公开(公告)号：US11901203B2

公开(公告)日：2024-02-13

申请号：US17344787

申请日：2021-06-10

Applicant: APPLIED MATERIALS, INC.

Inventor： Pengyu Han ,  Lei Lian ,  Shu Yu Chen ,  Todd Egan ,  Wan Hsueh Lai ,  Chao-Hsien Lee ,  Pin Ham Lu ,  Zhengping Yao ,  Barry Craver

IPC: H01L21/67 ,  G01N21/95 ,  G06N20/00 ,  G05B13/02

CPC classification number: H01L21/67253 ,  G01N21/9501 ,  G06N20/00 ,  G05B13/0265

Abstract: Methods and systems for detection of an endpoint of a substrate process are provided. A set of machine learning models are trained to provide a metrology measurement value associated with a particular type of metrology measurement for a substrate based on spectral data collected for the substrate. A respective machine learning model is selected to be applied to future spectral data collected during a future substrate process for a future substrate in view of a performance rating associated with the particular type of metrology measurement. Current spectral data is collected during a current process for a current substrate and provided as input to the respective machine learning model. An indication of a respective metrology measurement value corresponding to the current substrate is extracted from one or more outputs of the trained machine learning model. In response to a determination that the respective metrology measurement satisfies a metrology measurement criterion, an instruction including a command to terminate the current process is generated.

公开(公告)号：US20220399215A1

公开(公告)日：2022-12-15

申请号：US17344787

申请日：2021-06-10

Applicant: APPLIED MATERIALS, INC.

Inventor： Pengyu Han ,  Lei Lian ,  Shu Yu Chen ,  Todd Egan ,  Wan Hsueh Lai ,  Chao-Hsien Lee ,  Pin Ham Lu ,  Zhengping Yao ,  Barry Craver

IPC: H01L21/67 ,  G06N20/00 ,  G01N21/95

Abstract: Methods and systems for detection of an endpoint of a substrate process are provided. A set of machine learning models are trained to provide a metrology measurement value associated with a particular type of metrology measurement for a substrate based on spectral data collected for the substrate. A respective machine learning model is selected to be applied to future spectral data collected during a future substrate process for a future substrate in view of a performance rating associated with the particular type of metrology measurement. Current spectral data is collected during a current process for a current substrate and provided as input to the respective machine learning model. An indication of a respective metrology measurement value corresponding to the current substrate is extracted from one or more outputs of the trained machine learning model. In response to a determination that the respective metrology measurement satisfies a metrology measurement criterion, an instruction including a command to terminate the current process is generated.

公开(公告)号：US20230306281A1

公开(公告)日：2023-09-28

申请号：US17668280

申请日：2022-02-09

Applicant: Applied Materials, Inc.

Inventor： Pengyu Han ,  Hong-Rui Chen ,  Shu-Yu Chen ,  Wan-Hsueh Lai ,  Pin Ham Lu ,  Zhengping Yao

IPC: G06N5/02 ,  G05B13/02

CPC classification number: G06N5/022 ,  G05B13/029

Abstract: A method includes determining that conditions of a processing chamber have changed since a trained machine learning model associated with the processing chamber was trained. The method further includes determining whether a change in the conditions of the processing chamber is a gradual change or a sudden change. Responsive to determining that the change in the conditions of the processing chamber is a gradual change, the method further includes performing a first training process to generate a new machine learning model. Responsive to determining that the change in the conditions of the processing chamber is a sudden change, the method further includes performing a second training process to generate the new machine learning model. The first training process is different from the second training process.

公开(公告)号：US20220397515A1

公开(公告)日：2022-12-15

申请号：US17344788

申请日：2021-06-10

Applicant: APPLIED MATERIALS, INC.

Inventor： Pengyu Han ,  Lei Lian ,  Shu Yu Chen ,  Todd Egan ,  Wan Hsueh Lai ,  Chao-Hsien Lee ,  Pin Ham Lu ,  Zhengping Yao ,  Barry Craver

IPC: G01N21/31 ,  G01N21/95 ,  G06N20/00 ,  H01L21/66

Abstract: A machine learning model trained to provide metrology measurements for a substrate is provided. Training data generated for a prior substrate processed according to a prior process is provided to train the model. The training data includes a training input including a subset of historical spectral data extracted from a normalized set of historical spectral data collected for the prior substrate during the prior process. The subset of historical spectral data includes an indication of historical spectral features associated with a particular type of metrology measurement. The training data also includes a training output including a historical metrology measurement obtained for the prior substrate, the historical metrology measurement associated with the particular type of metrology measurement. Spectral data is collected for a current substrate processed according to a current process. A subset of current data extracted from a normalized set of the spectral data for the current substrate is provided as input to the trained model. Metrology measurement data for the current substrate is extracted from one or more outputs of the trained model.