公开(公告)号：US11835927B2

公开(公告)日：2023-12-05

申请号：US18068469

申请日：2022-12-19

Applicant: APPLIED MATERIALS, INC.

Inventor： Kartik B Shah ,  Satish Radhakrishnan ,  Karthik Ramanathan ,  Karthikeyan Balaraman ,  Adolph Miller Allen ,  Xinyuan Chong ,  Mitrabhanu Sahu ,  Wenjing Xu ,  Michael Sterling Jackson ,  Weize Hu ,  Feng Chen

IPC: G05B13/02 ,  G05B13/04

CPC classification number: G05B13/0265 ,  G05B13/048

Abstract: Process recipe data associated a process to be performed for a substrate at a process chamber is provided as input to a trained machine learning model. A set of process recipe settings for the process that minimizes scratching on one or more surfaces of the substrate is determined based on one or more outputs of the machine learning model. The process is performed for the substrate at the process chamber in accordance with the determined set of process recipe settings.

公开(公告)号：US11586160B2

公开(公告)日：2023-02-21

申请号：US17360652

申请日：2021-06-28

Applicant: APPLIED MATERIALS, INC.

Inventor： Kartik B Shah ,  Satish Radhakrishnan ,  Karthik Ramanathan ,  Karthikeyan Balaraman ,  Adolph Miller Allen ,  Xinyuan Chong ,  Mitrabhanu Sahu ,  Wenjing Xu ,  Michael Sterling Jackson ,  Weize Hu ,  Feng Chen

IPC: H05B3/68 ,  G05B13/02 ,  G05B13/04

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.