Abstract: A method includes receiving trace sensor data associated with a first manufacturing process of a processing chamber. The method further includes processing the trace sensor data using one or more trained machine learning models that generate a representation of the trace sensor data, and then generate reconstructed sensor data based on the representation of the trace sensor data. The method further includes comparing the trace sensor data to the reconstructed sensor data. The method further includes determining one or more differences between the reconstructed sensor data and the trace sensor data. The method further includes determining whether to recommend a corrective action associated with the processing chamber based on the one or more differences between the trace sensor data and the reconstructed sensor data.

Abstract: A method includes receiving first data associated with measurements taken by a sensor during a first manufacturing procedure of a manufacturing chamber. The method further includes receiving second data. The second data includes reference data associated with the first data. The method further includes providing the first and second data to a comparison model. The method further includes receiving a similarity score from the comparison model, associated with the first and second data. The method further includes performance of a corrective action in view of the similarity score.

Abstract: A non-transitory machine-readable storage medium includes instructions that, when executed by a processing device, cause the processing device to perform operations. The operations include receiving (i) sensor data indicating a first state of a coating of a processing chamber subsequent to a chamber recovery procedure and (ii) substrate process data indicating a set of process parameter values associated with performing a substrate processing procedure by the processing chamber having the coating in a second state prior to the chamber recovery procedure. The operations further include processing the sensor data and the substrate process data to determine an update to at least one of the set of process parameter values. The operations further include performing a) preparing a notification indicating the update for presentation on a graphical user interface or b) causing the processing chamber to perform a selection of the substrate processing procedure based on the update.

Abstract: A method includes receiving first data associated with measurements taken by a sensor during a first manufacturing procedure of a manufacturing chamber. The method further includes receiving second data. The second data includes reference data associated with the first data. The method further includes providing the first and second data to a comparison model. The method further includes receiving a similarity score from the comparison model, associated with the first and second data. The method further includes performance of a corrective action in view of the similarity score.

Abstract: The subject matter of this specification can be implemented in, among other things, methods, systems, computer-readable storage medium. A method can include receiving (i) sensor data indicating a first state of an environment of a processing chamber processing a substrate subsequent to a chamber recovery procedure, and (ii) substrate process data indicating a set of process parameter values associated with performing a substrate processing procedure by the processing chamber having the environment in a second state prior to the chamber recovery procedure. The method further includes processing the sensor data and the substrate process data using one or more machine learning models to determine one or more outputs. The one or more outputs include an update to at least one of the set of process parameter values. The update is associated with performing the substrate processing procedure by the processing chamber having the environment in the first state.

Abstract: A method includes receiving spectral data of a substrate and metrology data corresponding to the spectral data of the substrate. The method further includes determining a plurality of feature model configurations for each of a plurality of feature models, each of the plurality of feature model configurations including one or more feature model conditions. The method further includes determining a plurality of feature model combinations, where each feature model combination of the plurality of feature model combinations includes a subset of the plurality of feature model configurations. The method further includes generating a plurality of input datasets, where each input dataset of the plurality of input datasets is generated based on application of the spectral data to a respective feature model combination of the plurality of feature model combinations.

Abstract: A method includes processing measurement data of a substrate that was processed according to a manufacturing process using a first trained machine learning model to predict a critical dimension (CD) profile for the substrate. The method further includes generating a CD profile prediction image based on the predicted CD profile for the substrate. The method further includes processing the CD profile prediction image using a second trained machine learning model to generate a synthetic microscopy image associated with the substrate.

Abstract: A method includes receiving trace sensor data associated with a first manufacturing process of a manufacturing chamber. The method further includes processing the trace sensor data by a processing device to generate summary data associated with the trace sensor data. Generating the summary data includes identifying a steady state and transient portion of the trace sensor data and generating a first and second portion of summary data based on the steady state and transient portions. The method further includes generating a quality index score based on the summary data. The method further includes providing an alert to a user based on the quality index score. The alert includes an indication that the manufacturing chamber performance does not meet a first threshold.

Abstract: A method includes receiving trace sensor data associated with a first manufacturing process of a manufacturing chamber. The method further includes processing the trace sensor data by a processing device to generate summary data associated with the trace sensor data. The method further includes generating a quality index score based on the summary data. The method further includes providing an alert to a user based on the quality index score. The alert includes an indication that the manufacturing chamber performance does not meet a first threshold.

Abstract: The subject matter of this specification can be implemented in, among other things, methods, systems, computer-readable storage medium. A method can include receiving (i) sensor data indicating a first state of an environment of a processing chamber processing a substrate subsequent to a chamber recovery procedure, and (ii) substrate process data indicating a set of process parameter values associated with performing a substrate processing procedure by the processing chamber having the environment in a second state prior to the chamber recovery procedure. The method further includes processing the sensor data and the substrate process data using one or more machine learning models to determine one or more outputs. The one or more outputs include an update to at least one of the set of process parameter values. The update is associated with performing the substrate processing procedure by the processing chamber having the environment in the first state.

Abstract: A method includes receiving first data associated with measurements taken by a sensor during a first manufacturing procedure of a manufacturing chamber. The method further includes receiving second data. The second data includes reference data associated with the first data. The method further includes providing the first and second data to a comparison model. The method further includes receiving a similarity score from the comparison model, associated with the first and second data. The method further includes performance of a corrective action in view of the similarity score.

Abstract: A method includes receiving trace sensor data associated with a first manufacturing process of a manufacturing chamber. The method further includes processing the trace sensor data by a processing device to generate summary data associated with the trace sensor data. The method further includes generating a quality index score based on the summary data. The method further includes providing an alert to a user based on the quality index score. The alert includes an indication that the manufacturing chamber performance does not meet a first threshold.

Abstract: A method includes receiving trace sensor data associated with a first manufacturing process of a processing chamber. The method further includes processing the trace sensor data using one or more trained machine learning models that generate a representation of the trace sensor data, and then generate reconstructed sensor data based on the representation of the trace sensor data. The method further includes comparing the trace sensor data to the reconstructed sensor data. The method further includes determining one or more differences between the reconstructed sensor data and the trace sensor data. The method further includes determining whether to recommend a corrective action associated with the processing chamber based on the one or more differences between the trace sensor data and the reconstructed sensor data.

Abstract: A flushing system comprises: an oil tank for storing oil; a pipe system connected to the oil tank by means of a pipe to circulate the oil by means of the operation of a main pump; a microbubble generator connected to at least any one of the oil tank and the pipe system to generate the microbubbles in the pipe along which the oil flows and in the oil tank and thus to inject the microbubbles into the pipe; a water remover connected to the oil tank to remove the microbubbles and water from the oil; and a particle remover connected to the oil tank to remove microbubbles and foreign substances from the oil by means of electric precipitation.