Applied Materials, Inc.

Alex J Tom

Todd J Egan

Lei Lian

Leonard Michael Tedeschi

Priyadarshi Panda

Pengyu Han

Prashant Aji

Eli Mor

Ming Xu

Yen-Kun Victor Wang

Ashley M Okada

Kenneth Le

A cool cluster comprises one or more transfer chambers; a plurality of process chambers connected to the one or more transfer chambers; and a computing device of the tool cluster. The computing device is to receive first measurements generated by sensors of a first process chamber during or after a process is performed within the first process chamber; determine that the first process chamber is due for maintenance based on processing the first measurements using a first trained machine learning model; after maintenance has been performed on the first process chamber, receive second measurements generated by the sensors during or after a seasoning process is performed within the first process chamber; and determine that the first process chamber is ready to be brought back into service based on processing the second measurements using a second trained machine learning model.

Autonomous substrate processing system

A method and system for monitoring and detecting a gas leak in a gas stick assembly is provided. The method includes measuring a pressure in a mass flow controller of the gas stick assembly at different time points and determining whether there is a difference in pressure at the different time points that exceeds a difference threshold.

Leak detection for gas sticks

A substrate processing system comprises one or more transfer chambers; a plurality of process chambers connected to the one or more transfer chambers; and a computing device connected to each of the plurality of process chambers. The computing device is to receive first measurements generated by sensors of a first process chamber during or after a process is performed within the first process chamber; determine that the first process chamber is due for maintenance based on processing the first measurements using a first trained machine learning model; after maintenance has been performed on the first process chamber, receive second measurements generated by the sensors during or after a seasoning process is performed within the first process chamber; and determine that the first process chamber is ready to be brought back into service based on processing the second measurements using a second trained machine learning model.

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San Francisco, CA, United States of America

Alex J Tom