prior substrate. Training data for training a machine learning model to predict metrology measurement values of a current substrate is generated. Generating the training data includes generating a first training input including the spectral data associated with the first prior substrate and generating a first target output for the first training input, the first target output including the determined metrology measurement value associated with the first portion of the first prior substrate. The training data is provided to train the machine learning model.

Abstract: Methods and systems for detecting and correcting substrate process drift using machine learning are provided. Data associated with processing each of a first set of substrates at a manufacturing system according to a process recipe is provided as input to a trained machine learning model. One or more outputs are obtained from the trained machine learning model. An amount of drift of a first set of metrology measurement values for the first set of substrates from a target metrology measurement value is determined from the one or more outputs. Process recipe modification identifying one or more modifications to the process recipe is also determined. For each modification, an indication of a level of confidence that a respective modification to the process recipe satisfies a drift criterion for a second set of substrates is determined.

Abstract: A method for training a machine learning model to predict metrology measurements of a current substrate being processed at a manufacturing system is provided. Training data for the machine learning model is generated. A first training input including historical spectral data and/or historical non-spectral data associated with a surface of a prior substrate previously processed at the manufacturing system is generated. A first target output for the first training input is generated. The first target output includes historical metrology measurements associated with the prior substrate previously processed at the manufacturing system. Data is provided to train the machine learning model on (i) a set of training inputs including the first training input, and (ii) a set of target outputs including a first target output.

Abstract: A method for a substrate measurement subsystem is provided. An indication is received that a substrate being processed at a manufacturing system has been loaded into a substrate measurement subsystem. First positional data of the substrate within the substrate measurement subsystem is determined. One or more portions of the substrate to be measured by one or more sensing components of the substrate measurement subsystem are determined based on the first positional data of the substrate and a process recipe for the substrate. Measurements of each of the determined portions of the substrate are obtained by one or more sensing components of the substrate measurement subsystem. The obtained measurements of each of the determined portions of the substrate are transmitted to a system controller.

Abstract: A method for determining whether to modify a manufacturing process recipe is provided. A substrate to be processed at a manufacturing system according to the first process recipe is identified. An instruction to transfer the substrate to a substrate measurement subsystem to obtain a first set of measurements for the substrate is generated. The first set of measurements for the substrate is received from the substrate measurement subsystem. An instruction to transfer the substrate from the substrate measurement subsystem to a processing chamber is generated. A second set of measurements for the substrate is received from one or more sensors of the processing chamber. A first mapping between the first set of measurements and the second set of measurements for the substrate is generated. The first set of measurements mapped to the second set of measurements for the substrate is stored.

Abstract: Methods and systems for using a time-series of spectra to identify endpoint of an etch process. One method includes accessing a virtual carpet that is generated from a time-series of spectra for an etch process. A polynomial with coefficients represents the virtual carpet. The method includes processing a fabrication etch process on a fabrication wafer and generating a carpet defined from a time-series of spectra while processing the fabrication etch process. While the processing the fabrication etch process and generating the carpet, comparing portions of the carpet and the virtual carpet to identify an endpoint metric of the fabrication etch process.

Abstract: Implementations generally relate methods, systems, and computer readable media for discovery of an open network adapter. In some implementations, a method includes receiving, by a software-defined network (SDN) controller, a notification of an open networking adapter (ONA) establishing a connection with a switch, the ONA having an address associated therewith. The method further includes identifying the switch based on a lookup of the address of the ONA in forwarding tables of a neighboring switch of the SDN controller. The method further includes applying a service profile configuration to a port of the switch where the ONA is connected.

Abstract: Methods and systems for using a time-series of spectra to identify endpoint of an etch process. One method includes accessing a virtual carpet that is generated from a time-series of spectra for an etch process. A polynomial with coefficients represents the virtual carpet. The method includes processing a fabrication etch process on a fabrication wafer and generating a carpet defined from a time-series of spectra while processing the fabrication etch process. While the processing the fabrication etch process and generating the carpet, comparing portions of the carpet and the virtual carpet to identify an endpoint metric of the fabrication etch process.

Abstract: Methods and systems for using a time-series of spectra to identify endpoint of an etch process. One method includes accessing a virtual carpet that is formed from a time-series of spectra for the etch process collected during a training operation. And, running a fabrication etch process on a fabrication wafer, such that while the fabrication etch process is performed portions of a carpet defined from a time-series of spectral is generated for the fabrication etch process. Then, comparing the portions of the carpet of the fabrication etch process to the virtual carpet. End pointing is processed for the fabrication etch process when said comparing indicates that a desired metric has been reached for the fabrication wafer. In one example, said portions of the carpet include a current frame of captured spectra and at least one previous frame of captured spectra.