Patents by Inventor Mitrabhanu Sahu

Mitrabhanu Sahu has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11835927
    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.
    Type: Grant
    Filed: December 19, 2022
    Date of Patent: December 5, 2023
    Assignee: APPLIED MATERIALS, INC.
    Inventors: 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
  • Publication number: 20230121513
    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.
    Type: Application
    Filed: December 19, 2022
    Publication date: April 20, 2023
    Inventors: 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
  • Patent number: 11586160
    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.
    Type: Grant
    Filed: June 28, 2021
    Date of Patent: February 21, 2023
    Assignee: Applied Materials, Inc.
    Inventors: 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
  • Publication number: 20220413452
    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.
    Type: Application
    Filed: June 28, 2021
    Publication date: December 29, 2022
    Inventors: 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