Applied Materials, Inc.

Pramod Nambiar

Sidharth Bhatia

Heng-Cheng Pai

Ganesh Balasubramanian

Garrett H Sin

Irfan Jamil

Garrett Ho-Yee Sin

Deenesh Padhi

Paul Brillhart

Hang Yu

Sanjay G Kamath

Ilker Durukan

Shawyon Jafari

Vinayak Veer Vats

Katty Marie Lydia Gamon Guyomard

A method includes identifying sets of sensor data associated with wafers processed via wafer processing equipment and identifying sets of metrology data associated with the wafers processed via the wafer processing equipment. The method further includes generating sets of aggregated sensor-metrology data, each of the sets of aggregated sensor-metrology data including a respective set of sensor data and a respective set of metrology data. The method further includes causing, based on the sets of aggregated sensor-metrology data, performance of a corrective action associated with the wafer processing equipment.

Sensor metrology data integration

A method includes receiving part data associated with a corresponding part of substrate processing equipment, sensor data associated with one or more corresponding substrate processing operations performed by the substrate processing equipment to produce one or more corresponding substrates, and metrology data associated with the one or more corresponding substrates produced by the one or more corresponding substrate processing operations performed by the substrate processing equipment that includes the corresponding part. The method further includes generating sets of aggregated part-sensor-metrology data including a corresponding set of part data, a corresponding set of sensor data, and a corresponding set of metrology data. The method further includes causing analysis of the sets of aggregated part-sensor-metrology data to generate one or more outputs to perform a corrective action associated with the corresponding part of the substrate processing equipment.

Part, sensor, and metrology data integration

Methods, systems, and non-transitory computer readable medium are described for sensor metrology data integration. A method includes receiving sets of sensor data and sets of metrology data. Each set of sensor data includes corresponding sensor values associated with producing corresponding product by manufacturing equipment and a corresponding sensor data identifier. Each set of metrology data includes corresponding metrology values associated with the corresponding product manufactured by the manufacturing equipment and a corresponding metrology data identifier. The method further includes determining common portions between each corresponding sensor data identifier and each corresponding metrology data identifier. The method further includes, for each of the sensor-metrology matches, generating a corresponding set of aggregated sensor-metrology data and storing the sets of aggregated sensor-metrology data to train a machine learning model. The trained machine learning model is capable of generating one or more outputs for performing a corrective action associated with the manufacturing equipment.

A process development visualization tool generates a first visualization of a parameter associated with a manufacturing process, and provides a GUI control element associated with a process variable of the manufacturing process, wherein the GUI control element has a first setting associated with a first value for the process variable. The process development tool receives a user input to adjust the GUI control element from the first setting to a second setting, determines a second value for the process variable based on the second setting, and determines a second set of values for the parameter that are associated with the second value for the process variable. The process development tool then generates a second visualization of the parameter, wherein the second visualization represents the second set of values for the parameter that are associated with the second value for the process variable.

Process development visualization tool

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Sunnyvale, CA, United States of America

Pramod Nambiar