Abstract: Disclosed herein is technology for performing predictive modeling to identify inputs for a manufacturing process.

Abstract: Methods and systems for RF pulse monitoring and RF pulsing parameter optimization at a manufacturing system are provided. A radio frequency (RF) signal is pulsed within a processing chamber in accordance with a set of process parameters. Sensor data is received from one or more sensors that indicates a RF pulse waveform detected within the processing chamber. One or more RF signal characteristics are identified in the detected RF pulse waveform. Each identified RF signal characteristic corresponds to at least one RF signal pulse of the RF signal pulsing within the processing chamber. A determination is made, based on the identified one or more RF signal characteristics, whether the detected RF pulse waveform corresponds to the target RF pulse waveform. An indication of whether the detected RF pulse waveform corresponds to the target RF pulse waveform is provided to a client device.

Abstract: Disclosed herein is technology for performing predictive modeling to identify inputs for a manufacturing process. An example method may include receiving expected output data for a manufacturing process, wherein the expected output data defines an attribute of an output of the manufacturing process; accessing a plurality of machine learning models that model the manufacturing process; determining, using a first machine learning model, input data for the manufacturing process based on the expected output data for the manufacturing process, wherein the input data comprises a value for a first input and a value for a second input; combining the input data determined using the first machine learning model with input data determined using the second machine learning model to produce a set of inputs for the manufacturing process, wherein the set of inputs comprises candidate values for the first input and candidate values for the second input.

Abstract: Methods and systems for RF pulse monitoring and RF pulsing parameter optimization at a manufacturing system are provided. A radio frequency (RF) signal is pulsed within a processing chamber in accordance with a set of process parameters. Sensor data is received from one or more sensors that indicates a RF pulse waveform detected within the processing chamber. One or more RF signal characteristics are identified in the detected RF pulse waveform. Each identified RF signal characteristic corresponds to at least one RF signal pulse of the RF signal pulsing within the processing chamber. A determination is made, based on the identified one or more RF signal characteristics, whether the detected RF pulse waveform corresponds to the target RF pulse waveform. An indication of whether the detected RF pulse waveform corresponds to the target RF pulse waveform is provided to a client device.

Abstract: Methods and systems for multi-level RF pulse monitoring and RF pulsing parameter optimization at a manufacturing system are provided. A radio frequency (RF) signal is pulsed within a processing chamber in accordance with a set of RF pulsing parameters. Sensor data is received from one or more sensors that indicates a multi-level RF pulse waveform detected within the processing chamber based on the RF signal pulsing. One or more peaks are identified in the detected multi-level RF pulse waveform. Each identified peak corresponds to at least one RF signal pulse of the RF signal pulsing within the processing chamber. A determination is made, based on the identified one or more peaks, whether the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform. An indication of whether the detected multi-level RF pulse waveform corresponds to the target multi-level RF pulse waveform is provided to a client device.

Abstract: A method includes: receiving film property data associated with manufacturing parameters of manufacturing equipment; determining that the film property data is correlated and is different from target data; selecting, by a processing device, a set of data points of the film property data that are orthogonal to the target data; performing, by the processing device, feature extraction on the set of data points; and determining, based on the feature extraction, updates to one or more of the manufacturing parameters to meet the target data.

Abstract: A method includes obtaining, by a processor, 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. The method further includes applying a machine-learning model to the plurality of sensor values, the machine-learning model trained based on historical sensor data of a sub-system of the process chamber and task data associated with the recipe for depositing the fil. The method further includes generating an output of the machine-learning model, wherein the output is indicative of a health of the sub-system.

Abstract: Methods, systems, and non-transitory computer readable medium are described for prescriptive analytics in highly collinear response space. A method includes receiving film property data associated with manufacturing parameters of manufacturing equipment. The method further includes determining that the film property data is correlated and is different from target data. The method further includes selecting a set of data points of the film property data that are orthogonal to the target data. The method further includes performing feature extraction on the set of data points. The method further includes determining, based on the feature extraction, updates to one or more of the manufacturing parameters to meet the target data.

Abstract: Embodiments disclosed herein generally relate to a method, system, and non-transitory computer readable medium for classifying an outlier in time series data collected by a sensor positioned in a substrate processing chamber. The client device receives time series data from the sensor positioned in the substrate processing chamber. The client device converts the time series data to a bounded uniform signal. The client device identifies signal sub-segments that do not match an expected behavior. The client device classifies the identified sub-segments that do not match the expected behavior.

Abstract: Disclosed herein is technology for performing predictive modeling to identify inputs for a manufacturing process. An example method may include receiving expected output data for a manufacturing process, wherein the expected output data defines an attribute of an output of the manufacturing process; accessing a plurality of machine learning models that model the manufacturing process; determining, using a first machine learning model, input data for the manufacturing process based on the expected output data for the manufacturing process, wherein the input data comprises a value for a first input and a value for a second input; combining the input data determined using the first machine learning model with input data determined using the second machine learning model to produce a set of inputs for the manufacturing process, wherein the set of inputs comprises candidate values for the first input and candidate values for the second input.

Abstract: Systems and methods for controlling device performance variability during manufacturing of a device on wafers are disclosed. The system includes a process platform, on-board metrology (OBM) tools, and a first server that stores a machine-learning based process control model. The first server combines virtual metrology (VM) data and OBM data to predict a spatial distribution of one or more dimensions of interest on a wafer. The system further comprises an in-line metrology tool, such as SEM, to measure the one or more dimensions of interest on a subset of wafers sampled from each lot. A second server having a machine-learning engine receives from the first server the predicted spatial distribution of the one or more dimensions of interest based on VM and OBM, and also receives SEM metrology data, and updates the process control model periodically (e.g., wafer-to-wafer, lot-to-lot, chamber-to-chamber etc.) using machine learning techniques.

Abstract: Systems and methods for controlling device performance variability during manufacturing of a device on wafers are disclosed. The system includes a process platform, on-board metrology (OBM) tools, and a first server that stores a machine-learning based process control model. The first server combines virtual metrology (VM) data and OBM data to predict a spatial distribution of one or more dimensions of interest on a wafer. The system further comprises an in-line metrology tool, such as SEM, to measure the one or more dimensions of interest on a subset of wafers sampled from each lot. A second server having a machine-learning engine receives from the first server the predicted spatial distribution of the one or more dimensions of interest based on VM and OBM, and also receives SEM metrology data, and updates the process control model periodically (e.g., wafer-to-wafer, lot-to-lot, chamber-to-chamber etc.) using machine learning techniques.

Abstract: This disclosure describes methods and systems for building a spatial model to predict performance of processing chamber, and using the spatial model to converge faster to a desired process during the process development phase. Specifically, a machine-learning engine obtains an empirical process model for a given process for a given processing chamber. The empirical process model is calibrated by using the in-line metrology data as reference. A predictive model is built by refining the empirical process model by a machine-learning engine that receives customized metrology data and outputs one or more spatial maps of the wafer for one or more dimensions of interest across the wafer without physically processing any further wafers, i.e. by performing spatial digital design of experiment (Spatial DoE).

Abstract: This disclosure describes methods and systems for building a spatial model to predict performance of processing chamber, and using the spatial model to converge faster to a desired process during the process development phase. Specifically, a machine-learning engine obtains an empirical process model for a given process for a given processing chamber. The empirical process model is calibrated by using the in-line metrology data as reference. A predictive model is built by refining the empirical process model by a machine-learning engine that receives customized metrology data and outputs one or more spatial maps of the wafer for one or more dimensions of interest across the wafer without physically processing any further wafers, i.e. by performing spatial digital design of experiment (Spatial DoE).

Abstract: A method includes receiving historical time-series data and generating training data comprising a plurality of randomized data points associated with the historical time-series data. The historical time-series data was generated by one or more sensors during one or more processes. The method further includes training a logistic regression classifier based on the training data to generate a trained logistic regression classifier. The trained logistic regression classifier is associated with a logistic regression that indicates a location of a transition pattern from a first data point to a second data point. The transition pattern reflects about a reflection point located on the transition pattern. The trained logistic regression classifier is capable of indicating a probability that new time-series data generated during a new execution of the one or more processes matches the historical time-series data.

Abstract: Methods, systems, and non-transitory computer readable medium are described for prescriptive analytics in highly collinear response space. A method includes receiving film property data associated with manufacturing parameters of manufacturing equipment. The method further includes determining that the film property data is correlated and is different from target data. The method further includes selecting a set of data points of the film property data that are orthogonal to the target data. The method further includes performing feature extraction on the set of data points. The method further includes determining, based on the feature extraction, updates to one or more of the manufacturing parameters to meet the target data.

Abstract: This disclosure describes methods and systems for building a spatial model to predict performance of processing chamber, and using the spatial model to converge faster to a desired process during the process development phase. Specifically, the method obtains virtual metrology (VM) data from sensors of the chamber and on-board metrology (OBM) data from devices on the wafers; obtains in-line metrology data from precision scanning electron microscope (SEM); and also obtains an empirical process model for a given process. The empirical process model is calibrated by using the in-line metrology data as reference. A predictive model is built by refining the empirical process model by a machine-learning engine that receives customized metrology data and outputs one or more spatial maps of the wafer for one or more dimensions of interest across the wafer without physically processing any further wafers, i.e. by performing spatial digital design of experiment (Spatial DoE).

Abstract: Systems and methods for controlling device performance variability during manufacturing of a device on wafers are disclosed. The system includes a process platform, on-board metrology (OBM) tools, and a first server that stores a machine-learning based process control model. The first server combines virtual metrology (VM) data and OBM data to predict a spatial distribution of one or more dimensions of interest on a wafer. The system further comprises an in-line metrology tool, such as SEM, to measure the one or more dimensions of interest on a subset of wafers sampled from each lot. A second server having a machine-learning engine receives from the first server the predicted spatial distribution of the one or more dimensions of interest based on VM and OBM, and also receives SEM metrology data, and updates the process control model periodically (e.g., wafer-to-wafer, lot-to-lot, chamber-to-chamber etc.) using machine learning techniques.

Abstract: This disclosure describes methods and systems for building a spatial model to predict performance of processing chamber, and using the spatial model to converge faster to a desired process during the process development phase. Specifically, the method obtains virtual metrology (VM) data from sensors of the chamber and on-board metrology (OBM) data from devices on the wafers; obtains in-line metrology data from precision scanning electron microscope (SEM); and also obtains an empirical process model for a given process. The empirical process model is calibrated by using the in-line metrology data as reference. A predictive model is built by refining the empirical process model by a machine-learning engine that receives customized metrology data and outputs one or more spatial maps of the wafer for one or more dimensions of interest across the wafer without physically processing any further wafers, i.e. by performing spatial digital design of experiment (Spatial DoE).

Abstract: Methods and systems for time-series transient analysis of data are disclosed herein. A method includes receiving time-series data, generating a training data set including randomized data points, generating randomized data point combinations using a set of the randomized data points that are within a time window, computing distance values based on the randomized data point combinations, generating a classifier based on a plurality of computed distance values, and determining, using the classifier, a probability that new time-series data generated during a new execution of the process matches the time-series data. A system for performing the method is also disclosed.