公开(公告)号：US20220092241A1

公开(公告)日：2022-03-24

申请号：US16948522

申请日：2020-09-22

Applicant: Applied Materials, Inc.

Inventor： Ala Moradian ,  James Omer L'Heureux ,  Shuran Sheng ,  Rohit Mahakali ,  Karthik Ramanathan ,  Lin Zhang ,  Umesh Madhav Kelkar ,  Gopalakrishna B. Prabhu ,  Zheng Yuan ,  Jeonghoon Oh

IPC: G06F30/28 ,  G01D21/02 ,  G01D18/00

Abstract: A method includes receiving measurement data from multiple sensors positioned along a delivery line that delivers a liquid as a gas to one of a gas panel or a processing chamber; simulating, using a computer-generated model, one or more process parameters associated with the delivery line and a plurality of heater jackets positioned around the delivery line; comparing the measurement data with values of the one or more process parameters; and determining, based on at least a threshold deviation between the measurement data and the values of the one or more process parameters, that a fault exists that is associated with maintaining temperature within the delivery line consistent with a gaseous state of the liquid.

公开(公告)号：US20230222264A1

公开(公告)日：2023-07-13

申请号：US17571370

申请日：2022-01-07

Applicant: APPLIED MATERIALS, INC.

Inventor： Rohit Mahakali ,  Elizabeth Kathryn Neville ,  Adolph Miller Allen ,  Xiaoxiong Yuan ,  Weize Hu ,  Karthik Ramanathan

IPC: G06F30/27 ,  H01L21/67

CPC classification number: G06F30/27 ,  H01L21/67276 ,  H01L21/67155 ,  H01L21/67248

Abstract: A method includes receiving, from sensors, sensor data associated with processing a substrate via a processing chamber of substrate processing equipment. The sensor data includes a first subset received from one or more first sensors and a second subset received from one or more second sensors, the first subset being mapped to the second subset. The method further includes identifying model input data and model output data. The model output data is output from a physics-based model based on model input data. The method further includes training a machine learning model with data input including the first subset and the model input data, and target output data including the second subset and the model output data to tune calibration parameters of the machine learning model. The calibration parameters are to be used by the physics-based model to perform corrective actions associated with the processing chamber.

公开(公告)号：US11768984B2

公开(公告)日：2023-09-26

申请号：US16948522

申请日：2020-09-22

Applicant: Applied Materials, Inc.

Inventor： Ala Moradian ,  James Omer L'Heureux ,  Shuran Sheng ,  Rohit Mahakali ,  Karthik Ramanathan ,  Lin Zhang ,  Umesh Madhav Kelkar ,  Gopalakrishna B. Prabhu ,  Zheng Yuan ,  Jeonghoon Oh

IPC: G06F30/28 ,  G01D21/02 ,  G01D18/00 ,  G06F119/02 ,  G06F111/10 ,  G06F113/08

CPC classification number: G06F30/28 ,  G01D18/00 ,  G01D21/02 ,  G06F2111/10 ,  G06F2113/08 ,  G06F2119/02

Abstract: A method includes receiving measurement data from multiple sensors positioned along a delivery line that delivers a liquid as a gas to one of a gas panel or a processing chamber; simulating, using a computer-generated model, one or more process parameters associated with the delivery line and a plurality of heater jackets positioned around the delivery line; comparing the measurement data with values of the one or more process parameters; and determining, based on at least a threshold deviation between the measurement data and the values of the one or more process parameters, that a fault exists that is associated with maintaining temperature within the delivery line consistent with a gaseous state of the liquid.