The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Feb. 21, 2023
Filed:
Jun. 28, 2021
Applied Materials, Inc., Santa Clara, CA (US);
Kartik B Shah, Saratoga, CA (US);
Satish Radhakrishnan, San Jose, CA (US);
Karthik Ramanathan, Bangalore, IN;
Karthikeyan Balaraman, Bangalore, IN;
Adolph Miller Allen, Oakland, CA (US);
Xinyuan Chong, Milpitas, CA (US);
Mitrabhanu Sahu, San Jose, CA (US);
Wenjing Xu, San Jose, CA (US);
Michael Sterling Jackson, Sunnyvale, CA (US);
Weize Hu, Sunnyvale, CA (US);
Feng Chen, San Jose, CA (US);
Applied Materials, Inc., Santa Clara, CA (US);
Abstract
Methods and systems for reducing substrate particle scratching using machine learning are provided. A machine learning model is trained to predict process recipe settings for a substrate temperature control process to be performed for a current substrate at a manufacturing system. First training data and second training data are generated for the machine learning model. The first training data includes historical data associated with prior process recipe settings for a prior substrate temperature control process performed for a prior substrate at a prior process chamber. The second training data is associated with a historical scratch profile of one or more surfaces of the prior substrate after performance of the prior substrate temperature control process according to the prior process recipe settings. The first training data and the second training data are provided to train the machine learning model to predict which process recipe settings for the substrate temperature control process to be performed for the current substrate correspond to a target scratch profile for one or more surfaces of the current substrate.