US 11,709,477 B2
Autonomous substrate processing system
Priyadarshi Panda, Newark, CA (US); Lei Lian, Fremont, CA (US); Pengyu Han, San Jose, CA (US); Todd J. Egan, Fremont, CA (US); Prashant Aji, San Jose, CA (US); Eli Mor, Garden City, ID (US); Alex J. Tom, San Francisco, CA (US); and Leonard Michael Tedeschi, San Jose, CA (US)
Assigned to Applied Materials, Inc., Santa Clara, CA (US)
Filed by Applied Materials, Inc., Santa Clara, CA (US)
Filed on Jan. 6, 2021, as Appl. No. 17/143,072.
Prior Publication US 2022/0214662 A1, Jul. 7, 2022
Int. Cl. G05B 19/4155 (2006.01); G06N 20/00 (2019.01)
CPC G05B 19/4155 (2013.01) [G06N 20/00 (2019.01); G05B 2219/31368 (2013.01); G05B 2219/45031 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A substrate processing system, comprising:
one or more transfer chambers;
a plurality of process chambers connected to the one or more transfer chambers, the plurality of process chambers comprising a first process chamber comprising a first plurality of sensors and a second process chamber comprising a second plurality of sensors; and
a computing device connected to each of the plurality of process chambers, wherein the computing device is to:
receive one or more first measurements from at least one of the first plurality of sensors of the first process chamber during or after a first instance of a seasoning process performed within the first process chamber after performing maintenance on the first process chamber, wherein the one or more first measurements comprise a first set of measurements from the first plurality of sensors generated during the first instance of the seasoning process;
process the one or more first measurements using a trained machine learning model, wherein the trained machine learning model is to generate a first output based on processing of the one or more first measurements, wherein the first output comprises an indication that the first process chamber is ready to be brought back into service;
cause a first action to be performed with respect to the first process chamber based on the first output of the trained machine learning model;
determine a first result of the first action; and
update a training of the trained machine learning model based on the one or more first measurements, the first output, and the first result of the first action.