公开(公告)号：US20240420025A1

公开(公告)日：2024-12-19

申请号：US18703486

申请日：2022-11-12

Applicant: ASML NETHERLANDS B.V.

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

IPC: G06N20/00 ,  G03F7/00

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：US20220179321A1

公开(公告)日：2022-06-09

申请号：US17442662

申请日：2020-03-05

Applicant: ASML NETHERLANDS B.V.

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

IPC: G03F7/20 ,  G06N3/08 ,  G06T7/00

Abstract: A method for training a patterning process model, the patterning process model configured to predict a pattern that will be formed by a patterning process. The method involves obtaining an image data associated with a desired pattern, a measured pattern of the substrate, a first model including a first set of parameters, and a machine learning model including 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 is reduced.