US 11,853,042 B2
Part, sensor, and metrology data integration
Garrett Ho-Yee Sin, Sunnyvale, CA (US); Sidharth Bhatia, Santa Cruz, CA (US); Katty Marie Lydia Gamon Guyomard, Los Altos, CA (US); Shawyon Jafari, Sunnyvale, CA (US); Heng-Cheng Pai, Cupertino, CA (US); Pramod Nambiar, Sunnyvale, CA (US); Paul Lukas Brillhart, Pleasanton, CA (US); and Ilker Durukan, San Jose, CA (US)
Assigned to Applied Materials, Inc., Santa Clara, CA (US)
Filed by Applied Materials, Inc., Santa Clara, CA (US)
Filed on Feb. 17, 2021, as Appl. No. 17/177,978.
Prior Publication US 2022/0260978 A1, Aug. 18, 2022
Int. Cl. G05B 19/418 (2006.01); G06N 20/00 (2019.01); G01N 33/00 (2006.01); G06F 30/398 (2020.01); G06F 119/22 (2020.01); G06F 119/18 (2020.01)
CPC G05B 19/41875 (2013.01) [G01N 33/00 (2013.01); G06F 30/398 (2020.01); G06N 20/00 (2019.01); G01N 2033/0095 (2013.01); G05B 2219/31264 (2013.01); G05B 2219/32179 (2013.01); G05B 2219/45031 (2013.01); G06F 2119/18 (2020.01); G06F 2119/22 (2020.01)] 17 Claims
OG exemplary drawing
 
1. A method comprising:
receiving a plurality of sets of part data associated with substrate processing equipment, wherein each of the plurality of sets of part data comprises corresponding part values and a corresponding part identifier, and wherein each of the plurality of sets of part data is associated with hardware parameters of a corresponding equipment part of substrate processing equipment;
receiving a plurality of sets of sensor data, wherein each of the plurality of sets of sensor data comprises corresponding sensor values associated with producing one or more corresponding substrates by the substrate processing equipment and a corresponding sensor data identifier;
receiving a plurality of sets of metrology data, wherein each of the plurality of sets of metrology data comprises corresponding metrology values associated with the one or more corresponding substrates and a corresponding metrology data identifier;
generating a plurality of sets of aggregated part-sensor-metrology data, each of the plurality of sets of aggregated part-sensor-metrology data comprising a corresponding set of equipment part data, a corresponding set of sensor data, and a corresponding set of metrology data; and
causing, based on the plurality of sets of aggregated part-sensor-metrology data, performance of a corrective action associated with the substrate processing equipment, wherein the causing of the performance of the corrective action comprises training a machine learning model using the plurality of sets of aggregated part-sensor-metrology data.