Applied Materials, Inc.

Mihyun Jang

Jeong Jin Hong

Sejune Cheon

Sang Hong Kim

Zhaozhao Zhu

Jin Kyeong Lee

Anders Andelman Nottrott

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. The method further includes training a plurality of machine learning models, where each machine learning model is trained to generate an output using an input dataset of the plurality of input datasets and the metrology data. The method further includes selecting a trained machine learning model from the plurality of trained machine learning models satisfying one or more selection criteria.

Machine learning model training

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.

Process control knob estimation

The subject matter of this specification can be implemented in, among other things, methods, systems, computer-readable storage medium. A method can include a processing device receiving sensor data indicating a state of an environment of a processing chamber processing a series of substrates according to a substrate processing procedure. The series of substrates may be processed subsequent to a preventive maintenance procedure. The processing device may determine a first set of values based on the sensor data. The set of values may indicate a likelihood an associated substrate comprises a first process result that meets a threshold condition of the substrate processing procedure. The processing device may further predict a first test result based on the first set of values. The first test result may indicate a likelihood a first substrate processed subsequent to processing the series of substrates comprises a second process result that meets the threshold condition.

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Seoul, South Korea

Mihyun Jang