Applied Materials, Inc.

Ramachandran Subramanian

Raman K Nurani

Bharath Ram Sundar

Bibhavendra Singh

Bharath Muralidharan

Ramkishore Sankarasubramanian

Ramaswamy Melatoor Narayanan

Ganapathi Raman Sankaranarayanan

Debkalpo Das

A method includes obtaining sensor data associated with a deposition process performed in a process chamber to deposit film on a surface of a substrate. A plurality of physics-based outputs are generated using a transformation function and the sensor data. The transformation function is used to at least one of estimate site availability for growth at an equilibrium condition for the process chamber or estimate boundary layer thickness in an equilibrium condition for the process chamber. The physics-based outputs are mapped to a training set and a virtual model is trained based on the training set and the sensor data. The virtual model is trained to generate predictive metrology data associated with the film.

Systems and methods for predicting film thickness using virtual metrology

A method includes obtaining sensor data associated with a deposition process performed in a process chamber to deposit film on a surface of a substrate. The method further includes generating a plurality of physics based outputs using a transformation function and the sensor data. The method further includes mapping the physics based outputs to a training set. The method further includes training a virtual model based on the training set and the sensor data, wherein the virtual model is trained to generate predictive metrology data associated with the film.

Methods and systems for detecting outliers at a manufacturing system using machine learning are provided. Data collected by a sensors at a manufacturing system during a current process performed for a first set of substrates is provided as input to a trained machine learning model. One or more outputs are obtained from the trained machine learning model. A first amount of drift of a first set of parameter values for the first set of substrates from a target set of parameter values for the first set of substrates is extracted from the one or more outputs. A second amount of drift of each of the first set of parameter values for the first set of substrates from a corresponding parameter value of a second set of parameter values for a second set of substrates processed according to the current process at the manufacturing system prior to the performance of the current process for the first set of substrates is also extracted from the one or more outputs. A substrate health rating is assigned for each of the first set of substrates based on the first amount of drift. A sensor health rating is assigned for each of the sensors at the manufacturing system based on the second amount of drift. An indication of the substrate health rating for each of the first set of substrates and the sensor health rating for each of the sensors are transmitted to a client device connected to the manufacturing system.

Detecting outliers at a manufacturing system using machine learning

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Chennai, India

Ramachandran Subramanian