公开(公告)号：WO2023084063A1

公开(公告)日：2023-05-19

申请号：PCT/EP2022/081686

申请日：2022-11-12

Applicant: ASML NETHERLANDS B.V.

Inventor： REN, Jiaxing ,  FAN, Yongfa ,  CHEN, Yi-Yin ,  ZHANG, Chenji ,  ZHENG, Leiwu

IPC: G03F7/20

Abstract: Machine learning models can be trained to predict imaging characteristics with respect to variation in a pattern on a wafer resulting from a patterning process. However, due to low pattern coverage provided by limited wafer data used for training, machine learning models tend to overfit, and predictions from the machine learning models deviate from physical trends that characterize the pattern on the wafer and/or the patterning process with respect to the pattern variation. To enhance pattern coverage, training data is augmented with pattern data that conforms to a certain expected physical trend, and applies to new patterns not covered by previously measured wafer data.

公开(公告)号：WO2020193095A1

公开(公告)日：2020-10-01

申请号：PCT/EP2020/055785

申请日：2020-03-05

Applicant: ASML NETHERLANDS B.V.

Inventor： MA, Ziyang ,  CHENG, Jin ,  LUO, Ya ,  ZHENG, Leiwu ,  GUO, Xin ,  WANG, Jen-Shiang ,  FAN, Yongfa ,  CHEN, Feng ,  CHEN, Yi-Yin ,  ZHANG, Chenji ,  LU, Yen-Wen

IPC: G03F7/20 ,  G06N3/08 ,  G06N20/00

Abstract: A method for training a patterning process model, the patterning process model configured to predict a pattern that will be formed on a patterning process. The method involves obtaining an image data associated with a desired pattern, a measured pattern of the substrate, a first model comprising a first set of parameters, and a machine learning model comprising a second set of parameters; and iteratively determining values of the first set of parameters and the second set of parameters to train the patterning process model. An iteration involves executing, using the image data, the first model and the machine learning model to cooperatively predict a printed pattern of the substrate; and modifying the values of the first set of parameters and the second set of parameters such that a difference between the measured pattern and the predicted pattern of the patterning process model is reduced.

公开(公告)号：WO2023088641A1

公开(公告)日：2023-05-25

申请号：PCT/EP2022/079676

申请日：2022-10-24

Applicant: ASML NETHERLANDS B.V.

Inventor： REN, Jiaxing ,  CHEN, Yi-Yin ,  FAN, Yongfa ,  LIANG, Jiao

IPC: G03F1/36 ,  G03F7/20 ,  G06T17/20

Abstract: A grid dependency check for a simulation model is described. According to embodiments of the present disclosure, a grid dependency check can be advantageously performed faster and more efficiently compared to prior grid dependency checks. Certain portions of a design layout are selected and cropped to the minimum size required by the model, and used to generate a second design layout. 5 The selected portions are rotated and/or shifted relative to the grid to form one or more moved portions. The second design layout includes the one or more selected portions and the one or more moved portions so that a modeling operation (e.g., model apply) needs to only run a single time instead of multiple times as in the prior grid dependency checks.

公开(公告)号：WO2021004725A1

公开(公告)日：2021-01-14

申请号：PCT/EP2020/066446

申请日：2020-06-15

Applicant: ASML NETHERLANDS B.V.

Inventor： WANG, Lei ,  CHEN, Yi-Yin ,  FENG, Mu ,  ZHAO, Qian

IPC: G03F7/20

Abstract: Systems and methods for reducing prediction uncertainty in a prediction model associated with a patterning process are described. These may be used in calibrating a process model associated with the patterning process, for example. Reducing the uncertainty in the prediction model may comprise determining a prediction uncertainty parameter based on prediction data. The prediction data may be determined using the prediction model. The prediction model may have been calibrated with calibration data. The prediction uncertainty parameter may be associated with variation in the prediction data. Reducing the uncertainty in the prediction model may include selecting a subset of process data based on the prediction uncertainty parameter; and recalibrating the prediction model using the calibration data and the selected subset of the process data.